When you have a problem, just code a solution. I suck at time tracking and management. I tried apps like ActivityWatch that just didn't work well, and apps like RIZE charge $13 a month. So I built this overnight. Local Mac native time management tool.

If such public space like this exist in India we wouldn't have to spend 2k ₹ in cafe just to meet friend.

Do a kickflip!

After reading for 15 mins: I think Nick is operating in a world where interpretability was just about enumerating the neurons and superposition was an obstacle to that plan, so now he's happy because maybe superposition isn't real. But: 1) there are v few empirical results. Unfortunately interp has a long history of significantly overclaiming how interpretable their findings are (SAEs, early circuits having poor causal scrubbing scores etc) so I'm not updating much at all on the evidence he's already shown. it's really got a long history of people getting overexcited. So looking forward to seeing the paper with all the empirical evidence! 2) I think it's not that big news for me even if true because superposition in something like the average case wasn't my main crux to thinking interpretability is tractable. some cruxes this doesn't really deal with at all: - I think the correct 'circuit' is usually just an overwhelmingly large bag of random-seeming heuristics (a la Redwood Research’s interpretation of how an LLM predicts is vs was) and having it isn't very informative/doesn't help with much/doesn't lead to good predictions. - More generally, It seems possible to enumerate all the 'low level' features in a neural network, and still have a hard time answering 'high level' questions like: "Is the model misaligned? / What would it do in this new situation? / If it is misaligned what should we do about it?" there's an abstraction/emergence gap to cross from the features to the questions of interest, and crossing that gap is likely much harder than enumerating features. That said, if his claims hold up, I think it will be quite a big deal, and a useful precursor to tackling the thornier problems! There may also be some useful things to do without tackling the thorny problems: eg we'll be able to make use of masses of automated agents to scour the features and circuits to try and make sense of the mess, and maybe that’s good for something. although I suspect that even the most optimistic outcome here will not make alignment of superhuman AIs much easier (as some on twitter have claimed) because the neuron interpretability will not be adversarially robust, among other issues

interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works already-clean.goodfire.com/

my worry about all these Codex resets is that when the resets are done, people are going to hate OpenAI and constantly yap about how horrible they are Giving free stuff away always seems to bring the worst people to your platform

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WE ARE GOING TO EAT SO GOOD IN 4 DAYS, BLACK CLOVER FANS! MARK YOUR CALENDARS FOR OCTOBER 17TH!

We discovered a sophisticated recording device that was hidden inside one of our offices. We used our models to understand the device and where it had come from, identifying that a specific employee had planted it a few days earlier. We referred the matter to the FBI, and we’re grateful to the agents and prosecutors who moved quickly to make an arrest. The FBI’s investigation is ongoing and we will continue to support their work. At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data. The employee was terminated. We take protecting our technology, data, and people seriously, and as a matter of national importance. Our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states, and this case is an example of how AI can help address these sophisticated security threats.

自分が博士学生の時に車輪の再発明をしたことがあり、恩師に同じ研究結果があると指摘されました。しかし、直後にボソッと「先駆者と同じ場所にたどり着いたな」と言われたんですよね。独力で思い付いたなら、それは、先駆者と同じレベル到達したわけでポジティブに捉えて良いことなんですよ。

every family has that one person who had so much potential but then became mentally ill and can't do anything anymore

Character AI has gone bankrupt. The AI startup announced its Chapter 11 filing through a late-night newswire press release

The smartest code model in the world would not be good at 3d stuff unless it was trained specifically for it. A lot of the labs recently got a big pile of 3d training data and an RL environment to improve 3d skills. Take a look at the jump in 3d from Fable 5.1 to Opus 5.5. It's at least 4x better at 3d despite being a 5% better model at best. Clearly Meta didn't go hard on 3d for this release. Doesn't mean the model is bad. 3dmaxxing is a different type of benchmaxxing, don't overthink it. I'll take some fault here by sharing all the Fishslop demos. The 3d stuff is fun and cool as hell, but it's not a good representation of a model's real world code capability.

what the fuck

A big announcement from Lexsi Labs: Lexsi Labs (@Lexsi_labs) , Cohere Labs (@cohere ) and Hugging Face(@huggingface) are partnering on the Lexsi AI Safety and Alignment Hackathon, one of the biggest hackathon on post-training and AI Safety & Alignment. Nov 1 to 21, 2026. Online, free, open to builders worldwide. Teams build real post-training pipelines: - Models: open-weights Tiny Aya and Aya Expanse from Cohere Labs - Tooling: six open-source Lexsi Labs toolkits (AlignTune, CuratorKIT, AuditKIT, SafeTune, CircuitKIT, AgentTune) - Artifacts: models, adapters, logs and datasets hosted on Hugging Face Three tracks: fine-tuning and data curation, AI red teaming and safety, agentic fine-tuning. Each submission chains at least two toolkits and reports before-and-after metrics. $30,000+ in prizes and credits, GPU credits for up to 800 teams, Demo Day for the top 20, and interviews at Lexsi Labs for standout builders. Kickoff: Bengaluru, Oct 31, with an online option. Registration starts today at: hackathon.lexsi.ai Thank you to @huggingface and @cohere for building this with us.

it even has misalignment

现在phd做学术也是简单了，找导师要点资金，找个use case, 把大模型跑一圈， 做个benchmark就可以发文了。 参见这篇： arxiv.org/abs/2610.12452 BrickBench是面向文本条件乐高组装的智能体基准，把市面上的模型跑一遍，看哪个设计的乐高组装水平最高。 这还是斯坦福出的论文。