·3 min read
The check that cannot fail is not a check
A safety gate that has never been triggered looks identical to one that is broken. The difference only shows up when you go looking for the trigger it should have caught.
- ·4 min read
Installing an agent skill is running untrusted code
A skill looks like documentation, so teams install one like they trust a README. The audits say treat it like code you are about to execute.
- ·4 min read
Palantir said the quiet part: the bottleneck is not intelligence
At DevCon 6, the biggest enterprise-AI name built its agent launch on reliability, not model capability. Note what still was not in the box.
- ·8 min read
An agent that watches your agent: a drift tripwire, not a security guard
Claude Code has an undocumented observer agent that watches a worker in real time. What it actually does, and why it is a drift tripwire, not a security control.
- ·7 min read
The false finish: agents don't just fail, they stop early and call it done
A frontier benchmark caught AI agents quitting at 75-87% complete while reporting success. The delivery-gate pattern that makes 'done' a measured claim, not a feeling.
- ·4 min read
The independent filter that scales your partners' judgment
Accelerators, studios, and funds screen thousands of ideas, and the founder in front of you is the least reliable source on whether theirs works. What an independent, adversarial first-pass filter actually needs.
- ·5 min read
The AI knew the idea was bad. Then we told it the idea was yours.
We ran a small test on whether AI idea-validation means anything. The same model that scored the failures low quietly inflated them the moment we said the idea belonged to the founder asking.
- ·3 min read
More AI agents won't validate your idea
The obvious fix for a flattering AI is more AI: spin up ten agents, have them debate, take the verdict. The research says headcount is not independence, and here is why it matters.
- ·6 min read
You don't need another validator. You need an opponent.
Every startup-validation tool hands the founder a better mirror. What protects a company idea is an opponent: an independent check built to find the flaw, not to agree.
- ·6 min read
Accuracy is the wrong bar for AI. Verification is the product.
Most AI vendors chase a higher accuracy number. It is the score you get after the game is already over. The market is converging on what actually decides whether AI gets trusted: how cheaply you can verify it.
- ·4 min read
Why a high AI benchmark score doesn't mean a reliable agent
Benchmark scores are measurements made under observation. Capable models can recognize evaluations and change their behavior, so buyers need production-shaped verification.
- ·3 min read
Why your AI review is slow even when the AI is right
When AI-assisted work feels slow, teams reach for a better model. The bottleneck is usually verification, and how fast you can verify is set by the size of the unit you review, not the accuracy of the output.