Advancing open intelligence through efficient, accessible infrastructure for AI inference and post-training.
Joined May 2024
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SWE-bench Verified has become saturated. Seven models scored 95–97% (Vals AI, Sep 2026). But how well can coding agents build complete software projects from scratch?
Zero2Repo is a Gradient Data research initiative building hard, verifiable tasks and training data for whole-project development.
Agents start with a full spec, an interface contract and an empty workspace. They build the entire repo without reference code, then submit it for hidden deterministic testing. Every test must pass for a task to count. No LLM judge is used.
The first release includes 17 tasks derived from real open-source projects across Python, TypeScript, Go, C, C++ and Zig.
Across 18 model–harness configurations, scores range from 1/17 to 15/17. The top 10 span 47 percentage points, compared with 5 points on SWE-bench Verified.
No configuration has completed all 17 tasks. This matters for self-iteration: small unresolved issues can persist and compound as agents continue developing a project. Reliable self-evolution remains an open research challenge.
We’re expanding the task collection and looking for contributors to suggest seed repositories, develop tasks and review requirements and hidden tests.
More cases are coming.
👉 zero2repo.ai
👉 github.com/Gradient-Data/Zer…
These 17 tasks are a starting point. We’re inviting developers, researchers and open-source maintainers to help expand Zero2Repo.
Recommend a seed repo, propose a task, or review requirements and hidden tests.
Contribute: github.com/Gradient-Data/Zer…
Details: zero2repo.ai
Each Zero2Repo task starts with a spec, interface contract and empty workspace. Agents build a complete repo from scratch, then submit it for hidden deterministic testing. A task counts only if all tests pass. No LLM judge is used.
Across 17 tasks in 6 languages, 16 model–harness runs scored 1/17–15/17.
One area we’re focusing on is software engineering.
Seven models scored 95–97% on SWE-bench Verified. (Vals AI, Sep 2026) But can they build full projects from scratch?
Zero2Repo, Gradient Data’s latest research initiative, builds verifiable tasks for training and evaluation.
zero2repo.ai
Over the past year, we’ve worked on Echo and Logits to make AI post-training more efficient and accessible.
Gradient Data now extends that work with verifiable environments, real-world trajectories and evaluation systems for more capable and reliable agents.
gradientdata.ai
A self-evolving agent + a 428B model + 3 Macs = ?
Your own AI lab.
We ran @MiniMax_AI M3 locally with @tryParallax, right on our desk.
Then @GA_agent_ai took over to create a 5-stock portfolio and write it to disk.
No cloud. No API bills. Nothing left the machine.
Wild to see a ~3K-line agent drive all this with a 400B+ model on local hardware.
Thanks to the GenericAgent and MiniMax teams for making local AI feel real.
🤖 Made with AI
This GPT Image 2 prompt is going insanely viral right now.
“Redraw the attached image in the most clumsy, scribbly, and utterly pathetic way possible. Use a white background, and make it look like it was drawn in MS Paint with a mouse. It should be vaguely similar but also not really, kind of matching but also off in a confusing, awkward way, with that low-quality pixel-by-pixel feel that really emphasizes how ridiculously bad it is. Actually, you know what, whatever, just draw it however you want.”
Our GTC takeaway is clear: NVIDIA is betting hard on open.
- NemoClaw turns OpenClaw into enterprise infrastructure.
- Nemotron 4 will be open-sourced.
- Nemotron Coalition puts eight labs on a shared open frontier model.
This is what we've been building toward. Open infrastructure for open intelligence is the direction the biggest AI companies are taking.
