New articles appear on page 1 first. As new stories are published, existing articles move down to later pages.
AI agents are taking on real work. Who should sign off?
How teams can define human accountability, approval boundaries and escalation before AI agents act.
Why smaller AI models can be better products
When on-device AI can improve privacy, latency and cost—and what teams give up in capability.
What AI regulation means for product teams in 2027
A practical look at changing AI rules, product obligations and the questions teams should track.
Humanoid robots are reaching factories. The real test is reliability
Why real-world reliability, safety and useful task performance matter more than impressive demos.
AI benchmarks are useful. They are not the whole story
How benchmark contamination and narrow tests can make a single score misleading.
AI pricing is changing because software usage is changing
What usage-based AI pricing means for budgeting, predictability and vendor comparisons.
Who is responsible when an AI system causes harm?
Responsibility can depend on the product, deployment, contract and jurisdiction.
The best warehouse robot may be the one that knows when to ask for help
Reliable automation needs a safe way to recognise uncertainty and ask for human help.
Open weights, open source and AI licenses are not the same thing
Weights, source code and licenses can grant very different rights. Here is how to check.
Drone delivery has solved part of the journey. The last 100 meters are different
The final delivery handoff introduces people, obstacles, regulation and uncertainty.
RAG vs fine-tuning: which approach does your AI product need?
A practical guide to retrieval-augmented generation and fine-tuning, including costs, updates and evaluation.
An AI data privacy checklist for product teams
A framework for deciding what data an AI feature can use, how it is retained and what users should know.
How to evaluate an AI feature before launch
Build a realistic evaluation set, measure failure severity and decide what evidence is enough for release.
AI customer support needs a reliable human handoff
How support teams can use AI without trapping customers in loops or obscuring accountability.
AI coding assistants still need code review
Where coding assistants help, where they fail and how teams can review AI-generated code safely.
AI model drift: what to monitor after launch
Understand data drift, performance drift and practical monitoring signals for AI systems in production.
Synthetic data for AI: useful tool, not a free shortcut
Where synthetic data can help testing and training, and how to guard against bias, leakage and unrealistic examples.
AI hiring tools need transparency and human review
A practical look at bias testing, candidate notice, accessibility and accountability in AI-assisted recruitment.
A practical AI adoption plan for small businesses
Identify useful AI workflows, protect data and measure whether automation actually saves time.
Multimodal AI explained: when text, images and audio meet
What multimodal AI systems do, where they can be useful and why mixed inputs create evaluation challenges.
The Ninth Post pulse
A practical snapshot of the ideas shaping our coverage.
- Accountability in AI products
- Infrastructure and energy constraints
- Practical governance, not blanket rules
Signal over noise.
We explain what changed, why it matters, what the evidence says and what remains uncertain.
- Primary sources before hype
- Practical context over recycled announcements
- Clear corrections and editorial standards