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Hi Kevin,

Your post on hiring hit a nerve over here. It’s the kind of truth everyone in the industry knows but rarely says so clearly. For years, we’ve all seen the process for what it is: a mess of bias and inconsistency, where most “fixes” are just rearranging the deck chairs.

Our take is that the problem starts because everyone is aiming for “neutrality,” and it’s a trap.

A “neutral” system just defaults to the dominant culture’s norms. It says, “we’ll treat everyone the same,” which in practice means, “we’ll judge everyone as if they came up through Silicon Valley.” It’s a broken model.

We’re building an “equitable” system instead. The core job of the engine is to adapt its interpretation to the candidate’s context—no, better said: it’s built to understand their context so we can fairly measure the skill underneath.

So under the hood, we’re essentially building a universal translator for communication styles. It’s a set of models trained to distinguish the signal (a candidate’s actual technical reasoning) from the noise (the specific cultural style they use to express it). This way, we’re actually measuring the shape of their thinking—how they build, connect, and trade off ideas—not just checking if they recited the textbook answer to some brain teaser.

Trying to judge a great engineer on a whiteboard algorithm is like judging a master chef on how well they operate a microwave. You’re testing the wrong interface.

The output isn’t a simple thumbs-up or down. It’s a high-fidelity profile of their actual engineering traits—problem-solving agility, architectural instinct, learning orientation—the stuff that actually predicts success on the job.

Anyway, your post nailed the “why.” We’re just trying to do the hard work of building the “how.”

Best!

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