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Learning to Reason in 13 Parameters

https://arxiv.org/abs/2602.04118
1•nicholascarolan•2m ago•0 comments

Convergent Discovery of Critical Phenomena Mathematics Across Disciplines

https://arxiv.org/abs/2601.22389
1•energyscholar•2m ago•1 comments

Ask HN: Will GPU and RAM prices ever go down?

1•alentred•2m ago•0 comments

From hunger to luxury: The story behind the most expensive rice (2025)

https://www.cnn.com/travel/japan-expensive-rice-kinmemai-premium-intl-hnk-dst
1•mooreds•3m ago•0 comments

Substack makes money from hosting Nazi newsletters

https://www.theguardian.com/media/2026/feb/07/revealed-how-substack-makes-money-from-hosting-nazi...
4•mindracer•4m ago•0 comments

A New Crypto Winter Is Here and Even the Biggest Bulls Aren't Certain Why

https://www.wsj.com/finance/currencies/a-new-crypto-winter-is-here-and-even-the-biggest-bulls-are...
1•thm•4m ago•0 comments

Moltbook was peak AI theater

https://www.technologyreview.com/2026/02/06/1132448/moltbook-was-peak-ai-theater/
1•Brajeshwar•5m ago•0 comments

Why Claude Cowork is a math problem Indian IT can't solve

https://restofworld.org/2026/indian-it-ai-stock-crash-claude-cowork/
1•Brajeshwar•5m ago•0 comments

Show HN: Built an space travel calculator with vanilla JavaScript v2

https://www.cosmicodometer.space/
2•captainnemo729•5m ago•0 comments

Why a 175-Year-Old Glassmaker Is Suddenly an AI Superstar

https://www.wsj.com/tech/corning-fiber-optics-ai-e045ba3b
1•Brajeshwar•5m ago•0 comments

Micro-Front Ends in 2026: Architecture Win or Enterprise Tax?

https://iocombats.com/blogs/micro-frontends-in-2026
1•ghazikhan205•8m ago•0 comments

These White-Collar Workers Actually Made the Switch to a Trade

https://www.wsj.com/lifestyle/careers/white-collar-mid-career-trades-caca4b5f
1•impish9208•8m ago•1 comments

The Wonder Drug That's Plaguing Sports

https://www.nytimes.com/2026/02/02/us/ostarine-olympics-doping.html
1•mooreds•9m ago•0 comments

Show HN: Which chef knife steels are good? Data from 540 Reddit tread

https://new.knife.day/blog/reddit-steel-sentiment-analysis
1•p-s-v•9m ago•0 comments

Federated Credential Management (FedCM)

https://ciamweekly.substack.com/p/federated-credential-management-fedcm
1•mooreds•9m ago•0 comments

Token-to-Credit Conversion: Avoiding Floating-Point Errors in AI Billing Systems

https://app.writtte.com/read/kZ8Kj6R
1•lasgawe•9m ago•1 comments

The Story of Heroku (2022)

https://leerob.com/heroku
1•tosh•10m ago•0 comments

Obey the Testing Goat

https://www.obeythetestinggoat.com/
1•mkl95•10m ago•0 comments

Claude Opus 4.6 extends LLM pareto frontier

https://michaelshi.me/pareto/
1•mikeshi42•11m ago•0 comments

Brute Force Colors (2022)

https://arnaud-carre.github.io/2022-12-30-amiga-ham/
1•erickhill•14m ago•0 comments

Google Translate apparently vulnerable to prompt injection

https://www.lesswrong.com/posts/tAh2keDNEEHMXvLvz/prompt-injection-in-google-translate-reveals-ba...
1•julkali•14m ago•0 comments

(Bsky thread) "This turns the maintainer into an unwitting vibe coder"

https://bsky.app/profile/fullmoon.id/post/3meadfaulhk2s
1•todsacerdoti•15m ago•0 comments

Software development is undergoing a Renaissance in front of our eyes

https://twitter.com/gdb/status/2019566641491963946
1•tosh•15m ago•0 comments

Can you beat ensloppification? I made a quiz for Wikipedia's Signs of AI Writing

https://tryward.app/aiquiz
1•bennydog224•16m ago•1 comments

Spec-Driven Design with Kiro: Lessons from Seddle

https://medium.com/@dustin_44710/spec-driven-design-with-kiro-lessons-from-seddle-9320ef18a61f
1•nslog•16m ago•0 comments

Agents need good developer experience too

https://modal.com/blog/agents-devex
1•birdculture•18m ago•0 comments

The Dark Factory

https://twitter.com/i/status/2020161285376082326
1•Ozzie_osman•18m ago•0 comments

Free data transfer out to internet when moving out of AWS (2024)

https://aws.amazon.com/blogs/aws/free-data-transfer-out-to-internet-when-moving-out-of-aws/
1•tosh•19m ago•0 comments

Interop 2025: A Year of Convergence

https://webkit.org/blog/17808/interop-2025-review/
1•alwillis•20m ago•0 comments

Prejudice Against Leprosy

https://text.npr.org/g-s1-108321
1•hi41•21m ago•0 comments
Open in hackernews

Show HN: First autonomous ML and AI engineering Agent

https://marketplace.visualstudio.com/items?itemName=NeoResearchInc.heyneo
2•svij137•1w ago
Founder here. I built NEO, an AI agent designed specifically for AI and ML engineering workflows, after repeatedly hitting the same wall with existing tools: they work for short, linear tasks, but fall apart once workflows become long-running, stateful, and feedback-driven. In real ML work, you don’t just generate code and move on. You explore data, train models, evaluate results, adjust assumptions, rerun experiments, compare metrics, generate artifacts, and iterate; often over hours or days. Most modern coding agents already go beyond single prompts. They can plan steps, write files, run commands, and react to errors. Where things still break down is when ML workflows become long-running and feedback-heavy. Training jobs, evaluations, retries, metric comparisons, and partial failures are still treated as ephemeral side effects rather than durable state. Once a workflow spans hours, multiple experiments, or iterative evaluation, you either babysit the agent or restart large parts of the process. Feedback exists, but it is not something the system can reliably resume from. NEO tries to model ML work the way it actually happens. It is an AI agent that executes end-to-end ML workflows, not just code generation. Work is broken into explicit execution steps with state, checkpoints, and intermediate results. Feedback from metrics, evaluations, or failures feeds directly into the next step instead of forcing a full restart. You can pause a run, inspect what happened, tweak assumptions, and resume from where it left off. Here's an example as well for your reference: You might ask NEO to explore a dataset, train a few baseline models, compare their performance, and generate plots and a short report. NEO will load the data, run EDA, train models, evaluate them, notice if something underperforms or fails, adjust, and continue. If training takes an hour and one model crashes at 45 minutes, you do not start over. Neo inspects the failure, fixes it, and resumes. Docs for the extension: https://docs.heyneo.so/#/vscode Happy to answer questions about Neo.