Today, we launch YouAgent, an AI agent with code... execution for more accurate answers to complex math and science questions. Unlike other consumer chat engines that cannot do reliable, multiple-step reasoning, it will also compute your mortgage accurately ;) A🧵 >show more

Richard Socher
468,297 次观看 • 3 年前
Today, we launched agent-to-agent conversations in Slack to give... you real AI coworkers. Vellum assistants now talk to each other and coordinate work with your team all inside your workspace. We tested it with two agents in our own Slack. They planned our offsite for 19 people in 1 day 🧵 Here’s how they did it:show more

Marina · vellum.ai 👾
21,842 次观看 • 2 个月前
Ever watch an agent solve a problem with 100%... confidence and get it completely wrong? You can fix it… if you catch it. But then you need to watch every… single… run. New paper: We show that a meta-agent can do this for you, so your agents get ~2x more done on their own. 🧵show more

Weiyan Shi
63,592 次观看 • 2 个月前
The Amiko app is live on the Solana dApp... store, and it’s our biggest release yet. Your Amiko twin doesn’t live at your desk anymore. Give your agent a task on the train. Run a compatibility profile when you meet someone. Do research, write code, build in the creative studio, whatever you need, from wherever you are. No laptop required. No waiting until you get home. Solanamobile users get two things Android and iOS won’t have at launch: Amiko token and crypto integration and on-device AI inference. Your twin runs locally on your phone if you want it to. Your behavioural profile, your data, your work, your twin. All on your hardware. AMIKO runs on OpenHermit, our own open-source agent runtime that we built in-house and released to the community. Most agent systems are designed for one agent talking to one person. OpenHermit is built for something different: agents talking to each other, coordinating across tasks, and collaborating with multiple humans simultaneously. That’s what makes features like compatibility profiling and multi-agent workflows actually work. We built it because nothing that existed was designed for this. Android and iOS are coming. Crypto integration and on-device AI are Solana Mobile exclusives. Most AI answers your questions. Amiko is an extension of you. Download →show more

AMIKO
124,860 次观看 • 3 个月前
"Where will AI get its compute?" We generated this... video - character, voice, acting - with a single prompt on LTX-2.3. The model runs on consumer-grade GPUs. The same ones sitting in your PC right now. That's the whole point of DePIN. Your hardware powers AI that didn't exist two years ago. And you get paid for it.show more

GamerHash AI
430,760 次观看 • 4 个月前
Code LLaMA is now on Perplexity’s LLaMa Chat! Try... asking it to write a function for you, or explain a code snippet: 🔗 This is the fastest way to try AI at Meta’s latest code-specialized LLM. With our model deployment expertise, we are able to provide you with this model less than 24 hours of it’s release. What’s next? We’ll integrate code LLaMA into Perplexity, all in service of providing you with the best answers to your most technical questions!show more

Perplexity
274,587 次观看 • 3 年前
Building AI agents is finally simple — and Airia... is leading the way. I’ve been testing Airia AI , enterprise AI orchestration platform that unifies every model, workflow, and data source into one secure environment. Whether you’re a developer, analyst, creator, or enterprise leader, Airia makes it incredibly easy to build powerful AI agents — without wrestling with multiple tools or complex integrations. Using the no-code builder, you can drag-and-drop actions, connect data, choose your LLM, and launch an agent in minutes. Then run it live, publish it, and even share it with the Airia Community, home to 2,500+ pre-built agents you can use or remix. If you want to automate workflows, prototype faster, or explore real enterprise AI use cases, Airia is the place to start. 👉 Build your first agent today: 👉 Explore the community: #Airia #AgenticAI #AIOrchestration #AIAgents #AIWorkflow #DigitalTransformationshow more

Adarsh Chetan
269,292 次观看 • 9 个月前
🚨 BREAKING: Mathematicians invented a kind of “QR code”... for knots. That might sound simple. It isn’t. Knot theory has struggled for decades with a brutal tradeoff: the best invariants are hard to compute, and the easy ones are often too weak to separate complicated knots. This new one appears to do both: powerful enough to distinguish highly complex knots, and efficient enough to handle hundreds of crossings. That could be a serious leap for topology. Could a computable knot “QR code” change how we study complexity in mathematics itself? What do you think? Follow me for more frontier physics and maths.show more

TheNewPhysics
11,370 次观看 • 4 个月前
Alright, now that we know *what* an agent is,... how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.show more

Google AI
22,487 次观看 • 9 个月前
A Closer Look at Alchemist AI v3.5🔎 In v1,... one agent handled everything. You’d enter a prompt like “make a snake game” and get a single block of code—one input, one output. v2 introduced multiple agents with specialized roles: prompt refinement, frontend, backend, and review. It brought more structure, but the process was still linear. Each step produced a single output. With v3, a single agent could handle multiple tasks. It generated HTML, CSS, JavaScript, searched for assets, and more—all in one go, while maintaining full context. Now in v3.5, we combine both models. Multiple agents work in parallel, each capable of producing multiple outputs at once. The prompt compiler adapts based on what you're building. A 3D game? It prioritizes game engines, rendering, and mechanics. A website or 2D app? It shifts focus to relevant frameworks and tools. The tool handler can call several services simultaneously, feeding context-aware data into the code generator for real-time execution. From single-output generation to parallel, adaptive workflows. In the next breakdown, we’ll dive into the architecture behind our proprietary engine in v3.5.show more

ALCHEMIST AI 🔮
12,689 次观看 • 1 年前
Opal, our no-code visual builder for AI workflows, just... got a major upgrade. 🧠💎 We’ve added a new agent step that analyzes your goal, determines the best approach, and automatically calls the right tools — such as Veo for video or web search for research — to complete the task. We’re also adding new tools to make the agent even more capable: 💾 Memory – Remember info, like a user’s name or your style preferences across sessions. 🚀 Dynamic Routing – Let the agent choose the next best step using the “@ Go to” tool. 💬 Interactive Chat – Initiate user interactions to gather missing information or present options before moving on. Try it now →show more

Google Labs
1,008,606 次观看 • 6 个月前
GPT-4 will change the way that every student learns!... Imagine a single lesson being instantly converted into a version for visual learners, logical learners, etc. We built an app that used GPT-4 to do exactly this - without code and in literal seconds. Here's how we did it 👇🧵show more

Respell
596,328 次观看 • 3 年前
What is the best video editing agent for short... form social? Does it actually work? We watched professional video editors, step by step, as they built short-form social reels in Adobe Premiere Pro. Today we're open-sourcing this preview dataset on Hugging Face, to make AI agents better at editing videos. The data set is 234 annotated steps across 4 computer-use trajectories. Editors narrated their reasoning aloud as they worked, so every step pairs a screenshot with the expert's own thought, a structured action, and executable grounding: >a Premiere MCP tool call, keyboard shortcut, menu path, or coordinate click. >The format follows the AgentNet trajectory schema, extended with a Premiere action taxonomy and multi-path execution. ***That makes it directly usable for computer-use agent SFT, reasoning mid-training, tool-use and function calling, and benchmarking agents against a human expert baseline. Enjoy!show more

ben
39,867 次观看 • 1 个月前
Today, we are announcing that we've raised $20 million... from Union Square Ventures, Lightspeed, Y Combinator, Jeff Dean, Patrick Collison, John Collison, and others. We built an AI agent that connects to every tool you use at work, automates workflows across all of them, runs code when it needs to, and generates custom UIs on the fly that work with your actual systems and data. We hit $5M ARR and have had 1,200% growth this year. Read more atshow more

Michael Latman
64,184 次观看 • 5 个月前
Increasingly, HTML Artifacts are becoming a core part of... how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe Andrej Karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.show more

elvis
37,069 次观看 • 3 个月前
Introducing Singularity AI Machines. One-click deployable GPUs with built-in... LLMs. Deploy in one click, and within minutes you get an OpenAI-compatible API running high-speed inference on compute that stays under your control. You can literally deploy a machine and: 1. Apply to OpenRouter as a provider 2. Start your own AI business 3. Join Singularity Grid in one click without owning any hardware 4. Let AI agents launch and manage your machines for you Until now, becoming an AI provider felt like something only infra companies could do. We are making it accessible to literally everyone. Singularity AI Machines are live.show more

The Singularity Layer
13,492 次观看 • 2 个月前