Interactive, community-driven learning roadmaps and guides that map skills, technologies, and curated resources for developer career paths — covering frontend, backend, DevOps, ML/AI, MLOps, prompt engineering and more. Clickable nodes link to tutorials, best practices and question banks to guide study and hiring prep.
Build, run, and monitor LLM agents across one stack: an open framework for chaining models and tools, LangGraph for stateful agent orchestration, and LangSmith for tracing, evaluation, and deployment in production.
Provides a searchable, community-curated library of prompts for chat and LLM models, with a browsable site, CSV/Hugging Face dataset, an interactive prompting guide, and self-hosting options. Focused on prompt examples and community contributions for ChatGPT and other LLMs.
Community-curated collection of ChatGPT-style prompts mirrored as a Hugging Face dataset; organized by task and model compatibility for quick reuse. Useful for prompt engineering, text-generation prototyping, and building conversational examples across multiple LLMs.
Lets you write compositional Python programs that compile into self‑improving LLM pipelines — replacing brittle prompt engineering with a declarative, programmatic approach and built‑in algorithms to optimize prompts and weights for RAG, multi‑stage pipelines, and agent loops.
Locally hosted frontend that connects to many text, image, and TTS backends (KoboldAI, Ooba, Tabby, OpenAI, Claude, OpenRouter, Mistral, NovelAI, Horde). Built around character cards, lorebooks, group chats, and extensions for deep prompt control.
Declarative CLI and library to evaluate and red-team LLM apps: run test cases against prompts and models, compare providers side-by-side, and scan for jailbreaks, prompt injection, and data leaks — with CI/CD and pull-request code scanning built in.
Probes LLMs for failure modes — prompt injection, jailbreaks, data leakage, toxicity, hallucination — the way nmap scans a network. Ships 20+ attack probes that run against Hugging Face, OpenAI, Bedrock, Cohere, or any REST endpoint.
Contains short, small-vocabulary stories synthetically generated by GPT-3.5 and GPT-4 for training and evaluating compact language models. Includes multiple splits, a GPT-4-only V2 subset, and archive files with prompts and metadata for reproducible experiments.
Synchronized desktop chat browser that opens multiple LLM webapps (ChatGPT, Claude, Bard, Bing, Llama2) and submits the same prompt to each for fast cross-provider comparison. Offers keyboard shortcuts, local-model hooks, and prompt-improvement utilities.
Runs Stable Diffusion XL behind a Midjourney-style interface, hiding samplers, model swaps, and LoRA weights. A built-in GPT2 expander rewrites prompts into richer styling, and it works fully offline on as little as 4GB of Nvidia VRAM.
Framework for unit-testing, evaluating and benchmarking LLM systems with ready-made metrics (G‑Eval, hallucination, task completion), support for local judge models and synthetic datasets, plus CI-friendly integrations for LangChain/OpenAI/Anthropic.