I discovered Julius.ai for data queries and AutoGen for multi-agent systems. I also explored high-performance LLM inference using vLLM and DeepSpeed, alongside architectural patterns and critiques of the Foundation Model Transparency Index.
I explored Grab’s map optimizations, Amex’s explainable credit models, and batch inferencing with vLLM. I also looked into Playwright for browser testing, Mixtral-8x7b-Instruct's performance, and Microsoft’s LIDA for LLM-powered data visualization.
I explored home networking and LLM infrastructure, discovering WiFi 6 beam-forming, Predibase's competitive pricing for fine-tuned models, RunPod's serverless vLLM endpoints for HuggingFace models, and Portkey's utility as an AI model router.
I explored recent AI updates including Microsoft's Everything of Thought, fine-tuning datasets without inputs, and Tamil-Llama. I also checked out Voyager's Minecraft agent, Langchain's evaluators, and the power of using Pydantic to unify code, data, and text.
I explored techniques for managing LLM coding agents, Anthropic's multi-agent architecture, and persona vectors. I also found handy tools like gitingest for repo ingestion, the O*NET database for job analysis, and modern browser APIs for file access.
I explored multi-agent architectures, refined my AI coding workflows using MCP and Cursor, and experimented with GPT 4.1 prompting. I also learned handy uv and jq tricks while investigating application-specific LLM evaluations.