I ran DeepSeek V4-Flash internals on 8x H100s — here’s what mHC actually does

<ul> <li> <strong>Book:</strong> <a href="https://www.amazon.com/dp/B0GX2SQ594" rel="noopener noreferrer">System Design Pocket Guide: Interviews</a> </li> <li> <strong>Also by

<!-- SC_OFF --><div class="md"><p>DeepSeek dropped the full V4 paper this week. preview from april was 58 pages, this version adds a lot of technical depth.</p> <p>What stood out

<p>In the first eight weeks of 2026, ten major open-weight LLM architectures were released.</p> <p>GLM-5 matched GPT-5.2 and Claude Opus 4.6 on benchmarks. Step 3.5 Flash

<p>Instead of replacing the main model with a more expensive multimodal model, I gave a text-only agent an on-demand visual sensor.</p> <p>Many coding agents can already read

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xavy8t/hardly_any_benchmarks_for_intel_b65_so_i_got_4x/"> <img alt="Hardly any benchmarks for Intel B65, so

<h1> DeepSeek V4: Million-Token Context That Actually Works </h1> <p>Most long-context models are benchmarks in search of a use case. DeepSeek V4 flips the script—it delivers 1

<!-- SC_OFF --><div class="md"><p>On my two DGX Sparks, Swift 1.5 (UkisAI's reasoning-efficient fine-tune of Qwen3.8-Flash-Next) running on veloGB10 (<a

<!-- SC_OFF --><div class="md"><p>We spent the last year building a code indexing system and tried pretty much every retrieval approach along the way. Sharing what actually worked