Yes, so Codex can actually run multiple MagicPath calls, or you can also ask Codex to spawn multiple agents if you want to do that more efficiently!
LLMS
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Complete Claude Cowork tutorial: set up agents and automation
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Tutorial completo de Claude Cowork (25 min)
— Nico (@nicos_ai) 16 mai 2026
• cómo configurar Cowork
• automatizar tareas y procesos
• convertir Claude en un asistente real para tu trabajo
Subtitulado al español.
Muy recomendado 👇 https://t.co/m7tAS5tPMYComplete Claude Cowork Tutorial (25 min) • how to set up Cowork
• automate tasks and processes
• turn Claude into a real assistant for your work Subtitled in Spanish. Highly recommended -
Technical workflow discussion using Cursor and Claude Code
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You can do that with Cursor or Codex, although their browser does not allow external website navigation, just localhost. So you can do exactly what I’ve shown here, but you have to have the Cursor or Claude Code app (terminal) in a separate window from having MagicPath open in
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Researchers convert autoregressive VLM into diffusion-based model
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What if you could get the smarts of an autoregressive AI model but with much faster generation? Researchers from Shanghai Academy of AI for Science and Fudan University present BARD. They convert a standard autoregressive VLM into a diffusion-based one using progressive block
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Malta offers free ChatGPT Plus to citizens after AI literacy course
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Malta just became the first country to offer ChatGPT Plus to every citizen – free for a year. The only requirement: complete an AI literacy course first. The course was built by the University of Malta, not by OpenAI. So it's not a vendor training citizens to use vendor
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Using Codex to Build a Mobile App for On-the-Go Work
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I only add complexity if I truly need it personally. For Agent-S, I needed to get more actual work done on the go, so I had Codex build me a mobile app. And because the platform is so simple, it was able to one-shot the app with almost no effort on my end. It all compounds 🙂
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AI Agent’s High Capability and Ease of Use for Various Tasks
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Basically, this approach means the base agent is insanely capable, and can do pretty much everything you ask it. I use it for almost everything now. I've literally spent less time building this than I used to spend debugging OpenClaw.
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Decoupling AI Models from Agentic Orchestration Layers
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Exactly. Skills as self-contained units is the key design choice. It means you can swap the model underneath without rewriting your capabilities. The harness becomes the durable layer, the model becomes interchangeable.
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Technical Discussion on Model Weighting and User Distribution
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Are you updating the weights for your experiences and focuses vs the normal distribution of all the users a model is being served to
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The Future of Continual Learning and Local AI
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Continual Learning has already been solved, it just requires the model weights to be running locally on your own hardware so big labs are avoiding the topic altogether Local / Opensource AI will win Inevitable