Choosing Your Agent Stack: Comparing Open-Source and Commercial Coding Agents

Språk
Engelsk
Format
Kategori

Fakta og dokumentar

Your autocomplete now reads your repository, plans a 6 file change, runs tests, and opens a pull request while you finish lunch. That shift from suggesting to doing is the largest change in how software gets built since open source, and it turned a $20 subscription into a decision about money, privacy, and safety you make every day.

Most teams choose by accident, whatever a coworker mentioned, whatever demo impressed on social media. They pay $20 without knowing what it covers, get a $180 overage in a busy month, and keep a tool that fights them because switching feels harder. This book fills the gap between marketing and experience with the durable skills that survive the next release.

You will learn the harness versus model distinction that explains why 2 agents running the same frontier model feel completely different, see side by side profiles of Cursor, GitHub Copilot, Claude Code, Codex, Devin, Jules, Amazon Q, and Replit Agent, and meet the open source stack of Aider, Cline, OpenHands, and Goose where you bring your own key.

Inside, you'll discover: • The true cost equation: tokens per task, the $0.60 per 1,000 versus $6.00 per 1,000 gap, and routing cheap work to cheap models • Privacy by plan: what free tiers train on, what enterprise keeps, and how to keep proprietary code local with Ollama • How to build a portable stack with the Model Context Protocol so you can migrate in a day • A 5 hour evaluation: 7 real tasks from your codebase, scored for correctness, safety, and cost • Safety habits that prevent the deleted database and the truncated file without killing speed

By the final chapter you will have a 1 page framework, work type, budget, privacy, autonomy, portability, that tells you which agent to reach for at 9 AM and why, plus a review date to revisit it when pricing or quality moves. Choose like an engineer, not a consumer, and stop renting surprise.

© 2026 BGB Learn (E-bok): 6610001361464

Utgivelsesdato

E-bok: 8. september 2026

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