Observed Signal · Mar 31, 2026 · Product Launch · Source: https://martechseries.com/feed/ · Impact: 2/5 · Sentiment: Positive
Thunkable Launches Thunkable AI No-Code App Builder
Thunkable announced Thunkable AI, an AI-powered, no-code platform for building native iOS, Android and web apps without technical skills. The release layers generative AI onto Thunkable’s decade of product development to generate starter projects from natural-language prompts, provide an integrated AI assistant (Discuss Mode) for iterative refinement, and automate native app publishing to Apple and Google app stores. Thunkable says its prior product has produced more than 12 million apps, that 29 of the Fortune 100 have used the platform (including Apple, Costco, Microsoft, Pfizer and Uber), and that roughly 80% of its customers are non-developers. Early beta customers include Rukus and CCM Systems. The company positions the product as democratizing mobile app creation by removing coding and app-store submission barriers for individuals, teams and enterprises.
Product launch lowers barriers to native mobile app creation and automates app‑store publishing, potentially increasing in‑app inventory and enabling nontechnical creators and enterprises to ship apps faster.
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Key Takeaways & Evidence Grounding
- Thunkable launched Thunkable AI, an AI-powered no-code native app builder for iOS, Android and web.
- Thunkable says more than 12 million apps were built on its prior no-code product.
- The platform automates app store submission and updates for Apple and Google app stores.
- Thunkable reports usage by 29 of the Fortune 100 (including Apple, Costco, Microsoft, Pfizer and Uber) and that 80% of customers are non-developers.
- Early beta customers named in the announcement include Rukus and CCM Systems.
Connected Companies & Entities
4 Entities mappedOntology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Non-Technical Builder Ships iPhone App Using AI
Bryce Rattner Keithley, a talent and recruiting leader with no formal coding background, built and shipped Daily Hundred — an iPhone fitness app — using AI tools and low-code services. She combined Replit for development, Claude and Claude Code as coding/architecture assistants, Gemini and Lovable for AI-generated imagery, and Railway and TestFlight for infrastructure and testing. Daily Hundred features custom AI-generated videos of anthropomorphic animals demonstrating exercises, created by compositing Gemini-generated characters with real exercise footage. The story is presented on Claire Vo’s How I AI podcast (published 2026-06-01) as a case study in how non-technical creators can execute app ideas using modern generative AI and developer platforms.
Lovable launches mobile app for on-the-go building
Lovable released a mobile version of its AI-powered "vibe coding" app for iOS and Android on April 28, 2026. The app lets users create websites and web apps via voice or text prompts, with an agent that can run autonomously and cross-device continuity between phone and computer. The launch follows recent App Store scrutiny of vibe-coding tools; Apple recently blocked updates to some competitors and has restricted apps from downloading or executing new code inside a host app, pushing generated app previews to web browsers. Lovable’s mobile app promotes compliance by focusing on creating working websites/web apps and delivering build notifications for review. The article adds context about the App Store policy actions affecting Replit, Vibecode and the app Anything, and positions Lovable among AI-assisted app and design tools.
Developer Builds Mobile App in 24 Hours Using AI
A developer (Mittal Technologies) documented an experiment to build a working mobile habit‑tracking app in 24 hours using AI as the primary builder. Tools used included Claude for architecture and code generation, Cursor as the editing environment, and FlutterFlow for UI scaffolding; the author also used Flutter knowledge to integrate and finish the prototype. The AI produced high‑quality architecture and data‑layer code and accelerated debugging and test generation, but generated UI code required substantial manual refinement and integration effort. The app shipped as a solid prototype at hour 24 but was not production-ready. The author concludes AI is a strong collaborator for boilerplate, planning and debugging, but an experienced developer’s platform knowledge remained essential to ship a usable product quickly.
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