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Context Window Management: When 1M Tokens Isn't Enough

Context Window Management: When 1M Tokens Isn't Enough

Claude Opus 4.6 scores 76% on MRCR v2 at 1 million tokens. Gemini 3 Pro drops to 26.3%. Bigger windows don't solve the context problem — they change it. Research-backed strategies for chunking, compression, and retrieval.

9 min read
The Accountability Gap When AI Agents Act

The Accountability Gap When AI Agents Act

When an AI agent causes harm, who pays? Current law can't answer that clearly.

4 min read
More Context Doesn't Kill RAG. It Just Changes the Fight.

More Context Doesn't Kill RAG. It Just Changes the Fight.

Long-context LLMs now hit a million tokens, but a persistent 10% accuracy gap and punishing costs keep RAG very much in the fight.

4 min read
The 12-to-72 Problem: Computer-Use Agents Hit Human Scores but Miss the Point

The 12-to-72 Problem: Computer-Use Agents Hit Human Scores but Miss the Point

Computer-use agents jumped from 12% to 72% on OSWorld in 18 months. The scores look like progress. The latency and efficiency numbers tell a different story.

4 min read
Models Training Models: The Promise and Peril of Synthetic Data

Models Training Models: The Promise and Peril of Synthetic Data

Microsoft's Phi-4 trained on more than 50% synthetic data and beat GPT-4o on graduate science benchmarks. The old rules about training data are changing fast.

4 min read
The Agent Project That Should Have Been One LLM Call

The Agent Project That Should Have Been One LLM Call

Some enterprise agent projects fail because autonomy was added where a bounded single-call LLM design would have delivered cleaner behavior and lower operational risk.

10 min read
Open Source AI Impact: Who Wins When Models Get Cheap

Open Source AI Impact: Who Wins When Models Get Cheap

Open source AI used to be the cheaper substitute. In 2026, that is too small.

11 min read
Why Multi-Agent Papers Don't Replicate in Production

Why Multi-Agent Papers Don't Replicate in Production

A paper from Tran and Kiela tested 28 multi-agent configurations across four architectures: Sequential, Parallel, Debate, and Ensemble. Every single one...

7 min read
Types of AI Agents: The 2026 Classification That Actually Helps

Types of AI Agents: The 2026 Classification That Actually Helps

The reactive/deliberative/hybrid taxonomy is broken. The 2026 classification that actually helps: coding agents, research agents, computer-use agents, task agents, multi-agent orchestrators, and self-improving agents.

18 min read
Knowledge Graphs for AI Agents: Beyond Vector Search

Knowledge Graphs for AI Agents: Beyond Vector Search

Vector databases power most retrieval-augmented generation systems in production today. They're fast, simple, and good enough for single-hop lookups...

10 min read
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