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I read five postings open in September 2026 at Palantir, OpenAI, and Anthropic. They describe the same arc, from an open question to production adoption, and they ask for two deliverables: the customer's system and what it teaches the product back home.
CLM reads the situation and each option separately and picks the closest one. It is fast when the options repeat, and weak when the sentence needs nuance.
The tables make Jev and Laya winners on different slices. The choice depends on where labels are defined and how much labelled data already exists.
Laya is a non-autoregressive decision model under Apache 2.0. It trades the convenience of a hosted API for control over running, evaluating, and specializing the model.
Jev trades text generation for typed decisions with probabilities. The idea works when the answer space is closed, the question is narrow, and code stays in control.
Coding agents look more capable when every task starts from a clean checkout. New benchmarks show the cost that appears when patches, decisions, and technical debt carry into the next job.
Your agent nailed the demo and everyone loved it. But how do you know it actually works? If the answer is 'we tested it and it seemed fine', you are operating in vibes mode. And vibes don't scale.