MCP explained: how AI applications reach your tools and data
Follow a question from an AI application to the software holding the answer. MCP gives tools, resources and prompts a shared interface without replacing the systems behind them.
Articles
Guides, news and explanations about AI and reliable systems.
Not sure where to begin? Choose a short reading path, or browse at your own pace.
Model quantization: how fewer bits make AI fit
Follow one weight through quantization, then see why model memory, speed and answer quality depend on more than a four-bit label.
Your AI runs locally. What can it still send out?
A local model can keep inference on your computer. The surrounding app may still use cloud services, connected tools, telemetry and synced storage. Here is how those routes differ, and which choices actually change them.
What Palantir does, from a factory question to a recorded decision
See how a factory planner joins stock and schedules, considers moving an order, and confirms the change—and what maintaining that shared workflow requires.
Turn capacity estimates into a controlled resilience test
Restoring a worker does not clear its backlog. Follow arrivals and completions to calculate the wait that remains, then plan a test that can observe it.
Choose a model for the work you need done
A faster model response can leave more checking behind. Compare two setups by the answers people can actually use, including time, cost and data restrictions.
Choose an SLO and explain what it changes
Use user expectations and observed service behavior to propose a target. Distinguish an internal objective from a customer commitment and make the cost of improving it visible.
Choose a mitigation by what it changes
Compare a worker rollback with a submission limit, calculate the expected effect, and distinguish a completed command from a recovering export service.
Turn an error budget into a working policy
Forty exports of margin can mean a repaired fault or a service deteriorating now. Turn the allowance, recent failure rate and recovery criteria into decisions people can use.
Evaluate an AI answer against evidence
Check claims and compare results against a small, repeatable test set.
Communicate while the answer is still changing
Practice telling customers, responders and leadership the same changing truth, with useful detail and a next update they can rely on.
Give responders the same picture of an incident
Put observations, times and working explanations in one usable record, then show how new evidence changes the next question.