Add Travel Time – Auto Travel Time in Google Calendar

Hacker News

Hi HN, I built a tool called addtraveltime.com to fix a daily annoyance: Google Calendar doesn’t actually account for travel time between meetings. It’s easy to end up with back-to-back events in totally different locations and no realistic way to get from A to B. What it does: Add Travel Time connects to Google Calendar and automatically inserts travel time blocks before and after your events, based on location and your preferred mode of transport. You can override the default mode and starting point per event. We also just added a “smart travel” feature: if your next event is soon after, it’ll check if it makes more sense to travel directly to the next one instead of back to your starting point. Why I made it: After enough missed meetings and stress from calendar overload, I wanted something focused that solves only this problem. Most other tools are either manual, buried in bigger platforms, or try to do too much. What’s unique: Focused only on realistic scheduling (commute time in/out, no extra features) “Smart travel” can optimize routes across multiple events 30-day free trial (no card needed), then €2/month You can try it now at addtraveltime.com. Would love feedback, questions, bug reports, or suggestions. Happy to share technical details or design choices if anyone’s interested.

Visit product View on Hacker NewsJul 31, 2025

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

70%70% 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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, soon · Missing: plus, intuitive, reviews

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

33%33% 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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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