Start with your software idea. Throughstone and your AI coding agent turn it into a planned, documented, well-architected project. Each step is turnkey and the system guides you throughout the process.
Vibe-coded projects often start fast, then decay when nobody can explain the architecture, tradeoffs, or next safe change. Throughstone makes that project memory explicit from the start.
Start with architecture docs and decision records, then code. You begin from a plan you can read and question.
Every meaningful choice is written down as a decision record, not buried in a chat log — so the “why” survives.
Build proceeds in phases → steps → substeps, each a runnable unit with check-ins, so progress stays reviewable.
Throughstone keeps the agent from starting with code. It first turns the idea into durable project state, then builds against that state in reviewable units.
Run the setup wizard once. It stamps the project name, license posture, repo layout, docs hub, prompts repo, and agent pointers.
Tell the agent to read AGENTS.md. It interviews you, records the project brief, and prepares STEP-1 for architecture.
Architecture sessions capture scope, data, security, interfaces, testing, deployment, ADRs, and the cross-cutting review before app code starts.
The planning session turns the locked architecture into implementation STEPs, each with substep prompts, verification, review, and archival.
Instead of leaving the plan buried in a long chat, Throughstone writes plain-language project documents your AI agent can reread before each new piece of work.
A short description of what you are building, who it is for, what is deliberately out of scope, and any important risks or constraints. It gives the agent a stable target instead of relying on memory from the chat.
The project plan broken into manageable units of work. It shows what is planned, what is in progress, what is done, and where the next conversation should resume.
The blueprint for the software: the major parts, how data moves, security expectations, deployment shape, testing approach, and other decisions future work must respect.
A short note explaining an important choice, the alternatives considered, and the tradeoffs accepted. It preserves the why, so the same decision does not get reopened by accident later.
Instructions for one small task at a time. Each prompt tells the agent what to read first, what to change, what not to touch, and how to verify the result.
A regular health check that compares the code back to the documents, reviews accepted risks, reconsiders skipped architecture sessions, and runs tests so drift does not silently pile up.
The docs hub records what the system is now; the prompts repo records how it got there.
See the artifact trailThe videos follow AlignedDating, a fictional dating app built with Throughstone. They work as a launch sequence or a start-here path for people evaluating the method.
A general overview of what Throughstone does and its benefits.
Watch video 1An overview of the first interactive session, describing your project.
Watch video 3Shows how the system guides you automatically so you can focus on your product, not the process.
Watch video 4An overview of how optional planning sessions work based on your needs.
Watch video 5The system glossary, what it is, and how it helps your project.
Watch video 8Shows what files get created by the architecture sessions.
Watch video 9Shows how the development work gets broken down into smaller feature sets.
Watch video 10For the full ordered list, including every AlignedDating session link, use the README video section.
The sessions are structured interviews. They adapt their explanation level to the user, recommend sensible defaults, and write architecture docs plus ADRs as decisions are made.
The scaffold includes runbooks and registers for the maintenance work that often gets skipped until production exposes it.
Every 10-20 STEPs, reconcile docs against code, re-check conditional sessions, review risks, and run the full test suite.
Prepare the rollback path before deploy, verify during a watch window, and make the return lever explicit.
Stabilize first, then run root-cause analysis, search for similar bugs, fix, harden, and write the postmortem.
Vet packages before adding them and audit installed dependencies for vulnerabilities, licenses, and provenance.
Rotate credentials deliberately and keep accepted risks or technical debt visible in a durable register.
You want to ship real software without inheriting a pile of generated code you don’t understand.
You want the project-shaping discipline that turns a prompt into an architecture and a plan.
Hand it to the juniors — and to everyone who keeps asking “how do I actually start building with AI?”
Throughstone is intentionally more structured than a blank chat. That is the point, and also the tradeoff.
Products, internal tools, serious open-source projects, and AI-assisted builds where architecture, scaling, tests, security, future changes, or team handoff matter.
Throwaway demos, quick scripts, school assignments, and prototypes you do not expect to maintain. For those, the process may cost more than it saves.
Throughstone reduces project risk, but it does not make AI agents reliable by default. Production software still needs experienced review, especially around security, data, payments, and compliance.
Run the wizard once, then hand the project to your AI agent in plain English. It drafts your architecture and then guides you through the code creation.