📝 SUMMARY This was a long, wide-ranging call that opened with OpenAI Dev Day excitement (GPT-6.1 Sol, the "Dots" ambient agent, GPT Space, a Decision API, and a $500/month Cerebras tier) and closed hours later with casual banter. In between, Brandon Hancock demoed his agentic software-development workflow and shared a startup update, while other members — Daniel Zivkovic, Shakur Abdullah, Andrew Nanton, Tom Welsh, and Patrick Chouinard — showed parallel workflows and tools (Fable, Herder, T3 Code, CMUX) and business updates, including Tom's Asset MS product sold to farmers and Ty Wells' rapidly scaling ERP business. A major throughline was the OpenAI-vs-Anthropic competitive debate, with Patrick predicting Anthropic's response to Dev Day and the group debating whether Anthropic's enterprise-first posture will cost it long-term. Scott Rippey demoed CC Blackbox and Tight Code plus his AI video pipeline, drawing go-to-market advice from Brandon. The back half shifted into hands-on coaching: Patrick's home-lab "Agent Operated Environment," Brandon's cold-outreach and data-pipeline tactics, and heavy group coaching for Juan Torres on his AI photo booth business — pricing, seasonal launches, and sales targeting. Paul Miller added SaaS-founder perspective on VC dynamics and rapid prototyping, and Brandon closed with candid reflections on financial strain and gratitude for the group. 💡 KEY INSIGHTS Model releases and strategy OpenAI Dev Day shipped GPT-6.1 Sol (same price as 5.6 Sol, ~4x cheaper than Astra/Fable-class), "Dots" (a proactive ambient agent across chat platforms and phone, paired with a cheap/fast Decision API), GPT Space (a ChatGPT-native Microsoft 365 competitor), and a $500/month ultra-fast Cerebras tier at ~3,000 tokens/sec. All major labs are compressing frontier intelligence into cheaper/faster models rather than shipping new capability jumps. Sonnet is positioned as the workhorse for tool/computer use and autonomous agents, while Opus/Fable remain the "brain."

AI Developer Accelerator — Coaching Call - September 29 VIEW RECORDING - 198 mins (No highlights) Meeting Purpose To share project updates, discuss AI developments, and strategize on business growth. Key Takeaways - OpenAI's Dev Day was a major strategic move. Key announcements included GPT-6.1 Sol (Astra-level performance at a fraction of the cost), DOTS (a proactive ambient agent), and GPT Space (a Microsoft 365 competitor). - AI-driven development is reaching new levels of autonomy. Brandon's "Deep Plan" system automates the entire SDLC, while Scott’s CC Blackbox uses multiple AI engines for adversarial code review on every Git push. - Rapid customer acquisition is now a reality. Ty Wells onboarded 6 ERP clients in two weeks, and Tom Welsh sold his Asset MS to two farms for £5,000 each, demonstrating the power of AI-enabled business models. - Business strategy for new products must prioritize market feedback over perfection. For Juan's AI photobooth, the advice was to lower the price to secure 10–20 paying clients for social proof, then scale pricing based on demand. Topics AI Market & Dev Day Analysis - OpenAI's Dev Day announcements are a direct challenge to Anthropic's recent Opus 5.5 release. - Key OpenAI Announcements: - GPT-6.1 Sol: Astra-level performance at a fraction of the cost, making it the new default for most advanced tasks. - DOTS: A proactive ambient agent for Slack, phone, and other clients, with unlimited usage. - Decision API: A new tool for enabling proactive agent behavior by detecting state changes. - GPT Space: A direct competitor to Microsoft 365, offering AI-driven word processing, spreadsheets, and presentations. - New Subscription Tiers: A $500/month plan offers "ultra-fast" mode (3,000 tokens/sec) on Cerberus GPUs. - Subscription Sharing: Users can now share their subscription with vendors, simplifying billing and legal compliance for AI-powered apps.

🦸🏻THIS IS A BRANDON WEEK, Come say hi!!!🦸🏻 Two fresh model drops, a token-budget experiment that built a whole site in ~3,000 tokens, and Patrick's Codex plan somehow dead by noon — last week was a lot. If you missed it, the "tokens per task completed" metric alone is worth the catch-up. 📞 HOW THE CALLS WORK The calls can run 2+ hours. We want to make sure we're respecting everyone's time. Especially those of you who actually show up. Here's the structure: 👉 Reply to this post with your questions before the call 👉 If you submit a question and you're on the call, you go first 👉 We work through questions in the order they came in 👉 Then we open it up for everyone else If you can't make the call but want your question answered, drop it in the comments. We'll get to it. But priority goes to people who are there. The goal is simple: if you're taking the time to show up, you shouldn't have to wait behind questions from people who aren't even on the call. Plenty of threads still open from last week: Ty's head-to-head test of T3 Code vs. his CMUX setup, Daniel's weekend experiment with Honcho.ai, and Patrick's deep dive into JEV as a decision-making router. If you've been trying any of these yourself — or want to know how they turned out — bring your questions. 🔗 ZOOM LINK (save this) https://us06web.zoom.us/j/81995207847?pwd=Xe6u6LmIQOmCP5VTnOwWYjDBfZNKGB.1 📅 WHEN Tuesday September 29th at 6PM ET Looking forward to seeing you on the call!

📝 SUMMARY This call delivered a full sweep of what our community does best: making sense of a fast-moving AI landscape and turning it into practical, working setups. With two major model releases dropping right before the sessions, the group compared early impressions and dug into what actually matters when evaluating models — efficiency per task, not just price. From there, members shared real projects in progress, from an open-source CRM and a resort venture to RAG apps and AI-native development thinking, followed by a deep dive into tooling architecture like meta-harnesses, personal memory layers, and multi-profile agent setups. The closing stretch covered hands-on workflow and security topics, including token budget management, voice-driven capture pipelines, and an important caution about chat-sharing privacy risks. Whether you missed it live or want a refresher, the takeaways below capture the ideas, tools, and lessons worth stealing for your own work. 💡 KEY INSIGHTS GPT-6 Sol is the price/quality workhorse — near Fable Low and Astra Low quality at half the price of prior 5.6 Sol. GPT-6 Luna is the smallest, cheapest model — about a quarter the price of Opus/Sol Low — ideal for high-volume tool-calling and operator tasks. Opus 5.5 is best for open-ended, loosely structured dev work; Sol/Luna win on cheaper, well-defined tasks. Ty Wells calls it "a leap, not a jump" over Opus 5. Opus 5.5 followed strict token-budget instructions well (built a site in ~3,000 tokens, though quality was poor); GPT-6 Sol ignored token limits and underperformed. The team now tracks "tokens per task completed" instead of raw token cost. Opus 5.5 inherited Fable 5's safety guardrails — sensitive security/biochem questions trigger a downgrade to Opus 4.8, with locked tracing logs. Anthropic is adding token refresh options on subscription plans, following OpenAI's lead. Claude "Projects" now works as a proper orchestrator for managing customer and project context. Enterprises running agentic apps on Bedrock face serious token budget issues — some burn millions monthly since cheaper models fail compliance needs, and enterprise billing is straight metered with no subscription flexibility.

AI Developer Accelerator — Coaching Call - September 22 VIEW RECORDING - 111 mins (No highlights) Meeting Purpose Review new AI models, developer tools, and ongoing projects. Key Takeaways - New Models Released: Opus 5.5 (strong coding), GPT-6 Sol (half-price workhorse), and GPT-6 Luna (fast, cheap tool-use). - T3 Code Adopted: The meta-harness for orchestrating multiple agents (Claude Code, Codex) is gaining adoption for its server-based isolation and mobile client. - Shared Memory Solved: Honcho.ai provides a cheap, LLM-curated vector store for persistent, cross-harness memory, addressing a key developer pain point. - Jev for Routing: Jev is emerging as a fast, cheap alternative to LLMs for model routing and real-time decision-making, enabling significant cost savings. Topics New Model Releases & Performance - Opus 5.5: A significant leap over Opus 5, now scoring higher than Fable. - Constraint Following: Shakur tested it by asking it to build a website and report token usage, which it did (3,000 tokens). - Limitation: Inherits Fable's safety guardrails, downgrading to Opus 4.8 for sensitive topics.