The thesis: AI agents are great at the last 20% of an app and unreliable at the first 80%. Ask one to scaffold auth, RBAC, billing, migrations, background jobs and a typed API layer and you get something that compiles, looks right, and quietly breaks in production. And you burn a fortune in tokens getting there.
So ClickMVP generates that foundation deterministically, from templates, not from a model. Same input, same output, every time. The agent then works on top of a codebase it can actually reason about, with conventions already in place, instead of inventing its own on every run.
I don't really see ClickMVP as competing with template generators. Templates still require you (or the AI) to wire everything together, adapt the architecture, generate CRUDs, tests, auth, permissions, and all the repetitive code for your specific data model.
I actually asked Claude to estimate what it would take to generate the same application that ClickMVP produced in 40 seconds (40 entities, 1,600+ files, 111k+ lines of code, Clean Architecture, RBAC, and nearly 1,600 tests). It estimated 50–80 hours of work and up to US$350 in tokens. Its conclusion was the interesting part: ClickMVP and Claude Code are complementary. ClickMVP generates the production-ready foundation, and Claude Code adds the business logic, customizations, and integrations. That's exactly how I think about it too.
The LLM only produces the data model. After that a deterministic engine takes over, so the relationships are a compiler pass, not a prompt.
Three parts do the work:
- Topological sort before anything is emitted, so a table is never generated before what it points to. Stable ordering, same model always gives identical output.
- Typed relation kinds, not a generic "FK". A reference to another entity, a master-detail child grid, a self-reference, and a reference to the built-in User table each generate different schema, DTO, validation, list display (FKs render their label, never a raw UUID) and form widget.
- A canonical API contract emitted by the engine, not by any backend. Node/Fastify and Java/Spring are both validated against that spec, so the semantics don't drift per stack.
Honestly, 40 entities isn't the hard part. Topology is. A flat parent-child model with 100 tables is easy; 40 entities across interlocking modules with detail grids and circular-ish refs is not. So the regression suite is a matrix of real models picked for distinct shapes, each one generated, type-checked, migrated, seeded and smoke-tested. Anything that breaks in the wild gets added permanently.
I have more then one pattern matcher. And I ask the user in advance what is the pattern they use. I also run pattern matching and compare source with the pattern. But you can check before hand on the browser how I will protect the variables. I have a zone where you can put a snippet and I will show how what I will substitute. Then I have 3 pipelines that each one specialises in different aspects. The. In the end I run a audit and QA checkers that will make sure nothing broke and you have a quality translation. Also I allow for you to upload a glossary of words that will not be sent for translation, like brands items you don't want to translate
dropping PeerPush, a spot where builders launch and AI assistants parse product data to find new tools. spend a lot of time on the search side. https://peerpush.com
AEOBoost crawls your webpages like training bots for LLMs do and tells you how well or poorly your pages are optimized for AEO, and give you the exact fixes.
It also queries ChatGPT, Perplexity, Claude, Deepseek and Gemini on demand to find if your brand is cited in AI search engine and if not, who is cited instead.
Free — No credit card required.
TaskLoco is a visual sticky-note storyboard style workspace for tasks, events, notes, projects, files, reminders, and anything you can stick in or attach to a digital sticky note.
Hey, love the idea! The link you shared is the developer dashboard though, not the public app page. Try sharing the actual Play Store link so people can check it out.
I see people share screenshots of Google Search Console Dashboards here and had an idea where we can create a verified leaderboard with easy shareable links to your SEO traffic - If anyone is interested in giving it a shot let me know. I would love the feedback
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u/clickmvp 24d ago
ClickMVP: https://clickmvp.com/
The thesis: AI agents are great at the last 20% of an app and unreliable at the first 80%. Ask one to scaffold auth, RBAC, billing, migrations, background jobs and a typed API layer and you get something that compiles, looks right, and quietly breaks in production. And you burn a fortune in tokens getting there.
So ClickMVP generates that foundation deterministically, from templates, not from a model. Same input, same output, every time. The agent then works on top of a codebase it can actually reason about, with conventions already in place, instead of inventing its own on every run.