Biq Blue – Destroy Your Google BigQuery Costs

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Hi! I've been working on Biq Blue, a tool engineered to analyze your Google BigQuery tables, storage, and requests with the goal of drastically reducing your costs. Currently in early free beta, Biq Blue has already demonstrated its effectiveness on some big data sets. Essentially, it's a server that connects to your BigQuery database via the gcloud CLI, conducts analyses, and opens an HTTP port to serve both results and recommendations over web pages. Your data stays local, ensuring it never leaves your enterprise (I may only collects anonymous usage statistics and the email tied to your gcloud account) I’ve developed versions for Windows, MacOS, and Linux, as well as a Docker version, which can be installed directly on your infrastructure, enabling multiple users to access Biq Blue simply through a web browser. I’ve spent some time working on the “packager” to ensure that the installation process is as smooth and easy as possible. Consequently, any feedback regarding installation would be particularly appreciated :) Screenshots and documentation are available on the public GitHub page ( https://github.com/biqblue/docs ). Is it clear enough for you to go through the installation and startup process without any issues? Any additional feedback or advice is also more than welcome! Thanks !

Visit product View on Hacker NewsSep 21, 2023

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

79%79% 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

56%56% 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: google, users · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

16%16% 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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