3abiton

Congrats, I remember seeing this few months ago, and it has improved a lot since then! I haven't tapped into Wolfram / Mathematica since my physics years, but I am curious now to try it again.

> Same, sad how far Claude has fallen. I still think Claude code patterns are amazing so I just port those.

I am curious, is that a plugin, or skill?

This was so well beautifully written, and poignant for our times. Almost 70 years old paper.

Gosh this gave a big myspace flashback

This is so awesome. I wish more realistic problems get solved like this instead of every new gadget getting shoehorned for gaming or porn.

I second that. I've always enjoyed his work!

Check out Thingino. It's an open source firmware you can flash on certain off-the-shelf chinese camera (including wyze), to turn them from cloud spyware to actual fully local ip devices.

I wonder to what extent these habits or quirks are self-learned or "self-imposed" (probably the wrong word). I guess, it gets us in the comfort zone to free up our brain energy to perform more important tasks.

Could be handicapped version with more limited functionalities. Noentheless, open-weight is open-weight, I just hope the quality is good enough to be sota.

I miss the days of 4changpt.

> Yeah, Qwen3.5-122B-A10B-NVFP4 produces better responses than Qwen3.6-27B-NVFP4 (both from unsloth), but I'm mostly using them for programming in various ways, mostly Rust, Clojure, Python and JavaScript, and some translations tasks, but not much more than that, so YMMV. > > Edit: as a concrete example, I'm working on a "optimization framework via agent harness" right now, Qwen3.6-27B-NVFP4 is often unable to actually complete the optimization within 100 turns, while Qwen3.5-122B-A10B-NVFP4 has no issues finishing within ~50 turns or so.

But why are you using Qwen3.6-27B-NVFP4 compared to the FP8 or full version? In my experience the Q8 of 27B is on par sometimes better than 122B. I am experiemnting witb higher quants for 122B to fit on my Strix Halo, but still, the difference honestly for my workflow is not that much. I just wish they released 3.6-122B version.

> in my experience of 1 month daily use, Qwen 3.7 Pro is just unusable. wastes too much time, goes off track, useless stuck loops, cannot debug at all. Deepseek V4 Pro is night-and-day compare to Qwen. actually Qwen models seems the worst SWE experience so far.

I have used both Qwen3.6-35B and Qwen3.6-27B locally (both Q8 quantized with llama.cpp). I have also used antirez's quant of DS4-flash. They all performed within the same tier, DS4 being a bit more efficient, but they all gave really good results, mainly used for bash scripting, debugging, python and some C++. I am curious what type of applications/langauges failed with Qwen? One thing to note, the chat templates were "broken" for qwen models and had to debug it, there are already effort on this. Tbh, the same with gemma.

Also at scale Kimi (or DeepSeek and others) don't have to worry about local deployment. The number of param keeps growing, I highly doubt consumer hardware will catch up. I am just glad Qwen team is still putting up great bangers.

It would be so cool as a feature to allow people on HN to make some of their favorited posts/comments public.

Another crucial difference is: ROCm is open source, while cuda isn't. Yes it's tough to port things to newer gpus, but in theory people did it (therock spear heading the ROCm patch for strix halo before the offical support)

Honestly, root detection is a cat and mouse game. So many ways to spoof it. Same with Play Integrity. If the goal is to prevent bad actors, it will never work really. It will be just a big headache for the citizens. Look at how gatekeeping certain websites is working. VPNs became mainstream. So are proxies too especially for bad actors. Malicious actors have just to pay up a service to do so. And I am sure it will be the same with Android (it already is with keyboxes you can purchase to spoof play integrity)

This was a very enjoyable read, and loved the distribution inclusion, rather than point statistics. I hope there are more tests on different factors like GPUs (nvidia vs amd vs intel), screens, mice, and ultimately windows to see if there is really a difference. I don't play competitve gaming, but careful data driven approach would put a lot of the debated questions to rest.

Funnily the current high end Mac Studio are not suited for current LLMs. M3 Ultra is "quite an old" chip for AI, despite its bandwidth. The issue for running local models (especially LLMs), you need few things to align really well: 1) compute power (affecting PP) 2) VRAM capacity (affecting model size you can load) 3) Bandwidth (somewhat affecting decoding speed).

The issue with the M3 chip is the compute performance, as it doesn't fit well the transformer architecture. This changed with the M5 (apple baked their own matmul into the chip), which would significantly speed up PP (and video/image generation btw), making the M5 Ultra significantly faster than the M3 Ultra and in practice much more usable. You can try to load Kimi or GLM on M3 Ultra, but it's not usable. Now the M5 Ultra is not out yet, but undoubtedly it will be a superior offering, and shilling 15k on 512GB version is actually reasonable (if it's every priced remotely around that tag).

This is technically impressive, but is it usable in practice?

> I just don't think that I can ever trust an xAI model knowing that they are actively trying to shape its replies to fit a political narrative. How can you trust their models to be reliable in a business setting with the foreknowledge that their models are being nudged around in the backend?

https://www.nature.com/articles/s44387-025-00048-0

Large language models reflect the ideology of their creators.

It has very interesting insight from analysis of LLMs political leanings. Spoiler alert: they all have political bias.

I still don't fully get the additional value over tmux, beside notification regarding the agent status?

I run OpenWrt on 2 of my routers. It's really amazing the level of control. Although, I am now building a better approach: a mini PC as a managed linux router replacing my ISP (no wifi). Then my 2 wifi routers for wifi.

The target audience is different. Coding is mainly a trade of the tech savvy, who like many on r/localllama users do not hesitate to deply on 16GB Vram gpus. Even if so, it is estimated that within 2 years we will be able to run Claude 4.8 on consumer hardware give the rate of improvement of open-weight LLMs, which will put more financial pressure on "paid" labs. It's just a matter of rate of improvement which is shrinking between open-closed models.

The "trick" is well documented in their Deepseek-OCR paper, that builds on plenty of other work. It's just not simple to just switch a commonly used LLM architecture to a new one, but I don't doubt most frontier labs are already experimenting with it. This by itself a very active field of research.

They are heavily bogged down by bandwidth unfortunately. The macs are on another level. If Apple decides to release AI dedicated hardware, it would dominate this space (consumer AI).

No wonder why deers are seen as snobbish. All the illiterate ones were shot.

Honestly it's just a hierarchy difference between the two countries. In the US, tech/fin/military companies have the upper hand compared to the government (fragmented between 2 parties). Despite the sharades with Anthropic, Tech-fluencers are in control. Compared to china, the government (dictatorship) has more control over Tech companies (take any example from the past 10 years). For them, undermining the US AI supremacy is an objective, and releasing open weight models is the way, and I'm all for it.

> One day, maybe not far from now, a breakthrough will allow huge LLMs (say 200B in size) to run well on an old 5 year old Dell desktop.

I think there will be specialized hardware (beside GPUs) that would be custom made for LLMs. Yes TPUs exist, but mainly for datacenter. GPUs exist, but they are adapted from mainly graphic application. Once all the demand from data center dries up, innovation will kick in.

And a big thing that's missing is ... the harness comparison. Ot plays a very big role. I use forge, and I have been inpressed with what it can do given all the limitations of local models.

I think nearly everyone mentioned Qwen, so my turn I guess. Qwen 3.6 35B Q8 (MTP), on a Strix Halo, with llama.cpp. Around 40-50 t/s. Really great pefromance, I get always suprised by its capability. I used with forge-code directly in zsh. For long context 150k+) it start degrading and forgetting.