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AppifyText – AI text-to-app tool that builds internal tools

Hacker News

AppifyText – AI text-to-app tool that builds internal tools

Hi HN, on Feb 2023 I shared AppifyText, an early experiment in AI-powered text-to-app generation: you type what app you want, and it builds it automatically. That first version was very much a proof of concept. Today I’m sharing v2, which is a significant step forward in terms of output quality. What’s new in v2: 1) Better prompt comprehension – It follows complex instructions more accurately. 2) Richer app content – Generates structured apps with more details (e.g. master-detail relationships are auto-detected). 3) User roles understanding – e.g. “Sales reps and customers can access the app” creates user groups with sample users and permissions. 4) Public-facing views – Great for exposing parts of your app (like a catalog) to anonymous users or embedding into a website, and it designs the layout based on your prompt 5) Automatic dashboards – Just say “Create a dashboard showing number of orders per product,” and it generates proper visual charts. S6) marter UI design instructions – understands prompts like “use a dark theme” or “shades of gray as main colors.” AppifyText builds apps on top of DaDaBIK, a mature no-code/low-code platform. This means you can export the generated app and fully tweak it using no-code features, while still adding custom PHP and JavaScript code if needed. Because it uses a no-code engine behind the scenes, the AI output is constrained within a defined, stable environment, resulting in: 1) Lower risk of hallucinated logic 2) More stable and secure app structures Yes, there’s a trade-off: you don’t have the same freedom or open-ended creativity as with raw AI tools (“vibe coding”) but, when it comes to Internal tools, you often don't need so much freedom. IMHO for internal tools – CRMs, dashboards, admin panels – structure is often a strength, not a limitation. I’ve recorded a full 4-hour video showing the build of a complete Library Management System from scratch, covering backend, frontend, deployment, and adding custom PHP/JS where needed: https://youtu.be/JxmuWePr2JQ Thanks for reading, and for any feedback, thoughts or critiques.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
97%97% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, new · Missing: mac, agents, macos
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%51% 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: apps, video, users · Missing: mobile apps, ios, personal
47%47% 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
23%23% 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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