williamtrask
williamtrask on Hacker News
"You are not at the beginning of the curve like you were in the"
Note: on an exponential, you can always say this. People will look back on today with the same "aww cute" eyes.
Posted by
williamtrask on Wednesday September 02, 2026 @10:58AM
from the storage.googleapis.com dept.
Posted by
williamtrask on Tuesday September 01, 2026 @10:55PM
from the deepmind.google dept.
Posted by
williamtrask on Wednesday July 22, 2026 @11:51AM
from the andrewtrask.substack.com dept.
Posted by
williamtrask on Monday June 15, 2026 @02:56PM
from the andrewtrask.substack.com dept.
Posted by
williamtrask on Sunday June 14, 2026 @01:04AM
from the andrewtrask.substack.com dept.
Posted by
williamtrask on Monday June 01, 2026 @07:28AM
from the show-hn dept.
Yeah... probably right. I do hold out hope that this is mostly a timeframe thing. Like, the library, printing press, etc. all had their moments of centralization. But eventually they federated.
fwiw - i think the design looks good.
I wonder if a popularization moment for local AI will ultimately be the pin-prick that pops the AI bubble. Like the deepseek or openclaw moments but bigger/next.
i like your comment better than mine. more please.
i'm not sure it's productive to think this way. senators could be making more money on prediction markets. they took a nice step which will lead them to make no money on prediction markets (less money overall). it also sets a precedent which could easily be applied to the stock market.
what you're saying is probably on the mind of at least one Senator, but all things considered, this feels like a net-positive move which they didn't have to do.
Sometimes I find joy in noticing the importance of comma placement:
Everything the incumbents ship, in an open codebase your firm owns.
vs
Everything the incumbents ship in an open codebase, your firm owns.
Posted by
williamtrask on Friday April 10, 2026 @03:47PM
from the youtube.com dept.
<3 - have ambitious plans to release a new video/blogpost each week, but that's pretty ambitious we'll see
Posted by
williamtrask on Wednesday April 08, 2026 @10:41AM
from the iamtrask.github.io dept.
Posted by
williamtrask on Tuesday April 07, 2026 @11:49PM
from the github.com dept.
Posted by
williamtrask on Thursday January 08, 2026 @05:45AM
from the openmined.org dept.
Posted by
williamtrask on Thursday January 01, 2026 @04:20AM
from the youtube.com dept.
"Conclusion
Our monorepo isn't about following a trend. It's about removing friction between things that naturally belong together, something that is critical when related context is everything.
When a feature touches the backend API, the frontend component, the documentation, and the marketing site—why should that be four repositories, four PRs, four merge coordination meetings?
The monorepo isn't a constraint. It's a force multiplier."
Thank you Claude :)
tried searching for "noodlesUK" and didn't find anything meaningful
Nit: regarding (2), Phil Blunsom did (same Blunsom from the article, and who was leading language modeling at DeepMind for about 7-8 years). He would often opine at Oxford (where he taught) that solving next word prediction is a viable meta path to AGI. Almost nobody agreed at the time. He also called out early that scaling and better data were the key, and they did end up being, although Google wasn’t as “risk on” as OpenAI on gathering the data for GPT-1/2. Had they been history could easily have been different. People forget the position OAI was in at the time. Elon/funding had left, key talent had left. Risk appetite was high for that kind of thing… and it paid off.
Posted by
williamtrask on Sunday September 28, 2025 @07:31PM
from the twitter.com dept.
"This is not the reason, the reason is that this data is private. LLMs do not just learn from data, they can often reproduce it verbatim, you cannot give medical records or bank records of real people, that will put them at a very real risk."
(OP) You make great points. I think we're actually more in agreement than might be obvious. Part of the reason you need to "give" data to an LLM is because of the way LLMs are constructed... which creates the privacy risk.
The principle of attribution-based control suggested in this article would break that principle, enabling each data owner to control which AI predictions they make more intelligent (as opposed to only controlling which IA models they help train).
So to your point... this is a very rigorous privacy protection. Another way to TLDR the article is "if we get really good at privacy... there's a LOT more data out there... so let's start really caring about privacy"
Anyway... I agree with everything in your comment. Just thought I'd drop by and try to lend clarity to how the article agrees with you (sounds like there's room for improvement on how to describe attribution-based control though).
With you on this one. I do think ABC is a step in the right direction to improve things. <3
"The claim that humans need petabytes of data to develop their mind seems completely indefensible to me."
And yet every human you know is using petabytes of data to develop their mind. :)
I'm relatively close to publishing my PhD thesis which is broadly a survey paper of what you're describing. Will share (almost done with revisions).