LLMOps · Eval

AirLLM slices giant transformers into layer shards so they fit in consumer VRAM without quantization or distillation.

Juggling Claude Code, Codex, Gemini CLI, and friends from separate terminals gets old fast.

Hermes exists so you can stop re-teaching assistants that are supposed to remember, and run them on a $5 VPS instead of your laptop.

Hello-Agents is a free, 16-chapter curriculum that teaches developers to construct truly AI-driven agents from first principles rather than wiring no-code workflows.

It strips the telltale signs of AI-generated writing from text, replacing statistical blandness with prose that sounds like it came from a person.

gstack turns Claude Code into a structured engineering team so a single founder can ship like a twenty-person startup without writing most of the code.

Most RAG systems treat PDFs as flat text; RAG-Anything parses them into text, images, tables, and equations so you can query the whole page.

Headroom sits between your agents and the LLM to compress tool outputs, logs, and RAG chunks, delivering the same answers with a fraction of the tokens.

A React SDK that lets AI agents render UI mid-thought and ask humans for help before continuing.

It exists because single-agent chat windows don't scale when you're juggling multiple models, tools, and long-running tasks.

LangGraph adds state machines and durability to the usual "prompt, pray, loop" agent pattern.

A local-first, Rust-built AI agent that connects to 15-plus LLM providers and 70-plus tools, now governed by the Linux Foundation instead of a single company.

JeecgBoot exists so Java teams can describe an ERP in plain language, generate the Spring Boot and Vue code, and then manually merge it to keep things flexible.

Sim exists so you can orchestrate LLMs, tools, and vector stores on a visual canvas instead of writing another ad-hoc Python script.

BMad Method brings structured agile workflows and specialized agent personas into AI IDEs so the machine collaborates instead of freelancing.

It is an open-source bridge between large language models and the instant messaging apps you already tolerate.

Langfuse gives teams a shared place to trace, evaluate, and version the prompts that are currently scattered across notebooks and Slack threads.

Most ML tutorials end at training; this course walks you through the engineering required to actually ship it.

Because stitching together LLM workflows, RAG, agents, and observability by hand is a full-time job.

It aggregates trending topics and RSS feeds so an LLM can decide which headlines actually deserve to interrupt your day.