Run three agents in one repo and merging becomes the bottleneck. How to partition work so conflicts stay rare, and land the ones that do happen safely.
Your AI coding agent hits a usage cap and stalls mid-task. How to spread work across models and sessions so one provider's limit doesn't stop your whole fleet.
A Docker sandbox stops an AI coding agent wrecking your laptop, not your credentials leaving it. How to pick an isolation level, and what it really costs.
Background coding agents build pull requests in cloud sandboxes while you do other things. Which tasks belong there, and which need a session you steer.
The best MCP servers for coding agents, picked by job: browser, database, docs, errors. Plus the bar each one must clear before it earns context space.
Claude Code headless mode (claude -p) runs the agent as a one-shot shell command: pipe logs in, get JSON out, script it in CI. When to script, when to chat.
A flaky suite is a broken grader. Your AI coding agent optimizes for green and papers over the flake. How to measure it, label it, and delegate the fix.
Prompt injection turns anything your AI coding agent reads (issues, docs, tool output) into instructions. Where the real risk sits and how to contain it.
Make your codebase agent-ready: fast scoped tests, predictable structure, instruction files that survive a cold read. Habits that pay on every agent run.
Claude Code plugins bundle skills, hooks, slash commands and MCP servers into one installable package. When a plugin beats copy-paste, and when it is overkill.
Claude Code resume, rewind and branch explained: get a closed session back with --continue and --resume, undo bad runs, and know when a fresh start wins.
Claude Code skills package instructions, templates and scripts into folders the agent loads on demand. How to move know-how out of CLAUDE.md into skills.
Claude Code subagents keep noisy exploration out of your main context, but they can hide work too. When to delegate, and when a task deserves its own session.
Claude Code vs Codex vs Gemini CLI: the durable differences between the terminal agents, which one to make your daily driver, and when to run all three.
A chat box is a queue of one. Here's why a Kanban board is the natural task manager for Claude Code and Codex agents, and what a good agent board needs.
Agents invent functions, flags and whole packages that were never real, then use them with total confidence. Here's why it happens and how to catch it early.
Patch, break, revert, repeat, agents thrash when nothing new comes back. Here's how to spot a loop in the first two attempts, and what actually breaks it.
Agents rarely fail because a task is hard. They fail because it's big. Here's how to size work so an agent finishes clean, and how to split one that's too big.
AI agents guess at speed because they read code, not runtime. Here's how to make one profile first, prove the win in numbers, and stop tuning the wrong line.
Picking a coding model isn't a leaderboard question. Here's how to route work by task shape: small and fast for mechanical jobs, your best for the hard ones.
Agents write docs that describe the code and miss the why, then the docs rot anyway. Here's how to get documentation worth reading, and keep it current.
Long agent sessions drift, forget and repeat themselves. Here's how the context window actually works, and the session habits that keep an agent sharp.
You retype your best prompts every week. Saving them as slash commands turns a paragraph into three words: here's what to codify, and what to leave out.
Agents write UI they can't see, so it compiles, passes the tests and still looks wrong. Here's the feedback loop that makes frontend work with agents reliable.
Agents ship 400 lines where 40 would do, adding new abstractions, new dependencies, defensive code. Here's how to get the smallest change that actually works.
AI agents write code that works and skip the security parts, missing authorization, trusted input, leaked keys. Here's where it slips and how to catch it.
AI agents patch symptoms fast and find root causes slowly. Here's a debugging workflow that makes an agent reproduce, isolate and prove the fix before it edits.
Dropped into unfamiliar code? AI agents can map a system in an afternoon, if you ask for the right artefacts. Here's the workflow, and its failure modes.
Framework upgrades and API migrations are mostly mechanical, the ideal agent job. Here's how to run one across hundreds of files without breaking main.
Agents burn tokens rediscovering your repo, restarting from zero and building the wrong thing. Here's where the spend leaks, and the habits that stop it.
MCP lets your AI coding agent reach past the repo, into your database, browser, and issue tracker. Here's what the Model Context Protocol is and how to use it well.
Your AI coding agent relearns your project every session. An AGENTS.md file tells it once: here's what belongs in one, what to leave out, and why it works.
Vague prompts produce vague code. Spec-driven development gives your AI coding agent a written brief before it writes a line; here's how to do it well.
AI agents write code faster than you can check it. Writing the test first turns your suite into a spec the agent can verify itself against. Here's how.
Parallel AI agents in one repo trip over each other's changes. Git worktrees give each agent its own checkout: here's the setup, the pitfalls and the workflow.
Technical debt survives because cleanup never feels urgent. Here's how to turn it into small AI agent tasks and pay it down continuously, without adding more.
Searching for the best AI IDE? In 2026 the real winner isn't a code editor with a chatbot bolted on: it's an agent workspace. Here's what changed, and why SanuDesk is the best of the new breed.
AI agents write code faster than you can read it, so review becomes the new bottleneck. Here's a workflow for reviewing AI-generated code without losing speed.
You don't need to hire to ship like a team. Here's how solo developers use SanuDesk to run a fleet of Claude Code, Codex and Gemini agents in parallel, turning one pair of hands into a whole engineering org.
Looking for a BridgeMind alternative? Here's an honest look at the agentic coding tools worth comparing in 2026, including desktop agent workspaces, credit-based platforms, IDE agents and terminal tools, and how to pick the right one for how you actually work.
A practical guide to running a fleet of Claude Code, Codex and Gemini agents in parallel: tiling sessions, dispatching work from a board, and keeping them all shipping without losing track.
Vibe coding means describing what you want in natural language and letting AI agents write and run the code. Here's what the term actually means, where it works, and how to do it without losing control of your codebase.
