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tintcd – directory-aware terminal theming for PowerShell

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tintcd – directory-aware terminal theming for PowerShell

I built a PowerShell module that hashes directory paths to generate unique background colors. Each folder gets a stable, deterministic tint – no configuration needed. Why? I always have several terminal windows open in different folders. I'd alt-tab back, squint at the prompt, and wonder if I'm still in the right place. The prompt alone wasn't enough. I wanted something low-effort: no manual config, no color-picking — just instant "wait, wrong folder!" recognition or a warm and fuzzy "ah yes, this one." https://github.com/ymyke/tintcd

Visit productView on Hacker NewsDec 31, 2025
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78%78% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% 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
38%38% 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.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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