To

Tokscale – See who's burning the most tokens across all platforms

Hacker News

Tokscale – See who's burning the most tokens across all platforms

I've been through all of them. Claude Code, Codex CLI, Gemini CLI, Cursor. Finally settled on OpenCode. But I had billions of token data scattered across 5 different tools with no unified view. So I built Tokscale. The problem: - Token usage scattered across multiple AI coding tools - Each stores data in different formats and locations - Claude Code deletes your history after 30 days by default (!) - No way to see total spend or compare with other developers What Tokscale does: - One command (`bunx tokscale`) shows all your AI usage in one place - GitHub-style contribution graph (2D/3D) for your token consumption - Global leaderboard to see where you rank among other developers - Spotify Wrapped-style year review image to share Built with: - Rust native core for fast parsing - OpenTUI, the same terminal UI framework that powers OpenCode Check it out: https://tokscale.ai CLI: `bunx tokscale` GitHub: https://github.com/junhoyeo/tokscale

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, claude, codex · Missing: mac, agents, macos
91%91% 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 · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
34%34% 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 · Missing: plus, intuitive, reviews
25%25% 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
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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