Open Benchmarks Grants– a $3M commitment to close the AI eval gap
Today, we're launching the Open Benchmarks Grants: a $3M commitment to fund open-source and academic teams building benchmarks for AI agents. In partnership with HuggingFace, PrimeIntellect, FactoryHQ, Together, Harbor, and PyTorch, the grants provide funding, data development support, and research collaboration. Our ability to measure AI has been outpaced by our ability to develop it, and we believe this evaluation gap is one of the most important problems in AI. Open benchmarks are one of the most important levers for advancing AI safely and responsibly—but the academic and open-source…
Open Benchmarks Grants– a $3M commitment to close the AI eval gap launched on February 11, 2026 with 6 votes, #1,572 of 16,627 launches that month and more than 75% of that year's launches.
What it does
Agents still fail in subtle ways. Learn how open benchmarks close the evaluation gap and define the next frontier of agentic AI performance.
Today, AI is marked by a growing asymmetry: the excitement around agentic AI is real — backed by quantitative progress on model cards and genuine leaps forward, especially in coding. But ask individuals or enterprises where they feel ready to deploy agentic automation in high-stakes, domain-specific settings outside of coding… and you will find hesitation. The reason: our ability to measure AI has been outpaced by our ability to develop it, and this evaluation gap is one of the most important problems in AI. The evaluation gap won’t be solved by public benchmarks alone, and the risk of “benchmaxxing” is real— closing it will also require use-case-specific field deployments and other…from benchmarks.snorkel.ai
In the maker’s words, at launch
Today, we're launching the Open Benchmarks Grants: a $3M commitment to fund open-source and academic teams building benchmarks for AI agents. In partnership with HuggingFace, PrimeIntellect, FactoryHQ, Together, Harbor, and PyTorch, the grants provide funding, data development support, and research collaboration. Our…
Does a similar job
all alternatives →- AGAI Grant – Get $2,500 for your AI project in minutes2018 · ▲323
Hi HN, Last year, we launched a non-profit AI research lab called AI Grant (http://aigrant.org). Our goal is to fund promising people around the world working on AI. No strings attached. We've since given away over $100,000 to 30 teams working on different projects. You can see some of them here: http://aigrant.org/#finalists. We just started accepting applications for our third batch! The academic grant application process is burdensome. It's only avalible to a choice few. Applications take days to complete. AI Grant is open to anyone on the internet. You can apply…
oqoqoAug 2026 · oqoqo.ai · ▲325Most benchmarks today exist in curated environments and do not translate well to the real world. We built Oqoqo to bridge this gap. Oqoqo makes it super simple to build realistic evals and custom benchmarks for tasks users actually care about. Oqoqo can: - Reliably measure how agent friendly your product surfaces are against Codex, Claude Code, OpenClaw, Hermes, Pi, Opencode, Cursor, GitHub Copilot - Regression test MCP, CLI, skills, SDK, and any agent facing interface (we are continuously using Oqoqo to dogfood and improve our own MCP/CLI) - Create and share custom benchmarks for how…

- ATA tool to benchmark LLM APIs (OpenAI, Claude, local/self-hosted)2025 · llmapitest.com · ▲55
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
AI Grants & Credit Search by HackerNoon2025 · hackernoon.ai · ▲127Find the best active AI software & compute grants & credits
ML-Dev-Bench – Benchmarking AI Agents on Real-World AI Workflows2025 · github.com · ▲6We’re excited to share ML-Dev-Bench, a new open-source benchmark that tests AI agents on real-world ML development tasks. Unlike typical coding challenges or Kaggle-style competitions, our benchmark simulates end-to-end ML workflows including: - Dataset handling and preprocessing - Debugging model and code failures - Implementing new model architectures - Fine-tuning and improving existing models With 30 diverse tasks, ML-Dev-Bench evaluates agents across critical stages of ML development. To complement this, we built Calipers, a framework that provides systematic performance evaluation and…
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