u/BinarySplit

less likely to alienate people who lean right wing

That's my thinking too. I've spoken to a few people who vote National because they think there's no viable alternative. Democracy's pretty unhealthy.

I want more parties, so that we can go back to it being about policy-vs-policy/issue-vs-issue, instead of the left-vs-right trap where the only winners are the big political donors.

Update: On macOS, the issue was that Chrome stopped being able to access the system keychain. A reboot fixed that, but I also had to restore my profile directory from Time Machine because Chrome had deleted all my logins and extensions. Silly feature - it only reset half my profile, leaving it with data missing and still broken.

Happened to me too. Definitely wasn't a browser crash - last time I used it I closed it normally. I think the update killed some profiles. The worst part is that they don't recover - I just see a "Relaunch the browser to load your profile data and keep it encrypted" bar, but relaunching doesn't fix it.

There was already a similar analysis/approach:

On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification (aka Dynamic Fine Tuning)

Yongliang Wu, Yizhou Zhou, Zhou Ziheng, Yingzhe Peng, Xinyu Ye, Xinting Hu, Wenbo Zhu, Lu Qi, Ming-Hsuan Yang, Xu Yang

https://arxiv.org/abs/2508.05629

Cool that this comes to the same conclusion - hopefully this time it catches on. This gives me hope that fine-tuners will always be able to cheaply distill from other models to catch up and keep the playing field competitive.

I had a few chats with it on OpenRouter (Poolside provider) side-by-side with Qwen3.6-35B-A3B. Just random riddles and critical thinking questions. I also tried Poolside Chat. Qwen 35B won every time.

Laguna feels like it's underthinking. It answers quickly and verbosely, misses big parts of instructions, and has that "llama feel" of a model that sometimes doesn't know what it's about to say and has to make something up on the spot. Fast though.

Either its thinking is currently broken, or it's so specialized to coding that it's useless for other tasks. Unfortunate - I hope this is just a template or infra issue.

EDIT: Looks like skipping thinking is a feature not a bug, but it skips way too often. It only seems to want to think when presented with tasks like math.

Do you also find that it just refuses to spend time thinking in German?

With Opus 4.8, even if I set the reasoning level to max and told it to triple-check its work before replying, it'd start replying to tough German questions within seconds and always get it wrong. If the question was in English, it'd take its time and get the answer right.

They're only base and instruct models so far. Without RL post-training, its CoT reasoning will be somewhat performative, and it won't be able to do long-horizon work, etc. It'll feel closer to a Llama model than a Qwen or Gemma.

It's cool that they're sharing this and have gotten this far, but skip this release - it's not in the same league yet.

It only runs at 30 tokens/sec, which is on the slow side, but not /that/ slow.

Try auditing your skills/AGENTS.md and cut out everything you don't recognize. Sol follows instructions very literally. If there's a "make sure you didn't make any mistakes", it will shift heaven and earth to double/triple/quadruple-check its work.

Or just ask it why it took so long and which skills are to blame.

It is just a layer on top of existing services for which you need at least a modicum of domain knowledge to use properly.

And if you don't have that modicum, Claude Science is actually a net negative because it locks you in to just Claude. You can't easily get other models to review each others' work in the same way you'd get with Hermes Agent, Cursor, Copilot, OpenWork, etc.

I haven't tested these, as I don't use local models for bigger coding projects, but here are random links I've collected that probably can help.

Fixing / preventing bugs

• mattpocock/skills has tdd to make it create high-value tests to verify its work, and improve-codebase-architecture can refactor a messy monolith into more tractable modules
• desloppify is a loop to automatically clean up your codebase and cover it in tests

Keeping context lean

• maki is a harness that generates a high-level skeleton of the codebase, so agents know where to look without needing to read full files.
• headroom is a context compressor proxy. It sits between your harness and uses heuristics to compact code/data.

If you try any of these out and they work, let us know!

The current nanoGPT speedrun record gets to 3.28 in under 80 seconds with 8xH100s, so roughly ~640 H100-seconds. What am I missing? Why are your numbers around ~5000 GPU-seconds and not ~640 GPU-seconds?

Let's get one thing out of the way: you won't be able to run decent LLMs or do deep learning research on either. While you can train PyTorch and MLX models on a mac, it's slow and you'll be debugging in hard mode due to random driver issues. If you want to do DL, I'd set aside some budget for cloud compute or API tokens.

16GB can be tight if you run Docker at all, but it's otherwise fine if you're not juggling tensors. The faster Pro CPU helps for general SWE and non-deep ML work, but it's won't feel like there's any difference for the 90% of the time that you're not training/compiling something.

Since you're a student though... Are you considering weight/portability? The Pro is fairly heavy. Can be annoying for travel.

The core idea is interesting, but please don't outsource evaluation to an LLM.

Even if all of the missing results were added and it was rewritten to explain the evals, nobody seriously considering trying a new optimizer is going to be convinced by synthetic and toy datasets and models small enough to train on CPU.

I wouldn’t normally recommend Ollama over building llama.cpp

Recommend LM Studio instead! It's an easy-to-use interface over llama.cpp that includes a model browser, automatic settings for offloading, and a toggleable local API.

Ollama has such a long history of problems... You can never trust that it's using the right prompt template or good quants. Stuff will just silently not work well.

IMO, use it, but make sure you're continually improving how you use it. Don't settle for ChatGPT - get the Codex app (and try others), have it build software properly for you, use Code Review or a review skill, read how other people are building agents.md and skill.md files, build habits to ensure that you catch the agent's mistakes and the agent catches yours.

As a software engineer, hands-on coding is undoubtedly going to go away in the next few years. The crucial skill is in making sure you+AI is better than just AI, even as AI gets better.

I'd try to spend those FLOPS elsewhere in your workflow. Whisper for speech-to-text is pretty awesome. Might even be worth trying to get an Omni model to function as a continuous conversational wrapper around other models.

Even people trained to support disabilities seem to do this. It's such a bizarre failure of empathy. I've even had a psychiatrist tell me I just need to exercise more to fix my ADHD...

10/10 on the comic - the art, pacing, and composition are just *chef's kiss*.

The i and e sounds make that sentence so hard. As a beginner, I found FYFV's "This is the voice I want to use" way better for tracking progress - it has a mix of easy and hard sounds.

I haven't had much time to look at it, sorry.

I should be burnt out several times over by now, but somehow I keep ending up in places where I'm deeply motivated by the company's mission.

Even when bureaucracy, politics and tech issues make it hard to get anything done, I'm working long hours to try to catch up to ever-increasing expectations, I'm struggling to find time for my hobbies, my health is acting up, my cats are screaming at me, etc. I can always find a path from what I'm working on to some societal benefit.

Have you given gullibility a shot? It has worked so far for me!

Are they subsidizing their own products, or overcharging on the API?

The prompt cache prices are insane when you consider how often they're only paying to hold the KV cache in RAM for a few seconds while a tool call runs.

Zeyuan Allen-Zhu compared them to ReLU² on synthetic tasks in this video @ ~45m.

They have less "knowledge capacity" than ReLU² because they have fewer tokens, but improve "reasoning ability" (given fixed depth and no CoT tokens) because they can represent more complex functions.

I broadly agree, but have an alternative explanation: bad empiricism-focused papers are easier to read & judge than bad theory-focused papers.

Rejection of theory may be collateral damage in backlash against time-wasting papers.

It's frustrating that there are now so many people frothing at the mouth to get results for investors that as soon as someone discovers the next big leap, the big techs will get to raise trillions by throwing compute at it, and the researcher will probably only get citations.

IMO, financially no, career-wise it's it's on the fence, but if you're passionate about a specific area (e.g. medicine or finance), a PhD may be your only way to get you on the right trajectory.

I only have a Bachelors, work in industry (and have worked in academia), and am in a team that's virtually all PhDs. I started as a plain old non-specific "programmer", and am now in a drug discovery AI/ML team working on practically every facet from research to deployment. The whole time I've worked, I've been gaining skills and experience at probably a similar rate to people who were focused only on learning. The difference is that I was earning while learning.

Career-wise, a PhD proves you can do a multi-year project, so you're likely to be entrusted with more autonomy in your first role. In a "junior" position, not having a PhD can mean a very short leash - daily check-ins and little ability to choose your work. Whereas I've seen fresh PhD graduates allowed to embark on long-term projects despite struggling to keep them on track. If you're great, this means you hit the ladder with a running start. If you're not, you're just starting with a delay.

The biggest reason to do one IMO is that you get to pick your path. Niches like biology are hard to break into without experience. I only got in through dumb luck - they needed web developers, and I incrementally took on responsibilities until I was doing research. However, if you only care about mainstream stuff like attention mechanisms and LLMs, you're probably not going to gain much vs breaking in as a software engineer.

EDIT: Also, if you're aiming for industry, don't bother trying to measure career trajectories in papers and conferences. Most companies only care if you have no other experience. Delivering a product trumps delivering a paper.