I have a separate Dutch Apple ID I can switch to, but each time I log out I risk accidentally deleting all my data.

Of course you can be lazy with LLMs and I can usually tell if it’s one—shotted as well, but if you are a good writer, you’ll get 10x out of using an LLM to write.

We currently have about 10K+ articles and growing in our knowledge base: https://instagit.com/knowledge-base/

The problem: AI coding agents hallucinate library internals constantly. They confidently describe how a function works based on stale training data when the actual implementation does something different.

How it works: you ask a question about a repo, Instagit scans the source, and returns an answer with file paths and line numbers. You can target specific commits, branches, or tags. You can also swap "github" to "instagit" in any repo URL to get an instant wiki with Q&A (e.g. https://instagit.com/pandas-dev/pandas).

The real power though is giving your agent access via MCP rather than being the human in the loop. Point your agent at a large library, have it understand a specific feature, then rip out just the part you need into self-contained code. Drop the dependency entirely, sometimes even get better performance. The agent catches implementation details you'd miss reading the code yourself and that maintainers rarely document.

I get asked this a lot so might as well answer it now: how is this different from Context7, DeepWiki, CodeWiki, or GitHub MCP?

Context7, DeepWiki, and CodeWiki all pre-generate static summaries or guides. They're fast when they have what you need, but they don't cover every repo, they go stale, and there are hundreds of questions about any codebase that can't be pre-answered. GitHub MCP checks out files one at a time, which burns through context tokens fast and doesn't scale to large codebases.

Instagit reads source on demand for any public repo, returns just the answer, and keeps your context clean.

No API key or account needed to try it out: https://instagit.com/install

Comments URL: https://news.ycombinator.com/item?id=47002537

Points: 2

# Comments: 0

- Use a static website generator (I use Astro but there are others) where the agent can fully control every aspect of the website

- Use skills to enhance the prompting, like this frontend-design one but can also combine it with copy writing/seo skills etc

- Give the agent control access to the browser to give it visibility into the result in order to iteratively improve upon it

- The last thing is to just be very demanding and setting a high bar, so ask for animations and ask the agent over and over again “ok and now let’s improve what you just did”

It’s not really meant to query your own code base (Claude Code already does a great job at that) but more to explore other code bases you want to integrate with.

I’ve been shipping AI-written code for 2 years now. I can build something amazing in 40 mins but then spend 4+ hours debugging because the agent has no idea how the libraries it’s calling actually work. Docs are stale, StackOverflow is dead, training data is outdated. Every engineer I talk to has the same problem.

So I built Instagit, an MCP server that lets your coding agent understand any GitHub repo in depth so it can get it right on the first try. Works with Claude Code, Codex, Cursor, OpenClaw, etc.

No API key or account needed to try it out. Just need to share these instructions with your coding agent to get started:

curl -s https://instagit.com/install.md

1:55pm Cancelled my Claude subscription. Codex is back for sure.