They've also announced Qwen3.8-27B being released open-weight next week. Qwen3.6-27B is widely regarded as one of the best local models, especially since nothing else comes close to it, that isn't benchmaxxed, without being significantly larger. If 3.8 truly improves upon it that would be awesome.
Qwen3.6-35B is my daily driver for AI, and what convinced me to cancel my Claude subscription back in April. The Qwen3.6 line is easily the best local model I've tried, and I've tried a lot. I've got it diligently grinding away on my laptop right now, reviewing and fixing some bugs in my F# code.
Qwen-3.6-35B-A3B was our "gateway drug" into switching our organisation to agent/harness-first coding.
Particularly, I had one team member who was extremely sceptical of AIs/LLMs/harnesses and refused to use them. One day he said "Well, I have an RTX 5090 doing nothing... should I try to get something up on it?" and a few minutes later he had 3.6-35B loaded up, running OpenCode.
It continues to be a workhorse to this day, running on both my local Mac for various types of jobs, an AMD R9700 at the office, and said teammember still uses it on his 5090, although in practical terms we do a lot more with DS-V4-Flash-0731 these days.
I’ve run 3.6-27B and 3.6-35B on 32GB locally for a lot of bulk non-code tasks. Let it run overnight and wake up to millions of output tokens worth of results without data having left my house, all for the price of electricity.
I haven’t found it very useful for code. It can do some code, but I’ve tried a dozen different quants and context lengths and the output is always bad enough that it has to be discarded for anything other than really easy tasks. It has been useful for exploring codebases for search and summary, though.
DS Flash is where local models begin to feel useful for coding, but the quants we run locally are sharply reduced in intelligence from the benchmarks for the full models.
For applications where data cannot leave the local network it’s good to have them. For actual coding work I can’t actually justify the power of electricity and cooling, let alone the expensive hardware, compared to hosted APIs.
But I admit I do enjoy playing with them anyway. I think it’s one of those hobbies where it’s most fun if you never do the math on how much you’re paying for the privilege. If someone has a requirement that data stay local then it’s different, of course.
automatic coding is a flow thing with 3.6, but I agree it isnt fully there, it does need specific per function instructions to maintain coherence, with a few exceptions Im unsure hold in general which I got via an experimental harness.
Not OP, but I use it for a ton of smaller things. I have it hooked into Hermes and have been using it to help bulk rename my media folders so they all follow a common format, add titles that sort of thing which wouldn't be easy to 'script'. Another thing I use it for is comparing data sets, looking at my exported Spotify artists and compare to what I have locally, and letting me know where there are missing artists, or albums, and recommendations based on similar artists that I may not have locally.
Sure a lot of this could be done without AI, but it's certainly quicker and easier, and since my AI box is on solar, it's just the power of the sun to keep it going.
I started with Karpathy's LLM wiki, and did everything he said not to do - downgraded the model to mere tool usage and summarization, and it works great.
I am a data hoarder, and finally I can just dump all the content I remotely like, and get something interesting to browse for the price of electricity.
Agentic long-running tasks, as others have mentioned:
- Groom and triage tickets for agentic SWE workflows
- bug hunt — the probability of Qwen fixing a complex bug is 50/50 but often it is capable of identifying the root cause or at least laying the ground work for a more capable model to pick it up.
SmartConnections but heads up, Connections Pro asks $300/year, or, more for the plugin than for an LLM subscription, more than Microsoft Office for that matter.
Not OP, but I use it for personal tasks that are just not worth the claude tokens -- rooting through historical medical records to unify prescription history, super-OCR'ing thousands of PDF pages (i.e. beyond PDF dumping -- vision means it can look at tables, understand tricky things like a continuation of a block quote or aside on the next page, etc.), and when power is cheap I'll just let it noodle on little projects on my data. During my agent's "free time" I give it with a tremendously open ended prompt last week, it did a linguistic analysis of how my texting changes in the lead up to, initiation, maintenance, and ending of romantic relationships.
The A3B models are super fast but I found the A3B Q4 model ran in circles a lot and ended up taking longer to complete tasks that 27B Q6 because it kept having to redo/rethink/fix something.
I was writing extensive prompts to rein it in and it would still ignore basic directives like "never force push on the repo, ask me instead". I ended up switching back to 27B after about a week of frustration and lost productivity.
What!? You are skeptical of AI but will go through the manual process of hosting a model that’s less than frontier intelligence (talking about Qwen 3.6)? Anti-AI folks are always odd to me
A local model needs 0 investment and 0 commitment, takes literal minutes to get started (especially if you have someone who is into that stuff showing you the ropes) and if you end up disliking the experience of using AI you can just `rm -fr` it and forget the whole thing existed.
- You at least need a capable machine, so that's not 0 monetary investment.
- You need to spend at least an hour decicding between ollama, llamacp, mlx, etc.
- You need to find the correct quantized version of the model that works for you based on the architecture.
- You need to figure out the correct context window size to get reasonable performance.
- You need to setup a harness that works against your model
- You might need to setup additional websearch tools, image tools, etc since harnesses like pi don't come with the model. Ofc you can't use codex and claude code, because those aren't opensource and you are anti-AI.
Or, you could sign up for Opencode for $10 and just be productive.
I'm particularly calling out the hypocrisy of the original comment. Being Anti-AI, and then spending hours on setting up a less than frontier AI model.
All of your objections have already been addressed by the previous comments.
The original comment states that the person in question already had a suitable graphics card to hand, so it did not require a monetary investment.
GP clearly states that "someone who is into that that stuff" was guiding the process, so it did not require a significant time investment.
> I'm particularly calling out the hypocrisy of the original comment. Being Anti-AI, and then spending hours on setting up a less than frontier AI model.
I see no hypocrisy in the original comment.
You've also assumed the skeptic in question doubts the capabilities of AI. That may be the case (like you, I have no idea), but they may also have privacy concerns, in which case a local model is the appropriate choice.
There are plenty of reasons to be skeptical of AI.
install LM Studio, download the automatically selected quant based on your hardware, start a conversation with the automatic context size. 10 minutes at best and zero effort
Maybe not '0 investment and 0 commitment', but incredibly little depending on what you have laying around. It takes less than 5 minutes to download say LM Studio and an Open Model and as long as you have the hardware to support it, you start moving along. If you are on AMD in some ways it's even 'easier', you can download Lemonade and it will tell you exactly what will fit and best options based on what you are trying to do.
For me at least the local AI stuff, powered with solar has been pretty great. Would that scale to a large business? Goodness no, but for my tinkering and learning, it works great.
I'm running Qwen 27B no problem with an AMD 9070XT + 24gb DDR5 ram. Does basic web search for me (tool call with tavily, costs nothing I get 1000 searches a month) and is great for creative writing (primarily breaking writer's block). Until the recent surge in ram costs, that wouldn't be hard to do. I built the computer for ~$1600 a year ago.
I am getting ~13-15 tps with my 9070XT for the 27B (~35tps for the 35B-A3B), but I think for me the main bottleneck is the 64gb of DDR4 3600 memory. What kinda speeds are you getting with what speed of DDR5?
Back in “the day” nerds just bought the hardware to fuck with. Some of us still do. Claiming that compute is the barrier to entry just means you’re not a nerd. That’s ok.
> You need to setup a harness that works against your model - You might need to setup additional websearch tools, image tools, etc since harnesses like pi don't come with the model
How is that hypocrisy? Self hosting is somehow anti AI? Its not anti AI. Its literally using AI!
…and honestly, at a higher technical level than slapping your wallet against a token provider and running prompts in a hosted sandbox you can't even see the prompts in.
Look man I am incredibly skeptical of how LLM’s have been rolled out and all the promises people make (it’s so much snake oil and pipedreams), but I also found it very trivial to hop on LM studio and start tinkering with models. If you’ve already got a decent midtier computer on hand, which I imagine a lot of us already do, then it’s really not hard to get started and get immediate results.
Local models on regular hardware aren't really capable of anything. Whatever you're testing is nowhere near a measly $20/mo subscription, so it's of limited use.
i really like the idea of running local models but i'm always in the position of wanting the best model(s) available and i don't have any severe privacy concerns. as such i have yet to justify ever using local models.
This is the diametric opposite of the rent-vs-buy scenario that this entails.
Local: You need to invest $thousands into GPU and/or very-high-end CPU+Memory hardware.
Vendor: You can use any existing device, even a phone or tablet. A very low-end laptop is fine.
> takes literal minutes to get started
Local: Typical scenario is hours just to download the software, the model weights, and then faffing around with CUDA and matching your GPU drivers.
Vendor: Free-tier available instantly on a web URL. Even local agents have free tiers from multiple vendors. Install is a single command and/or download and "next,next,next,finish" wizard that takes ~1 minute.
> you can just `rm -fr` it and forget the whole thing existed.
I'm still cleaning up multi-GB model weights floating around in hidden subdirectories under my user profile from months ago when I was experimenting with local models!
Meanwhile I simply... stopped using Gemini. That was the entire process: I no longer actively use it. They stopped billing me for my token usage, because it is now zero. That's... it.
> I'm still cleaning up multi-GB model weights floating around in hidden subdirectories under my user profile from months ago when I was experimenting with local models!
Are you trying to say that local models are hard to use because... you're having issues handling files properly? I am not sure I get the argument.
I get the rest of the comment: local models require an investment upfront, and it is less convenient. It doesn't say that it is not cheaper, though.
It took me about three hours total to set up a local model. I already have a GPU and I have fiber for the download. llama.cpp is not difficult to compile and has many backends. It can run parts of the model on different backends, like in the common case that the GPU doesn't have enough VRAM for everything. There are many step-by-step guides available.
Takes even less depending on your system. LM Studio or Lemonade and you are set up in minutes and now they can even tell you what models will fit with the memory you have.
And it would be in seconds if models weren’t that large and slow-ish to download! LM studio is such a noob friendly experience, pretty neat first experience!
> "I'm still cleaning up multi-GB model weights floating around in hidden subdirectories under my user profile from months ago when I was experimenting with local models!"
I used to deal with these kinds of frustrations too.
fd --unrestricted --size +1G
fd --help
-u, --unrestricted...
Perform an unrestricted search, including ignored and hidden files. This is an alias for
'--no-ignore --hidden'.
-S, --size size
Limit results based on the size of files using the format
<+-><NUM><UNIT>
Full model or a 4-bit quant? I have a 5090 and I'm not sure whether I should use a quant that fits within the VRAM or a much bigger version where I'd have to offload a lot to 64GB RAM and a beefy CPU (but still a CPU)
I personally run the Q6 quant on my RX 9070 XT (16GB VRAM). On r/LocalLlama there was a post recently as well, which talked about the degradation of different quants (for the 27B version)[0]
A lot of people already have 12GB+ GPUs lying around for playing games, doing video editing, etc. I would not get a GPU or mac just to run LLMs personally, but if one wants to get such a device for other tasks too, it may make sense to eg choose a slightly higher (v)RAM variant if they want to run some bigger models. Then what you pay for the local llms is just the difference.
“If you have the prerequisite hardware, then… know which model you want out of thousands of a variants… and your drivers are up to date, then it is fast!”
This largely describes me. I'm skeptical of AI in that it's capabilities, while very impressive, are vastly oversold and overblown. Being skeptical of AI is not being "Anti-AI". That's largely the AI data centers are using up all the water and electricity types.
Maybe you're anti-AI because you're really anti-outsourcing your thinking to some remote corporation you don't control?
That's one of my main issues with AI anyways, the thought of having all my data go through some sketchy foreign (to me) entity with questionable motives and under a questionable regime.
I'm not against AI. I'm calling out the hypocrisy in the comment. I'm anti-AI, but will spend hours trying to setup a local model, instead of just getting access to frontier intelligence in 15 mins, and actually getting useful work done.
If you're learning about model inference, then it's a different and you are definitely not anti-AI in that case.
I think learning how to set up a local AI now requires less time than learning about potential pitfalls of token-plans and processing payments in corporate environments.
It's not that billing is complicated, but learning to set up a local AI is a lot more useful and more rewarding.
35B MoE is certainly a good and fast local model. I find 27B dense to be quite a bit smarter, so I daily drive that. I wish there was a ~100B MoE with maybe 10B active. It would be super smart and fast!
I tried it for a bit, and It was not really worth its size. It got swept up in all the other AI news recently, but laguna s 2.1 I think is the best ~100B moe model right now
I didn't mention it above, but Laguna S is my other favorite model. I use Qwen a lot more, it's smaller and faster, but I like to switch to Laguna when I feel like I need a "heavy hitter" for certain huge or complex tasks.
What on earth hardwares do you guys have to be able to run 100gb models locally?! That's crazy! I'm here struggling to even get 27b models to run in somewhat usable way
Strix Halo as well. Bought it for $1,800 new on sale and shoved an extra 4tb drive into it. Been amazing for local AI. Maybe not the absolute fastest thing (usually around 30t/s depending on the task) but has been awesome for a local AI box that I can solar power.
Couple of rack mount batteries and roughly 5kw of solar panels. Feeds into a subpanel so I can flip it when I want a couple rooms of solar on the house, or hook a generator up if needed. Can't power the entire house, but works well for thinks like computers, lighting, etc. And if I want to expand, just throw on more panels, or realistically, just throw on more batteries to store the juice.
Haha I'm on an Mac Studio with an M1 Ultra, 64gb ram. I bought it when it first came out, it just happens to be good for local LLMs. I have to use a smaller quant of Laguna S though (I think 4-bit? Not at my machine to check), as 8-bit and full size definitely don't fit in the 64gb I have.
Yeah, a good rule of thumb is that the weights take up ~100% of the size of the model, so 100B bytes (8-bit quant) would be, well, 100GB and a 4-bit quant would be half that.
Yeah, here I am sitting deeply deeply deeply regretting not buying couple CMP 170HX at $200 or $350, knowing I could just flip them ethically at purchase price if nothing came of it... I could have just casually built a 128GB dual A100 local AI monster
I'm working with a lab that has a few Ampere GPUs on infiniband and they are just not compatible with the latest quants and vLLM updates. FP8 is about as low as you can go.
I have an RX 6700 XT with 12gb vram and 64gb system ram. running dense models like 27b is difficult, but i can run IQ4/IQ5 qwen 122b-a10b or 35b-a3b at ~20tok/s
IQ4 qwen 122b-a10b would mean 61GB total size and 5GB active, so about 5GB of the model loaded into GPURAM plus any generated context, and 61GB of weights loaded into system RAM? I don't know if that math is correct, but does that run well? Wouldn't that only leave 3GB of system RAM?
Not badly so because MoE models(identifiable by "CoolName-xxxB-AxxB" naming scheme) have bunch of branches in the middle that only one out of all gets non-zero values. Each of branches aka "Experts" as well as top/bottom parts are significantly smaller than the whole, and so CPU emulation of CUDA operations mixed with GPU taking as much as possible become not so out of question, unlike for dense models("CoolName-xxxB" without "-AxxB")
I use 27B in plan mode and 35B MoE in act mode. I noticed that is the best balance for me for consistent tool calls and intelligent planning. Takes some time to switch, but it's worth it for me.
Honestly the 27b dense one punches way above its weight in a lot of domains, especially coding in my testing, so I think you will probably be disappointed.
Can you elaborate on DeepSeek (deepseek-v4-flash, i assume?). What does your typical usage pattern look like and what is your weekly/monthly spend?
I gave it a try for a few days (pi + openrouter + deepseek-v4-flash via deepinfra) and ended up paying ~$18 for rather light usage. Yes it's still cheap, yes it's fast, but i feel i would still get a better deal with a Claude subscription plan.
I agree with parent. OpenRouter might be cheaper list-price, but i have been using 10$ on DS platform since April/May, still have 2$ left. Using OpenRouter i depleted the same dollar-amount in a 1-2 weeks with same usage pattern. No idea why.
I would recommend looking into Ornith1.0 - it's using Qwen3.6 35B-A3B and excels in coding, at least for my coding needs, Python, web-dev, SQL scripting and some C#. Using Pi harness.
Apple Silicon. But: there's no need to use the FP16 version. At 8-bit precision the quality loss is almost imperceptible. That cuts the footprint to 36GB. Which is great for a 64GB Mac, because you have room for plenty of context. 6-bit also works nicely at 26GB + context.
You want to use the newer quantization formats like Unsloth's UD quants or oQe, where the weights are selectively quantized using a calibration dataset so that important weights are left at/closer to full precision.
use a quantized version. since it's MoE, what matters is that the 3b parameters that are used for every token fit in gpu vram, the rest can stay in system ram. really great if you don't have unified memory.
I host the models on my Mac Studio, an M1 Ultra with 64gb ram (I bought it when it came out, just happens to be good at LLMs). So when I work on my laptop, I have my oh-my-pi setup configured to use the models on my Mac over my local "bonjour" network or whatever Apple calls it. That way I have a nice cool lap, while using models that my M4 MacBook Air with its 16gb ram couldn't possibly run.
Strix Halo for me. If I am running something on my laptop, it's a much smaller usually around 12b model, but those are a bit less functional. I mean I think there is a a ROG FLow Z that has the Strix Halo setup, but that thing was super expensive.
It's just an M4 MacBook Air with 16gb ram – probably incapable of running models itself. I actually run the models on my Mac Studio which is an M1 Ultra with 64gb, and oh-my-pi on my laptop is configured to use the models over the local network.
On my SpacemiT K3 SBC with 32GB RAM (where models run on the eight A100 RISC-V cores with 1024 bit vectors) doing the same task I got 5, 5.8, 6.5 tok/s using gemma-4-26B-A4B-it-QAT-Q4_0.gguf, Qwen3.6-35B-A3B-Q4_K_M.gguf, Qwen3.5-35B-A3B-Q4_K_M.gguf. The corresponding dense models are more in the 2.5-3 tok/s range.
Kind of slow, but using only 14W of electricity so the Wh per task is twice as good as using my i9-13900 laptop with 4060 GPU.
I use it with a strix halo server. 35B runs stupidly fast. 27B is about 700 TPS prefill and 30 TPS token generation. Which interestedly is about what Kimi K3 gives me depending on provider.
While the laptop option is nice, for an inference server you're probably going to want the desktop form factor as it has significantly more thermal overhead and thus better performance. In the desktop models most of the internal volume is a gigantic heatsink
I have a framework desktop, but depending on your need, DGX spark might be better. The prefill and NVFP4 is a significant advantage. But framework desktop is a better general computer. I expect to be able to use it for years to come. Where as DGX Spark you’re at the mercy of NVIDIA BSP.
On an 8GB GPU and 32GB laptop: ~5 words/s while running in Qubes via ollama with completely default settings (I don't have an install at the moment that'll tell me tokens/s). Not exactly a highly tuned setup, but it's a ballpark at least :)
Tolerable and usable for some things, though thinking makes it take about a minute to reply in many cases. But getting this kind of thing to run on 8GB of VRAM is the main benefit of the mix-of-experts setup: it can do partial GPU loading and get a ton better throughput than a similarly-sized dense model (like 5-10x, sometimes more).
It's pretty fast, faster than I could type anyway, but not as fast as Claude of course. My oMLX dashboard says I get about 45 tokens per second from the Qwen model I'm running (I host it on my M1 Mac Studio, not on my laptop).
Qwen is running on my Mac Studio, an M1 Ultra 64gb. My harness (oh-my-pi) on my laptop is configured to use the models hosted on my local network, since it's just a MacBook Air 16gb and probably incapable of running anything useful itself.
I get about 45-55 tokens per second using Qwen with this setup. I could probably squeeze out more if I messed around with the settings, but I'm mostly using oMLX's defaults for the model.
I recommend trying pi.dev as your agent harness for local models. In my experience it has been the sweet spot of functionality (which you can and should extend with plugins) vs performance (OpenCode just swamps local models on my hardware).
you can try ollama, omlx or llama.cpp for instance to download a model and get an inference server running locally. They expose „open ai compatible“ endpoints, so you can configure almost any harness to use them.
Depends on how you are doing it. LM Studio and tool calling models can use the web, or you could go for something like Perplexica, or if you want to go real crazy, something like Hermes or OpenClaw.
It's just a Macbook Air with the base M4 and 16gb ram, but I'm hosting the models on a Mac Studio with M1 Ultra and 64gb ram that I had purchased when it came out. I get about 45-55 tokens per second with this setup. I think I could get more if I spent some time fiddling with the parameters, but I don't really know what I'm doing there so I've just left most of it on oMLX's defaults.
Not the GP, but I run this model as daily driver too. It runs great on a Macbook Pro 64GB (M3 Max). Token generation speed can be about 100 tokens/sec with multi-token prediction, although it depends on the context. Worst case speed is around 50 tokens/sec.
The weaker point is prompt prefill, which starts at 1,400 tokens/sec but decreases significantly at high contexts. That said, for agentic scenarios, if you're using a harness that doesn't needlessly bust the cache, it doesn't feel slow.
I really hope they release a Qwen 3.8 35B, although the lack of a mention seems ominous.
I'm on the verge over here, the new Anthropic models have been a disappointment. I've tried the A3B variant, but had mixed results. What do you use as the coding agent, and have you heavily customized your workflows?
It's just a MacBook Air with an M4, cheap and nothing special. I host Qwen on my Mac Studio, an M1 with 64gb ram. The model uses around 20-25gb ram depending on what it's doing.
Kimi K3 is more like "weights available" in that you can download and use them but it is under a custom license that has a bunch of limitations where you have to pay Moonshot for doing some stuff. GLM 5.2 on the other hand is plain old MIT.
Not sure how Qwen3.8-Max is going to be licensed, hopefully it'll be Apache like the smaller ones.
You can do whatever you want with the model within your own organization. If you use it commercially—either as a model-as-a-service business or in a very large-scale product—you should check the additional license terms, which go beyond MIT. My interpretation is that Moonshot cares about the exact inference behavior and accurate representation of their model or derivatives, and perhaps also about capturing some additional value despite their own GPU limitations, so the extra license terms focus on those large-scale commercial deployments.
Considering that very few orgs are going to be able to host a 3T parameter model internally, chances are most deployments would be subject to these restrictions and require a separate license from Moonshot.
US AI companies are already sweating and 100% pressuring the Trump administration for more anti-Chinese regulation, since there have already been talk of Trump considering banning Chinese models. There's however another push back from the startup industry urging them not to ban it, since it will stifle the innovation. In other recent news OpenAI also greatly cut their model prices, 20% for 5.6 Terra and 80% for 5.6 Luna, to stay competitive.
> In other recent news OpenAI also greatly cut their model prices, 20% for 5.6 Terra and 80% for 5.6 Luna, to stay competitive.
I’ve seen comments on HN saying how bad this is for the Chinese model developers since the cheaper option like Deepseek Flash are not longer as price competitive to justify the hassle/risk/lack of multimodal… but isn’t this a gigantic red flag for OpenAI/Anthropic at their current valuations?
Sure, it’s just the lowest end for now, and the enterprise money is at the top of the market. And there’s protectionism/enterprise lock-in/etc that complicate things somewhat.
But still, if the US AI labs ever tap the training brakes for a millisecond, the “inference is still a money maker” argument seems to evaporate when they’ll immediately have to fight a race to the bottom until margins are virtually nothing.
Or if the benchmaxing “line goes up” FOMO mindset starts to lose its luster and companies find their individual niches for productive use of AI and stop bothering with all the latest and greatest churn for top dollar.
Which might be even worse if it means the training arms race is still ongoing but neither Anthropic or OpenAI want to be the first to “lose”. While the marginal value of each new model training run keeps decreasing and enterprises signal they’re more concerned with cost reductions than solving ARC-AGI-7 puzzles.
Huh? V4Flash is still incredibly price competitive. The new version is right around GLM 5.2 and maybe slightly worse than opus 4.8 depending on which benchmark you use while being much cheaper(even factoring how most US zdr providers charge 10x Deepseek’s api caching price). K3 is also a tad behind fable/sol while being alot cheaper
Some advice I got from another HN Mac user was to run local models in energy saver mode. You'll get slightly reduced tokens, but the laptop won't overheat and the fans won't go wild.
Oh. I've been using an icepack under my laptop to keep mine cool. I'm watching it with llamatop to see if the GPU is actually active or not, aw activity monitor wasn't showing me what I wanted.
This is what I've been waiting for. We are still using fine-tuned deployments of Qwen3.6-27B with a lot of success but could use a bump in intelligence. Here's hoping.
I also "evolved" into 27b (q8 unsloth) and pi.dev (tried many combinations) feels for me the same as opus 4.5 that i use at work, faster even (using 2x 3080 20GB gives me 60-80tk/s). Though you do need to feed it more details up front (about what exactly you are planning to do and a good written skill.md) but I work that way anyways, im hyped for 3.8
Having invested in a machine with 128GB of RAM, I would love seeing something a bit larger than 27B / 35B, possibly a 54B dense model or 70B MoE would be much closer to the Qwen 3.8 Max experience.
All of us with a 96gb rtx 6000 would love to see a 70b moe. Maybe they are waiting for OpenAI and Anthropic to IPO so they can short their stock and release. Local LLM is going to get very interesting in the next 2 years.
The 27B have many more active parameters than much bigger models such as DS4Flash, MiniMax etc, which makes it punch above its tiny weight. A great fit for a 5090 in a closet for meat-and-potatoes, kind of work.
I've been running Qwen3.6-27B-IQ4 (4-bit quantized) locally and it's been great. I can't run the non-quantized version as I only have a 4090 w/24 GB of VRAM and it won't fit and leave any context room, but the quantized version only uses 18GB.
Yeah, Qwen3.8-Max is the new Flagship model for coding and harness system and many other benchmarks are reaching equal performance as Claude and other close models. That's gonna drop the price of LLM in agent landscape a lot.
the bonsai 27B 1bit quant version of Qwen3.6 27B is even more nuts, model fits in 4GB, and with 100k of content model+kv cache fits in 8GB. I’ve been running it locally on my mac mini 16GB. it gets around 4-6 tok/s, so not quite real-time ready, but good enough to let it run on task async for 20 min and come back. The 1bit model struggles a bit with multi-turn conversations though (e.g. when switching from plan to act mode it will still keep trying to make a plan) but that’s easy enough to reformat prompts into multiple one shot sessions of smaller work.
For those of us who don't have the time to follow closely, Qwen3.6-27B being Open Source and Open Weight, what level is this compared to other Western paid version?
Just so that we know what 3.8 would be like.
I currently have about 150 Tabs of Antirez posting on AI and running local model I haven't had the time to read. And there are probably some prerequisite reading or other research in between as well. I just wish there are some very high level overview and news coverage on all these.
> what level is this compared to other Western paid version?
IMHO this is a difficult question to answer. Part of the power of paid models comes from the software supporting it. With local models, you have tons of workflows that can severely influence the quality of the result.
In my personal experience, the SOTA models are way more consistent and can handle more complex questions. Part of that is (probably) because I don't let my local model access the internet, while paid models do use the internet to look at docs etc.
You absolutely need to let models access the Internet if you want consistently good results. Pretty much any non-trivial task requires the model to do things like look up APIs, code examples, or existing discussions of a given topic.
You don't really need the Internet. Tons of documentation is available for download (either as a zip, or with the documentation site as its own git repo). Wikipedia is available for download. You can get reddit dumps, HN dumps, stack exchange dumps, etc. This can all easily fit on one hard drive.
Reading the actual code is also always a better source of truth than docs anyway (this is true for people and LLMs). Just clone whatever libraries you use.
If they trained it well, and can do computer use, it will be a new era. Companies can keep PCs, put Qwen 3.8 27b on it and get rid of the employees, lol...
I am surprised that they keep going with it, seeing how fast it improves and basically soon running themselves too out of business.
What's even their end goal? Open source models make sense, if profit is not the target, but for OpenAI and the rest, once they achieve "AGI", don't they basically become useless?
There was an interesting interview by MLST with a team doing well on ARC AGI 3 who are using Qwen 3.6 27B, and said that it's actually better at coding than the larger 3.6 35B.
I guess which of the smaller 3.8 models is best for coding will depend on which one they put the training effort into.
The larger 3.6 35B is actually a mixture of experts (MOE). This means a small proportion of those B's are actually active. It's fast and suitable for agentic tasks but nowhere near good as the dense 27B model, which has all of its parameters loaded.
As someone who is searching for a new programming contract right now, reading all of the incredible abilities here is pretty intimidating. Especially since I get almost all of my projects from Upwork which is an outsourcing site.
I believe I am competing directly with these frontier models in some circumstances. Like there are a ton of programmers who previously would be outsourcing work to that site, but now they assign that same work to AI agents.
Ever since November 2022 when ChatGPT blew up, I have been focusing on agents in order to try to get ahead of the curve. But I haven't managed to get an agent business off the ground and have been doing poorly paid agentic projects from that site instead.
But now everyone is building agents, and this crazy list of accomplishments makes it look like we are close to the point where the agents are building agents.
In fact the next time I get an Upwork contract for another agent, I actually should run it through my agent and see how far it can get. What I'm seeing a lot of now is requests to automate as much of a business as possible.
Anyway the point is these models are just about capable of doing the entire job of analyzing a small business and building out all the agents and iterating on them with the business owner.
That's actually what I should build is a SaaS that does that. Which I would if I wasn't basically desperate to get another contract this week.
And I know Upwork is bad but I have not had much success with other options on short notice.
> Anyway the point is these models are just about capable of doing the entire job of analyzing a small business and building out all the agents and iterating on them with the business owner.
If you search for LLM benchmarks focused on real-world tasks, you'll quickly learn this ain't the case. No point in telling business owners about that though, they need to see/learn by themselves.
he is the real world. he is telling you that it is the case. when will you people wake up. this shit is real and happening. people who would have reached out to you asking for help are now not doing that. people to whom you would otherwise have gone for help are no longer hearing from you or never met you in the first place.
According to a few tasks from my little personal coding benchmark it's very good at coding and kinda bad at web design. (Also excellent at "draw me a picture" one-shot prompts, for whatever that's worth)
On a sneaky one that involved parsing MIME headers and dealing with character encodings it did better than Kimi K3 at Max and for 38% lower cost.
Interestingly it seems noticeably better than the qwen3.8-max-preview model they offered just a few weeks ago.
What does the benchmark even mean when people are using AI to make real world things that solve real world problems?
I see people, and my self making amazing things with AI and fixing old projects and having real world impact at the fraction of the cost it would take me to hire people, or hours spent on my own coding.
I have built tools and systems with AI that have allowed me to build windows drivers, android apps, web apps, iOS apps, vm occultation, custom block drivers, custom file systems and more. To the point where entire products have been created.
Not trying to be a doomsday, but yes. It seems as though with the right infrastructure we are at the point where businesses owners can go from idea to product very fast and not need or hire much external talent.
Idk about sold. But it’s loaded on all the windows machines in a fairly big company that solves a real world problem.
It allows us to apply custom ACLs to AI agents and the child process spawned by AI agents. Giving us the ability to control what files an AI agent can read or write to, while still being in the calling users context. It allows us to force all ai derived processes to use a transparent MITM proxy so we can then also apply robust access rules to remote host allow or deny access to specific urls and not others. It also allows us to monitor access to windows Credential Manager with rules ti allow specific singed binaries to access some credentials but not others. It give us complete control of what AI agents on windows can see or not see or access.
Windows native sandboxing is lacking. You have some stuff in WSL that completely are broken once you call a windows native app. Or you have app containers which are too restrictive and result in applying expensive file system ACL to all files the app containers would access, which can take hours when dealing with million of files, and would be required to be applied every time you chains your app container (there are some workarounds, for them but they still have a one time cost a long with a fairly flaky maintenance process). You can get the network part done by running commands as a different user but that would result in the same file system ACL nightmare that app containers has.
Result is we get seatbelt level sandboxing in windows native, and can apply dynamic rules like preventing access to .aws folders regardless of the OS level ACLs, using glob rules like */.aws, so we don’t have to be aware of the exact path ahead of time.
It also has registry tree ACLs and, can prevent process and process trees from gaining administrative access, the list of features goes on and on.
Suppose I wanted you or someone else on Upwork or Fiverr to port a Rails 4 app to Rails 8 (or React or HTMX or anything up to date and maintainable).
Assume the business logic and all edge cases work in the legacy app. The app is "done", just too old to work on or run on modern hosts. Hence the project.
Would/could you use AI to deliver the project at 10x the speed? Or at 1/10 the price? Or charge the same amount as ever?
I have a feeling the lowest-cost developers on freelance marketplaces are not very good at using the AI tools (and whatever related acumen) and need a lot of hand-holding, the same amount of effort almost as if I decided to guide AI to do it myself.
IMO, there's value you can sell in being someone that (even developers) can hand off a project to, to do end to end, if it's fully specified. Even though the buyer could do it themselves, the developer would be taking on managing it and having AI do it until it's correct, and all the little things, few as they may be.
AI should be giving contract developers superpowers. If it's not, then the limited set of contractors with the AI superpowers will be able to charge 10x as much for their time, and the actual cost of the project remains the same even if it's done 10x as fast/easily.
Curious if you see/feel this, or if there's a marketplace for "please vibe code this for me, make sure it works and is what I want".
There is still some use to having someone else supervise it and/or test it etc. But most of that gap could probably be closed by setting up your harness so that it can efficiently do the end to end testing including UI itself. Recent model releases seem to know how to set up playwright or whatever and test the live page on its own. But I still see some improvement to be made when I look at it myself.
I would say 90% of outsourced developers are going to use AI just like someone making top US dollar. Actually for the majority of projects on Upwork, the need to generate with AI is much greater. And I think it's nonsense the idea that an overseas programmer can't figure out how to make adequate requests to a frontier model.
The models are smart enough that someone could literally just say "convert to Rails 8 like you are real real good best Railsdev" and follow up with a few "now test and make the bestest code Base" and if they are repeating that maybe with a few different leading models.. will a real Rails dev be able to find some fault? Sure. Would they have also found issues with the work of another human Rails dev? Yes probably.I just don't think it takes as much actual skill at this point. And certainly there is no secret ability that people in San Francisco have but not in Bangladesh or only certain neighborhoods or whatever.
One guy I was talking to said he was frustrated because his developer was not using AI enough. I came to the conclusion based on his requirements and relatively generous project timeline that that developer was deliberately avoiding using AI or admitting that he was using AI because he knew if he did then the main project phase would be finished in less than a month.
It does help dealing with what previously were often impossible timelines. Recently my Upwork profile has focused on AI agents so I have not been seeing many of those typical line of business type jobs.
But also more than half of those more typical business web apps are now starting with "I have already generated a prototype" and they want it to be made. "production ready" which I think contractors are taking advantage of how vague that is especially since the majority of these projects have pretty modest actual usage. I actually don't like that because it's much less concrete than delivering actual features.
Im sorry to hear about your situation. Have you blogged about it? I’m curious about how the volume of your type of work has been changing over the past 2-3 years.
That working is as not common as college dropouts becoming successful entrepreneurs. But both become stereotypes. But I know you might also just want to read about it.
PS. Not everyone (in fact the rare few) write as if no one is reading. For most, literally no one ever reads :D
You need to run away from any client who thinks your primary purpose is to write code. You must run away from the business if YOU think your primary purpose is to write code.
You should be able to write code and do it well, But the AIs, and I'm not even talking about the ones on the frontier, have been able to write code faster than I have for quite some time.
What you need to explain to your customers is all the things around writing code: software architecture, performance, and so on. Also make sure you have some understanding of the customers business, so you can suggest additional ways to make their lives better/make more money.
Software developers have a chance. Code monkeys? None.
bingo. to give an analogy in terms of law firms - you can either be a partner who brings in new business or the associate who does the grunt work of reviewing/writing contracts.
First, I am aware they have been able to write code for some time. I made my first LLM coding agent experiment the day after ChatGPT first blew up in November 2022.
I haven't been a "code monkey" in the last say 15 or 20 years of my programming. I would argue also that code money was never really a thing, it was just an excuse that pretentious overpaid developers used for the existence of underpaid exploited programmers. 95% of programmers have to do requirements analysis and design regardless of their pay grade. I learned a long time ago how important requirements analysis and good design is. And the jobs I am applying to largely specify architecture and requirements analysis in the description. Employers are aware of code generation tools.
But also, the premise that writing the code is not the hard part is just nonsense. Sure requirements analysis is harder than people realize, good design is hard and so is understanding things about proper and closed iteration. But that stuff is not, in the aggregate, harder than the code.
And also, frontier models can absolutely do requirements analysis and architecture. And the sheer speed of implementation means that there is a huge built-in advantage for iterating more effectively.
"Code monkey" absolutely was and still is a thing. I was hired by Big Name Co originally to be staff but a reorg had me do typical senior position duties for around a year, and it was hell. Every decision was made over our heads, feedback was unwelcome because the feature had already gone through design and gotten layers of sign-offs from different areas of the org, etc. My sole purpose was to write code and review peer code. Staying there a full year was way too much.
Second point, requirements analysis is hard because:
* Different stakeholders have conflicting goals, sometimes mutually exclusive
* Some stakeholders have ideas that are actively harmful to users, and you must flex political capital to nip it in the bud
* Some stakeholders think they know what they want with a far greater degree of clarity than they actually do, and lack the time to sit down and suss out the nuances, because you're a side-show in the grander scheme of their own priorities.
Writing code is a heck of a lot easier than dealing with people at the edges.
Those are good points, but that mostly comes down to lack of organizational status or office politics etc. which is independent of capability level and largely orthogonal to whether it's a human programmer or an AI agent. Unless you are redefining "software engineer" to be more like a political operator. But that goes for any human group and any job. And anyway after a certain level, not providing the senior engineers and or project managers with sufficient political sway to realize projects goals is a management failure. Unless again you are defining software engineer as someone who is a master social manipulator in the context of their job.
A big part of an engineering performing requirements analysis is owning the technical understanding of the requirements and identifying areas where the requirements are not technically feasible, can be done with less expense with adjustments, or can be better achieved with a different solution.
AI will help you draft the communication of that knowledge, but it won't help you actually deliver the knowledge or ensure that the knowledge was appropriately received. As you say, it is a fundamentally human problem, which is why I maintain that writing code is easier.
To say that you're building agents, is based on a way of viewing things that isn't at all pervasive. Some might say you're building a customer support chatbot. To talk of building agents as a common activity makes sense if you use LangChain I guess, where its title is "Open Source AI Agent Framework | Build Agents Faster". But for instance, YC just released qm, and in its README it talks about customizing the agent, which is quite a bit different from just casually dropping the phrase "I'm building an agent". https://github.com/yc-software/qm
Not sure what you are trying to say but thanks for reminding me about qm, it has some cool features.
My MindRoot framework had some of qm's main features awhile ago though. I do try to use it to build solutions by customizing rather than from scratch when possible. But a lot of clients or potential clients don't like that idea even though it's MIT. They want some that has clearly been invented by them as a new programming project.
I actually turned down a project last year from someone who wanted me to start over in LangGraph -- he had already decided on it before he knew about my thing.
The most recent project I have been using MindRoot and building up the voice capabilities so I can now handle voice agents end to end including SIP.
But on that website now I try not to emphasize my framework at all for most proposals. You don't have leverage on Upwork. And also with code generation it isn't a critical point necessarily.
Why are you looking for projects on upwork if you know they don’t pay well and that you don’t have leverage? That sounds like a pretty bad place where to find contracting projects
Please recommend somewhere else. I have started on LinkedIn but especially without much time and having so much experience being shortish Upwork contracts it's a hard sell without lying on my resume and so I am having to be careful where I spend my time.
The biggest advantage of Upwork is I have a track record there and there are tons of RFPs. Other places usually don't have RFPs and are going to look down on the Upwork record.
But I appreciate any ideas for places or people to look for work. My email is in my profile.
I am not sure about funding for rent if I don't pick something up this week.
This makes me wonder if AI companies even have a MOAT in the first place.
All requests to an LLM are idempotent, for every API call you need to send it the entire conversation history so that it can process it. LLMs do not learn or remember anything, which makes it super easy for users to switch LLMs on the fly. Most popular AI frameworks, make this a one-liner change these days.
And that makes me wonder if the trillion dollar valuations for OpenAI and Claude are even justified. Cause if that is justified, then Kimi, Qwen, Deepseek etc are also valued at a trillion dollars. Or all of them are worth a lot less. One of those statements is true.
Also this makes me wonder if the next iteration of LLMs would be based on fine-tuning, where LLMs actually learn from your past behaviour so that it would grant some amount of stickiness to the product. OpenAI used to offer fine tuning runs for GPT-3.5, but they don't seem to do that anymore.
While that particular API might be nice, and people and companies should probably push back against the obfuscation, in the end it doesn't really matter. When I hand off between different models I already have the first model prepare a markdown file for the second rather than just importing the entire original thread wholesale, because that's expensive anyhow, and also rather unfocused. They can't get their models to stop generating that sort of checkpoint because that's a fundamental operation necessary for all the harnesses to work anyhow.
The fundamental technology of LLMs and arguably AI in general strongly cuts against that sort of lockin. Handoff is a fundamental capability. There's no option to encrypt the docs or write it in some dialect only one model understands because humans need to understand it to, which stops that whole line dead in its tracks for at least the forseeable future. An AI can already today pick up such pieces, how much more easily will they do it tomorrow?
If they want to lock me in, they're going to need to provide a feature that I need so badly I can't switch and nobody else has. It is hard to see what that would be, other than being a generally better model.
As a heavy pi user - this doesn’t help, for codex. The thinking traces are encrypted and you cannot move that session over to eg your local ds4. It will lack those precious thinking traces that you absolutely need to continue a session productively.
They can be deterministic. We did this at Groq, if you sent a request with exactly the same input token, seed and temperature value you would get precisely the same result every time.
This is harder to do on other architectures that themselves aren't fully deterministic though.
Do you mean that you get the exact same result (as word for word) because from Groq's documentation, it says that determinism is best effort, and also it's not guaranteed across model versions : https://console.groq.com/docs/prompting?_sm_vck=tMW2S7k17245...
My understanding is that it's best effort because even if you control the seed/temperature, the model version and hardware specs you still have floating point math that will introduce rounding differences
Was the temperature 0? Cause unless I don't understand it right, any non-zero temperature implies probabilistic next token prediction.
You did mention, seed, which I haven't seen available anywhere else (in my limited experience) and it can explain a reproducible inference result, but I feel like this matches the "letter of the law" meaning of deterministic rather than the "spirit of the law".
Using a PRNG and a seed, you can have paeudo-randomness and determinism. The pseudo-randomness is the key piece, it’s random to the extent that it cannot be distinguished from true randomness in polynomial time classically.
I appreciate the replies on the determinism point and I’ve learned some new things here. In any case I probably should not have tagged that on, as my main point was to share that the sort of property that parent is talking about (whether true for all LLMs/providers/harnesses or not) is statelessness, not idempotency.
It's a more or less solved problem (e.g. DeepSeek has batch-invariant kernels, vLLM also has reproducible outputs etc), with an obvious caveat that you need to lock your hardware setup, model snapshot, and inference stack if you want any useful reproducibility across longer time periods. It's up to specific providers to actually implement it to any extent, of course commercial providers are incentivized to do the opposite.
>LLM responses are certainly not idempotent, as they are not even deterministic.
Isn't that more due to an optimization and not how the LLM itself runs?
Like a MoE LLM run on a single input should give the same output each time. But this is inefficient, as any given token is hitting 1 (or maybe 2 or 3) experts at a time, meaning all the other experts are doing absolutely nothing. So you upgrade it to take in multiple requests. But then any given expert can become a bottleneck, so when too many requests need a given expert, some of them are routed to a second or third best expert instead. Within the context of any single request, this looks like non-determinism, but it is still deterministic when considering the full batch.
For everyday users and everyday use cases, that is enough to treat it as non-deterministic (the harness might also send in unique data like current time which means one can never have the exact same request twice), but when talking about LLMs more theoretically, I think we need to consider they can still be ran deterministically even if that isn't as optimized.
Similar with temperature. 0 means deterministic, but anything higher with a seeded value is deterministic. If anything, temperature is us purposefully adding non-determinism to agents because they were too deterministic.
It is much more subtle than your specific example, which is strictly speaking a bug, though ofc it has been used during pretraining for efficiency purposes. Sglang and miles have been working towards full determinism in open source codebases, so the LLMs can help explain the subtleties encountered in actual projects if you point them to these repos.
In the simplest possible case, a distributed addition of floating point numbers is not deterministic if you don't specify the order of the addition operations. If floats are added in a first-come first-serve fashion (the simplest implementation of a reduction operation) you already lose determinism. These could be activations from multiple experts, but it could also be adding chunks of a matrix operation that uses multiple CUDA cores.
If you manage to achieve determinism in temperature zero, it is possible to extend it to determinism at higher temperatures, because at that point you only have to keep the pseudo-random-number generator state in sync across parallel instances and this problem has been solved.
Google figured this out with their paper from 2023, We have no moat and neither does OpenAI. The moat now is the harness and being able to recursively self improve from RLHF, a great example is how Grok used to be pretty bad but since SpaceX bought Cursor, they used that data to train Grok 4.5 which is now very competent at coding and even exceeds frontier models in certain benchmarks.
Moat is not the harness. Harness itself is temporary until the models get better and slowly the code in harness will go down.
Note that the biggest GPU providers in the world are the hyper scalers and even they couldn’t allocate more if you pay for it. Because the rich companies and well funded ones are gobbling them up to the point where if tomorrow a 5T model that smokes every other model in the world is released you just can’t afford inference.
Agree. Harness can not be a moat. There are many open harnesses and they are at least on par with the providers ones. It looks like Anthropic/OpenAI's approach to vendor lock-in is not so much the inference or the harness it is functional integration across the individuals and teams in a company. I don't think this will be a moat either, but I think it's all they have outside of compute.
The training data, at least up to now, is very abundant and basically every lab has the same data from scraping the Internet. RLHF data is what's now valuable.
One possibility is that they're counting on another bailout package like the banks and others got in 2008.
The bets are absolutely huge and have been spread throughout the financial system via securitization. If the number does not continue to go up, a lot of people are going to find themselves backing their numbers with money that simply isn't there anymore. Therefore, the number will continue to go up and we (meaning American taxpayers) will back whatever it takes for that to happen.
Now, were the bets stupid to begin with? Of course. Who wants a machine that potentially unemploys major percentages of the population? No one. But the bets were made regardless, and then more bets were made on the hypothetical returns of the first round of bets, and so on and so on.
I think Anthropic/OpenAI do have a moat in the (western) enterprise market. Chinese hosted models are a no-go, and my experience with enterprise IT departments is that they will not self-host. So far signing up with a known product (e.g. Claude) seems to be the way they will go and this is the moat that the AI companies have. Alternatively there is Copilot, but that for now seems to mostly be backed by Anthropic/OpenAI models[1].
Will this continue? The field is moving too fast to tell. Kimi, Qwen, Deepseek also produce very capable models but that doesn't automatically translate into trillion dollar valuations.
However, trillion dollar valuations on Anthropic and OpenAI, such new companies, never publicly traded and such huge valuations decided just by investors. This is just asking for trouble.
The actual moat is the same as in web services - data and user base.
Why is Google a monopoly? Do they have so advanced software that no one can outperform? I doubt it. What they have is a giant user base that generate loads of real-time data, which make Google services more accurate.
So how AI company can build a moat? Exactly the same way: by making a giant user base produce loads of real time data.
Just imagine a service that will generate answers not only based on data they were trained on, but on all data from all user conversations. Imagine being at a concert, looking for a certain type of beer and instantly receiving an answer from an AI assistant about that only because some other guy in a crowd looking for exactly the same thing said to his agent "ah, here they are!".
It is not happening just yet because of making it secure and private is not yet solved, but it's just a matter of time I think.
> that makes me wonder if the trillion dollar valuations for OpenAI and Claude are even justified.
They aren't, not even if we forget about the capable Chinese models.
I suspect Anthropic will implode soon when employees are unable to get the cash-out that they expected. Having so much compensation locked up in company stock is risky on a good day.
The first is the compute. OpenAI and Anthropic secured huge amounts of compute, Google, Meta and xAI have their own huge datacenters. Now anyone can rent some cloud machines and start serving Kimi K3, but it's going to be impossible to get to a similar scale as the big 5 above. And inference has economies of scale: the more people you serve in parallel, the more efficient you are.
The second is the data. By now (and maybe even by one year ago), all the data on the internet has been used for training. You need new data. The big AI companies sit on top of trillions or quadrillions of tokens that they have generated over the years. They can use that to train new models. That data is gold, and the proof is that SpaceX was happy to pay $60B to acquire Cursor.
If you want to overtake the frontier labs, you have 2 options: use their models to generate synthetic data, and provide lots of (cheap, maybe below cost) inference to generate your own new data. The frontier labs know about the first, and I'm sure they try to limit how much others milk their models. As for the second, that's the "honest" way to compete, but it's not easy.
compute is not a moat, it's a rapidly depreciating physical asset. buying up all the shovels in a gold rush does not give you a moat, it gives you a slight advantage for the time being. someone else will just start making shovels. and the data is clearly available, hence the number of open-weight models.
well the boards have been laid across the two shores, and the enemies are now climbing the walls, i guess is what i'm saying. if they had a moat, it's been breached
your post helped me realize a change Meta is pursuing on Instagram that is to give more weight to captions and long text posts so they can have more data to training that would usually go to websites/Google. Even AI slop is good for this.
> This makes me wonder if AI companies even have a MOAT in the first place.
Generally speaking they do, at least from my experience when switching from one model to the other - their performance decreases, and they often do large refactors outside of the requested scope as they try to bring the code closer to 'their' style.
Which makes sense imo - they'v been trained to iterate over the code they wrote, and not code that was modified by someone else in the interim.
These companies have AI and enough money to lobby the Pope. They can afford to reanimate members of congress and push some tactical legislation through.
But all the money in the world cannot move government too quickly.
Other moats exist too. OS or browser can undermine performance and availability of alternatives.
With their current API approach they're essentially a commodity. They need to start moving parts of the harness behind the API, otherwise they'll remain a commodity.
Recursive self-improvement changes the parameters a bit, especially for the market-leaders, and it's the one thing that makes me wonder if they'll be able to extend their lead faster than the smaller labs can keep up, but it's an option available to everyone.
> Cause if that is justified, then Kimi, Qwen, Deepseek etc are also valued at a trillion dollars
It’s more like a bunch of people are placing different bets. Only a few bets are going to generate a return, possibly only one, but the profit on that one bet will make it all worthwhile. That’s the theory, anyway.
This isn’t technically true. Most model providers don’t send the thinking tokens anymore, so if you switch from one provider to another, you will be missing large parts of the conversation.
fine tuning runs of models the size of gpt 5.6 are absurdly expensive. I'd guess at least $100k in cloud gpu time for a single run, and you have to do a few iterations to get things right
I think you're right and I think it's why Google have taken their pedal off the metal for model releases to focus on integrations and tools. And why Microsoft have backed off from the OpenAI partnership to do the same. Anthropic and OpenAI are going to massively struggle to maintain their pace and reach profitability just selling commodity tokens.
Fine tunes are a possibility but I think it offers very little uplift for the vast majority of uses beyond just stuffing enough context.
From my experience these open source models are nowhere near the performance offered by Fable/Opus/GPT-5.6. Whenever I tried Qwen, Kimi, Deepseek, the results were much worse and it just took much more time to get something usable. When you consider that, the frontier offerings are still much cheaper.
That might be true right now, but how long until you have to move the goalposts? In my experience with DeepSeek and Kimi, they're as capable as the frontier was four months ago, which already solves a big chunk of the coding tasks that I'm interested in.
Same prompt for both for the conversion. I used OpenCode for the qwen version, but I encountered a significant amount of errors / timeouts while it was running. Claude finished in around 16 min, but I spent close to 2 hours shepherding the Qwen build. For the implementation, there were signs it had good vision, but the timeouts make this very hard to use in a production setting.
Update: Tried using Qwen Desktop. It told me it succeeded, and linked me to the folder it created it in.
That folder didn't exist, I asked Qwen where the files were, and it attempted to create them again.
Stopped it, and asked what happened to the original files. Qwen Desktop apparently doesn't have access to the local file system, but continued merrily along without warning about that.
I've had nothing but trouble with Qwen on opencode. GLM too. I know a few people who use them on Ollama could without issue though, so I don't think it's a model issue.
Both horrific (from a user's perspective, perhaps not the underlying code).
I've noticed another type of AI slop that's prevalent in all the popular models; font sizes and variations like you wouldn't believe. It's very hard to instruct LLM's not to do this.
I thought it was pretty cool, its the kind of stuff that I wish there was more of on the internet. I guess some people's imagination runs a little more than others.
Whilst these coding models improve, they seem to cater for multiple languages, and for many, a trimmed-down LLM that supports just one language would be fantastic. This would be smaller, more able to run on the hardware people have at home, realistically (even on the CPU). Having one LLM that knows assembler, java, bain, C etc is neat, but when you only work and use one language at a time, it would be kinda neat to have those broken out into dedicated, smaller LLMs. After all Qwen3.8-Max handles over 90 programming languages - ask any programmer to name a list of computer languages and if they get over 20, they are doing well.
I believe the knowledge of multiple languages brings something onto the table that having narrow language knowledge does not. The LLM will be able to better generalize a problem, apply universal concepts and avoid mistakes that would otherwise be too "echo chambery". Even as a human, knowing multiple languages has made me better at my primary language.
That's not how LLMs work. If you're talking about number of parameters, you wouldn't be able to reduce the size much by "removing" support for other languages.
Imagine our LLM as this one-dimensional matrix (instead of unfathomably many, many more dimensions):
0.728201017
-1.9293839303
2.93020202929
5.82290101728
-0.2844920917
…
And then, all of these values are multiplied by some scaling factor, essentially vectors pointing in some direction (in our case, only on a one-dimensional line instead of in some higher-dimensional space).
Now imagine it’s possible to “remove” the portion of the weights’ directions that’s responsible for some undesired property like history knowledge, or another programming language:
0.428201017
-1.6293839303
2.63020202929
5.52290101728
-0.5844920917
…
(For demonstration and faster-to-type-out purposes, I’ve changed each vector by deducting a constant amount of 0.3, which doesn’t make much sense. In reality, it’d modify probably all digits in some direction.)
Now we can see: the number of rows has stayed the same, but the value has changed. This means the required storage (or computational) space remains the same.
Please do correct me if this simplified too much or I had a mistake in my understanding.
This is oversimplified. The proposed question is whether a model with fewer parameters could achieve performance on one language similar to that of a larger model that’s been trained more broadly, which isn’t straightforward to do.
Oh, I didn’t interpret the above question as asking in that direction; but yeah, that’s of course something I didn’t attempt to answer with my comment.
Although I’d be intrigued in the answer to that small-narrow vs. large-broad model question, too!
The exception being the token embeddings and lm head (which scale with the number of tokens the model knows and presumably you need a smaller number in the tokenizer for only English and python). But those are a pretty small % of the total model weights on most LLM sizes
Only a tiny, tiny fraction of the parameters are encoding information that's specific to a particular programming language. Even if you could remove those without degrading performance, it would have a negligible effect on the model size.
For a set parameter size, if you benchmark an LLM on a single language, you'd get better results by training it on multiple languages rather than just the one. Scaling quality training data almost always increases performance as the neural network picks up general rules and an understanding of code that are language agnostic.
I think you'd want to remove e.g. knowledge of harry potter universe and ancient egypt. Training on a bunch of high quality java code bases is still likely to improve your python model.
Actually, it's shown that even general knowledge helps coding models because their input is natural language itself so they need to understand it well enough to even turn into code.
Teaching model to code in say python does indeed increase its effectiveness in other seemingly unrelated areas. On the other hand I remember that early models of ChatGPT were great in chess notation and later models aren’t as good as OpenAI doesn’t prioritize this now
I think the window for a ban of open weight models is closing fast so let's hope US administration is going to miss it and we get Fable-level models (at least in some aspects) with open weights without infringing any newly introduced law as a long-term local baseline.
Even if the US does ban it, Europe due to its lack of European competitors, has no current similar protectionist incentives. Even if there is regulatory capture in the US, the rest of the world isn't going to follow suit until the current market leaders outright are replaced with other competitors that Europe would want to favor. The genie is out of the bottle in the West already.
They are profitable and active on the enterprise local model territory. You can RL a model with them for your own purposes and I heard good things about it.
If China keeps releasing LLMs with such permissive licenses it probably works better since the pretraining rnd is subsidized and de-risked - but that’s a big if.
Would be hard for them to act alone due to free movement in EU. Would be hard for them to outlaw a German business from offering the models from a German datacenter to French customers.
Theres a pretty big chance imho that europe will follow suit , or at least the segmenets that are fully aligned with the US on all things china even to their own detriment.
Remeber rip and replace Huawei 5G because of national security , leading to some parts of europe having worse coverage than some tiny african markets.
Best bet would be for europe to look at it like what it is an open source commodity that is useful to build on rather than a moat to extract rents with.
Doubt it now when US declared that Europe is an enemy, Europe is banned from the best models and that US is considering an invasion of Europe (Greenland). The days of US influence in Europe is limited.
If it was 2021, probably. But Trump has soured relations in the last 18 months enough that the soft power about technology isn't there like it once was assumed to be. What's he going to do if Europe doesn't step in? Jack up tariffs? Is it that day of the week already?
Open weights is more beneficial to Europe spring-boarding a catch up than defending Claude/Anthropic.
If CADA passes in its current state I doubt we'll be banning Chinese models any time soon. I think most people misunderstand the extent of the permanent damage caused by Trump
He threatened to go to war with Denmark over Greenland. In the US, that might just be a side story, a little joke from insane grandpa president. But it's not in Europe. And it's only one of the crazy adversarial things he did.
well, the EU just brought down the regulatory hammer which complicates things a bit. we will have to see if it operates as a proxy ban on open models depending on how they go about enforcing all the safety, bias, transparency and data provenance requirements.
>Europe due to its lack of European competitors, has no current similar protectionist incentives [...] The genie is out of the bottle in the West already.
I agree, but you are underestimating EU regulatory incompetence
You can send armed men to your house, or restrict companies in America from using these models.
They can also restrict US providers from serving these models. Looking at OpenRouter, there is currently 11 providers for 'DeepSeek V4 Flash 0731' with the only non-US one being Deepseek themselves, which aren't even the cheapest.
> the only non-US one being Deepseek themselves, which aren't even the cheapest.
Interesting.
If I were a western lab, might I be tempted to subsidize at least one faster & cheaper provider for Chinese models to keep even more data from leaving the US?
(I know in this case there can just be more efficient providers, am asking more than speculating)
The NSA will run Mythos against the entire US population and it will find the patterns. Powerful computer purchases. Energy consumption suddenly spiking. HN comments demonstrating intent and/or contempt for the glorious self-evident freedom preserving laws of the United States.
Then they call your local law enforcement with an anonymous tip about this suspicious dude who's long overdue for a totally random home inspection.
When has enforceability ever been a concern of the government? This is the same government that had anti-sodomy laws on the books until the Supreme Court ruled it unconstitutional in 2003.
Making it illegal even without having a realistic means of enforcing it still chills behavior, and so just because it isn't practically enforceable in the general case doesn't mean it isn't without consequences.
Same way they detect if you do business with Russia, North Korea, Iran. They don’t detect 100% of the cases but the punishment are severe enough to deter most companies.
They can't enforce a ban. Companies can just download the models and run them on MacBooks or GPUs (or the cloud) and USG has no idea. It would also hurt the administration's funding from corporate tech (companies don't like to fund politicians who restrict them). I'm not saying the administration won't do it, it would just be very dumb.
It was a matter of time for China to catch up with the US. In terms of infrastructure, manufacturing, and engineering workforce, China has the upperhand and I foresee them becoming the SOTA leaders. Maybe if the US wasn't so busy gatekeeping and keeping things proprietary, they would've had more trust from the open source community.
First and foremost, China isn't energy-limited like the US is. A modern grid, lost of cheap power coming online every day. The worlds most advance ultra-high voltage transport links are tying the deserts in the west to the cities in the south.
It probably sucks for you if the proposed powerline goes through your house, but the need of the many come first.
Having cheap and plentiful power is a huge market advantage. It was one of the factors driving Norway out of poverty 100 years ago.
I think this is really a rosy image of China’s electrify situation. They have huge needs in the east and green energy in the west, they can’t build their UHV links fast enough. They are doing ok at keeping up, but there isn’t a surplus of cheap electricity lying around in the same places where you have cheap water as well.
What is stopping them from just building their data centers in the west where they have energy surplus? Not like you need to locate a data center inside a city.
Water. The west is really dry. We could throw in data lines as well but it’s really bottlenecked by water. Actually the only place in China with water in abundance is southeast China.
Also they really don’t have the chips to supply those data centers, and the government isn’t so crazy to go that aggressive on AI, they have lots of other priorities as well.
Chinese data centers like to use air cooled chillers for cooling, not evaporative coolers. It's simple because power for the chillers is cheaper than water for the evaporative coolers. The opposite is true in the US
Evaporative cooling is a matter of convenience and cost, not a necessity. Other cooling technologies can be used. Intel has already proven a closed-loop system can work just as well.
One of China's big sources of power is hydroelectric such as the Three Gorges Dam, so if you want electricity and water in one place, then there it is.
The US also isn't energy-limited. They'll just price out all humans living there from buying any electricity.
EDIT: This was obviously meant to be facetious with the intent to highlight the negative effects on the general population that datacenter buildout has in the US.
That graph features a completely-made-up superexponential AI growth curve coincidentally reaching 100% of US electricity generation in 2030, but even if you take the prediction up to 2026 at face value, it also agrees that AI is currently a small fraction of overall demand. Of course any individual data center site might struggle with insufficient local supply, but China would have the same problem if they wanted to build bigger data centers. (Production capacity is actually north of 25 000 TWh, but poorly utilized because of a mismatch in place and time between producers and consumers.)
Also give it to Chinese labs for the vision of going open source and open models to compete. Apart from their great models, I quite like how they publish papers of their research too.
> Today, we are officially releasing Qwen 3.8-Max, the most capable model in the Qwen family to date. This also marks the first time we will open-source the weights of a Qwen-Max-class model — the open weights will be released next week.
> Qwen3.8 is launching and going open-weight soon! [...] You don't have to wait to test it. Just now, the Qwen3.8-Max-Preview made its debut on Alibaba’s Token Plan, Qoder, and QoderWork.
July 19th post mentions “Max-Preview” vs. today’s post dropping the “Preview”. Unclear what changed if anything though.. Maybe broader availability or it’s a slightly improved checkpoint
My understanding is that these "preview" models are usually earlier RL checkpoints, and that "official release" happens when they're happy with the training run?
I believe they mentioned around the preview announcement that they'd be releasing improvements to capability, which I assume means continued training.
K3 also forgot the wheels when I tried it, but it turned out to be a bug in llama-server's built-in SVG viewer. I know you have your own rasterizer but just FYI, sometimes it isn't the model's fault.
Agree, I don't necessarily see a strong argument favoring OpenAI or Anthropic here. In the interest of perspective, can anyone (perhaps playing devil's advocate) give one?
The open models are now good enough for what I want to do with them, let alone any future improvements. And factoring in efficiency gains, a model in the ~70b range starting to satisfy my needs would completely obviate the need to pay others for inference. This does not seem far-fetched to me, comparing with where open models were at this time last year. What am I missing?
Seems to me OpenAI and Anthropic are kinda following the Apple business strategy. Those two offer a premium service that gets better results and works more seamlessly. I.e. the integration between Anthropic models and Claude Code is apparently nice and gets better results, and I've heard anecdotally that Codex is currently the best.
So just like in IPhone vs Android, you could end up with a situation where Chinese firms compete and get most of the revenue and usage with low profit margins but OpenAI and Anthropic capture a premium side of the market and still get a lot of profits.
> In the interest of perspective, can anyone (perhaps playing devil's advocate) give one?
I have numerous cases where Sol failed and only Fable could solve a problem. For example yesterday I was merging a Q2 curved with a Bezier curved face in 3D using OpenSCAD. I tried for over 2 hours with Sol 5.6 high and x-high.
Fable two-shotted it in about 30 minutes.
In my experience open models (or GLM, DS and Kimi) are radically worse than either of Claude or ChatGPT at these tasks.
I think there is a huge "long tail" of tasks like this where the frontier labs are ahead, and I think this long tail is quite important.
I settled on Fable for design, Opus for build routine, and every now and then I'd try out the Chinese models. In my experience they do fine on small codebases, and quickly get confused on anything larger than 500k LOC.
My use case is: mature, very well documented, fully Ai written code, with about 1:10 ratio of prompts/specs to code, and codebase sizes >500k and <2m LOC. Once one achieves the state of good, comprehensive design documentation I can literally vibe code with single sentence prompts thanks to the extensive test coverage, ADRs, and tens of thousands of lines of specs. Fable/Opus works predictably well, the Chinese models are literally dangerous to the codebase.
Hardware and electrical costs including power usage and electric wiring/outlet costs of such machine.
Unless you are spending more than a max subscription (200 a month+) its cheaper to use the cloud.
But things are priced cheaper in the cloud now to lock you in and restrictions around hosted models are getting worse.
If you only have a $300 dollar laptop its probably not worth the upgrade.
I'm personally excited by local AI but the experience for the average isn't the same. I'm willing to get .5/s running on 10-15 years old machines but what I can do with it is limited.
Their marketing department :-) . I'm only half-joking; those guys are hard at work finding the best product-market fit for ChatGPT/Claude. "Product market fit" means "strongest revenue", which is not necessarily going to bring the best tool for you or me, but the one that can either get more consumers to shell off money, or more enterprises to cough money for licenses, and in both cases those consumer basis will be narrowed down to what legal and geopolitical circumstances allow OpenAI and Anthropic (and this is why they want to ban the competition!). It also means dark patterns and enshitification, of which I'm already seeing some both in the Codex interface (it was just renamed "ChatGPT"!!!) and in Claude Code (which also is just "Claude" now and can't '@' properly any longer). So in the medium run most people will be better off running an open source harness that can use any model.
you can't create a new drug by running a model on a laptop. You can't serve a customer support bot running on a laptop. You can't generate video in bulk for many users on a laptop. So there is still a case for paying others for inference.
Does it justify the valuations? No idea, but some major use cases are still there. That's why they are rushing to implement, OpenAI creating a "deployment company", Anthropic having some pharma rumors, etc.
yes, but at that point even open weight models are not free. Can someone set up the best open-weight model for free and charge pretty much just for GPU usage+operations+margin for inference? yes. But they would have to keep up with how good the private models are. The bet is private models would sell in a similar way at a premium and invest in staying ahead. Is this bet going to work? no idea, don't shoot the messenger.
> yes, but at that point even open weight models are not free. Can someone set up the best open-weight model for free and charge pretty much just for GPU usage+operations+margin for inference? yes.
Or, the company itself can rent the GPU and host the model of their choice on it.
> But they would have to keep up with how good the private models are.
Why? The whole point of the thread is that the marginal gains of using private models is a fraction of the additional cost of using those private models.
IOW, if you really need that improvement over the open models, you can go ahead and pay it. What we are seeing though is that the improvement in general is barely noticeable but the cost is 4x as much.
Can they still go public ? MiniMax M3 Pro is also coming, then DeepSeek-v4-Pro GA, then GLM5.5. There will only be bad news for them in the coming few weeks/months.
My guess is their strategy is to aim for clueless developers, with more focus on "proactivity" with Fable and very large models. I have a few non-technical friends who dabble in AI coding and I am watching with amazement how much better their creations get with the newer models. 3 months ago, they would "ship" stuff with a backend at 127.0.0.1 and api keys in the frontend. Right now they produce, without an iota of understanding, decently designed, but not very secure or efficient cloud native stuff that sort of works. I bet in 3-6 months they will be releasing secure, scalable, well architected stuff without spending a single thought on any of those things. And I think this is where the really big models will be a bit of a moat, for a short while at least.
US AI labs really rub me the wrong way, especially with the doom and scare tactics they use. Both Altman and Dario keep talking about how AI will replace workers and how we should regulate LLMs for national security, Dario’s main point.
LLMs are useful. We can all see that in agentic coding. But replacing everyone’s job? Hardly. And what’s with the scare tactic of trying to get the US government to ban foreign models?
LLMs are useful, and dare I say they’re on par with the internet. Making them cheaper and affordable is good for everyone. The fear mongering from Anthropic and OpenAI looks like an attempt to corner the US market into using only US models so they can keep the profits, especially since China has proven that LLMs are a commodity. US AI labs should work on making LLMs cheaper or better harness. Altman and Dario are not trustworthy.
> "Anthropic believes that they are the ones who should have final say over how Anthropic is used; given that they think only they should be developing leading edge AI, they by extension think that only they should have final say over AI generally. When you further combine this realization with the company’s pronouncements about AI’s ability to conduct all economic activity, you realize that Anthropic’s leadership effectively wants to have power over everything and everyone."
To be fair, we're simultaneously mocking anthropic for believing in safety so much and also for them thinking they're the only ones that care enough about it. It's true that no one else seems to care as much. Judging by reactions from everyone, all their safety talk is very bad PR.
The criticism isn't that they believe in safety too much. It's that they patently using safety as a red herring with the actual goal of regulating away competition that they know the cannot beat.
The people involved have been talking about safety long before they had any users or a company.
"Concrete Problems in AI Safety" by Dario and another founder was published 2016, anthropic was founded 2021. Among a bunch of other examples, including other founders.
If safety is seen as a joke now, it was really seen as a joke back then. The reason they keep shooting themselves in the foot with bad PR is because they truly believe in risks.
I’m sorry, but this do-gooder concept doesn’t jibe with their other actions. For example, swindling their business partners (such as Figma). Filing an unnecessary trademark lawsuit against a customer whose brand predates Anthropic.
And for safety? Oh yeah, they literally sell the removal of guardrails in exchange for minimum spend commitments.
For example, swindling their business partners (such as Figma). Filing an unnecessary trademark lawsuit against a customer whose brand predates Anthropic.
Completely unrelated to AI safety.
they literally sell the removal of guardrails in exchange for minimum spend commitments
It seems blindingly obvious to me that you'd want to let trusted organizations use the models defensively, which means without guardrails, and that you'd probably gate that behind some kind of enterprise sales process, for multiple reasons.
Honestly, it just seems like you have an axe to grind and aren't really particularly knowledgeable (or curious) about AI safety.
1. This is not “unrelated to AI safety”. The things they seek in the name of AI safety always seem to be aligned with the path that will eliminate competition and give them maximum economic power. Thus, to disentangle what is sincere and what is ulterior, we have to look at the leadership’s character holistically. A ruthlessly unethical and cutthroat corporation that is also seeking unprecedented power doesn’t deserve the benefit of the doubt when it claims to be doing this for altruistic reasons.
2. You misstated what I wrote. My gripe is not that they have an enterprise sales process. It’s that in negotiations, they say “we’ll drop the safeguards if you agree to spend more”. I haven’t heard of any groundbreaking AI safety research concluding that spending more with Anthropic makes the models safer, but maybe Dario has a new blog post coming.
What they believe now or care about is fundamentally not very relevant: allowing them a monopoly would be a big mistake. Google was supposed to do no evil and it probably started that way but nothing guarantees the future other than having alternatives.
If there are genuine society risks in a tech I don't want to discourage CEOs from talking about them. I feel like we've spent decades talking about how evil chemical companies (etc.) were about covering up issues in the 20th century. But yes, that's different to being a reason to ban external models.
I agree re: Dario quietly pushing for government control. He also said LLMs would replace a lot of entry-level information jobs, doubling the unemployment rate from 4-5% to 10%.
but this crap may take forever to play out even if the outcome is well-known. Self-driving is "here", it's obvious that once it's cheap enough having a human behind a car wheel or a freight truck wheel is an absurd waste of human life (kinda like digging canals with bare hands instead of an excavator), yet truckers and uber drivers are still employed. But everyone knows the writing is on the wall for them.
It's not so simple, if such a headline can get them closer to the regulatory capture they want to lock in American businesses and forbid them from using Chinese AI.
AWS Bedrock still lacks the support for the latest open weights models like GLM 5.2, DSV4 Flash 0731, Kimi K3 etc. Even they only support Qwen 3 which is a very old model. Any specific reasons they are reluctant to support open weights models?
AWS Bedrock do support many, older, open weight models. Perhaps there is a reluctance to support anything more powerful that would compete with their partner Anthropic?
In practice I'm not sure how big a deal this is - Bedrock is not the cheapest or best provider (try Fireworks AI or DeepInfra who do support more recent models), and would seem to be of more interest to corporate users who already have an AWS account.
Perhaps also of relevance the US government is hassling US companies that are using Chinese models (currently DoorDash, previously AirBnB & Cursor), which may limit demand for corporate use.
I was wondering the same thing. Maybe it’s a licensing issue. Or the models are too big and there isn’t enough demand. But size is not a solid argument because they also don’t support Qwen3.6 27B and 35B-A3B, especially since they just added Gemma 31B and 26B-A4B.
2.4 Trillion parameters with open weights releasing next week? The open-source community is going to need a collective GoFundMe just to buy enough VRAM to host this monster.
Qwen3.6 35B-A3B Is my current backup model after Ornith—1.0, which has performed slightly better and faster at Python, db and some backend tasks. About 75 tokens/sec on a 5060ti 16gb depending on context size (usually 64k)
At $2/$6, it can be much more "approachable" than K3. Wondering what have they done differently to be able to afford this price (that K3 couldn't, apparently - most other providers offer similar prices to K3 itself).
Moonshot is printing money on k3. It likely costs the same to serve as qwen3.8. The license requires all major inference providers to sign an extra (secret) licensing agreement with moonshot that almost certainly requires them to agree to this price and pay royalties to moonshot. Watch as the k3 price plummets over the next 1-2 weeks.
If k3 is open-weight (and it's available on hugging face [1]), how could they force providers to sing an extra (secret) licensing agreement?
Edit to answer my own question:
License file [2] states:
> If the Licensee or any of its affiliates operates a Model as a Service business,
and the aggregate revenue of the Licensee and its affiliates exceeds 20 million
US dollars (or the equivalent in other currencies) in total over any consecutive
12 months, the Licensee must enter into a separate agreement with Moonshot AI
before using the Software or its derivative works for any commercial purpose.
Licenses are not copyright law, they are contract law. Copyright is automatic and does not require any terms, as long as the work is covered by copyright. Contract law can then override the innate copyright rights (for example to permit people to use your copyrighted works).
If you create something that isn't copyright-able, you can still create a contract which people must agree to in order to use your work. The enforcement/validity of that contract then depends on making sure the user knew about the contract and agreed to it. The way they did that here (just publishing a LICENSE file) could be interpreted as a judge as no confirmation of agreement of contract - depending on if they find a "reasonable" user would have known to look for the file.
Copyright is an automatic protection (meaning you don’t need to register it, like registered trademarks), for certain types of creative works. Contracts are one way to dish out these rights (eg. for a fee).
If you have a work that is not copyrightable, and you give it to people lawfully, then you cannot protect it via copyright.
Qwen is Alibaba. Alibaba rents hardware to Moonshot. So Qwen always has the option of cheaper hardware. Qwen might also break even on inference as competitive advantage since Alibaba has wider pockets. Alibaba also owns a 36% stake in Moonshot, which must make pricing discussions interesting... but then again Google rents hardware to OpenAI and Anthropic.
I like that Alibaba are emphasizing more challenging real-world "coding" tasks as well as just benchmarks.
The "Reproduce a research paper — then improve it" seems particularly impressive as well as actually useful - perhaps as close as we're going to get to "recursive self-improvement" given that these are data-driven not code-driven models.
I'm not sure how much real world use of AI is 100% hands off agentic coding, but I guess for objective evaluation purposes it needs to be something without a human in the loop.
It'd be interesting to see a comparison of each company's best models on a suite of real-world challenging tasks like this, but I guess difficult since each group of tasks would need to be one-time-use only to prevent subsequent benchmaxxing which makes comparisons useless.
I would achieve a lot too if I wasn't interrupted with "how is progressing meetings"
And if with put those meetings in the model it would also be as bad as everyone
> How Qwen Cloud handles your data during inference?
> Qwen Cloud does not use your API inputs or outputs to train or improve models. > Learn more about how your data is handled during the inference process from Zero data retention.
If this is true, this is a big deal for me, but unfortunately I cannot find anything in their legal agreements, so this marketing sentence is worthless.
I have a 5080 Super RTX but its still not enough to run these big models. I use Gemma 4 right now on a Debian Linux with no GUI that I access remotely. Its works pretty well, but still not as good as any of the big models like Opus 5... Can't wait to be able to buy a personal home server that can run much bigger models. I heard some companies have started building AI PCs only used to have a personal AI model at home, have any of you tried one yet?
Their base models and architecture has quickly become the go-to for local inference and fine-tuning, even when they introduced some tricky things like GDN, so many people use it, that it was matter of days/weeks until lots of OSS frameworks adopted it.
The models are good even by skeptics standard, it's just that evangelists are overselling the capabilities. If you understand the limits of LLMs not using them as a business is shooting yourself in the foot.
However, they are not at the point where they can effectively train themselves, nor did they are capable of researching their own method of learning. SWEs in mid-corps on my country are right now relegated to reviews and sanity check, basically babysitting the LLMs and making sure they're not spouting nonsense. If you think about it, that's basically QA and can also be delegated to another AI. If Bun's rust rewrite that they tout as fully LLM-led can pass the test of time in a year or so I think that's it.
I believe all that is now constrained by compute and capital, not tech.
I mean its pretty obvious right? This models are not flawless and sometimes reach stupid conclusions so there needs to be some one who watches it. Thought i must say u are right. Every one of them pretends that this new model is gonna finally take ur jobs lol
There's infinite work to be done, so higher productivity makes people worth more. (Obviously this doesn't apply if AI can do everything but we're not there yet.)
The percentage of the population that needs to work will continue to go down because of aging and automation although you might not see it as a reduction of hours for a given individual employed person.
Already 38% of Americans aged 16 or older do not work and are not looking for work and yet are not dying from hunger or exposure. This would have seemed like a utopian dream to someone from the 1800s.
Being 16, 17 and 18 and not working is expected, given they are still at school. A good percentage of people aged 18 to 24 are studying full time.
This stat seems a bit misleading and doesn't support the "you can live and not work argument". How many of those people are retired? How many of those are under 18? How many of those simply cannot work? How many of those live at home or are on food stamp?
government, where two thirds of spending comprises transfer payments that help households bridge the gap between their spending, retirement, and health care needs and their wages and salaries [1]
The market comment linked in the paragraph above that one features a toy model of the US economy which illustrates this (section "Equilibrium in output and income"). [2]
Maybe you should ask AI to review your statistics and reasoning. A large amount of 16-22 year old kids are in school. And retirees 80+ are retired. I don't see how any of this is relevant to the idea of what would happen to the economy if there is an employment collapse. I don't know that this would also seem like a utopian dream to someone in the 1800s; I'm not sure what that means. I think someone from the 1800s would be surprised by a great many things and that statistic would probably be low on their list.
> I love their video with the idea that people will be able to do their hobbies while ai does their job...
Are you not already experiencing this? I think this is fairly common for people using AI now, though the time may not always go into hobbies or sports. It's common for me to setup Claude with an hour+ task while I catch up on housework, or while I'm getting ready in the morning.
In the last couple of weeks I've unfortunately had multiple family illnesses - it has been helpful to have Claude keep up with much of my product development programming work while I visit my mother in hospital and check on my father's recovery. I'm able to give more time to family without worrying that business progress isn't keeping up. The overnight Claude sessions while I'm asleep have been particularly helpful.
We are in the golden period where this IS possible. Once it becomes the norm to "do something else while your agent works", we'll be asked to do more WORK while the agent works, rather than do hobbies/housework/nap/etc.
I used Qwen3.8 Max Preview for 2 weeks and can't tell if I truly miss Fable.
Qwen doesn't overuse em-dashes, weird sentence structures with bold text: normal text.
In my private GitHub Repo Qwen finished as much tasks as Fable did. Without hitting 5-hour session limits. Qwen's token-per-second performance fluctuated greatly. From 20 tps up to 80 tps.
Qwen failed on some "reading between the lines", but so did Fable.
The only noticeable difference is skill loading. Until "Must use" in a skills front matter, Qwen tends to be very lightly loading skills. Superpowers works, because of the same Must use wording. All my local skills use the same wording now. For installed Skills, it's more or less my responsibility to remind Qwen using a certain skill.
I used Qwen together with Qwen Code. I didn't compare Qwen+Claude Code.
With my Owl code scanner, Claude and Qwen produced similar results. Qwen repeatedly used the same output format, despite no format provided and disabling memory. Fable varied greatly. Both followed the file format output and produced valid files.
With Qwen 3.8 being now a visual understanding model, I don't really miss Fable/Opus/Sonnet for my private projects.
At work we are still exclusively using Anthropic models with Claude Code. No change incoming on that front.
Privately, I'm sold on Qwen and Qwen Token plan. No session limits, many open weights models available via a single API.
Since it's horrifying thinking about what US companies do with my data, it's no worse or better thinking about what Chinese companies do. It's a choice between the Plague and Cholera.
In a 30M+ LOC repo we blew out Qwen3.6’s context thinking about a code review. This was via direct call to model. Do harnesses facilitate better context management, or is there something else to accommodate its smaller context window?
What was it reviewing? Was it just a "take a look at this pile of code over here"? Because I think asking any model to review 30M lines of code is a stretch...
in general with automated code reviews I've found it fruitful to tell the model what specifically to look for and where. I usually don't get over 10% of the claude opus context window for code reviews, but it of course depends on how your code is structured, how much the agent has to explore etc.
Does this desideratum make any sense? The whole point is that you write to it in English or Urdu, and it writes the specified code. If you cut off everything but the code writing, you cut off everything.
Domain modelling as well: we have clear programming concepts, but the kind of autocomplete that can autocomplete a customer sales process needs to be able to refer/leverage ontological connections on words like ‘bass’, ‘season’, ‘rebate’, and ‘jamboree’.
Intuitively it feels like focused models should be better models, but human programmers are ‘better’ knowing programming alongside general stuff. We’re not reduced by reading economics or Tolkien, removing such knowledge would be premature optimization.
Thanks..non AI expert here just wanted to understand..cos one of the most talked about topics is running models locally on cheaper hardware. Helps many esp in poorer places.
Would be nice if a genuinely useful coding model can fit into an old laptop, give decent performance and know nothing but (say) Go or c language.
no, apparently, otherwise we'd already have specialized models. every bit of meaningful human-generated data appears to improve the overall capability of the model.
Kind of yes, but this be get you are pretty stupid autocomplete yuo can probably avhieve without an LLM at all.
>we have professors with specialisation
Yes, but any professor with a specialisation is an erudite with vast knowledge across the board.
I'd argue we can have a meaningful BIG model with a specialisation but not vice versa.
PS: all of this is about a model that is expected to be able to execute development tasks on a human level. Obviously we can have small models that are very capable in things like TTS or STT.
I guess it can but it will be useless. After all the model superpower is awareness and ability to guess and infer some stuff. Right now a model saves you time not only by coding faster, but that it can figure out some stuff about the shape of the data and its purpose.
If you throw general purpose model at a codebase - it will look at the table and data logical connections beyond what is explicitly declared. It will figure out on its own that Salaries should be displayed on SalariesTable.php and it will "know" that your prices should include vat and so on.
A human knows that VAT and price go together and are related, full size LLM does too, stripped one - doesn't.
I'm trying and failing to find value running a potential Qwen 3.8 27b dense model on a 16 core, 128 GB of ram, 2080ti box. Yes, the GPU yells for help, but the problem is that no math works to upgrade this machine even when pouring $200 in rent every month into the large model providers...
How are you all justifying economical use of these local models right now? What's the cost efficient way to do this and do better (even with models evolving over time and losing now vs later) than the big labs?
You will simply not get more value out of running a local model vs paying for a subscription/API from the cloud in 2026. There is no math that will make local models come out ahead in $/intelligence/token.*
The point of local models is privacy, offline use, and maybe no guard rails.
* Not talking about enterprises that buy DGX racks and host Chinese models for internal use.
Points are starting to be made in favor of value, to the contrary of what you are affirming. Specifically because the new open weights models lower the TCO of hardware in an environment where new open weights were previously thought to be a thing of the past.
There is for $/creativity/token. LLM sampling settings are poorly supported even in open source serverless providers but are the single best lever you have for getting better outputs in regards to creativity (and quality for long context or highly quantized models).
There's no point, except if you want privacy and independence. I am playing with a personal assistant that checks my emails, calendars, sends me an agenda and maintains my TODO list. I am not sending such data outside.
Running locally for me is mainly about learning, maintaining control+privacy, and helping shift my coding+design process to leverage LLMs. I guess if you made me boil it down to a single word to justify the cost I would just say: tuition.
Sounds like we have similar boxes - mine has a 10 core CPU, 64 GB of ram, and a 2070 Super. My motherboard had two unused PCIe3x8 slots and doesn't support Blackwell GPUs. I bought a couple of brand new Ada generation RTX 2000s with 16GB of memory for under $1400 to get to 40 GB of VRAM. That will easily run Qwen3.6-27b at a 6-bit quantization and 80,000 token context size. It isn't fast (19-21 t/s), but using pi-coding-agent is fine.
Now, my instinct is that I am giving up SOTA performance on agentic coding with this setup and LLM. But the gap between my setup and SOTA commercial models is small enough that it doesn't matter to me.
The only scenario is if you have enough work to do batch inference. Using a tiny fraction of GPU capacity to decode a single request at a time just doesn't make sense, as you say.
> $200 in rent every month into the large model providers
We all know that is hugely subsidized, and I guarantee that OpenAI and Anthropic are looking to enshittify that ASAP.
The enterprise users, however, are not subsidized like that. They pay per token. And some developers in those companies are chewing down a lot of tokens. Self-hosting an open weight model could be a massive savings very quickly. It also gives them negotiation leverage when talking to OpenAI and Anthropic.
Well, it is always possible that OpenAI and Anthropic have some secret sauce that they are hiding that nobody knows about. However, the pricing of tokens on places like HuggingFace and OpenRouter suggests that probably isn't true. In addition, the lack of reliability and general slowness of Opus and Fable at various times also suggests that.
Now, the one wildcard in all of this could be Google. They are on the eighth generation of their TPU and have been holding their cards extremely close to the vest. I don't think anybody has a good read on exactly how much capacity they have. Most things you can kind of figure out the overall business numbers and what's going on in Google--the TPU area is one of the exceptions. I know a couple of big customers and even they don't have any visibility on that front.
Yes, I don't. I have my own harness where the agent is only able to do a set of predefined things and none of them involves the internet. However, I imagine the model could be a lot smaller if it does not have the baggage of coding and programming in general.
I mostly just need it to mostly be a very advanced NLP model that is able to figure the set of tools that it needs to call with what argument. Then it will just follow a predefined decision tree.
AI is a commodity. This is proven now. And valuations will have to drop 90%.
There's a lot of AI models that each 'win' one week over the next. The pure definition of a commodity market and not a 'winner takes all market' as valuations would imply
> In this case, Qwen3.8-Max was asked to create the oh-my-cli project from scratch and, over a 10+ day long-horizon autonomous coding run, build a self-evolving harness.
They don't explain how successful that went but it's a bit hilarious seen that an Anthropic dev explained that it's been 15 days Claude was hard at work --with nothing to show yet-- trying to rewrite itself in another language.
"You rewrite Claude Code, we rewrite oh-my-pi."
"You're nowhere after 15 days, we do it in 10."
Sure, it's apples to oranges and all that. But part of me thinks they know fully well what they did there.
It's meaningless. Models have always been able to do this and this capability is strengthened during RL since being able to explore the solution space to figure something out will give it a reward.
What is important is how long it can go without requiring human intervention. Not just that it's possible to run on its own for a time.
The second sentence of the page: "This also marks the first time we will open-source the weights of a Qwen-Max-class model — the open weights will be released next week."
Reddit has become a fairly worthless site because people just ask questions instead of taking anytime at all to figure out the answer. He just needed to wait for the page to load instead of adding noise to the discussion
Particularly, I had one team member who was extremely sceptical of AIs/LLMs/harnesses and refused to use them. One day he said "Well, I have an RTX 5090 doing nothing... should I try to get something up on it?" and a few minutes later he had 3.6-35B loaded up, running OpenCode.
It continues to be a workhorse to this day, running on both my local Mac for various types of jobs, an AMD R9700 at the office, and said teammember still uses it on his 5090, although in practical terms we do a lot more with DS-V4-Flash-0731 these days.
I haven’t found it very useful for code. It can do some code, but I’ve tried a dozen different quants and context lengths and the output is always bad enough that it has to be discarded for anything other than really easy tasks. It has been useful for exploring codebases for search and summary, though.
DS Flash is where local models begin to feel useful for coding, but the quants we run locally are sharply reduced in intelligence from the benchmarks for the full models.
For applications where data cannot leave the local network it’s good to have them. For actual coding work I can’t actually justify the power of electricity and cooling, let alone the expensive hardware, compared to hosted APIs.
But I admit I do enjoy playing with them anyway. I think it’s one of those hobbies where it’s most fun if you never do the math on how much you’re paying for the privilege. If someone has a requirement that data stay local then it’s different, of course.
Do you mind sharing your use cases?
Sure a lot of this could be done without AI, but it's certainly quicker and easier, and since my AI box is on solar, it's just the power of the sun to keep it going.
I started with Karpathy's LLM wiki, and did everything he said not to do - downgraded the model to mere tool usage and summarization, and it works great.
I am a data hoarder, and finally I can just dump all the content I remotely like, and get something interesting to browse for the price of electricity.
Agentic long-running tasks, as others have mentioned:
- Groom and triage tickets for agentic SWE workflows
- bug hunt — the probability of Qwen fixing a complex bug is 50/50 but often it is capable of identifying the root cause or at least laying the ground work for a more capable model to pick it up.
https://smartconnections.app
Keep in mind Obsidian is open standard JS plugins... You know what can write open standard JS plugins?
Classic which comes first, LLM or the plugin, though. :-)
The A3B models are super fast but I found the A3B Q4 model ran in circles a lot and ended up taking longer to complete tasks that 27B Q6 because it kept having to redo/rethink/fix something.
I was writing extensive prompts to rein it in and it would still ignore basic directives like "never force push on the repo, ask me instead". I ended up switching back to 27B after about a week of frustration and lost productivity.
Gemma QAT is an honourable mention.
- You at least need a capable machine, so that's not 0 monetary investment. - You need to spend at least an hour decicding between ollama, llamacp, mlx, etc. - You need to find the correct quantized version of the model that works for you based on the architecture. - You need to figure out the correct context window size to get reasonable performance. - You need to setup a harness that works against your model - You might need to setup additional websearch tools, image tools, etc since harnesses like pi don't come with the model. Ofc you can't use codex and claude code, because those aren't opensource and you are anti-AI.
Or, you could sign up for Opencode for $10 and just be productive.
I'm particularly calling out the hypocrisy of the original comment. Being Anti-AI, and then spending hours on setting up a less than frontier AI model.
You forgot the step before where you spend months waiting for security to vet it, legal to sign off and procurement to approve it.
Or you could use hardware your team has lying around. Everyone isn’t working on cloud-hosted CRUD APIs.
Local AI is almost perfect. But its like all democracy: its history is marred with lots of crap.
The original comment states that the person in question already had a suitable graphics card to hand, so it did not require a monetary investment.
GP clearly states that "someone who is into that that stuff" was guiding the process, so it did not require a significant time investment.
> I'm particularly calling out the hypocrisy of the original comment. Being Anti-AI, and then spending hours on setting up a less than frontier AI model.
I see no hypocrisy in the original comment.
You've also assumed the skeptic in question doubts the capabilities of AI. That may be the case (like you, I have no idea), but they may also have privacy concerns, in which case a local model is the appropriate choice.
There are plenty of reasons to be skeptical of AI.
For me at least the local AI stuff, powered with solar has been pretty great. Would that scale to a large business? Goodness no, but for my tinkering and learning, it works great.
It is 0 monetary investment if I already have said machine lying around doing nothing.
Which is exactly the story OP talked about.
Pi has a nice guide on it (https://pi.dev/docs/latest/llama-cpp) and it is really not that hard.
How is that hypocrisy? Self hosting is somehow anti AI? Its not anti AI. Its literally using AI!
…and honestly, at a higher technical level than slapping your wallet against a token provider and running prompts in a hosted sandbox you can't even see the prompts in.
Local: You need to invest $thousands into GPU and/or very-high-end CPU+Memory hardware.
Vendor: You can use any existing device, even a phone or tablet. A very low-end laptop is fine.
> takes literal minutes to get started
Local: Typical scenario is hours just to download the software, the model weights, and then faffing around with CUDA and matching your GPU drivers.
Vendor: Free-tier available instantly on a web URL. Even local agents have free tiers from multiple vendors. Install is a single command and/or download and "next,next,next,finish" wizard that takes ~1 minute.
> you can just `rm -fr` it and forget the whole thing existed.
I'm still cleaning up multi-GB model weights floating around in hidden subdirectories under my user profile from months ago when I was experimenting with local models!
Meanwhile I simply... stopped using Gemini. That was the entire process: I no longer actively use it. They stopped billing me for my token usage, because it is now zero. That's... it.
You have it totally backwards.
Are you trying to say that local models are hard to use because... you're having issues handling files properly? I am not sure I get the argument.
I get the rest of the comment: local models require an investment upfront, and it is less convenient. It doesn't say that it is not cheaper, though.
I guess they were using ollama, which does not tell you where it puts the models it downloads.
I used to deal with these kinds of frustrations too.
Same story on an RTX 4060 Ti 16 GB. MTP is a serious boost to tg.
Downloading the model is a simple hf command that HuggingFace's web UI even gives you.
llama.cpp is trivial to use, and so is llama-swap, if you want to use other models too.
If you don't know what arguments to run it with, you download ggrun and use that.
Local LLMs are incredibly capable and don't need expensive hardware. A $500 GPU will do. Or even cheaper.
This is all trivial.
[0]: https://www.reddit.com/r/LocalLLaMA/comments/1vef79c/quantiz...
Download LM studio, search models, click download, wait minutes, prompt and have fun
That's one of my main issues with AI anyways, the thought of having all my data go through some sketchy foreign (to me) entity with questionable motives and under a questionable regime.
Local AI solves for all of those.
If you're learning about model inference, then it's a different and you are definitely not anti-AI in that case.
It's not that billing is complicated, but learning to set up a local AI is a lot more useful and more rewarding.
https://huggingface.co/Qwen/Qwen3.5-122B-A10B
And the context takes space +kv cache. KV cache drives usefulness as your context grows, it needs to pull the kv cache.
Simplified, the context has to be run on every turn, so the KV cache supplies the processed tokens, so it just needs the new inpute.
I have an RX 6700 XT with 12gb vram and 64gb system ram. running dense models like 27b is difficult, but i can run IQ4/IQ5 qwen 122b-a10b or 35b-a3b at ~20tok/s
Curious for any more experiences
Isn't that just the definition of MoE vs dense ?
So it can be dumber but its quite capablr.
On the other hand having 90t/s with any local model is nice and Pi with loop police extension can prevent looping a lot.
I honestly think that with my electricity prices running qwen 36B myself is more expensive than hitting the cache rate at deepseek.
I gave it a try for a few days (pi + openrouter + deepseek-v4-flash via deepinfra) and ended up paying ~$18 for rather light usage. Yes it's still cheap, yes it's fast, but i feel i would still get a better deal with a Claude subscription plan.
https://openrouter.ai/deepseek/deepseek-v4-flash-20260731#pr...
You want to use the newer quantization formats like Unsloth's UD quants or oQe, where the weights are selectively quantized using a calibration dataset so that important weights are left at/closer to full precision.
Kind of slow, but using only 14W of electricity so the Wh per task is twice as good as using my i9-13900 laptop with 4060 GPU.
I personally have the Framework Desktop, but there's also systems from other brands like Bosgame
Huge fan of that thing, it's th e Linux MBP I've always wanted.
Tolerable and usable for some things, though thinking makes it take about a minute to reply in many cases. But getting this kind of thing to run on 8GB of VRAM is the main benefit of the mix-of-experts setup: it can do partial GPU loading and get a ton better throughput than a similarly-sized dense model (like 5-10x, sometimes more).
I get about 45-55 tokens per second using Qwen with this setup. I could probably squeeze out more if I messed around with the settings, but I'm mostly using oMLX's defaults for the model.
The weaker point is prompt prefill, which starts at 1,400 tokens/sec but decreases significantly at high contexts. That said, for agentic scenarios, if you're using a harness that doesn't needlessly bust the cache, it doesn't feel slow.
I really hope they release a Qwen 3.8 35B, although the lack of a mention seems ominous.
Kimi K3, GLM 5.2 and now Qwen3.8-Max - open weight models.
DeepSeek V4 Flash outperforming Gemini 3.1 pro, probably DeepSeek V4 Pro update is also coming soon
Chinese labs are cooking very hard. US closed weight labs are probably hard time to resist not calling Washington DC for more AI regulations
Not sure how Qwen3.8-Max is going to be licensed, hopefully it'll be Apache like the smaller ones.
Sure, it’s just the lowest end for now, and the enterprise money is at the top of the market. And there’s protectionism/enterprise lock-in/etc that complicate things somewhat.
But still, if the US AI labs ever tap the training brakes for a millisecond, the “inference is still a money maker” argument seems to evaporate when they’ll immediately have to fight a race to the bottom until margins are virtually nothing.
Or if the benchmaxing “line goes up” FOMO mindset starts to lose its luster and companies find their individual niches for productive use of AI and stop bothering with all the latest and greatest churn for top dollar.
Which might be even worse if it means the training arms race is still ongoing but neither Anthropic or OpenAI want to be the first to “lose”. While the marginal value of each new model training run keeps decreasing and enterprises signal they’re more concerned with cost reductions than solving ARC-AGI-7 puzzles.
There seems to be quite a gap between the small ones and the enormous ones these days.
Just so that we know what 3.8 would be like.
I currently have about 150 Tabs of Antirez posting on AI and running local model I haven't had the time to read. And there are probably some prerequisite reading or other research in between as well. I just wish there are some very high level overview and news coverage on all these.
IMHO this is a difficult question to answer. Part of the power of paid models comes from the software supporting it. With local models, you have tons of workflows that can severely influence the quality of the result.
In my personal experience, the SOTA models are way more consistent and can handle more complex questions. Part of that is (probably) because I don't let my local model access the internet, while paid models do use the internet to look at docs etc.
Reading the actual code is also always a better source of truth than docs anyway (this is true for people and LLMs). Just clone whatever libraries you use.
What's even their end goal? Open source models make sense, if profit is not the target, but for OpenAI and the rest, once they achieve "AGI", don't they basically become useless?
I guess which of the smaller 3.8 models is best for coding will depend on which one they put the training effort into.
https://humanparadox.org/local-vs-frontier-benchmarks-for-my...
It can complete multi-step tasks much better, and has a bit more curiosity.
I believe I am competing directly with these frontier models in some circumstances. Like there are a ton of programmers who previously would be outsourcing work to that site, but now they assign that same work to AI agents.
Ever since November 2022 when ChatGPT blew up, I have been focusing on agents in order to try to get ahead of the curve. But I haven't managed to get an agent business off the ground and have been doing poorly paid agentic projects from that site instead.
But now everyone is building agents, and this crazy list of accomplishments makes it look like we are close to the point where the agents are building agents.
In fact the next time I get an Upwork contract for another agent, I actually should run it through my agent and see how far it can get. What I'm seeing a lot of now is requests to automate as much of a business as possible.
Anyway the point is these models are just about capable of doing the entire job of analyzing a small business and building out all the agents and iterating on them with the business owner.
That's actually what I should build is a SaaS that does that. Which I would if I wasn't basically desperate to get another contract this week.
And I know Upwork is bad but I have not had much success with other options on short notice.
If you search for LLM benchmarks focused on real-world tasks, you'll quickly learn this ain't the case. No point in telling business owners about that though, they need to see/learn by themselves.
The cleanup pay is mighty generous as well.
Still no Artificial Analysis benchmark yet. Or benchmark for Laguna S 2.1 or Meituan models or lots of other models.
On a sneaky one that involved parsing MIME headers and dealing with character encodings it did better than Kimi K3 at Max and for 38% lower cost.
Interestingly it seems noticeably better than the qwen3.8-max-preview model they offered just a few weeks ago.
I see people, and my self making amazing things with AI and fixing old projects and having real world impact at the fraction of the cost it would take me to hire people, or hours spent on my own coding.
I have built tools and systems with AI that have allowed me to build windows drivers, android apps, web apps, iOS apps, vm occultation, custom block drivers, custom file systems and more. To the point where entire products have been created.
Not trying to be a doomsday, but yes. It seems as though with the right infrastructure we are at the point where businesses owners can go from idea to product very fast and not need or hire much external talent.
It allows us to apply custom ACLs to AI agents and the child process spawned by AI agents. Giving us the ability to control what files an AI agent can read or write to, while still being in the calling users context. It allows us to force all ai derived processes to use a transparent MITM proxy so we can then also apply robust access rules to remote host allow or deny access to specific urls and not others. It also allows us to monitor access to windows Credential Manager with rules ti allow specific singed binaries to access some credentials but not others. It give us complete control of what AI agents on windows can see or not see or access.
Windows native sandboxing is lacking. You have some stuff in WSL that completely are broken once you call a windows native app. Or you have app containers which are too restrictive and result in applying expensive file system ACL to all files the app containers would access, which can take hours when dealing with million of files, and would be required to be applied every time you chains your app container (there are some workarounds, for them but they still have a one time cost a long with a fairly flaky maintenance process). You can get the network part done by running commands as a different user but that would result in the same file system ACL nightmare that app containers has.
Result is we get seatbelt level sandboxing in windows native, and can apply dynamic rules like preventing access to .aws folders regardless of the OS level ACLs, using glob rules like */.aws, so we don’t have to be aware of the exact path ahead of time.
It also has registry tree ACLs and, can prevent process and process trees from gaining administrative access, the list of features goes on and on.
It might please you to know that this driver was vibe coded from a linux vm based sandbox.(that was also vibe coded)
Assume the business logic and all edge cases work in the legacy app. The app is "done", just too old to work on or run on modern hosts. Hence the project.
Would/could you use AI to deliver the project at 10x the speed? Or at 1/10 the price? Or charge the same amount as ever?
I have a feeling the lowest-cost developers on freelance marketplaces are not very good at using the AI tools (and whatever related acumen) and need a lot of hand-holding, the same amount of effort almost as if I decided to guide AI to do it myself.
IMO, there's value you can sell in being someone that (even developers) can hand off a project to, to do end to end, if it's fully specified. Even though the buyer could do it themselves, the developer would be taking on managing it and having AI do it until it's correct, and all the little things, few as they may be.
AI should be giving contract developers superpowers. If it's not, then the limited set of contractors with the AI superpowers will be able to charge 10x as much for their time, and the actual cost of the project remains the same even if it's done 10x as fast/easily.
Curious if you see/feel this, or if there's a marketplace for "please vibe code this for me, make sure it works and is what I want".
I would say 90% of outsourced developers are going to use AI just like someone making top US dollar. Actually for the majority of projects on Upwork, the need to generate with AI is much greater. And I think it's nonsense the idea that an overseas programmer can't figure out how to make adequate requests to a frontier model.
The models are smart enough that someone could literally just say "convert to Rails 8 like you are real real good best Railsdev" and follow up with a few "now test and make the bestest code Base" and if they are repeating that maybe with a few different leading models.. will a real Rails dev be able to find some fault? Sure. Would they have also found issues with the work of another human Rails dev? Yes probably.I just don't think it takes as much actual skill at this point. And certainly there is no secret ability that people in San Francisco have but not in Bangladesh or only certain neighborhoods or whatever.
One guy I was talking to said he was frustrated because his developer was not using AI enough. I came to the conclusion based on his requirements and relatively generous project timeline that that developer was deliberately avoiding using AI or admitting that he was using AI because he knew if he did then the main project phase would be finished in less than a month.
It does help dealing with what previously were often impossible timelines. Recently my Upwork profile has focused on AI agents so I have not been seeing many of those typical line of business type jobs.
But also more than half of those more typical business web apps are now starting with "I have already generated a prototype" and they want it to be made. "production ready" which I think contractors are taking advantage of how vague that is especially since the majority of these projects have pretty modest actual usage. I actually don't like that because it's much less concrete than delivering actual features.
I don't know why but this made me laugh out loud. I know you're trying to help but just a funny jump
I think the volume for custom agents is probably higher than ever but so is the competition for that work.
PS. Not everyone (in fact the rare few) write as if no one is reading. For most, literally no one ever reads :D
You need to run away from any client who thinks your primary purpose is to write code. You must run away from the business if YOU think your primary purpose is to write code.
You should be able to write code and do it well, But the AIs, and I'm not even talking about the ones on the frontier, have been able to write code faster than I have for quite some time.
What you need to explain to your customers is all the things around writing code: software architecture, performance, and so on. Also make sure you have some understanding of the customers business, so you can suggest additional ways to make their lives better/make more money.
Software developers have a chance. Code monkeys? None.
guess one which is valuable ?
First, I am aware they have been able to write code for some time. I made my first LLM coding agent experiment the day after ChatGPT first blew up in November 2022.
I haven't been a "code monkey" in the last say 15 or 20 years of my programming. I would argue also that code money was never really a thing, it was just an excuse that pretentious overpaid developers used for the existence of underpaid exploited programmers. 95% of programmers have to do requirements analysis and design regardless of their pay grade. I learned a long time ago how important requirements analysis and good design is. And the jobs I am applying to largely specify architecture and requirements analysis in the description. Employers are aware of code generation tools.
But also, the premise that writing the code is not the hard part is just nonsense. Sure requirements analysis is harder than people realize, good design is hard and so is understanding things about proper and closed iteration. But that stuff is not, in the aggregate, harder than the code.
And also, frontier models can absolutely do requirements analysis and architecture. And the sheer speed of implementation means that there is a huge built-in advantage for iterating more effectively.
"Code monkey" absolutely was and still is a thing. I was hired by Big Name Co originally to be staff but a reorg had me do typical senior position duties for around a year, and it was hell. Every decision was made over our heads, feedback was unwelcome because the feature had already gone through design and gotten layers of sign-offs from different areas of the org, etc. My sole purpose was to write code and review peer code. Staying there a full year was way too much.
Second point, requirements analysis is hard because:
* Different stakeholders have conflicting goals, sometimes mutually exclusive * Some stakeholders have ideas that are actively harmful to users, and you must flex political capital to nip it in the bud * Some stakeholders think they know what they want with a far greater degree of clarity than they actually do, and lack the time to sit down and suss out the nuances, because you're a side-show in the grander scheme of their own priorities.
Writing code is a heck of a lot easier than dealing with people at the edges.
AI will help you draft the communication of that knowledge, but it won't help you actually deliver the knowledge or ensure that the knowledge was appropriately received. As you say, it is a fundamentally human problem, which is why I maintain that writing code is easier.
but then you totally jumped the shark!
My MindRoot framework had some of qm's main features awhile ago though. I do try to use it to build solutions by customizing rather than from scratch when possible. But a lot of clients or potential clients don't like that idea even though it's MIT. They want some that has clearly been invented by them as a new programming project.
I actually turned down a project last year from someone who wanted me to start over in LangGraph -- he had already decided on it before he knew about my thing.
The most recent project I have been using MindRoot and building up the voice capabilities so I can now handle voice agents end to end including SIP.
But on that website now I try not to emphasize my framework at all for most proposals. You don't have leverage on Upwork. And also with code generation it isn't a critical point necessarily.
The biggest advantage of Upwork is I have a track record there and there are tons of RFPs. Other places usually don't have RFPs and are going to look down on the Upwork record.
But I appreciate any ideas for places or people to look for work. My email is in my profile.
I am not sure about funding for rent if I don't pick something up this week.
All requests to an LLM are idempotent, for every API call you need to send it the entire conversation history so that it can process it. LLMs do not learn or remember anything, which makes it super easy for users to switch LLMs on the fly. Most popular AI frameworks, make this a one-liner change these days.
And that makes me wonder if the trillion dollar valuations for OpenAI and Claude are even justified. Cause if that is justified, then Kimi, Qwen, Deepseek etc are also valued at a trillion dollars. Or all of them are worth a lot less. One of those statements is true.
Also this makes me wonder if the next iteration of LLMs would be based on fine-tuning, where LLMs actually learn from your past behaviour so that it would grant some amount of stickiness to the product. OpenAI used to offer fine tuning runs for GPT-3.5, but they don't seem to do that anymore.
A more appropriate term is “stateless”. LLM responses are certainly not idempotent, as they are not even deterministic.
https://earendil.com/posts/session-portability/
“Just take the session thread to another provider” might not be feasible anymore soon-ish.
The fundamental technology of LLMs and arguably AI in general strongly cuts against that sort of lockin. Handoff is a fundamental capability. There's no option to encrypt the docs or write it in some dialect only one model understands because humans need to understand it to, which stops that whole line dead in its tracks for at least the forseeable future. An AI can already today pick up such pieces, how much more easily will they do it tomorrow?
If they want to lock me in, they're going to need to provide a feature that I need so badly I can't switch and nobody else has. It is hard to see what that would be, other than being a generally better model.
(Only half-/s)
Which is why folks should be jumping to pi / oh my pi as soon as possible
This is also why anthropic no finger lets you use the flat rate sub in non-claude-code harnesses
(They do but you are charged at api rates)
This is harder to do on other architectures that themselves aren't fully deterministic though.
My understanding is that it's best effort because even if you control the seed/temperature, the model version and hardware specs you still have floating point math that will introduce rounding differences
an article explaining this in detail : https://news.ycombinator.com/item?id=45200925
You did mention, seed, which I haven't seen available anywhere else (in my limited experience) and it can explain a reproducible inference result, but I feel like this matches the "letter of the law" meaning of deterministic rather than the "spirit of the law".
Greedy decoding a single batch in most libraries will give you mostly deterministic outputs. Higher batch sizes can increase variance.
But all of this is down to CUDA and/or kernel implementation issues.
1: https://earendil.com/posts/session-portability
Isn't that more due to an optimization and not how the LLM itself runs?
Like a MoE LLM run on a single input should give the same output each time. But this is inefficient, as any given token is hitting 1 (or maybe 2 or 3) experts at a time, meaning all the other experts are doing absolutely nothing. So you upgrade it to take in multiple requests. But then any given expert can become a bottleneck, so when too many requests need a given expert, some of them are routed to a second or third best expert instead. Within the context of any single request, this looks like non-determinism, but it is still deterministic when considering the full batch.
For everyday users and everyday use cases, that is enough to treat it as non-deterministic (the harness might also send in unique data like current time which means one can never have the exact same request twice), but when talking about LLMs more theoretically, I think we need to consider they can still be ran deterministically even if that isn't as optimized.
Similar with temperature. 0 means deterministic, but anything higher with a seeded value is deterministic. If anything, temperature is us purposefully adding non-determinism to agents because they were too deterministic.
In the simplest possible case, a distributed addition of floating point numbers is not deterministic if you don't specify the order of the addition operations. If floats are added in a first-come first-serve fashion (the simplest implementation of a reduction operation) you already lose determinism. These could be activations from multiple experts, but it could also be adding chunks of a matrix operation that uses multiple CUDA cores.
If you manage to achieve determinism in temperature zero, it is possible to extend it to determinism at higher temperatures, because at that point you only have to keep the pseudo-random-number generator state in sync across parallel instances and this problem has been solved.
https://www.semianalysis.com/p/google-we-have-no-moat-and-ne...
Note that the biggest GPU providers in the world are the hyper scalers and even they couldn’t allocate more if you pay for it. Because the rich companies and well funded ones are gobbling them up to the point where if tomorrow a 5T model that smokes every other model in the world is released you just can’t afford inference.
The bets are absolutely huge and have been spread throughout the financial system via securitization. If the number does not continue to go up, a lot of people are going to find themselves backing their numbers with money that simply isn't there anymore. Therefore, the number will continue to go up and we (meaning American taxpayers) will back whatever it takes for that to happen.
Now, were the bets stupid to begin with? Of course. Who wants a machine that potentially unemploys major percentages of the population? No one. But the bets were made regardless, and then more bets were made on the hypothetical returns of the first round of bets, and so on and so on.
Will this continue? The field is moving too fast to tell. Kimi, Qwen, Deepseek also produce very capable models but that doesn't automatically translate into trillion dollar valuations.
However, trillion dollar valuations on Anthropic and OpenAI, such new companies, never publicly traded and such huge valuations decided just by investors. This is just asking for trouble.
1. https://docs.github.com/en/copilot/reference/ai-models/suppo...
Why is Google a monopoly? Do they have so advanced software that no one can outperform? I doubt it. What they have is a giant user base that generate loads of real-time data, which make Google services more accurate.
So how AI company can build a moat? Exactly the same way: by making a giant user base produce loads of real time data. Just imagine a service that will generate answers not only based on data they were trained on, but on all data from all user conversations. Imagine being at a concert, looking for a certain type of beer and instantly receiving an answer from an AI assistant about that only because some other guy in a crowd looking for exactly the same thing said to his agent "ah, here they are!".
It is not happening just yet because of making it secure and private is not yet solved, but it's just a matter of time I think.
They aren't, not even if we forget about the capable Chinese models.
I suspect Anthropic will implode soon when employees are unable to get the cash-out that they expected. Having so much compensation locked up in company stock is risky on a good day.
The first is the compute. OpenAI and Anthropic secured huge amounts of compute, Google, Meta and xAI have their own huge datacenters. Now anyone can rent some cloud machines and start serving Kimi K3, but it's going to be impossible to get to a similar scale as the big 5 above. And inference has economies of scale: the more people you serve in parallel, the more efficient you are.
The second is the data. By now (and maybe even by one year ago), all the data on the internet has been used for training. You need new data. The big AI companies sit on top of trillions or quadrillions of tokens that they have generated over the years. They can use that to train new models. That data is gold, and the proof is that SpaceX was happy to pay $60B to acquire Cursor.
If you want to overtake the frontier labs, you have 2 options: use their models to generate synthetic data, and provide lots of (cheap, maybe below cost) inference to generate your own new data. The frontier labs know about the first, and I'm sure they try to limit how much others milk their models. As for the second, that's the "honest" way to compete, but it's not easy.
They don't. The moat will mainly be the tooling around AI, not the AI itself. You don't hear any company claiming their moat is the Internet.
Generally speaking they do, at least from my experience when switching from one model to the other - their performance decreases, and they often do large refactors outside of the requested scope as they try to bring the code closer to 'their' style.
Which makes sense imo - they'v been trained to iterate over the code they wrote, and not code that was modified by someone else in the interim.
These companies have AI and enough money to lobby the Pope. They can afford to reanimate members of congress and push some tactical legislation through.
But all the money in the world cannot move government too quickly.
Other moats exist too. OS or browser can undermine performance and availability of alternatives.
Recursive self-improvement changes the parameters a bit, especially for the market-leaders, and it's the one thing that makes me wonder if they'll be able to extend their lead faster than the smaller labs can keep up, but it's an option available to everyone.
This isn't without it's challenges however.
1. This will increase costs drastically, since they would need to a run a sandbox per use to ensure data isolation.
2. Increased latency, and this directly limits how much of the harness can be moved to the cloud before the users notice sluggishness
It’s more like a bunch of people are placing different bets. Only a few bets are going to generate a return, possibly only one, but the profit on that one bet will make it all worthwhile. That’s the theory, anyway.
Fine tunes are a possibility but I think it offers very little uplift for the vast majority of uses beyond just stuffing enough context.
Which anyone who hasn't been sitting in the SV echo chamber could have told you years ago after applying even the smallest bit of thought.
Original designs: https://image.non.io/257dc9cb-9e6b-4e00-8f12-23ea5e073649.we...
These are fairly rich, pattern-heavy, nuanced designs. I've asked each to create it as a SPA where the map flows behind it.
Opus 5 results: https://html.non.io/opusAcmeBooks
Qwen 3.8 max results: https://html.non.io/qwenAcmeBooks
Same prompt for both for the conversion. I used OpenCode for the qwen version, but I encountered a significant amount of errors / timeouts while it was running. Claude finished in around 16 min, but I spent close to 2 hours shepherding the Qwen build. For the implementation, there were signs it had good vision, but the timeouts make this very hard to use in a production setting.
That folder didn't exist, I asked Qwen where the files were, and it attempted to create them again.
Stopped it, and asked what happened to the original files. Qwen Desktop apparently doesn't have access to the local file system, but continued merrily along without warning about that.
https://image.non.io/07a153cd-c31c-4f7b-a89d-363faec05a91.we...
It did a self update and it worked for a single request (me just saying hi). Pasted in the prompt to build the page and it 401'd.
I'm quite surprised at the difference.
I've noticed another type of AI slop that's prevalent in all the popular models; font sizes and variations like you wouldn't believe. It's very hard to instruct LLM's not to do this.
However, that would just change the weights values and not their dimensions.
Imagine our LLM as this one-dimensional matrix (instead of unfathomably many, many more dimensions):
0.728201017
-1.9293839303
2.93020202929
5.82290101728
-0.2844920917
…
And then, all of these values are multiplied by some scaling factor, essentially vectors pointing in some direction (in our case, only on a one-dimensional line instead of in some higher-dimensional space).
Now imagine it’s possible to “remove” the portion of the weights’ directions that’s responsible for some undesired property like history knowledge, or another programming language:
0.428201017
-1.6293839303
2.63020202929
5.52290101728
-0.5844920917
…
(For demonstration and faster-to-type-out purposes, I’ve changed each vector by deducting a constant amount of 0.3, which doesn’t make much sense. In reality, it’d modify probably all digits in some direction.)
Now we can see: the number of rows has stayed the same, but the value has changed. This means the required storage (or computational) space remains the same.
Please do correct me if this simplified too much or I had a mistake in my understanding.
Although I’d be intrigued in the answer to that small-narrow vs. large-broad model question, too!
There's Mistral. Although they've been surprisingly quiet for the last 6 months.
https://trackingai.org
Mistral: eq.IQ ~70
Kimi, Claude, Grok, Gpt, Gemini: eq.IQ ~130+
Open weights is more beneficial to Europe spring-boarding a catch up than defending Claude/Anthropic.
They seem to be oriented more toward customizing models for the concrete needs of a company, on-prem deployment, proprietary knowledge-bases, etc.
I agree, but you are underestimating EU regulatory incompetence
They can also restrict US providers from serving these models. Looking at OpenRouter, there is currently 11 providers for 'DeepSeek V4 Flash 0731' with the only non-US one being Deepseek themselves, which aren't even the cheapest.
Interesting.
If I were a western lab, might I be tempted to subsidize at least one faster & cheaper provider for Chinese models to keep even more data from leaving the US?
(I know in this case there can just be more efficient providers, am asking more than speculating)
Crazy how all the 5 eyes nations banned Huawei within like 18 months of each other.
Level 2: ... or with anyone who does.
Level 3: Anyone caught with unauthorized model weights is subject to the same prosecution they could expect if they had been caught with CSAM.
Then they call your local law enforcement with an anonymous tip about this suspicious dude who's long overdue for a totally random home inspection.
Making it illegal even without having a realistic means of enforcing it still chills behavior, and so just because it isn't practically enforceable in the general case doesn't mean it isn't without consequences.
It probably sucks for you if the proposed powerline goes through your house, but the need of the many come first.
Having cheap and plentiful power is a huge market advantage. It was one of the factors driving Norway out of poverty 100 years ago.
Also they really don’t have the chips to supply those data centers, and the government isn’t so crazy to go that aggressive on AI, they have lots of other priorities as well.
EDIT: This was obviously meant to be facetious with the intent to highlight the negative effects on the general population that datacenter buildout has in the US.
States have had trouble delivering expected EV charging capacity btw
Why aren’t we considered energy limited?
I have not looked into it, but I have heard a lot of new data center have to run on site gas turbine on trailers to have enough power.
If the US is not energy limited, why they do that?
The need of the rich, you mean. In these kinds of situations it's never about the many, if it was about them different choices would be made.
volunteers in the open source community use whatever is easiest and cheap.
open weights is not open source. there is no "trust".
I don't understand. That's dated today, but:
https://twitter.com/alibaba_qwen/status/2078759124914098291
> Qwen3.8 is launching and going open-weight soon! [...] You don't have to wait to test it. Just now, the Qwen3.8-Max-Preview made its debut on Alibaba’s Token Plan, Qoder, and QoderWork.
That was on July 19th. I used it to draw this pelican: https://simonwillison.net/2026/Jul/20/afraid-of-chinese-mode...
So what are they releasing today?
I believe they mentioned around the preview announcement that they'd be releasing improvements to capability, which I assume means continued training.
3.8 Max Preview ran 90% discount on QwenCloud exactly as when Moonshot couldn't keep up with all the signups.
Here's a pelican I just got out of the new model. It took 11 minutes and forgot the wheels! https://tools.simonwillison.net/markdown-svg-renderer#url=ht... (scroll to bottom)
The reasoning trace is pretty great:
> More additions: basket with fish in it? Cute detail — a fish poking out of a basket on the handlebars! This adds charm and pelican context.
If the price is $2/$6 that cost me 17 cents: https://www.llm-prices.com/#it=90&ot=29734&ic=2&oc=6
The open models are now good enough for what I want to do with them, let alone any future improvements. And factoring in efficiency gains, a model in the ~70b range starting to satisfy my needs would completely obviate the need to pay others for inference. This does not seem far-fetched to me, comparing with where open models were at this time last year. What am I missing?
So just like in IPhone vs Android, you could end up with a situation where Chinese firms compete and get most of the revenue and usage with low profit margins but OpenAI and Anthropic capture a premium side of the market and still get a lot of profits.
I have numerous cases where Sol failed and only Fable could solve a problem. For example yesterday I was merging a Q2 curved with a Bezier curved face in 3D using OpenSCAD. I tried for over 2 hours with Sol 5.6 high and x-high.
Fable two-shotted it in about 30 minutes.
In my experience open models (or GLM, DS and Kimi) are radically worse than either of Claude or ChatGPT at these tasks.
I think there is a huge "long tail" of tasks like this where the frontier labs are ahead, and I think this long tail is quite important.
I settled on Fable for design, Opus for build routine, and every now and then I'd try out the Chinese models. In my experience they do fine on small codebases, and quickly get confused on anything larger than 500k LOC.
My use case is: mature, very well documented, fully Ai written code, with about 1:10 ratio of prompts/specs to code, and codebase sizes >500k and <2m LOC. Once one achieves the state of good, comprehensive design documentation I can literally vibe code with single sentence prompts thanks to the extensive test coverage, ADRs, and tens of thousands of lines of specs. Fable/Opus works predictably well, the Chinese models are literally dangerous to the codebase.
Unless you are spending more than a max subscription (200 a month+) its cheaper to use the cloud.
But things are priced cheaper in the cloud now to lock you in and restrictions around hosted models are getting worse.
If you only have a $300 dollar laptop its probably not worth the upgrade.
I'm personally excited by local AI but the experience for the average isn't the same. I'm willing to get .5/s running on 10-15 years old machines but what I can do with it is limited.
Their marketing department :-) . I'm only half-joking; those guys are hard at work finding the best product-market fit for ChatGPT/Claude. "Product market fit" means "strongest revenue", which is not necessarily going to bring the best tool for you or me, but the one that can either get more consumers to shell off money, or more enterprises to cough money for licenses, and in both cases those consumer basis will be narrowed down to what legal and geopolitical circumstances allow OpenAI and Anthropic (and this is why they want to ban the competition!). It also means dark patterns and enshitification, of which I'm already seeing some both in the Codex interface (it was just renamed "ChatGPT"!!!) and in Claude Code (which also is just "Claude" now and can't '@' properly any longer). So in the medium run most people will be better off running an open source harness that can use any model.
you can't create a new drug by running a model on a laptop. You can't serve a customer support bot running on a laptop. You can't generate video in bulk for many users on a laptop. So there is still a case for paying others for inference.
Does it justify the valuations? No idea, but some major use cases are still there. That's why they are rushing to implement, OpenAI creating a "deployment company", Anthropic having some pharma rumors, etc.
I think GP was asking specifically about the use case for closed models.
Or, the company itself can rent the GPU and host the model of their choice on it.
> But they would have to keep up with how good the private models are.
Why? The whole point of the thread is that the marginal gains of using private models is a fraction of the additional cost of using those private models.
IOW, if you really need that improvement over the open models, you can go ahead and pay it. What we are seeing though is that the improvement in general is barely noticeable but the cost is 4x as much.
LLMs are useful. We can all see that in agentic coding. But replacing everyone’s job? Hardly. And what’s with the scare tactic of trying to get the US government to ban foreign models?
LLMs are useful, and dare I say they’re on par with the internet. Making them cheaper and affordable is good for everyone. The fear mongering from Anthropic and OpenAI looks like an attempt to corner the US market into using only US models so they can keep the profits, especially since China has proven that LLMs are a commodity. US AI labs should work on making LLMs cheaper or better harness. Altman and Dario are not trustworthy.
> "Anthropic believes that they are the ones who should have final say over how Anthropic is used; given that they think only they should be developing leading edge AI, they by extension think that only they should have final say over AI generally. When you further combine this realization with the company’s pronouncements about AI’s ability to conduct all economic activity, you realize that Anthropic’s leadership effectively wants to have power over everything and everyone."
"Concrete Problems in AI Safety" by Dario and another founder was published 2016, anthropic was founded 2021. Among a bunch of other examples, including other founders.
If safety is seen as a joke now, it was really seen as a joke back then. The reason they keep shooting themselves in the foot with bad PR is because they truly believe in risks.
And for safety? Oh yeah, they literally sell the removal of guardrails in exchange for minimum spend commitments.
Completely unrelated to AI safety.
they literally sell the removal of guardrails in exchange for minimum spend commitments
It seems blindingly obvious to me that you'd want to let trusted organizations use the models defensively, which means without guardrails, and that you'd probably gate that behind some kind of enterprise sales process, for multiple reasons.
Honestly, it just seems like you have an axe to grind and aren't really particularly knowledgeable (or curious) about AI safety.
2. You misstated what I wrote. My gripe is not that they have an enterprise sales process. It’s that in negotiations, they say “we’ll drop the safeguards if you agree to spend more”. I haven’t heard of any groundbreaking AI safety research concluding that spending more with Anthropic makes the models safer, but maybe Dario has a new blog post coming.
3. Check your tone.
> “My scorecard, at the highest level, would be we’ve been roughly right on technological predictions and pretty wrong on the social and economic implications” https://www.cxtoday.com/ai-automation-in-cx/sam-altman-softe...
I agree re: Dario quietly pushing for government control. He also said LLMs would replace a lot of entry-level information jobs, doubling the unemployment rate from 4-5% to 10%.
Yale did a study recently showing little impact on employment in high-AI exposed jobs https://budgetlab.yale.edu/research/ai-probably-not-yet-reas...
Yet here we are, still driving our own cars, and laughing at silly behavior of most advanced self-driving systems.
I am certain we'll get to something of similar value, but I expect we'll have to improve infrastructure too (smart roads).
That's what Hollywood has been telling me my entire life!
In practice I'm not sure how big a deal this is - Bedrock is not the cheapest or best provider (try Fireworks AI or DeepInfra who do support more recent models), and would seem to be of more interest to corporate users who already have an AWS account.
Perhaps also of relevance the US government is hassling US companies that are using Chinese models (currently DoorDash, previously AirBnB & Cursor), which may limit demand for corporate use.
Edit to answer my own question:
License file [2] states:
> If the Licensee or any of its affiliates operates a Model as a Service business, and the aggregate revenue of the Licensee and its affiliates exceeds 20 million US dollars (or the equivalent in other currencies) in total over any consecutive 12 months, the Licensee must enter into a separate agreement with Moonshot AI before using the Software or its derivative works for any commercial purpose.
[1] https://huggingface.co/moonshotai/Kimi-K3
[2] https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE
If you create something that isn't copyright-able, you can still create a contract which people must agree to in order to use your work. The enforcement/validity of that contract then depends on making sure the user knew about the contract and agreed to it. The way they did that here (just publishing a LICENSE file) could be interpreted as a judge as no confirmation of agreement of contract - depending on if they find a "reasonable" user would have known to look for the file.
If you have a work that is not copyrightable, and you give it to people lawfully, then you cannot protect it via copyright.
The "Reproduce a research paper — then improve it" seems particularly impressive as well as actually useful - perhaps as close as we're going to get to "recursive self-improvement" given that these are data-driven not code-driven models.
I'm not sure how much real world use of AI is 100% hands off agentic coding, but I guess for objective evaluation purposes it needs to be something without a human in the loop.
It'd be interesting to see a comparison of each company's best models on a suite of real-world challenging tasks like this, but I guess difficult since each group of tasks would need to be one-time-use only to prevent subsequent benchmaxxing which makes comparisons useless.
If this is true, this is a big deal for me, but unfortunately I cannot find anything in their legal agreements, so this marketing sentence is worthless.
Nice!
Their base models and architecture has quickly become the go-to for local inference and fine-tuning, even when they introduced some tricky things like GDN, so many people use it, that it was matter of days/weeks until lots of OSS frameworks adopted it.
> Qwen3.8-Max comes with the official support for reasoning_effort, which can be used to adjust reasoning depth and control cost:
> xhigh (default): for complex tasks demanding thorough analysis
> medium: balancing accuracy and speed
> low: efficient reasoning optimizing for speed and cost
I hope this is significantly cheaper. I've been loving Deepseek for it's nearly free usage costs, hard to justify switching from cents per day.
> Pricing: Input: $2.0 / M tokens Output: $6.0 / M tokens Implicit Caching: $0.25 / M tokens
[0]: https://x.com/Alibaba_Qwen/status/2084100707423289643
https://www.qwencloud.com/models/qwen3.8-max
https://openrouter.ai/qwen/qwen3.8-max
Surely Alibaba is leading by example here by reducing work hours per week while keeping pay the same right? Right?
Ironically it's Electron BS, so actually, Sol could probably build itself...
However, they are not at the point where they can effectively train themselves, nor did they are capable of researching their own method of learning. SWEs in mid-corps on my country are right now relegated to reviews and sanity check, basically babysitting the LLMs and making sure they're not spouting nonsense. If you think about it, that's basically QA and can also be delegated to another AI. If Bun's rust rewrite that they tout as fully LLM-led can pass the test of time in a year or so I think that's it.
I believe all that is now constrained by compute and capital, not tech.
Already 38% of Americans aged 16 or older do not work and are not looking for work and yet are not dying from hunger or exposure. This would have seemed like a utopian dream to someone from the 1800s.
Being 16, 17 and 18 and not working is expected, given they are still at school. A good percentage of people aged 18 to 24 are studying full time.
This stat seems a bit misleading and doesn't support the "you can live and not work argument". How many of those people are retired? How many of those are under 18? How many of those simply cannot work? How many of those live at home or are on food stamp?
... these days. 100 years ago you'd have a job at 12 in many cases, 14-16 if you were more lucky.
I'm not even talking about peasants from previous centuries.
The market comment linked in the paragraph above that one features a toy model of the US economy which illustrates this (section "Equilibrium in output and income"). [2]
[1] https://www.hussmanfunds.com/comment/mc260219/
[2] https://www.hussmanfunds.com/comment/mc260104/
Are you not already experiencing this? I think this is fairly common for people using AI now, though the time may not always go into hobbies or sports. It's common for me to setup Claude with an hour+ task while I catch up on housework, or while I'm getting ready in the morning.
In the last couple of weeks I've unfortunately had multiple family illnesses - it has been helpful to have Claude keep up with much of my product development programming work while I visit my mother in hospital and check on my father's recovery. I'm able to give more time to family without worrying that business progress isn't keeping up. The overnight Claude sessions while I'm asleep have been particularly helpful.
It's infinite work, I just did more work while codex was doing it's thing in the background.
Before AI:
I have 10 tasks that take 1 hour each to do.
After AI:
I have 100 tasks that take 10 minutes each to do.
Same amount of time spent working, maybe even more stressful, just more productive.
And then you were doing 100 x 10 minutes = 100 / 6 hours = ~16 hours & 40 minutes.
Qwen doesn't overuse em-dashes, weird sentence structures with bold text: normal text.
In my private GitHub Repo Qwen finished as much tasks as Fable did. Without hitting 5-hour session limits. Qwen's token-per-second performance fluctuated greatly. From 20 tps up to 80 tps.
Qwen failed on some "reading between the lines", but so did Fable.
The only noticeable difference is skill loading. Until "Must use" in a skills front matter, Qwen tends to be very lightly loading skills. Superpowers works, because of the same Must use wording. All my local skills use the same wording now. For installed Skills, it's more or less my responsibility to remind Qwen using a certain skill.
I used Qwen together with Qwen Code. I didn't compare Qwen+Claude Code.
With my Owl code scanner, Claude and Qwen produced similar results. Qwen repeatedly used the same output format, despite no format provided and disabling memory. Fable varied greatly. Both followed the file format output and produced valid files.
With Qwen 3.8 being now a visual understanding model, I don't really miss Fable/Opus/Sonnet for my private projects.
At work we are still exclusively using Anthropic models with Claude Code. No change incoming on that front.
Privately, I'm sold on Qwen and Qwen Token plan. No session limits, many open weights models available via a single API.
Since it's horrifying thinking about what US companies do with my data, it's no worse or better thinking about what Chinese companies do. It's a choice between the Plague and Cholera.
Also I love how literal Qwen 3.5 was, hopefully 3.8 is still extremely literal. The token explosions were actually helpful in debugging prompts.
Just like we have professors with specialisation wondering if AI models can also be so.
Intuitively it feels like focused models should be better models, but human programmers are ‘better’ knowing programming alongside general stuff. We’re not reduced by reading economics or Tolkien, removing such knowledge would be premature optimization.
Would be nice if a genuinely useful coding model can fit into an old laptop, give decent performance and know nothing but (say) Go or c language.
But I see yours + others points.
>we have professors with specialisation
Yes, but any professor with a specialisation is an erudite with vast knowledge across the board.
I'd argue we can have a meaningful BIG model with a specialisation but not vice versa.
PS: all of this is about a model that is expected to be able to execute development tasks on a human level. Obviously we can have small models that are very capable in things like TTS or STT.
If you throw general purpose model at a codebase - it will look at the table and data logical connections beyond what is explicitly declared. It will figure out on its own that Salaries should be displayed on SalariesTable.php and it will "know" that your prices should include vat and so on.
A human knows that VAT and price go together and are related, full size LLM does too, stripped one - doesn't.
You can always try out this model for free on Qwen Chat. Alibaba Cloud has too much friction for me.
> Qwen3.8-Max was asked to create the oh-my-cli project from scratch and, over a 10+ day long-horizon autonomous coding run
10+ days of building what exactly? Is that a shell prompt customization toolkit? Or a coding harness??
Neither - well, sort of the second. The poorly named thing is a self evolving coding harness, the self evolving part makes it a big deal.
I wish they had picked a different example.
https://www.hhgproject.org/entries/bistromathics.html
How are you all justifying economical use of these local models right now? What's the cost efficient way to do this and do better (even with models evolving over time and losing now vs later) than the big labs?
The point of local models is privacy, offline use, and maybe no guard rails.
* Not talking about enterprises that buy DGX racks and host Chinese models for internal use.
But also, control and consistency. A local model cannot be changed out under your feet like an API model can be.
Sounds like we have similar boxes - mine has a 10 core CPU, 64 GB of ram, and a 2070 Super. My motherboard had two unused PCIe3x8 slots and doesn't support Blackwell GPUs. I bought a couple of brand new Ada generation RTX 2000s with 16GB of memory for under $1400 to get to 40 GB of VRAM. That will easily run Qwen3.6-27b at a 6-bit quantization and 80,000 token context size. It isn't fast (19-21 t/s), but using pi-coding-agent is fine.
Now, my instinct is that I am giving up SOTA performance on agentic coding with this setup and LLM. But the gap between my setup and SOTA commercial models is small enough that it doesn't matter to me.
We all know that is hugely subsidized, and I guarantee that OpenAI and Anthropic are looking to enshittify that ASAP.
The enterprise users, however, are not subsidized like that. They pay per token. And some developers in those companies are chewing down a lot of tokens. Self-hosting an open weight model could be a massive savings very quickly. It also gives them negotiation leverage when talking to OpenAI and Anthropic.
For all we know, inference might be dirt cheap, they might just be hiking the API prices so high for us to think subscriptions are subsidized.
Now, the one wildcard in all of this could be Google. They are on the eighth generation of their TPU and have been holding their cards extremely close to the vest. I don't think anybody has a good read on exactly how much capacity they have. Most things you can kind of figure out the overall business numbers and what's going on in Google--the TPU area is one of the exceptions. I know a couple of big customers and even they don't have any visibility on that front.
The last thing I want my personal agent to do is to write up code and run arbitrary commands. That is practically a legalized RCE.
I mostly just need it to mostly be a very advanced NLP model that is able to figure the set of tools that it needs to call with what argument. Then it will just follow a predefined decision tree.
There's a lot of AI models that each 'win' one week over the next. The pure definition of a commodity market and not a 'winner takes all market' as valuations would imply
i might end up cancelling claude, anybody else thinking of the same ?
It is clearly distilled with an aggressive pro West bias to increase sales in the West.
They don't explain how successful that went but it's a bit hilarious seen that an Anthropic dev explained that it's been 15 days Claude was hard at work --with nothing to show yet-- trying to rewrite itself in another language.
"You rewrite Claude Code, we rewrite oh-my-pi."
"You're nowhere after 15 days, we do it in 10."
Sure, it's apples to oranges and all that. But part of me thinks they know fully well what they did there.
Does this mean they distilled Claude? Sounds like what Claude Code will often do.
What is important is how long it can go without requiring human intervention. Not just that it's possible to run on its own for a time.