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pi pod runs sessions of the pi coding agent in isolated sandboxes (&quot;pods&quot;) on a server you run, in composable environments.----Since moving my company towards AI-native work, I have been really frustrated by the state of &quot;agentic engineering&quot; environments. Products by the labs (claude code, codex) lock you into a single provider for your tokens. Agnostic solutions (factory, devin, arguably cursor) make you pay per-token costs. None of these products allow you to fully customize the harness, and of course they all run on someone else&#x27;s infrastructure.I&#x27;ve been an early and fervent user of pi, which I think is fantastically simple and beautiful software. I have felt it needs an environment for it to work across platforms with fully functional composability for teams.This is very much a work in progress, but for my team this has been a much needed solution and has helped us tremendously. I hope you will give it a try and let me know how you would like it to improve.

Show HN: Pi pod – Run your pi coding agent in sandboxes on your own server

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My friend and I have worked at a number of BI companies and thought: Can’t a coding agent do most of this now?Almost. They just need a context&#x2F;semantic layer to ensure query correctness and some kind of artifact for publishing findings and visualizations.We built an open source project called Graphene that provides these tools:- Semantic layer that’s more token-efficient than YAML, more deterministic than Markdown (eg, composable, callable metric macros), with a query API that’s well in-distribution (SQL).
- MDX-like files for dashboards (Markdown with inlined SQL + HTML components for viz with support for CSS and Javascript)
- Connects to popular data warehouses or local DuckDBYour coding agent will build really in-depth reports and can ofc leverage any other skills or context that you&#x27;ve made available to it. We dogfood it inside a monorepo with our website, app source code, planning docs, etc so the agent has access to a ton of context. It’s also nice that the agent can add instrumentation, adjust pipelines and transformations, and add dashboards all in one PR.If you want to try it out, just point your coding agent at <a href="https:&#x2F;&#x2F;github.com&#x2F;graphene-data&#x2F;graphene&#x2F;blob&#x2F;main&#x2F;docs&#x2F;setup.md" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;graphene-data&#x2F;graphene&#x2F;blob&#x2F;main&#x2F;docs&#x2F;set...</a> and ask it to set up Graphene.If you don’t have data to play with, you can clone our example project:1. `git clone &lt;<a href="https:&#x2F;&#x2F;github.com&#x2F;graphene-data&#x2F;example-flights.git`" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;graphene-data&#x2F;example-flights.git`</a>&gt;
2. `cd example-flights &amp;&amp; npm install`Would love any&#x2F;all feedback!

Show HN: Graphene – Data analysis toolkit for your coding agent

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