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Notes from building apps, shows and AI systems with a small team.

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  1. 5 min read

    How to report a local problem in India so it gets fixed

    A plain guide to reporting a civic problem in India: what to write, which office to use, the official deadlines, and how to follow up when nothing happens.

  2. 4 min read

    Agent Skills left Anthropic. Write yours like this

    On 22 October Anthropic donated Agent Skills to the Linux Foundation's AAIF. What a skill actually is, why we keep ours short, and what not to install.

  3. 4 min read

    Independent evals after Anthropic let METR in

    On 15 October Anthropic gave METR employee-like access to its models. What independence really means, and a version a small product team can run.

  4. 4 min read

    Why a chatbot that agrees with you can lead you wrong

    Two studies found AI chatbots flatter users and do not help people decide better. How to ask an AI for advice without being told what you want to hear.

  5. 4 min read

    Claude Haiku 5.5: when the cheap model is enough

    Haiku 5.5 costs about 75% less than Haiku 4.5. What we would move onto it in Cabin, what we would not, and why evals come first.

  6. 5 min read

    How to stop overthinking a decision: a 6-step method

    How to stop overthinking a decision: name the question, set a time limit, define good enough, cut the options, get distance and act. With sources.

  7. 4 min read

    Why Anthropic asked a Vedanta monk about Claude

    Anthropic met religious scholars, including Swami Sarvapriyananda, about Claude's values. What we know, what it means, and lessons for AI teams.

  8. 5 min read

    5 Indian apps to try in 2026: decisions, quizzes, jobs

    Five Indian apps from one small team: Cabin for decisions, QuesMo for friend quizzes, Karma, an interview prep app in beta and a local shop finder.

  9. 12 min read

    Running a 24/7 AI software factory: builders and one advisor

    How we set up a team of AI coding agents and one advisor agent to ship tested tickets around the clock: roles, ledgers, release trains and what broke.

  10. 5 min read

    Social quiz apps: why questions beat passive scrolling

    What a social quiz app changes: answering with friends instead of scrolling alone, and what research on active and passive social media use says.

  11. 3 min read

    How we ship software with AI agents without losing control

    Specs, small plans, task-scoped agents, independent review and real checks. How agents do the work while people keep the decisions.

  12. 4 min read

    How to design a responsible AI companion app

    Practical rules for a responsible AI companion app: say it is an AI, skip engagement hooks, handle crisis moments, and give people control of memory.

  13. 4 min read

    Building a Hinglish AI chatbot: lessons for India

    How to build a Hinglish AI chatbot people enjoy: match the user's register, handle Roman and Devanagari script, test code-mixed prompts and watch cost.

  14. 3 min read

    Routing between LLM providers to balance cost and quality

    Why we route requests across more than one model provider, how prompt caching cuts repeat costs, and why routing changes pass offline evals first.

  15. 4 min read

    llms.txt in 2026: what it does and what it doesn't

    A practical llms.txt guide: what the file is, what Google says about it, who uses it, and how to generate one from your site's content in minutes.

  16. 3 min read

    Designing AI memory that stays accurate

    Long-term memory for an AI companion is about keeping facts current, not storing more. Recall, supersession, and why contradictions cost trust.

  17. 4 min read

    AI content labelling rules in India: a creator's guide

    India's AI content labelling rules and YouTube's synthetic content label, explained for creators: what counts, what to disclose and a simple checklist.

    • ai-video
    • policy
    • creators
  18. 5 min read

    LLM evals for small teams: a practical starting point

    How to build LLM evals with a small team: start from real failures, use pass/fail checks, calibrate an LLM judge and gate every prompt change.