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Install and configure Open WebUI as your Ollama frontend. Docker setup, model management, RAG, tools, and multi-user auth on Linux and macOS. Tested on Docker 27.

Configure LM Studio multi-GPU to split Llama 3.3 70B, Mixtral, and DeepSeek across 2–4 GPUs. Layer-splitting, VRAM balancing, and GPU offload settings explained.

Together AI fast inference API for open-source LLMs: run Llama 3.3, Mistral, Qwen, and DeepSeek at scale with Python or TypeScript. Free tier, USD pricing included.

vLLM vs TGI compared on throughput, latency, model support, Docker self-hosting, and USD pricing. Choose the right LLM inference server for production.

Use Claude Computer Use API to automate desktop tasks with Python and Docker. Control browsers, GUIs, and files with AI vision on Ubuntu. Starts at $3/MTok.

Build a Claude Code custom agent with tool use in Python 3.12. Wire bash, file read/write, and web search tools into an autonomous agentic loop. Tested on macOS & Ubuntu.

Claude 4.5 JSON mode structured output patterns using Python 3.12 and the Anthropic SDK. Extract validated data, avoid parse errors, build production pipelines.

Automate pull request reviews using Claude Code and GitHub Actions. Add AI code review to any repo in 20 minutes with claude-code-action. Tested on Node 22 + Ubuntu.

Claude Sonnet 4.5 API function calling and streaming guide for developers. Ship tool use, real-time output, and production patterns with Python 3.12 + Node 22.

Use Windsurf Cascade to scaffold FastAPI routes, Django models, and async endpoints. Boost backend productivity with AI-assisted Python development. Python 3.12 + uv.

Connect Claude to Amazon Bedrock Knowledge Bases via MCP. Query private S3 docs, enable reranking, and wire IAM permissions for enterprise RAG on AWS. Tested on us-east-1.

Connect Claude and Cursor to your Notion workspace using MCP. Query pages, databases, and docs with natural language. No custom code. Setup in 15 min with Node 22.

Claude 4.5 vs GPT-4o coding benchmarks compared on HumanEval, SWE-bench, agentic tasks, latency, and API pricing in USD. For developers choosing an LLM in 2026.

Claude Code multi-file refactoring walkthrough: plan, execute, and verify large-scale codebase changes using agentic AI. Tested on Python 3.12 and Node 22.

Master .claude files in Claude Code for persistent project memory. Configure CLAUDE.md, slash commands, and settings to automate your dev workflow. Tested on Claude Code CLI.

Qwen 2.5-Max vs Qwen3-Max vs Qwen3.5 compared by benchmark, pricing, and context window. Pick the right qwen-max API version for your stack.

Set up Windsurf Rules to give AI agents persistent project context, coding standards, and memory. Works with .windsurfrules and global rules. TypeScript & Python tested.

Continued pre-training vs fine-tuning compared for LLM customization. Learn which method fits your data, budget, and use case. Python 3.12 + Hugging Face.

GGUF quantization after fine-tuning with llama.cpp: convert, quantize to Q4_K_M or Q8_0, and run locally. Tested on Python 3.12, CUDA 12, Ubuntu 24.

Claude Haiku 4.5 for high-volume production workloads: batch API setup, cost optimization, and throughput tuning in Python 3.12 + Docker. Starts at $0.80/MTok.

Run Qwen2.5-VL 7B or 72B locally with Ollama or vLLM for image understanding, OCR, and visual reasoning. Tested on Python 3.12, CUDA 12, and Apple Silicon.

Run MMLU, MT-Bench, and custom eval suites on fine-tuned models using lm-evaluation-harness and FastChat. Tested on Python 3.12 + CUDA 12 + Hugging Face.

Fine-tune Llama 3.3 70B with Unsloth for 5x faster training and 60% less VRAM. Step-by-step guide using QLoRA, Python 3.12, CUDA 12, and Google Colab or local RTX GPU.

Fine-tune LlamaIndex embeddings on your own data to boost RAG retrieval accuracy. Covers synthetic dataset generation, training, and evaluation. Python 3.12 + CUDA 12.

Fine-tune LLMs to return reliable JSON output using Unsloth, Axolotl, and Pydantic schema enforcement. Tested on Python 3.12, CUDA 12, and 4-bit QLoRA.

Fine-tune Llama 3, Mistral, or Qwen2.5 on RunPod GPU cloud using Axolotl and QLoRA. Step-by-step setup for A100/H100 pods, pod config, and cost control. Tested on Python 3.12.

Fine-tune large language models using LISA layer-wise importance sampling. Cut GPU memory 60% vs LoRA with better convergence. Tested on Python 3.12 + CUDA 12.

Fine-tune Mistral 7B for SQL generation using QLoRA and Unsloth on 16GB VRAM. Covers dataset prep, training, evaluation, and deployment. Python 3.12 + CUDA 12.

Generate high-quality synthetic training datasets with GPT-4o, format them for fine-tuning, and train a smaller model. Python 3.12, OpenAI SDK, JSONL output.

ShareGPT vs Alpaca dataset formatting for LLM fine-tuning explained. Convert, validate, and pick the right format for Unsloth, Axolotl, and TRL. Python 3.12.

Master LM Studio model management: download GGUF models, organize your local library, and switch between models instantly. Tested on Windows 11 and macOS Sequoia.

Claude Code slash commands speed up your dev workflow with 15 shortcuts for memory, context, git, and custom commands. Tested on Claude Code CLI, Node 22, macOS & Ubuntu.

Fine-tune LLMs with ORPO to align model behavior without a separate reward model or preference dataset. Tested on Python 3.12, TRL 0.8, and Llama 3 8B.

Use Windsurf Cascade Agent to autonomously refactor large codebases. Multi-file edits, terminal commands, and safe rollback on TypeScript and Python projects.

Run and fine-tune LLMs on Apple Silicon using MLX-LM. Covers install, quantization, LoRA fine-tuning, and serving — tested on M2/M3 with 16GB RAM.

Install Qwen 2.5 72B locally with Ollama or LM Studio. Covers GGUF quantization, VRAM requirements, GPU offloading, and inference config on Linux and macOS.

Run Qwen2.5 7B or 14B GGUF quantized models on 8GB VRAM using llama.cpp or Ollama. Covers Q4_K_M vs Q5_K_M tradeoffs, GPU offload layers, and inference speed.

Deploy Qwen2.5-Math 7B or 72B for scientific computing and LLM reasoning with CoT and Tool-Integrated Reasoning. Tested on Python 3.12 + CUDA 12.

Deploy Qwen2.5-VL 7B or 72B for multimodal vision tasks: image analysis, OCR, document parsing, and visual reasoning. Tested on Python 3.12 + CUDA 12.

Spectrum fine-tuning targets only high signal-to-noise layers, cutting GPU memory up to 50% while matching full fine-tune quality. Tested on Python 3.12 + CUDA 12.

Run LM Studio as an OpenAI-compatible local API server. Connect any OpenAI SDK client to local LLMs — Python, Node.js, curl. Free, no API key needed.

Configure MCP browser automation for Claude Desktop and Claude Code. Covers Puppeteer MCP setup, deprecation warning, and Playwright MCP migration. Node.js 18+.

Configure MCP servers in Zed editor to supercharge AI-powered coding. Add filesystem, GitHub, and custom servers via settings.json or the Agent Panel UI.

Configure Windsurf IDE from scratch and master Cascade AI, custom rules, and multi-file workflows in your first week. Tested on macOS and Ubuntu with Node 22.

Configure Windsurf Memories to persist codebase context across sessions. Covers auto-generated vs manual memories, Rules files, and .windsurfrules setup. Tested on Windsurf 1.13.x.

Configure Windsurf remote development over SSH and Docker containers. Connect to cloud VMs, WSL, and dev containers with Cascade AI intact. Tested on Ubuntu 24.04.

GaLore cuts LLM training memory by 65% with full-parameter learning. Run LLaMA 3 on a single 24GB GPU using Python 3.12, PyTorch 2.3, and the galore-torch library.

Windsurf Flow explained: how the RAG-based context engine, Cascade, Memories, and .windsurfrules work together to keep you in flow state. Tested on Windsurf 1.x.

TRL 0.12 ships PPO rename, unified ScriptArguments, WPO for DPO, pairwise judges for Online DPO, and a new trl env CLI. Tested on Python 3.12 + CUDA 12.

Windsurf Supercomplete explained: how it predicts multi-line intent, differs from Copilot, and how to configure it for TypeScript and Python. Tested on Windsurf 1.x.