You’ve got 100 GB of PDFs, notes, and exported chat logs you’d love to query with natural language. So you reach for the standard RAG playbook: chunk everything, embed it, store the vectors in FAISS or a hosted vector DB. Then you check the index size and it’s 150–700 GB — larger than the data […]
Read More →Your agentic app just ran a search. The tool returned 500 results as JSON. Your agent appended all of it and fired off an API call — 45,000 tokens to answer a question that needed maybe 4,500. Tejas Manohar, a senior engineer at Netflix, hit this problem every day. He was running out of tokens […]
Read More →Forward Deployed Engineer (FDE) is the most in-demand technical role in AI right now. Not because of the title, because of the problem it solves. AI research labs are shipping capabilities faster than enterprises can absorb them. The gap between what is technically possible and what is actually running in production is enormous, and closing […]
Read More →Python still owns the models. TypeScript ate the application layer. Here’s why — and what it means for what you build next. Table Of Contents The framing nobody puts on the cover If you read tech headlines in late 2025 and early 2026, you’ve probably seen the claim that TypeScript “overtook Python” or “won AI.” […]
Read More →A plain-English reference guide covering the jargon that shows up every time a new language model drops, from parameter counts to quantization methods. Contents 01 · Architecture & Model Design — Transformer · Dense Model · Mixture of Experts · Active Parameters · Feed-Forward Network · Layers · Hidden Dimension · Attention Heads 02 · Attention Mechanisms — Multi-Head Attention · Multi-Query Attention · Grouped-Query Attention · KV Cache · Sliding Window Attention · RoPE · RoPE Theta 03 · Sizing, Scale & Counting — Parameters · Embedding Parameters · Non-Embedding […]
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