llms
10 GitHub Repositories To Master Claude Code - KDnuggets
Learn faster, build smarter, and unlock the full power of Claude Code through real examples, reusable templates, prompts, workflows, subagents, and system design.
LLMs+
LLMs are evolving. The next generation of the world’s hottest technology will be cheaper, more efficient, and able to solve bigger problems without going off the rails.
Qwen Studio
Qwen Studio offers comprehensive functionality spanning chatbot, image and video understanding, image generation, document processing, web search integration, tool utilization, and artifacts.
A coding implementation on qwen b ab covering multimodal inference thinking control tool calling moe routing rag and session persistence
AMD 3D V-Cache Turns Ryzen Into a Surprise RAG AI Weapon, With An 88% Boost Over Non-X3D CPUs
AMD's 3D V-Cache CPUs deliver a huge boost versus the Non-X3D part in AI benchmarks, showcasing why they are best suited for RAG pipelines.
But LLMs' limitations can be seen when it needs to generate responses on data it wasn't trained on.
That's where RAG or Retrieval-Augmented Generation) comes into play
Anthropic Courses
Learn to build with Claude through Anthropic's comprehensive courses and training programs.
Codex for (almost) everything
The updated Codex app for macOS and Windows adds computer use, in-app browsing, image generation, memory, and plugins to accelerate developer workflows.
Models overview
Claude is a family of state-of-the-art large language models developed by Anthropic. This guide introduces the available models and compares their performance.
Breaking Down the .claude Folder
The .claude folder is created by tools that integrate with Claude to store local state. It keeps track of how the model behaves inside your project and that includes configuration, cached data, task definitions, and sometimes context that helps the system stay consistent across…
We Tried 100 Claude Skills. These Are The Best
Tested, ranked, and ready to use
Sam Altman May Control Our Future—Can He Be Trusted?
New interviews and closely guarded documents shed light on the persistent doubts about the head of OpenAI, Ronan Farrow and Andrew Marantz write.
Components of A Coding Agent
How coding agents use tools, memory, and repo context to make LLMs work better in practice
six of the main building blocks
application layer, a so-called agentic harness
Anthropic Maps 171 Emotion-like Concepts Inside Claude - Dataconomy
Anthropic's interpretability team published research revealing that its Claude Sonnet 4.5 model contains 171 internal representations that function analogously to
Qwen
Qwen Chat offers comprehensive functionality spanning chatbot, image and video understanding, image generation, document processing, web search integration, tool utilization, and artifacts.
Gemma 4
Our most intelligent open models, built from Gemini 3 research.
Claude Code Unpacked
What actually happens when you type a message into Claude Code? The agent loop, 50+ tools, multi-agent orchestration, and unreleased features, mapped from source.
Meta's new prompting technique makes LLMs significantly better at code review — boosting accuracy to 93% in some cases
This technique can be used out-of-the-box, requiring no model training or special packaging. It is code-execution free, which means you do not need to add additional tools to your LLM environment.
One major bottleneck: the need to set up dynamic execution sandboxes for every repository,
Claude Code’s Real Secret Sauce Isn’t the Model
Skimming the leaked Claude Code TypeScript snapshots suggests that much of its coding performance comes from the surrounding software harness, including repo context, tooling, caching, memory, and subagents.
drona23/claude-token-efficient: Universal CLAUDE.md - cut Claude output tokens by 63%. Drop-in. No code changes.
Universal CLAUDE.md - cut Claude output tokens by 63%. Drop-in. No code changes. - drona23/claude-token-efficient
How to Build Advanced Cybersecurity AI Agents with CAI Using Tools, Guardrails, Handoffs, and Multi-Agent Workflows
Getting Started with Smolagents: Build Your First Code Agent in 15 Minutes
Build an AI weather agent in 40 lines of Python using Hugging Face's smolagents library. Learn to create tools, connect LLMs, and run autonomous tasks.
An agent is essentially an LLM equipped with tools
approach to reasoning
Anatomy of the .claude/ Folder
A complete guide to CLAUDE.md, custom commands, skills, agents, and permissions, and how to set them up properly.
Metannoying
Your brain on "not X but Y"
This happens in seconds. A student with high verbal aptitude processes this almost instantaneously.
Psychometricians have called antonyms the most efficient measure of verbal intelligence in the toolkit: high g-loading, high discrimination at the top of the ability range, no contextual scaffolding.
Matmul Flow | AI by Hand Academy
IndexCache, a new sparse attention optimizer, delivers 1.82x faster inference on long-context AI models
Researchers at Tsinghua University and Z.ai built IndexCache to eliminate redundant computation in sparse attention models like DeepSeek and GLM. The training-free technique cuts 75% of indexer operations, delivering up to 1.82x faster time-to-first-token at 200K context lengths…
Google's new compression algorithm cut memory stocks within hours of publication
Google published a research blog post on Tuesday about a new compression algorithm for AI models. Within hours, memory stocks were falling. Micron dropped 3 per cent, Western Digital ...
Getting Started with Nanobot: Build Your First AI Agent
Learn how to set up Nanobot, connect it to WhatsApp, and power it with OpenAI GPT-5.3-Codex for a practical, always-on AI agent.
Claude Code Cheat Sheet
How BM25 and RAG Retrieve Information Differently? - MarkTechPost
When you type a query into a search engine, something has to decide which documents are actually relevant — and how to rank them. BM25 (Best Matching 25), the algorithm powering search engines like Elasticsearch and Lucene, has been the dominant answer to that question for…
The clever part is that BM25 doesn’t reward keyword stuffing
saturation
Release: llm 0.29
Access large language models from the command-line
A Visual Guide to Attention Variants in Modern LLMs
From MHA and GQA to MLA, sparse attention, and hybrid architectures
An exclusive tour of Amazon’s Trainium lab, the chip that’s won over Anthropic, OpenAI, even Apple
Shortly after Amazon announced its $50 billion investment in OpenAI, AWS invited me on a private tour of the chip lab at the heart of the deal.
Note: Based on the article **[An exclusive tour of Amazon’s Trainium lab, the chip that’s won over Anthropic, OpenAI, even Apple](#rid:1653401870)**, here are the key points: ### Strategic Impact & Partnerships * **Major Adoption:** Amazon’s custom chips have secured massive commitments from leading AI labs. **Anthropic** runs Claude on over 1 million Trainium2 chips, and **OpenAI** recently signed a…
Safely Deploying ML Models to Production: Four Controlled Strategies (A/B, Canary, Interleaved, Shadow Testing) - MarkTechPost
Safely Deploying Machine Learning Models to Production: Four Controlled Strategies (A/B, Canary, Interleaved, Shadow Testing)
Note: This bookmark, **[Safely Deploying ML Models to Production: Four Controlled Strategies](#rid:1652757915)**, is an article from MarkTechPost that outlines how to release machine learning models into production while minimizing risk. The article details four key deployment strategies: * **A/B Testing**: Randomly splits a small percentage of total traffic to the new model while the rest stays on the…
LLM Architecture Gallery
A gallery that collects architecture figures from The Big LLM Architecture Comparison and related articles, with fact sheets and links back to the original sections.
Mistral's Small 4 consolidates reasoning, vision and coding into one model — at a fraction of the inference cost
Mistral's Small 4 combines reasoning, multimodal analysis and agentic coding in a single open-source model with configurable inference effort, offering enterprises a lower-cost alternative to running separate models for each task.
Observability for your LLM-powered apps: OTel Instrumentation for RubyLLM
LLM calls are black boxes in production. Learn how to add structured observability to your RubyLLM-powered app with OpenTelemetry.
Nvidia says it can shrink LLM memory 20x without changing model weights
Nvidia's KV Cache Transform Coding (KVTC) compresses LLM key-value cache by 20x without model changes, cutting GPU memory costs and time-to-first-token by up to 8x for multi-turn AI applications.
Unsloth Docs | Unsloth Documentation
Train your own model with Unsloth, an open-source framework for LLM fine-tuning and reinforcement learning.
Coding agents for data analysis
Here's the handout I prepared for my NICAR 2026 workshop "Coding agents for data analysis" - a three hour session aimed at data journalists demonstrating ways that tools like Claude …
New LLM Architecture Gallery
I put together a new LLM Architecture Gallery that collects the architecture figures from my recent comparison articles in one place, together with compact fact sheets and links.
CanIRun.ai — Can your machine run AI models?
Detect your hardware and find out which AI models you can run locally. GPU, CPU, and RAM analysis in your browser.
Billion-Parameter Theories
We assumed good theories are small. But the minimum viable compression of a complex system might be billions of parameters large.
Chomsky showed that all human languages share deep recursive structure.
Elegant equations might not exist for complex systems.
The Sword of Damocles in Software
"GitHub Copilot had 20 million users. First to market. Then Claude Code arrived and installs peaked within six months. If the sword can cut the leader, no one is safe."
Claude Code told me what tools it needs to work faster. Oh boy I was missing so many things.
My Claude was craving for better tooling.
5 Powerful Python Decorators to Optimize LLM Applications
Learn these five Python decorators based on diverse libraries, that take particular significance when used in the context of LLM-based applications.
New GPT-5.4 Model To Feature "extreme" Reasoning - Dataconomy
OpenAI has indicated that a new version of its large language model, GPT-5.4, is in development following a post on
Harness Engineering: How to Supervise Code You Can’t Read
AI writes the code now. The skill that matters is controlling what it builds.
Every time you accept that output without checking, you’re running up a tab. Call it trust debt: the accumulated cost of all the assumptions you never audited. Trust debt doesn’t announce itself. It compounds quietly until Future You has to pay it all back at once, usually at the worst possible…
Meet SymTorch: A PyTorch Library that Translates Deep Learning Models into Human-Readable Equations - MarkTechPost
Cambridge Researchers Introduce SymTorch: A PyTorch Library that Translates Deep Learning Models into Human-Readable Equations
Claude Code Power Tips
Use Claude Code to speed up data science. Master data cleaning, visualization, and model prototyping with Python, pandas, and scikit-learn. Get actionable power tips.