Objectiv – Open-source product analytics infrastructure

Hacker News

Hi! One of the creators here. Very proud to finally be able to show you what we've been working on for over a year now. Curious to hear your thoughts! Objectiv is open-source (APLv2) product analytics infrastructure. It's built around a generic but strict event taxonomy, open/common data- and infra tools (currently PG, snowplow, working on bigquery with more to come), and the analyses are done using our pandas-like, SQL speaking modeling library called Bach. As a result, we’re moving towards a vision wherein models can be shared openly, independent of product, platform[1] or data platform[2]. How? - Fully assist the dev doing the instrumentation using tools for ide support, run-time validation, ci integration. No auto capture, but very low-effort[1] instrumentation. - Scale using proven tech: a single collector writing to PG for small or snowplow to anything[2] for big. - Feed data into your own data warehouse after validation. - No tracking plan, but an open event taxonomy designed for modeling, that fits most user interfaces out of the box or can be extended otherwise. - Take some of our pre-built models off the shelf or use Bach directly to model on the raw data in a notebook. - Bach generates SQL for the target platform: productionize without lock-in, feed to a BI system, dbt, etc. Where? Github: https://github.com/objectiv/objectiv-analytics Docker demo: https://objectiv.io/docs/home/quickstart-guide/ Website/docs: https://objectiv.io/ Let me know what you think and what platform/SQL backend you’d like to see supported first. [1] Currently on plain js, react, angular, react native. Future: vue, native android/ios. [2] Once we’re done building Bach support for all SQL platforms. Now: PG, bigquery in development. Future: redshift, clickhouse, athena, etc.

Visit product View on Hacker NewsMay 19, 2022

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

95%95% 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: ios · Missing: supports, reddit linkedin, podcasting

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

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

70%70% 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: ios · Missing: mobile apps, personal, entrepreneurs

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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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