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
[ Tag ]
#ai coding
24 articles
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
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
Production 6 min
Keeping Secrets Out of Your AI Coding Sessions
A practical guide to sharing the right context with AI assistants without exposing API keys, credentials, and sensitive data that creates security risk.
0degrees Team
Tooling 7 min
Writing a CLAUDE.md That Actually Changes How Your AI Codes
How to write a project instruction file that reliably steers AI coding assistants toward your conventions — what to include, what to leave out, and how to test it works.
0degrees Team
Tooling 6 min
When AI Invents APIs: Handling Code Hallucinations in Practice
How to recognize, prevent, and recover from AI-generated code that confidently calls methods, packages, or APIs that don't exist.
0degrees Team
Prompt Engineering 7 min
Prompting AI to Write Testable Code by Default
How to prompt AI coding assistants to produce pure functions, injectable dependencies, and explicit interfaces — so the generated code is easy to test before you write a single assertion.
0degrees Team
Prompt Engineering 6 min
Type-Driven AI: Getting Better Code by Sharing TypeScript Types First
How sharing your TypeScript types before asking for implementations dramatically improves AI-generated code — and how to use the compiler as a fast verification loop.
0degrees Team
Tooling 7 min
Documenting Code with AI: Keeping Docs Honest
How to get AI coding assistants to write documentation that accurately reflects your code — not plausible-sounding summaries that drift from reality.
0degrees Team
Production 7 min
Profiling First, Prompting Second: Using AI to Optimize Slow Code
How to structure AI-assisted performance work — what profiling data to share, how to frame bottlenecks, and how to validate that a suggested fix actually helps.
0degrees Team
Tooling 7 min
Using AI to Review Your Own Code Before It Ships
How to use AI coding assistants as a first-pass code reviewer — prompting for real feedback on code you wrote, interpreting what it finds, and fitting it into your workflow.
0degrees Team
Tooling 7 min
Briefing AI on Your Codebase: The Architecture Context That Actually Matters
How to give an AI coding assistant exactly the architectural context it needs to write code that fits your project — without overwhelming it with noise.
0degrees Team
Agents 6 min
Task Decomposition for AI Coding Agents: How to Break Down Complex Features
How to split a complex feature into agent-sized sub-tasks that execute reliably — with concrete patterns and scope rules that prevent drift and rework.
Josh
Tooling 7 min
Upgrading Dependencies with AI: A Systematic Migration Workflow
How to use AI coding assistants to navigate major library upgrades — feeding changelogs, mapping the breaking change surface, and validating the result.
Josh
Agents 7 min
Steering a Drifting AI Agent: How to Correct Course Mid-Task
Practical techniques for recognizing when an AI coding agent has gone off track and redirecting it effectively — before it digs you deeper into the wrong solution.
Josh
Tooling 7 min
Using AI to Explore Unfamiliar Codebases
How to use AI coding assistants as a guide when navigating code you didn't write — from getting an architectural overview to tracing execution paths.
Josh
Tooling 6 min
Refactoring with AI: A Step-by-Step Strategy for Modernizing Legacy Code
How to use AI coding assistants for safe, incremental refactoring — scoping the work, writing safety tests, reviewing generated diffs, and avoiding common pitfalls.
Josh
Agents 7 min
MCP Servers: Giving Your AI Coding Agent Real Tools
How to extend your AI coding assistant with Model Context Protocol servers — connecting it to your database, APIs, and custom tools in about ten minutes.
Josh
Production 7 min
Writing Tests with AI: Getting Edge Cases, Not Just Coverage
How to prompt AI coding assistants to generate tests that actually catch bugs — edge cases, failure modes, and boundary conditions, not just happy-path boilerplate.
Josh
Tooling 7 min
Managing Context in Long AI Coding Sessions
How to keep AI coding assistants accurate across a long feature branch — when to trim, reset, and summarize context so the model stays focused.
Josh
Tooling 6 min
Debugging with LLMs: Give the Model What It Can't Guess
How to structure debugging sessions with an AI assistant — what runtime context to share, how to frame the problem, and how to verify a diagnosis before patching.
Josh
Production 6 min
Evaluating AI-Generated Code Before It Ships
A practical checklist for reviewing AI-written code — catching hallucinated APIs, subtle logic bugs, and security gaps before they reach production.
Josh