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Adam West
4.4K posts
Adam West
@bitcoin_west
The old way is quietly being replaced
USA Katılım Ekim 2018
2.1K Takip Edilen903 Takipçiler
@kimmonismus try normalizing the spend calculation with inflation/gdp data
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The Manhattan Project is a joke compared to the expansion of data centers. Let's hope that chip production continues despite the war in Iran.
OpenAI Newsroom@OpenAINewsroom
In January 2025, we committed to generating 10GW of compute and have already identified over 8GW of that. Now, we're planning for 30GW of compute by 2030. A milestone that scales with the rapidly accelerating demand for intelligent systems. Image generated by @ChatGPTapp Images 2.0 😉 #YouCanJustBuildThings
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@systemdesignone “You guys should really turn this single DB monolith that barely has downtime into 10 different services that all communicate with each other where each have their own DB while completely ignoring it’s one scaling issue”
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My ChiliPad Ooler seems to be reaching end of life, and I need a replacement. Bed cooling has been a game changer for me.
I've read horror stories on the Dock Pro (and customer support under the new ownership of the company).
Eight Sleep is crazy expensive (subscription on top of ~$2K purchase price).
I found the AquaBed on Amazon which seems to have decent reviews, esp for the price, but the cooling capacity is a concern. It's almost a "roll the dice" price tag compared to the others.
Any experience among the folks here?
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Big Misconception with AI Agents...
Do NOT do this:
User -> Agent -> External API -> Agent decides everything every time, lazily & un-deterministically pulls your data
Do THIS:
External API -> Python Sync App -> Postgres -> FastAPI -> Next.js -> User
Use Claude code or Codex to build the apps that sync & structure your data first.
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Most people don’t know this…
Brian Chesky and the Airbnb team couldn’t raise money.
Not because the idea was bad.
Because investors thought it was insane.
“People won’t let strangers sleep in their homes.”
So they got creative.
During the 2008 election…
they launched a side hustle selling cereal.
Obama O’s. Cap’n McCain.
They made ~$30,000 just to stay alive.
Let that sink in.
A company that would become worth $100B+
was literally funded by novelty cereal boxes.
But that’s not the interesting part.
The interesting part is what it signals.
Most founders think funding comes first.
It doesn’t.
Survival comes first.
The best founders don’t wait for permission.
They manufacture momentum.
They find any leverage point:
• attention
• culture
• timing
• distribution
and turn it into oxygen.
Airbnb didn’t win because it was obvious.
It won because the founders refused to die.
And they were willing to do things that didn’t scale…
just to get to something that would.
Today, we’re watching the same thing happen again.
AI isn’t just creating billion-dollar companies.
It’s giving founders new ways to survive early:
build faster
test faster
reach people without permission
The people who win won’t be the ones with the best ideas.
They’ll be the ones who stay alive long enough
to find the one that works.
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Might be a game changer
Andrej Karpathy@karpathy
Three days ago I left autoresearch tuning nanochat for ~2 days on depth=12 model. It found ~20 changes that improved the validation loss. I tested these changes yesterday and all of them were additive and transferred to larger (depth=24) models. Stacking up all of these changes, today I measured that the leaderboard's "Time to GPT-2" drops from 2.02 hours to 1.80 hours (~11% improvement), this will be the new leaderboard entry. So yes, these are real improvements and they make an actual difference. I am mildly surprised that my very first naive attempt already worked this well on top of what I thought was already a fairly manually well-tuned project. This is a first for me because I am very used to doing the iterative optimization of neural network training manually. You come up with ideas, you implement them, you check if they work (better validation loss), you come up with new ideas based on that, you read some papers for inspiration, etc etc. This is the bread and butter of what I do daily for 2 decades. Seeing the agent do this entire workflow end-to-end and all by itself as it worked through approx. 700 changes autonomously is wild. It really looked at the sequence of results of experiments and used that to plan the next ones. It's not novel, ground-breaking "research" (yet), but all the adjustments are "real", I didn't find them manually previously, and they stack up and actually improved nanochat. Among the bigger things e.g.: - It noticed an oversight that my parameterless QKnorm didn't have a scaler multiplier attached, so my attention was too diffuse. The agent found multipliers to sharpen it, pointing to future work. - It found that the Value Embeddings really like regularization and I wasn't applying any (oops). - It found that my banded attention was too conservative (i forgot to tune it). - It found that AdamW betas were all messed up. - It tuned the weight decay schedule. - It tuned the network initialization. This is on top of all the tuning I've already done over a good amount of time. The exact commit is here, from this "round 1" of autoresearch. I am going to kick off "round 2", and in parallel I am looking at how multiple agents can collaborate to unlock parallelism. github.com/karpathy/nanoc… All LLM frontier labs will do this. It's the final boss battle. It's a lot more complex at scale of course - you don't just have a single train. py file to tune. But doing it is "just engineering" and it's going to work. You spin up a swarm of agents, you have them collaborate to tune smaller models, you promote the most promising ideas to increasingly larger scales, and humans (optionally) contribute on the edges. And more generally, *any* metric you care about that is reasonably efficient to evaluate (or that has more efficient proxy metrics such as training a smaller network) can be autoresearched by an agent swarm. It's worth thinking about whether your problem falls into this bucket too.
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I see WordPress people moving to Astro.
Why Astro compared to others like NextJS?
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Adam West retweetledi
URGENT PSA - New supply chain attack vector that I found WILD > AI LLMs hallucinate package names roughly 18-21% of the time.
Hackers have started pre-registering those hallucinated names on PyPI and npm with malicious payloads; they call it "slopsquatting"
You can only imagine what's next
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