I've been thinking about how LLM coding costs scale across the life of a project, and I'm not sure the way we usually frame it holds up. Greenfield is where the velocity multiplier looks the best — small context, clean abstractions, low blast radius — and that's also where the per-change token cost is lowest. Both of those move in the wrong direction as the codebase gets bigger and harder to reason about. Each change pulls in more files, more constraints, and more historical decisions the model has to re-discover. The bill goes up while the speedup goes down. I'm guessing this gets worse on projects built primarily by LLMs, because the patterns the model laid down in the easy phase don't always hold up under the weight of real product complexity, and the inefficiencies are harder to spot when nobody hand-wrote them. That's a hunch, not a finding — I'd want to see real cost-per-feature curves on a few projects of comparable scope before I'd commit to it. But it's the question I keep coming back to when people quote a velocity multiplier without saying what month they measured it in.
I use Claude with the mabl MCP to create mabl tests. A lot.
New CLI feature? Sure, I write the integration test. Then I also ask Claude to create a mabl test that proves the feature works for real.
This week I'm improving the flow merge page. To work on that page I need a merge conflict, and a merge conflict takes some setup. Create a test with a flow. Branch it. Edit the flow on the branch. Go back to master and change the same flow there. That's a lot of clicking just to reach the screen I want to fix.
So now I type "Claude, use mabl and do that for me!" My job is to fix the merge page, not to make merge conflicts by hand.
Time to optimize your cloud estate. "Ship it" is no longer the end of development.
Dust off the 12 optional optimizations you never shipped because they died in Jira. Feed the fallow tickets into the agent forge. Now is the time for the work we never had bandwidth to do.
Last week, I dusted off an old ML pipeline—long considered too complex to replumb. No problem: select the fanciest LLM model, crank it to MAX effort, and feed it some good ideas.
Flatten and optimize queries? Done.
Materialize all views? Done.
Cache expensive calls in Redis? Done.
Vectorize computations and push down to machine instructions? Done.
Build a comprehensive end-to-end validation harness and suite to ensure these changes break nothing? Check.
OK... 98% cheaper? Pencils down.
It's time to revisit everything you've ever shipped. Juice that lemon until it turns to diamond. Capacity and expertise are no longer the limit. With agent weights and your horse sense, 98% optimization is table stakes.
Lean in. Today you can ask harder questions and produce more thorough analyses than ever before. You're the most capable you've ever been. Time to live your best coding life. Let's say you want to optimize your org's 100 CI workflows? You better instrument disk, memory, CPU, and network usage — OK... done. Now analyze the last 10K builds: what's the optimal build instance shape for _each individual_ workflow? Done. Now shadow-roll five custom instance shapes, raise dozens of PRs to consume them, and run 300 test builds. Done. Data? Reviewed. Statistical analysis, completed. Resources saved: 40%. Builds broken: zero.
You wouldn't have allocated the month it'd take to do this kind of work in 2024, but it's 2026 and your agent fleet just did it for you over the weekend.
It's always been true that if you don't give a team enough scope, they'll fill whatever scope you do give them, usually further than you anticipated. I've watched that at the team level for years. What's new is watching it happen to individuals. The people who were already deep in devops are building more devops, and better devops. Same with the framework people, the networking people, the ones who care about UX polish. Everyone leans into what they already know. Personal backlogs are filling up and getting churned through faster than they used to. This is largely a very good thing. But it does create some interesting challenges around how we think about architecture, prioritization, and team commitments, and we're still working through those.
I've been deleting a lot of code lately and I cannot tell you how good it feels. I cleared out about 400 stale translation keys — the kind that accumulate over years and that nobody dares touch, because who still knows what references them? The agent knows; it checks every reference in seconds, so the sweep I'd always filed under "someday" turned into an afternoon. And Eugene and Jeff finally pulled out Bootstrap (BOOTSTRAP! the framework we've been trying to get rid of for years!) — thank you both, truly. I re-ran our UI library market share report afterward and that's when the impact really landed: Bootstrap is finally gone.
What surprised me is how the agents change the math. Migrating class components to function components with hooks, ripping out an old framework, pruning dead i18n — these were always "we should do this eventually" jobs that lost every prioritization fight. The cost of removing code and cleaning up our codebases dropped enough that now we just... do it. I keep catching myself opening diffs hoping they're mostly red.