Hey, Luca here, welcome to a weekly edition of theš” Monday Ideas š” from Refactoring! To access all our articles, library, and community, subscribe to the full version.
Reporting from Salesforceās TDX event! š§āš»
This weekās sponsor is Salesforce!
Our friend Conor Bronsdon was our eyes and ears on the floor at TDX last week, and wrote a full breakdown on Chain of Thought.
He says Agentforce is now Salesforce's fastest-growing product, and the signs were everywhere at TDX:
60+ new MCP tools in Headless 360
Agent Script open-sourced on GitHub
Agentforce Vibes 2.0 (their vibe coding tool),
plus new Slack agent capabilities.
Salesforce went as far as publishing the full language spec, grammar, parser, and compiler for their agent-definition language.
They're betting an open agent ecosystem wins developer adoption, while enterprise-grade upgrade paths let businesses actually ship reliable agents to production.
š” Coding is not solved, but bad codeā¦ kinda is
Last week I launched Tolaria, which by now is one of the fastest growing repos on the whole Github! It got 6000+ stars in less than a week.
Tolaria is a lot of code: 100K+ LOCs, 2K+ commits, 3K+ tests, 70+ ADRs. I publicly said many times I didnāt write any of it, which made some people think I believe coding is solved.
I donāt think coding is solved at large, but bad code feels solved. To understand what I mean by that, a useful way to think about tech debt is to split it into two kinds:
Bad code
Misaligned code
Bad code is basic hygiene failure: lack of tests, high complexity, tight coupling, duplication, outdated libraries. It is bad regardless of business context, and we know it increases lead time, outages, and maintenance cost.
As of today this kind of debt is largely avoidable if you choose to enforce quality. On Tolaria, AI-generated code must meet three gates: high test coverage, strong CodeScene health scores, and current library/docs usage. These rules live both in CLAUDE.md and CI, because agents still forget or ignore instructions. Once those checks are in place, the basics can be trusted without manually reviewing every line.
Misaligned code is harder, and is definitely not solved yet. Code may be clean, tested, and well-structured, but still wrong for the product direction you want. Itās code that is a good circle when you really needed a square: a leaky abstraction, an old design assumption, or a model that no longer matches intent.
Reviewing architecture and abstractions still feels supremely important, because they're the things that the AI model canāt figure out by itself, because they are about what you want to build in the future, and why.
I wrote more about this in my series of articles about my AI coding workflow:
My AI Coding Workflow (Feb 2026)
Updates to My AI Coding Workflow (Apr 2026)
Introducing Tolaria (last week!)
šļø Progressive disclosure of complexity
In May last year I interviewed Guillermo Rauch, founder and CEO of Vercel
Guillermo talked about the qualities of great technology, and one principle that stuck with me was what he called the progressive disclosure of complexity.
A defining feature of great tech is thatās approachable for beginners but powerful enough for enterprise needs.
This principle guides the design of Vercel, Next.js, and everything they do.
āIām building a platform that should not be intimidating for your first line of code, and gives you the superpower that you could be one day a top 30 internet website with the same infrastructure, with the same tools, with the same access.ā
Guillermo draws inspiration from products like the iPhone, which can be used by both children and the elderly while still serving business professionals with complex needs.
His approach to API design focuses on what he calls token minimization (not AI tokens!) ā requiring minimal code to get started, then gradually introducing more sophisticated features as needed:
š Low barrier to entry ā a new Next.js project can be started with just a few lines of code.
š± Growing with users ā complexity is only introduced when needed for specific requirements.
š Same technology at all scales ā the technology that powers a beginnerās first project is the same that can scale to handle millions of users.
Here is the full interview with Guillermo:
You can also find it on š§ Spotify and š¬ Substack
š Weekly Readings
Finally, here are the best articles I have read this week:
š„ My CTO Daily Driver
8 min ā¢ by James Stanier
I basically recommend all of Jamesā articles, and this makes no exception. He explains how he uses Claude Code to drive a personalized CTO workspace with memory. With custom slash commands tied to Linear, Notion, and BetterStack.
š„ The Peril of Laziness Lost
12 min ā¢ by Bryan Cantrill
Laziness has always been a programmerās virtue ā itās what drives us to build abstractions instead of doing the same thing twice. LLMs donāt have that instinct: you have to supply it to them. If you donāt, you lose the elegance, efficiency, and maintainability that comes from human constraints.
š„ 99% of Our Production Code Is Written by AI
20 min ā¢ by Peter Pang
Took a lot of ideas from this post. Feels a bit AI-generated at times, but the ideas around feature gates, self-healing feedback loop, and bug fixing are awesome. Peak āharness engineeringā, as kids call it these days.
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See you next week!
Luca