Dmitry Noranovich

Codex for work | OpenAI share.google/qZn2nS0qiQxZKY…

PromptScout: Track your brand visibility across AI models by @lukaszstarosta producthunt.com/products/promp…

Basedash: AI-native Business Intelligence Platform by @MaxMusing and @drk producthunt.com/products/based…

WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation huggingface.co/papers/2605.10…

Work with Codex from anywhere | OpenAI share.google/aLBpARgN36bub5…

Codex is getting easier to automate and customize around your code. 🪝 Hooks customize the Codex loop with scripts that run at key points in a task: • Run validators before or after work • Scan prompts for secrets • Log conversations to internal systems • Create memories or customize behavior by repo or directory ⚙️ Programmatic access tokens provide scoped credentials for Business and Enterprise teams: • Create tokens from ChatGPT workspace settings • Use them in CI, release workflows, and internal automations • Set expirations or revoke access when needed • Keep usage tied back to the workspace

New course: Transformers in Practice. You'll get a practical view of how transformer-based LLMs work, so you can reason about their behavior, diagnose problems like slow inference, and make smarter decisions about deployment. This course is built in partnership with @AMD and taught by @realSharonZhou. You'll see how transformers generate text one token at a time, how the model decides which earlier words matter most when predicting the next one, and how techniques like quantization speed up inference on GPUs. This is not a video-only course; interactive visualizations throughout let you play with these concepts and build intuition that sticks. Skills you'll gain: - Understand why LLMs hallucinate, and RAG and chain-of-thought shape what they generate - Look inside the model to see how attention and layers combine to predict the next token - Diagnose inference bottlenecks and learn the techniques that speed up transformers on GPUs Join and understand what's really happening inside your LLMs: deeplearning.ai/courses/transf…

Introducing Suggest Queries Mode 💚 Weaviate’s Query Agent can now suggest queries that are answerable by the data in your collections. This helps users discover what kinds of questions they can ask! 💬

Paper page - Qwen-Image-VAE-2.0 Technical Report share.google/Qjz7exBnOJBwNJ…

HackerPulse: Measures AI impact across engineering teams by @GlebBraverman producthunt.com/products/hacke…

Raindrop: Monitoring for AI agents by @AlexisGauba and @koushik producthunt.com/products/raind…

Starting today, you can run cloud agents inside fully configured development environments. Set them up the same way you'd set up a laptop for an engineer: cloned repos, installed dependencies, and toolchain credentials.

How do you keep Claude working until the job is done? Claude Code helps with this in a few ways, including one we shipped recently: /goal.

AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation huggingface.co/papers/2605.13…

Building an Evaluation Harness for Production AI Agents: A 12-Metric Framework From 100+ Deployments in @TDataScience towardsdatascience.com/building-an-ev…

PyTorch 2.12 introduces major updates across compilation, export, distributed training, and accelerator support. Highlights include up to 100x faster batched linalg.eigh on CUDA, the new torch.accelerator.Graph API, Microscaling quantization support in torch .export.save, and fused Adagrad. The release includes 2,926 commits from 457 contributors since PyTorch 2.11. Have questions? Join @AndreyTalman (@Meta), @albanDesmaison (@Meta), and @joespeez (@reflection_ai), moderated by @Chris_AI_HPC (@Meta), on May 20 at 10:00 AM PT for a live Q&A covering the release and answering questions from the community. 🔗 Read the release blog and register for the webinar: pytorch.org/blog/pytorch-2… #PyTorch #OpenSourceAI #MachineLearning #AIInfrastructure

What I Learned From Implementing LLM Architectures From Scratch youtu.be/TXzQ7PGpO6w?si… by @rasbt

Introducing Claude for Small Business \ Anthropic share.google/ML6jwvzIiF53oe…

A little talk on what we can learn from implementing LLM architectures from scratch in Python and PyTorch. And how I approach new open-weight models, compare them against reference implementations etc: youtube.com/watch?v=TXzQ7P…

apideck: Hosted platform solution for SaaS companies by @gertjanwilde, @tomschouteden, @nickleplated, and more producthunt.com/products/apide…