Thoughts on AI, data science, and the technology shaping our future.
Anthropic's April 14 Mythos Preview announcement is the first time a major lab has shipped a model as a capability demonstration rather than a product. Here's what the dual-use frontier means for data scientists, Project Glasswing's implications, and three concrete takeaways for any team building on frontier models.
Read more →Shopify's April 9 AI Toolkit release makes it the first major enterprise platform to go MCP-native. Here's why the architecture pattern matters for every data scientist building agent tools, even outside e-commerce, and three concrete takeaways for your own MCP server designs.
Read more →Meta shipped its first flagship model since the $14.3B Scale AI deal — and quietly closed it. The weights aren't coming. Here's what the pivot means for the open-source ecosystem, why Qwen, Gemma, and Nemotron are now the real open-weights leaders, and three concrete things data scientists should do this week.
Read more →Eighteen months after Anthropic shipped MCP, it's become the default transport layer for every serious agent stack — quietly adopted by LangChain, LlamaIndex, DSPy, and every major model provider. Here's why the boring standard won, what it changes for data scientists, and the gaps that remain.
Read more →NVIDIA shipped reasoning, multimodal RAG, voice, and content safety as a single unified family of fully-open models — weights, training data, and recipes included. The individual benchmarks don't tell the real story. Integration cost does, and this release dissolves most of it.
Read more →Google Research's TurboQuant compresses LLM KV caches to 3 bits with zero accuracy loss — delivering 6x memory reduction and up to 8x faster attention on H100 GPUs. No retraining, no calibration data. Here's why this infrastructure breakthrough matters more than the next model release.
Read more →Google DeepMind's Gemma 4 ships four model sizes under Apache 2.0 with native function calling, 256K context, and MoE efficiency that puts 3.8B active parameters within striking distance of 31B dense quality. Local agentic AI just became production-viable.
Read more →Google's Gemini Embedding 2 is the first production embedding model that natively maps text, images, video, audio, and documents into a single vector space. Early adopters report 70% latency reduction and 20% recall improvement — here's what it means for your RAG pipeline architecture.
Read more →Prompt engineering has evolved from ad-hoc craft into disciplined practice. From structured outputs and DSPy optimization to agentic system prompts and retrieval-grounded patterns — here's what actually works in production, and the common mistakes data scientists make.
Read more →MLflow 3.0 introduces LoggedModel as a first-class entity, comprehensive tracing for GenAI, LLM-as-judge evaluation, and deployment jobs that validate models before production. Here's what matters after migrating two production pipelines — and what's still missing.
Read more →The vector database market has matured from a handful of startups into a crowded, competitive landscape. Here's a practical comparison of Pinecone, Weaviate, Chroma, Qdrant, Milvus, and pgvector — with guidance on which to choose for your use case.
Read more →From LoRA to QLoRA, the barrier to fine-tuning has shifted from compute to judgment. Here's the practical playbook for choosing your base model, preparing data, setting hyperparameters, and deploying fine-tuned 8B models on consumer hardware.
Read more →From naive retrieve-and-generate to agentic self-reflective pipelines and GraphRAG, the RAG landscape has evolved dramatically. Here's a practical framework for choosing between RAG, fine-tuning, and long context windows — and why most production systems in 2026 use all three.
Read more →B200s range from $2.25 to $16/hour depending on where you rent. H100s have dropped 60% in two years. Here's how to think about cloud vs. on-prem vs. marketplace for your ML workloads — with real numbers and practical recommendations.
Read more →Anthropic's leaked internal documents reveal Claude Mythos, a model that represents a "step change" in reasoning and cybersecurity capabilities. Markets reacted, but the real story is what this tells us about the accelerating frontier model race.
Read more →The US just passed its most significant AI legislation ever, requiring mandatory bias audits for AI in hiring, lending, healthcare, and criminal justice. Here's how it changes the way we build and deploy models.
Read more →From autonomous code generation to supply chain orchestration, agentic AI systems are moving from research demos to production deployments. Here's what that means for data scientists and why Morgan Stanley says most of the world isn't ready.
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