We built Atono because we kept losing context

Hacker News

We kept asking "why did we build it this way?" and spending 20 minutes hunting through Jira, Slack, and GitHub for answers. It was a symptom of a bigger "Jira-problem". So we built Atono ( https://atono.io ). The idea: Stories that don't die when you ship. Everything - requirements, code changes, bugs, decisions - stays in one place. Our take (might not be for everyone): -Feature flags live in the story, not another tool -Feature engagement for you to see if people use what you built -Context > process What's in it: -Toggle feature flags right in the story -See usage graphs without another analytics tool -One-click bug reports that actually capture everything -Free forever for 5 users We use Atono to build Atono. Works for us, but we're pretty opinionated about Product Teams. Need your take: Does this match how you work? What's missing? What's wrong with our approach? Try it at https://atono.io

Visit product View on Hacker NewsSep 24, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, context · Missing: mac, agents, macos

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

54%54% 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

51%51% 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 · Strong signals: users · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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

15%15% 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.

Incorrect prediction on native model

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