Taste Came First: Build It, Then Use AI
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Taste isn’t an AI-era novelty—it’s the timeless discipline of judgment; those who already had it are the ones winning with AI.
Prompted to Perform: A 22% Lift for GPT-5-mini on Tau² Telecom
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A structured prompt rewrite turned vague policies into checklists, boosting GPT-5-mini’s telecom benchmark accuracy by 22% and unlocking previously unsolvable tasks.
Deep Orchestrator: A Simple MCP Loop That Makes Deep Research Work
Keep the agent simple: plan–execute–deterministically verify in a loop, with MCP tools, targeted memory, and a small policy engine.
ApeRAG: Production-Ready Multimodal GraphRAG with Agents and MCP
ApeRAG is a production-grade, multimodal GraphRAG platform with AI agents and MCP, built for hybrid retrieval and scalable K8s deployment.
GPT-5 Thinking Makes ChatGPT a Surprisingly Competent Research Assistant
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GPT-5 Thinking turns ChatGPT into a competent, mobile-friendly research agent that interleaves reasoning with web search and tools to deliver verifiable, deep results—provided you guide and sanity-check it.
Trust-First Architecture Beats Smarts for AI Agents
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Users adopt AI agents that are architected for trust—start simple, integrate thoughtfully, expose limits, and escalate gracefully.
Stop Building Multi-Agents: Context Engineering for Reliable LLM Agents
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Skip multi-agents for now: unify decisions in a single-threaded agent that shares full context, and use summarization to scale.
Standardizing the AI Orchestrator as a Model Virtual Machine
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Treat the AI orchestrator as a secure, standardized virtual machine so models can safely and portably use tools and data under strict governance.