QA teams deserve better than SnagIt

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Hey HN, Screenshots of bugs are no substitute for reproducing issues, but clearly annotated/laid out images do go a long way for developers to understand what’s going on and start working on them sooner. These remain an odd amount of work/overhead to do well— no one is going to fire up photoshop here, and most QA folks I’ve talked to have a simple trick or two up their sleeve for assembling screenshots/repro content quickly. A friend of mine’s spent the last few years working to improve this process and threw together what’s quite possibly the most functional screenshot tool (on windows) I’ve seen out there, meant specifically for QA teams (one click share to issue trackers, some annotation tools useful there, etc), and I wanted to share it with the HN community on behalf of them. They’ve put in pretty much zero marketing efforts so far and I’m trying to encourage them to get started— I think this could be useful for quite a few people, but other than some surface blog content, this is literally the first public post of their product. There’s a free version (and like, $2/mo for premium), it’s my hope some of you can try it out and find it useful. A bit on their product here: https://www.pixtel.us/product.html, and you can grab a download here: https://www.pixtel.us/download.html.

Visit product View on Hacker NewsApr 18, 2022

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

84%84% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

64%64% 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: way · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% 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

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.

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