136 related articles

jlens-gguf is an open-source tool bringing Anthropic's Jacobian Lens interpretability method to GGUF and llama.cpp, enabling internal observation, real-time steering, and abliteration for both dense and MoE models.

Five key AI industry trends: Doubao surpasses 180 trillion daily calls, OpenAI's in-house AI chip, NVIDIA's $3-4 trillion compute forecast, China catching up, and the GPT-5.6 cheating scandal.

DeepSeek's paper 'Thinking with Visual Primitives' was online for just 4 hours before being pulled. It uses bounding boxes and points as reasoning primitives, letting models 'point at' images to outperform GPT, Gemini, and Claude on maze navigation and counting.

Hit the Vibe Coding ceiling? This guide covers the three-stage AI coding progression path, Claude Code vs. Codex, SuperPower SDD, and how to go from vibe coding to enterprise-grade AI engineering.
Kronos Financial Foundation Model: Usi…
Kronos is the first open-source foundation model treating candlestick data as the "language of financial markets," using an autoregressive Transformer and earning 32K GitHub Stars. A deep dive into its principles, applications, and limits.

Claude Sonnet 5 review: 63.2% SWE-bench, near Opus 4.8 performance, but new tokenizer hides real costs. Ranks 13th on CursorBench. Most tasks: stick with Opus 4.8.

Bilibili creator KaterSony tests Claude Sonnet 5 across 8 real-world tasks—image recognition, 3D modeling, web generation—comparing it against GPT-5.5, Gemini 3.1 Pro, and revealing its true capability limits and cost traps.
Outlines: The Open-Source Tool for Get…
Outlines is an open-source Python library for structured LLM output. Using constrained decoding with FSMs, it guarantees JSON, regex, and Pydantic-compliant generation with near-zero overhead.

Claude Sonnet 5 leak: rumored input price of just $2/million tokens with near-Opus 4.8 performance. We break down the evidence, pricing, and what it means for developers.

Anthropic's Claude Sonnet 5 claims near-OPUS 4.8 performance at lower cost. Real-world tests reveal hidden tokenizer costs, weak creative output, and only 13th place on Cursor rankings.

LLMs explained through the lens of functions: input is x, output is y, training solves for parameters, inference computes results. Trillion parameters, next-token prediction — no advanced math needed.
Goku: A Browser-Based LLM Inference En…
Goku is a browser-based LLM inference engine built on WebAssembly and wllama, enabling local AI inference, model management, and offline use — no cloud API needed.

Leaked financials show OpenAI's -122% operating margin — losing money on every sale. MIT research reveals 95% of enterprise AI investments yield zero returns. A deep dive into the AI cost paradox.
TurboVec: A Deep Dive into the Rust-Po…
TurboVec is a Rust-based vector index library powered by TurboQuant, with Python bindings for RAG, semantic search, and AI applications. A deep-dive into its architecture.

An indie dev attempts to train a CPU-native LLM on $0 budget using ternary quantization, sparsity, and fine-grained MoE — with pre-registered success criteria and full public reporting.
Soofi Open-Source Foundation Model Tra…
Soofi is an open-source project providing complete foundation model training code, covering data preprocessing, tokenizer construction, model architecture, and training optimization. A deep dive into its value for the LLM ecosystem.

One used RTX 3090, one 16.8GB GGUF file, and Qwen3.6 27B runs locally offline. SWE-bench score of 77 rivals Claude Sonnet. MTP boosts speed to 59 tok/s. Full local AI coding assistant deployment guide.

An ML engineer trained SmoLLM, a 109M-parameter LLaMA-style model from scratch for under $50. Full breakdown of architecture, training pitfalls, instruction tuning, and real-world performance.

Claude Code is Anthropic's local AI programming assistant that reads your entire codebase, auto-debugs, and delivers far higher accuracy than Cursor and Trae. Here's why it's the strongest AI coding tool today.

Why do ChatGPT and Gemini both use Transformer? This deep dive explains how self-attention solves RNN's serial inefficiency and CNN's local limitations across three key dimensions.