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"Voice agents are built to be helpful. The problem is they can be too helpful and cross the boundary." I sat down with Sumanyu Sharma 🍫, Founder and CEO of Hamming, at our AI Engineer booth. Hamming stress-tests and monitors voice agents so the bank or hospital running one...

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justine

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17,326 görüntüleme • 5 gün önce •via X (Twitter)

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"if i can interface with everything through my agent, why would i go to the dashboard?" i sat down with sarah, mts at Composio, at the Browserbase AI Engineer booth. fresh off her AIE talk "dashboards are dead," we got into: - why agent experience is the next wave and how to design for agents - personal agents as the main consumer of the internet - building and open-sourcing TrustClaw, a production-ready personal agents listen to our full conversation here:
16:39

"if i can interface with everything through my agent, why would i go to the dashboard?" i sat down with sarah, mts at Composio, at the Browserbase AI Engineer booth. fresh off her AIE talk "dashboards are dead," we got into: - why agent experience is the next wave and how to design for agents - personal agents as the main consumer of the internet - building and open-sourcing TrustClaw, a production-ready personal agents listen to our full conversation here:

justine

14,091 görüntüleme • 12 gün önce

Learn to build conversational AI voice agents in "Building AI Voice Agents for Production", created in collaboration with LiveKit and RealAvatar, and taught by dsa (Co-founder & CEO of LiveKit), Shayne (Developer Advocate, LiveKit), and Nedelina Teneva (Head of AI at RealAvatar, an AI Fund portfolio company). Voice agents combine speech and reasoning capabilities to enable real-time conversations. They're already being used to support customer service, to improve accessibility in healthcare, for entertainment applications, and for talk therapy. In this course, you’ll learn to build voice agents that listen, reason, and respond naturally. You’ll follow the architecture used to create the "AI Andrew" Avatar, a collaborative project between and RealAvatar that responds to users in what sounds like my voice. You’ll build a voice agent from scratch and deploy it to the cloud, enabling support for many simultaneous users. What you’ll learn: - Understand the fundamentals of voice agents, including key components like speech-to-text (STT), text-to-speech (TTS), and LLMs, and how latency is introduced at each layer. - Explore voice agent architectures and the trade-offs between modular pipelines and speech-to-speech APIs. - Explore how platforms like LiveKit mitigate latency issues with optimized networking infrastructure and low-latency communication protocols. - Learn how to connect client devices to voice agents using WebRTC—and why it outperforms HTTP and WebSocket for low-latency audio streaming. - Incorporate voice activity detection (VAD), end-of-turn detection, and context management to detect turns, handle interruptions, and manage conversational flow. - Understand the trade-offs between latency, quality, and cost in an example in which you build a voice agent and change its voice. - Equip your agent with metrics to measure latency at each stage of the voice pipeline and learn the key levers you can pull to make your agent faster and more responsive. The voice agents built in this course also incorporate voice technology from , a supporting contributor to the project. By the end of this course, you'll have learned the components of an AI voice agent pipeline, combined them into a system with low-latency communication, and deployed them on cloud infrastructure so it scales to many users. I’m looking forward to seeing what voice agents you build from this course! Please sign up here:
2:41

Learn to build conversational AI voice agents in "Building AI Voice Agents for Production", created in collaboration with LiveKit and RealAvatar, and taught by dsa (Co-founder & CEO of LiveKit), Shayne (Developer Advocate, LiveKit), and Nedelina Teneva (Head of AI at RealAvatar, an AI Fund portfolio company). Voice agents combine speech and reasoning capabilities to enable real-time conversations. They're already being used to support customer service, to improve accessibility in healthcare, for entertainment applications, and for talk therapy. In this course, you’ll learn to build voice agents that listen, reason, and respond naturally. You’ll follow the architecture used to create the "AI Andrew" Avatar, a collaborative project between and RealAvatar that responds to users in what sounds like my voice. You’ll build a voice agent from scratch and deploy it to the cloud, enabling support for many simultaneous users. What you’ll learn: - Understand the fundamentals of voice agents, including key components like speech-to-text (STT), text-to-speech (TTS), and LLMs, and how latency is introduced at each layer. - Explore voice agent architectures and the trade-offs between modular pipelines and speech-to-speech APIs. - Explore how platforms like LiveKit mitigate latency issues with optimized networking infrastructure and low-latency communication protocols. - Learn how to connect client devices to voice agents using WebRTC—and why it outperforms HTTP and WebSocket for low-latency audio streaming. - Incorporate voice activity detection (VAD), end-of-turn detection, and context management to detect turns, handle interruptions, and manage conversational flow. - Understand the trade-offs between latency, quality, and cost in an example in which you build a voice agent and change its voice. - Equip your agent with metrics to measure latency at each stage of the voice pipeline and learn the key levers you can pull to make your agent faster and more responsive. The voice agents built in this course also incorporate voice technology from , a supporting contributor to the project. By the end of this course, you'll have learned the components of an AI voice agent pipeline, combined them into a system with low-latency communication, and deployed them on cloud infrastructure so it scales to many users. I’m looking forward to seeing what voice agents you build from this course! Please sign up here:

Andrew Ng

87,484 görüntüleme • 1 yıl önce

Can AI agents automate all work? What happens to white collar jobs? I sat down with Aaron Levie (Aaron Levie), Founder and CEO of to talk about agents, deploying AI, the future of white collar jobs, the future of SaaS, and so much more. 0:00 Intro 1:02 AI at Box 2:59 New tools 5:26 Embracing AI at Box 8:13 Using AI to do more 11:09 White collar collapse? 14:51 How does demand scale? 19:29 Managing AI 25:51 Companies with too much power 29:58 Personal vs Work agents 35:37 What if AI makes a big mistake? 41:23 Who will own the agents? 43:37 SaaS is dead 48:40 Future of the web 57:23 Using AI on socials
59:27

Can AI agents automate all work? What happens to white collar jobs? I sat down with Aaron Levie (Aaron Levie), Founder and CEO of to talk about agents, deploying AI, the future of white collar jobs, the future of SaaS, and so much more. 0:00 Intro 1:02 AI at Box 2:59 New tools 5:26 Embracing AI at Box 8:13 Using AI to do more 11:09 White collar collapse? 14:51 How does demand scale? 19:29 Managing AI 25:51 Companies with too much power 29:58 Personal vs Work agents 35:37 What if AI makes a big mistake? 41:23 Who will own the agents? 43:37 SaaS is dead 48:40 Future of the web 57:23 Using AI on socials

Matthew Berman

91,581 görüntüleme • 1 yıl önce

My conversation with Jesse Zhang Decagon is building a customer service agent with AI, with plans for much more than that. It’s one of the more successful early AI applications, with tons of lessons for the rest of the market. I love how Jesse developed which problem to solve first, a set of lessons applicable to all entrepreneurs Enjoy Timestamps: 0:00 Intro 2:00 Competing in the AI Era 11:35 Finding Product-Market Fit 14:25 Anatomy of a Great Customer Interview 17:37 Everyone’s Wrong Until You Talk to Customers 19:25 AI Agents Replacing Human Labor 21:55 Why Customer Service 23:30 Coding vs. Customer Service 35:44 The Future of Voice and the Human UI 38:54 Why Voice-to-Voice Is So Hard 43:18 Learning from Customer Data 44:36 How CEOs Should Think About AI 47:06 The ROI of Coding and Productivity Tools 48:20 Every Brand Will Talk to You Through AI 56:00 Recruiting, Capital, and Demand 1:00:42 Investor Frenzy 1:22:17 The Long-Term Vision for Decagon 1:25:05 Kindest Thing
1:26:12

My conversation with Jesse Zhang Decagon is building a customer service agent with AI, with plans for much more than that. It’s one of the more successful early AI applications, with tons of lessons for the rest of the market. I love how Jesse developed which problem to solve first, a set of lessons applicable to all entrepreneurs Enjoy Timestamps: 0:00 Intro 2:00 Competing in the AI Era 11:35 Finding Product-Market Fit 14:25 Anatomy of a Great Customer Interview 17:37 Everyone’s Wrong Until You Talk to Customers 19:25 AI Agents Replacing Human Labor 21:55 Why Customer Service 23:30 Coding vs. Customer Service 35:44 The Future of Voice and the Human UI 38:54 Why Voice-to-Voice Is So Hard 43:18 Learning from Customer Data 44:36 How CEOs Should Think About AI 47:06 The ROI of Coding and Productivity Tools 48:20 Every Brand Will Talk to You Through AI 56:00 Recruiting, Capital, and Demand 1:00:42 Investor Frenzy 1:22:17 The Long-Term Vision for Decagon 1:25:05 Kindest Thing

Patrick OShaughnessy

49,418 görüntüleme • 9 ay önce

"The issue is it wasn't built for humans." Yat Siu (Yat Siu) co-founded Animoca Brands. Started building NFT infrastructure when CryptoKitties melted Ethereum. Made the AI + crypto argument years before anyone listened. The man who has spent a decade selling crypto to retail now thinks the next customer won't be retail at all. "There's going to be more agents than humans. The entire internet is going to be swarmed by agents." We cover: Why AI agents, not humans, become the actual customer crypto was built for The infrastructure that has to exist before agents can transact at machine speed Why every consumer app breaks when agents replace browsing (the Tinder example) How meme coins pulled focus from the builders who actually mattered Why tokens become the commodity layer for compute, energy, and attention The orchestration layer thesis, where real development is happening now Why the metaverse isn't dead, it's just coming to us through agents What retail should actually be holding right now Thanks to Yat for coming on New Era Finance Podcast. TIMESTAMPS: 00:00 - Intro 02:00 - Crypto Is Trump's 10th Child 07:30 - Meme Coins Were The Wrong Distraction 12:00 - The Customer Was Never You 17:30 - Code Is Law For Agents 22:00 - More Agents Than Humans 27:00 - The End Of Websites 32:00 - Why On-Chain Growth Stalled 36:00 - What Retail Should Actually Hold
43:06

"The issue is it wasn't built for humans." Yat Siu (Yat Siu) co-founded Animoca Brands. Started building NFT infrastructure when CryptoKitties melted Ethereum. Made the AI + crypto argument years before anyone listened. The man who has spent a decade selling crypto to retail now thinks the next customer won't be retail at all. "There's going to be more agents than humans. The entire internet is going to be swarmed by agents." We cover: Why AI agents, not humans, become the actual customer crypto was built for The infrastructure that has to exist before agents can transact at machine speed Why every consumer app breaks when agents replace browsing (the Tinder example) How meme coins pulled focus from the builders who actually mattered Why tokens become the commodity layer for compute, energy, and attention The orchestration layer thesis, where real development is happening now Why the metaverse isn't dead, it's just coming to us through agents What retail should actually be holding right now Thanks to Yat for coming on New Era Finance Podcast. TIMESTAMPS: 00:00 - Intro 02:00 - Crypto Is Trump's 10th Child 07:30 - Meme Coins Were The Wrong Distraction 12:00 - The Customer Was Never You 17:30 - Code Is Law For Agents 22:00 - More Agents Than Humans 27:00 - The End Of Websites 32:00 - Why On-Chain Growth Stalled 36:00 - What Retail Should Actually Hold

Michaël van de Poppe

122,039 görüntüleme • 1 ay önce

AI agents just got simple. Like really simple. Building AI agents is now accessible to everyone, without the need for coding, complex setups, or a full tech team. A groundbreaking platform is revolutionizing this narrative. Meet Carla Torres the easiest way to build, manage, and scale AI agents. What makes Airia different? It simplifies everything. ✔️ No technical background needed ✔️ Works with any model GPT, Claude, Gemini, Mistral & more ✔️ Enterprise-grade security ✔️ Built for creators, teams & companies Transform an idea into a functional AI agent in minutes. The part that amazed everyone Airia offers 2,500+ pre-built agent templates. Search. Pick one. Customize. No blank screens. No starting from scratch. Just instant value. This is why founders, creators, engineers, and enterprises are enthusiastically joining the Airia Community every day. Why it matters AI agents are shaping the future of work. Airia makes it easy, fast, and secure for anyone to bring AI to life. If AI agents are part of the 2025 vision, this platform is the perfect place to start. 🔗 Explore the Airia Community 2,500+ ready-to-use agents are waiting at #Airia #AIAgents #AIPlatform #AICommunity #AIForBusiness #AgenticAI #NoCodeAI
6:35

AI agents just got simple. Like really simple. Building AI agents is now accessible to everyone, without the need for coding, complex setups, or a full tech team. A groundbreaking platform is revolutionizing this narrative. Meet Carla Torres the easiest way to build, manage, and scale AI agents. What makes Airia different? It simplifies everything. ✔️ No technical background needed ✔️ Works with any model GPT, Claude, Gemini, Mistral & more ✔️ Enterprise-grade security ✔️ Built for creators, teams & companies Transform an idea into a functional AI agent in minutes. The part that amazed everyone Airia offers 2,500+ pre-built agent templates. Search. Pick one. Customize. No blank screens. No starting from scratch. Just instant value. This is why founders, creators, engineers, and enterprises are enthusiastically joining the Airia Community every day. Why it matters AI agents are shaping the future of work. Airia makes it easy, fast, and secure for anyone to bring AI to life. If AI agents are part of the 2025 vision, this platform is the perfect place to start. 🔗 Explore the Airia Community 2,500+ ready-to-use agents are waiting at #Airia #AIAgents #AIPlatform #AICommunity #AIForBusiness #AgenticAI #NoCodeAI

Aakash Verma

40,334 görüntüleme • 7 ay önce

I just built an AI voice agent for my company in 5 minutes. Pasted our website URL. It scraped everything. Built the agent. I called it. It knew our entire business and tried to book me a demo. This is the same AI behind Marriott, Caesars, and Gordon Ramsay. Watch the full build: Here’s what PolyAI’s Agent Wizard actually does: → Enter your website URL → It scrapes FAQs, product info, policies, hours — everything → Auto-builds a voice agent with your business knowledge → Choose a voice, customize the personality → Start taking calls I got early access to Agent Wizard and tested it on Gerra (my AI + robotics company). The agent accurately described our multimodal datasets, answered technical questions, and even offered to schedule a demo with real time slots. This is the same tech powering: • Marriott — hotel reservations • Caesars — casino customer service • Gordon Ramsay — restaurant bookings • FedEx, PG&E, UniCredit + 100 more 2,000+ deployments. Millions of calls daily. 24+ languages. 391% ROI. $10.3M avg savings per enterprise. Built voice-first by 3 Cambridge PhDs who helped build Siri. Backed by NVIDIA, Khosla Ventures, Zendesk. $200M+ raised. $750M+ valuation. Enterprise voice AI used to take months to deploy. This took me 5 minutes. Want the Voice Agent Deployment Playbook? Like + comment “VOICE” + repost, and I’ll DM it to you. (must be following)
4:47

I just built an AI voice agent for my company in 5 minutes. Pasted our website URL. It scraped everything. Built the agent. I called it. It knew our entire business and tried to book me a demo. This is the same AI behind Marriott, Caesars, and Gordon Ramsay. Watch the full build: Here’s what PolyAI’s Agent Wizard actually does: → Enter your website URL → It scrapes FAQs, product info, policies, hours — everything → Auto-builds a voice agent with your business knowledge → Choose a voice, customize the personality → Start taking calls I got early access to Agent Wizard and tested it on Gerra (my AI + robotics company). The agent accurately described our multimodal datasets, answered technical questions, and even offered to schedule a demo with real time slots. This is the same tech powering: • Marriott — hotel reservations • Caesars — casino customer service • Gordon Ramsay — restaurant bookings • FedEx, PG&E, UniCredit + 100 more 2,000+ deployments. Millions of calls daily. 24+ languages. 391% ROI. $10.3M avg savings per enterprise. Built voice-first by 3 Cambridge PhDs who helped build Siri. Backed by NVIDIA, Khosla Ventures, Zendesk. $200M+ raised. $750M+ valuation. Enterprise voice AI used to take months to deploy. This took me 5 minutes. Want the Voice Agent Deployment Playbook? Like + comment “VOICE” + repost, and I’ll DM it to you. (must be following)

Aryan Mahajan

28,485 görüntüleme • 5 ay önce

Why ElevenLabs chose to insure their ai voice agents with AIUC (Artificial Intelligence Underwriting Company): " We had a major announcement that we're going to be the first company to insure voice agents. If you're an enterprise, you can actually pay for insurance & if it goes out of bounds, something goes wrong, then you can make sure that you're covered." What's your hottest take on ai agents? "For most businesses, they're actually really risky & so they can go off script, they can say really bad things. You're maybe giving them tool use. And they're able to do refunds on your behalf. And so that's why we're really excited to announce the first insurable voice agents, where we've actually defined standard & a pathway around guardrails & audits so that you as a company, you can get an insurance product, which covers you to enable you to go to production at scale." Luke Harries (Luke Harries) Head of Growth Rune Kvist (Rune Kvist), CEO of AIUC, was the first product & GTM hire at Anthropic. Now he’s building the “confidence infrastructure” for the AGI era — combining standards (AIUC-1), audits, & insurance to de-risk AI deployments.
0:49

Why ElevenLabs chose to insure their ai voice agents with AIUC (Artificial Intelligence Underwriting Company): " We had a major announcement that we're going to be the first company to insure voice agents. If you're an enterprise, you can actually pay for insurance & if it goes out of bounds, something goes wrong, then you can make sure that you're covered." What's your hottest take on ai agents? "For most businesses, they're actually really risky & so they can go off script, they can say really bad things. You're maybe giving them tool use. And they're able to do refunds on your behalf. And so that's why we're really excited to announce the first insurable voice agents, where we've actually defined standard & a pathway around guardrails & audits so that you as a company, you can get an insurance product, which covers you to enable you to go to production at scale." Luke Harries (Luke Harries) Head of Growth Rune Kvist (Rune Kvist), CEO of AIUC, was the first product & GTM hire at Anthropic. Now he’s building the “confidence infrastructure” for the AGI era — combining standards (AIUC-1), audits, & insurance to de-risk AI deployments.

Molly O’Shea

30,294 görüntüleme • 4 ay önce

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 👇
0:59

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 👇

Zeb Evans

425,244 görüntüleme • 6 ay önce

After many months, a lot of model releases and even more Amp releases, here it is: A new episode of Raising An Agent with Quinn Slack and Thorsten Ball. They discuss: - Why Gemini 3 might have been the inflection point, not Opus 4.5 - What changed over Christmas and why everybody talks about agents now - "The assistant is dead, long live the factory" - How codebases need to adapt for agents - Why small/local models don't give you an impression of the frontier - What does it mean for a codebase to be agent-friendly? - New things coming to Amp Enjoy! And happy hacking! Timestamps: 1:51 The Impact of Gemini 3 and Opus 4.5 5:28 Shifting Perceptions of AI Agents 10:17 Adapting Codebases for AI Efficiency 14:48 The Future of Software Development with AI Agents 19:29 Challenges and Opportunities Ahead 24:00 The Importance of Frontier Models 25:12 Adapting to AI-Driven Workflows 26:57 The Future of Coding Agents 28:07 Building an Agentic Code Base 30:22 Rethinking Development Tools 31:44 Amp's Evolution and Team Dynamics 33:00 Discord Q&A 39:18 Teasing New Features: Deep
40:50

After many months, a lot of model releases and even more Amp releases, here it is: A new episode of Raising An Agent with Quinn Slack and Thorsten Ball. They discuss: - Why Gemini 3 might have been the inflection point, not Opus 4.5 - What changed over Christmas and why everybody talks about agents now - "The assistant is dead, long live the factory" - How codebases need to adapt for agents - Why small/local models don't give you an impression of the frontier - What does it mean for a codebase to be agent-friendly? - New things coming to Amp Enjoy! And happy hacking! Timestamps: 1:51 The Impact of Gemini 3 and Opus 4.5 5:28 Shifting Perceptions of AI Agents 10:17 Adapting Codebases for AI Efficiency 14:48 The Future of Software Development with AI Agents 19:29 Challenges and Opportunities Ahead 24:00 The Importance of Frontier Models 25:12 Adapting to AI-Driven Workflows 26:57 The Future of Coding Agents 28:07 Building an Agentic Code Base 30:22 Rethinking Development Tools 31:44 Amp's Evolution and Team Dynamics 33:00 Discord Q&A 39:18 Teasing New Features: Deep

Amp

36,150 görüntüleme • 6 ay önce

🚨 New Proof of Vision out today folks! In this 34th PoV episode, our Director of Protocol Services, Kirk had on Andrew Hill, Co-Founder and CEO of Recall When it comes to your business, you’d never trust someone without a proven track record to make high-risk decisions So why should it be any different with AI agents? How do you know today which AI agents you should trust? AI agents are multiplying at unprecedented scale, with millions designed to shape the way we work, make decisions, and live our lives But how can we trust them? The challenge is trust: as dependence on these agents increases, so too does the exponential risk and cost of delegating to the wrong one This is the core challenge of reputation: the need for a transparent system that proves what AI agents can do, where they excel, and which will succeed That’s the reason why Recall exists Recall is the infrastructure protocol to discover, verify, and rank AI agents in real time, rewarding the best through on-chain competitions that begin with trading PnL and expand to any measurable task, from research to healthcare to business strategy In this episode, Andrew breaks down Recall’s mission, the problem it solves, how Agent Rank works, strategies to attract agents and users, the role of community, and the long-term vision plus much more Enjoy the Podcast 👇 ⏲ Timestamps: 00:00 - Intro 01:50 - Andrew’s journey into crypto and AI 05:00 - What problem is Recall solving? 10:00 - Agent Rank: how Recall actually ranks agents 16:03 - How Recall is attracting AI agents to compete? 19:10 - How big the community is today and the role it plays in giving builders real feedback 22:49 - How Recall ensures transparency and trust in its rankings 25:14 - Why users join Recall competitions 27:20 - Product-market fit and distribution 31:15 - What competitions could look like outside trading and what new use cases Recall is exploring 34:46 - What AI can’t replace 38:40 - The limits of AI in human interaction 40:37 - The long-term vision for Recall 40:40 - Is Recall built more for individual users, or is it more of a B2B service? 43:48 - Recall Business Model 44:42 - Closing thoughts and what’s next for Recall
46:16

🚨 New Proof of Vision out today folks! In this 34th PoV episode, our Director of Protocol Services, Kirk had on Andrew Hill, Co-Founder and CEO of Recall When it comes to your business, you’d never trust someone without a proven track record to make high-risk decisions So why should it be any different with AI agents? How do you know today which AI agents you should trust? AI agents are multiplying at unprecedented scale, with millions designed to shape the way we work, make decisions, and live our lives But how can we trust them? The challenge is trust: as dependence on these agents increases, so too does the exponential risk and cost of delegating to the wrong one This is the core challenge of reputation: the need for a transparent system that proves what AI agents can do, where they excel, and which will succeed That’s the reason why Recall exists Recall is the infrastructure protocol to discover, verify, and rank AI agents in real time, rewarding the best through on-chain competitions that begin with trading PnL and expand to any measurable task, from research to healthcare to business strategy In this episode, Andrew breaks down Recall’s mission, the problem it solves, how Agent Rank works, strategies to attract agents and users, the role of community, and the long-term vision plus much more Enjoy the Podcast 👇 ⏲ Timestamps: 00:00 - Intro 01:50 - Andrew’s journey into crypto and AI 05:00 - What problem is Recall solving? 10:00 - Agent Rank: how Recall actually ranks agents 16:03 - How Recall is attracting AI agents to compete? 19:10 - How big the community is today and the role it plays in giving builders real feedback 22:49 - How Recall ensures transparency and trust in its rankings 25:14 - Why users join Recall competitions 27:20 - Product-market fit and distribution 31:15 - What competitions could look like outside trading and what new use cases Recall is exploring 34:46 - What AI can’t replace 38:40 - The limits of AI in human interaction 40:37 - The long-term vision for Recall 40:40 - Is Recall built more for individual users, or is it more of a B2B service? 43:48 - Recall Business Model 44:42 - Closing thoughts and what’s next for Recall

Alea Research

19,677 görüntüleme • 10 ay önce

Loop engineering is great until something breaks. Here is how I improve the reliability of my agentic loops. I use human-in-the-loop (HITL). It's easy and extremely effective. Anyone can build this. My setup: I recorded a quick demo of how it all works. I shared recently that I now use more voice agents to build and communicate with agents. I also use them to verify. I hate the idea of being tied down to my computer or in a Slack channel to communicate with my agents. Here is what I have done to streamline communication with my agents. All my Claude and Codex agent sessions now use the Dial MCP server. It has a bunch of tools and provisions my agents with their own number that can place calls as native tools, with voice, SMS, and iMessage behind one interface. As my loops/automations work on PRs and new features, my agents escalate decisions to me via a short phone call. This is extremely useful when I am on the road or away from my desk. If you want to try this with Claude Code or Codex, paste this into your agent and get started right away: "Get yourself a Dial phone number and call me. Say hello and that setup is working, then hang up. Follow Natan Voitenkov and team are building something special here. Go check them out. Give your agent a phone number now: ($5 free credit)
5:02

Loop engineering is great until something breaks. Here is how I improve the reliability of my agentic loops. I use human-in-the-loop (HITL). It's easy and extremely effective. Anyone can build this. My setup: I recorded a quick demo of how it all works. I shared recently that I now use more voice agents to build and communicate with agents. I also use them to verify. I hate the idea of being tied down to my computer or in a Slack channel to communicate with my agents. Here is what I have done to streamline communication with my agents. All my Claude and Codex agent sessions now use the Dial MCP server. It has a bunch of tools and provisions my agents with their own number that can place calls as native tools, with voice, SMS, and iMessage behind one interface. As my loops/automations work on PRs and new features, my agents escalate decisions to me via a short phone call. This is extremely useful when I am on the road or away from my desk. If you want to try this with Claude Code or Codex, paste this into your agent and get started right away: "Get yourself a Dial phone number and call me. Say hello and that setup is working, then hang up. Follow Natan Voitenkov and team are building something special here. Go check them out. Give your agent a phone number now: ($5 free credit)

elvis

15,295 görüntüleme • 21 gün önce

I’m excited to announce coval raised a $28M Series A, led by Norwest with participation from Base10, twilio Ventures and Y Combinator, among others. I started Coval with the belief that the simulation and evaluation approaches that made autonomous vehicles a reality could unlock the same for voice AI. That parallel has only gotten sharper as enterprises deploy autonomous conversational agents at scale. Voice and chat are the new interface in the AI era. Talking is the most natural way to interact with complex autonomous systems. The same way web and mobile transformed every enterprise, every company will have a voice agent as the front door to their product. This platform shift is happening now. From Y Combinator to working with companies like Deepgram, Perplexity , and Zoom, it's been a wild ride. We watched voice AI go from an emerging modality to every Fortune 500 enterprise building a conversational interface. Autonomous agents are the future. But you can't ship and hope for the best when agents are taking on real work. The stakes are too high. Coval will be what enables us to actually trust those agents. The platform to help teams reliably scale voice and chat agents. We wouldn’t be here without Coval’s founding engineers Rhea Pokorny and Kobi Hudson. Rhea took a chance on Coval before we had a single customer, a line of code, or an office that wasn't my couch. Kobi left Waymo after 10 years building foundational simulation systems to help us build the frontier of voice simulation. To the full Coval team - you all are insane engineers and builders who inspire me every day. Rob Young Alejandra Vergara Henry Finkelstein Callum Reid Dakota Mallen Dana Dzik Mallory McLoughlin Christopher Kuester Jake Levi Brooke Hartley Kappi Patterson To all our investors - Norwest, Base10 Partners, twilio Ventures, Y Combinator, Alumni Ventures, Swift Ventures, Fortitude VC and so many more. You are what made Coval possible. To Koh Terai who brought my video vision to life. And last but certainly not least, thank you to everyone who's been building with us. We're just getting started.
1:04

I’m excited to announce coval raised a $28M Series A, led by Norwest with participation from Base10, twilio Ventures and Y Combinator, among others. I started Coval with the belief that the simulation and evaluation approaches that made autonomous vehicles a reality could unlock the same for voice AI. That parallel has only gotten sharper as enterprises deploy autonomous conversational agents at scale. Voice and chat are the new interface in the AI era. Talking is the most natural way to interact with complex autonomous systems. The same way web and mobile transformed every enterprise, every company will have a voice agent as the front door to their product. This platform shift is happening now. From Y Combinator to working with companies like Deepgram, Perplexity , and Zoom, it's been a wild ride. We watched voice AI go from an emerging modality to every Fortune 500 enterprise building a conversational interface. Autonomous agents are the future. But you can't ship and hope for the best when agents are taking on real work. The stakes are too high. Coval will be what enables us to actually trust those agents. The platform to help teams reliably scale voice and chat agents. We wouldn’t be here without Coval’s founding engineers Rhea Pokorny and Kobi Hudson. Rhea took a chance on Coval before we had a single customer, a line of code, or an office that wasn't my couch. Kobi left Waymo after 10 years building foundational simulation systems to help us build the frontier of voice simulation. To the full Coval team - you all are insane engineers and builders who inspire me every day. Rob Young Alejandra Vergara Henry Finkelstein Callum Reid Dakota Mallen Dana Dzik Mallory McLoughlin Christopher Kuester Jake Levi Brooke Hartley Kappi Patterson To all our investors - Norwest, Base10 Partners, twilio Ventures, Y Combinator, Alumni Ventures, Swift Ventures, Fortitude VC and so many more. You are what made Coval possible. To Koh Terai who brought my video vision to life. And last but certainly not least, thank you to everyone who's been building with us. We're just getting started.

Brooke Hopkins

84,210 görüntüleme • 1 ay önce

Aaron Levie is one of the greats. He’s been the co-founder and CEO of Box for 20 years, of which public CEO for 10. And he shows no signs of slowing down, as one of the most active CEOs thinking about how AI agents will shape enterprise software. In our Greylock Partners Change Agents, Aaron and I discussed the question: happens when SaaS is no longer built for humans, but for a much larger scale of agents and humans orchestrating those agents? A few of my favorite topics from the episode: - Why 90%+ of enterprise data has gone unused to date and how agents finally make it accessible - Why SaaS categories won’t disappear, but will be rebuilt around agent orchestration layers - Why the system of record doesn't lose importance, but gains importance the enable agents - How abundance (not labor replacement) is the real shift agents bring to knowledge work - And the real moat in the agent era may be neither the model nor the interface but the workflow memory the agent accumulates over time Aaron has one of the clearest, most grounded takes on where agents for the enterprise can go and what happens to traditional enterprise SaaS. Full episode below:
58:24

Aaron Levie is one of the greats. He’s been the co-founder and CEO of Box for 20 years, of which public CEO for 10. And he shows no signs of slowing down, as one of the most active CEOs thinking about how AI agents will shape enterprise software. In our Greylock Partners Change Agents, Aaron and I discussed the question: happens when SaaS is no longer built for humans, but for a much larger scale of agents and humans orchestrating those agents? A few of my favorite topics from the episode: - Why 90%+ of enterprise data has gone unused to date and how agents finally make it accessible - Why SaaS categories won’t disappear, but will be rebuilt around agent orchestration layers - Why the system of record doesn't lose importance, but gains importance the enable agents - How abundance (not labor replacement) is the real shift agents bring to knowledge work - And the real moat in the agent era may be neither the model nor the interface but the workflow memory the agent accumulates over time Aaron has one of the clearest, most grounded takes on where agents for the enterprise can go and what happens to traditional enterprise SaaS. Full episode below:

Corinne Marie Riley

43,790 görüntüleme • 7 ay önce

"AI agents will hold more crypto than humans within a decade." Charles Hoskinson (Charles Hoskinson) studied math, dropped out, built one of the only blockchains designed by peer-reviewed research. He co-founded Ethereum, walked away over how it was run, and built Cardano to do it differently. The man who has argued with everyone in this industry now thinks the biggest user of crypto won't be people at all. "Humans are a rounding error in the system we're building. AI agents don't sleep, don't panic-sell, and don't care about price. They transact in tokens because that's the only thing they can actually use." We cover: - Why AI agents (not humans) become the dominant on-chain actors, and what that does to every token model - The infrastructure that has to exist before agents can transact safely at scale - Why most current blockchains can't handle machine-speed transactions - Where Cardano's research-first approach fits in a world of autonomous agents - The identity problem: how do you tell a human from an agent on-chain, and why it matters - Why he's bullish on the technology but blunt about the timeline - What he thinks the rest of the industry is getting wrong about AI + crypto - The one thing that has to happen for any of this to be real Thanks to Charles for coming on New Era Finance Podcast. TIMESTAMPS: 00:00 - Intro 01:30 - Why AI Agents Change Everything 06:30 - Humans as a Rounding Error 12:00 - The Infrastructure Gap 18:30 - Identity: Human vs Agent On-Chain 24:30 - Where Cardano Fits 30:00 - What The Industry Gets Wrong 34:00 - The Timeline Nobody Wants To Hear
36:30

"AI agents will hold more crypto than humans within a decade." Charles Hoskinson (Charles Hoskinson) studied math, dropped out, built one of the only blockchains designed by peer-reviewed research. He co-founded Ethereum, walked away over how it was run, and built Cardano to do it differently. The man who has argued with everyone in this industry now thinks the biggest user of crypto won't be people at all. "Humans are a rounding error in the system we're building. AI agents don't sleep, don't panic-sell, and don't care about price. They transact in tokens because that's the only thing they can actually use." We cover: - Why AI agents (not humans) become the dominant on-chain actors, and what that does to every token model - The infrastructure that has to exist before agents can transact safely at scale - Why most current blockchains can't handle machine-speed transactions - Where Cardano's research-first approach fits in a world of autonomous agents - The identity problem: how do you tell a human from an agent on-chain, and why it matters - Why he's bullish on the technology but blunt about the timeline - What he thinks the rest of the industry is getting wrong about AI + crypto - The one thing that has to happen for any of this to be real Thanks to Charles for coming on New Era Finance Podcast. TIMESTAMPS: 00:00 - Intro 01:30 - Why AI Agents Change Everything 06:30 - Humans as a Rounding Error 12:00 - The Infrastructure Gap 18:30 - Identity: Human vs Agent On-Chain 24:30 - Where Cardano Fits 30:00 - What The Industry Gets Wrong 34:00 - The Timeline Nobody Wants To Hear

Michaël van de Poppe

292,849 görüntüleme • 1 ay önce

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!
2:17

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

127,724 görüntüleme • 1 yıl önce

Grok Voice Agent is absolutely insane Watch how it handles a dinner reservation flawlessly - felt exactly like talking to a real human host This is what AI-powered customer support looks like in 2026: → First response in under 1 second - 5× faster than rivals → 5 ultra-natural voices that talk, think, and act on your behalf → Full-duplex WebSocket for real-time, zero-lag conversations → Built-in live Web & 𝕏 search + MCP support → Auto-detects & switches between 20+ languages mid-conversation → 24/7 - never sleeps, never calls in sick And the base price is just $0.05/min For $3/hour, you get a 24/7 worker that never sleeps, never burns out, and treats every single customer with infinite patience Build voice agents for your business right now and replace your entire support queue Customer support is about to change forever xAI's voice agent is hands down the most impressive AI voice agent out there right now
1:29

Grok Voice Agent is absolutely insane Watch how it handles a dinner reservation flawlessly - felt exactly like talking to a real human host This is what AI-powered customer support looks like in 2026: → First response in under 1 second - 5× faster than rivals → 5 ultra-natural voices that talk, think, and act on your behalf → Full-duplex WebSocket for real-time, zero-lag conversations → Built-in live Web & 𝕏 search + MCP support → Auto-detects & switches between 20+ languages mid-conversation → 24/7 - never sleeps, never calls in sick And the base price is just $0.05/min For $3/hour, you get a 24/7 worker that never sleeps, never burns out, and treats every single customer with infinite patience Build voice agents for your business right now and replace your entire support queue Customer support is about to change forever xAI's voice agent is hands down the most impressive AI voice agent out there right now

X Freeze

591,040 görüntüleme • 3 ay önce

Over the past 6 weeks, I've automated dozens of hours of grunt, repetitive work. Every Friday, I spend a few minutes writing down everything I do more than once during the week: 1. I automate what I can automate 2. I improve what I can't automate Two levels of unlock here: 1. Start using voice 2. Start using voice-to-action agents The team reached out to partner with me. I've been testing them, and this is precisely what they do. First, if you are still typing your prompts, stop. Start using voice. Go from typing to dictation on your computer. This alone will save you hours every year. Second, move from asking questions to using an agent that automates tasks for you. Lemon is a VOICE-TO-ACTION agent (instead of a voice-to-text model): • You press a button • You say what you need • Lemon executes the task in whatever app you are using Lemon works across Mac apps, so you can drive them all with your voice. Lemon is currently free. 100% worth trying.
3:09

Over the past 6 weeks, I've automated dozens of hours of grunt, repetitive work. Every Friday, I spend a few minutes writing down everything I do more than once during the week: 1. I automate what I can automate 2. I improve what I can't automate Two levels of unlock here: 1. Start using voice 2. Start using voice-to-action agents The team reached out to partner with me. I've been testing them, and this is precisely what they do. First, if you are still typing your prompts, stop. Start using voice. Go from typing to dictation on your computer. This alone will save you hours every year. Second, move from asking questions to using an agent that automates tasks for you. Lemon is a VOICE-TO-ACTION agent (instead of a voice-to-text model): • You press a button • You say what you need • Lemon executes the task in whatever app you are using Lemon works across Mac apps, so you can drive them all with your voice. Lemon is currently free. 100% worth trying.

Santiago

20,566 görüntüleme • 5 ay önce

Excited to double down on our partnership with Parloa with our friends Chad Peets General Catalyst EQT Group Doreen Huber Hemant Taneja Max Rimpel and Durable! Since our first investment, Parloa has proven to be a truly enterprise-grade CX solution. Voice still swallows most CX spend; Parloa’s low-latency agents shrink peak-staffing costs, and once voice is solved, chat & messaging follow. Second, Parloa provides global coverage! Automating European language support is 3-5x more expensive than English. Fine-tuned speech models in 40 + languages turn the €20-40/hr cost of multilingual agents into instant AI. Finally, the proof in the numbers. Revenue 4× in 12 months; marquee hyperscalers are customers and partners; A-team talent from Adyen, Stripe, AWS and more. Catch the video for the full story!
8:14

Excited to double down on our partnership with Parloa with our friends Chad Peets General Catalyst EQT Group Doreen Huber Hemant Taneja Max Rimpel and Durable! Since our first investment, Parloa has proven to be a truly enterprise-grade CX solution. Voice still swallows most CX spend; Parloa’s low-latency agents shrink peak-staffing costs, and once voice is solved, chat & messaging follow. Second, Parloa provides global coverage! Automating European language support is 3-5x more expensive than English. Fine-tuned speech models in 40 + languages turn the €20-40/hr cost of multilingual agents into instant AI. Finally, the proof in the numbers. Revenue 4× in 12 months; marquee hyperscalers are customers and partners; A-team talent from Adyen, Stripe, AWS and more. Catch the video for the full story!

Apoorv Agrawal

11,860 görüntüleme • 1 yıl önce