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| 🔗 Stories, Tutorials & Articles |
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| Towards self-driving codebases |
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OpenAI spun up a swarm of GPT-5.x agents - thousands of them. Over a week-long sprint, they cranked out runnable browser code and shipped it nonstop. The system hit 1,000 commits an hour across 10 million tool calls.
The architecture? A planner-worker stack. Hierarchical. Recursive. Lean on agent chatter. Heavy on self-steering behavior. |
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| Generative Pen-trained Transformer |
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Meet GPenT, an open-source, wall-mounted polargraph pen plotter with a flair for generative art. It blends custom hardware, Marlin firmware, a Flask web UI running on Raspberry Pi, and Gemini-generated drawing prompts.
The stack? Machina + LLM. Prompts go in, JSON drawing commands come out. That drives a real-world plotter that spits out SVGs and algorithmic patterns like it’s no big deal. |
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| Nathan Lambert: Open Models Will Never Catch Up |
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| Open models will be the engine for the next ten years of AI research, according to Nathan Lambert, a research scientist at AI2. He explains that while open models may not catch up with closed ones due to fewer resources, they are still crucial for innovation. Lambert emphasizes the importance of intentional investment in open models to maintain influence in the AI research field. |
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| Self-Optimizing Football Chatbot Guided by Domain Experts on |
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| Generic LLM judges and static prompts fail to capture domain-specific nuance in football defensive analysis. The architecture for self-optimizing agents built on Databricks Agent Framework allows developers to continuously improve AI quality using MLflow and expert feedback. The agent, such as a DC Assistant for American Football, can interact with users via Databricks Apps, creating a tool-calling agent for specific domain expertise. The build phase creates an initial prototype, while the optimize phase accelerates to production by continuously optimizing the agent based on feedback. |
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| My AI Adoption Journey |
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| A dev walks through the shift from chatbot coding to agent-based AI workflows, think agents that read files, run code, and double-check their work. Things only clicked once they built out custom tools and configs to help agents spot and fix their own screwups. That’s the real unlock. |
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