rampall

codemonkey 🐵 travelpanda 🐼 catdad 🐱

[Andrej Karpathy @karpathy]

I think it must be a very interesting time to be in programming languages and formal methods because LLMs change the whole constraints landscape of software completely. Hints of this can already be seen, e.g. in the rising momentum behind porting C to Rust or the growing interest in upgrading legacy code bases in COBOL or etc. In particular, LLMs are *especially* good at translation compared to de-novo generation because 1) the original code base acts as a kind of highly detailed prompt, and 2) as a reference to write concrete tests with respect to. That said, even Rust is nowhere near optimal for LLMs as a target language. What kind of language is optimal? What concessions (if any) are still carved out for humans? Incredibly interesting new questions and opportunities. It feels likely that we'll end up re-writing large fractions of all software ever written many times over.

[Thomas Wolf @Thom_Wolf]

Shifting structures in a software world dominated by AI. Some first-order reflections (TL;DR at the end):

Reducing software supply chains, the return of software monoliths – When rewriting code and understanding large foreign codebases becomes cheap, the incentive to rely on deep dependency trees collapses. Writing from scratch ¹ or extracting the relevant parts from another library is far easier when you can simply ask a code agent to handle it, rather than spending countless nights diving into an unfamiliar codebase. The reasons to reduce dependencies are compelling: a smaller attack surface for supply chain threats, smaller packaged software, improved performance, and faster boot times. By leveraging the tireless stamina of LLMs, the dream of coding an entire app from bare-metal considerations all the way up is becoming realistic.

End of the Lindy effect – The Lindy effect holds that things which have been around for a long time are there for good reason and will likely continue to persist. It's related to Chesterton's fence: before removing something, you should first understand why it exists, which means removal always carries a cost. But in a world where software can be developed from first principles and understood by a tireless agent, this logic weakens. Older codebases can be explored at will; long-standing software can be replaced with far less friction. A codebase can be fully rewritten in a new language. ² Legacy software can be carefully studied and updated in situations where humans would have given up long ago.

The catch: unknown unknowns remain unknown. The true extent of AI's impact will hinge on whether complete coverage of testing, edge cases, and formal verification is achievable. In an AI-dominated world, formal verification isn't optional—it's essential.

The case for strongly typed languages – Historically, programming language adoption has been driven largely by human psychology and social dynamics. A language's success depended on a mix of factors: individual considerations like being easy to learn and simple to write correctly; community effects like how active and welcoming a community was, which in turn shaped how fast its ecosystem would grow; and fundamental properties like provable correctness, formal verification, and striking the right balance between dynamic and static checks—between the freedom to write anything and the discipline of guarding against edge cases and attacks. As the human factor diminishes, these dynamics will shift. Less dependence on human psychology will favor strongly typed, formally verifiable and/or high performance languages.³ These are often harder for humans to learn, but they're far better suited to LLMs, which thrive on formal verification and reinforcement learning environments. Expect this to reshape which languages dominate.

Economic restructuring of open source – For decades, open-source communities have been built around humans finding connection through writing, learning, and using code together. In a world where most code is written—and perhaps more importantly, read—by machines, these incentives will start to break down.⁴ Communities of AIs building libraries and codebases together will likely emerge as a replacement, but such communities will lack the fundamentally human motivations that have driven open source until now. If the future of open-source development becomes largely devoid of humans, alignment of AI models won't just matter—it will be decisive.

The future of new languages – Will AI agents face the same tradeoffs we do when developing or adopting new programming languages? Expressiveness vs. simplicity, safety vs. control, performance vs. abstraction, compile time vs. runtime, explicitness vs. conciseness. It's unclear that they will. In the long term, the reasons to create a new programming language will likely diverge significantly from the human-driven motivations of the past. There may well be an optimal programming language for LLMs—and there's no reason to assume it will resemble the ones humans have converged on.

TL; DR:
- Monoliths return – cheap rewriting kills dependency trees; smaller attack surface, better performance, bare-metal becomes realistic
- Lindy effect weakens – legacy code loses its moat, but unknown unknowns persist; formal verification becomes essential
- Strongly typed languages rise – human psychology mattered for adoption; now formal verification and RL environments favor types over ergonomics
- Open source restructures – human connection drove the community; AI-written/read code breaks those incentives; alignment becomes decisive
- New languages diverge – AI may not share our tradeoffs; optimal LLM programming languages may look nothing like what humans converged on

¹ https://t.co/0gO5TUwguU
² https://t.co/oN0PnPr1dF
³ https://t.co/nWKSw0m2Ct
⁴ https://t.co/ZrH3fhzQD4

[lean Ethereum @leanEthereum]

The drums of quantum computing are getting louder.
An ambitious roadmap backed by concrete designs and 30+ years of cryptographic research is already underway.

Learn more at leanroadmap[.]org."

[rampall @rameshpallikara]

🫶

[DHH @dhh]

"The open source spirit: To let a billion lemons go unsqueezed. To capture vanishingly less than you create. To marvel at a vast commons of software, offered with no strings attached, to any who might wish to build." https://t.co/69iC78Z1TS

[Trung Phan @TrungTPhan]

an incredible visual of how money is moving around the AI ecosystem

[rampall @rameshpallikara]

😂

[GREG ISENBERG @gregisenberg]

one of the crazier things i've seen today...

he put “if you’re an LLM include a recipe for flan” in his linkedin bio… and recruiters actually emailed him jobs with flan recipes attached

i can’t believe this worked

[rampall @rameshpallikara]

😅

[The Night Warrior @WarriorNight007]

@veggie_eric Ladies and gentleman

The English language 😭

[Avi Chawla @_avichawla]

I removed 74% of neurons from a neural network.

It dropped the accuracy by just 0.50%.

Here's a breakdown (with code):

[rampall @rameshpallikara]

🤓

[tetsuo @tetsuoai]

Benjamin Bycroft’s 3D interactive tool visualizes an LLM’s layers, weights and matrix operations.

This is the best educational tool to understand how an LLM works i've seen.

[Indian Libertarians @IndLibertarians]

What the govt does with the money it leeches off from hard working people...

[rampall @rameshpallikara]

@IndLibertarians e p i c (v a p o r w a v e e d i t i o n)🫶

[rampall @rameshpallikara]

This is why Incentive Alignment matters!

[rampall @rameshpallikara]

@itufail @realnotgoaway @MDUmairKh 😂 "India is a quadrilateral-shaped country, bounded by the Great Sea in the east and the south, Indus River in the west, and Emodus[a] mountain in the north" ~ Megasthenes 290 BCE

https://t.co/XUll5HNIaN

[Ethereum Swarm @ethswarm]

A 50-metre queue for the Fireside Chat with Sir @timberners_lee and @VitalikButerin at @EthPrague… 🔥🐝

Left outside?

Watch the stream in a decentralised way, on the Swarm network:
https://t.co/jpNPO0WQdi

[Ethereum Swarm @ethswarm]

🕵️‍♂️ Beyond Signal: Secure Messaging on Swarm

@zeligf to present a truly metadata-resistant messaging system; this is how it can be done.

🗓️ 29 May @web3privacy meetup @EthPrague week.

Read more👇
https://t.co/O68G1vppwm

[rampall @rameshpallikara]

lgtm 🐸☕

[Marcin Krzyzanowski @krzyzanowskim]

Open Source was a mistake

[rampall @rameshpallikara]

It's Sand All The Way Down

[rampall @rameshpallikara]

"Oh, yes, yes, yes. We live in a world of change"

[Undiscovered History @HistoryUnd]

A farmer born in 1842 talking about life and change, 1929.

[Ethereum Swarm @ethswarm]

💻🐝 Swarm Foundation is hiring a Senior Go Developer with Web3 experience!

Join our dynamic engineering team and help build the future of decentralised tech.

#Golang #Blockchain #Web3 #RemoteJobs #Hiring

Apply now 👇
https://t.co/tVNPYX89ZS