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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 🔜 Paris 🇫🇷 booth. Hamming stress-tests and monitors voice agents so the bank or...

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justine

2,576 subscribers

18,670 просмотров • 1 месяц назад •via X (Twitter)

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Комментарии: 7

Фото профиля Sumanyu Sharma 🍫
Sumanyu Sharma 🍫1 месяц назад

@HammingAI @aiDotEngineer Super fun chat, thanks for having me!

Фото профиля justine
justine1 месяц назад

@HammingAI @aiDotEngineer learned so much from you! thanks for coming on!

Фото профиля Raymond Chen
Raymond Chen1 месяц назад

@sumanyu @HammingAI @aiDotEngineer let's go @sumanyu !!!

Фото профиля justine
justine1 месяц назад

@HammingAI @aiDotEngineer watch the interview on youtube:

Фото профиля Michael Kuznetsov
Michael Kuznetsov1 месяц назад

@sumanyu @HammingAI @aiDotEngineer such good insights as usual @sumanyu!

Фото профиля Lawrence Lin Murata
Lawrence Lin Murata1 месяц назад

@sumanyu @HammingAI @aiDotEngineer great interview @sumanyu!!

Фото профиля Sahil Gandhi
Sahil Gandhi1 месяц назад

@sumanyu @HammingAI @aiDotEngineer Facts that's why we are building @talentpluto

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Amp

36,551 просмотров • 7 месяцев назад

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

85,937 просмотров • 2 месяцев назад

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

15,614 просмотров • 2 месяцев назад

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 просмотров • 9 месяцев назад

Introducing PhoneLLM, an open model for voice agents. GPT 5.6 Terra performance on typical voice agent tasks at 1/3 the latency and 1/18 the cost. For voice agents, we need models that are both very low latency and very good at tool calling and instruction following. There's a trade-off here, and we often have to compromise on either latency or capability when building voice agents. With PhoneLLM (and the training and data stack that made this model possible) we're fixing this problem. For the last couple of years, most of the effort in frontier model development has gone towards leveraging test-time compute. Which is awesome! Models of all shapes and sizes are available that perform really, really well ... if you have "thinking" turned on for your model. But if you need your agent to respond at voice conversation speed, you can't use thinking models. PhoneLLM is a full-weights fine-tune of NVIDIA Nemotron Nano 30B. We trained on a wide range of real-world telephone and customer support use cases. The training focused on taking the excellent Nano 30B base capabilities and teaching the model to do typical voice agent tasks with thinking disabled. The results are really good: accurate tool calling and concise, on-topic responses in long conversations. And fast: TTFAT measured server-side is <100ms if you run PhoneLLM on a lightly loaded B200. :-) But seriously, when we characterize model latency, we do it with full, end-to-end, batched request simulations using real Pipecat voice agent pipelines. You can serve more than 80 concurrent agents on a single B200 with P95 end-to-end TTFAT <600ms. Including network overhead. That's an LLM cost-per-minute around $0.0025. (1/4 of a cent.) At a latency lower than any third-party API offers today. More details about this model, including weights on Hugging Face, how to spin it up with one click on Modal, and a starter project repo you can clone, are in the thread ...
3:28

Introducing PhoneLLM, an open model for voice agents. GPT 5.6 Terra performance on typical voice agent tasks at 1/3 the latency and 1/18 the cost. For voice agents, we need models that are both very low latency and very good at tool calling and instruction following. There's a trade-off here, and we often have to compromise on either latency or capability when building voice agents. With PhoneLLM (and the training and data stack that made this model possible) we're fixing this problem. For the last couple of years, most of the effort in frontier model development has gone towards leveraging test-time compute. Which is awesome! Models of all shapes and sizes are available that perform really, really well ... if you have "thinking" turned on for your model. But if you need your agent to respond at voice conversation speed, you can't use thinking models. PhoneLLM is a full-weights fine-tune of NVIDIA Nemotron Nano 30B. We trained on a wide range of real-world telephone and customer support use cases. The training focused on taking the excellent Nano 30B base capabilities and teaching the model to do typical voice agent tasks with thinking disabled. The results are really good: accurate tool calling and concise, on-topic responses in long conversations. And fast: TTFAT measured server-side is <100ms if you run PhoneLLM on a lightly loaded B200. :-) But seriously, when we characterize model latency, we do it with full, end-to-end, batched request simulations using real Pipecat voice agent pipelines. You can serve more than 80 concurrent agents on a single B200 with P95 end-to-end TTFAT <600ms. Including network overhead. That's an LLM cost-per-minute around $0.0025. (1/4 of a cent.) At a latency lower than any third-party API offers today. More details about this model, including weights on Hugging Face, how to spin it up with one click on Modal, and a starter project repo you can clone, are in the thread ...

kwindla

329,729 просмотров • 22 дней назад

"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

293,517 просмотров • 3 месяцев назад

AI power users are here and the case for agent-friendly software is materializing. Aaron Levie on agents outnumbering people 1000x: "If you start to imagine that we all have to build software for agents… we spend as much time now thinking about the agent interface to our tool as we do the human interface." "If you have a hundred or a thousand times more agents than people, then your software has to be built for agents… it's going to be through an API or a CLI or MCP." "What if you give a coding agent access to your SaaS tools and a coding agent access to your knowledge work, workflows and context?" "It can actually code its way or use APIs through whatever task it's trying to achieve." "That appears to be like a paradigm that is starting to compound… and I actually think it kind of makes sense as the ultimate manifestation of this stuff." Aaron Levie
1:23

AI power users are here and the case for agent-friendly software is materializing. Aaron Levie on agents outnumbering people 1000x: "If you start to imagine that we all have to build software for agents… we spend as much time now thinking about the agent interface to our tool as we do the human interface." "If you have a hundred or a thousand times more agents than people, then your software has to be built for agents… it's going to be through an API or a CLI or MCP." "What if you give a coding agent access to your SaaS tools and a coding agent access to your knowledge work, workflows and context?" "It can actually code its way or use APIs through whatever task it's trying to achieve." "That appears to be like a paradigm that is starting to compound… and I actually think it kind of makes sense as the ultimate manifestation of this stuff." Aaron Levie

a16z

42,152 просмотров • 23 дней назад