Dmitry Noranovich

Cursor: The AI Code Editor by @dwetterau, @msfeldstein, @, and more producthunt.com/products/curso…

AI Skills Manager: One place for all your AI skills by @IdoEvergreen producthunt.com/products/ai-sk…

Google AI Studio 2.0: Full-stack vibe coding powered by Antigravity + Firebase by @addyosmani, @joshtwoodward, @ammaar, and more producthunt.com/products/googl…

3DreamBooth: High-Fidelity 3D Subject-Driven Video Generation Model huggingface.co/papers/2603.18…

Netlify: The platform for shipping web apps fast by @thisiskp_, @biilmann, @JavaSquip, and more producthunt.com/products/netli…

NVIDIA: The official handle for NVIDIA. by @steipete and @vincent_koc producthunt.com/products/nvidi…

Billy.sh: Local AI coding assistant for your terminal using Ollama by @jd4codes producthunt.com/products/billy…

Introducing the new @stitchbygoogle, Google’s vibe design platform that transforms natural language into high-fidelity designs in one seamless flow. 🎨Create with a smarter design agent: Describe a new business concept or app vision and see it take shape on an AI-native canvas. ⚡️ Iterate quickly: Stitch screens together into interactive prototypes and manage your brand with a portable design system. 🎤 Collaborate with voice: Use hands-free voice interactions to update layouts and explore new variations in real-time. Try it now (Age 18+ only. Currently available in English and in countries where Gemini is supported.) → stitch.withgoogle.com

MetaClaw: Just Talk -- An Agent That Meta-Learns and Evolves in the Wild huggingface.co/papers/2603.17…

Next.js 16.2 • Up to ~60% faster rendering • Up to ~400% faster 𝚗𝚎𝚡𝚝 𝚍𝚎𝚟 startup • Server Function 𝚍𝚎𝚟 logging • Redesigned error page • Better hydration errors • 𝙴𝚛𝚛𝚘𝚛.𝚌𝚊𝚞𝚜𝚎 display in error overlay nextjs.org/blog/next-16-2

Announcing the Colab MCP Server: Connect Any AI Agent to Google Colab - Google Developers Blog share.google/gfOzU71qHCEY9b…

Today, we’re evolving @StitchbyGoogle from @GoogleLabs into an AI design canvas transforms natural language prompts into production-ready front-end code. Some highlights from what’s new: 1. A complete redesign of the Stitch UI, which can now ingest multimodal references (text prompts, images, or code) as creative seeds for your design ideas 2. A brand new, context-aware design agent that can share feedback on builds, generate PRDs, and ask questions to better understand your vision. You can even talk to the agent if you prefer a verbal sounding board 3. A new agent-friendly markdown file, DESIGN.md, which you can use to export or import your design rules to or from other design and coding tools Whether you’ve been designing for decades or you’re whiteboarding your first software idea, Stitch can help you turn concepts into prototypes in minutes rather than days ➡️ stitch.withgoogle.com

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with @Oracle and taught by @richmondalake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time! deeplearning.ai/short-courses/…

Introducing MiniMax-M2.7, our first model which deeply participated in its own evolution, with an 88% win-rate vs M2.5 - Production-Ready SWE: With SOTA performance in SWE-Pro (56.22%) and Terminal Bench 2 (57.0%), M2.7 reduced intervention-to-recovery time for online incidents to 3-min on certain occasions. - Advanced Agentic Abilities: Trained for Agent Teams and tool search tool, with 97% skill adherence across 40+ complex skills. M2.7 is on par with Sonnet 4.6 in OpenClaw. - Professional Workspace: SOTA in professional knowledge, supports multi-turn, high-fidelity Office file editing. MiniMax Agent: agent.minimax.io API: platform.minimax.io Token Plan: platform.minimax.io/subscribe/toke…

Meet the new Stitch, your vibe design partner. Here are 5 major upgrades to help you create, iterate and collaborate: 🎨 AI-Native Canvas 🧠 Smarter Design Agent 🎙️ Voice ⚡️ Instant Prototypes 📐 Design Systems and DESIGN.md Rolling out now. Details and product walkthrough video in 🧵

Open SWE: An Open-Source Framework for Internal Coding Agents in @LangChain blog share.google/QtnIZO5ZZpOnrK…

Many people start learning AI by reading about it. But the real shift happens when you start building. Going from understanding AI to creating applications usually happens step by step: first understanding how generative AI works, then learning the programming behind it, then working with LLMs, and eventually building full AI systems. Here are a few courses that walk through that path: Generative AI for Everyone hubs.la/Q047g3-d0 AI Python for Beginners hubs.la/Q047g3_60 ChatGPT Prompt Engineering for Developers hubs.la/Q047g8_n0 LangChain for LLM Application Development hubs.la/Q047g40-0 Agentic AI hubs.la/Q047fR7g0 A practical path from learning AI to building with it. Share it with someone starting their AI journey.

💫 New LangChain Academy Course: Building Reliable Agents 💫 Shipping agents to production is hard. Traditional software is deterministic – when something breaks, you check the logs and fix the code. But agents rely on non-deterministic models. Add multi-step reasoning, tool use, and real user traffic, and building reliable agents becomes far more complex than traditional system design. The goal of this course is to teach you how to take an agent from first run to production-ready system through iterative cycles of improvement. You’ll learn how to do this with LangSmith, our agent engineering platform for observing, evaluating, and deploying agents. Enroll for free ➡️ academy.langchain.com/courses/buildi…