Last week we watched a $6 AI avatar video, a six-day autonomous "Fable" run, and a Rust-based Adobe Fireworks clone — and somehow the biggest debate was whether one "monster" machine beats a cluster of smaller Proxmox nodes. (Patrick says: get a hybrid NAS and scale out.) If you missed it, you missed a lot — so let's make this week even better. 📞 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: Brandon's digging into "Potato"-style smaller PR techniques and a possible second Claude subscription, Paul's sharing his Blacksmith CI results after jumping ship from GitHub Actions, and Scott's still weighing whether Stack Sentinel becomes a SaaS (liability questions pending). If any of that sparks a question — or you've got your own — drop it below and we'll dig in together. 🔗 ZOOM LINK (save this) https://us06web.zoom.us/j/81995207847?pwd=Xe6u6LmIQOmCP5VTnOwWYjDBfZNKGB.1 📅 WHEN Tuesday November 3rd at 6PM ET Looking forward to seeing you on the call!

📝 SUMMARY This week's call packed an unusually wide range of member updates into one session. After some casual catch-up — including token-spend jokes and members comparing personal AI assistants like "Dot," "Hermes," and the newly-released "Duet" — Brandon Hancock led an extended infrastructure discussion on scaling parallel AI coding work-trees, covering compute, storage, and CI/CD cost bottlenecks (~$600/month in GitHub Actions) and trading ideas with Patrick Chouinard on NAS-based shared storage, multiple smaller Proxmox nodes, and hybrid NVMe-plus-spindle designs. The middle of the call turned into show-and-tell, with Patrick sharing an internal Claude "support skill" pipeline (97% opt-in in two weeks), a six-day autonomous "Fable" run, and a ~$6 AI avatar training video; Ty Wells, Paul Miller, Scott Rippey, and Juan Torres each demoed projects ranging from a Rust-based Adobe Fireworks clone to a "Stack Sentinel" monitoring app and a photo-booth AI image pipeline. Later segments covered Ryan C's rebuilt digital-signage business, Shakur Abdullah's CRM rebuild and FOIA/public-records workflow, a tip on reverse-engineering SaaS tools via GitHub, and a philosophical thread on AI-driven code review, before Brandon closed with an update that his company remains pre-revenue, awaiting acquisition or investment. 💡 KEY INSIGHTS Patrick Chouinard reported extreme token usage polarization among go-live users. He recommended separating storage on a shared NAS from compute on multiple smaller nodes rather than using one "monster" machine. Patrick built a FinOps dashboard spanning Claude, Codex, OpenAI API, Wavespeed, Tavily, 11Labs, FireCrawl, and GitHub. Patrick's agents use a base personality prompt nicknamed "Clara" and store API keys on a physical secret-manager server. Patrick uses Sonnet 5.5 for heavy transcript ingestion and GLM 5.3 Flash for cheap bulk chunk-processing. Brandon Hancock uses "Dot" as a co-pilot for managing GitHub issues, travel booking, and cancelling unused subscriptions.

🧠 THIS IS A BRANDON WEEK!!! 🧠 Last week, we debated whether self-hosted inference could ever beat a commercial API, and the consensus was surprisingly clear! If you missed the deep dive into VLLM vs. GPT-6, you missed some serious insights on where the real value lies in AI deployment. 📞 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. We still have some fascinating threads open, from Andrew's forensic psychiatry AI work to the practical application of decision models. Daniel is also preparing to share his Canadian AI governance guide, and Patrick is continuing to document his T3/Hermes orchestration architecture. Come discuss these next steps! 🔗 ZOOM LINK (save this) https://us06web.zoom.us/j/81995207847?pwd=Xe6u6LmIQOmCP5VTnOwWYjDBfZNKGB.1 📅 WHEN Tuesday October 27th at 6PM ET Looking forward to seeing you on the call!

📝 SUMMARY This week's call packed a lot in: it opened with casual hardware talk that turned into a bigger observation about AI widening the gap between tech workers who adapt and those who don't, then moved through a wide range of member updates — Patrick Chouinard's company-wide Claude rollout and personal tooling projects, Juan Torres's photo booth business, Andrew Nanton's document-review and decision-model work, Paul Miller's productivity gains from pruning Claude skills, Ryan C's custom Android remote-management software, and Daniel Zivkovic's pivot toward AI governance work. The second half went deeper into technical and business debates, including whether self-hosted inference (VLLM/TensorRT LLM) can beat commercial APIs (a discussion where attendees largely converged on APIs rarely being beaten without a governance or scale justification), Canadian AI governance trends, advice on entering financial-industry contracting, project demos from Shakur Abdullah, a comparison of a code security/review tool against Greptile and CodeRabbit, and a closing segment on AI video-generation tooling for corporate training. Whether you're interested in enterprise AI adoption, infrastructure choices, governance, or hands-on tooling, there was something here — and the recording captures it all. 💡 KEY INSIGHTS Patrick Chouinard: people aren't replaced by AI directly, but by colleagues who adapted faster — those not keeping up are effectively "signing their name on the list" for future layoffs. Patrick Chouinard: just keeping up with the pace of AI field evolution is now filling people's schedules. Paul Miller suggested the surge of interest in his client's AI training courses may be partly driven by fear of job irrelevance rather than genuine learning interest; Patrick Chouinard agreed, noting a split between people building capability and people attending out of fear. Patrick Chouinard: data providers (e.g., Bloomberg) weren't ready for the AI "tidal wave" — restrictive data-usage contracts must now be rethought for MCP-based direct AI access.

AI Developer Accelerator — Coaching Call - October 20 VIEW RECORDING - 99 mins (No highlights) Meeting Purpose A weekly sync on AI projects, challenges, and enterprise adoption. Key Takeaways - Enterprise AI Adoption Surges: A major company's Claude rollout saw 2,000 licenses requested in a 4-day week, with training booked solid for four weeks. The primary driver is user fear of job irrelevance, making integration and governance the top enterprise priorities. - T3 Code Beta is a Game-Changer: The T3 Code beta's new inter-server/inter-thread orchestration and Orchestrator 2.0 are enabling true agent collaboration and resource pooling, significantly boosting productivity. - Build vs. Buy for Inference: For client work, benchmark quality with top models (e.g., GPT-4o) via OpenRouter. Use a router (e.g., JEV) to optimize cost by triaging tasks to cheaper models, avoiding the complexity and poor ROI of building a custom inference engine. - Automated Code Review is Essential: Tools like Tidecode provide continuous, multi-model security and quality checks on every push, catching hundreds of issues that manual reviews miss and saving significant time and cost. Topics Enterprise AI Adoption & Challenges - A company's Claude rollout saw unprecedented demand, with 2,000 licenses requested in a 4-day week. - Training Demand: In-person sessions (75 people/day) are booked solid for four weeks, driven by user fear of job irrelevance. - Primary Challenge → Integration & Governance: