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A human-curated, CLI-driven Context Layer for AI agents

Hacker News

A human-curated, CLI-driven Context Layer for AI agents

The Context Layer is my take on treating context as a first-class, external layer that exists and evolves alongside your codebase. It's an agent-agnostic CLI tool designed for keeping long-lived project context structured and accessible across projects. It's intentionally human-curated - nothing is auto-collected or inferred. The goal is control, granularity, and explicit structure rather than embeddings or opaque memory systems. I've been using it across a few projects so far. Curious to hear from others experimenting with AI-assisted workflows! Website: https://ctxlayer.dev/ Tutorial: https://ctxlayer.dev/docs/ GitHub: https://github.com/anatoliykmetyuk/ctxlayer

Visit product View on Hacker NewsFeb 25, 2026

Actual performance

2points

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

91%91% 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

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

26%26% 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

18%18% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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