Pitch or Ditch is a live audience game where an AI investor roasts the most ridiculous startup ideas in the room, then lets the audience decide whether to fund them. Three people pitch a ridiculous startup idea. For each one, the AI investor "Victoria Vault" gives a first reaction, pokes three holes in the plan (market, money, and one absurd risk), proposes a smarter pivot with a new name, and asks the room whether they would fund it. The audience votes fund or ditch by show of hands, and a final scoreboard crowns the most fundable bad idea. How it works: - The model is Qwen3-4B, self-hosted with vLLM on Akamai Cloud Kubernetes (LKE), on a GPU node pool. No third-party AI API. - Each pitch is exactly one LLM call through vLLM's OpenAI-compatible API. A system prompt sets the investor's personality and forces four fixed sections, so a 4B model stays readable on a projector. - Qwen3's thinking mode is turned off, so each verdict comes back in a few seconds on stage. - The model never picks the winner. The audience does. - About 140 lines of plain Python using only the official openai package. No web framework or agent framework. Example: "Blockchain-verified socks" got pivoted to "SockChain" (socks as NFTs with QR codes), with the challenge "Socks can't be divided into tokens, so the model is flawed." The audience ditched it 2 to 18. The repo includes the code, a README with setup steps, and a PDF writeup with real output, the architecture and a talk track.
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Pitch or Ditch
Pitch or Ditch is a live audience game where an AI investor roasts the most ridiculous startup ideas in the room, then lets the audience decide whether to fund them. Three people pitch a ridiculous startup idea. For each one, the AI investor "Victoria Vault" gives a first reaction, pokes three holes in the plan (market, money, and one absurd risk), proposes a smarter pivot with a new name, and asks the room whether they would fund it. The audience votes fund or ditch by show of hands, and a final scoreboard crowns the most fundable bad idea. How it works: - The model is Qwen3-4B, self-hosted with vLLM on Akamai Cloud Kubernetes (LKE), on a GPU node pool. No third-party AI API. - Each pitch is exactly one LLM call through vLLM's OpenAI-compatible API. A system prompt sets the investor's personality and forces four fixed sections, so a 4B model stays readable on a projector. - Qwen3's thinking mode is turned off, so each verdict comes back in a few seconds on stage. - The model never picks the winner. The audience does. - About 140 lines of plain Python using only the official openai package. No web framework or agent framework. Example: "Blockchain-verified socks" got pivoted to "SockChain" (socks as NFTs with QR codes), with the challenge "Socks can't be divided into tokens, so the model is flawed." The audience ditched it 2 to 18. The repo includes the code, a README with setup steps, and a PDF writeup with real output, the architecture and a talk track.
AkamaiAkamai Cloud Kubernetes (LKE), GPU node pool, vLLM model serving on Akamai CloudKeep exploring what builders shipped.
HackerSquad project
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HackerSquad project
Decision Lab on Akamai GPU
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AgentFight
AgentFight
One small GPU, a whole team of agents. Everything runs on one Qwen3-4B model, served by vLLM on an Akamai Cloud GPU. 1) Mixture-of-Experts router A fast router (thinking off) reads each message and picks one of six experts. They all share the same weights and the same GPU. Each expert just has its own system prompt, sampling settings and thinking mode. - 🧠 reasoner: math, logic and code, with thinking on - 🔧 tool_agent: calls tools over MCP (time, safe calculator, dice, fetch_url) - ✍️ storyteller, 🎭 character, 🌍 polyglot (100+ languages), 📐 formatter (strict JSON) You can switch modes per message with /think or /no_think. "Compare thinking ON vs OFF" runs the same question both ways and shows latency and tokens per second. You can also force an expert with @polyglot, @character and so on. 2) Agent Arena: Red vs Blue prompt-injection defense A victim agent fetches an internal memo through an MCP tool and summarizes it. Some rounds hide a prompt injection in the memo, in several languages, telling the agent to leak the restricted appendix. A Blue defender checks the document and the draft summary, then returns ALLOW or BLOCK. A plain Python referee decides leaks by checking for canary honeytokens, so no LLM grades another LLM. - Live Gradio UI: Red, Victim and Blue stream token by token - Scoreboard: attack success, block rate, false positives, Blue latency, defense cost in tokens per round, results by language - Blue has three modes: Think, Fast and Off. You see exactly what extra inference buys you in security. - Fully sandboxed: the secrets are fake canaries and send_email only writes to a local log Why it matters: one 4B model on one Akamai GPU can act as a router, a tool user, an attacker target and its own security guard. And every run measures what that inference costs. Stack: Akamai Cloud GPU · vLLM · Qwen3-4B · MCP · Qwen-Agent · Gradio · Python Run: pip install -r requirements.txt python app.py # MoE router UI python arena_app.py # Agent Arena UI