Blog & Developer Guides | TOOlover

Learn how to effectively integrate AI tools into your development workflow. From setup to best practices, this guide covers the essentials for beginners.

Learn proven techniques to make code reviews more effective, faster, and less painful for your team.

Learn actionable techniques to write code that is easy to read, maintain, and extend.

Curated extensions for VS Code, browsers, CLI, and productivity — chosen for real workflow impact.

Verified free-tier services for hosting, databases, email, monitoring, analytics, AI APIs, and developer tools — with official pricing references as of 2026-09-17.

Inspect portable skills, MCP servers, client extensions, permissions, and failure paths before connecting an agent plugin to a real repository.

Why context engineering replaced prompt engineering as the core production-agent skill, with the 5-layer stack, anti-patterns, and implementation patterns.

The 5 types of agent memory, why immortal memory destroys agent performance, and practical patterns for memory management in production AI systems.

How multi-agent systems moved from toy demos to production architectures, with practical orchestration patterns and the A2A protocol reshaping agent interoperability.

Understand where an AI coding agent plans work, where commands execute, and how to define safer boundaries for code, secrets, network access, and review.

Separate agent planning from code execution with controlled workers, outbound-only networking, least-privilege secrets, and reviewable pull requests.

Write a small repository contract that gives coding agents useful context, safe commands, validation rules, and a reviewable handoff.

Use bounded authority, reproducible checks, and explicit human approval to review agent-generated changes before a pull request.

Review agent-authored changes with bounded scope, reproducible checks, behavior-focused diff review, and explicit human approval.

Define bounded agent roles, choose the right autonomy level, and require evidence before accepting AI-assisted code.

Make resumable agent work reviewable with explicit permissions, observable diffs, repeatable checks, and a concise handoff.

Use separate working directories for agent tasks, keep branches reviewable, and clean up without treating a worktree as a security sandbox.

A practical evaluation checklist for agent-generated changes: define guardrails, test realistic cases, inspect failures, and review the diff.

Use shared AI coding-agent channels without losing scope, approvals, tests, or human ownership of the final change.

Use go.mod as a compatibility boundary, load focused agent guidance, and validate generated Go code with the toolchain.

How to give AI coding agents clear intent, useful boundaries, and executable checks without creating unnecessary process.

A repeatable way to delegate coding-agent work without giving up review: define scope, inspect the diff, test failure cases, and approve deliberately.

TOOlover has a new official home. Same free tools, same privacy-first approach — just a new address.

Frontier model showdown: benchmarks, coding quality, pricing, speed, and which one you should use for what.

Where AI fits in the software development lifecycle — planning, coding, reviewing, testing, and deploying — and where it still falls short.

How to get the most out of Claude Code — from writing better prompts to structuring your workflow for real productivity gains.

Learn why JSON formatting matters, when to use it, and how to choose the right indentation style for your project.

Compare JMESPath and JSONPath query languages for JSON. Learn when to use each and which one is right for your use case.

A short post on why client-side processing matters for developer tools — privacy, speed, and offline use.