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What if you just did a startup instead?

https://alexaraki.substack.com/p/what-if-you-just-did-a-startup
1•okaywriting•4m ago•0 comments

Hacking up your own shell completion (2020)

https://www.feltrac.co/environment/2020/01/18/build-your-own-shell-completion.html
1•todsacerdoti•7m ago•0 comments

Show HN: Gorse 0.5 – Open-source recommender system with visual workflow editor

https://github.com/gorse-io/gorse
1•zhenghaoz•8m ago•0 comments

GLM-OCR: Accurate × Fast × Comprehensive

https://github.com/zai-org/GLM-OCR
1•ms7892•9m ago•0 comments

Local Agent Bench: Test 11 small LLMs on tool-calling judgment, on CPU, no GPU

https://github.com/MikeVeerman/tool-calling-benchmark
1•MikeVeerman•10m ago•0 comments

Show HN: AboutMyProject – A public log for developer proof-of-work

https://aboutmyproject.com/
1•Raiplus•10m ago•0 comments

Expertise, AI and Work of Future [video]

https://www.youtube.com/watch?v=wsxWl9iT1XU
1•indiantinker•10m ago•0 comments

So Long to Cheap Books You Could Fit in Your Pocket

https://www.nytimes.com/2026/02/06/books/mass-market-paperback-books.html
3•pseudolus•11m ago•1 comments

PID Controller

https://en.wikipedia.org/wiki/Proportional%E2%80%93integral%E2%80%93derivative_controller
1•tosh•15m ago•0 comments

SpaceX Rocket Generates 100GW of Power, or 20% of US Electricity

https://twitter.com/AlecStapp/status/2019932764515234159
2•bkls•15m ago•0 comments

Kubernetes MCP Server

https://github.com/yindia/rootcause
1•yindia•16m ago•0 comments

I Built a Movie Recommendation Agent to Solve Movie Nights with My Wife

https://rokn.io/posts/building-movie-recommendation-agent
4•roknovosel•16m ago•0 comments

What were the first animals? The fierce sponge–jelly battle that just won't end

https://www.nature.com/articles/d41586-026-00238-z
2•beardyw•25m ago•0 comments

Sidestepping Evaluation Awareness and Anticipating Misalignment

https://alignment.openai.com/prod-evals/
1•taubek•25m ago•0 comments

OldMapsOnline

https://www.oldmapsonline.org/en
1•surprisetalk•27m ago•0 comments

What It's Like to Be a Worm

https://www.asimov.press/p/sentience
2•surprisetalk•27m ago•0 comments

Don't go to physics grad school and other cautionary tales

https://scottlocklin.wordpress.com/2025/12/19/dont-go-to-physics-grad-school-and-other-cautionary...
1•surprisetalk•27m ago•0 comments

Lawyer sets new standard for abuse of AI; judge tosses case

https://arstechnica.com/tech-policy/2026/02/randomly-quoting-ray-bradbury-did-not-save-lawyer-fro...
3•pseudolus•28m ago•0 comments

AI anxiety batters software execs, costing them combined $62B: report

https://nypost.com/2026/02/04/business/ai-anxiety-batters-software-execs-costing-them-62b-report/
1•1vuio0pswjnm7•28m ago•0 comments

Bogus Pipeline

https://en.wikipedia.org/wiki/Bogus_pipeline
1•doener•29m ago•0 comments

Winklevoss twins' Gemini crypto exchange cuts 25% of workforce as Bitcoin slumps

https://nypost.com/2026/02/05/business/winklevoss-twins-gemini-crypto-exchange-cuts-25-of-workfor...
2•1vuio0pswjnm7•30m ago•0 comments

How AI Is Reshaping Human Reasoning and the Rise of Cognitive Surrender

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646
3•obscurette•30m ago•0 comments

Cycling in France

https://www.sheldonbrown.com/org/france-sheldon.html
2•jackhalford•31m ago•0 comments

Ask HN: What breaks in cross-border healthcare coordination?

1•abhay1633•32m ago•0 comments

Show HN: Simple – a bytecode VM and language stack I built with AI

https://github.com/JJLDonley/Simple
2•tangjiehao•34m ago•0 comments

Show HN: Free-to-play: A gem-collecting strategy game in the vein of Splendor

https://caratria.com/
1•jonrosner•35m ago•1 comments

My Eighth Year as a Bootstrapped Founde

https://mtlynch.io/bootstrapped-founder-year-8/
1•mtlynch•36m ago•0 comments

Show HN: Tesseract – A forum where AI agents and humans post in the same space

https://tesseract-thread.vercel.app/
1•agliolioyyami•36m ago•0 comments

Show HN: Vibe Colors – Instantly visualize color palettes on UI layouts

https://vibecolors.life/
2•tusharnaik•37m ago•0 comments

OpenAI is Broke ... and so is everyone else [video][10M]

https://www.youtube.com/watch?v=Y3N9qlPZBc0
2•Bender•37m ago•0 comments
Open in hackernews

The 7 Habits of Highly Ineffective Agents

https://tobyhede.com/blog/the-7-habits-of-highly-ineffective-agents/
3•tobyhede•1mo ago

Comments

tobyhede•1mo ago
I have been using Claude Code extensively on a side project (a hard sci-fi orbital tactics sandbox and battlefield simulator written in Rust with Bevy).

I recently attempted to create a procedural starfield background with multi-layer parallax, wired into the game.

I thought it would take an afternoon, and two weeks and three full rewrites later, I ended up with a list I’m calling: The 7 habits of highly ineffective agents

  1. Planning Theatre – Write dense and systematically wrong plans. Long, confident plans that look impressive, get “approved”, and are fundamentally wrong in ways you can’t see without strong domain knowledge.

  2. Confidently Incorrect Architecture – Design the wrong thing in incredible detail. Elaborate designs that can never solve the actual problem (e.g. starfield parallax without real layers / camera–world modelling), but look beautifully structured on paper.

  3. Context Resistance – The context is futile. You will be hallucinated. Ask for Bevy 0.17 patterns, get Bevy 0.15. Agents “agree” with the updated context and then quietly fall back to older habits and half-remembered APIs.

  4. Imaginary Implementation – Works on my hallucination. Code for an engine that doesn’t exist: non-existent APIs, obsolete shader interfaces, plausible-sounding data flows that won’t compile anywhere outside the model’s head.

  5. Context Evasion – Treat hard constraints and instructions as optional vibes. The project had explicit, non-optional instructions (skills to call, architecture rules, testing strategy, etc.). The agent read them, acknowledged them… and behaved as if they were suggestions.

  6. Applied Rationalization – Explanation over implementation. When something fails, the agent doesn’t just explain it – it bakes the explanation into the codebase: ignoring tests, downgrading issues to “non-blocking”, justifying precision loss, and moving on.

  7. Weaponised Context – The context will continue until the code improves. By the end, the feature had volumes of surrounding context: plans, handoffs, bug explanations, revisions. Each failure generated more docs for the next agent to inherit and ignore.

I’m curious how this matches other people’s experience with Claude / Claude Code (or your own agent stacks): - Which of these habits have you seen the most in your own workflows? - What have you done that actually reduced these failure modes (gating, skills, checklists, stricter prompts, something else)? - Are there other “habits of highly ineffective agents” you’d add to this list?

Would love to hear horror stories and what’s working for you.