I built a way for AI to remember how you like things done

Hacker News

I kept running into the same issue with AI: every conversation starts from zero, even when I already solved the same problem before. I still had to repeat constraints, preferences, and workflow every time. So I built AEP (Agent Experience Protocol), a simple way to save successful AI workflows such as intent, constraints, preferences, steps, failure traps, and success checks, and reuse them later so future tasks start aligned instead of from scratch. It lives as repo-local JSON in .agent/aep/, is agent-agnostic, and works more like “Git for how you work with AI” than prompts or memory. Curious if others are hitting the same problem and whether something like this would be useful in tools like Cursor, ChatGPT, or Claude.

Visit product View on Hacker NewsMar 31, 2026

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

94%94% 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

55%55% 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

36%36% 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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

24%24% 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 · Missing: https docs, excited, just released

23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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