Andrew Rosca

[Andrew Rosca @andrewr]

@lporiginalg @grok what is the source of this data and is it correct

[Andrew Rosca @andrewr]

This new research from MIT CSAIL proposes Recursive Language Models (RLMs), a paradigm that shifts the prompt from the model’s direct input to an external Python environment.

https://t.co/BE2uyL435T

[Andrew Rosca @andrewr]

Experimental orchestration platform designed to manage and scale "swarms" of AI coding agents, specifically Claude Code instances, to automate complex software development workflows.

https://t.co/1xwyjaca2s

[Andrew Rosca @andrewr]

https://t.co/b4UVfaaszK

[Andrew Rosca @andrewr]

What could possibly go wrong...

https://t.co/gjzV5hjfqT

[Andrew Rosca @andrewr]

[W]hen we had a few weak computers, programming became a mild problem, and now we have gigantic computers, programming had become an equally gigantic problem.
-- The Humble Programmer, Dijkstra, 1972

https://t.co/OmYgWhp6qb

[Andrew Rosca @andrewr]

While AI makes development "easy" through speed and proximity, it often sacrifices "simplicity," leading to systems burdened by tangled accidental complexity.

https://t.co/yJqetds990 https://t.co/yJqetds990

[Andrew Rosca @andrewr]

We've done a great job emulating real developers with these coding LLMs 😂

[Andrew Rosca @andrewr]

This article from Anthropic Engineering explores solutions for maintaining context in AI agents working across multiple sessions.

https://t.co/vUsFHbs8Kc

[Andrew Rosca @andrewr]

This study suggests that the systematic structure of human language--from words to grammar--emerges from a cognitive bottleneck where the brain must minimize "predictive information" to process sequences efficiently.

https://t.co/aaHVfdxlnc

[Andrew Rosca @andrewr]

An interactive, live 3D map of satellites currently in orbit (all 28,500 of them)

https://t.co/zGLG6U8gef

[Andrew Rosca @andrewr]

AI models can analyze text, images, and video, but they still fail at something as basic as reading an analog clock.

https://t.co/lez8bKh3ia

[Andrej Karpathy @karpathy]

Sharing an interesting recent conversation on AI's impact on the economy.

AI has been compared to various historical precedents: electricity, industrial revolution, etc., I think the strongest analogy is that of AI as a new computing paradigm (Software 2.0) because both are fundamentally about the automation of digital information processing.

If you were to forecast the impact of computing on the job market in ~1980s, the most predictive feature of a task/job you'd look at is to what extent the algorithm of it is fixed, i.e. are you just mechanically transforming information according to rote, easy to specify rules (e.g. typing, bookkeeping, human calculators, etc.)? Back then, this was the class of programs that the computing capability of that era allowed us to write (by hand, manually).

With AI now, we are able to write new programs that we could never hope to write by hand before. We do it by specifying objectives (e.g. classification accuracy, reward functions), and we search the program space via gradient descent to find neural networks that work well against that objective. This is my Software 2.0 blog post from a while ago. In this new programming paradigm then, the new most predictive feature to look at is verifiability. If a task/job is verifiable, then it is optimizable directly or via reinforcement learning, and a neural net can be trained to work extremely well. It's about to what extent an AI can "practice" something. The environment has to be resettable (you can start a new attempt), efficient (a lot attempts can be made), and rewardable (there is some automated process to reward any specific attempt that was made).

The more a task/job is verifiable, the more amenable it is to automation in the new programming paradigm. If it is not verifiable, it has to fall out from neural net magic of generalization fingers crossed, or via weaker means like imitation. This is what's driving the "jagged" frontier of progress in LLMs. Tasks that are verifiable progress rapidly, including possibly beyond the ability of top experts (e.g. math, code, amount of time spent watching videos, anything that looks like puzzles with correct answers), while many others lag by comparison (creative, strategic, tasks that combine real-world knowledge, state, context and common sense).

Software 1.0 easily automates what you can specify.
Software 2.0 easily automates what you can verify.

[Andrew Rosca @andrewr]

Chrome now has a feature to display two web pages side-by-side in the same tab

https://t.co/kdwt7LsdYN