Guide AI with constraints by stating invariants, hard limits, and acceptable tradeoffs so fixes honor contracts, not convenience. This principle-first method keeps CI/CD reproducible and advice trustworthy.
Accuracy isn’t intelligence. This piece shows why to evaluate AI decision quality—shifting from prediction to aligned, agentic choices with human override, logging, and accountability.
Start simple—even with a wrapper—to validate demand fast, then layer moats through UX, domain workflows, integrations, and data before scaling. Build what users retain.
When you code with AI, security must be continuous. Embed AI code security best practices by prompting for a vulnerability scan on every change to keep speed and resilience in sync.
Tired of seeing 'classic' from your assistant? Learn how to stop AI generic answers by insisting on evidence, context, and concrete next steps for faster, better debugging.
A practical look at how to operationalize AI in workflows—building reliable data-to-decision pipelines that embed insights where work happens and measure decisions changed.
Combine domains, add constraints, and revive old scraps—this guide shows how to brainstorm with AI to expand options and spark original, shippable ideas for engineers. Think sandbox sessions, pressure-tests, and a five-step mini-sprint.
Automation scales, but trust comes from principled pauses and visible lifelines. Here’s how to keep human judgment in AI without losing velocity.
Continuity-first agentic systems anchor today’s changes to yesterday’s decisions with memory and rationale. Learn how to prevent AI agent context loss using constraints, ledgers, and fail-closed workflows.
Most demos confuse delegation with intelligence. This piece explains when to use multi-agent LLMs—only when distinct value functions conflict—and how to design arbitration to resolve real tradeoffs.
