Co

Cognitive Layers

Hacker News

Cognitive Layers

Like everybody else, I straddle the line of agentic coding psychosis – excited about moving faster than ever, but deeply concerned about losing the thread and producing something incomprehensible and hard to evolve. This has been a problem before – underspecified projects, specifications going out of sync with the first line of code. We've just amplified it now. A lot of people (myself included) have tried to maintain good specifications in markdown to give LLMs and humans maximal context. But this is still walls of text that poison anyone's context, regardless of their artificiality. So I built a way to model knowledge as a graph that both people and LLMs can consume progressively – by navigating the graph or searching for specific connectivity patterns rather than ingesting everything at once. At its core it's an open specification for layers that add progressively more semantic value: starting with prose, growing into terminology, tasks, concepts, API surfaces, and structured plans. The most critical component is a layer that maps artifacts (such as code) to the knowledge model with good enough precision to track drift and coverage automatically.

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, agentic · Missing: mac, agents, macos
95%95% 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.
Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
54%54% 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
40%40% 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
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.

Incorrect prediction on native model

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