Learn AI by connecting concepts with real decisions.
Technical guides, patterns and readings to go from explanation to verifiable implementation.
Practical guides
Complete tours to resolve a specific decision.
How to choose a local model
Memory, quantization, context and speed without buying blindly.
Complete guide ↗ ASSESSMENTHow to read a benchmark
Dataset, metrics, conditions and limits before comparing results.
Method ↗ DECISIONHow to compare models
A common table for capacities, prices and traceability.
Comparator ↗APIs and development
Decisions that appear when taking a prototype to production.
Structured Outings
Schemes, validation and recovery from invalid responses.
Integration ↗ CONTEXTWindow and recovery
What to send to the model, what to recover and what to keep outside.
Architecture ↗ OPERATIONBudget and observability
Cost per task, latency, failures and end-to-end quality.
Production ↗Agent patterns
More autonomy requires better boundaries and observability.
Research explained
Technical readings without turning an isolated result into a universal conclusion.
More tokens do not always provide more signal
Attention, recovery and degradation in extensive tasks.
Assessment ↗ AGENTSMeasure processes as well as results
Traces, intermediate decisions and recovery from failures.
Evals ↗ LOCALInference moves to device
Small models, multimodality and real restrictions.
Deployment ↗Related analysis
How to Audit AI-Generated SQL Before Using It
A query can run without errors and still calculate a metric incorrectly. This protocol explains how to inspect joins, filters and aggregations, test in a controlled environment, and compare results with known references.
18 Oct 2026 ↗
How to instrument an AI application to investigate failures, latency, and usage
A practical guide to logging each request and its stages without indiscriminately storing sensitive data. It covers an event schema, an instrumentation example, privacy criteria, diagnostic dashboards, and a process for investigating failures and distinguishing API-reported usage from final charges.
18 Oct 2026 ↗
Designing Safe Degradation: A Guide to AI Automations
Learn how to define failure states, recovery policies, and acceptance tests that prevent duplicate effects in AI-powered automated workflows.
18 Oct 2026 ↗
How to Assess the Strength of Evidence in AI Papers: A Methodological Guide
This guide offers a structured way to examine the evidence chain in AI research. It shows how to distinguish direct observations from implicit assumptions and interpretive conclusions, so readers can identify which claims the data actually support and which depend on premises that may not hold.
17 Oct 2026 ↗
Beyond the headline result: how to read robustness tests in an AI paper
A methodological guide to distinguishing experimental robustness from generalization and reproducibility, reconstructing test conditions, and drawing conclusions proportionate to the evidence observed in AI research.
17 Oct 2026 ↗
From Launch Date to Metrics: How to Audit Real Progress in AI
A recent release, more parameters, or a longer context window does not by itself prove that a model has improved. This framework helps separate announcements from measurable evidence and decide what to verify before adopting a new tool.
17 Oct 2026 ↗