Technical Deep Dives.

Multimodal LLMs in production: how image tokens drive cost and latency, why MMMU-Pro saturation hides gaps, and how to pick a model by the modality you ship.

Sparse attention in 2026: NSA, DeepSeek DSA, MoBA and MiniMax MSA. Why native trainable sparsity ships where post-hoc masking of a dense model stalls.

State space models in 2026: Mamba, RWKV, and Jamba versus Transformers. Where linear-time SSMs win, where recall breaks, and why hybrids at 3:1 won production.

Thinking Machines Inkling debuted at Artificial Analysis index 41, behind Kimi K3's 57. For an open-weights fine-tuning base, rank is the wrong axis. What to measure instead.

Eval harness security after the July 2026 OpenAI/Hugging Face sandbox escape: egress as the failed control, answer keys as secrets, and a threat model you can run.

RL post-training mapped: RLHF and PPO through Constitutional AI, DPO, and GRPO to RLVR and rubric rewards. Every jump changed the reward, not the optimizer.

Synthetic data for post-training: when distillation helps, when self-generated data triggers model collapse, and how to detect the narrowing before it ships.

Test-time compute scaling explained: best-of-N, self-consistency, and verifier-guided search, where each saturates, and when more inference compute is wasted.

Self-hosting a frontier open MoE like DeepSeek V4 or Kimi K3 means holding all 1.6-2.8T parameters in VRAM, not the active count. The real GPU bill and when the API wins.

vLLM v0.25.0 removed the legacy PagedAttention code and made Model Runner V2 the default for dense models. What changed, and whether it moves your numbers.

Kimi K3 runs linear attention in 3 of 4 layers and claims it beats full attention. What the 48B evidence shows, and what stays unverified at 2.8T scale.

Agent memory is not RAG: it needs a write path, not just retrieval. Memory types, temporal knowledge graphs, forgetting by design, and why chat logs fall short.

Agent evaluation for tool use: why pass@1 hides unreliability, the pass^k metric, BFCL and tau-bench, state-based vs LLM-judge scoring, and building a harness.

The KV cache is the real bottleneck in LLM serving: 70-90% of GPU memory at long context. PagedAttention, prefix caching, MLA, and quantization explained.

World models explained: latent dynamics models, generative simulators like Genie 3, and language world models inside LLMs, plus concrete tests for coherence.

DeepSeek DSpark adds semi-autoregressive drafting and load-aware verification to speculative decoding. What is new versus EAGLE-3, and why the benchmarks are not yet independently verified.

Effective context length is far shorter than the advertised window. What RULER and NoLiMa reveal about 1M-token models, why context rots, and when RAG still wins.

A practical guide to speculative decoding in vLLM: how it works, n-gram and draft-model setups on Qwen3, EAGLE-3, and where the speedup quietly disappears.

A technical guide to LLM quantization: FP8 training, NVFP4 and MXFP4, W4A4 inference, the outlier problem, and where low-bit precision quietly breaks accuracy.

LLM evaluation is breaking down: benchmark saturation, contamination, and biased LLM-as-a-judge setups make leaderboard numbers misleading. Here is what to measure instead.

Explore how AI agents, open protocols like MCP and A2A, and computer-use models are transforming the internet from a document-retrieval system into an agentic web where software reasons, acts, and collaborates autonomously.

Qwen-Scope and Anthropic's Natural Language Autoencoders are reshaping LLM interpretability in 2026. Inside the two releases, what they ship, and where each breaks.

Inside DeepSeek V4: hybrid attention (CSA + HCA), 1.6T MoE, 1M context, and the lineage from MLA to NSA to DSA that made it possible.

Learn the architecture, frameworks, and reliability patterns needed to deploy AI agents in production. Covers LangGraph, CrewAI, multi-agent systems, and more.

Explore RAG in 2026: from naive vector search to GraphRAG, agentic retrieval, ColPali, and context engines. A deep technical guide for AI practitioners.

Learn how Model Context Protocol (MCP) became the universal standard for connecting AI models to tools and data, reshaping the entire AI ecosystem.

Mechanistic interpretability lets researchers reverse-engineer neural networks to understand how AI thinks. Learn about sparse autoencoders, circuits, and safety.

Explore how open-source LLMs like Qwen, DeepSeek, Mistral, and Nemotron closed the gap with proprietary models in 2025-2026, reshaping AI's competitive landscape.

Learn how Mixture of Experts (MoE) powers frontier AI models like DeepSeek-V3 and Mixtral: sparse routing, load balancing, and why MoE beat dense scaling.

Explore how reasoning models like o1, o3, and DeepSeek-R1 use inference-time compute scaling and chain-of-thought to solve problems standard LLMs cannot.

Explore DeepSeek's architecture breakthroughs: Multi-Head Latent Attention, auxiliary-loss-free MoE, FP8 training, and GRPO: frontier AI for $5.5M.

Master LLM inference optimization: speculative decoding, KV-cache compression, quantization, FlashAttention, and serving frameworks compared for fast, cost-effective AI.

Master the transformer architecture from first principles: self-attention, multi-head attention, positional encodings, encoder-decoder design, and modern innovations like RoPE, GQA, and SwiGLU, with code.

Explore vibe coding: the AI development paradigm coined by Karpathy. Compare Cursor, Claude Code, Google Antigravity & Copilot — with honest takes on which tools actually deliver.