Turing Community

Live at our Happy Hour with Foundation Capital, and the conversation is already diving deep. We’re talking RL Environments, coding, STEM, and what it really takes to push model capabilities forward. From structured evals to agent workflows, the room is buzzing with ideas on how frontier AI research evolves from benchmarks to real-world systems. Great energy. Even better questions. Let’s see where this one goes.

Most AI models can pass standard benchmarks. But real-world risk does not live at the surface. As frontier models improve, many STEM benchmarks are saturating. High pass rates on academic datasets reduce evaluation signal and can mask weaknesses in advanced reasoning. That’s why we built HLE++. HLE++ is a calibrated STEM evaluation framework designed to preserve measurable pass@k separation beyond baseline benchmarks like Humanity’s Last Exam (HLE). It measures how leading large language models perform on graduate-to-PhD-level, multi-step math and science tasks under strict structural constraints. Why this matters: • Advanced STEM reasoning underpins high-impact AI use cases across finance, life sciences, manufacturing, energy, and defense. • Small reasoning errors compound at scale in automated decision systems. • Evaluation design, ambiguity control, and rubric structure materially affect reported model performance. What we’re seeing: • Performance gaps widen on structured, multi step domain tasks. • Models that perform well on general benchmarks degrade under domain-specific stress. • Calibrated difficulty bands reveal weaknesses leaderboard scores often hide. If you're deploying AI for technical decision-making, the question is no longer "Is the model powerful?" It’s: How does it perform when the reasoning truly matters? Explore HLE++ and see how today’s frontier models perform under domain-grade scrutiny:: https://bit.ly/4ugOqTC

Open-RL is trending at #1 on Hugging Face Appreciate the community support! You can download the dataset below! https://lnkd.in/gr9yBb6

AI strategy is shifting from pilots to production. Join us with Foundation Capital to explore what it takes to build AI systems that are reliable, reproducible, and built for real-world impact, from agent evaluation to governance and scale. If you’re thinking long-term about how AI drives durable advantage, this conversation is for you. When: Thursday, March 5 5:30 PM - 8:30 PM PST Where: Mountain View, California Register here: https://bit.ly/46bcPzE

AI strategy is shifting from pilots to production. Join us with Foundation Capital to explore what it takes to build AI systems that are reliable, reproducible, and built for real-world impact, from agent evaluation to governance and scale. If you’re thinking long-term about how AI drives durable advantage, this conversation is for you. When: Thursday, March 5 5:30 PM - 8:30 PM PST Where: Mountain View, California Register here: https://bit.ly/46bcPzE

Turing is featured in Unicorn Focus by Igor Ryabenkiy, a tactical field guide for founders shaping product strategy in the AI era. In the book, our CEO, Jonathan Siddharth shares how the company’s origin traces back to a challenge he faced while building his first startup. He and his co-founder struggled to hire and retain top engineers in Silicon Valley. Competing with large technology companies proved expensive and inefficient. That experience led to a clear insight: exceptional talent is distributed globally, but opportunity is not. Turing was built on that belief. The company developed AI-powered infrastructure to identify, evaluate, and orchestrate world-class engineering talent at scale. As leading AI labs began requiring expert human intelligence to train and refine frontier models, this foundation enabled Turing to evolve into a trusted partner advancing cutting-edge AI systems. As Jonathan shares in the book, “If you're choosing between problems, ask yourself which one kept you up at night. That’s where your deepest insight is. Find Unicorn Focus on Amazon: https://a.co/d/b0a43jw

We’re excited to announce that Turing is joining Anthropic as a launch partner to customize Claude Enterprise for complex enterprise agents. Advanced capabilities matter. But the distance between capability and business impact is execution. Together with Anthropic, we’re embedding Claude directly into enterprise systems, designing human-in-the-loop workflows, and deploying agentic AI with the governance, oversight, and operational rigor required to scale. From finance and sales to legal and operations, this partnership is about moving AI from assistant to infrastructure. If you're thinking about structured deployment, not experimentation, let’s talk. More details here: https://bit.ly/40qaeyf

Frontier labs are pushing past toy benchmarks and into environments that look like real systems. As part of our collaboration with AI at Meta and Hugging Face on OpenEnv, Turing contributed production-grade RL Environment technology that combines: A standardized, Gymnasium-style interface for agents Stateful WebSocket sessions instead of stateless HTTP Containerized microservices with health checks, metrics, and tracing Typed actions and observations with Pydantic validation The result is an evaluation layer where agents interact with real tools and APIs, under real constraints, while labs keep the properties they care about most: isolation per session, reproducibility, and production observability. This is how we move agent research closer to what actually happens in deployment. See it on Hugging Face <https://bit.ly/4ahiZAI>

Real progress begins with thinking deeply, questioning assumptions, and learning continuously. On World Thinking Day, we recognize the power of thoughtful problem solving in shaping a better future. #WorldThinkingDay

AI is now embedded in GxP workflows across pharma and life sciences, shaping quality, safety, supply chain, and regulatory decisions. Traditional compliance was built for predictable systems. Agentic AI isn’t predictable. Risk can emerge mid-execution. Static controls won’t catch it. Leading orgs are moving to in-flight governance: -Real-time monitoring -Context-aware data controls -Auto audit trails The result? ~45% less audit prep, faster reviews, $1M+ in annual savings, and the confidence to scale AI safely. In regulated industries, compliance can’t be retrospective. It has to run with the system. Talk to a Turing Strategist about implementing continuous, in-flight compliance without disrupting validated systems: https://bit.ly/4rVc5H4