Coegil – Ship enterprise-grade, full-stack AI apps with Claude Code

Hacker News

Hi HN, We built Coegil - dev tools + frontend/backend infra for vibe coders. You can vibe code a frontend in minutes — but you hit the wall when you need a real backend. So we built the OS we wanted - cloud-grade infra, minus the DevOps: - Self-serve access control, data storage, and versioning - Scalable compute sessions for your favorite vibe coder + libraries - Multi-step job pipelines with logs + monitoring - GenAI agent & endpoint hosting with logic + memory - Build dashboards + traditional ML models - Ready-made auth + payments - Multi-cloud + on-prem connectivity - 99.999% uptime No config setup. No stitching tools. Just build full-stack apps fast. We used a version of this at Bridgewater Associates — and realized the rest of the world needed it too. * Signup: https://coegil.com/signup-hn * Check us out: https://coegil.com Would love feedback from vibe coders trying to go from prototype to prod.

Visit product View on Hacker NewsJul 28, 2025

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

94%94% 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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% 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 · Strong signals: apps · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

26%26% 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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