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Fine-tuning or prompting? A practical decision guidePrompting gets you started; fine-tuning gets you consistent. A checklist for choosing the right tool for each task.Most teams reach for prompting first, and they should. It's fast, cheap to change and easy to review. But prompts hit a ceiling when a task needs a consistent format, specialised vocabulary or behaviour that's hard to describe in words.Continue readingProducts
Turn one-off answers into scheduled reports with CoworkerAsk once, then let Coworker deliver the answer every Monday, shared with the people who need it.3 mins readResearch
The silent failure in long RL runs: trainer–inference mismatchA tiny numerical gap between rollout and training log-probs can bend a week of learning. How we detect it early.8 mins readModel training
What's new in the model catalog: 174 open-weight modelsNew MoE releases, longer contexts and per-model correctness tests: the autumn update to the catalog.4 mins readResearch
Human preference optimization, explainedRLHF, DPO and what preference data really needs to look like for enterprise assistants.7 mins readProducts
Pinned story packets: how Developer keeps builds traceableEvery coding task carries the exact scope, PRD and TDD versions it was approved against.4 mins readGuides
Build an evaluation set your team will actually trustFifty real cases beat five thousand synthetic ones. A step-by-step guide to evals for production models.6 mins readModel training
Collecting multimodal training data with consentFrom egocentric video to contact-centre calls: how we record, label and deliver training-ready datasets.5 mins readProducts
Turning the GPUs you own into a model labRun the same training, inference and Console on your own cluster, with your data and weights in your environment.4 mins readGuides
A red-teaming checklist for enterprise assistantsTwelve probes every assistant should survive before it meets a customer, with examples from regulated industries.6 mins readResearch
How much domain data is enough?Early results on scaling curves for domain adaptation, and why the first thousand examples matter most.9 mins readProducts
A tour of the Training ConsoleRuns, checkpoints and deployments in one place, without losing sight of the experiment.3 mins readGuides
Serving your fine-tuned model behind an OpenAI-compatible APISwap a base URL and keep your existing client code. A short guide to going from checkpoint to endpoint.4 mins read
The silent failure in long RL runs: trainer–inference mismatch
Research
Introduction
In on-policy reinforcement learning the trainer assumes the samples came from the policy it's optimising. In practice, rollouts are generated by a separately optimised inference engine, and the two rarely agree to the last decimal.
8 mins readContinue ReadingModel training
Training agents to use tools reliablyEnvironments, rewards and the small details that decide whether an agent finishes a multi-step task.7 mins readGuides
Data governance for AI teams, without the slowdownWho can train on what, and how to prove it later. Practical controls that keep projects moving.5 mins readResearch
Understanding inference cost per tokenBatching, context length and model size: the three levers that decide your serving bill.6 mins readProducts
Clearing claims paperwork in minutes, not daysHow a healthcare team used Coworker and a fine-tuned model to triage claims and flag at-risk renewals.5 mins read
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