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Hoop.dev, Golang server for a web database client

Hacker News

Hoop.dev, Golang server for a web database client

Hi HN, I’m Andrios, founder and CEO of Hoopdev, and am excited to show you our web database client with a Go-based language server that’s easy to use and built for teams. As of today, we’re launching our Free plan and anyone can start using it. It works with any browser. Desktop database clients have always felt clunky to me. After 10 years of programming, I still find myself struggling with basic tasks—like securely configuring access or just trying to pull data from a different device. Every time I'm away from my main setup, it feels like I'm locked out of my own data. These hurdles slow me down and remind me why we need to push for web-based solutions that are not just powerful, but also intuitive and accessible right from the browser. At Hoopdev we are building a Clojurescript client and Golang server that keeps what’s best about database clients while modernizing the experience in the web. We’ve built 1) Schema navigation and autocompletion that works with any db 2) Grouped outputs: so you can easily copy, search, and share query outputs 3) AI-powered Data Masking and Community-sourced Workflows [0]: so you can build your own middlewares 4) The ability to share your outputs with teammates: no more pasting long unformatted SQL into Slack 5) Runbooks: save your team’s common queries into a Git repo so your teammates can run them form a web form. We built a new type of language server that runs on Kubernetes, it is a lightweight Golang GRPC proxy that can scan and modify layer 7 packets in real-time (this is what enables real-time AI data masking). UI uses Clojurescript. You can self-host the full solution. Our business model is to make the database client so useful for individuals and small teams that their companies will want to pay for the team and security features. We will never sell your data. You will notice that a log-in is required and that we do collect usage data and crash reports. We do so because we’re spinning up backend resources for each user and also to keep improving the product. We’ll soon allow users to opt out of usage data. You can see our privacy policy here [1]. It is early, but we are confident that even today the experience is meaningfully better than in many desktop database clients. Please give it a shot at Hoop.dev and let us know how it goes. Follow us on Twitter [2]. Let me know what you think! Ask me anything! [0] https://github.com/hoophq/plugin-secretsmanager [1] https://hoop.dev/docs/more/privacy-policy [2] twitter.com/twitter.com/hoopdotdev

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Actual performance

6points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, slack, user · 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
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive, host, soon · Missing: plus, platform, reviews
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, users, way · Missing: mobile apps, personal, entrepreneurs
26%26% predicted probability of success on TrustMRR, 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.

Incorrect prediction on native model

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