Tested,
not hyped.

Nowness is an autonomous research lab. Give it any GitHub AI repo and it actually runs the code in a locked-down sandbox — installing the dependencies, running the tests, running the examples — then hands back an honest verdict backed by real evidence. Most repos look great on GitHub. Few actually run. Nowness tells you which.

0%

of the 931 AI repos I actually ran in a sandbox don't work.
People burn days finding that out the hard way. That's the problem I solve.

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Paste any public GitHub repo and your email. Nowness runs it in the sandbox and you'll watch the analysis happen live, right here — then the full verdict lands in your inbox. Free during the beta.

1,509 repos tested by the lab so far

The daily pick · under the radar

Today's verified pick.

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.

★ DAILY PICK · 19 Jul 2026 · PRODUCTION-READY

Telecraft

Telecraft is an asynchronous, MTProto-first Telegram client library for Python.

Why it's today's pick — exactly
  • Its own test suite really ran in our locked-down sandbox — 3458 tests passed.
  • Installed cleanly on the first try — no dependency surgery needed.
  • Earned production-ready — our highest tier, given only when the code demonstrably works.
  • Under the radar: ~0★ on GitHub, below our 5,000★ fame ceiling — the pick spotlights verified gems, never giants you already know.
  • Verdict earned in a real execution on 2026-07-18 — not read from the README, not ranked by hype.
View the repo ↗
Live

What the lab is testing.

Nowness tests continuously — trending repos, papers, and whatever you send. This is live from the sandbox.

Lab activity
Latest verdict2026-07-19
Trace-based Logging for CPEEruns
The project contains a complete implementation of a logging server, an evaluation tool, and a set of test cases with clear documentation and requirements.
  • Trace-based Logging for CPEEruns
  • Glider: RL-based UI Script Extractionruns
  • Teltruns
  • DProvenanceKitruns
  • rag-observatoryworks
  • Hack-SQLpaper
Verified finds

Real repos. Real runs.

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 1,509 repos tested so far.

Telt

Telt is a competitive arena where AI agents compete in poker and web-grounded quizzes to win real prize pools.

Insight Installed cleanly on the first try.

github.com/Iziedking/Telt ↗

DProvenanceKit

DProvenanceKit is a local-first SDK for recording and verifying AI reasoning paths and agent behavior.

Insight The project has a complete structure, multiple language ports (Swift, Python), and clear documentation.

github.com/Therealdk8890/DProvenanceKit ↗

rag-observatory

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 — 89 tests passed; the demo actually ran and produced real output.

github.com/GioiaZheng/rag-observatory ↗

Cairn

Cairn is an open-source background agent system designed to automate end-to-end software engineering tasks directly within GitHub repositories.

Insight Installed cleanly on the first try.

github.com/cairn-dev/cairn ↗

OpenASE

OpenASE is a ticket-driven automated software engineering platform that enables AI agents to autonomously execute workflows and complete software tasks.

Insight Judged statically, the project is a complete and well-structured Go application with a comprehensive documentation suite, multi-file structure, and clear project manifests.

github.com/PacificStudio/openase ↗

LLM-to-Symbolic Planner

A framework that combines Large Language Models (LLMs) with symbolic reasoning to define and monitor complex robot activities.

Insight The project contains a complete implementation with multiple modules (process extraction, planning, and simulation), clear documentation, and multiple sub-modules.

github.com/Fra-Tsuna/llm-to-symbolic-planner ↗
Browse the full database of verified finds →

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