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Learning AI

Browse 31 articles in learning ai.

Learning AI Articles

31 articles · Page 1 of 3

Person connecting protocol cables between two glowing devices with diagrams on a whiteboard

Learning AI·22 min read

Build the MCP + A2A agent protocol stack from scratch

Wire an MCP server to an A2A agent that delegates tasks and calls tools. TypeScript and Python examples, Streamable HTTP transport, Agent Cards, and auth.

Person sorting through stacks of documents, crossing out wrong ones, with a magnifying glass on the desk

Learning AI·22 min read

Agentic RAG: from dumb retrieval to self-correcting agents

Your RAG pipeline retrieves wrong documents and nobody catches it. Build a self-correcting agent that grades results, rewrites queries, and knows when to stop.

Watercolor illustration of a colorful workspace with a main monitor surrounded by floating screens showing different code, warm plum tones

Learning AI·18 min read read

Claude Code subagents and the orchestrator pattern

How to structure Claude Code subagents, write dispatch prompts, and coordinate parallel work across services, SDKs, and frontends in a monorepo.

Person drawing a web of connected nodes on a glass wall with colorful sticky notes around the edges

Learning AI·22 min read read

Graph memory for AI agents: when vector search isn't enough

Build graph memory for AI agents in TypeScript and Python. Extract entities, track relationships over time, and compare Mem0, Zep, and Letta in production.

Person wearing a headset at a desk with sound waveforms visible on screen, golden amber atmosphere

Learning AI·22 min read

Voice AI pipeline: STT, LLM, TTS and the 300ms budget

Build a real-time voice pipeline with Pipecat. How STT, LLM, and TTS stream concurrently under a 300ms latency budget, with turn detection and interruptions.

Engineering team reviewing real-time AI agent monitoring dashboards with metrics and conversation traces

Learning AI·22 min read read

Build an AI Agent Observability Pipeline from Scratch

Build a production observability pipeline for AI agents using TypeScript and the Chanl SDK. Covers metrics, traces, quality scoring, drift detection, and alerting.

Visualization of an AI agent context window filling up with system prompts, tool definitions, and conversation history

Learning AI·20 min read read

Your AI Agent's Context Window Is Already Half Full

System prompts, tool schemas, MCP descriptions, memory injection, conversation history. They all eat tokens before the user says a word. Learn where your context budget goes and how to manage it.

Illustration of a quality monitoring dashboard showing score trends and alert thresholds across production AI agent conversations

Learning AI·20 min read

Production Agent Evals: Catch Score Drift, Ship Confidently

Your evals pass in staging but miss production failures. Build three eval pipelines with the Chanl SDK: automated scorecards, scenario regression, and drift detection that catches quality degradation before customers do.

Watercolor illustration of a traffic control tower overlooking a busy intersection of code agents, warm amber and teal tones

Learning AI·14 min read read

How to enforce the orchestrator pattern in Claude Code

The main Claude Code thread plans and reviews. Subagents implement. Three enforcement layers make this mandatory: CLAUDE.md, skills, and hooks. Includes a starter kit you can copy.

Illustration of a balance scale tilted by invisible weights, representing hidden biases in AI evaluation systems

Learning AI·18 min read

12 Ways Your LLM Judge Is Lying to You

Research identifies 12 systematic biases in LLM-as-a-judge systems. Learn to detect and mitigate each one before they corrupt your eval pipeline.

Visualization of the widening gap between AI agent capability scores and reliability metrics across model generations

Learning AI·15 min read

Your Agent Is Getting Smarter. It's Not Getting More Reliable.

Reliability improves at half the rate of accuracy. Three 85%+ tools combine to just 74%. Here's the math, the research, and the testing protocols that close the gap.

Person exploring geometric shapes representing vector space

Learning AI·20 min read

Embeddings Turn Text Into Meaning. Here's the Math and the Code

What embeddings are, how similarity search works under the hood, and how to build a semantic search engine, from cosine similarity math to production vector databases.

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