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Insights on AI, security, software architecture, and building what's next for ambitious businesses.
1574 articles in Guide · Page 42 of 79
Apache Superset Performance: Worker Pool Sizing
Master Apache Superset worker pool sizing with real numbers and configuration patterns. Optimise performance, reduce latency, and scale analytics reliably.
Apache Superset vs Qlik Sense: 2026 Decision Framework
Compare Apache Superset and Qlik Sense across TCO, governance, embedding, and team experience. Decision matrix for data leaders in 2026.
APRA CPS 234 and Third-Party AI: What Trustees Demand
APRA CPS 234 compliance for third-party AI. Controls, audit prep, and implementation steps trustees demand. PADISO's practitioner guide.
The Healthcare AI Operating Model in 2026
End-to-end guide to building a healthcare AI operating model: governance, build vs buy, vendor selection, and the maturity curve from pilot to portfolio-wide deployment.
How to Bench an Unreleased Model Without an API Key
Framework for benchmarking unreleased LLMs without API access. Repeatable process for engineering teams to evaluate models before production release.
Implementing ISO 42001: A Practitioner's Path
Practical guide to ISO 42001 implementation for mid-market AI companies. Evidence patterns, tooling, review cadence, and audit-ready controls.
MCP Servers vs REST APIs for Tool Integration
Compare MCP servers and REST APIs for AI tool integration. Learn design tradeoffs, security models, and developer experience to choose the right approach.
Migrating from Qlik to Apache Superset: The D23.io Playbook
Step-by-step Qlik to Superset migration guide: data remapping, dashboard rebuilds, semantic layers, training, and cutover. PADISO's proven playbook.
Using Opus 4.6 for Agent Orchestration: Patterns and Pitfalls
Production-grade patterns for Opus 4.6 agent orchestration: prompt design, validation, cost optimisation, and failure modes engineering teams hit most.
Using Opus 4.7 for Financial Reconciliation: Patterns and Pitfalls
Production-grade patterns for deploying Opus 4.7 on financial reconciliation. Prompt design, validation, cost optimisation, and failure modes.
Opus 4.7 in Government: A 2026 Adoption Playbook
Deploy Opus 4.7 in government securely. Real architectures, data residency, governance, ROI benchmarks and production tasks for 2026 federal adoption.
Opus 4.7 in Mining: A 2026 Adoption Playbook
Deploy Opus 4.7 in mining operations. Real architectures, governance, data residency, ROI benchmarks, and production tasks where Opus 4.7 delivers measurable value.
Portfolio-Wide AI Operating Model for Energy
Build a scalable AI operating model across energy portfolio companies. Diligence, capability rollout, governance, and exit positioning benchmarks.
Using Sonnet 4.5 for Meeting Note Summarisation: Patterns and Pitfalls
Production-grade patterns for Sonnet 4.5 meeting summarisation. Prompt design, cost optimisation, validation, and the failure modes engineering teams hit most.
Updating Production Prompts for New Claude Versions
Framework for safely updating production prompts across Claude model releases. Testing, versioning, and rollback strategies for 2024–2027.
Using Opus 4.7 for RAG Retrieval: Patterns and Pitfalls
Production-grade patterns for deploying Opus 4.7 on RAG retrieval workflows. Prompt design, output validation, cost optimisation, and failure modes.
Agent Evaluation: Beyond LLM-as-Judge
Master production AI agent evaluation. Reference-based, behavioural, and outcome metrics that actually predict real-world performance.
AI Incident Response Playbooks for Compliance Teams
Build AI incident response playbooks that pass audits. Controls, evidence patterns, and implementation steps for SOC 2, ISO 27001, and regulatory readiness.
Anthropic's Next Release: Pre-Built Evaluation Suite
Master Anthropic's pre-built evaluation suite for Claude models. Framework for engineering teams to test AI agents through 2027.
Apache Superset Alerts and Reports: Patterns from Real Deployments
Production patterns for Apache Superset alerts and reports. Code examples, performance benchmarks, scheduler config, and deployment gotchas from live systems.