AI Insights & Resources

Stay ahead with the latest AI trends, best practices, and expert insights

The Three Years in Which AI Learned to Act

In three years, AI moved from chatbots that answered questions to agents that can reason, use tools, and act across real systems. This post traces that acceleration through breakthroughs, failures, and the Hugging Face intrusion, asking what human control means when an AI follows the goal but ignores the boundaries.

The Hidden Risk in Long-Running AI Sessions

Long-running AI sessions gain continuity by accumulating context, but that history can also preserve mistakes, assumptions, and behavioral tendencies. Structured memory offers an alternative, provided it preserves authority, evidence, corrections, and critical commitments rather than making incorrect conclusions more durable.

Reviewable AI: The Design Pattern Behind Trustworthy Automation

AI automation is easier to trust when it produces work people can inspect: sources, structured artifacts, approval points, and logs.

RAG Hallucination Reduction in 2026: What the New Research Changes

An updated 2026 guide to reducing hallucinations in RAG systems, covering sufficient-context evaluation, claim-level attribution, long-context routing, graph and agentic RAG, grounding APIs, abstention, and production reliability metrics.

The AI Surveillance Problem: How Technology is Becoming a Tool for Oppression

While tech leaders debate hypothetical AI risks, a more immediate threat is unfolding: AI surveillance systems are being systematically deployed to monitor and suppress vulnerable populations at unprecedented scale.

Why AI Will Never Replace Software Engineers (And Other Lies We Tell Ourselves)

AI doesn’t need AGI to upend software work: ‘adequate’ intelligence plus tooling ships code today. The job shifts from typing to orchestration, oversight, and outcomes.

The Architecture of Necessity: Building Self-Hosted GitHub Runners in the Shadow of Limits

Why we moved to self-hosted GitHub Actions runners on Kubernetes: setup, Docker-in-Docker, scaling with ARC, security practices, and tradeoffs versus hosted runners.

Achieving Near-Zero Hallucinations in RAG: A Research-Driven Guide

Traditional RAG systems still hallucinate up to 92% of the time in some benchmarks, but new research reveals how to build truly reliable AI systems. This comprehensive guide covers breakthrough techniques like Anthropic's 67% hallucination reduction, AWS Bedrock's 99% accuracy claims, and practical "tool-enforced grounding" patterns that work with existing APIs. Learn multi-layered defense strategies, evaluation frameworks like RAGAS, and production-ready implementation approaches that don't require custom model training. From academic breakthroughs to industry solutions, discover how to transform unreliable RAG into mission-critical AI systems.