Weed–Minimalist AI/ML inference and backprogation in the style of Qrack

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"Weed" is an AI/ML library in the style of vm6502q/qrack (now unitaryfoundation/qrack, on GitHub). I wrote the (C++) Qrack quantum computer simulator framework (now with +2.5M downloads of its ctypes Python wrapper) to have absolutely minimal dependencies and supply-chain vulnerability attack surface: it only requires pure language standard at bare minimum, with optional OpenCL or CUDA for (vendor-agnostic) hardware acceleration, and with optional Boost library inclusion for performance. "Weed" aims to provide the same standards and utility for AI/ML inference and back-propagation as Qrack does for quantum computing: never be locked into a hardware vendor, never be locked out of deploying on a platform due to lack of upstream dependency support, and let fastidious engineering and design point to the way to novel optimizations, all of which work together through a "transparent" interface of optimal default user settings.

Visit product View on Hacker NewsJan 31, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, computer, open · Missing: mac, agents, macos

58%58% 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.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

39%39% 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 · Strong signals: platform, interface · Missing: plus, intuitive, reviews

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

31%31% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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