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Captan – Open-Source Cap Table Management CLI

Hacker News

Captan – Open-Source Cap Table Management CLI

I built Captan ( https://github.com/acossta/captan ), a tiny open-source CLI tool for managing startup cap tables. Instead of juggling spreadsheets or paying for expensive SaaS cap table solutions, Captan stores everything in a simple JSON file that you can version-control in Git. It supports: - Stakeholders (founders, employees, investors) - Security classes (Common, Preferred, Option Pool) - Share issuances - Option grants with vesting schedules (monthly, cliff) - SAFEs (record + simulate conversion at a priced round) - Cap table math (Outstanding vs Fully Diluted) - CSV/JSON exports - Audit log ("the ship’s log") Overall a JSON in git will offer better auditability and version control than most commercial solutions out there. Modeling different scenarios is super easy, just create a git branch and model whatever you need. Quick taste: --------------------------------------- npm install -g captan $captan init --name "Acme, Inc." --pool-pct 20 $captan enlist stakeholder --name "Alice Founder" $captan issue --security sc_common --holder sh_alice --qty 5000000 $captan chart Example output: Captan — Cap Table (as of today) Name Outstanding % Alice Founder 5000000 100.00% Totals Issued equity: 5000000 Vested options: 0 Outstanding total: 5000000 Fully diluted total: 7000000 --------------------------------------- Why I built it: early-stage founders (myself included) often don’t need Carta or Pulley yet — just a clean, hackable way to track ownership. I wanted something transparent, developer-friendly, and Git-native. Repo: https://github.com/acossta/captan npm: https://www.npmjs.com/package/captan I’d love feedback on what features would make this more useful to you? Thanks!

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, ios · Missing: reddit linkedin, podcasting, created
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, tiny, open · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released
45%45% 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: ios, month, monthly · Missing: mobile apps, personal, entrepreneurs
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
16%16% 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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