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Learning AI

Browse 31 articles in learning ai.

Learning AI Articles

31 articles · Page 2 of 3

Person building with tool components at a desk

Learning AI·20 min read

Function Calling: Build a Multi-Tool AI Agent from Scratch

Build a multi-tool AI agent from scratch using function calling across OpenAI, Anthropic, and Google. Runnable TypeScript and Python code, validation with Zod and Pydantic, and production hardening patterns.

Illustration of an AI agent navigating branching knowledge paths across interconnected document nodes

Learning AI·18 min read

Your RAG Pipeline Is Answering the Wrong Question

Naive RAG scores 42% on multi-hop questions. Agentic RAG hits 94.5%. The difference: letting the agent decide what to retrieve, when, and whether the results are good enough. Build both in TypeScript and Python.

Illustration of an engineer assembling context layers for an AI agent, with memory, tools, and knowledge sources flowing into a central pipeline

Learning AI·21 min read

Context Engineering Is What Your Agent Actually Needs

Prompt engineering hits a wall with production AI agents. Context engineering fixes it. Build a full context pipeline with memory, RAG, history compression, and tool resolution.

Developer comparing small and large AI model outputs on a monitor

Learning AI·18 min read

A 7B Domain Model Beat Everything We Tried

Domain-specific language models are beating trillion-parameter generalists on vertical tasks. Here's when a 7B model is the right call, how the training pipeline works, and what production teams are shipping today.

Illustration of a neural network with low-rank adapter matrices injected between layers, showing only a small percentage of parameters highlighted for training

Learning AI·19 min read

Fine-Tune a 7B Model for $1,500 (Not $50,000)

Full fine-tuning costs $50K in H100s. QLoRA on an RTX 4090 costs $1,500. Learn how LoRA and QLoRA let you train only 0.1-1% of parameters with nearly identical results, with working code for fine-tuning models that understand your agent's tool schemas.

Neural network distillation visualization showing a large teacher model transferring knowledge to a compact student model

Learning AI·16 min read

A 1B Model Just Matched the 70B. Here's How.

How to distill frontier LLMs into small, cheap models that retain 98% accuracy on agent tasks. The teacher-student pattern, NVIDIA's data flywheel, and the Plan-and-Execute architecture that cuts agent costs by 90%.

Small chip outperforming a rack of servers

Learning AI·14 min read

Why Your AI Bill Is 30x Too High

Small language models match GPT-3.5 at 2% of the size and 95% less cost. Benchmarks, code, and a migration story from $13K/month to $400.

Watercolor illustration of developers at a cafe terrace with MCP diagram on whiteboard — Teal & Copper style

Learning AI·15 min read

Part 1: Claude's 7 Extension Points — The Mental Model

CLAUDE.md, Skills, Hooks, MCP Servers, Connectors, Claude Apps, Plugins — Claude's extension ecosystem is powerful but confusing. Here's the mental model that makes sense of all 7.

Watercolor illustration of developers at a cafe terrace with LLM layered diagram on whiteboard — Terra Cotta style

Learning AI·17 min read

Part 2: CLAUDE.md, Hooks, and Skills — Three Layers

CLAUDE.md sets conventions. Hooks enforce them. Skills teach workflows. Understanding these three layers — and their reliability spectrum — is the key to a Claude Code setup that actually works.

Watercolor illustration of developers at a cafe terrace with MCP plug-and-socket diagram on whiteboard — Sage & Olive style

Learning AI·17 min read

Part 3: MCP Servers vs. Connectors vs. Apps

All Claude Apps are Connectors. All Connectors are MCP Servers. Understanding this hierarchy — and when to build vs. use managed integrations — saves weeks of unnecessary engineering.

Watercolor illustration of developers at a cafe terrace with rocket deployment diagram on screen — Dusty Blue style

Learning AI·20 min read

Part 4: All 7 Extension Points in One Production Codebase

50+ skills, multiple MCP servers, scoped rules, safety hooks — here's how all 7 Claude extension points compose in a real NestJS monorepo with 17 projects. What works, what fights, and what we'd do differently.

AI agent memory architecture with semantic search vectors

Learning AI·20 min read read

Build your own AI agent memory system — what breaks when real users show up?

Build a complete memory system for customer-facing AI agents — session context, persistent recall, semantic search. Then learn what breaks when real customers start returning.

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