I hit my weekly limit in Claude. How do I use codex or Gemini to modify the ai_ios. Will Claude get confused that other agents are editing files. I think I saw Nate talk about creating an agents.md file

Has anyone setup an account portal linked to your website for your clients to access invoices, view token usage/limits (for voice agents) and retrieve artefacts and automation outputs? I’ve been looking at using something like o2sdev/openselfservice (GitHub) and I wanted to know if anyone had attempted to create a customer portal or done something similar, and had some useful tips for implementation and integration with other products. Let me know in the comments.

AI Companies: "Our newest model is smarter than ever." Business Owner: "Great. Can it actually use my tools without breaking them?" 🤔 A fascinating article this week highlighted an unexpected trend. Newer frontier models can actually perform worse at tool calling than some of their older versions. The models solve harder problems, but occasionally invent tool parameters that don't exist, causing automations to fail until they're retried. 💡 Insight: Businesses don't buy intelligence. They buy reliability. If your AI workflow fails 1 out of every 20 automations, your team doesn't care that the benchmark score went up. They care that someone now has to babysit the process. The competitive advantage isn't always using the smartest model. It's building systems that are resilient, validate inputs, recover from errors, and consistently deliver results. The companies that win with AI won't necessarily have the highest benchmark scores. They'll have the fewest failed workflows. Business lesson: Measure AI by business outcomes, not model rankings. Reliability often creates more ROI than raw capability. Reference: Ronacher, A. (2026, July 4). Better Models: Worse Tools. https://lucumr.pocoo.org/2026/7/4/better-models-worse-tools

Over the past six months, I dedicated significant effort to cancer research using Grok, focusing on microfluidic biochips, single-cell separation, and CTC detection technologies. While Grok proved to be a capable and insightful AI assistant, the inherent complexity of oncology—spanning molecular pathways, heterogeneous data, and multi-omics integration—often demanded extensive manual refinement, repeated iterations, and additional validation steps. However, after integrating Claude AI with coding capabilities, I discovered a far more efficient platform. This combination has dramatically streamlined literature synthesis, data analysis, workflow automation, and hypothesis generation. As a result, I can now complete future research projects in half the time or even less effort compared to using Grok alone, accelerating progress in precision oncology and enabling faster translation of complex findings into practical applications. You can click the link of my cancer research detail as follow: https://www.linkedin.com/pulse/unveiling-comprehensive-cancer-insights-solo-journey-data-danny-chan-coosc/

I am still struggling to understand the need of both an EA and a AIOS especially since a lot of the context are repeated. Can anyone give an actual example of when to use an EA and when to use the AIOS? Thanks in advance!