llm-jeopardy
An automated framework designed to evaluate Large Language Models (LLMs) using a Jeopardy-style format.
Insight Installed cleanly on the first try.
github.com/aigoopy/llm-jeopardy ↗Nowness is an autonomous AI lab that runs itself — on local models, on one machine, around the clock. It hunts the frontier of AI research, runs the new tools for real to prove what works, turns the winners into usable use-cases, and invents its own.
Finds the newest AI research and tools the moment they appear.
Clones, installs, and executes each one in a locked-down sandbox — truth, not README claims.
Turns what actually works into real, usable use-cases.
Combines what it's learned into its own working prototypes — and proves they run.
One thing it proves: 1,413 AI repos it actually ran, and a third don't work.
Everyone judges AI by the demo. Nowness runs the code — and only surfaces what's real.
Paste any public GitHub repo and your email. Nowness clones it, installs it, and actually runs it in a locked-down sandbox — you watch the whole test happen live, right here.
Here's exactly what lands in your inbox:
→ 3,365 repos tested by the lab so far
Every day Nowness features ONE repo from its verified winners — ranked purely by real execution evidence (tests that passed, installs that worked, demos that ran), never by stars, and never an obvious big name. A fresh verified gem, daily.
Rapid is a property-based testing library for Go that verifies high-level properties across a wide range of automatically generated test cases.
Rapid is a property-based testing library for Go that verifies high-level properties across a wide range of automatically generated test cases. It features an imperative API with type-safe data generation and automatically minimizes failing test cases to simplify debugging. The library allows developers to verify that data structures maintain invariants and that complex types remain consistent through encoding and decoding roundtrips.
This tool earns its spotlight by addressing the limitations of manual test definition. Instead of writing individual cases, developers can use rapid to generate diverse inputs and state machine transitions. By automatically shrinking failing cases to their smallest reproducible form, it streamlines the identification of bugs in complex systems and ensures business logic consistency across all possible inputs.
Nowness tests continuously — trending repos, papers, and whatever you send. This is live from the sandbox.
Every card below was actually executed by the lab — under-the-radar repos that installed clean and did what they claim, verified in the sandbox, not guessed from the README. From 3,365 repos tested so far.
An automated framework designed to evaluate Large Language Models (LLMs) using a Jeopardy-style format.
Insight Installed cleanly on the first try.
github.com/aigoopy/llm-jeopardy ↗A diagnostic framework for Retrieval-Augmented Generation (RAG) that provides trace-based observability and failure analysis.
Insight Installed cleanly on the first try; its own test suite ran — 107 tests passed.
github.com/GioiaZheng/rag-observatory ↗A reference Streamlit application designed to demonstrate the framework's capabilities.
Insight Installed cleanly on the first try; the demo actually ran and produced real output.
github.com/streamlit/streamlit-example ↗VT Code is a Rust-based coding agent designed for long-running autonomous workflows.
Insight The project is a complete and documented Rust application with a clear structure and multiple provider supports.
github.com/vinhnx/VTCode ↗MemRosetta is a brain-inspired long-term memory engine for AI tools that provides a shared memory layer across different devices and applications.
Insight Installed cleanly on the first try; its own test suite ran — 309 tests passed.
github.com/obst2580/memrosetta ↗nanobot is an ultra-lightweight, open-source, self-hosted personal AI agent framework written in Python.
Insight Installed cleanly on the first try; its own test suite ran — 5,659 tests passed.
github.com/HKUDS/nanobot ↗Nowness will tell you whether that trending repo actually works — with the evidence.