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AI Developer Accelerator — Coaching Call - June 16th
AI Developer Accelerator — Coaching Call - June 16 VIEW RECORDING - 89 mins (No highlights) Meeting Purpose Share project updates and discuss strategies for AI development. Key Takeaways - Fable's absence is a setback, but developers are adapting with multi-model workflows (e.g., Claude for planning, Codex for coding) and focusing on deterministic scaffolding to maintain control. - New projects include an AI-native CRM for UK estate agents (Ryan), a positive-only "KindMark" platform for service workers (Ty), and an AI photo booth (Juan). - Patrick developed "AgentOps," a meta-scaffolding for Hermes using NATS as a "nervous system" to monitor and manage his home lab infrastructure. - A key strategy is using an "adversarial" system prompt to force the AI to challenge assumptions and clarify the core business problem, preventing it from amplifying broken processes. Topics Fable's Absence & Mitigation Strategies - Fable's shutdown is a significant setback, but developers are adapting with multi-model workflows. - Ryan: Uses Opus for iteration on a Fable-generated V1 plan. - Ty: Used Fable for initial problem-solving but found it unusable for security auditing, a critical step. - Paul: Combines Opus for projects with Codex for validating requirements documents. - Patrick: Prefers a collaborative model that challenges assumptions over one that simply executes instructions.
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RecapFlow : June 16th Coaching call analysis
📝 SUMMARY This week's call opened with the community offering condolences to Patrick Chouinard and Paul Miller, who both recently experienced family losses. Patrick shared how Claude helped him compress two months of estate administration into 48 hours during his bereavement. The technical discussion centered on coping strategies following the sudden unavailability of Anthropic's Fable model, with members sharing alternative workflows combining Claude Opus 4.8, Codex GPT-5.5, and the emerging Fusion architecture. A strong consensus emerged around the danger of automating broken business processes, alongside practical demonstrations of adversarial prompting and agent scaffolding strategies. 💡 KEY INSIGHTS Estate administration acceleration: Claude processed funeral home paperwork and proactively searched government websites for required forms and benefits, reducing administrative burden from months to hours while demonstrating unexpected emotional sensitivity by pacing tasks and flagging only time-sensitive items. Deterministic over autonomous: Keep systems as deterministic as possible, using AI decision-making only where necessary. The value in coming years lies in scaffolding and infrastructure rather than end-to-end autonomy. Model specialization: For terminal, infrastructure, and script work, GPT-5.5 (Codex) currently outperforms Claude Opus 4.8, while Opus remains superior for UI-backed application development. Adversarial prompting: Patrick's system prompt configures Claude as a challenging business analyst that asks "what problem are you actually trying to solve?" rather than accepting stated solutions at face value. Placed in Claude.md at the user level, it applies to every session including Claude Code. Process integrity warning: AI amplifies broken business processes rather than fixing them, making dysfunction bigger and more visible. Intention is a muscle that atrophies when over-relying on highly autonomous models. Intent queue workflow: Ty's method uses Claude's background commands to capture context-rich questions, storing them locally to surface as a primed queue at the next session, saving token spend on re-priming.
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🚀 Welcome to AI Developer Accelerator (Start Here)
👋 Hey there! Ready to supercharge your dev skills with AI? You're in the right place. Watch the intro video below for a walkthrough of our community and a peek at our AI-enhanced future. 🛠 CLASSROOMS: - Full Stack Development with AI: Where code meets cognition. Sharpen your skills and build AI-- powered apps. - CrewAI: Your squad for all things AI. Share ideas, collaborate on projects, and celebrate successes together. - Code Bugs & Project Issues: Debug like a pro. Get help on tricky bugs and offer your wisdom to others. - Monetize Your AI Dev Skills: From AI code to income: Collaborate, innovate, and monetize your dev skills! - YouTube Tutorial Requests: Please let me know what you want to learn more about when it comes to Fullstack Developement and AI. 📜 RULES: - Promotions are a no-no. Let's keep the focus on learning and growing. - We appreciate quality contributions. Enhance your posts with visuals and use ChatGPT for refining your content. - No talking about politics or religion. Go to X if you want to talk about that. - See something off? Help us maintain the community spirit by reporting any issues to me. 🥇 FIRST STEP: Introduce yourself with a post about your AI journey and what you're working on in the General Discussion group. **Bonus points for sharing a screenshot of your current app!** 🎯 ACTIONS: Be proactive, engage in discussions, and collaborate on group projects. 👩‍💻👨‍💻 Let's code, innovate, and thrive together!
Cheapest Google Search API I could find?
Has anyone found a cheaper Google Search API for AI agents? I’m trying to give an agent live Google results without messing with Playwright, proxies, captchas, etc. Mostly just need clean JSON back so the agent can pull title/url/snippet into context and move on. I looked at a few options and the cheapest I’ve found so far is SerpBase: https://serpbase.dev Seems pay as you go, which is better for side projects than getting locked into a monthly plan. Before I wire it into the agent, curious if anyone here knows an even cheaper option?
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I SOLVED Claude Code forgetting what it was building
Deep in a session, right when I understood the work best, I wrote down what to build next as a short structured capsule. Then I cleared the context. A fresh session with none of that understanding read the capsule cold and built it to spec. It did not know it was finishing its own work. Handoff notes fail two ways: they ROT (you forget) or they CANNOT TRAVEL (a fresh session can't read your mind). And terse notes silently drop the two things that matter most: WHERE the work goes and what DONE looks like. I measured it. Labeled-field capsules scored 10/10 on intent fidelity vs terse prose at 9.67, and prose's weakest spots were exactly WHERE and ACCEPTANCE. The honest part: the model still ran ~31% of malformed input instead of refusing it, so that check is deterministic code, not a model call. It understands the capsule. It does not get to decide the capsule is safe. Free CLI and Claude Code plugin, MIT. Try authoring one capsule next time you are deep in something, then clear and let a fresh session build it. github.com/gtsbahamas/intent-capsule
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AI Developer Accelerator
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Master AI & software development to build apps and unlock new income streams. Transform ideas into profits. 💡➕🤖➕👨‍💻🟰💰
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