Quicklog – I used this app to journal for 13 years, now you can too

Hacker News

Hi folks! I'm delighted to present Quicklog, a macOS app for near-instant journaling. I have ADHD and for the life of me I couldn't keep a journal. Having to go through the sequence of launching an app > creating a new entry > typing > saving > quitting the app, was enough steps for me to lose the will to keep doing it day after day. So back in 2013 I wrote a tool for quick journaling in Objective-C for personal use. It did only one thing: on a hotkey press it would open a small window on top of everything to capture a single entry, and when the return key was pressed the window would disappear and the entry would be time-stamped and appended to a txt file. I have been using it for 13 years (I'm at 14557 entries). Some days have a dozen entries, some days have one entry, and that's ok. I use it for capturing a snapshot of each day, logging work stuff so I can look back for what I need, and even simply scrolling back years and enjoy remembering my life's small moments, which won't go lost like tears in rain. A couple months ago I decided to port the app to Swift, polish it, and make it available on the macOS app store. It is a privacy-first app (no servers, no account), it saves to a local .md file so there's no proprietary format that locks you in. The free version has 200 entries (which roll over, so the 200 most recent entries are kept), and the pro version allows unlimited entries. Obviously a skilled user can just write a simple terminal script to do something analogous, but Quicklog is thought with everyone in mind. I'll be happy to answer any question. Enjoy!

Visit product View on Hacker NewsApr 22, 2026

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

95%95% 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: mac, macos, user · Missing: agents, agent, cursor

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

41%41% 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

24%24% 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

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

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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