Independent analysis of models, agents, automation and research—written for people responsible for what ships, what it costs and what can fail.
A production-readiness framework for engineering leaders
A coding agent should earn autonomy through controls and repeatable evidence—not through an impressive demo. This framework turns repository access into a staged engineering decision.
Turn model selection into a workload decision
A practical way to select models using task evidence, constraints and switching costs rather than a single public score.
Design approval around consequence, not anxiety
A control map for deciding which actions can run automatically and which require informed human approval.
An evidence ladder for technical teams
How to decide whether a research result deserves a prototype, a controlled trial or no engineering time yet.
We separate vendor claims, public evidence and our own recommendations. When evidence is thin, we say so.
Capability, cost, deployment and model-selection decisions.
Explore desk →02Coding agents, tool use, orchestration, control and reliability.
Explore desk →03Operational workflows that survive contact with production.
Explore desk →04What research changes, what it proves and what teams can use now.
Explore desk →05Evaluation design, reproducibility and evidence beyond leaderboards.
Explore desk →06Evidence-led frameworks for expensive AI tooling decisions.
Explore desk →Primary sources first
Reproducible methods
Limitations in plain view
Commercial ties disclosed
Research updates, evaluation frameworks and production lessons for technical leaders.
Measure systems, interventions and failure—not demos
A benchmark design for teams evaluating tool-using agents on their own operational tasks.
A buyer guide for engineering and AI leads
A vendor-neutral scorecard covering evidence, integration, security, economics, operations and exit risk.