Reassign – a 24-hour dial for planning your day

Hacker News

For years I've used paper and a printed clock to organise my day. Found it way better than any list or timeline app. Played around a bit and made a digital clone to speed things up: Reassign.app (best experience is on desktop) Anyone else think of their day as a circle rather than a list? edit: it also has MCP integration ( https://reassign.app/api/mcp ) — my Claude already connects to Linear and Calendar, so I use it to start and plan my day. I can quickly get a skeleton of a day out and then polish until done.

Visit product View on Hacker NewsMay 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, mcp · Missing: mac, agents, macos

82%82% 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.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, 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

37%37% 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

24%24% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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