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Richard Seroter's Architecture Musings

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Blog Featuring Links, Code, Thoughts, and Experiences with Software, AI, and Cloud Services

https://seroter.com

Daily Reading List – August 19, 2026 (#849)

19 Aug 2026

Today's links look at doc-driven development, why harnesses are basically situated agents, what's new in Go 1.27.

Daily Reading List – August 18, 2026 (#848)

18 Aug 2026

Today's links look at data about AI usage patterns in software teams, some best practices for AI code reviews, and what to do when the hard part stops being hard.

Daily Reading List – August 17, 2026 (#847)

17 Aug 2026

Today's links look at how companies are doing agentic engineering in real life, what to do about skill sprawl, and whether you need skills at all.

Daily Reading List – August 14, 2026 (#846)

14 Aug 2026

Today's links look at how to speed up generative UI, which patterns and problems in multiagent systems to be aware of, and whether software factories actually work.

Daily Reading List – August 13, 2026 (#845)

13 Aug 2026

Today's links look at the flashy Gemini 3.7 Flash model, why reviewing code is a skill, and what technical debt in the AI era looks like.

Daily Reading List – August 12, 2026 (#844)

13 Aug 2026

Today's links look at why local models won't win, whether TDD inside the agent loop makes sense, and where a watermark can hide in plain text.

Daily Reading List – August 11, 2026 (#843)

11 Aug 2026

Today's links look at whether agents are killing the UI, whether agents can use computers yet, and a fresh definition of the product role.

Daily Reading List – August 10, 2026 (#842)

10 Aug 2026

Today's links look at why 'code was never the hard part' is insulting, what code review are for, and why open source matters for AI.

Daily Reading List – August 7, 2026 (#841)

7 Aug 2026

Today's links look at when a loop should stop, how WebMCP is picking up momentum, and what has to be true to rewrite the code all the time.

Daily Reading List – August 6, 2026 (#840)

6 Aug 2026

Today's links look at the new Agent Plugins spec, when you should self-host inference, and what nobody tells you about writing agent skills.

Daily Reading List – August 5, 2026 (#839)

5 Aug 2026

Today's links look at a big change in Google AI leadership, what name we should use when referring to AI coding, and whether lacking a long-term career plan is fine.

Daily Reading List – August 4, 2026 (#838)

5 Aug 2026

Today's links look at how to work with 5 coding agents simultaneously, scaling agents with session-aware load balancing, and why engineering management is a career change instead of a promotion.

Daily Reading List – August 3, 2026 (#837)

4 Aug 2026

Today's links look at the economic benefits of refactoring, the one line that 95% of agent skills are missing, and how to give (and take) credit in big tech companies.

Daily Reading List – July 30, 2026 (#836)

30 Jul 2026

Today's links look at what a loop is, how to apply eval-driven development, and why GitHub might be the wrong shape for this AI world.

Which programming languages offer built-in tools for modernizing your code?

30 Jul 2026

If you’ve been a software developer for more than five minutes, then you probably have some old code running somewhere. It probably works fine, so who cares if it’s a little dusty and based on an

Daily Reading List – July 29, 2026 (#835)

29 Jul 2026

Today's links look at how much you can delegate to agents, how we killed reading and can get it back, and why your moat shouldn't be the models.

Daily Reading List – July 28, 2026 (#834)

28 Jul 2026

Today's links look at a big update to the Model Context Protocol, why it's good to be bored, and how to vibe code your first Android app.

Daily Reading List – July 27, 2026 (#833)

28 Jul 2026

Today's links look at the AI productivity paradox, why mentoring matters more in the AI era, and why you should stop engineering your agent harness.

Daily Reading List – July 24, 2026 (#832)

24 Jul 2026

Today's links look at what the right amount of spec is for agentic development, what data you need to determine the ROI of AI, and which architecture mistakes make systems hard to change.