I solo built OpenAI Codex 3 days before launch

Hacker News

Three days before the release of Codex I launched Vibe-1. Today Google released Jules. With the paywall of Codex and the load times of Jules, I thought this would be a good opportunity to showcase Vibe-1. Vibe-1 was one of the first asynchronous coding agents that ran directly inside of Github code bases. I built this by myself in roughly 2 weeks (1 week of building it, and 1 week of marketing). IMO, when it works, it feels like magic. A day before my big launch, X stopped running my ads, killing the momentum-- before X support could get back to me, OpenAI came out with Codex. Despite what OpenAI and Google do with their products, I still plan on maintaining and contributing towards Vibe-1. My next big feature is a digital "project manager" layer that sits on top of project (focused towards vibe coders).

Visit product View on Hacker NewsMay 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, google · Missing: mac, macos, cursor

93%93% 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 · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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