Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

How we're using AI agents at Wallaroo Media to improve performance for clients, part 5: Implementing Triple Whale 🐳's Web Performance Agent to find actionable improvements to increase mobile conversion rates and client revenue 📊

Brandon Doyle

49,439 subscribers

1,349,973 görüntüleme • 1 yıl önce •via X (Twitter)

Bilim & Teknoloji Finans

Anya Rossi• Live Now

Private livecam show

7 Yorum

Brandon Doyle (🦘,🦘) profil fotoğrafı

Brandon Doyle (🦘,🦘)1 yıl önce

@AY_Orbach

NICK profil fotoğrafı

NICK1 yıl önce

@WallarooMedia @triplewhale

Jenna (Weiner) Crane profil fotoğrafı

Jenna (Weiner) Crane1 yıl önce

@WallarooMedia @triplewhale 🔥

Arvo Digital profil fotoğrafı

Arvo Digital1 yıl önce

@WallarooMedia @triplewhale Super smart 🧠

Wallaroo Media profil fotoğrafı

Wallaroo Media1 yıl önce

@triplewhale

Jerry Dearden profil fotoğrafı

Jerry Dearden1 yıl önce

@WallarooMedia @triplewhale This is amazing!

Brandon Doyle (🦘,🦘) profil fotoğrafı

Brandon Doyle (🦘,🦘)1 yıl önce

@WallarooMedia @triplewhale 🫡

Benzer Videolar

If you're in eCommerce this video is a MUST WATCH. I started using new AI agents from Triple Whale 🐳 that are specifically designed for Black Friday / Cyber Monday and they're incredible. I compiled our favorite agents at Wallaroo Media, why we love them, how to use them, etc, and put it in a nice little PDF for you. Get it via the link in the comment below!

If you're in eCommerce this video is a MUST WATCH. I started using new AI agents from Triple Whale 🐳 that are specifically designed for Black Friday / Cyber Monday and they're incredible. I compiled our favorite agents at Wallaroo Media, why we love them, how to use them, etc, and put it in a nice little PDF for you. Get it via the link in the comment below!

Brandon Doyle

761,409 görüntüleme • 9 ay önce

Today we're launching the PlanetScale hosted MCP server. Use your favorite AI agent to explore your data, identify slow queries, and improve database performance.

Today we're launching the PlanetScale hosted MCP server. Use your favorite AI agent to explore your data, identify slow queries, and improve database performance.

PlanetScale

29,423 görüntüleme • 6 ay önce

ExACT combines Reflective-MCTS and Exploratory Learning to improve AI agents' decision-making, enabling test-time compute scaling. Learn how these methods help agents refine strategies for state-of-the-art performance and improved computational efficiency:

ExACT combines Reflective-MCTS and Exploratory Learning to improve AI agents' decision-making, enabling test-time compute scaling. Learn how these methods help agents refine strategies for state-of-the-art performance and improved computational efficiency:

Microsoft Research

13,274 görüntüleme • 1 yıl önce

Yesterday I met with a $100M brand completely swamped trying to build AI agents. They were duct taping together N8n, Gumloop, Make, Lindy and Zapier hoping it would help automate. Here’s the thing: Those tools are great but only if your data is clean, connected, and actually usable by AI. For example you can’t build a real media buying agent if it doesn’t know: – What’s in stock – What your customers LTV – How your sales change by season Triple Whale brings all of your data into one place. So when you ask our AI a question, it doesn’t guess because it already has the data. And it knows how to use it. We’ve spent 920 days and hired 72 full-time engineers to build the most advanced data layer in ecom designed for AI from day one. Wallaroo Media figured it out👇 They dropped every other platform for Triple Whale. ❌ No more scattered agents ❌ No more platform-hopping ✅ Better answers, better automations, better results Shout out to Justin Doyle and the whole Wallaroo team for the incredible partnership!

Maxx Blank 🐳

47,002 görüntüleme • 1 yıl önce

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!

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 görüntüleme • 8 ay önce

MCT AI Agent Update: We've developed an AI model to forecast memecoin performance using historical data, X sentiment (likes, followers, posts), Rugcheck scores, real-time whale activity, and other in-depth metrics, including trading volume, liquidity patterns, and holders distribution.

MCT AI Agent Update: We've developed an AI model to forecast memecoin performance using historical data, X sentiment (likes, followers, posts), Rugcheck scores, real-time whale activity, and other in-depth metrics, including trading volume, liquidity patterns, and holders distribution.

Chain Alpha

38,964 görüntüleme • 1 yıl önce

How to Fine Tune Llama 3 for Better Instruction Following? 🚀 Setup Environment 📊 Train Model 💾 Save Model Locally 🌐 Upload Model to Hugging Face 📈 Evaluate Performance 🛠 Using 🦥 Unsloth AI Subscribe: YT: Meta AI at Meta Daniel Han

How to Fine Tune Llama 3 for Better Instruction Following? 🚀 Setup Environment 📊 Train Model 💾 Save Model Locally 🌐 Upload Model to Hugging Face 📈 Evaluate Performance 🛠 Using 🦥 Unsloth AI Subscribe: YT: Meta AI at Meta Daniel Han

Mervin Praison

14,107 görüntüleme • 2 yıl önce

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:

Andrew Ng

126,507 görüntüleme • 1 yıl önce

Chris Camillo reveals anyone can make $500,000/year by implementing AI agents to automate sales👀 “In 24 hours, you can set up an AI agent that will answer calls, send out immediate automated text responses, and get them a quote bid in next to real-time. You might increase that company’s revenue by 5 to 15% in two or three days. Immediately, you become invaluable.”

Chris Camillo reveals anyone can make $500,000/year by implementing AI agents to automate sales👀 “In 24 hours, you can set up an AI agent that will answer calls, send out immediate automated text responses, and get them a quote bid in next to real-time. You might increase that company’s revenue by 5 to 15% in two or three days. Immediately, you become invaluable.”

The Iced Coffee Hour

41,296 görüntüleme • 4 ay önce

Everyone's Using OpenClaw Wrong (Here's How I Make $31K/Month): 00:00 Intro 00:32 Biggest Mistake 1:50 Specialized Agents 2:58 X Agent 4:00 YouTube Agent 4:51 Trading Agent 5:18 Web Agent 5:52 Revenue Breakdown 8:42 Copy Paste This Prompt 10:43 What To Build First

Everyone's Using OpenClaw Wrong (Here's How I Make $31K/Month): 00:00 Intro 00:32 Biggest Mistake 1:50 Specialized Agents 2:58 X Agent 4:00 YouTube Agent 4:51 Trading Agent 5:18 Web Agent 5:52 Revenue Breakdown 8:42 Copy Paste This Prompt 10:43 What To Build First

Sharbel

76,270 görüntüleme • 5 ay önce

Chris Camillo explains the path to $500,000 per year through implementing AI agents to automate sales👀 “In 24 hours, you can set up an AI agent that will answer calls, send out immediate automated text responses, and get them a quote bid in next to real-time. You might increase that company’s revenue by 5 to 15% in two or three days. Immediately, you become invaluable.”

Chris Camillo explains the path to $500,000 per year through implementing AI agents to automate sales👀 “In 24 hours, you can set up an AI agent that will answer calls, send out immediate automated text responses, and get them a quote bid in next to real-time. You might increase that company’s revenue by 5 to 15% in two or three days. Immediately, you become invaluable.”

The Iced Coffee Hour

286,724 görüntüleme • 5 ay önce

This is a powerful combination: 1) Directing an AI agent to generate characters, locations and all the angles I need for my dialogue shots 2) Using human actors to drive the performance of my characters The agent handles the technical side and I can focus on performance:

This is a powerful combination: 1) Directing an AI agent to generate characters, locations and all the angles I need for my dialogue shots 2) Using human actors to drive the performance of my characters The agent handles the technical side and I can focus on performance:

Halim Alrasihi

51,221 görüntüleme • 5 ay önce

Is your Agent ready for real-world users? Learn how to run an evaluation using Agentverse’s Response QA Agent - a built-in tool that stress-tests your Agent with realistic queries and scores its performance. This is the first step to improving discoverability, trust, and performance. Run your first QA check and see how your Agent responds under pressure. Watch 👇

Is your Agent ready for real-world users? Learn how to run an evaluation using Agentverse’s Response QA Agent - a built-in tool that stress-tests your Agent with realistic queries and scores its performance. This is the first step to improving discoverability, trust, and performance. Run your first QA check and see how your Agent responds under pressure. Watch 👇

Agentverse by Fetch.ai

20,202 görüntüleme • 8 ay önce

#AI platforms can be a great team-building tool 🏃🏽‍♀️⚽ 🏅 Learn how coaches and scouts are using Intel-powered 3D Athlete Tracking (3DAT) platform technology to analyze performance data, and using #AI to find top talent. #Paris2024

#AI platforms can be a great team-building tool 🏃🏽‍♀️⚽ 🏅 Learn how coaches and scouts are using Intel-powered 3D Athlete Tracking (3DAT) platform technology to analyze performance data, and using #AI to find top talent. #Paris2024

Intel

31,628 görüntüleme • 2 yıl önce

Give your AI agents access to the entire live web. Web Search, Fetch, Browser, and Agent. Four web primitives. One API key. Every layer built in-house. And we're just getting started! Sign up at and get 500 steps. No credit card.

Give your AI agents access to the entire live web. Web Search, Fetch, Browser, and Agent. Four web primitives. One API key. Every layer built in-house. And we're just getting started! Sign up at and get 500 steps. No credit card.

TinyFish

437,104 görüntüleme • 4 ay önce