BuildShip – Low-Code Visual Backend Builder, Powered by AI

Hacker News

Hello HN! I am Harini and along with my co-founder Shams, we just launched BuildShip. We are the team behind Rowy.io and we created BuildShip based all the feedback/and user requests we got from Rowy's community of no-code/low-code builders and developers. Today you can create your backend workflows using nocode tools (but they lack scalability, flexibility, and extensibility and end up being expensive). Or you have to code them and deploy them to a cloud platform of your choice (you end up spending a lot of time reading docs, looking up CLIs, and context switching) - this is especially unnecessarily complex and time-consuming for no-low-/low-code product builders and developers. With BuildShip, you can create powerful APIs, schedule jobs, database CRUD operations, and any backend task by simply combining nodes into a workflow. We have hundreds of pre-built nodes (from core nodes like parallel run, loop, branch etc to third-party tools, APIs,, support for 1M+ NPM packages and any AI models via OpenAI, Google Cloud, Replicate, Hugging Face, and many more as well as database connections to Firestore, Supabase, PostGres, BigQuery and much more). But MOST Importantly - if an integration is missing - you don’t wait for us to release it - you can simply ask AI what you want and it gives you a custom workflow node generated just for you. This will change the way you build your backend! Btw the generated node is not a black box, you can dig into its code - right in the browser, tweak it anyway you like in JavaScript/TypeScript with AI code gen, test, and when ready - Ship in one click instantly! Website: https://buildship.com Documentation: https://docs.buildship.com Product demo: https://buildship.com/youtube We would love for your feedback and thoughts on this as we continue to Build → Ship!

Visit product View on Hacker NewsSep 26, 2023

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

98%98% 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: created, para · Missing: supports, reddit linkedin, podcasting

93%93% 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

65%65% 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, way, para · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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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