Links from January 2026

OpenCode appears to be pretty interesting and could be worth investigating further. Even though Anthropic blocked Claude subscriptions on it, afterwards GitHub Copilot announced official support for it, so you can still access Opus via Copilot credits. At first glance I was able to copy my ~/.claude/commands to ~/.opencode/commands and they appeared to just work (with Opus via Copilot). The larger benefit of opencode would be testing other models that Claude Code does not support, notably local models.

Addy Osmani’s LLM coding workflow going into 2026 appears very similar to my own, though an interesting note on articles like this is that the reader can interpret the language used in different ways. Claude Code has an excellent /plan capability that Opus 4.5 excels on. I do plan, and I do write specs from time to time, but I also one-shot and two-shot changes and my actual development looks like a mixture of these. At no time do i generate pages and pages of specs and then have the LLM embark on a long horizon delivery period and not look at the code it wrote. Addy points out in anther post the importance of understanding the code with ideas like:

If you skip review, you don’t eliminate work - you defer it

And also links to another article noting that skipping review results in:

No consistency, no overarching plan. It’s like I’d asked 10 junior-mid developers to work on this codebase, with no Git access, locking them in a room without seeing what the other 9 were doing

This is inline with my findings and against a popular (hype) argument that an increase in code volume due to LLMs needs to be accompanied with a decrease in human-in-the-loop because speed. This is a drop in quality, which is deferred until your customer’s are affected and your DORA metrics deteriorate.

Model-Market Fit has some bangers:

When MMF exists, human-in-the-loop is a feature. It maintains quality, builds trust, handles edge cases. The AI does the work; the human provides oversight. When MMF doesn’t exist, human-in-the-loop is a crutch. It hides the fact that the AI can’t perform the core task. …The test is simple: if all human correction were removed from this workflow, would customers still pay? If the answer is no, there’s no MMF. There’s only a demo.

and

The gap between 80% and 99% accuracy is often infinite in practice.

I’m finding promise in having agents use a CLI directly rather than having them generate API code. This tweet elaborates on the context window and efficiency improvements.

How to Pair With an Agent is a short but good example of to make your prompting less vague.