Alexander Watson

Run Autonomous, Self-Evolving Agents More Safely with NVIDIA OpenShell | NVIDIA Technical Blog developer.nvidia.com/blog/run-auton…

Been so much fun cooking OpenShell and NemoClaw with the @NVIDIAAI folks! 🙏🦞 Huge step towards secure agents you can trust. What’s your OpenClaw strategy?

🚨 BREAKING: 4OGS Racing enters the professional motorsport arena after extensive R&D program. Sears Pointless 2026 marks our championship assault. Very racy. Questionably pacey. #4OGSRacing #VeryRacy #QuestionablyPacey #24HoursOfLeMons #MotorsportNews

synthetic data is also a big feature of our Nemotron 3 Nano pre & post-training. Gretel joined Nvidia in April, and it's been an amazing ride to bring SDG & Data Designer to help build our latest SoTA Nemotron. here are some datasets we worked on 🧵

🚀 @nvidia just open-sourced NeMo Data Designer, the Python framework we use to generate synthetic data for Nemotron. Structured outputs ✅ LLM columns ✅ Dependency-aware fields ✅ Validators ✅ LLM-as-judge ✅ Jump in & build your own pipelines: github.com/NVIDIA-NeMo/Da…

Psyched!

Blog post and notebook: gretel.ai/blog/building-…

📊 Excited to share how teams are using @gretel to evaluate RAG systems! Here's our guide to: - Generate diverse test scenarios - Evaluate different configs - Find edge cases automatically Something we've learned from lots of customer convos. Check it out ⬇️

3/ We’re already catching glimpses of this shift with techniques like synthetic data and RL, excited to see where this can go 🚀

2/ The new blueprint for building AI: keep it lean, iterative, and guided by the best ideas both humans and machines can collectively generate

🧵What we’re seeing emerge with models like DeepSeek R1, @MSFTResearch Phi-4, @Meta's self-play and now as OSS with @huggingface Open-R1 is a data pipeline as sophisticated and original as the models themselves- and I’m here for it

@karpathy Reminds me of the logic in "if you can dodge a wrench, you can dodge a ball" from the movie #dodgeball. Maybe we can all try making models that are actually good at the tasks we need

The most bullish AI capability I'm looking for is not whether it's able to solve PhD grade problems. It's whether you'd hire it as a junior intern. Not "solve this theorem" but "get your slack set up, read these onboarding docs, do this task and let's check in next week".

The future of AI customization won't need armies of labelers - just synthetic data and expertise 🚀 @AIatMeta's ALMA paper just proved this: matching Llama3-Instruct performance with synthetic data + just 9k examples (vs. millions!) Paper: arxiv.org/abs/2412.04305

Neat paper from NeurIPS around scaling synthetic data, focused on diversity in prompt and response synthesis and iteration (100s of examples and 10 iterations) as keys to alignment matching or exceeding exclusively human data, similar to what we have seen at @gretel_ai

Everyone’s talking about synthetic data generation — but what’s the recipe for scaling it without model collapse? 🤔 Meet ALMA: Alignment with Minimal Annotation. We've developed a new technique for generating synthetic data and aligning LLMs that achieves performance close to Llama3-instruct with only 9000 labeled examples, that's less than 1% of the millions of human annotation data typically used for alignment. Check out the full paper here: arxiv.org/abs/2412.04305

Now organizations can customize any model on AOAI with Gretel, leveraging their proprietary and sensitive data as a source of knowledge to ensure models are customized to their business, and customer needs. Details here: gretel.ai/blog/privacy-p…

Thrilled to showcase the results of months of work between @Azure and Gretel, bringing safe, high-quality synthetic data as a native integration to Azure @OpenAI Services

Today, during his Keynote @satyanadella said "There is no AI without data" and "Our work with @gretel_ai helps remove data bottlenecks"