Chronos a Drag-and-Drop Scheduler

Hacker News

I built this because every scheduler I tried starts you on the calendar. I always found the easiest way to plan is to have a list of tasks and then put them on the schedule. Chronos is built around that flow. Tasks live in a panel on the right, you drag them onto the calendar on the left, and done. I'm a high school student and built this primarily for my own scheduling. Sharing it as I think it's a better way to schedule. I wrote it with AI assistance just to speed up the process. A few things that might be interesting technically: - The entire app is a single HTML file with no frameworks, no build step, no backend. You can download it and open it in a browser with no setup. - Save/load works via a base64 JSON blob you copy and paste to load your schedule. - There's a Telegram bot assistant that reads the save code and sends you reminders before events. Keeping it free to run was a challenge. - I think all the functions I ever needed are there. You can schedule the normal way ofcourse as well. Known issues: the PDF export and iCal integration are kinda bad. If you run into problems with those I'd genuinely like to know. Built this around my own needs so it might have blind spots once again. Would appreciate any feedback on the UX, thank you!! Try it: https://chronos_v1.oneapp.dev Telegram bot: https://t.me/chronos_1bot Source: https://github.com/alekseiavgustin/ChronOs

Visit product View on Hacker NewsMar 25, 2026

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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.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: single, tasks, code · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

28%28% 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

18%18% 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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