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Many people still remember when ManusAI ManusAI was released. It was astonishing to see what AI agents are already capable of today, and its in-depth research was impressive. But what else can ManusAI actually do? Unlike traditional chatbots, Manus can independently plan, execute, and optimize complex, multi-step tasks from...

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Chubby♨️

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50,359 görüntüleme • 1 yıl önce •via X (Twitter)

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Boris Cergol profil fotoğrafı
Boris Cergol1 yıl önce

@ManusAI_HQ Manus is impressive, but it lacks grounding. Can produce very complex results but give it a simple form to fill out and the result is quite underwhelming.

Places Visited & Pictures Taken profil fotoğrafı
Places Visited & Pictures Taken2 yıl önce

Wondering what happens when AI is used? Here’s the answer 🙂

Ilyas Salaoui profil fotoğrafı
Ilyas Salaoui1 yıl önce

@ManusAI_HQ Impressive — ManusAI is more than a chatbot; it’s a full-stack autonomous coworker. From market intel to live websites, it closes the loop without hand-holding. The big question now: how do we scale guardrails when one prompt can spin up code, content, and insights?

JC Gilbert profil fotoğrafı
JC Gilbert1 yıl önce

@ManusAI_HQ imo manus is still one of the best ai products in the market the ux is just unparalleled imo, no manual error handling

RiskDataScience profil fotoğrafı
RiskDataScience1 yıl önce

@ManusAI_HQ It can reverse-engineer (simple) SaaS solutions when prompted appropriately. 🤯

akim profil fotoğrafı
akim1 yıl önce

@ManusAI_HQ ai agent is future No multi ai agent is future

Eryn Littel profil fotoğrafı
Eryn Littel1 yıl önce

@ManusAI_HQ Manus isn't just dialogue. It plans independently. This was the dream years ago, now unfolding. Truly profound.

Cengiz profil fotoğrafı
Cengiz1 yıl önce

@ManusAI_HQ manus is a fantastic AI product, shame they have completely fallen out of the common consciousness

{Author} profil fotoğrafı
{Author}1 yıl önce

@ManusAI_HQ

Johannes Miertschischk profil fotoğrafı
Johannes Miertschischk1 yıl önce

Have you ever considered why Large Language Models (LLMs) are increasingly being promoted as 'AI agents'? Artificial personal assistants who can do all kinds of annoying tasks for you at any time. Tax returns, insurance stuff, official business... All this and much more will be handled by your individually tailored personal AI assistant. Even independently and without your permission! The only prerequisite to be able to handle all sorts of things on your behalf is total access to your digital devices. The best thing to do is simply entrust it with your entire life. Unlike your unreliable friends, it will never forget even the smallest detail. And since you also talk to your AI friend on a daily basis about all sorts of topics, it can practically read your mind and anticipate your every wish! Isn't this a wonderful, brave, new world?

Akshay Bapat profil fotoğrafı
Akshay Bapat1 yıl önce

@ManusAI_HQ @ManusAI_HQ is gpt 5

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Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:
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Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

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Genspark AI agent just released AI Sheets. You can now upload any data and the agent automatically analyzes it, generates reports, and can research the web to find the data for you. 5 powerful use cases + how to try👇:
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Genspark AI agent just released AI Sheets. You can now upload any data and the agent automatically analyzes it, generates reports, and can research the web to find the data for you. 5 powerful use cases + how to try👇:

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Introducing Wide Research Tackle complex research in a single step. With Wide Research, Manus breaks massive, large-scale tasks into multiple sub-tasks — and runs them all at once — without missing a beat. - Research a long list of sales leads - Profile hundreds of companies across multiple metrics - Batch-generate images, web pages, slides, and more You define the goal. Manus handles the rest — simultaneously. Prompt once. Run wide. This is research at scale. Launching today for Pro users, with a gradual rollout planned for Plus and Basic tiers.

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Manus is joining Lenny Rachitsky Product Pass. Lenny subscribers now get 1 year of complimentary access to Manus, a general AI agent that delivers research, slides, websites, images, videos, and more. To our users: thank you for pushing the boundaries. To new builders: welcome. We can't wait to see what you create.

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Our AI engineer can now build simple agents using the English language! Tell it what to do, and it will try to generate, execute, and deploy code. AI has already replaced SQL. The next step is to replace Python. It's officially in its 3rd month of internship!!
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Our AI engineer can now build simple agents using the English language! Tell it what to do, and it will try to generate, execute, and deploy code. AI has already replaced SQL. The next step is to replace Python. It's officially in its 3rd month of internship!!

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4/ Crew AI Orchestration framework for building reliable autonomous AI agents This example shows an agent that automatically creates and schedules social media posts, but it can research, code, and even write apps João Moura あい

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Genspark AI Genspark Slides makes creating professional presentations easier than ever before. At its core is the Super Agent—an AI that independently researches, structures content, and automatically creates engaging slides. Equipped with additional design tools, this agent transforms into a powerful tool for visual presentations. Users can customize the style, add graphics, or insert sources—all with a simple command. Even existing files such as PDFs, Word documents, or Excel spreadsheets can be converted into high-quality slides in just a few minutes. Genspark AI Slides offers a seamless combination of intelligent content creation and visual design – ideal for anyone who regularly needs to deliver compelling presentations. In this example I use an Arxiv-Paper to create a Presentation of it.

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2:06

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

Andrew Ng

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Lior Alexander

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1:03

The future of automation at the Department of War 🇺🇸 has arrived. We are excited to announce the launch of Agent Designer, now available in first AI instance, Google Google Gemini. All 3 million Department of War personnel now have the power to create their own custom AI assistants to automate tasks and streamline complex workflows, with absolutely no coding experience needed. These custom agents can perform multi-step tasks, ingest various data sources, and be shared with teams for immediate deployment. Now, operators can automatically generate after-action reports, analysts can synthesize CUI images into memos, and comptrollers can build apps to analyze financial data. From logistics to strategy, Agent Designer empowers you to build the tools needed for increased efficiency. Several pre-built agents are already available in Gemini to address common workflows. An “AI-first” Department of War empowers the warfighter to address bigger problems, rather than being burdened by tedious tasks.

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ChatGPT connecting to external data sources offers a glimpse into what AI interoperability looks like. Ask any question in ChatGPT and it can now pull data from Box. The future of AI is AI Agents that can access your systems and data to complete any task for you.

Aaron Levie

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0:52

"Vibe coding is NOT the same as using AI to write code that's reviewed, tested, and maintained." Here's my new episode with Lee Robinson (Cursor's Head of AI Education) where he shows how to build AI apps properly using planning, test-driven development, agent workflows, and more. Some tips from Lee: 1. Work with AI to create a detailed technical plan upfront including requirements, design preferences, and your preferred tech stack. 2. Ask AI to write tests so that it can check its own work. Add this to the plan: "Write unit tests for business logic, e2e tests for core user journeys." 3. Add "make descriptive commits" to your initial plan so that you can track what AI changed and why. 📌 Watch our beginner-friendly tutorial now: Also available on: Spotify: Apple: Newsletter:

Peter Yang

108,315 görüntüleme • 10 ay önce