README.md

metadata

title: DIY Creator
emoji: 🛠️
colorFrom: gray
colorTo: red
sdk: docker
app_port: 7860
pinned: false

DIY Creator 🛠️ — a multi-agent team you can watch work

Name something you want to build. A team of three agents hands off to each other to produce a vetted, step-by-step build guide — and you watch them do it in real time.

Project #17 of the "30 AI Projects in 15 Days" challenge. Focus: multi-agent orchestration.

Above (local model): 🔍 Researcher gathers the brief → ✍️ Writer drafts a guide → 🧐 Critic scores it 5/10 and sends it back with concrete fixes → Writer revises → the guide ships (honestly noting any minor items that remain). On a stronger backend it converges to a clean pass.

The team

Agent	Role
🔍 Researcher	Gathers a practical brief: materials (with quantities), tools, techniques, safety hazards, common mistakes, and honest difficulty/time/cost.
✍️ Writer	Turns the brief into a clear, start-to-finish guide a beginner could actually follow.
🧐 Critic	An independent reviewer — scores the draft, flags issues by severity, and sends it back. The writer revises; the critic re-checks. The loop runs until the guide passes (or hits the round cap).

The lesson: roles, handoffs, and an honest gate

A multi-agent "team" is three things done well:

1. Role-specialized agents — each one has a single job and a prompt tuned for it.
2. Explicit handoffs — the researcher's brief feeds the writer; the writer's draft feeds the critic.
3. A critique → revise loop — nobody ships their own first draft.

Two design choices worth calling out:

• The critic is independent (the "diamond"). It reviews the writer's work, ideally as a different model, so the review isn't the author grading itself. Set DIY_CRITIC_MODEL to a second local model for a sharper, more independent review.
• The gate is honest-by-construction. The critic finds issues (what LLMs are good at), but the pass/revise decision is made in code from issue severity — a guide passes only when no high- or medium-severity issues remain. The model can't rubber-stamp its own work.

Run it

pip install -r requirements.txt
python app.py            # http://127.0.0.1:7860
# Local Ollama by default (free/private). For the cloud model instead:
#   DIY_LLM_BACKEND=claude ANTHROPIC_API_KEY=sk-... python app.py

Config (env): DIY_LLM_BACKEND (ollama|claude) · DIY_OLLAMA_URL · DIY_OLLAMA_MODEL (default qwen2.5:7b) · DIY_CRITIC_MODEL (optional independent critic) · ANTHROPIC_API_KEY / ANTHROPIC_MODEL for the Claude backend.

Note on models: a small local model self-critiquing is genuinely noisy — it will sometimes ship "after review, minor notes remain" rather than a clean pass. That's honest. A stronger model (or a DIY_CRITIC_MODEL diamond) converges to a clean pass more often.

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Built by Robert Lucyk · GritAI Solutions · part of the 30-in-15 challenge.