Architecture as the Source of Truth Instead of Source Code<br>LIVE<br>Get API Key
18 months · zero budget<br>Architecture as the source of truthinstead of source code.<br>Describe your system once. A deterministic compiler emits the whole codebase — backend, database, API, frontend, tests, deployment. Then an ecosystem of agents maintains it, remembers what it learned, and repairs what breaks.<br>“We keep trying to build smarter drivers.<br>I think the bigger opportunity is building better roads.”<br>Try it now — free keyHow it works<br>ArchitectSidhant Channa<br>HardwareMinimal stack<br>Budget$0 raised
18 monthszero budget48 tools30 external · 18 internal363 linesof ADL → a full clinic app160 filesgenerated per spec8,116 linesof emitted code3exemplars compiled end to end18 monthszero budget48 tools30 external · 18 internal363 linesof ADL → a full clinic app160 filesgenerated per spec8,116 linesof emitted code3exemplars compiled end to end
I was born on April 1st. Either the universe has a profoundly dark sense of humor, or this whole project is the longest setup for a punchline in history. I’m fine with either outcome.<br>For the last 18 months, one thing kept bothering me about the AI industry. Every few months we announce a bigger model. More parameters. Larger context windows. We keep building smarter and faster drivers. But we keep dropping these Formula 1 drivers into an empty field in 1820 and asking them why they aren’t winning races. We ask the driver to build the car, pave the track, refine the fuel, and act as the pit crew.<br>The problem isn’t the driver. The problem is the missing track.
Capability is a combination of intelligence and infrastructure.
Right now we take a probabilistic model, hand it the keys to a massive, legacy codebase, and act shocked when it hallucinates. But you don’t hand a first-day intern the keys to a law firm and expect them to win in the Supreme Court. Law firms have protocols. Engineering teams have infrastructure.<br>Current software infrastructure was designed around a massive, hidden assumption: that humans are the operating system. Git, pull requests, Jira, Slack, Confluence, stand-ups — all of them assume a human being is there to carry the continuity. We are dropping AI into infrastructure built exclusively for human psychology, and then blaming the model when things break. We are optimizing at the wrong abstraction layer.<br>Burning GPUs to expand context windows doesn’t solve this. An LLM with a massive context window is just a brilliant scientist who wakes up with severe amnesia every single day. Yes, they can read a lot of notes before breakfast, but there is a physical limit. Intelligence only compounds when infrastructure exists to preserve and amplify it.<br>The biggest problem isn’t that AI writes bad code. It’s that AI writes code faster than humans can reason over it. And every time you ask it to iterate, the abstraction drifts.<br>So what if LLMs stopped owning the implementation entirely? The last programming language a human needs to learn is no language at all. Instead of asking an LLM to generate implementation directly, I ask it to describe the system as an Architecture Definition Language (ADL) specification.<br>The mental model I keep coming back to is HDL for hardware. ADL isn’t another programming language. It’s a language for describing what a system is rather than how every line is implemented. Entities, relationships, operations, contracts, authorities, and deployment topologies all live in one place, condensed into a format that both humans and AI can easily reason over. From there, a deterministic compiler takes over.<br>IntentLLMADL SpecDIS CompilerRunning System<br>The compiler validates topology, checks invariants, and enforces authority graphs — before a single source file is generated.
Backend<br>Rust · Axum · SQLx
Frontend<br>React · TypeScript
Data & API<br>PostgreSQL · OpenAPI · GraphQL
Ops & Reliability<br>Docker · Kubernetes · Tests · CI
Declared target matrix — validated, current emission stack<br>Backends<br>axumactixexpressfastapihonofastifygo-ginbinary
Frontends<br>react-appnextjs-appvue-appsvelte-appsolid-appleptos-appdrogon-static
Databases<br>postgresqlsqliteclickhousemysqlmongodbredisnone
The compiler validates the declared kinds through the live gateway (18 of 22 kind probes pass validate_adl; the 4 un-emitted backends fail loudly at validation). Emission is build-verified end to end through the production gateway: backends axum (Rust), express (Node/TS), and fastapi (Python) all compile and serve real API round-trips — axum+sqlite via cargo check and live POST/GET, express via tsc, a node:test suite, and a live POST/GET round-trip, fastapi via pytest, a live POST/GET round-trip, and FK enforcement; the remaining backend kinds (actix, hono, fastify, go-gin) are rejected with a loud diagnostic at validate_adl instead of silently emitting the wrong server. Frontends react, vue, svelte, and solid each build into real CRUD apps (bun run build: react 244 kB, vue 73 kB, svelte 8 kB, solid...