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AI

AI means the end of software as we know it

Hacker News

AI means the end of software as we know it

As agents scale in intelligence per token per watt, software architecture is starting to shift: away from CRUD databases and directional tree structures, toward multidimensional hypergraphs where knowledge, governance, and code are all the same kind of node. No single human can hold that structure in their head at once — but an agent can traverse it at inference speed. ILC is our open-source infrastructure project: a self-compiling morphogenetic hypergraph where the software literally builds itself from the same structure it uses to organize knowledge and settle it's own governance and disputes. Thinking and code, becoming one thing.. This ILC open source project is moving the needle towards that. Thanks for your critical feedback.

Visit productView on Hacker NewsSep 8, 2026
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Actual performance

2points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, single · Missing: mac, macos, cursor
92%92% 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
52%52% 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: open source, io · Missing: https docs, excited, just released
43%43% 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 · Strong signals: way · Missing: mobile apps, ios, personal
41%41% 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
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
14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
13%13% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Correct prediction on native model

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