nsypteras

I do wonder if the labs have already started including weight updates in their training sets as a means of teaching RSI via RL and this is basically just discovering that. Regardless if this ability is emergent or achieved through finetuning though, it's pretty incredible.

These are truly maddening. I’ve recently fallen prey to another trick - email from a famous-ish tech person starting a new company and asking to chat, but then responding with interest just enters you into the normal pipeline with a hiring manager. Nothing wrong with that pipeline, but don’t misrepresent the outreach as an opportunity to chat with an influential person when it’s actually just an automated recruitment email.

This is so cool! For someone unfamiliar with modern game design but interested in building something similar, how did you get the assets such as the seats for the game? Are they open source, vibe-coded or made by hand? Would also be curious what the underlying game engine is, or any other details you wouldn't mind providing. Thanks!

Analyzing frontier LLM performance on my favorite daily puzzle game (https://www.nicksypteras.com/blog/cbs-benchmark.html) Next step is to assess how well the LLMs can create their own new, logically satisfiable puzzles in the same style. Then I'll have them battle it out, with one model creating a puzzle and the other attempting to solve it!

Congrats on launching! One immediate thought is that people will always be wary of running LLM-generated code on their machines even if it's sandboxed. Is one of the future business cases for this to host a remote execution environment that pctx can call out to rather than running the code locally?

Ya interesting thought - would be fascinating if generating games w/solutions is part of the training data pipeline. There's been previous work done on on testing LLMs on logic puzzles[1][2][3] so they could possibly be building off those ideas to improve performance.

[1] https://huggingface.co/papers/2504.00043 [2] https://huggingface.co/blog/yuchenlin/zebra-logic [3] https://arxiv.org/pdf/2403.12094

I'm impressed it recommended so many books i've already read and liked! I have a big reading backlog but once it's whittled down I will likely come back to this. One feature request would be to also show a "why this is recommended" for each recommendation so I can further narrow down the list for what I'm looking for

"Counter Chinese Influence in International Governance Bodies" and grouping them in with US "adversaries" and "rivals" is quite undiplomatic language to throw in under "Lead in International AI Diplomacy and Security" section. Diplomacy with China should be an important part of this initiative but will inevitably be bungled.

1984: U.S. withdraws. 2003: U.S. rejoins. 2011: U.S. stops paying dues after Palestine joins. 2017: U.S. announces withdrawal (effective end of 2018). 2023: U.S. rejoins, pledges to repay dues. 2025: U.S announces withdrawal

Seems to be a revolving door

Same here! Kiwix comes in clutch on flights. I've used it so many times to get background knowledge on topics mid-read. Plus free and open source. Such a great service.

I think that would be one of the success cases described in the article because HITL is an integral part of good customer support chatbots. Support chats can be escalated to a human whenever the agent is unable to provide a satisfactory answer to the user.

> The transportation agency has spent years looking for an innovative way to allow passengers to move faster through the security checkpoints.

I think the writer had some fun with this one

That's pretty cool! Now I can imagine a tool that gives you a prediction before you even post and then offers suggestions for how to increase performance...

A while back I made a little script (for fun/curiosity) that would do this for HN profiles. It’d use their submission and comment history to infer a profile including similar stuff like location, political leaning, career, age, sex, etc. Main motivation was seeing some surprising takes in various comment threads and being curious about where it might have came from. Obviously no idea how accurate the profiles were, but it was similarly an interesting experiment in the ability of LLMs to do this sort of thing.

Cofounder splits are extremely common. Cofounder "couples" counselors are a thing you could look into to help resolve your differences. Your VC might have recommendations for one. If you ultimately decide to split, I'd recommend at least one of you (or "the company"?) getting a lawyer to draw up a formal separation agreement you both sign in order to split in the cleanest possible way.

So much nostalgia for my Pebble. Got it right as I was seriously getting into programming. I still remember how magical it felt writing the C code to build my first watch face and how proud I was to show people. Amazing news <3

Reminds me of a modern version of The Entertainment from Infinite Jest.

> Is there an ML engineering practice that isn't focused on building models but more on managing/deploying/scaling models? i.e. can I avoid learning all the maths underneath?

I transitioned to this in 2018. It was called MLOps. I was a mobile developer before that. Transitioning was pretty easy at that time (it might be more competitive now). What I did: worked on an intensive ML project for myself and realized I enjoyed working across the ML stack. I wrote a blog post on the project here: https://www.nicksypteras.com/blog/aisu.html. Then I applied to an MLOps team and leveraged the project to demonstrate skills/experience. As for avoiding the math, you could probably get away with it but learning the basics will make everything way easier. I feel like without some basic ML math under my belt I would have been flying very blindly.

thanks, just confirmed can create Signals via the API now :) Look forward to trying this out

Cool feature! Any plans to offer limited API credits in order to trial things? I just created an account but I'm unable to test (settings shows 0/300 credits).

Ditto. I would pay big money for this if it came in an inconspicuous form factor like airpods. Hopefully it's just a matter of time before Airpods themselves can do this.

So glad you said this! For some reason that's always stuck out to me as having been my biggest personal "wow" moment while watching AI development progress. ChatGPT is awesome but for some reason I've never felt as awed by it.

Clearly an extremely impressive demo and congrats on the launch. I do wonder how often the bugs Devin encounters will be solvable from the simple fixes that were demonstrated. For instance, I notice in the first demo Devin hits a KeyError and decides to resolve it by wrapping the code in a try-catch. While this will get the code to run, I immediately imagined cases where it's not actually an ideal solution (maybe it's a KeyError because the blog post Devin read is incorrect or out of date and Devin should actually be referencing a different key altogether or a different API). Can Devin "back up" at this point and implement a fix further back in its "decision tree" (e.g. use a different API endpoint) or can it only come up with fixes for the specific problem it's encountering at this moment (catch the KeyError and return None)?

Back in high school, I spent a good chunk of time reading the guides on this site purely just to fantasize about doing the trips myself one day. What a cool blast from the past, particularly given that the site hasn't changed!

Not sure how comfortable the "climatization" is given that many people in the pictures are wearing winter coats inside. Hopefully it's not sweltering in the summer...