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| 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 | |
| ```bash | |
| 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. | |
| --- | |
| Built by **Robert Lucyk** · [GritAI Solutions](https://gritai.solutions) · part of the 30-in-15 challenge. | |