AI Coding

A well-built outer harness serves two goals: it increases the probability that the agent gets it right in the first place, and it provides a feedback loop that self-corrects as many issues as possible before they even reach human eyes. Ultimately it should reduce the review toil and increase the system quality, all with the added benefit of fewer wasted tokens along the way.

Two releases this week, however, suggest we’re starting to enter a second phase of open source agentic coding projects. The first brought us clones, this next phase brings us reimaginings.

Claude Code went from 0.7% to 4.5% of all public GitHub commits in six months

cross-posted from: https://lemmy.bestiver.se/post/1018360

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Here's what I keep seeing: the specification-driven development (SDD) conversation has exploded. The internet is overflowing with people saying you should write a spec before prompting. Describe the behavior you want. Define the constraints. Give the agent guardrails. Good advice. I often follow it myself.

But almost nobody takes the next step. Encoding those specifications into automated tests that actually enforce the contract.

You’re paying for the AI to write code, so surely the expensive part is what it produces, right? I would have guessed something like 60/40 or maybe 70/30 skewed towards output. The reality is nothing like that. Real-world data from OpenRouter’s programming category shows 93.4% input tokens, 2.5% reasoning tokens, and just 4.0% output tokens. It’s almost entirely input.

It turns out Claude Opus 4.6 and GPT-5.4 are both very competent at SwiftUI—and a full SwiftUI app can fit in a single text file, which means I can use them to spin something up without even opening Xcode.

The biggest risk in AI adoption isn’t moving too slowly. It’s mandating too hard, watching it backfire, and oscillating between full commitment and full retreat. The organizations that found their footing got better at paying attention, not better at writing policy.

AI conversations are ephemeral by design — decisions made early fade as sessions lengthen, and nothing survives the session boundary. Developers hold on to long conversations not because long sessions are productive, but because the context lives nowhere else. I propose externalizing decision context into a living document — external memory that persists what the context window cannot, turning transient alignment into durable shared understanding.

After building Curling IO Version 3 in Gleam alongside AI coding agents, I'm convinced Gleam is the best language for this workflow. Agents don't write better Gleam - there's less training data. But Gleam's compiler lets agents self-correct without waiting for a human.

AI scales the groundwork; teams that successfully adopt AI typically already have solid foundational practices in place, while those lacking them struggle to get value from their AI investments.

cross-posted from: https://lemmy.bestiver.se/post/944071

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cross-posted from: https://lemmy.bestiver.se/post/939521

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