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Copper – Data-Oriented Robotics Engine in Rust

Hacker News

Copper – Data-Oriented Robotics Engine in Rust

Hello HN, Today we are proud to launch and share with you Copper (cu29), an open source Rust-first robot software engine. Why another robot framework? Simply because : 1. nothing really good existed in C++: out of my past 4 robotics company experiences we built 4 robot runtimes, often from scratch 2. nothing really exists in Rust while we see a very good fit in robotics for the language and its general popularity is growing. But also we want to introduce a new way of building complex robots: 1. historically robots were very simple, hard real time embedded programs (industrial arms etc..) 2. complex robots, like autonomous robots used ROS or a variation of it. 1 is not enough for today's robots, and 2 is very geared toward experimentation by design. With Copper we try to get the best of both worlds by stealing a page from the gaming industry where programs are both real team and very complex. You'll see concepts in Copper like for example: - data oriented programming - structured login - determinism of the runtime that both makes your runtime really fast (so in a sense more reliable as it can get more observations!) and easier to debug. This project has been releases very early by choice so we can get the feedback from the community and build the best possible tool for robot creators! Enjoy Edits: grammar

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos
80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, io · Missing: https docs, excited, just released
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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
31%31% 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
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time, introduce · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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