Built Loony for builders who want to spin up data infrastructure fast

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I've come across this problem a few times, and I am a recently upskilled AI engineer with no prior data engineering experience. My most recent example was CRM related - I wanted to see my entire sales funnel and I had a few different data sources and APIs like linkedin (I'm in EU so I get access to crazy data access via Member portability APIs), app events, gmail/calendar, apollo for enrichment etc... that I wanted to combine, track, and store over time. I have so much data from different sources and getting these together is a massive pain (I also didn't need a complex CRM for this - just wanted to make it queryable via Claude and create some visuals with Streamlit.) The way I think about it is: these are just APIs and data sources. I fetch the data, and then I want to pipeline it and structure it in my own little database, in a way that makes sense to me. Then I can enrich it, expand it, change the schema, query it, build dashboards, apps, whatever (freedom...). The problem is that every time I started doing this I'd end up yak shaving for days. Which DB? Where do the pipeline files live? How do I productionise cron jobs? I want to make it operational via API and MCP (agentic-friendly semantics built in). Essentially, my vision is to have a dedicated app for each of my departments, but doing this every time is a massive pain, even with a boilerplate. So... me and my bro built Loony.dev, which is essentially a CLI optimised for your Claude agent, and it does all of this out of the box. Just bring your data sources - deploy your env variables to Loony, and it can pull the data, build the pipelines, set the update schedules and build agentic-ready views with MCP / REST-API out of the box. Would love to know if this resonates with anybody else? Feels like I was running into this issue every other week and I just gave up because it felt like I was building another tiny startup each time.

Visit product View on Hacker NewsMar 27, 2026

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

99%99% 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: started · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released

30%30% 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: friendly, builder · Missing: plus, platform, intuitive

19%19% 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

18%18% 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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