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Ran a hands-on workshop in Tokyo where ~50 engineers built a CLI coding agent from scratch in Python and now, I've converted it into a self-paced exercise.

GitHub Repo: https://github.com/primaprashant/alduin

This should help in getting a better understanding of what goes on inside the coding agents. You will iteratively implement the core agent loop present in all coding agents like Claude Code and Codex starting from a skeleton repo with a basic input loop and no LLM. Over 7 phases, you will add the Anthropic API and implement tools (read file, edit file, bash). Each phase has hints and a reference implementation if you get stuck.

Should take ~3-5 hours and in the end, you'll have your own coding agent which you can use on other project and codebases as well.

Feedback and PRs welcome. Happy to answer any questions.

Comments URL: https://news.ycombinator.com/item?id=47133182

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Article URL: https://blog.rust-lang.org/2026/02/23/rust-debugging-survey-2026/

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Article URL: https://arxiv.org/abs/2602.20082

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Article URL: https://www.iranintl.com/en/202602234502

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Article URL: https://www.phinite.ai/

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Article URL: https://en.wikipedia.org/wiki/Cassandra_(metaphor)

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Article URL: https://www.wired.com/story/how-to-organize-safely-in-the-age-of-surveillance/

Comments URL: https://news.ycombinator.com/item?id=47133126

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COLT converts natural language instructions into browser automation. You say "Create a user with email admin@test.com and admin role" — it executes it on a live browser and exports a standalone Playwright script. How it works:

1.Discover — crawls your web app autonomously, maps every page, form, modal, and element 2.Index — LLM-summarizes each state into a vector search index (discover once, run unlimited tasks) 3.Execute — ReAct agent loop drives the browser in real time with self-healing on failures 4.Record — exports reusable Playwright scripts, pytest tests, and typed Python functions

It handles Shadow DOM, cross-origin iframes, infinite scroll, pagination traps, and modals — the stuff that breaks most automation tools. What it's not: Not a consumer AI browser. Not an RPA tool. It's for engineering teams who want to generate E2E tests or automate internal tool workflows without writing selectors by hand. The key difference from other AI browser agents: they start from scratch every execution. COLT builds a persistent knowledge base of your app, so task #100 is as fast as task #1. Built with Python, Playwright, and ChromaDB. Works with Groq, OpenAI, Anthropic, or local Ollama models. Currently in closed beta — launching soon. Would love feedback on the approach, especially from anyone doing browser automation at scale

Comments URL: https://news.ycombinator.com/item?id=47133121

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Article URL: https://www.theregister.com/2026/02/24/anthropic_misanthropic_chinese_ai_labs/

Comments URL: https://news.ycombinator.com/item?id=47133103

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Hey HN. I built memctl because every AI coding agent starts each session with zero context. No memory of past decisions, no shared knowledge across your team. memctl is a memory server that gives AI coding agents persistent context that carries over across sessions. Memory is shared across your team so every agent works with the same knowledge. It's branch-aware so context follows your git workflow, and everything is tracked with full history. It works with any AI coding agent. Open source and self-hostable.

GitHub: https://github.com/memctl Website: https://memctl.com

Launches on March 1st. Waitlist open. Would to hear any feedback!

Comments URL: https://news.ycombinator.com/item?id=47133074

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Article URL: https://www.nytimes.com/2026/02/24/technology/taiwan-china-chips-silicon-valley-tsmc.html

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Article URL: https://www.theregister.com/2026/02/23/open_source_devs_column/

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Article URL: https://github.com/GreatScott/enveil

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Article URL: https://old.reddit.com/r/selfhosted/comments/1rckopd/huntarr_your_passwords_and_your_entire_arr_stacks/

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Article URL: https://danielmiessler.com/blog/why-you-should-hate-anthropic

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Hey everyone,

I’ve been working on a project called L88 — a local RAG system that I initially focused on UI/UX for, so the retrieval and model architecture still need proper refinement.

Repo: https://github.com/Hundred-Trillion/L88-Full

I’m running this on 8GB VRAM and a strong CPU (128GB RAM). Embeddings and preprocessing run on CPU, and the main model runs on GPU. One limitation I ran into is that my evaluator and generator LLM ended up being the same model due to compute constraints, which defeats the purpose of evaluation.

I’d really appreciate feedback on:

Better architecture ideas for small-VRAM RAG

Splitting evaluator/generator roles effectively

Improving the LangGraph pipeline

Any bugs or design smells you notice

Ways to optimize the system for local hardware

I’m 18 and still learning a lot about proper LLM architecture, so any technical critique or suggestions would help me grow as a developer. If you check out the repo or leave feedback, it would mean a lot — I’m trying to build a solid foundation and reputation through real projects.

Thanks!

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Article URL: https://thunderseethe.dev/posts/compiler-education-deserves-a-revoluation/

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Article URL: https://www.phoronix.com/news/Torvalds-Unseeded-Random

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Comments URL: https://news.ycombinator.com/item?id=47132771

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Serious question.

Outside government or heavily regulated enterprise, what is Microsoft’s core value prop in 2026?

It feels like a lot of adoption is inherited — contracts, compliance, enterprise trust, existing org gravity. Not necessarily technical preference.

If you were starting from scratch today with no legacy, no E5 contracts, no sunk cost — how many teams would actually choose the full MS stack over best-of-breed tools?

Curious what people here have actually chosen in greenfield builds.

Comments URL: https://news.ycombinator.com/item?id=47132766

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