ICortex: LLM Powered Python Interpreter
A no-code development framework—Let AI do the coding for you 🦾 - textcortex/icortex
ICortex: LLM Powered Python Interpreter launched on March 26, 2023 with 8 votes, #1,999 of 5,007 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 3 years on.
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My Attempt to Organize the World of AI Dev Tools2025 · aicode.danvoronov.com · ▲101I've been exploring the (not so=) amazing potential of AI in coding and have compiled a list of tools. From AI-powered IDEs to code generators, this resource is my contribution to the community. I'm still on the fence about including txt2sql projects, as their functionality seems too basic to me. And I'm personally maintaining this, so your feedback is wellcome.
Open-source SDK for creating custom code interpreters with any LLM2024 · github.com · ▲64Python & JS/TS SDK for running AI-generated code/code interpreting in your AI app - GitHub - e2b-dev/code-interpreter: Python & JS/TS SDK for running AI-generated code/code interpreting in your AI app
Open-source CLI coding framework using Claude2024 · github.com · ▲84AI powered cli coding agent that monitors your dev/test server and fixes errors and adds features - vysakh0/dravid
Maybe Haskell2015 · books.thoughtbot.com · ▲60See what it’s like to program in a language without null.
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An application stack Claude coded directly in LLVM IRMar 2026 · github.com · ▲10This repo is the result of a debate about what kind of programming language might be appropriate if humans are no longer the primary authors. Initially the thought was "LLMs can just generate binaries directly" (this was before a more famous person had the same idea). But that on reflection seems like a bad approach because languages exist to capture program semantics that are elided by translation to machine code. The next step was to wonder if an existing "machine readable" program representation can be the target for LLM code generation. It turns out yes. This project is the result of…
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I'm a big fan of the BBC podcast In Our Time -- and (like most people) I've been playing with the OpenAI APIs. In Our Time has almost 1,000 episodes on everything from Cleopatra to the evolution of teeth to plasma physics, all still available, so it's my starting point to learn about most topics. But it's not well organised. So here are the episodes sorted by library code. It's fun to explore. Web scraping is usually pretty tedious, but I found that I could send the minimised HTML to GPT-3 and get (almost) perfect JSON back: the prompt includes the Typescript definition. At the same time I…
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