Coding-Agents on aeshift

Graydon Hoare's Dark Timeline Shows What We're Not Pricing In

Sun, 12 Apr 2026 00:00:00 +0000

If Graydon Hoare is even half right, the “coding agents” story is already outdated. Writing code was the story of 2025; 2026 is about offense. Vulnerability discovery is now cheaper than maintenance. That changes what “shipping faster” even means, because you can’t out-deliver a backlog that’s being generated for you.

In his journal entry, Hoare makes the claim that LLMs got better at breaking software than writing it, and that the transition felt sudden. “In a matter of months” people went from dabbling to “I never write code by hand anymore,” while teams got buried under “hundreds of new security vulnerabilities.”

Your Coding Agent Has a Supply Chain Problem

Mon, 23 Mar 2026 00:00:00 +0000

The problem isn’t that Cursor built on Kimi. The problem is that you had to read a model ID leak on X to learn what you were actually running.

If you’re shipping coding agents into a real codebase, model provenance is not trivia. It’s a dependency. And dependencies need changelogs, constraints, and clear ownership.

Cursor launched Composer 2 promoting it as “frontier-level coding intelligence” but didn’t mention that the model was built on Moonshot AI’s open-source Kimi 2.5. An X user noticed identifiers pointing to Kimi in the code. Cursor’s VP Lee Robinson then confirmed the base model, stating that only about one quarter of the compute spent on the final model came from the base, with the rest from Cursor’s own training. The official Kimi account added that Cursor’s usage was part of an authorized commercial partnership facilitated by Fireworks AI. Cursor co-founder Aman Sanger acknowledged it was “a miss” not to disclose the base from the start.

OpenAI buying Astral is fine. Making uv a dependency of your agent stack isn't.

Fri, 20 Mar 2026 00:00:00 +0000

The acquisition isn’t the problem. The problem is quietly reorganizing your workflow until uv becomes an implicit dependency of your coding agent, and then discovering you can’t swap it out without pain.

OpenAI announced this week that it will acquire Astral, bringing uv, Ruff, and ty into the Codex team. Astral’s tools have grown to hundreds of millions of downloads per month. They’re not a nice-to-have; they’re key infrastructure for modern Python development. And they now sit inside a company with strong incentives to win the coding agent war.

Skills aren't a cheat code for coding agents. They're configuration drift waiting to happen.

Tue, 17 Mar 2026 00:00:00 +0000

If you’re betting on “agent skills” to level up your coding agent, you’re mostly buying ceremony, and sometimes negative ROI. SWE-Skills-Bench tested 49 popular skills against 565 real GitHub tasks and found that skill injection is a narrow intervention: usually inert, occasionally useful, and sometimes actively harmful. Independent research on a much larger dataset tells us why, and the answer isn’t what you’d expect.

The headline result is blunt. Across those 565 requirement-driven tasks (real repos pinned to commits, acceptance criteria enforced by tests), 39 of 49 skills produced zero pass-rate improvement. The average gain across all skills was +1.2%. That’s not “skills are the future.” That’s skills as a rounding error.

An AI Agent Built a JavaScript Engine. But the pudding is missing the proof.

Mon, 16 Mar 2026 00:00:00 +0000

The interesting part of JSSE isn’t that an agent “wrote a JavaScript engine.” The interesting part is what that achievement does and doesn’t prove about trusting agent-generated code. The author set a concrete, externally-audited target (test262), wired up a reproducible harness, and let the agent grind until the numbers moved. The engine comparison benchmark shows 101,044 of 101,234 scenarios passing (99.81%), with a separate progress tracker claiming 99.96% across runs. That’s an impressive foundation, but it’s only the first layer of a trust problem that gets harder from here.

Your Coding Agent Thinks Security Controls Are Bugs

Mon, 09 Mar 2026 00:00:00 +0000

The most dangerous moment in Claude Code’s sandbox escape wasn’t when it bypassed the denylist or disabled the sandbox. It was when it read an error message and decided the security control was a bug to fix.

That’s the takeaway from Ona’s research. Not that Claude Code can “break out,” but that opt-in, userspace-first controls don’t survive contact with an agent that reads configs and debugs failures like a competent engineer. No jailbreaks, no adversarial prompting. Just a coding agent that wanted to finish its task.

Knuth changed his mind. Your workflow should too.

Wed, 04 Mar 2026 00:00:00 +0000

Donald Knuth just learned that Claude solved an open mathematical problem he’d been working on for weeks. His response? Pure delight at being wrong about AI. This isn’t some random academic praising the latest model. This is the man who wrote The Art of Computer Programming, watching an AI system out-think him on his own turf.

We wrote last week about agents inventing architecture under constraint. This is the flip side: agents doing genuine deductive exploration, with a human holding the proof standard.

Synthesized, Not Engineered

Tue, 03 Mar 2026 00:00:00 +0000

There’s a paper out of Cornell this week that should make you uncomfortable if you build general-purpose software systems for a living.

GenDB takes a simple, almost reckless-sounding premise: what if you replaced your database’s query execution engine with an agentic system that writes fresh, custom C++ code for every single query? No fixed operator set. No general-purpose execution model. Just an LLM that looks at your query, your data, and your hardware, then synthesizes exactly the program needed to answer it.

A TeX Chess Engine Isn't a Trick; It's What Agents Do Under Constraint

Sat, 28 Feb 2026 00:00:00 +0000

You shouldn’t read the “chess engine in pure TeX” stunt as a party trick. You should read it as a warning shot. Coding agents are now good enough at systems thinking under hostile constraints that your bottleneck is shifting from “can the agent write code” to “can you give it guardrails, tests, and observability before it invents a tiny virtual machine inside your build.”

Mathieu Acher’s write-up tells the whole story.