Op

Open Codex – OpenAI Codex CLI with open-source LLMs

Hacker News

Open Codex – OpenAI Codex CLI with open-source LLMs

Hey HN, I’ve built Open Codex, a fully local, open-source alternative to OpenAI’s Codex CLI. My initial plan was to fork their project and extend it. I even started doing that. But it turned out their code has several leaky abstractions, which made it hard to override core behavior cleanly. Shortly after, OpenAI introduced breaking changes. Maintaining my customizations on top became increasingly difficult. So I rewrote the whole thing from scratch using Python. My version is designed to support local LLMs. Right now, it only works with phi-4-mini (GGUF) via lmstudio-community/Phi-4-mini-instruct-GGUF, but I plan to support more models. Everything is structured to be extendable. At the moment I only support single-shot mode, but I intend to add interactive (chat mode), function calling, and more. You can install it using Homebrew: brew tap codingmoh/open-codex brew install open-codex It's also published on PyPI: pip install open-codex Source: https://github.com/codingmoh/open-codex

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Actual performance

106points
37comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, codex, models · Missing: mac, agents, macos
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, introduce · Missing: web3, crypto, cryptocurrency
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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