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node-mlx – Machine learning framework for Node.js

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node-mlx – Machine learning framework for Node.js

I started studying machine learning this year, and I found python in my way to understand how things actually work, so I decided to port a machine learning framework to JavaScript first. I chose to port MLX, a library focused on Mac GPUs, because I mainly use Mac and I don't want to spend time on NVIDEA's environment, also I believe MLX and JavaScript are perfect match since so many JavaScript developers are using Mac. The examples of node-mlx (see the Examples section on GitHub) are written in a straightforward way as I'm still a beginner myself, which should make them very easy to understand. I'm currently still learning basic conceptions and gradually implementing them in JavaScript to understand better. I'll write more examples as part of my learning, and whom want to get updates of the new examples can watch my twitter (handle is the same with hacker news).

Visit productView on Hacker NewsJul 20, 2024
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2points
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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, 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: hacker news, ide, io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new, using · Missing: agents, macos, agent
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
36%36% 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
32%32% 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
16%16% 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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