I kept burning through Claude Code tokens with no idea where they were going. No breakdown. No visibility. Just usage disappearing. So I built Token Bleed. Open source. Runs locally. Zero setup. One command and you get: - Per-project, per-session, per-prompt cost breakdown - Cache hit rate tracking (this is the biggest lever most builders are ignoring) - Model distribution -- see if you're running Opus on work that doesn't need it - Session compare -- diff two sessions side by side to find the cheaper approach - Log retention fix -- Claude Code deletes your logs after 30 days by default, this stops that The code is public on GitHub (MIT) and runs with a simple npx command. Watch the build, grab the command and run it against your own logs. Code - https://TokenBleed.dev or, just run it from the terminal - npx token-bleed

Everyone has a take on which AI model is best. Almost nobody runs the test. I put five models through the same coding task: GPT-5.5, Opus 4.7, Gemini 3.1, and two Qwen 3.6 models running locally for free via Ollama. Same prompt. One shot each. Final rankings: 1. Qwen 3.6 27B dense - 8/10 - 794 lines - $0 (49 min on M1 Max) 2. Claude Opus 4.7 - 7/10 - 613 lines - $1.65 (4.5 min) 3. Gemini 3.1 Pro - 585 lines - $0.26 (~5 min) 4. GPT-5.5 - 6/10 - 750 lines - $0.37 (~7 min) 5. Qwen 3.6 35B A3B MoE - last - 855 lines - $0 (~6 min) The 20%: bigger parameters did not mean better output. The MoE model was faster but inconsistent. The dense model was slower but delivered. That tradeoff is worth understanding before you pick a model for a real build. Full video breakdown is on YouTube. Play every build: GPT-5.5 (medium): https://assets.airevenueclub.com/playground/video-2/gpt-medium-snake.html GPT-5.5 (xhigh): https://assets.airevenueclub.com/playground/video-2/gpt-xhigh-snake.html Claude Opus 4.7: https://assets.airevenueclub.com/playground/video-2/claude-snake.html Gemini 3.1 Pro: https://assets.airevenueclub.com/playground/video-2/gemini-snake.html Qwen3.6-27B: https://assets.airevenueclub.com/playground/video-2/qwen_27b_snake.html Qwen3.6-35B-A3B: https://assets.airevenueclub.com/playground/video-2/qwen_35b_snake.html

You're getting bad answers because you're only asking one model. I built a system in n8n that sends the same prompt to Claude, ChatGPT, Gemini, and Grok. They all compete. Then a fifth model reads every response and tells you what to actually do. The part that surprised me: the models disagree more than you'd think. Especially on strategy and positioning questions. Having four competing opinions forced into a synthesis is genuinely better than trusting any single one. What's in the free guide: - Full step-by-step build (7 steps, every node and setting) - Architecture table showing how the pieces connect - Resource list with every tool and URL - Credentials checklist so you don't miss any API keys The full system, the n8n workflow exports, the MCP server config, and the deployment guide are all inside Pro. AIRevenueClub.com/Pro