AI hackathons are short, brutal, and unforgiving. The winning teams at BYTEFORGE rarely have the most novel idea — they have the tightest execution loop. This guide is the shortlist we wish every first-time participant read the night before.
1. Build the right team
Three to four people is the sweet spot. Any smaller and you cannot parallelize; any larger and coordination overhead eats your sprint. A balanced roster looks like:
- One "AI plumber" — comfortable wiring up LLM APIs, embeddings, and prompt chains.
- One full-stack builder — owns the UI, auth, and deployment.
- One domain / pitch lead — writes the problem framing and rehearses the 3-minute demo.
- One utility player (optional) — handles data prep, design polish, or the on-stage demo.
2. Pre-install everything the night before
You lose the sprint if you are downloading Node at hour one. Before you walk into the venue, every teammate should have:
- Node.js 20+ and Bun (or npm/pnpm) working from the terminal.
- Python 3.11+ with
pipand a virtualenv tool. - Git, SSH keys added to GitHub, and a shared empty repo already created.
- Your IDE of choice with an AI autocomplete extension logged in.
- API keys for at least one LLM provider — free tiers are fine.
- A deployment target (Vercel, Cloudflare, Netlify) linked to the repo.
3. Is AI allowed in hackathons?
Yes. At BYTEFORGE 2026 — and virtually every modern AI-themed hackathon — LLMs, code assistants, and third-party AI APIs are not just allowed, they are the point. Judges score you on the finished product, the technical depth of what you assembled, and the clarity of your demo. Using an LLM to scaffold code, generate copy, or draft your pitch deck is expected. What is not allowed is submitting a project you built before the event or copying another team's repository — that is disqualification territory.
4. The permitted-tools shortlist
These are the categories worth having warm before the clock starts:
LLMs & chat APIs
OpenAI, Anthropic Claude, Google Gemini, Groq, Mistral, and open-source models via Ollama or Hugging Face Inference. Bring at least two so you can fail over.
Agent & orchestration
LangChain, LlamaIndex, Vercel AI SDK, and the OpenAI Agents SDK for wiring tool calls, memory, and multi-step reasoning without reinventing the loop.
Vector DBs & retrieval
Supabase pgvector, Pinecone, Chroma, Qdrant, or Weaviate. Pick one and stick with it — swapping mid-sprint costs an hour.
Spatial computing APIs
WebXR, Three.js, react-three-fiber, Google ARCore Depth API, and Meta's Spatial SDK for AR/VR and 3D experiences that judges remember.
Vision & multimodal
GPT-4o / Claude vision, MediaPipe, Roboflow, and Replicate for image, audio, and video pipelines without training your own model.
Ship & demo
Vercel or Cloudflare Pages for the app, tldraw or Excalidraw for architecture slides, Loom for the fallback demo video, and OBS if you plan to stream.
5. The 6-hour timebox
Steal this schedule. Adjust the ratios, not the discipline.
- 0:00 – 0:30 · Frame it. Read the problem statement, pick a narrow slice, write a one-sentence promise. Cut scope until it fits.
- 0:30 – 1:00 · Wire the skeleton. Empty deploy, empty database, empty LLM call — end-to-end, running on the internet.
- 1:00 – 4:00 · Build the core loop. Two people on the AI path, one on UI, one on data. No meetings, just shipping.
- 4:00 – 5:00 · Freeze features. Polish only. No new endpoints. Fix the top three bugs, write the empty state, style the hero.
- 5:00 – 5:30 · Rehearse the demo. Out loud. Twice. Time it.
- 5:30 – 6:00 · Record the fallback video. Wifi will fail on stage. Assume it.
6. What judges actually reward
Innovation, technical depth, impact, and presentation — each worth 25% at BYTEFORGE. Judges see forty demos in a row; they remember the ones that opened with the problem in a single sentence, showed the working product in the first minute, and ended with a specific "what's next." Skip the intro slides. Show the product.