Four of today's launches verify agent output rather than produce it

Four launches today focus on verifying agent output rather than generating it: Conveyor requires every code diff to trace back to an approved intent, VinvAI evaluates fixes against hidden tests, SkillWorks scores whether published Claude Code skills load, and SemiAnalysis proposes tokens per megawatt as the serving metric. The shared assumption is that generation is cheap, making reviewer attention the scarce resource, so differentiation shifts to evidence humans can check quickly.

Four separate launches today spend their engineering on checking agent work rather than generating more of it. Conveyor makes an approved intent the unit of review and traces every change back to it, VinvAI runs the service and judges a fix on observed behavior against tests the agent never sees, and SkillWorks scores whether a published Claude Code skill actually loads before anyone wires it in. The shared assumption is that generation is now cheap enough that the scarce resource is a reviewer's attention, which is also the argument behind today's essay on reading agent output as a trained skill. If that holds, the next round of differentiation is in evidence a human can check quickly, not in throughput. Read: Conveyor puts approval on intent before the code exists, then requires every diff to trace back to an approved item. That inverts the usual order, where review is the last gate and the only one. Paired with today's argument that reading agent output is a distinct skill teams have to train for, the implication for anyone running several coding agents is that adding agents without adding review structure only moves the queue. Read: VinvAI runs the services, watches them, and evaluates a proposed fix against tests the agent cannot read. Hidden tests are the cheapest defense against a model optimizing for the checker instead of the bug, and self-reported success is the failure mode being targeted. Read: SkillWorks indexes Claude Code skills, subagents and plugins and scores each one on whether it actually loads. The number that matters is not the ranking but how many published listings fail a basic load check, which turns directory installs into a supply chain question. Read: SemiAnalysis proposes tokens per megawatt as the serving metric, on the argument that power rather than GPU count binds capacity. At the other end of the same arithmetic, Can I Run This Model shows why weight size understates what a card actually needs, and a MiniMax H3 port streams weights to fit a 33B multimodal model onto a 16GB Mac. Planning at both ends is now denominated in memory and power, not chip counts. Read: Knowl attacks contradictory context in long running agent projects by superseding facts rather than appending them, pisesh goes after session sprawl, and GitX moves commit discipline into a skill so agent history stays reconstructable. Three independent tools converging on the same neglected half of memory work suggests deletion is the step most current setups skip. Watch: Two interviews published today attach specific tools to the claim: Ryan Carson describes running a startup solo on OpenClaw, Codex and Devin, and Josh Pigford walks through shipping five products at once. Named tools make the workflows checkable rather than aspirational, which is the difference between a practitioner report and a pitch. Discuss: The argument is that capabilities keep migrating out of the scaffold and into the weights, so harness logic written for today's model gaps is temporary, and the durable surface is the one managing human attention. The simulation piece pushes in the same direction from the data side, arguing for a worse signal when it arrives orders of magnitude cheaper and faster.