scresswell

Yadda 3 makes it easier to write executable specifications in the natural language of the business. Here are the principles and practices I use to keep those specifications simple, expressive and useful, whether they're being read and written by humans or agents.

Announcing `drizzle-transact` and `drizzle-explain`: two small libraries that turn good intentions into good mechanisms.

Yadda 3.0.0 is out. The release modernises the JavaScript BDD library, but more interestingly, it was largely built by Claude Code and points to why executable specifications may become even more valuable in an agentic development world.

Further experience with Claude has strengthened my view that code quality matters more, not less, in the age of vibe coding. Left unguided, it amplifies duplication and weak abstractions; tightly constrained, it can be impressively precise. Generative AI is an amplifier. It accelerates coherence, and it accelerates mess.

Yes, SKIP LOCKED is great. In practice you nearly always want LIMIT, which the article did not mention. Be careful if your selection spans multiple tables: only the relations you explicitly lock are protected (see SELECT … FOR UPDATE OF t1, t2). ORDER BY matters because it controls fairness and retry behaviour. Also watch ANALYZE: autoanalyze only runs once the dead to live tuple threshold is crossed, and on large or append heavy tables with lots of old rows this can lag, leading to poor plans and bad SKIP LOCKED performance. Finally, think about deletion and lifecycle: deleting on success, scheduled cleanup (consider pg_cron), or partitioning old data all help keep it efficient.

I genuinely like the framing of advisory versus authoritative AI, and I agree with the core observation that authority, when it is genuinely granted, is what unlocks step change improvements rather than marginal efficiency gains. In the environments where it is appropriate, allowing systems to act rather than merely suggest can dramatically accelerate development and reshape workflows in ways that advisory tools never will. In that sense, you are right: authority is the AI bottleneck.

My concern with your article is that, without clearer caveats, you imply that authority is the right answer everywhere. As you rightly note, AI systems make mistakes and they make them frequently. In many real world contexts, those mistakes are not cleanly reversible. You cannot roll back a data leak. You cannot always recover fully from data loss. You cannot always undo millions of pounds of lost or refunded revenue caused by subtle failures or downtime. You cannot always roll back the consequences of an exploited security vulnerability. And you certainly cannot reliably undo reputational damage once trust has been lost.

Even in cases where you can mostly recover from a failure, you cannot recover the organisational and human disruption it causes. A recent UK example is the case where thousands of drivers were wrongly fined for speeding due to a system error that persisted from 2021. Given the scale, some will have lost their licences, some may have lost their jobs, and many will have experienced long term impacts such as higher insurance premiums. Even if fines are refunded or records corrected later, the downstream consequences cannot simply be undone. While the failure in this example was caused by human error, the fact that some mistakes are unrecoverable is just as true for AI.

Part of the current polarisation in opinions about AI comes from a lack of explicit context. People talk past each other because they are optimising for different objectives in different environments, but argue as if they are discussing the same problem. An approach that is transformative in a low risk internal system can be reckless in a public, regulated, or security sensitive one.

Where I strongly agree with you is that authoritative AI can be extremely powerful in the right domains. Proofs of concept are an obvious example, where speed of learning matters more than correctness and the blast radius is intentionally small. Many internal or back office applications fall into the same category. However, for many public facing, safety critical, or highly regulated systems, authority is not simply a cultural or organisational choice. It is a hard constraint shaped by risk, liability, regulation, and irreversibility. In those contexts, using AI in a strictly advisory capacity may be a bottleneck, but it is also a deliberate and necessary control measure, at least for now.