AI

AI-org – org-mode powered by AI

Hacker News

AI-org – org-mode powered by AI

Hey guys. I made an attempt at using AI agents to help organize your tasks and life. I liked orgmode before AI. But just wondered how it could look with AI. So I created ai-org, a non-taken name it seems. It's an opencode fork with a custom org agenda task view. (and a simple file browser). (wouldn't be possible without opencode team, truly what a great MIT licensed product) The template includes an AGENTS.md and accompany file structure to the effect of: matt@mbp:~/doc/dev/pro/ai-org/template $ tree -L 1 projects/ duties/ resources/ archive/ goals.org ideas.org scratch.org todo.org AGENTS.md HUMAN.md It's been a fun experiment, would love to hear any thoughts, thank you. https://ai-org.net/

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, tasks · Missing: mac, macos, cursor
71%71% 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: created · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released
35%35% 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 · Missing: mobile apps, ios, personal
28%28% 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
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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