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Deep-dive guides on AI agents, agent orchestration, MCP, and developer tooling.
11 posts found
DeepSeek, Profiled: How a Quant Hedge Fund's AI Lab Rewrote the Scaling Debate
DeepSeek's R1 release wiped roughly $589 billion off Nvidia's market cap in a single trading day by suggesting frontier-level reasoning didn't require frontier-level spend. Here's what the company actually built, what its cost claims do and don't mean, and why its chip workarounds matter more than its benchmark scores.
Rubin, Ironwood, and the Groq LPU Aren't Competing for the Same Job
The 2026 AI chip race looks like one contest on a spec sheet, but Nvidia's Rubin, Google's Ironwood, and Groq's LPU are actually built for three different jobs. Comparing their PFLOPS numbers head-to-head misses why each one wins where it does.
Test-Time Compute Scaling: Why Letting a Model "Think Longer" Sometimes Beats Making It Bigger
A 2024 DeepMind study found that spending more compute at inference time, not training time, can let a small model match one many times its size on math problems. Here's the mechanism, and why it only works in a narrow band of difficulty.
Colorado, California, Texas: Mapping the 2026 US State AI Law Patchwork
Texas bans specific AI uses, California regulates frontier model developers, and Colorado rewrote its flagship AI law under litigation pressure. Here's what each actually requires, and why Trump's preemption executive order hasn't changed any of it yet.
Perplexity's Browser Bet: Why It Doubled Down on Comet the Same Summer OpenAI Killed Atlas
When OpenAI shut down its ChatGPT Atlas browser in August 2026, Perplexity was raising money at a $30B+ valuation to build Comet into the opposite bet: that the AI browser, not the chat window, is where the agent economy actually lives.
AI Copyright Litigation, Mapped: Where Fair Use Actually Stands After the Anthropic Settlement
A field guide to the AI training-data lawsuits that have actually produced rulings (Thomson Reuters v. Ross, Bartz v. Anthropic, Kadrey v. Meta, Getty v. Stability) and what they do and don't establish for anyone building on top of a foundation model.
Mistral AI, Profiled: The Product Isn't the Model, It's the Deployment Terms
ASML led a ~€1.7B round in Mistral AI in 2025: an industrial policy trade dressed as a venture round. A grounded look at what the Paris lab actually ships, why sparse MoE was the right asymmetric bet, and where the open-weight-plus-sovereignty strategy is fragile.
Your Model's Reasoning Trace Is Not a Log: What Chain-of-Thought Faithfulness Research Actually Shows
Reasoning models write confident step-by-step explanations that often omit the thing that actually changed their answer. Here's the experimental evidence, the mechanism behind it, and which of your uses of chain-of-thought survive it.
The Memory Wall: Why On-Device LLM Inference Is Bottlenecked by Bandwidth, Not Compute
Your phone's NPU isn't the reason local LLMs feel slow; the memory bus is. Here's why token generation is bandwidth-bound, and why quantization, KV-cache tricks, and unified memory are all attacks on the same wall.
The EU AI Act's High-Risk Rules Moved to December 2027: What Actually Changes for Builders
The EU AI Act high-risk obligations were due on August 2, 2026, until the AI Omnibus moved Annex III to December 2, 2027. Here is what is required, of whom, and how it compares with the US, UK, and China.
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