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17 posts
For three years, on-device AI was a demo that almost worked. In June 2026 it stopped being one. Sparse models like Apple's AFM 3 and Google's Gemma 4 made intelligence large in flash, small in motion, free to run, and offline by default.
In 2024, you needed a 70B model to get good answers. In 2026, a 7B model trained on the right data beats it on most real-world tasks. The mechanism isn't a secret — it's distillation done well, and it's reshaping the entire model economy.
In April 2026, Anthropic blocked the year's most viral open-source agent framework from spending Claude subscriptions. The fight wasn't really about one developer in Vienna — it was about an unspoken truth the labs had been dodging for a year: chat is the past, agents are the interface, and subscriptions cannot price what comes next.
Every frontier model now claims a 1M-token context window. In production, almost no one uses more than 64K. Here's the gap between the benchmark and the reality, and what to do about it.
Model Context Protocol started as Anthropic's spec for hooking Claude into tools. A year later, every major AI provider, IDE, and SaaS vendor speaks it. This is what protocol-winning looks like in real time.
Enterprise teams are ditching traditional SaaS dashboards for autonomous AI agents that monitor, decide, and act. Here's what's driving the shift and what it means for software builders.
RAG is the most deployed LLM pattern in production today. After building RAG systems for 18 months, here are the architectural decisions that matter and the mistakes that don't show up until scale.
Token costs are just the tip of the iceberg. After running LLM workloads in production for a year, here's where the money actually goes — and how to cut costs without cutting quality.
AI systems fail differently than traditional software. After investigating 50 production incidents across ML systems, here are the patterns — and the engineering practices that prevent them.
Three approaches to customizing LLM behavior, each with different tradeoffs. A decision framework based on your data, budget, and accuracy requirements.
AI agents don't just write code — they review it. Autonomous code review catches bugs, security flaws, and design issues that human reviewers miss. Here's how it works.
The single most important capability that turned language models into agents wasn't better reasoning — it was tool use. Here's the technical story of how function calling changed everything.
Traditional RAG retrieves documents and stuffs them into context. Agentic RAG plans queries, evaluates results, and iterates until it finds the right answer.
Building a demo agent is easy. Shipping one that handles edge cases, recovers from failures, and earns user trust is hard. Here are the lessons learned.
Not all AI agents are created equal. A practical comparison of Claude, GPT-4, and Gemini on real software engineering tasks — coding, debugging, and system design.
Single agents are powerful. Teams of specialized agents working together are transformative. Here's how multi-agent architectures are reshaping complex problem-solving.
The shift from AI-as-tool to AI-as-agent represents the biggest paradigm change since the internet. Here's how we got here and where it's heading.