This post is my Agentic Coding 2025 Reading List: the best community-written posts I found this year on using LLMs and coding agents to write and ship professional code.
I organized them into following 9 categories based on the themes I kept seeing all year:
If you want to catch up on the year quickly, or you’re new to agentic coding and want a solid starting point, this list should give you the main ideas, debates, and failure modes without the noise.
The main trend this year is that my role (and a lot of other developers’ roles) shifted from writing code to reviewing it. That shift caused an identity crisis for some of us, plus real concerns about technical debt, security risks, and whether the promised productivity gains are real. This reading list captures how the community navigated these challenges.
Curation principles:
I aimed for ~50 posts (one per week). Ended up with 42 after aggressive filtering.
I only included community posts. No official launches, funding news, or company drama.
I optimized for practical value: workflows, failure modes, and lessons you can apply immediately.
Each link has a short "why it matters" note so you can triage fast.
I highlighted must-reads in each section.
Theme 1: Practical Workflows & Playbooks
Actionable patterns and tactics for shipping with agents
Theme 2: Case Studies: Wins and Limits
Real projects showing what worked, what didn't, and why
Building a Mac app with Claude code (Jul 1): Native macOS almost 100% built by Claude Code but the author has been building software for the Mac since 2008.
Theme 3: The Developer's Evolving Role
From code writer to reviewer
Vibe engineering (Oct 7): When seasoned professionals accelerate their work with LLMs while staying accountable for the software they produce.
Theme 4: Tool Internals & Architecture
How AI coding agents actually work under the hood
The Magic of Claude Code (Sep 30): Commands that power Unix happen to be perfectly suited for use by LLMs.
You Should Write An Agent (Nov 6): You only think you understand how a bicycle works, until you learn to ride one.
Theme 5: Code Quality & Technical Debt
The hidden costs of AI-generated code
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Theme 6: The Productivity Debate
Measuring real impact
Writing Code Was Never the Bottleneck (Jun 30): Argues code reviews, testing, debugging, coordination and communication were, and still are the actual bottlenecks.
Theme 7: Security Risks
Attack surfaces and defensive strategies
Theme 8: Cognitive Friction & Limitations
Where current AI hits walls
Why LLMs can't really build software (Aug 14): They cannot maintain clear, dual mental models of both the requirements and the code's actual behavior.
Theme 9: Big Picture: The Future of Software
Industry-level shifts and what's coming
Whether you're catching up during the holidays or bookmarking for later, I hope this list helps you build better software with AI in 2026.