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Mar 13 •
👋 Welcome to AI Revenue Club
Really glad you're here. This community exists for one reason: to help builders like you create AI systems that actually make money. 👋 Introduce yourself below, tell us: ▸ Your name ▸ Where you're from ▸ The AI system you want to build 💬 One thing before you dive in, be honest — which one are you?
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Pinned
Dec '25 •
Why I'm Here
I’ve spent my life building software. I started coding at 12. Built startups for over 20 years. Sold one. Lost more money than most people will ever see. That experience taught me something most founders learn too late: Leverage through systems outlasts teams, hustle, and exits. Too many founders build for the shiny outcome. Big team. Big burn. Hope for a big payday. I’ve been there. I’ve had the exit. I’ve also watched it evaporate. Now I build differently. - Revenue over valuation - Systems over headcount - Freedom over optionality A business that generates revenue with minimal people and minimal ongoing effort isn’t a lifestyle business. It’s the smartest asset you can own. I don’t want to work for my business. I want my business to work for me. Currently doing all of this on the side of a full-time job, just like most of you. 🔧 WHAT I'VE BUILT - 20+ mobile apps, some serving tens of millions of users - Multiple SaaS products from idea to scale - Ad networks and location-based advertising platforms - Automated trading systems managing $100M+ running at NY4, the NYSE data center - AI-driven products and agent pipelines - Physical products with fully automated operations - A company I took public on three European stock exchanges, then sold the IP I’ve also led cloud architecture at a Fortune 200, where scale, reliability, and failure actually matter. Not demos. Not screenshots. Not hype. ⚡ WHY THIS COMMUNITY EXISTS Most AI and automation content looks good in demos, breaks in production, and never survives real usage. Here, we focus on building real products with AI, then replacing the roles around them with systems so they run without you. - Real builds - Real failures - Real trade-offs - Production-grade thinking No hype. No toy workflows. No “just prompt it” nonsense. If you want to build products that run without you and scale without hiring, you're in the right place. 🫡 USAF Veteran — Richard
17d •
Claude Code, Codex, OpenCode token visibility finally exists
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
24d •
Qwen 3.6 Dethrones Opus 4.7, GPT 5.5 and Gemini 3.1
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
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Apr 21 •
Qwen 3.6 vs Opus 4.7 - Test 1
Playing with Qwen 3.6 vs Opus 4.7, on an M1 Max with 64gb of ram: Qwen is hosted locally for FREE on my machine whereas Opus 4.7 is part of my paid Anthropic subscription. You tell me, is it worth it? This is a simple test, I'll be doing more complex tests in the days to come. Prompt: make a simple html based snake game inside a retro looking handheld device Qwen 3.6 - Time: 4min 24sec - Size: 20.25 kb - Lines of Code: 676 - Try it out here: https://assets.airevenueclub.com/playground/snake-qwen_3_6.html Opus 4.7 - Time: 1min 8sec - Size: 12.31 kb - Lines of Code: 511 - Try it out here: https://assets.airevenueclub.com/playground/snake-opus_4_7.html What do you think?
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