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Article URL: https://zellij.dev/roadmap/

Comments URL: https://news.ycombinator.com/item?id=46985911

Points: 1

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Article URL: https://configmesh.app/

Comments URL: https://news.ycombinator.com/item?id=46985896

Points: 1

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Article URL: https://reclaimthenet.org/uk-fines-for-lack-of-age-verification

Comments URL: https://news.ycombinator.com/item?id=46985888

Points: 1

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Article URL: https://github.com/skorotkiewicz/grab

Comments URL: https://news.ycombinator.com/item?id=46985882

Points: 2

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With this extension, you can save and manage all your important links in one convenient place.

Comments URL: https://news.ycombinator.com/item?id=46985877

Points: 1

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Article URL: https://www.unite.ai/increasingly-hipaa-cant-stop-ai-from-de-anonymizing-patient-data/

Comments URL: https://news.ycombinator.com/item?id=46985857

Points: 1

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Article URL: https://korru.app

Comments URL: https://news.ycombinator.com/item?id=46985831

Points: 2

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Article URL: https://twitter.com/prestonjbyrne/status/2021599606841172352

Comments URL: https://news.ycombinator.com/item?id=46985820

Points: 1

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Article URL: https://www.thetimes.com/uk/technology-uk/article/ai-researchers-quit-openai-chatgpt-anthropic-pfhgpxztr

Comments URL: https://news.ycombinator.com/item?id=46985813

Points: 1

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Reinforcement learning has become the secret weapon behind AI's most impressive specialized achievements.

From robotics with Tesla's Autopilot to DeepMind's AlphaFold 2 for predicting protein structures with 90%+ accuracy to even hedge funds deploying RL for algorithmic trading, there is a need for reinforcement learning.

And the market proves this demand further: RL grew from $1.5B (2020) → $12B (2024) with projections hitting $79B by 2030.

BUT THERE IS A BRUTAL REALITY!!!

Just to get one production line or train one model, the companies spend $100 million+ EVERY YEAR, many of which goes to computational engineering and RL engineers. Moreover, only after days or even weeks of training will you know the RL algorithm didn't work, and those days of costs and time need to just be ABSORBED into production costs.

This makes only tech giants and heavily-funded startups play this game, and that too with hard scalability.

With firsthand experience over a 3 day period training a CV line on a NVIDIA DGX Spark and months of experience with multi-agent frameworks, I know this problem as a developer just trying to work on projects. THIS IS WHY I BUILT CADENZA -> the RL-alternative, mem-native memory layer for agent specialization.

I am still developing and building the idea, but I know this problem is real so any support or guidance would be EXTREMELY valuable. Thanks!

Comments URL: https://news.ycombinator.com/item?id=46985812

Points: 1

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I built an open-source system called Horaculo that analyzes coordination and divergence across financial news sources. The goal is to quantify narrative alignment, entropy shifts, and historical source reliability. Pipeline Fetch 50–100 articles (NewsAPI) Extract claims (NLP preprocessing) Generate sentence embeddings (HuggingFace) Compute cosine similarity in C++ (AVX2 + INT8 quantization) Cluster narratives Compute entropy + coordination metrics Weight results using historical source credibility Output structured JSON signals Example Output (query: “oil”) Json Copiar código { "verdict": { "winner_source": "Reuters", "intensity": 0.85, "entropy": 1.92 }, "psychology": { "mood": "Fear", "is_trap": true, "coordination_score": 0.72 } } What it measures Intensity → narrative divergence Entropy → informational disorder Coordination score → cross-source alignment Credibility weighting → historical consensus accuracy per source Performance 1.4s per query (~10 sources) ~100 queries/min ~150MB memory footprint Python-only version was ~12s C++ optimizations: INT8 embedding quantization (4x size reduction) AVX2 SIMD vectorized cosine similarity PyBind11 integration layer Storage SQLite (local memory) Optional Postgres Each source builds a rolling credibility profile: Json Copiar código { "source": "Reuters", "total_scans": 342, "consensus_hits": 289, "credibility": 0.85 } Open Source (MIT) GitHub: [https://github.com/ANTONIO34346/HORACULO] I'm particularly interested in feedback on: The entropy modeling approach Coordination detection methodology Whether FAISS would be a better fit than the current SIMD engine Scalability strategies for 100k+ embeddings

Comments URL: https://news.ycombinator.com/item?id=46985791

Points: 3

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Article URL: https://pi.dev/

Comments URL: https://news.ycombinator.com/item?id=46985780

Points: 3

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Hi HN,

PaperLab is a Markdown editor that deliberately does less.

No plugins. No AI. No "productivity features". No collaboration.

Just: write → save locally → sync when you want.

Why? Because Notion/Obsidian/Roam became operating systems when all I needed was a notepad.

Built with IndexedDB for instant startup and optional cloud sync for multi-device.

Try it: https://paperlab.ink

It's intentionally boring. That's the point.

Comments URL: https://news.ycombinator.com/item?id=46985767

Points: 1

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Article URL: https://www.technologyreview.com/2026/02/11/1132768/is-a-secure-ai-assistant-possible/

Comments URL: https://news.ycombinator.com/item?id=46985759

Points: 1

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Article URL: https://www.theguardian.com/technology/2026/feb/10/apple-google-third-party-apps-app-store

Comments URL: https://news.ycombinator.com/item?id=46985753

Points: 1

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Article URL: https://www.theguardian.com/technology/2026/feb/11/big-ai-job-swap-white-collar-workers-ditching-their-careers

Comments URL: https://news.ycombinator.com/item?id=46985752

Points: 1

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Article URL: https://www.maastrichtuniversity.nl/news/thanks-ai-we-can-play-roman-game-again

Comments URL: https://news.ycombinator.com/item?id=46985736

Points: 1

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What are you working on? Any new ideas that you're thinking about?

Comments URL: https://news.ycombinator.com/item?id=46985555

Points: 1

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Article URL: https://www.devutilityhub.me/

Comments URL: https://news.ycombinator.com/item?id=46985549

Points: 1

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Article URL: https://www.sefaria.org/texts

Comments URL: https://news.ycombinator.com/item?id=46985537

Points: 1

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