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.
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.
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.
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.
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.
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 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.
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?
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.
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.
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.
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...
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.
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.
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.
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.
>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.
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.
> 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.
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.
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.
> 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.
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.
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
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.
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.
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.
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.
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.
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
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.
> 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.
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.
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.
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."
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%.
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.
> 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.
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.
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.
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.
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 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.
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.
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?
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.
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.
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.
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."
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.
> they need to see/learn by themselves
The cleanup pay is mighty generous as well.
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If only LLM benchmarks could benchmark it in the first day!
Still no Artificial Analysis benchmark yet. Or benchmark for Laguna S 2.1 or Meituan models or lots of other models.
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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.
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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.
> Im sorry to hear about your situation. Have you blogged about it?
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 stopped blogging years ago when I realized that only bots were reading the posts.
I think the volume for custom agents is probably higher than ever but so is the competition for that work.
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
As someone else who is an IT consultant.
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.
guess one which is valuable ?
The problem is Claude Fable is now better than most programmers I know at software architecture and performance optimization as well.
That's a popular type of AI cope.
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.
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I thought this comment was going to be about leveraging agents in ways your existing clientele demographic cannot or will not (yet|ever),
but then you totally jumped the shark!
Can you give me a hint about wtf you are talking about?
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.
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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.
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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!
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It was for me too but the new deepseek pricing is too good to ignore for now.
I honestly think that with my electricity prices running qwen 36B myself is more expensive than hitting the cache rate at deepseek.
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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.
I have been using Qwen3.6-35B-A3B as my daily driver as well and its been phenomenal when it comes to coding
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What kind of machine do you have running that? My attempts at local have always resulted in a very hot lap
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With laptop being ...?
Is your Qwen3.6 locally run on your laptop? What kind of tokens/s are you getting from your laptop GPU?
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compared to claude - how 'fast' is it in terms of throughput on your laptop?
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What do you use to pair it with web search?
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I'm a big ole noob when it comes to local AI. What are you using for a harness? Or platform to interact with it?
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On what hardware do you run the model locally, if so?
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What are the specs of your laptop and what tokens per second do you get?
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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?
What laptop?
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Qwen3.8-Max is the first in Qwen-Max series to be open-weight as well.
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
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.
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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.
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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.
And if you're running it on a dGPU, power limit it, because you lose very little in terms of token generation performance, since it's memory-bound.
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.
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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.
How and where do you finetune it?
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Really awesome. Though I wish they'd do a dense 48B, 60B or 72B.
There seems to be quite a gap between the small ones and the enormous ones these days.
Qwen 3.6 27b has been the sweet spot for me in terms of local models. I've had good luck using it with Pi harness. Looking forward to this.
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.
You consider that even Gemma 4 31B is not even competing with Qwen 3.6 27B?
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.
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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...
Yes let’s get rid of employees so no one is employed but somehow they can afford to buy my stuff
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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.
I have found 27 to just be so much more coherent than 35:
https://humanparadox.org/local-vs-frontier-benchmarks-for-my...
It can complete multi-step tasks much better, and has a bit more curiosity.
Do you have a news source for 3.8 27B pls?
https://xcancel.com/Alibaba_Qwen/status/2084100707423289643#...
as much as im excited for it, sadly it gonna be one of the reasons to push ram prices higher
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.
> All requests to an LLM are idempotent, for every API call you need to send it the entire conversation history
A more appropriate term is “stateless”. LLM responses are certainly not idempotent, as they are not even deterministic.
Which is why big labs have been working hard on making their harness not be stateless any longer:
https://earendil.com/posts/session-portability/
“Just take the session thread to another provider” might not be feasible anymore soon-ish.
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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.
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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.
LLMs are, in theory, deterministic. Sampling is not intrinsic to LLMs.
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.
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According to [1] there is - unfortunately - increasingly more state in LLM sessions.
1: https://earendil.com/posts/session-portability
>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.
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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.
https://www.semianalysis.com/p/google-we-have-no-moat-and-ne...
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.
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Surely the moat is the training data... with the data you can explore new architectures much easier and get step changes in performance.
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Is it RLHF if there is no human feedback?
And yet investment is continuing. What are they counting on?
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> 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.
They have 2 moats.
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.
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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.
China has the moat that they are cheap/free/open. The US corps have the moat that the other option is Chinese models. At least for some time.
> This makes me wonder if AI companies even have a MOAT in the first place.
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.
Burdensome regulatory compliance is a moat.
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.
> 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.
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.
> They need to start moving parts of the harness behind the API
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
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> 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.
There's no mandate that says any of those bets are going to generate a return
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
The major labs don’t allow assistant prefill, so you have to “summarize”
Mamba/SSMs could change this picture.
The moat is the US government.
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.
They have a moat; they don't have $1 trillion valuations.
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.
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.
What provider did you use ? Synthetic's Kimi is a beast
The visual web development / perceptionbench scores are very promising for image->html flows. Here are some test results.
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.
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.
https://image.non.io/07a153cd-c31c-4f7b-a89d-363faec05a91.we...
Also tried Qwen Code. Tried it with a coding plan and it 401'd. Tried it with an api key I loaded up with pay-as-you-go credits and it 401'd.
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.
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Wow the Opus version is a lot more functional (try clicking some links).
I'm quite surprised at the difference.
So Opus introduced a container with overflow:scroll in the middle of the page, while Qwen didn't? That's interesting in terms of "smartness".
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.
Yeah, I'd assume it's possible to extract all languages as steering vectors from a model and then substract the ones you don't need from its weights.
However, that would just change the weights values and not their dimensions.
Can you please explain why?
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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
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.
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There is economic benefits of the cost per a lumen continuously coming down.
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.
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> but the need of the many come first.
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.
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.
> trust from the open source community
volunteers in the open source community use whatever is easiest and cheap.
open weights is not open source. there is no "trust".
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.
> lack of European competitors
There's Mistral. Although they've been surprisingly quiet for the last 6 months.
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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.
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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.
I mean there is Mistral?
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>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
Yeah, but the world is not limited to the US so who cares…
If it's fable level then I am sold. I didn't have so much success with Qwen compared to claude x supermax plus thinking, no matter what I did.
What can the US administration do about it?
Send you to jail.
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What they always do. Send in armed men with guns? Export Controls. Import Controls. National Security Laws.
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Level 1: Anyone using unauthorized model weights in their business is prohibited from doing business with the government...
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.
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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.
> 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.
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?
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.
In Qwen's case, both 3.8 Max releases seem rushed may be in response to Kimi K3 getting all that attention.
3.8 Max Preview ran 90% discount on QwenCloud exactly as when Moonshot couldn't keep up with all the signups.
... other comments were right, this is the full qwen3.8-max model, two weeks ago was the qwen3.8-max-preview release.
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
It gave the svg for the wheels in the reasoning trace then forgot to include them in its final answer. Lol.
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Wow, bike geometry is really good! Except for the missing wheels lol
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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.
they releasing Max.
Once OpenAI and Anthropic are public, every such announcement will become a reliable sell signal
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.
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.
> What am I missing?
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.
[dead]
coding on a laptop is only one use case
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.
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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.
Fable 5.1 is coming, then GPT-6...
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.
You are right to feel that way about the frontier labs, especially Anthropic. From https://stratechery.com/2026/anthropics-safety-superpower/
> "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."
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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.
Sam drank the "superintelligence" kool aid early on and said 30-40% of jobs could be impacted by AI, but recently admitted he was wrong
> “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...
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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.
But the US are the country of freedom!
That's what Hollywood has been telling me my entire life!
Only the ones that beat expectations
It'll sell after 1-2 times, then the market will get jaded and ignore.
3.8 27b is the real news here
Can't wait, been running the 3.6 locally and it's been a boon.
real
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.
If I recall most of the open weight models have provisions about large scale commercial hosting. So probably licensing issues.
> 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.
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.
Kimi K3 is about the same
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.
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.
[1] https://huggingface.co/moonshotai/Kimi-K3
[2] https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE
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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 used the qwen 3.6 models and convinced myself to go ahead and get a gpu. I got the 7900 xt with 20 gb vram.
How’s that been going?
Bad move. Should have used the money to buy NVIDIA stock
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?
Just arrived on open router. Was waiting for this.
https://openrouter.ai/qwen/qwen3.8-max
I noticed they are careful not show Apple products, what operating system are they using in the demo videos?
> 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.
Nice!
Waiting for Qwen3.8-27B :)
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.
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.
It seems this is the only mention of cost?
> 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.
They mention it explicitly in the Twitter post [0].
> 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
The linked qwencloud page has pricing; it's $2/6.
https://www.qwencloud.com/models/qwen3.8-max
Thanks, totally missed that!
I've been waiting for a tiny model release! Kimi, Deepseek, and GLM were all monsters, the 27B 3.8 is filling a gap thats been open since Qwen 3.5
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.
Lmao I love their video with the idea that people will be able to do their hobbies while ai does their job.
Surely Alibaba is leading by example here by reducing work hours per week while keeping pay the same right? Right?
That's the thing. Wny are companies like OpenAI/Anthropic/Alibaba/Kimi/Deepseek still hiring SWEs if their models have become so good?
https://en.wikipedia.org/wiki/Jevons_paradox
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OpenAI can't even build Codex app (not cli) for Linux... I thought code was solved?!
Ironically it's Electron BS, so actually, Sol could probably build itself...
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.
The world never runs out of problem. There is so much work to do.
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.
Source?
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?
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But how is that possible? How can one survive without income barring homelessness, begging, prostitution, and other not so utopian options?
In the 1800s most families only had a single provider, so this percentage may have been higher back then than it is now.
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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.
No I haven't had time to spend my afternoon rock climbing while ai generates documentation.
It's infinite work, I just did more work while codex was doing it's thing in the background.
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.
Anecdotally, I'm even more busy with AI than before AI. I'm expected to do a lot more even if doing one thing is faster.
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.
So you were working 10 x 1 hour = 10 hours before.
And then you were doing 100 x 10 minutes = 100 / 6 hours = ~16 hours & 40 minutes.
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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.
The real answer is to stop trying to have AI reviewing AI code because it's just the blind leading the blind.
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Qwen3.8-35B-A3B would be really appreciated (-/\-)
The dream. Or a qwen-3.8-coder model.
Can a model be stripped off anything not relevant to coding and get a lot lighter? Or is that impossible?
Just like we have professors with specialisation wondering if AI models can also be so.
You can... but the trick is to do so without killing performance. Turns out a lot of random things help make coding performance good.
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.
If you're literally using it as code autocomplete that's not functionally true. But it's still neither well specified nor a good idea.
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.
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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.
we do though and it can be stripped
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.
Yes, look up REAP.
Linking here since the pruning method you're talking about wasn't the top search result when I looked: https://github.com/CerebrasResearch/reap
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So the news here is that this model left the preview stage, and they are also releasing an open-weight version of the Max series for the first time.
You can always try out this model for free on Qwen Chat. Alibaba Cloud has too much friction for me.
unfortunately the alibaba cloud does not seem like it can keep up with the demand caused by this announcement, i'm just getting endless timeouts
I was super excited to read this but lost the plot when I go to
> 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.
> The open weights will be released next week.
Has anyone know whatever they will release any video / image generation capabilities? Video input obviously suppose to be present.
KimiK3-Thinking is not a matching model for comparsion?
I'm so glad exciting releases like this are still going to have components with open weights like Qwen 3.8 27B dense
Is "cowork" a general industry term now? Here I was just getting used to "coding" replacing "programming".
Cowork is to work as coding is to... ding?
Has anyone tried Qwen with the Fusion 360 MCP server? I feel like drawing with python is close enough but I'm curious
We will eventually need a self evolution benchmark to see where these large models can create recursive solutions that improve
the benchmark I trust most is whether the model can explain its own pricing page without getting confused
Not even humans can do that, you're literally asking for something beyond AGI
Bistromathics
https://www.hhgproject.org/entries/bistromathics.html
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.
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> The point of local models is privacy, offline use, and maybe no guard rails.
But also, control and consistency. A local model cannot be changed out under your feet like an API model can be.
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).
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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 $1400 to get to 40 GB of VRAM. That will easily run Qwen3.6-27b at a 6-bit quantization and 80,000 token context size. It isn't fast (19-21 t/s), but using pi-coding-agent is fine.
Now, my instinct is that I am giving up SOTA performance on agentic coding with this setup and LLM. But the gap between my setup and SOTA commercial models is small enough that it doesn't matter to me.
I use opencode go and pay 10$ a month. Works good for 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.
If you can do inference on the CPU, drop the GPU : it should be faster.
> $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.
We all ASSUME that.
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.
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What is the best LLM that I can use right now that is optimized for everything other than coding?
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.
that is not an model issue but a harness one, if you don't want it to have io to files or run command then simple don't give it access to the tools.
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.
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is it the right time to perhaps switch to QwenCode ?
i might end up cancelling claude, anybody else thinking of the same ?
Already done it. But switched to Codex instead. See you on the other side buddy.
I'm oh so seriously tempted to switch at least for a month or two.
Have you retrained Qwen not to give Hoover Institution answers to geopolitical questions?
It is clearly distilled with an aggressive pro West bias to increase sales in the West.
Does the page actually load for anyone? I get stupid spa skeleton spinners.
they release new qwen model every two weeks?
It's been 2.5 months since Qwen 3.7-Max. Unless you mean since the preview of this 3.8-Max model.
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
Tokenpocalypse canceled
Does anyone know how token- and reasoning efficient it is? The charts don't show how many tokens were used in any benchmark.
The imminent third-party benchmarks will cover that.
Now, if only we could afford a setup decent enough to run 2/3 instances at the same time...
> 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.
Where did you read that Anthropic are experimenting with rewriting Claude Code?
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ah so they distilled fable and sol, eh?
“self-evolves through feedback loops”
Does this mean they distilled Claude? Sounds like what Claude Code will often do.
Of course they did.
I mean sure but I’m curious to what extent and why it’s so obvious to some. I haven’t seen this sort of language in a marketing post before.
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.
Are these latest Qwen models still open weights or has Qwen moved away from that?
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."
Page won’t load for me it’s just grey bars fading back and forth forever.
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