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Meet Quest 1.0 ！ - Built-in Agent Skills with elevated design taste - Parallel execution: local + remote, multiple tasks at once - From 0→1 to 1→∞, MVP to refactors - Walk away while it builds. Check results when ready 👇 Watch our PM adds two features. No code written.

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Qoder

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1,982,600 просмотров • 6 месяцев назад •via X (Twitter)

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Cursor for Design? This Claude Code UI workflow is insane And many vibe coders not aware of it: 1. Claude Code can assign parallel tasks to sub agents 2. Integrate with git worktree to create sandbox env This means you can get Claude Code generate 10 design at the same time, fork & iterate 🔁 Design is an explorative process, by leveraging sub agent + parallel task capabilities, many interesting product UX can be built; Also turned this workflow into an open-source IDE extension here:
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“When I was with my boyfriend, he never sent me up to 500k at once. But this lady transferred 1 million in a single transaction. That was when I realized it was time to walk away from that relationship.” — Woman shares her experience dat!ng another woman.👀🤔

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🚨 OpenAI just launched Codex, a brand-new autonomous coding agent that can build features and fix bugs on its own. We’ve been using it Every 📧 for a few days, and I’m impressed. I invited Alexander Embiricos (ben davies), a member of the product staff responsible for Codex, to demo Codex and talk about it live on a special edition of AI & I: What Codex is and how it works Codex is designed to be used by senior engineers—it performs coding tasks like adding features or fixing bugs autonomously. It's built to allow you to start many sessions at once, so you can have multiple agents working in parallel. Codex is built to have "taste" OpenAI trained Codex to have the taste of a senior software engineer. It knows how big codebases work, how to write a good PR, and uses clean, minimal code. Why an “abundance mindset” is best for interacting with agents Codex is designed to allow users to delegate many tasks at once without getting caught up in the details. This lets you point an abundance of agents at a specific task like a difficult bug—it’s worth it even if only one of them succeeds. How OpenAI is thinking about agents Codex is one piece of a unified super-assistant OpenAI wants to eventually build—an agent that helps users easily get things done by selecting the right tools for them behind the scenes. OpenAI’s vision for the future of programming In the future developers will probably spend less time writing routine code and more time guiding agents, reviewing their work, and making strategy decisions. Programming will become more social, letting teams easily delegate multiple tasks at once, allowing people to focus on ideas and collaboration instead of routine coding. Watch below!

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🎨 Introducing Genspark Design The next-gen AI for design and creation, powered by Claude Opus 4.7. From rough idea to professional design, no design background needed. - One tool for everything you design. UI prototypes, videos, HTML animations, posters — all in one place and production-ready. - Stay on-brand with your own design system. Upload your Figma files or save your designs, reuse them across every project, and share them with your team. - Ship it, don't just mock it. Turn any app or website design into working code with one click, built on Genspark Code. Start building at our launch price 👉

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Cursor pays engineers $1,100,000 a year to run teams of AI agents that ship code while they sleep. [The CEO of Cursor explained in 9 minutes how they ship at 100x speed using team of agents] ↓ Save this before everyone copies the playbook 1. Engineers no longer babysit one assistant. They manage dozens of agent colleagues working in parallel, each on its own remote machine 2. Validation contract before code, not after. Humans only at scoping and review. 3. The agent team handles the full loop : planning, coding, testing, shipping PRs with each agent specialised for a role. Watch the guide. Then read the guide below by Khairallah AL-Awady

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Want to win a trip Into The Millennium to meet us at Sphere?! We’re teaming up with Fandiem to give away a VIP trip for two to see us at our ‘Into The Millennium’ Sphere Residency this August 17 in Las Vegas, NV! Trip includes 2 VIP tickets to our show, meet & take a photo with us, roundtrip airfare and hotel accommodations, and more! Donate for a chance to win, all while supporting the incredible Make-A-Wish America at close Friday, August 1, 2025 11:59 pm PT.

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17,701 просмотров • 1 год назад

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Google Labs just updated Stitch and it's insane: → Voice-activated design (talk and it builds) → AI agent that critiques your UI and writes PRDs → Instant multi-device prototyping → Exportable design systems via markdown → Manages multiple ideas in parallel I've designed and built dozens of apps throughout the years – a formerly tedious process that's now available to anyone. No, this doesn't kill designers. But the bottlenecks are collapsing. It's a beautiful time to build. This video is not sponsored, but I did receive early access to test Stitch from my friends at Google Labs which was much appreciated 🙏

Roberto Nickson

30,412 просмотров • 5 месяцев назад

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New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by Brian McBrayer 🐬 from NVIDIA. Many teams struggle to turn agent demos into reliable systems that are ready for production. This short course teaches you to harden agentic workflows into reliable systems using Nvidia's open-source NeMo Agent Toolkit (NAT). Whether you built your agent in raw Python or using a framework like LangGraph, or CrewAI, NAT provides building blocks for observability, evaluation, and deployment that turn proofs-of-concept into production-ready systems. NAT makes it easy to troubleshoot and optimize agent performance with execution traces, systematic evaluations, and CI/CD integration. Skills you'll gain: - Build configuration-driven agent workflows with REST APIs and minimal code - Add observability with tracing to visualize agent reasoning and debug performance bottlenecks - Create systematic evaluations using gold-standard datasets to measure and improve agent reliability - Deploy multi-agent systems with authentication, rate limiting, and professional web interfaces - Orchestrate agents from different frameworks to collaborate on complex tasks Join and learn how to turn agent demos into reliable systems!

Andrew Ng

64,033 просмотров • 8 месяцев назад

HERMES AGENT BUILT A SKILL THAT AUTOMATED AN 8-HOUR WORKFLOW INTO 80 MINUTES. THE AGENT BUILT IT FROM ITS OWN COMPLETED SESSIONS. one user ran their customer pipeline through Hermes repeatedly: meetings → recordings → product requirements → technical specs → development → delivery. after enough runs, Hermes identified the pattern and created a skill that handles the full pipeline: → takes meeting recordings → extracts requirements automatically → runs research on the extracted data → generates product requirement documents → generates technical specifications → delegates the build to a coding agent → sends a link to the finished product what Hermes skills contain: → when to use (trigger conditions) → step-by-step procedure → known pitfalls to avoid → verification steps (how to confirm it worked) stored as markdown in ~/.hermes/skills/. readable, editable, permanent. agents with 20+ self-created skills finish similar future tasks ~40% faster. month 1 you explain everything. month 3 the agent runs your core workflows from the skills it built along the way. full breakdown of Hermes as a Personal AI OS in the article 👇
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HERMES AGENT BUILT A SKILL THAT AUTOMATED AN 8-HOUR WORKFLOW INTO 80 MINUTES. THE AGENT BUILT IT FROM ITS OWN COMPLETED SESSIONS. one user ran their customer pipeline through Hermes repeatedly: meetings → recordings → product requirements → technical specs → development → delivery. after enough runs, Hermes identified the pattern and created a skill that handles the full pipeline: → takes meeting recordings → extracts requirements automatically → runs research on the extracted data → generates product requirement documents → generates technical specifications → delegates the build to a coding agent → sends a link to the finished product what Hermes skills contain: → when to use (trigger conditions) → step-by-step procedure → known pitfalls to avoid → verification steps (how to confirm it worked) stored as markdown in ~/.hermes/skills/. readable, editable, permanent. agents with 20+ self-created skills finish similar future tasks ~40% faster. month 1 you explain everything. month 3 the agent runs your core workflows from the skills it built along the way. full breakdown of Hermes as a Personal AI OS in the article 👇

YanXbt

29,819 просмотров • 2 месяцев назад

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Excited to share our latest progress on DYNA-1 pre-training! 🤖 The base model now can perform diverse, dexterous tasks (laundry folding, package sorting, …) without any post-training, even in unseen environments. This powerful base also allows extremely efficient fine-tuning to ~100% success on challenging new tasks with as little as 1 hour of data! 🤯 Watch it master two of them: cup stacking & celery chopping on repeat, no failures. 👇

Dyna Robotics

67,746 просмотров • 9 месяцев назад