Dive deep into the world of Artificial Intelligence with our curated collection of articles, covering the latest breakthroughs and insights from leading researchers and engineers.
A practical guide to MoE post-training, covering the tradeoff between load balancing and task quality, why RL becomes unstable when routing changes across engines or policy versions, and how to choose EP versus ETP in large-scale deployments.
Qing Ke Ai
If OpenClaw cron jobs are not firing, the root cause is usually not “cron is broken” but a mismatch in where scheduling is configured, which gateway is actually running, or whether the real execution path was ever verified. This guide walks through the shortest path to diagnosis.
LLM Practical Experience Hub
Use OpenClaw to turn Vercel deployment events into useful chat alerts instead of raw webhook noise. Trigger wakeups from webhooks, schedule follow-up checks with cron, and deliver production context to Telegram, Feishu, or your team’s preferred chat surface.
LLM Practical Experience Hub
Connect OpenClaw to Telegram so your AI assistant can reply in DMs and groups, remember context, run scheduled workflows, and proactively send useful updates instead of waiting in another tab.
LLM Practical Experience Hub
Context engineering turns prompt management into a runtime systems problem for AI agents, covering reversible offload, just-in-time retrieval, lossy but recoverable summarization, sub-agent isolation, and cache-stable request design.
Qing Ke Ai
Learn how to connect OpenClaw to Feishu or Lark so your AI assistant can chat in DMs, handle group mentions, remember context, and proactively run real workflows for your team.
LLM Practical Experience Hub
Learn how to connect OpenClaw to Telegram so your AI assistant can message you proactively, monitor workflows, and handle real operational tasks from a chat surface you already use every day.
LLM Practical Experience Hub
WideSeek-R1 reframes broad information seeking as an organizational problem, using a lead-agent-subagent MARL system to parallelize search and approach DeepSeek-R1-level performance with a 4B model.
Qing Ke Ai
A practical guide to policy entropy collapse in RLVR and GRPO, covering why PPO clipping drives entropy decay and how dynamic clipping schedules restore exploration.
Qing Ke Ai
PlugMem turns raw AI agent logs into reusable knowledge, improving long-term memory quality while lowering context cost across LongMemEval, HotpotQA, and WebArena.
Qing Ke Ai
Learn how advanced importance sampling techniques like GEPO and VESPO solve data staleness in off-policy reinforcement learning for stable and efficient training.
Qing Ke Ai
How Ling 2.5 replaces part of GQA with a 1:7 MLA + Lightning Attention design to improve long-context throughput, reduce KV cache cost, and keep training quality stable.
Qingke AI
A practical LLM Reinforcement Learning guide covering REINFORCE to PPO/GRPO derivations, plus production engineering patterns like async rollouts, importance sampling, and token-stream stability.
Chi Guo Dong Bu Tu Guo Dong Pi
Clawdbot is an open-source AI agent with memory, proactive notifications, and task automation. Learn how to set it up for $5/month and why developers call it early AGI.
Sai Bo Fen Wei cybervibe
After 6 months of LLM RL training failures and breakthroughs, I share battle-tested solutions for training collapse, GRPO instability, exploration bottlenecks, and why Thinking models need special handling. Practical fixes you can apply today.
Qing Ke Ai
Master Google Nano Banana Pro to create publication-ready academic diagrams. Learn our proven 2-step workflow combining LLMs with AI image generation for CVPR/NeurIPS-quality scientific illustrations.
Datawhale
Master Nano Banana Pro (Google's AI image editor) with 50+ expert prompts. Learn multi-turn refinement, character consistency, product shots, and scene replacement techniques.
Xi Yang Shi Ai Shi Pin
Discover 12 battle-tested lessons from months of production RL training. Learn why stability trumps everything, how agentic RL differs from reasoning RL, and practical strategies to avoid reward hacking in LLM training pipelines.
Chi Guo Dong Bu Tu Guo Dong Pi
Discover why file systems are making a comeback in AI agent architecture. Learn how AGFS's 'everything is a file' philosophy enables seamless multi-agent collaboration and simplifies LLM tool integration with Unix-style commands.
thinkingloop
DeepSeek-V3.2 rivals Gemini 3.0-Pro with 3 breakthrough innovations: DSA sparse attention, scalable RL framework, and 85K+ agent training tasks. Compare V3.2 vs Speciale for your use case.
Chi Guo Dong Bu Tu Guo Dong Pi
Discover how AI inference engines evolved from edge-optimized CNNs to cloud-scale LLMs. Learn the key differences between vLLM, TensorRT-LLM, and traditional frameworks like MNN and TVM in this comprehensive 2025 guide.
Qing Ke Ai
Best AI models for 2025 and 2026 compared in one guide: GPT-5.1 vs Claude Opus 4.5 vs Gemini 3 Pro vs Claude Sonnet 4.5 vs Grok 4.1, with pricing, context windows, and use-case picks.
Alex
OpenAI co-founder Ilya Sutskever declares the 'Age of Scaling' is over in exclusive interview. Discover why pre-training limits are here, what's next for AI research, and SSI's mission for safe superintelligence.
Ji Qi Zhi Xin
xAI launches Grok 4.1 with 2M context window, 3x lower hallucination rate, EQ-Bench3 #1 ranking, and ultra-affordable API pricing at $0.20 input/$0.50 output per 1M tokens. Full performance breakdown & pricing guide.
Alex
Explore Google's Nano Banana Pro (Gemini 3 Pro Image), the new AI image generation model with perfect text rendering and character consistency. Find out where to use it.
Jin Shuo Xin Yu
Google Gemini 3 Pro tops LMSYS Arena with record 1501 Elo score and dominates GPT-5.1 on AGI-critical benchmarks including Humanity's Last Exam (37.5% vs 26.5%) and ARC-AGI (45.1%), while achieving 100% on AIME 2025 with code execution.
Alex
Explore Kimi K2, the 1.04T parameter open-source MoE model. Our deep dive covers its MuonClip optimizer, agentic AI training, and benchmark performance.
Ji Zhi Liu
Explore Jason Wei's three laws of AI: the Verifier's Law, Commoditization of Intelligence, and the Jagged Edge. A framework for understanding AI's future progress and automation timeline.
Founder Park
Master distributed RLHF frameworks: Compare OpenRLHF and veRL architectures. Learn Ray Actors, GPU colocation, PPO implementation, and hybrid engine design for scalable reinforcement learning systems.
Qing Ke Ai
Best multi-agent AI frameworks for 2025 and 2026 compared: CrewAI, AutoGen, LangGraph, OpenAI Agents, Swarm, MetaGPT, Dify, and more. See the fastest picks for beginners, prototyping, and production.
Datawhale
Cursor 2.0 review: Composer model codes 4x faster (200 tokens/s), integrated browser for UI dev, Agents Mode. Tested vs GitHub Copilot. Is the $20/mo subscription worth it?
Liu Xiao Pai R
Master prompt engineering with this complete 2025 guide. Learn zero-shot, few-shot, and Chain-of-Thought techniques for ChatGPT, Claude, and Gemini. Includes 20+ ready-to-use templates.
Agi Luo Pan
AI agent challenges explained: solve latency issues, fix brittle planning, and avoid reflection loops. Advanced engineering patterns and RL techniques for production-ready agentic AI systems.
Qing Ke Ai
Step-by-step TensorRT-LLM tutorial: Deploy Llama 3/GPT models 3x faster on A100/H100. Includes Python setup, Docker configuration, KV Cache optimization, and benchmarks vs vLLM. Complete guide in 20 minutes.
Qing Ke Ai
Benchmarked 4 vector databases with 10M vectors: Milvus wins on cost ($500/mo vs $1,200), Pinecone on ease (zero-ops). Real latency tests, throughput metrics, scalability comparisons. Includes migration guide & Python code.
Yi Yan
Boost RAG accuracy with chunking: 9 core strategies + 2026 upgrades (contextual retrieval, late chunking, cross-granularity). Practical defaults: 256-512 tokens, 10-20% overlap. Includes Python/LangChain code and production tips.
Ai Yun Shu
Complete LangChain ecosystem comparison: LangChain for building chains, LangGraph for complex agents, LangSmith for monitoring. Decision matrix, code examples, and use cases for each tool.
Shen Du Xue Xi Shi Jue
Learn how to add special tokens to LLMs during fine-tuning without causing catastrophic forgetting. Our guide covers smart initialization and PEFT/LoRA.
Bao Bao Suan Fa Bi Ji
Master Context Engineering achieving 40-70% better LLM accuracy with 3-stage RAG pipeline: pre-retrieval data prep, in-retrieval optimization (hybrid search + re-ranking), pre-generation construction. Reduce hallucinations, improve reliability.
Quan Ge Tan Ai
Discover the top 10 AI tools thriving in the underground economy. Based on real API data, we reveal the AI coding agents and role-playing apps developers use.
Shi Zi Lu Kou Crossing
Explore the future of AI programming assistants. Learn about a local-first, secure AI coding tool that automates refactoring, testing, and deployment from your CLI.
Lao Yang Ai Gao Sheng Huo
Best RAG frameworks 2025 compared with real benchmarks: LangChain for fast prototyping, Haystack for production, LlamaIndex for ingestion, and RAGFlow for visual workflows. Includes latency, cost, and Python examples.
Chen Jin Shi Xue Ai
Complete visual guide to LLM agents. Explore 5 types of AI agents: single-agent, multi-agent, ReAct, autonomous loops. With architecture diagrams & real examples.
Lao Liu Shuo Nlp
Master RAG evaluation: 7 critical metrics from Recall@K to answer faithfulness. Code examples, benchmarks, RAGAS framework. Reduce hallucinations, boost accuracy by 40%.
AI Author
Learn how GRPO-RoC fixes outcome-based reward issues. This training method improves AI reasoning by 40% through data curation. With code examples & benchmarks.
Qing Ke Ai
Discover how the Checkpoint-Engine achieves a 30x speedup in LLM RL training. Learn about its innovative approach to parameter updates for large-scale RL.
Qing Ke Ai
DeepSeek V3 Multi-head Latent Attention (MLA) cuts KV cache 4-8x vs standard MHA. Learn low-rank compression, matrix absorption, prefill vs decode phases. Complete PyTorch implementation with tensor shapes.
Chen Jin Shi Xue Ai
Learn how to build an iOS app with AI. This Vibe Coding guide covers AI pair programming, using v0 for UI, and leveraging generative AI for faster development.
Shan Gui Er
Explore the architecture of Alibaba's Qwen3-Next, a powerful large language model. Learn about its Mixture of Experts (MoE) design and performance.
Zheng Li
Build agentic RAG with LangGraph and Qwen achieving 5x better accuracy on complex queries. Learn agent-based retrieval, multi-step reasoning, self-correction. Complete code tutorial with real benchmarks.
Ning Si Ai
Learn how to train a language model with this PyTorch training loop guide. Explore text generation, the AdamW optimizer, and Mixture of Experts models.
Zheng Li
Learn how to build a Llama-style MoE language model from scratch. This guide covers the Mixture of Experts architecture, tokenization, and model setup.
Zheng Li
Learn the complete Supervised Fine-Tuning (SFT) pipeline to enhance LLM reasoning. This guide covers the DeepSeek R1 process, from SFT to knowledge distillation.
Ning Si Ai
Learn to implement a full GRPO training pipeline. This guide covers Supervised Fine-Tuning (SFT) with cold-start data, CoT prompting, and the GRPOTrainer.
Ning Si Ai
Explore the 5 core reward functions powering DeepSeek-Coder-V2. Learn how its modular reward model for accuracy, reasoning, and format shapes AI behavior.
Ning Si Ai
Learn to replicate the DeepSeek R1 training process. This guide covers building a reinforcement learning pipeline from scratch using GRPO for advanced LLM reasoning.
Ning Si Ai
Learn how Prefill-Decode separation in LLM serving boosts goodput by 4.48x. Discover DistServe, a new architecture that optimizes latency and meets strict SLOs.
GiantPandaLLM
Compress LLMs 10-100x smaller using knowledge distillation. Learn teacher-student training, temperature scaling (T=3-5), soft targets. DistilBERT case: 40% smaller, 60% faster, 97% accuracy. Complete tutorial.
Chen Jin Shi Xue Ai
AI agents fail without proper infrastructure: 80% effort should go to infra, not models. Build 6-component pipeline: data pipelines, vector DBs (Pinecone/Weaviate), model serving, orchestration, monitoring, security. Cut costs 40-70% with optimization.
Pingxingjilu
Fine-tune LLaMA 3 with zero coding in 3 steps using LLaMA Factory WebUI. Save 80% GPU memory with QLoRA on RTX 3090/4090. Beginner-friendly tutorial with CUDA setup. Supports 100+ models.
Number in the Moutain
What is a transformer model in AI? Learn the Transformer architecture, self-attention, encoder-decoder flow, and how Transformers power GPT, BERT, Claude, and modern LLMs with diagrams and examples.
Quan Ge Tan Ai
Run Llama 3, Mistral, CodeLlama locally with Ollama: 5-10x speedup on GPU, $0 API costs, complete privacy. One-command install on macOS/Linux/Windows. 8GB RAM minimum for 7B models, 16GB for 70B. Complete setup guide.
Jordan Lee
Comprehensive guide to Large Language Models: how LLMs work, Transformer architecture, training process, prompt engineering, and real-world applications. Learn about GPT, Claude, Gemini, and more.
Quan Ge Tan Ai
Explore the shift to separated architectures for RL post-training of LLMs. Learn how systems like AsyncFlow & TransferQueue solve data orchestration challenges.
Little Boji
Explore LLM inference optimization on H800 SuperPods. Learn how a disaggregated architecture with SGLang tackles the prefill bottleneck to boost throughput.
yiakwy
Monitoring **PyTorch GPU memory usage** during model training can be perplexing. To demystify this, we'll dive into the **PyTorch memory snapshot** tool, a powerful utility for detailed **GPU memory ...
Panda
Discover a critical flaw in Supervised Fine-Tuning (SFT) that limits LLM performance. Learn how a simple learning rate tweak unifies SFT and DPO for a 25% gain.
Alex
Master GraphRAG workflow achieving 3x better accuracy on multi-hop queries vs vector RAG. Learn graph traversal, node-edge architecture, centrality ranking, PageRank. Complete knowledge graph setup tutorial for 2025.
Mi
Master GPU performance optimization: Matrix multiplication achieves 90%+ FLOPS on A100, while CNNs get only 20% due to memory bandwidth bottleneck. Learn compute-bound vs memory-bound operations, fused kernels, Tensor Cores, and H100 FP8 improvements.
xiaodong gong
Traditional reinforcement learning models struggle with real-time applications due to "AI lag." Two ICLR 2025 papers from Mila introduce groundbreaking solutions to tackle inaction and delay regret, enabling large AI models to operate in high-frequency, dynamic environments without compromising speed or intelligence.
Alex
Build production agentic AI with 7 critical infrastructure components: MicroVM runtime, memory service, zero-trust security, tool gateway. Complete AWS Bedrock AgentCore setup guide. Learn session isolation, S3 vector storage, 8-hour workflows.
Alex
Compare DeepSeek V3 vs Llama 4 architecture: MLA vs GQA attention, MoE vs dense models. Learn how 671B parameters run at 37B speed. Includes code examples and design trade-offs.
Alex
Learn how to deploy Kimi K2, a state-of-the-art Mixture-of-Experts (MoE) model, on a massive 128 H200 GPU cluster. This guide covers the key challenges and solutions using OME and SGLang for scalable, high-performance inference, achieving 4800 tokens/second with low latency.
Alex
Learn how to select the best ldmatrix operation in CUTLASS CuTe for high-performance GPU matrix multiplication. Optimize data movement and performance.
Alex
Unlock the full potential of your CUDA kernels by mastering memory coalescing with TiledCopy. This article dives deep into optimizing data transfers from Global to Shared Memory on NVIDIA GPUs, covering cp.async, row-major vs. column-major layouts, and cache line alignment to maximize memory bandwidth and accelerate your deep learning workloads.
Alex
# Fine-Tuning Qwen3 with Unsloth: Step-by-Step Guide Qwen3, the latest generation of large language models, is redefining AI with advanced reasoning, instruction following, and robust multilingual s...
Alex
# Baidu ERNIE 4.5: Advancements in Multimodal Large Language Models Baidu's ERNIE 4.5 marks a major leap in artificial intelligence, especially in the development of **multimodal large language mode...
Alex
# MemOS: Persistent Memory for LLMs & Next-Gen AI Agents  transforming base models to chat assistants. Complete 3-stage pipeline: base → instruct → chat model. LoRA reduces cost 70%, 7B model SFT in 2-4 hours on A100 ($10-20). Alpaca vs Dolly vs Open-Orca datasets compared.
Alex
Discover how linear layers enable multi-head attention in Transformers, powering advanced NLP models with parallel processing and rich representations.
Alex
Explore single-controller vs multi-controller in veRL, inspired by Google's Pathways, and learn their impact on distributed reinforcement learning systems.
Alex
The field of artificial intelligence has seen rapid advancements in reinforcement learning for reasoning, particularly within large language models (LLMs). This article reviews influential research s...
Alex
xAI's Grok 4 dominates AI benchmarks with 45% on Human Last Exam (Gemini: 21%), doubles ARC AGI scores, and introduces multi-agent architecture. Trained on 200K GPU Colossus supercomputer. Full performance breakdown.
Alex
## Qwen3 Training Pipeline: Pre-training, Reinforcement Learning, and Model Distillation ### Qwen3 Pre-training: Building a Robust Foundation Qwen3 training begins with a comprehensive three-stage ...
Alex
Google dominates 2025 LLM API market with 42% share vs OpenAI 28%. Gemini Flash is 10x cheaper ($0.075 vs $0.50/1M tokens). OpenRouter data reveals shocking shift. Full cost comparison + market trends.
Alex
Master training 671B parameter LLMs with RL. Solve 5 critical challenges: Megatron vs FSDP, memory offloading, weight conversion, 1000+ GPU scaling. Real DeepSeek V3 workflow with GRPO achieving 10x speedup.
Alex
Migrating from traditional to AI infrastructure? Master 5 critical differences: GPU vs CPU scaling, KV Cache vs web caching, 3D parallelism vs load balancing. Real migration strategies for LLM systems in 2025.
Alex
SGLang crushes vLLM with 3x throughput and 40% cost savings via prefill-decode separation. Real H800/A100 benchmarks, architecture deep-dive, production deployment guide. The future of LLM inference.
Alex
### Why Direct Reinforcement Learning on Base Language Models is the Next Frontier Direct reinforcement learning (RL) on base language models is emerging as a transformative approach in LLM optimiza...
Alex
Learn Andrew Ng
Alex
Reinforcement learning for LLMs (large language models) is revolutionizing the field of artificial intelligence by enabling models to learn beyond the constraints of supervised learning. This article...
Alex
Compare 7 LLM sampling methods: Top-P (Nucleus), Temperature, Beam Search, Min-P, Mirostat. Fix repetitive outputs, improve quality. Includes parameter tuning guide for GPT/Claude/Gemini.
yong qiang
## Kimi Researcher: Advancing AI Agents with End-to-End Reinforcement Learning Kimi Researcher is the flagship product of the Kimi Agent initiative, designed to revolutionize research automation thr...
Alex
Fix Qwen3 FP16 overflow on mobile devices: QK-Norm explained with code examples. Deploy LLMs on edge hardware (RTX 3060, mobile chips) with 90% error reduction.
Alex