0xAF🧑‍💻

pretending I know everything 😉

[0xAF🧑‍💻 @itsafroj]

where is AGI?

[OpenAI @OpenAI]

Building apps has never been easier.

With Sites, Codex can turn your work, ideas, and plans into an interactive website or app your team can explore, use, and share with a URL.

Rolling out to Business and Enterprise plans, before expanding more broadly.

[0xAF🧑‍💻 @itsafroj]

Define Programmer, coder and developer.
then we will talk who is going to replace by ai.

[Kritika @kritikakodes]

One minute silence for those, who thinks:

"AI will replace developers"

[0xAF🧑‍💻 @itsafroj]

@KhaliqHussainnn @vinx_codes Done

[0xAF🧑‍💻 @itsafroj]

IQ doesn't exist.

[φ @QuanticASI]

really high IQ people know God exists. it is not a belief, it is a fact

[0xAF🧑‍💻 @itsafroj]

@isha_singh06 Ubuntu 24.04 LTS

[0xAF🧑‍💻 @itsafroj]

@193993933j Critical thinking increment ( c++) everytime you ask.

[0xAF🧑‍💻 @itsafroj]

@tamimbuilds @ayesha_fatiima Forcing snap package.
Ai integration.
Security.
Rustifying everything tool

[0xAF🧑‍💻 @itsafroj]

@tamimbuilds @ayesha_fatiima You’d be surprised by how much people have started to hate Ubuntu.

[0xAF🧑‍💻 @itsafroj]

@nalinrajput23 Use ubuntu 24.04 LTS

[0xAF🧑‍💻 @itsafroj]

@0xPrajwal_ Windows?
Whole OS is paid Spyware.
Unnecessary ads.

Linux is great in many ways.
It's FOSS
Ad free.
Freedom.

[0xAF🧑‍💻 @itsafroj]

@NanouuSymeon Yes.

[0xAF🧑‍💻 @itsafroj]

@NehraWorkss 👋

[0xAF🧑‍💻 @itsafroj]

Linux Commands
A-Z

#Linux

[0xAF🧑‍💻 @itsafroj]

'AGI winter' is coming!

[0xAF🧑‍💻 @itsafroj]

React basics:
🧩 Components: Reusable UI building blocks.
📜 JSX: Writing HTML-like code in JavaScript.
📥 Props: Passing data down to components.
⚡ State: Managing dynamic data within a component.
🪝 Hooks: Powering logic with useState & useEffect.

#ReactJS #WebDev

[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.
https://t.co/WAz8aIztKT

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.

[0xAF🧑‍💻 @itsafroj]

@Loverism__ clearly, you can not think beyond physical appearance
you don not know about love.