Op

Opty – A Zig-based HDC that reduces token use by up to 90%

Hacker News

Opty – A Zig-based HDC that reduces token use by up to 90%

I kept seeing people all over social media talking about how they were making custom local-LLM systems to help reduce the token load injected into their context window. On the side my recent project has me looking at Hyperdimensional Distributed Memory. I couldn't help but wonder if I could make an MCP server to improve token usage. Sure enough, using a combination of HDC + TOOL format, I was able to get opty's own self audit down by 93% in token usage. Still experimenting with large codebases but feel pretty good about how this should drive overall token usage down. Happy to hear any feedback.

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, context, using · Missing: mac, agents, macos
88%88% 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
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
56%56% 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
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
43%43% 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
15%15% 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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