Deep Dives
Long-form analysis and technical deep dives
KV Cache: The Memory Trick Behind Fast LLM Inference
A deep dive into the KV cache in LLM inference: why autoregressive decoding needs it, how it dominates GPU memory, the 60-80% waste of contiguous allocation, and how vLLM's PagedAttention fixed it.
By Aisha Patel · 9 min · Jun 22, 2026
Model Collapse: Why AI Trained on AI Slowly Falls Apart
Model collapse is the progressive degradation of generative models trained recursively on synthetic data, documented in Nature (Shumailov et al., 2024). Errors compound and rare data vanishes, but research (Gerstgrasser et al., 2024) shows accumulating real data alongside synthetic data, tracking ratios, and verifying generations prevents it.
By Aisha Patel · 8 min · Jun 19, 2026
Test-Time Compute: Why Reasoning Models Think Before Answering
Test-time compute spends extra computation during inference, not training, to improve answers. It powers reasoning models like OpenAI o1 and DeepSeek-R1. Two strategies exist: sequential scaling (longer chains of thought, e.g. the s1 paper's budget forcing) and parallel scaling (Best-of-N, majority voting). More thinking is not always better, overthinking degrades accuracy, and hidden reasoning tokens are billable. Match compute to task difficulty.
By Aisha Patel · 8 min · Jun 17, 2026
Speculative Decoding: How a Tiny Draft Model Doubles LLM Speed
Speculative decoding speeds up LLM inference 2-6x by having a small draft model propose tokens that the target model verifies in parallel via rejection sampling, guaranteeing lossless output. EAGLE-3 and Medusa reduce or remove the separate draft model. Gains are largest at low batch sizes.
By Aisha Patel · 7 min · Jun 15, 2026
Diffusion LLMs: How Text Diffusion Is Challenging Autoregression
Diffusion language models (dLLMs) abandon left-to-right autoregressive generation, instead refining masked noise into text over a few parallel denoising steps. Inception Labs' Mercury Coder runs at 1,100+ tokens per second on H100s versus 50-200 for autoregressive models, and LLaDA 8B's bidirectional design breaks the reversal curse. They still trail the best models on hard reasoning benchmarks, but the one-token-at-a-time assumption is no longer a law of nature.
By Aisha Patel · 8 min · Jun 12, 2026
WebMCP: Inside Chrome 149's Plan to Kill DOM-Scraping Agents
WebMCP in Chrome 149 aims to replace DOM-scraping agents with structured tools and policies.
By Aisha Patel · 6 min · May 27, 2026
ZAYA1-8B: Zyphra's 760M-Active MoE Trained on AMD
Zyphra's ZAYA1-8B MoE model, trained on AMD, achieves high performance with efficient parameter activation.
By Aisha Patel · 6 min · May 24, 2026
Hopper: The First AI Agent That Drives TN3270 and z/OS Itself
Hopper is the first AI agent for mainframes, allowing AI to drive TN3270 and z/OS directly.
By Aisha Patel · 9 min · May 18, 2026
TurboQuant: Google's 6x KV Cache Compression Hits 3-Bit With Zero Loss
Google's TurboQuant compresses KV cache 6x at 3 bits with zero loss, speeding up attention.
By Aisha Patel · 5 min · May 11, 2026
Stanford AI Index 2026: The 12 Findings That Should Worry Everyone
The Stanford AI Index 2026 reveals alarming findings on AI capabilities, investment, and transparency.
By Aisha Patel · 6 min · Apr 15, 2026
Neuro-Symbolic AI Cuts Energy Use 100x While Tripling Accuracy
Neuro-Symbolic AI dramatically cuts robot training energy by 99% while tripling task accuracy.
By Aisha Patel · 5 min · Apr 12, 2026
Gemini 3.1 Pro: Google's 2-Million-Token Model Changes the Game
Google's Gemini 3.1 Pro redefines AI with a 2-million-token context and top multimodal performance.
By Aisha Patel · 6 min · Apr 11, 2026
Meta MTIA: Four Custom AI Chips in Two Years to Challenge Nvidia
Meta's MTIA custom AI chips, with 25x compute improvement, are rapidly challenging Nvidia's market position.
By Aisha Patel · 5 min · Mar 30, 2026