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We've been working with the top frontier labs to improve their design model capabilities. Today we're bringing that to your agents. Introducing our first agent tool: the Brand API. As the cost of creation goes to zero, and slop floods around us, the challenge is creating things worth existing.... Agents need the right tools to craft great things. This helps gen AI agents solve 3 problems: - Extract: produce on-brand assets when users already have a brand - Search: find and retrieve brands for inspiration, from our curated index - Verify: check their own work against, see what's off brand and how to fix it Anyone can try it for free today at Check out the full blog, and case study with General Intelligence Company below.show more

Thais Castello Branco

12,658 subscribers

186,984 次观看 • 6 天前 •via X (Twitter)

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64 条评论

Thais Castello Branco 的头像

Thais Castello Branco6 天前

"Ultimately, the outputs are now just better. In today’s world, better outputs are the edge. And now we have better outputs than our competitors." Check out the case study here:

Thais Castello Branco 的头像

Thais Castello Branco6 天前

You can try it for free at: And integrate easily via API or MCP:

Thais Castello Branco 的头像

Thais Castello Branco6 天前

You can read our full blog "Helping agents create things worth making" here:

Tadeo Donegana Braunschweig 的头像

Tadeo Donegana Braunschweig6 天前

tasteful! 🫶

Thais Castello Branco 的头像

Thais Castello Branco6 天前

wonder who made it

Rémi Kalbe 的头像

Rémi Kalbe6 天前

Congrats on the launch @lgesuelli_p @tadeodonegana 🫶

Thais Castello Branco 的头像

Thais Castello Branco6 天前

@lgesuelli_p @tadeodonegana 🐐🐐 the goats!!!!

Luisa Toledo 的头像

Luisa Toledo6 天前

very cool!

Thais Castello Branco 的头像

Thais Castello Branco6 天前

lets go!

alexis 的头像

alexis6 天前

let’s go congrats on launch!!

Thais Castello Branco 的头像

Thais Castello Branco6 天前

lets gooo!

Caio Fonseca 的头像

Caio Fonseca6 天前

insane!!!

Thais Castello Branco 的头像

Thais Castello Branco6 天前

lfg

Owominire 的头像

Owominire6 天前

It's about to get tasty

Thais Castello Branco 的头像

Thais Castello Branco6 天前

very!

John Rodrigues 的头像

John Rodrigues6 天前

Congratulations on the launch. Will check it out

Thais Castello Branco 的头像

Thais Castello Branco6 天前

yay! feedback is super welcome

andrew pignanelli 的头像

andrew pignanelli6 天前

Super happy with this collab! We use taste to power brand extraction and search in Cofounder making it very easy to port design systems over to agentic companies.

Thais Castello Branco 的头像

Thais Castello Branco6 天前

two design obsessed companies fighting slop 🤝

Sarah Zeng 的头像

Sarah Zeng6 天前

Amazing!! Congrats

Thais Castello Branco 的头像

Thais Castello Branco6 天前

thank you!! 🚀

luisadallacosta 的头像

luisadallacosta6 天前

Great win for the agents!

Thais Castello Branco 的头像

Thais Castello Branco6 天前

they need a lil help ya know

Jeronimo 的头像

Jeronimo6 天前

Agents are about to get some taste

Thais Castello Branco 的头像

Thais Castello Branco6 天前

lets go!

Marcelo Afonseca 的头像

Marcelo Afonseca6 天前

solving taste across the stack!

Anton Gridz 的头像

Anton Gridz6 天前

This is huge! Congrats on the launch! 🚀

Thais Castello Branco 的头像

Thais Castello Branco6 天前

thank you!!

Amplify Partners 的头像

Amplify Partners6 天前

👁️👁️

Luiz Matias 的头像

Luiz Matias6 天前

awesome!

mathurah 的头像

mathurah6 天前

so cool!! congrats on the launch <33

Thais Castello Branco 的头像

Thais Castello Branco6 天前

thank you!!!

Roman 的头像

Roman6 天前

Soo coool!!!

Thais Castello Branco 的头像

Thais Castello Branco6 天前

let's gooo

zane 的头像

zane6 天前

love it

Thais Castello Branco 的头像

Thais Castello Branco6 天前

wooo!

Bautista García 的头像

Bautista García6 天前

great work @tadeodonegana @BauGuerra and team

Thais Castello Branco 的头像

Thais Castello Branco6 天前

@tadeodonegana @BauGuerra amazing work

Gabriela Berenholc 的头像

Gabriela Berenholc6 天前

amazinggg!

Thais Castello Branco 的头像

Thais Castello Branco6 天前

💫

Reid Christian 的头像

Reid Christian6 天前

yes.

Thais Castello Branco 的头像

Thais Castello Branco6 天前

mmmhm

Ana Abreu 的头像

Ana Abreu6 天前

😃

Thais Castello Branco 的头像

Thais Castello Branco6 天前

the best of the best

weisser 的头像

weisser6 天前

Excited to share this on the Solo Founders newsletter. Congrats on the launch!

Thais Castello Branco 的头像

Thais Castello Branco6 天前

love it!!!!

Kyle Sandstrom 的头像

Kyle Sandstrom6 天前

Super cool :)

Thais Castello Branco 的头像

Thais Castello Branco6 天前

:) thank you

Kudzo S Ahegbebu 的头像

Kudzo S Ahegbebu6 天前

So cool! Congrats!

Thais Castello Branco 的头像

Thais Castello Branco6 天前

thank you!!!

AJ 的头像

AJ6 天前

Yo I was running the brand extraction and while it was "profiling" so I changed the tab now I can't see the progress anywhere

faizan khan 的头像

faizan khan6 天前

this is cool, whats the pricing on this.

0xjean.eth @ 🇦🇷⤴ 的头像

0xjean.eth @ 🇦🇷⤴6 天前

geist and geist mono spotted!

farias 的头像

farias6 天前

lets gooo

Thais Castello Branco 的头像

Thais Castello Branco6 天前

lets go!

Eric Stevens 的头像

Eric Stevens6 天前

the off brand stuff almost never comes from bad taste. its round 4 of edits at 11pm when everyone stopped checking against the book. something that never gets tired of that check is worth more than the fun generation part.

Andrew Carr 🤸 的头像

Andrew Carr 🤸6 天前

A great launch!! Congrats

Thais Castello Branco 的头像

Thais Castello Branco6 天前

thank you!!

Tonye💡 的头像

Tonye💡6 天前

Verify is the one I'd pay for. Models invent their own hex, so prompting the brand in never held for us. We ended up rewriting the colours they hardcoded into our own tokens after generation.

Thais Castello Branco 的头像

Thais Castello Branco6 天前

tell me more!

Aaliya 的头像

Aaliya6 天前

Finding brand inspiration and checking your work in one place is really useful.

Thais Castello Branco 的头像

Thais Castello Branco6 天前

epic! tell me more about your usecase!

Perly 🦈 的头像

Perly 🦈6 天前

@mathurahravi So coooool

Thais Castello Branco 的头像

Thais Castello Branco6 天前

@mathurahravi thank you!!

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Introducing our first agent tool: the Brand API This helps gen AI agents solve 3 problems: - Extract: produce on-brand assets when users already have a brand - Search: find and retrieve brands for inspiration, from our curated index - Verify: check their own work against, see what's off brand and how to fix it Anyone can try it for free today at Check out the full blog, and case study with General Intelligence Company andrew pignanelli below.

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We're only year 3 of a decade (if not multi-decades) long transformation of work. 3 years ago we bet on building an horizontal platform for work with agents, a chance to invent a new operating system for companies, from scratch, with AI as a fundamental premise. Many people considered us crazy for going after that, praising verticalized AI products as the winning strategy. But here's the thing: the time horizon of tasks successfully handled by agents has been predictively increasing form minutes to hours and will in all likelihood reach the equivalent of days and weeks of human work equivalent in the coming quarters. This is were verticalized and/or single-player AI falls short. Single-player tools, one person, one agent, confined to your machine is the wrong architecture for what's coming. We're shifting from using AI to produce things, to managing fleets of agents that do the producing. 3 years ago I wrote[1]: "ChatGPT is the Pong of LLMs. [...] Imagine, one day we'll get the DOOM, Civ, Red Alert, and Counter Strike of LLMs. Let alone multiplayer modes." Weeks long tasks in companies are inherently collaborative and mechanically spanning multiple teams. The new bottleneck in harnessing agents within organizations is coordination: multiple humans and multiple agents need to work together, with shared context, shared tools, shared goals. Agents that can hand work off to other agents or surface decisions to the right person at the right time. Humans who can review, steer, and step in without losing the thread. Teams that can run parallel workstreams and actually stay aligned. This is Multiplayer AI, and that's what we've been building at Dust. Across Datadog, Clay, Persona, 1Password, Doctolib and 3,000+ organizations globally, we've watched teams figure out what this looks like in practice. 300,000+ agents deployed. 70% weekly active. 240%+ NRR. Today we're announcing a $40M Series B with Abstract, Sequoia, Snowflake, and Datadog to accelerate our vision. Designing the right interfaces for multiplayer AI is the next frontier. Join us to redefine work by defining multiplayer AI.

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How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

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Rui Costa

45,907 次观看 • 4 个月前

Today, we're shipping our Marketing Intelligence Layer 120,000+ top Meta ads, emails, and growth playbooks, all curated for agents. Every good marketer does the same thing before they write anything. They go study what's already working. That takes days (sometimes weeks). We just made it take seconds. Helena now reads from the largest curated marketing best practice repository ever built into an AI before she writes a single word for you. → 20k+ email templates from the best brands in dtc, saas, b2b → 100k+ Meta ad creatives with the hooks, copy, and angles that actually converted → brand briefs from award-winning campaigns → growth playbooks with real tactics and real numbers → brand identity references across thousands of companies Live now for all Helena users today. Try it →

Seijin Jung

35,807 次观看 • 1 个月前

Building CRED garage has been an incredible pursuit for us at Design Mafia and we had a great time crafting it. It’s the culmination of some incredible collaboration between product, business, engineering, product design, brand marketing, brand design, creative and motion design teams. CRED garage is rolling out from today to our members. Do check out

Harish

35,916 次观看 • 3 年前

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 次观看 • 1 年前

People are still sleeping on WebMCP.. You can now give agents tool calls to interact with your website natively. Including full UI/UX support and interactive elements to build their own views. Sharing a demo below that I submitted for the WebMCP challenge - see how agents can interact with the El Nino situation tracker. It creates it's own views, preserves human edits, and creates a shareable link to share with other agents (or humans). Video and code by Codex ChatGPT & Railway Live at elneenyo dot com to try out today!

People are still sleeping on WebMCP.. You can now give agents tool calls to interact with your website natively. Including full UI/UX support and interactive elements to build their own views. Sharing a demo below that I submitted for the WebMCP challenge - see how agents can interact with the El Nino situation tracker. It creates it's own views, preserves human edits, and creates a shareable link to share with other agents (or humans). Video and code by Codex ChatGPT & Railway Live at elneenyo dot com to try out today!

Nikunj Kothari

21,055 次观看 • 20 天前

Take a behind-the-scenes look at how FridonAI works! 👀 In the video, you’ll see our main agent, the Supervisor (or Chat Agent), managing everything. This agent connects to other specialized agents, known as plugins in FridonAI, to handle specific tasks. Each crypto agent in FridonAI is equipped with tools designed for different tasks within its domain. For example, the Coin Technical Chart Searcher Agent helps users find coins based on chart similarity, indicator values, or descriptions. It decides which tools to use, gathers data from them, and creates the final response for the user. The Supervisor Agent oversees everything. It figures out which crypto agent to call based on the user’s request, collects their answers, validates them, and crafts the final, polished response sent back to the user. So now, it’s clear how new agents can be injected into our agentic system, this is a perfect example of how swarm intelligence works in action. With our infrastructure and framework, developers can seamlessly contribute new agents, enhancing the system’s capabilities and making it more sophisticated together. Shoutout to the LangChain team for their amazing tools and LangGraph Studio, which makes building, debugging, and visualizing agents and utilization of LLMs super easy! 👇

Take a behind-the-scenes look at how FridonAI works! 👀 In the video, you’ll see our main agent, the Supervisor (or Chat Agent), managing everything. This agent connects to other specialized agents, known as plugins in FridonAI, to handle specific tasks. Each crypto agent in FridonAI is equipped with tools designed for different tasks within its domain. For example, the Coin Technical Chart Searcher Agent helps users find coins based on chart similarity, indicator values, or descriptions. It decides which tools to use, gathers data from them, and creates the final response for the user. The Supervisor Agent oversees everything. It figures out which crypto agent to call based on the user’s request, collects their answers, validates them, and crafts the final, polished response sent back to the user. So now, it’s clear how new agents can be injected into our agentic system, this is a perfect example of how swarm intelligence works in action. With our infrastructure and framework, developers can seamlessly contribute new agents, enhancing the system’s capabilities and making it more sophisticated together. Shoutout to the LangChain team for their amazing tools and LangGraph Studio, which makes building, debugging, and visualizing agents and utilization of LLMs super easy! 👇

Fridon AI

81,188 次观看 • 1 年前

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!

Sam Rodriques

726,150 次观看 • 1 年前