Real-Time Bluesky Post Firehose in < 256MB

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Hello everyone, I'm still trying to get some traction with sqlflow! I'm completely disenchanted with the modern data stack, and am trying to create a toolchain that helps treat data as if it were software: Observability, Integrated Testing, Performance and a real UX. The linked page uses sqlflow (the foundation of this datastack) to consume and window the bluesky post firehose, save it to postgres, then serve it back over HTTP for dashboarding. What're you using for analytics? Do you serve lines a chart to your customers? What tech are you using? Are you happy with the JVM (flink)? i'm very interested in learning how you tackle streaming data and analytics today! Thank you

Visit product View on Hacker NewsSep 17, 2026

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Launch Intel predictions

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

83%83% 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

61%61% 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

55%55% 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 · Missing: mobile apps, ios, personal

47%47% 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

23%23% 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

19%19% 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

11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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