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  10. TPM-Attest: Hardware-Rooted Anti-Cheat Alternative for Linux(arxiv.org ↗)
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  12. Show HN: Bitcoin-rs – An AI-assisted Bitcoin full node in Rust(github.com/gosuda ↗)
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  17. Gabor Fields: Orientation-Selective Level-of-Detail for Volume Rendering(arcanous98.github.io ↗)
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  18. OpenXLR – A Linux Variant of Elgato Wave Link(github.com/emaspa ↗)
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  19. Show HN: A GeoJSON viewer that checks the bbox you declared(9revolution9.com ↗)
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  20. Thomson-1.0-Small LLM Developed by Thomson Reuters(featherless.ai ↗)
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  21. FSD Supervised now approved in Czech Republic(twitter.com/teslaeurope ↗)
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  22. WhatsApp Business Paid Support Is a Scam
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  23. NILM to recover the energy consumption of individual appliances(github.com/goruck ↗)
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  24. Show HN: Grade text from the CLI with custom rulesets and Jev(github.com/lukstei ↗)
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  25. Show HN: Gleamit – dental health tracker for Android/iOS(gleamit.app ↗)
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  26. BicDB – a PostgreSQL-compatible database for local-first apps(github.com/nikoma ↗)
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  27. AlternativeTo – Crowdsourced Software Recommendations(alternativeto.net ↗)
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  28. AI Data Center Basics: What Is Linear Pluggable Optics (LPO)?(semtech.fr ↗)
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  29. Don't Use AI to Write(paulbakker.io ↗)
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Hackathons in the vibe coding era: our token economics experiment

2 pointsby 1h agoquesma.com
2 comments
58m agoHN ↗

did the teams that spent the most tokens actually ship more, or did spend mostly track how lost they got?

25m agoHN ↗

I would say so. In this exercise, more tokens meant better results. Though DeepSeek tokens were as good as frontier ones.

In this hackathon, how much data you analyzed was correlated with tokens spent.

Analyzing the whole dataset gave the best insights, but cost more tokens. Using a small dataset could save tokens, but some insights were premature.