An ultra-lightweight application auto-updater

Hacker News

Wanted an updater for a CLI utility so that a user could run "my-utility update" and have it self-updated. Did not want to go through package managers, in order to have better distribution control. Looked into Sparkle (and WinSparkle), Squirrel, GoUpdate, Google Omaha, and others. All were very heavy, hard or impossible to use across multiple platforms, and carried their own UI component baggage. None came even close to meeting our needs. So we rolled our own. `sxupdate` weighs in at about 100k, works on any platform and can be integrated with virtually any programming language. Using a bring-your-own-UI-component approach, it is extremely lean, platform-independent, applicable to either console or UI-based applications, and avoids duplication of and potential inconsistency between the application and the updater UIs. Hope someone else can get some mileage out of it. We sure have.

Visit product View on Hacker NewsJun 21, 2023

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

77%77% 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

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

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

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

51%51% 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 · Strong signals: arr · Missing: mrr, revenue, profit

17%17% 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.

Incorrect prediction on native model

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