Agents 4 min
Build Your First AI Agent in TypeScript
A from-scratch walkthrough of the agent loop — tools, reasoning, and termination — using the Claude API and plain TypeScript. No frameworks.
Josh
Field manual · AI engineering
Learn to build AI software, from zero degrees.
Practical, code-first tutorials on agents, RAG, prompt engineering and shipping LLM apps to production. No hype — just what works.
- Articles
- 32
- Topics
- 06
- Hype
- 00
[ 01 · Featured ]
Featured article
[ 02 · Latest ]
Recent articles
Tooling 7 min
AI for Shell Scripts: Getting Safe, Correct Bash from an LLM
How to prompt AI for shell scripts that handle errors, edge cases, and environment differences — and how to validate the output before you trust it.
0degrees Team
Tooling 7 min
Multi-Model AI Coding Workflows: Routing Tasks to the Right Model
Most developers default to one model for everything. Here's a practical framework for matching each coding task to the right model — cutting costs without sacrificing quality.
0degrees Team
Agents 6 min
Parallel AI Agent Workflows: Fanning Out Tasks to Multiple Agents
How to identify independent workstreams, fan them out to multiple AI agent sessions simultaneously, and merge results without conflicts.
0degrees Team
Prompt Engineering 6 min
Generating Realistic Test Fixtures with AI
How to prompt AI coding assistants to produce diverse, realistic test fixtures and seed data — not just placeholder values that pass but don't catch real bugs.
0degrees Team
Prompt Engineering 8 min
Prompting AI for Defensive Code: Getting the Unhappy Path Right
AI coding assistants default to the happy path. Here's how to systematically prompt for input validation, error boundaries, and the failures that break production.
0degrees Team
Production 7 min
Using AI to Analyze Logs and Diagnose Production Incidents
A practical workflow for feeding production logs to an LLM to surface root causes, correlate errors, and cut mean-time-to-resolution — without leaking sensitive data.
0degrees Team
[ 03 · Topics ]
Explore by topic
01
Fundamentals
Core concepts every AI engineer should internalise first.
02Agents
Designing, building and orchestrating autonomous LLM agents.
03RAG
Retrieval-augmented generation, embeddings and vector search.
04Prompt Engineering
Reliable prompting patterns for real production systems.
05Tooling
The frameworks, SDKs and dev tools that ship AI faster.
06Production
Evals, observability, cost and reliability at scale.