Learning Persian with Anki, ChatGPT, and Dual-Subtitle Loops
A practical, repeatable system that fuses Anki, ChatGPT, and dual-subtitle YouTube loops to progress toward real-time comprehension of Persian.
A practical, repeatable system that fuses Anki, ChatGPT, and dual-subtitle YouTube loops to progress toward real-time comprehension of Persian.

Engineer the agent’s context—cache, tools, memory, attention, and errors—and you’ll get faster, cheaper, more reliable agents than model power alone can deliver.

Make AI work in big, messy repos by compacting context and reviewing specs, not just code: research → plan → implement, with humans focused upstream.

AI is now standard in development, delivering productivity gains—but real success requires organizational change, not just tool adoption.

Faster LLMs will reshape coding workflows and productivity, but escalating demand, hardware limits, and pricing pressures mean a bumpy, fast-changing road ahead.
Today, AI amplifies senior engineers’ impact instead of democratizing coding for juniors.
A general-purpose AI coding agent can already do real Lean proof engineering with guidance, hinting that theorem proving may soon be cheap and automated despite today’s rough edges.

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.

A structured prompt rewrite turned vague policies into checklists, boosting GPT-5-mini’s telecom benchmark accuracy by 22% and unlocking previously unsolvable tasks.

Keep the agent simple: plan–execute–deterministically verify in a loop, with MCP tools, targeted memory, and a small policy engine.

ApeRAG is a production-grade, multimodal GraphRAG platform with AI agents and MCP, built for hybrid retrieval and scalable K8s deployment.

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.

Users adopt AI agents that are architected for trust—start simple, integrate thoughtfully, expose limits, and escalate gracefully.

Skip multi-agents for now: unify decisions in a single-threaded agent that shares full context, and use summarization to scale.

Treat the AI orchestrator as a secure, standardized virtual machine so models can safely and portably use tools and data under strict governance.