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Dropbase AI – A Prompt-Based Python Web App Builder

Hacker News

Dropbase AI – A Prompt-Based Python Web App Builder

Hey HN, Dropbase is an AI-based Python web app builder. To build this, we had to make significant changes from our original launch: https://news.ycombinator.com/item?id=38534920 . Now, any web app can be entirely defined using just two files: `properties.json` for the UI and `main.py` for the backend logic, which makes it significantly easier for GPT to work with. In the latest version, developers can use natural language prompts to build apps. But instead of generating a black-box app or promising an AI software engineer we just generate simple Python code that is easily interpreted by our internal web framework. This allows developers to: (1) See and understand the generated app code. We regenerate the `main.py` file and highlight changes in a diff viewer, allowing developers to understand what exactly was changed. (2) Edit the app code: Developers can correct any errors, occasional hallucinations, or edit code to handle specific use cases. Once they like the code, they can commit changes and immediately preview the app. Incidentally, if you’ve tried Anthropic’s Artifacts to create “apps”, our experience will feel familiar. Dropbase AI is like Claude Artifacts, but for fully functional apps: you can connect to your database, make external API calls, and deploy to servers. Our goal is to create a universal, prompt-based app builder that’s highly customizable. Code should always be accessible and developers should be in control. We believe most apps will be built or prototyped this way, and we're taking the first steps towards that goal. A fun fact is that model improvements were critical here: we could not achieve the consistent results we needed with any LLM prior to GPT-4o and Claude 3.5 Sonnet. In the future, we’ll allow users to modify the code to call their local GPT/LLM deployment via Ollama, rather than relying on OpenAI or Anthropic calls. If you’re building admin panels, database editors, back-office tools, billing/customer dashboards, and internal dev tools that can fetch data and trigger actions across any database, internal/external service or API, please give Dropbase a shot! We're excited to get your thoughts and questions! Demos: - Here’s a demo video: https://youtu.be/RaxHOjhy3hY - We also introduced Charts (beta) in this version based on suggestions from cjohnson318 in our previous HN post: https://youtu.be/YWtdD7THTxE Useful links: - Repo here: https://github.com/DropbaseHQ/dropbase . To setup locally, follow the quickstart guide in our docs - Docs: https://docs.dropbase.io - Homepage: https://dropbase.io

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Actual performance

141points
11comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, apps · 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 · Missing: supports, reddit linkedin, podcasting
89%89% 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, llama, ide · Missing: https docs, just released, exist
83%83% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps, video, users · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: occasional, builder, users · Missing: plus, platform, intuitive
39%39% 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
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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