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CarbonLint – Open-source real-time energy&carbon profiler for software

Hacker News

CarbonLint – Open-source real-time energy&carbon profiler for software

Hi HN, I'm excited to share CarbonLint, an open-source tool I've been building to help developers measure the actual energy consumption and carbon emissions of their software in real-time. While there are great tools for tracking CPU/RAM, there aren't many accessible ways to translate those hardware metrics into actual environmental impact natively on your local machine. How it works: • It's built with Rust & Tauri (v2) for a tiny footprint. • It monitors CPU, memory, disk I/O, and network activity of specific processes. • It calculates energy usage based on user-configurable hardware profiles (e.g., Apple M-series, Intel, AMD). • It translates energy into CO2-equivalent emissions using real-time, region-specific power grid carbon intensity data. • Available natively across Windows (.exe/.msi), macOS (.dmg), Linux (.deb/.AppImage/.rpm), and Android (.apk). Key features: Global keyboard shortcuts to start/stop profiling sessions instantly. UI runs quietly in the system tray. Comprehensive session reports detailing exactly how much energy a specific software run consumed. The code is fully open-source (MIT), and everything runs locally on your machine. I'd love to hear your feedback on the architecture, the energy estimation models, or any feature requests you might have. Repo: https://github.com/nishal21/CarbonLint Website: https://nishal21.github.io/CarbonLint/ Thanks!

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, model · Missing: agents, agent, cursor
96%96% 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
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
70%70% 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
34%34% 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
30%30% 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
14%14% 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.

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