Four package managers have shaped a decade of JavaScript tooling. npm is the incumbent that ships with Node, Yarn was
Continue readingnpm vs Yarn vs pnpm vs Bun: Fifteen Years of Package-Manager Wars, as a Timeline
What this is: a chronology of the major LLM releases of 2026, paired with the loudest things the internet said
Continue readingEvery 2026 LLM Release, Paired With the Hype That Announced It — Scored for Accuracy
Most teams that ship a retrieval-augmented generation feature never measure it. The demo looks good, a few hand-picked questions return
Continue readingStop Vibing Your RAG Pipeline: Recall@k, MRR, and NDCG in an Afternoon
Run two unrelated documents through a large language model at the same time, and you assume the model keeps them
Continue readingYour Transformer Can Hold Two Thoughts at Once: The New Linear Superposition Result
September 2026 has been one of the densest months for AI releases in recent memory. Within the first 72 hours,
Serving a large language model to thousands of concurrent users is, underneath all the marketing, a memory management problem. Every
Continue readingDeepSeek-V4.1-Flash: Why the Most Interesting AI Paper This Month Is About Storage
Autoregressive decoding is the reason large language models feel slow: every token is generated by a full forward pass, and
When an LLM serves a 2,000-token response to a prompt of 10,000 tokens, it does something that looks absurd from
Continue readingPagedAttention and the KV Cache: How OS Paging Explains Modern LLM Serving
Every RAG system has two halves: a retriever that decides which text the model sees, and a generator that answers
Continue readingRAG Chunking Strategies in 2026: What the Benchmarks Actually Show
Serving a 7B model in fp16 takes roughly 14 GB of VRAM. A 70B model takes around 140 GB —
Continue readingLLM Quantization Explained: GPTQ, AWQ, GGUF, and When Each One Wins