308 related articles

Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.

OpenAI's GPT-5.6 requires case-by-case government approval, and Claude Mythos was pulled after breaching classified systems. A full breakdown of frontier AI hitting the national security red line.

OpenAI released GPT-5.6 but it requires case-by-case government approval, while Claude Mythos was pulled after breaching classified systems. A full breakdown of AI capabilities hitting national security red lines.

Python tops the language rankings again, but AI teams are quietly swapping its internals for Rust and Mojo. A look at Python's speed and GIL pains, the two-language problem, and the rise of Rust tooling and Mojo on GPUs.

Master the full DeepSeek-OCR deployment and fine-tuning workflow: vLLM inference deployment, efficient Unsloth fine-tuning, dataset preprocessing, LoRA training, validation, and RAG vector database integration.

This week's GitHub trending focuses on AI coding: Skills sets rules for Agents, Omniroute is a never-down AI gateway, Code Review Graph is a code knowledge graph, PI is an open-source Agent toolbox, and AI Engineering from Scratch teaches from zero.

This week's GitHub trending focuses on AI coding: Skills sets rules for Agents, Omniroute is a never-down AI gateway, Code Review Graph builds a code knowledge graph, PI is an open-source Agent toolkit, and AI Engineering from Scratch teaches from the ground up.

DeepSeek partners with Peking University to open-source DSpark, an inference acceleration tech boosting single-user speed by 57%-85% under high concurrency. Learn its three core designs and the DSpec framework.

DeepSeek and Peking University open-source DSpark, an inference acceleration tech boosting single-user generation speed by 57%-85% under high concurrency. Learn its 3 core designs and the DSpec framework.

Want to break into AI from scratch? This article breaks down an efficient self-study roadmap: from Python, math, and machine learning basics to PyTorch, then to CV, NLP, and data mining—reaching entry-level career-switching intensity in 3 months.

Poolside releases Laguna S 2.1, an open-source agentic coding model: 118B total params with only 8B active, scoring 40.4% on DeepSWE — ~4.5x DeepSeek V4 Pro Max. Supports 1M-token context, deployable on a single workstation.

DeepSeek open-sources DeepSpec, a full-stack speculative decoding library delivering 60–85% inference speedup and up to 661% throughput gains in production. MIT license, 9 checkpoints, 9 benchmarks.

Kimi, Alibaba Qwen, and DeepSeek leverage MoE architecture, home-field data advantages, and the open-source flywheel to close in on OpenAI and Anthropic at ultra-low cost. A deep dive into China's open-source LLM catch-up.

How does DeepSeek founder Liang Wenfeng's open-source philosophy inspire indie game developers? This article unpacks three core methodologies for long-term thinking in technical creation.
Intelligent Model Routing: The Core Te…
Intelligent Model Routing is becoming key AI infrastructure. This article explores its principles, solution types, technical challenges, and implementation considerations to help developers balance cost, latency, and quality.

CivitAI's paid "Early Access" mechanism has sparked heated debate on Reddit: should functional models stay locked behind paywalls long-term? An in-depth look at creator monetization, community consensus, and platform responsibility.

A real case: a creator launched an AI photo generation product in 3 hours with zero code, and got paid the next day. This article breaks down the full loop methodology.

A real case: a creator launched an AI photo generation product in under 3 hours with zero code, and got paid the next day. This article breaks down the full loop methodology.

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.

A developer fine-tunes a small model with LoRA to extract conversation state, tackling the LLM long-conversation memory problem. A deep dive into the technical approach, dataset design, and the real trade-offs between fine-tuning and prompt engineering.