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RecapFlow : October 13th Coaching call analysis
πŸ“ SUMMARY This week's call was a wide-ranging mix of infrastructure talk, business updates, and honest discussion about what it actually takes to scale AI usage. Members swapped notes on managing multiple AI subscriptions and their costs, Patrick Chouinard walked through leading a large enterprise Claude rollout at a Canadian financial institution (including the token-economy culture shift and employee confusion between models and "harnesses"), and Ty Wells shared progress on his Island Flow ERP/accounting product for Bahamas businesses plus a demo of a redesigned T3 interface with draggable widgets. Juan Torres reported on deploying his AI photo booth at a high school homecoming β€” Wi-Fi interference near a military base included β€” and the feedback that he needs a proper landing page to attract institutional clients. The group also dug into technical territory: Patrick's NTFY-based secure push-notification setup tied to Tailscale, his "AOE" (Agent Operated Environment) project, Ty's multi-machine Proxmox setup, and ChatGPT's "Dots" feature. A recurring theme throughout: the gap between AI power users running multi-agent setups and "normies" just getting started, and how to navigate the cost, confusion, and adoption challenges in between. πŸ’‘ KEY INSIGHTS Ty Wells (business owner) frames his ~$2,000/month in AI subscriptions as cheaper than hiring "a senior software architect, engineer, QA tester," justifying heavy usage. Brandon Hancock noted that after Codex's usage limits were cut in half, the subscription "isn't that good anymore," and he has been timing resets to maximize usage. Patrick Chouinard described enterprise Claude rollout dynamics: personal AI subscriptions are blocked entirely at his financial-institution client due to data privacy/audit requirements β€” all usage must go through the company tenant. Patrick observed a cultural shift at that client: employees used to negotiate over return-to-office days; now the key negotiating point is "which model can we access," with people requesting more tokens instead of overtime or pay.
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AI Developer Accelerator β€” Coaching Call - October 13th
AI Developer Accelerator β€” Coaching Call - October 13 VIEW RECORDING - 91 mins (No highlights) Meeting Purpose Share AI development progress, challenges, and enterprise adoption insights. Key Takeaways - Island Flow's Success Drives Automation: Island Flow hit 20/25 customers in its first month, forcing a shift from white-glove service to AI-driven self-onboarding to manage data migration at scale. - Enterprise AI Adoption Creates New Challenges: A major financial firm's Claude launch saw >50% of staff request licenses on day one. The main challenge is now educating users on the critical differences between models (e.g., Claude Opus) and their various harnesses (e.g., Copilot). - AI-Powered Infrastructure Management: Patrick built an Agent Operated Environment (AOE) project, a central AI context repo that enables T3 Code to manage the entire infrastructure (servers, projects, deployments) from a single thread. - AI Photo Booth Proves Market Fit: Juan's AI photo booth was a success at a high school event, generating new leads despite a major connectivity issue. The next step is building a professional website to attract corporate clients. Topics Enterprise AI Adoption: Claude Launch - A major financial firm's Claude launch was a massive success, with >50% of staff requesting licenses on day one. - Budgeting: A pure "token economy" is in effect.
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πŸš€ Welcome to AI Developer Accelerator (Start Here)
πŸ‘‹ 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. πŸ›  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. πŸ“œ 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. πŸ₯‡ 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. πŸ‘©β€πŸ’»πŸ‘¨β€πŸ’» Let's code, innovate, and thrive together!
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Exploring feature screening under multicollinearity β€” built a Python library for it
Hey everyone! πŸ‘‹ I wanted to share something I've been working on recently. I've been exploring feature selection in high-dimensional datasets, particularly a problem where a feature can be genuinely important to the target but still have little or even zero marginal correlation with it. This becomes especially interesting when predictors are highly correlated. While exploring this, I came across a research paper by Tanaka and Matsui (2023) on Truncated Preconditioned Profiled Independence Screening (TPPIS). I decided to implement the method as an open-source Python library so it would be easier to experiment with and use in actual ML workflows. The package includes TPPIS along with baseline methods such as SIS, FPSIS, FPSIS-BIC, and PPIS, with scikit-learn compatibility. One interesting result from reproducing the paper's first simulation example was that TPPIS recovered the hidden predictor in all 50 runs, while SIS failed to recover it in every run under that specific setup. Obviously, that's just one simulation setting, and I'd like to see how the implementation behaves on other datasets too. If you're interested in feature selection, ML research, statistics, or working with high-dimensional data, I'd genuinely love to hear your thoughts. Here are the links if you'd like to explore it: πŸ“„ Original paper: https://arxiv.org/abs/2306.05702 πŸ“¦ GitHub: https://github.com/Mohammed-Aswath/tppis πŸ“š Documentation: https://mohammed-aswath.github.io/tppis/ 🐍 PyPI: https://pypi.org/project/tppis/ I've also shared a post about the project on LinkedIn, with a visual explanation of the problem and the simulation results. Here is the linked post link : https://lnkd.in/p/drWPGSpP I'm sharing this here because I'd like to learn from people who might approach the problem differently, try it on their own datasets, or spot limitations I've missed. Feedback and constructive criticism are very welcome!
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Exploring feature screening under multicollinearity β€” built a Python library for it
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