emregucerr

I would love to see someone build it as some kind of an SDK. App builders could use it as a local LLM plugin when dealing with data involving sensitive information.

It's usually too much when an app asks someone to setup a local LLM but this I believe could solve that problem?

Did they pick the word "uncrewed" to not use the word "unmanned"? If so, I'm not hopeful. Might be another EuroDrone disaster.

People dismiss this as a meme too quick but I think this is a good thought experiment not only for drawing a comparison for energy consumption but learning efficiency. AI is often criticized for its low learning efficiency but if you compare it to a human it's not looking too bad. Let's say a human becomes an AGI-level learner by the time they are 14yo. Human vision is approx. 500 megapixels and that is approx. 1.7 Gb per second of vision data. That means it takes approx. 800 PETABYTES of data to 'pre-train' a human to become a well-enough generalist learner. Take Llama 4 from Meta whose training data set consisted of 30 trillion tokens - his is equivalent of 120 Tb which is a mere 0.12 peta-bytes.

I am well aware this is a flimsy napkin math at best but I find comparing LLM models to humans with a more serious tone is fun and useful thought.

> I don’t think the gates should animate up into the air.

I agree! It feels off compared to the overall aesthetic of the game.

Awesome game though! Loved it.

I think most people blindly try to get controls in Vanta/Drata to pass like us. I'd much rather build a dumb bot than having to talk to my auditor. But still

> we realize how stupid this is

Hey HN! We recently got our SOC2 certification. One thing that really annoys us is having to get all deployment PRs approved by at least one person per guidelines. This might not sound like a lot but it gets annoying pretty fast when you are 2 people and deploy multiple times a day.

We built a very simple Github bot that snap-approves all pull requests for the default branch. For some extra flair, it sends a very dry joke about the contents of the PR.

In the off-chance that you are also a small team that has a Slack channel filled with PR links just to get them blindly approved, you can download it for yourself.

p.s. This is obviously mostly parody. Even though we have a small use-case for it, we realize how stupid this is.

i wonder how good is R1 at counting pixels from a screenshot. what enabled claude and OAI's CUA to develop computer use was being able to precisely give x-y coordinates of a click location.

also, how big of a gain to have reasoning for computer use? i feel like reasoning unlocks a lot when there is a single complex question but not so much better at taking actions in a long term plan.

why specifically books? i never was able to get real value out of them in the case of programming.

imo, the more senior counterpart benefits less from pair programming and therefore enjoys less. however, it's still the fastest way to get someone familiarized with a concept/project when done correctly. might be really valuable in a cs curriculum.

Am I missing something with the latest buzz around the 'founder mode'? It's a new concept for startups that transitioned into an enterprise. Not seed/series A stage startups. In those cases you don't have any other option at all?

I believe going against the notion that CEOs should delegate work as the company grows is great but I don't get why seed stage startup founders act like they are enlightened from this and brag about being in 'founder mode' is something special. You simply don't have any other option?

So how is 'founder mode' - which lost all of its meaning at this point is an edge for 'startups'?

> The best lens for future performance of large models is uncertainty. 100% agree. I think to better way to phrase my argument there would be to reject the notion that LLMs are destined to get exponentially smarter (twitter fallacy). This is not to say I believe they are not going to get any smarter in the future. We simply don't know and building a company/product on the expectation of another Moore's Law is dangerous.

> Why not? The 'original' part is more important than the 'beautiful' part - which should have been more clear in my writing. This argument also triggers the question "is true originality even possible" but I think the difference for LLMs at the moment is their incapability of building non-obvious analogies. I've yet to be inspired something written by an AI and I don't think simply overfitting a model with all human generated data is enough for that. As I also mentioned in the blog, I would be happily proven in future.

> _if_ you split the process up appropriately I believe this pre-requisite is very important. LLMs so are terrible at planning and splitting a complex task into simpler steps. This might be natural limitation of `next token prediction`. For complex planning, each step should be the result of both the previous and speculative future steps. We try to tackle this by dividing a plan into two: a macro and a micro plan but still a lot to improve there.

p.s. thanks! aider is awesome too!