Automatically keep track of all features implemented in a project

Hacker News

I built this because I wanted to automate my least favourite part of documentation writing - expressing at a high level what was implemented. There are a couple of situations where you could need that: - to show to the client what changed since the last update - to show to an executive, where the fucus went lately - to explain to a new team member, what the project is about Because the report (you can see how it looks here: https://github.com/netizer/feature-ledger/blob/main/docs/cli... ) is automated, I also use it as a discovery tool in projects I just started working on. The way it works: - `ledger init` - creates a `.ledger` directory; descriptions of all new features will be stored there - `ledger bootstrap` - prints out a prompt to be used in a coding agent; it will analyse a new codebase and create the JSON files describing all features - `ledger audit` - when you usa a coding agent it will automatically keep the feature list up-to-date, audit is to discover features implemented without a coding agent - `ledger release cut --name "Release name" --date 2026-09-11` - run it after you've just shown to the client what had changed and you want to start working on a new set of features; in the new ledger you will see new features in green, updated features with blue, and deleted ones in red - `ledger build` - run it whenever you want to see the report of new features; there are 3 files generated based on JSON files describing features: a PDF for the client, FEATURES.md (the same but in an MD format), FEATURES_EXTENDED.md (the same but including implementation details - this is for the dev team)

Visit product View on Hacker NewsSep 11, 2026

Share card

Actual performance

1points

2comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

80%80% 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, including · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io, including · 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.

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

32%32% 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

14%14% 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

Similar products

Flipadelphia flips your features50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Flipadelphia flips your features

Hacker News2

Voyager 3.2.0 is out with lots of handy features43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Voyager 3.2.0 is out with lots of handy features

Hacker News3

Automatically Controlling a Dehumidifier with a Nest57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automatically Controlling a Dehumidifier with a Nest

Hacker News1

Automatically Thank Patreon Backers with OpenFaaS38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automatically Thank Patreon Backers with OpenFaaS

Hacker News11

Sorts Evernotes, Automatically57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Sorts Evernotes, Automatically

Hacker News3

TempHub41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automatically call your temps, one by one

Indie Hackers1b2b

BulletWeek GTD app packed with features39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

BulletWeek GTD app packed with features

Hacker News4

Track BGP Resources Automatically48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Track BGP Resources Automatically

Hacker News2

My afternoon project, Clustergram55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

My afternoon project, Clustergram

Hacker News1

Project Maya72%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Project Maya

Hacker News88

Browse all products Analyze your launch