News from May 2026

Guide and benchmarks showing how Multi-Token Prediction (MTP) layers can roughly double local LLM generation speed with minimal extra RAM, tested across Qwen 3.6 variants and complex long-context prompts.

A cautionary argument that relying solely on AI to write and read code leaves developers vulnerable to hidden errors, security risks, vendor lock-in, and career fragility unless they learn to understand and fix code themselves.

Demonstrates running Qwen3.6 27B GGUF on llama.cpp and boosting throughput from ~67 to ~120 tokens/sec by enabling MTP (multi‑token prediction) and stacking N‑gram speculative decoding, with setup steps and VRAM notes.

Overview of MTP (multi-token prediction) now merged into llama.cpp, how it works, which models support it, required GGUF updates, and tuning tips showing up to ~25% TPS gains with minimal downsides.

A practical guide to building a sovereign AI stack: separate risky agents from core data, blend frontier cloud models for architecture and reviews with fast, stable local models for day‑to‑day work, and choose balanced hardware (e.g., 128 GB RAM, token speed over sheer size) instead of chasing extremes.

Explains how DeepSeek V4 Flash achieves near-frontier performance at ultra-low cost and can run fully offline on consumer hardware using mixture-of-experts, hybrid attention for million-token context, and aggressive quantization, along with real-world strengths and limitations.

Yann LeCun argues that while LLMs are useful, they cannot lead to general intelligence, outlining JEPA-based world models that plan via abstract prediction for robotics and real-world control, his Tapestry vision for sovereign open AI, and reflections on Meta and research culture.

The creator compares Llama, Qwen, and Gemma running locally on a Mac Mini across logic, technical explanation, and a real-world task, finding the smallest model (Gemma 3 4B) fastest and most useful while explaining tradeoffs like open weights, size, and quantization.

Explains Yann LeCun’s JEPA world-model approach as a non-generative, joint-embedding alternative to LLMs, tracing its roots (Barlow Twins, DINO) and showing how it avoids blurry video prediction to enable action-conditioned planning.

Explains Yann LeCun’s JEPA world-model approach as a non-generative, joint-embedding alternative to LLMs, tracing its roots, the representation collapse fix (Barlow Twins), and how JEPA enables predictive control and planning.