Pipy 0.30.0

Hacker News

Pipy 0.30.0

Pipy 0.30 is now available. It adds improvements to a number of areas including better documentation, more core controls, new filters, enhanced Cache and Metrics API, and some bug fixes. The Pipy Runtime API has expanded its coverage of SSL engines, asynchronous file read/write operations. This release was truly a community effort and could not have been made possible without all of the hard work from everyone involved in active discussions and the Pipy project on GitHub.The Pipy community provides code submissions covering new functionality and bug fixes, documentation improvements, quality assurance testing, continuous integration environments, bug reports, and much more. Everyone has done their part to make this release possible! If you’d like to join this amazing community, you can find it on GitHub, Slack, and the Pipy discussion groups.

Visit product View on Hacker NewsMar 25, 2022

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

85%85% 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.

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

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% 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.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

23%23% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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