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SuprLogs – Autopilot changelogs from GitHub commits

Hacker News

SuprLogs – Autopilot changelogs from GitHub commits

I hate writing changelogs. So usually, I don't. When I have, they've gotten stale or were way too technical for my audience. At the same time I recognize they are critical for illustrating momentum to customers, investors, and colleagues. So I built a tool to make it effortless, automatic, and something I'd never have to think about again. My solution is simple: 1. Connect a repo 2. Done SuprLogs reads every commit diff, generates analysis, writes unified changelog entries from that analysis, publishes, updates, etc. all on autopilot. It's basically a set-it-and-forget it system. A few really cool features that I haven't seen anywhere else include: - Ability to connect multiple repos to create a single unified changelog - Ability to ingest every historic commit and backfill a changelog to the present day - Ability to set a custom system prompt to guide the writing style - Kanban style theme, similar to how PostHog does their changelog I'd especially love feedback on: - Is the tool is useful with it's current features? What's it missing? - Do you like the themes? - Do you trust it with private repos? - Do you understand the value prop from the homepage? Anyone can use it. It's free forever for public repos. And there's a no-credit-card free trial with all the bells and whistles if you want to give it a deeper dive (private repos, analytics, teams, etc). Happy to answer any questions about the tech, orchestration, durability, security, etc.

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Product HuntOn track for Day 1 leaderboard · Strong signals: single · Missing: mac, agents, macos
73%73% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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.

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