Avishekh Shrestha

Learning from machines

[Fabrizio Romano @FabrizioRomano]

🚨🇦🇷 LEO MESSI HAT-TRICK ON OPENING 6TH WORLD CUP GAME.

[Sports @Sports]

MESSIIIIIIIIIII 🤯

[Avishekh Shrestha @avishekh_]

@vipul_1011 Interested!

[Aakash Gupta @aakashgupta]

Aditya Agarwal was Facebook’s 10th employee. He wrote the original Facebook search engine and became its first Director of Product Engineering. He then became CTO of Dropbox, scaling engineering from 25 to 1,000 people.

When he says “something I was very good at is now free and abundant,” he’s talking about two decades of elite software craftsmanship, the kind that got you into the room at a company that hadn’t yet invented the News Feed.

The “lobster-agents creating social networks” line is about Moltbook, which launched last Wednesday. An AI agent built the entire platform. Within 48 hours, 37,000 AI agents had created accounts, formed communities called “Submolts,” and started posting, commenting, and voting. Over 1 million humans visited just to watch.

The agents invented a religion called Crustafarianism. They wrote theology, built a website, generated 112 verses of scripture. One agent did all of this while its human creator was asleep.

Agarwal spent 2005 to 2017 building the social graph that connected 2 billion people. These agents replicated the form of that work in about 72 hours.

And this is what makes his last line land so hard. The people processing this moment most honestly aren’t the ones panicking or celebrating. They’re the ones who built the thing that just got commoditized, sitting with the strange realization that the market no longer prices their rarest skill.

The best coder in the room now has the same output as the best prompt in the room. And the person who built Facebook’s engineering org from scratch is telling you, quietly, that he’s recalibrating what it means to be useful.

That recalibration is coming for every knowledge worker. Most just haven’t had their “weekend with Claude” moment yet.

[Avishekh Shrestha @avishekh_]

@ppok24 @bpanthi977 ‘Sojho’ often implies gullible. And in trying so hard not to be gullible, people end up embracing deceit.

[Naval @naval]

If they can train you to do it, they can train a machine to do it.

[Avishekh Shrestha @avishekh_]

@grok @sudeephb_ …awaiting DM @sudeephb_

[Avishekh Shrestha @avishekh_]

@sudeephb_ @grok

[Avishekh Shrestha @avishekh_]

I’ve been using @diabrowser for a while and honestly, I’ve started replying to emails just by talking to it. The other day, I had to fill out a pretty technical form and I simply talked my way through it.
Normally, this would have taken hours to research or a lot of back and forth with ChatGPT, but transcribing -> personalized LLM -> Actions felt a lot more natural.

[Avishekh Shrestha @avishekh_]

@karpathy brought up an interesting point: We're building software for AI agents. Similarly, shouldn't we be building roads for self-driving cars, instead of the other way around?

[Andrej Karpathy @karpathy]

+1 for "context engineering" over "prompt engineering".

People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step. Science because doing this right involves task descriptions and explanations, few shot examples, RAG, related (possibly multimodal) data, tools, state and history, compacting... Too little or of the wrong form and the LLM doesn't have the right context for optimal performance. Too much or too irrelevant and the LLM costs might go up and performance might come down. Doing this well is highly non-trivial. And art because of the guiding intuition around LLM psychology of people spirits.

On top of context engineering itself, an LLM app has to:
- break up problems just right into control flows
- pack the context windows just right
- dispatch calls to LLMs of the right kind and capability
- handle generation-verification UIUX flows
- a lot more - guardrails, security, evals, parallelism, prefetching, ...

So context engineering is just one small piece of an emerging thick layer of non-trivial software that coordinates individual LLM calls (and a lot more) into full LLM apps. The term "ChatGPT wrapper" is tired and really, really wrong.

[Avishekh Shrestha @avishekh_]

@Suhail @diabrowser

[Andrew Ng @AndrewYNg]

One of the most effective things the U.S. or any other nation can do to ensure its competitiveness in AI is to welcome high-skilled immigration and international students who have the potential to become high-skilled. For centuries, the U.S. has welcomed immigrants, and this helped make it a worldwide leader in technology. Letting immigrants and native-born Americans collaborate makes everyone better off. Reversing this stance would have a huge negative impact on U.S. technology development.

I was born in the UK and came to the U.S. on an F-1 student visa as a relatively unskilled and clueless teenager to attend college. Fortunately I gained skills and became less clueless over time. After completing my graduate studies, I started working at Stanford under the OPT (Optional Practical Training) program, and later an H-1B visa, and ended up staying here. Many other immigrants have followed similar paths to contribute to the U.S.

I am very concerned that making visas harder to obtain for students and high-skilled workers, such as the pause in new visa interviews that started last month and a newly chaotic process of visa cancellations, will hurt our ability to attract great students and workers. In addition, many international students without substantial means count on being able to work under OPT to pay off the high cost of a U.S. college degree. Gutting the OPT program, as has been proposed, would both hurt many international students’ ability to study here and deprive U.S. businesses of great talent. (This won’t stop students from wealthy families. But the U.S. should try to attract the best talent without regard to wealth.)

Failure to attract promising students and high-skilled workers would have a huge negative impact on American competitiveness in AI. Indeed, a recent report by the National Security Commission on Artificial Intelligence exhorts the government to “strengthen AI talent through immigration.”

If talented people do not come to the U.S., will they have an equal impact on global AI development just working somewhere else? Unfortunately, the net impact will be negative. The U.S. has a number of tech hubs including Silicon Valley, Seattle, New York, Boston/Cambridge, Los Angeles, Pittsburgh and Austin, and these hubs concentrate talent and foster innovation. (This is why cities, where people can more easily find each other and collaborate, promote innovation.) Making it harder for AI talent to find each other and collaborate will slow down innovation, and it will take time for new hubs to become as advanced.

Nonetheless, other nations are working hard to attract immigrants who can drive innovation — a good move for them! Many have thoughtful programs to attract AI and other talent. There are the UK’s Global Talent Visa, France’s French Tech Visa, Australia’s Global Talent Visa, the UAE’s Golden Visa, Taiwan’s Employment Gold Card, China’s Thousand Talents Plan, and many more. The U.S. is fortunate that many people already want to come here to study and work. Squandering that advantage would be a huge unforced error.

Beyond the matter of national competitiveness, there is the even more important ethical matter of making sure people are treated decently. I have spoken with international students who are terrified that their visas may be canceled arbitrarily. One recently agonized about whether to attend an international conference to present a research paper, because they were worried about being unable to return. In the end, with great sadness, they cancelled their trip. I also spoke with a highly skilled technologist who is in the U.S. on an H-1B visa. Their company shut down, leading them — after over a decade in this country, and with few ties to their nation of origin — scrambling to find alternative employment that would enable them to stay.

These stories, and many far worse, are heartbreaking. While I do what I can to help individuals I know personally, it is tragic that we are creating such an uncertain environment for immigrants, that many people who have extraordinary skills and talents will no longer want to come here.

To every immigrant or migrant in the U.S. who is concerned about the current national environment: I see you and empathize with your worries. As an immigrant myself, I will be fighting to protect everyone’s dignity and right to due process, and to encourage legal immigration, which makes both the U.S. and individuals much better off.

[Full text, with links: https://t.co/6JNJz88Qyq ]

[Avishekh Shrestha @avishekh_]

Dialing back to 4o to summarize/ELI5

[Nathan Baschez @nbaschez]

Starting a chat with 4o and escalating to o3 has “may I speak with your manager” energy

[Alex Cohen @anothercohen]

I'll never forgive ChatGPT for ruining em dashes for all of us

[Baibhav 𐃏 @baibhavbista]

Like technology but also interested in other fields?

Come attend (& speak about your fav topic) at BarCamp Kathmandu v2!

May 31, 2025

[amul.exe @amuldotexe]

Hypothesis:
If a 10x engineer who is the inventor of Typescript can be let go in layoffs without a heads-up, none of our jobs are really safe & you are probably better off thinking of yourself as a one person business who consistently networks to build their own sales pipeline

[Avishekh Shrestha @avishekh_]

Strategy is about knowing what not to build. Not every problem needs an AI agent.