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Ana Sayfaya Dön

Played around with TypeSafe AI Jev today, mostly to understand what it does for tool calling. Instead of an LLM deciding what to do, I substituted that part with Jev. My learning is that we will be able to make the agent act before the user finishes the sentence.... show more

Pratim Bhosale

42,486 subscribers

110,761 görüntüleme • 2 gün önce •via X (Twitter)

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Sidhant profil fotoğrafı

Sidhant2 gün önce

@typesafeai @GradiumAI I built a memory system with it

Shivay Lamba profil fotoğrafı

Shivay Lamba2 gün önce

@typesafeai @GradiumAI loved the explanation, and this unlocks so many use-cases previously not possible with LLM based systems

Dapping profil fotoğrafı

Dapping2 gün önce

@typesafeai @GradiumAI Cool stuff!

Jatin K Malik profil fotoğrafı

Jatin K Malik2 gün önce

@typesafeai @GradiumAI It's crazy this is going to expand into endless use cases i can think from top of my head

Ashwin Kumar Uppala profil fotoğrafı

Ashwin Kumar Uppala2 gün önce

@typesafeai @GradiumAI This is how i see it.

Pratim Bhosale profil fotoğrafı

Pratim Bhosale2 gün önce

@typesafeai @GradiumAI That’s what I meant to build as well😂. Codex’s UI skills went only so far. I'm integrating the blender MCP now.

Yatish Goel profil fotoğrafı

Yatish Goel2 gün önce

@typesafeai @GradiumAI 'Cancel my booking... actually, just check the cancellation fee' would be a useful test. I'd start the lookup on partial speech, but wait before doing anything that changes the booking.

ethereagle · building profil fotoğrafı

ethereagle · building2 gün önce

@typesafeai @GradiumAI acting on a prefix only works if the wrong tool is cheap to undo. for voice, do you wait for a Jev confidence cutoff, or fire as soon as one tool scores highest?

Neeraj Mukta profil fotoğrafı

Neeraj Mukta2 gün önce

@typesafeai @GradiumAI Great leaning project you got there

suggy profil fotoğrafı

suggy2 gün önce

@typesafeai @GradiumAI acting before the user finishes is powerful and slightly terrifying I'd want a confidence threshold + an easy undo path before letting prediction turn into side effects

Gates 🦞 profil fotoğrafı

Gates 🦞2 gün önce

@typesafeai @GradiumAI Can you open source?

Harsh Gehlot profil fotoğrafı

Harsh Gehlot2 gün önce

@typesafeai @GradiumAI This looks cool

Phillip Forrence profil fotoğrafı

Phillip Forrence2 gün önce

@typesafeai @GradiumAI Someone put a ring on her finger!

AI News profil fotoğrafı

AI News2 gün önce

@typesafeai @GradiumAI Cool intro

Surf profil fotoğrafı

Surf2 gün önce

@typesafeai @GradiumAI Would be nice to be able to see the code on a github repo

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Jev Engineering is the layer most agent stacks are still missing. state → decision → action → verification → next state up to 193x faster and 444x cheaper in our tests. everyone is optimizing the model. Jev Engineering optimizes what happens between model calls. every agent eventually hits the same fork: state → multiple possible actions → one route has to win instead of sending every decision back to an expensive LLM call, Jev turns that into a dedicated layer: so the loop becomes: LLM → reason Jev → decide tools / agents → execute state → update Jev → decide again once decisions become their own engineering layer, you can benchmark, batch and verify them separately. full blueprint in article ↓

Jev Engineering is the layer most agent stacks are still missing. state → decision → action → verification → next state up to 193x faster and 444x cheaper in our tests. everyone is optimizing the model. Jev Engineering optimizes what happens between model calls. every agent eventually hits the same fork: state → multiple possible actions → one route has to win instead of sending every decision back to an expensive LLM call, Jev turns that into a dedicated layer: so the loop becomes: LLM → reason Jev → decide tools / agents → execute state → update Jev → decide again once decisions become their own engineering layer, you can benchmark, batch and verify them separately. full blueprint in article ↓

Ricker

83,098 görüntüleme • 1 gün önce

Wow, TypeSafe AI Jev is a game changer for voice-driven UI. Responsiveness really matters for an experience to 'feel' right, and this certainly does the job! Latency here is not optimal given I'm in London and calling out to us-west. But still, super snappy. In this video, Jamcat is using Pipecat to orchestrate a real-time handover between a command agent (Jev) that drives the UI, and voice agent (PhoneLLM) for handling generations and (not in the video) discussing ideas and session setup with the user.

Wow, TypeSafe AI Jev is a game changer for voice-driven UI. Responsiveness really matters for an experience to 'feel' right, and this certainly does the job! Latency here is not optimal given I'm in London and calling out to us-west. But still, super snappy. In this video, Jamcat is using Pipecat to orchestrate a real-time handover between a command agent (Jev) that drives the UI, and voice agent (PhoneLLM) for handling generations and (not in the video) discussing ideas and session setup with the user.

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Andy Hall

70,844 görüntüleme • 8 ay önce

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Aish

590,837 görüntüleme • 1 yıl önce

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!

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fxnction

27,570 görüntüleme • 3 ay önce

Jev is HERE and this is the CLEAREST explanation of what it is and what NEW businesses it unlocks. (and at the end I'll tell you how to get Jev even if you're on the waitlist) WHAT IT IS You know how you open your inbox and have to decide what's junk, what needs a reply, and what can wait? Jev does that part. It looks at each thing and says "this is junk, I'm 94% sure." It doesn't write anything back to you. It just sorts. 1,700 emails for 18 cents, instantly. That sounds kinda trivial but the important part WHAT IT UNLOCKS My explanation of Jev sounds small until you realize HOW MANY jobs are exactly this. Someone reading a stack of applications. Someone deciding which support ticket goes to which team. Someone looking at inbound and deciding who's worth calling back. A few ideas on what it unlocks: 1/ Instant quotes that are actually instant. Every quote form on the internet says "we'll email you by end of day." Build the version that answers in under a second, for roofers, movers, insurance, legal intake. 2/ Lead scoring as a product. Every agency and service business has a contact form full of junk. Score every submission and send the real ones straight to the owner's phone. 3/ Support triage for companies with no support team. The ticket gets classified and routed before anyone opens it. 4/ Clipping tools. Pass in a transcript, get the best moments scored in three seconds. Every clipping product just got a cheaper engine. 5/ Application piles. Grants, permits, insurance claims, job apps, loan docs. Someone reads that stack one item at a time today. 6/ Marketplace matching. Someone types what they need and gets matched to the right local business instantly instead of waiting for callbacks. 7/ Browser agents that actually move FAST. That makes bulk browser work practical: pulling quotes from five carriers, filing the same form for 200 clients, checking supplier inventory in real time etc. TLDR; find an expensive queue and put Jev at the front of it. HOW TO GET IT I didn't realize you can skip the waitlist because Jev is live on the Vercel AI Gateway right now, so you can start calling it today. In this episode, we share how. Episode now live on The Startup Ideas Podcast (SIP) 🧃 (thanks to vogel for coming on and spilling the sauce today) Watch: Jev is a big deal because this is a whole new way to do AI Really cool Happy Jev day.

Jev is HERE and this is the CLEAREST explanation of what it is and what NEW businesses it unlocks. (and at the end I'll tell you how to get Jev even if you're on the waitlist) WHAT IT IS You know how you open your inbox and have to decide what's junk, what needs a reply, and what can wait? Jev does that part. It looks at each thing and says "this is junk, I'm 94% sure." It doesn't write anything back to you. It just sorts. 1,700 emails for 18 cents, instantly. That sounds kinda trivial but the important part WHAT IT UNLOCKS My explanation of Jev sounds small until you realize HOW MANY jobs are exactly this. Someone reading a stack of applications. Someone deciding which support ticket goes to which team. Someone looking at inbound and deciding who's worth calling back. A few ideas on what it unlocks: 1/ Instant quotes that are actually instant. Every quote form on the internet says "we'll email you by end of day." Build the version that answers in under a second, for roofers, movers, insurance, legal intake. 2/ Lead scoring as a product. Every agency and service business has a contact form full of junk. Score every submission and send the real ones straight to the owner's phone. 3/ Support triage for companies with no support team. The ticket gets classified and routed before anyone opens it. 4/ Clipping tools. Pass in a transcript, get the best moments scored in three seconds. Every clipping product just got a cheaper engine. 5/ Application piles. Grants, permits, insurance claims, job apps, loan docs. Someone reads that stack one item at a time today. 6/ Marketplace matching. Someone types what they need and gets matched to the right local business instantly instead of waiting for callbacks. 7/ Browser agents that actually move FAST. That makes bulk browser work practical: pulling quotes from five carriers, filing the same form for 200 clients, checking supplier inventory in real time etc. TLDR; find an expensive queue and put Jev at the front of it. HOW TO GET IT I didn't realize you can skip the waitlist because Jev is live on the Vercel AI Gateway right now, so you can start calling it today. In this episode, we share how. Episode now live on The Startup Ideas Podcast (SIP) 🧃 (thanks to vogel for coming on and spilling the sauce today) Watch: Jev is a big deal because this is a whole new way to do AI Really cool Happy Jev day.

GREG ISENBERG

148,535 görüntüleme • 2 gün önce

.Mati Staniszewski on The Future of Human-AI Interaction: “Voice will 100% will be one of the biggest ways you interact with AI.” “AI will be able to connect the IQ plus the EQ part of it. It will be able to emotionally understand you. It will know how you're feeling and can adjust based on that. It can pause and can think and can reinsert itself into the conversation.” “Voice is the most primal way we communicate and now we can bring technology back on our terms.” “So 12 months from now I think AI will be able to understand us in an incredibly better way —both our knowledge and emotional part. And deliver similar conversation to the one we are having now.”

.Mati Staniszewski on The Future of Human-AI Interaction: “Voice will 100% will be one of the biggest ways you interact with AI.” “AI will be able to connect the IQ plus the EQ part of it. It will be able to emotionally understand you. It will know how you're feeling and can adjust based on that. It can pause and can think and can reinsert itself into the conversation.” “Voice is the most primal way we communicate and now we can bring technology back on our terms.” “So 12 months from now I think AI will be able to understand us in an incredibly better way —both our knowledge and emotional part. And deliver similar conversation to the one we are having now.”

David Senra

11,582 görüntüleme • 11 gün önce

Notice how off the bat he completely flipped it back onto her? Instead of actually answering the question. And then he proceeds to bulldoze her with bs that has nothing to do with the fact that men - can never, ever understand what it is to be a woman. His experience of being a man pretending to be a woman is irrelevant to what women are. We already exist. We aren’t an idea.

Notice how off the bat he completely flipped it back onto her? Instead of actually answering the question. And then he proceeds to bulldoze her with bs that has nothing to do with the fact that men - can never, ever understand what it is to be a woman. His experience of being a man pretending to be a woman is irrelevant to what women are. We already exist. We aren’t an idea.

Stellar_Fox

17,054 görüntüleme • 4 ay önce

Zelensky says he is ready for direct talks with Putin to end the war "I am ready for direct negotiations with Putin to end this war, instead of waiting until all the conflicts in the world are resolved, and only then will it be Ukraine's turn."

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Visegrád 24

97,001 görüntüleme • 3 ay önce

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Sub-agents in (latent) space! We’ve been working on a side project. As far as I know, this is the first massively multiplayer, completely LLM-driven game. Come play Gradient Bang with us. See if you can catch me on the leaderboard. This whole thing started because I wanted to explore a bunch of things I’m currently obsessed with, in an application of non-trivial size, that felt both new and old at the same time. So … a retro-style space trading game built entirely around interacting with and managing multiple LLMs. Factorio, but instead of clicking, you cajole your ship AI into tasking other AIs to do things for you. Some of the things we’ve been thinking about as we hack on Gradient Bang: - Sub-agent orchestration - Partial context sharing between multiple LLM inference loops - Managing very long contexts, and episodic memory across user sessions - World events and large volumes of structured data input as part of human/agent conversations - Dynamic user interfaces, driven/created on the fly by LLMs - And, of course, voice as primary input If you’ve been building coding harnesses, or writing Open Claw agents, or doing pretty much anything that pushes the boundaries of AI-native development these days, you’re probably thinking about these things too! This is all built with Pipecat AI, the back end is Supabase, the React front end is deployed to Vercel, and all the code is open source.

kwindla

456,432 görüntüleme • 5 ay önce

A look at the future: chatting with my robot via Discord to ask it to find something in my house. - Use an LLM agent (Qwen, OpenAI's GPT-4o, whatever) to understand what the user wants and generate a task plan - Use open-vocabulary 3d scene representations to build up a representation of the world that we can use to find objects - Interface with social media chat programs to do things like send a picture of what the robot sees

A look at the future: chatting with my robot via Discord to ask it to find something in my house. - Use an LLM agent (Qwen, OpenAI's GPT-4o, whatever) to understand what the user wants and generate a task plan - Use open-vocabulary 3d scene representations to build up a representation of the world that we can use to find objects - Interface with social media chat programs to do things like send a picture of what the robot sees

Chris Paxton

40,472 görüntüleme • 1 yıl önce

I’m excited to kick off the first of our short courses focused on agents, starting with Building Agentic RAG with LlamaIndex, taught by Jerry Liu, CEO of LlamaIndex 🦙. This covers an important shift in RAG (retrieval augmented generation), in which rather than having the developer write explicit routines to retrieve information to feed into the LLM context, we instead build a RAG agent that that has access to tools for retrieving information. This lets the agent decide what information to fetch, and enables it to answer more complex questions using multi-step reasoning. In detail, you'll learn about: - Routing: Where your agent will use decision-making to route requests to multiple tools. - Tool Use: Where you'll create an interface for agents to select what tool (function call) to use as well as generate the right arguments. - Multi-step reasoning with tool use: Where you'll use an LLM to carry out multiple steps of reasoning, while retaining memory throughout the process. You’ll also learn how to step through what your agent is doing to debug and improve it iteratively. It’s an exciting time to build agents. Sign up and get started here!

I’m excited to kick off the first of our short courses focused on agents, starting with Building Agentic RAG with LlamaIndex, taught by Jerry Liu, CEO of LlamaIndex 🦙. This covers an important shift in RAG (retrieval augmented generation), in which rather than having the developer write explicit routines to retrieve information to feed into the LLM context, we instead build a RAG agent that that has access to tools for retrieving information. This lets the agent decide what information to fetch, and enables it to answer more complex questions using multi-step reasoning. In detail, you'll learn about: - Routing: Where your agent will use decision-making to route requests to multiple tools. - Tool Use: Where you'll create an interface for agents to select what tool (function call) to use as well as generate the right arguments. - Multi-step reasoning with tool use: Where you'll use an LLM to carry out multiple steps of reasoning, while retaining memory throughout the process. You’ll also learn how to step through what your agent is doing to debug and improve it iteratively. It’s an exciting time to build agents. Sign up and get started here!

Andrew Ng

297,131 görüntüleme • 2 yıl önce

"This is the best way of going back into Indian films... work with SS.Rajamouli, his vision is so far beyond limited imagination that just to be able to see what he's thinking for the story is great... my part is amazing" Priyanka Chopra about Varanasi #MaheshBabu

"This is the best way of going back into Indian films... work with SS.Rajamouli, his vision is so far beyond limited imagination that just to be able to see what he's thinking for the story is great... my part is amazing" Priyanka Chopra about Varanasi #MaheshBabu

NP LEGΛCY 🇨🇴🫰🏽

37,025 görüntüleme • 6 ay önce

"We've move to Resend for so much of our marketing communications. Why? Our AI Agents can talk to it. So easily. And that means we can vibe with it. We can get it to do almost anything we want it to do. That's the new bar today." The latest Agents #002 is out!

"We've move to Resend for so much of our marketing communications. Why? Our AI Agents can talk to it. So easily. And that means we can vibe with it. We can get it to do almost anything we want it to do. That's the new bar today." The latest Agents #002 is out!

Jason ✨👾SaaStr.Ai✨ Lemkin

18,823 görüntüleme • 4 ay önce

Introducing the Papers with Code MCP server! To celebrate its release, I've asked Claude Code to research the architecture of Jev by TypeSafe AI using the `search_papers` tool. Here's what it came up with: "If Jev is architecturally anything public, it's a large schema-conditioned bidirectional encoder with parallel label-query heads (GLiNER/ML-Decoder shape) trained with a proper-scoring-rule RL objective (RLCR shape), scaled far past the ~150M-param range those papers operate in — "neither small nor an LLM" fits that. The 40–200× speedup is consistent with removing autoregression, not with any exotic mechanism."

Introducing the Papers with Code MCP server! To celebrate its release, I've asked Claude Code to research the architecture of Jev by TypeSafe AI using the `search_papers` tool. Here's what it came up with: "If Jev is architecturally anything public, it's a large schema-conditioned bidirectional encoder with parallel label-query heads (GLiNER/ML-Decoder shape) trained with a proper-scoring-rule RL objective (RLCR shape), scaled far past the ~150M-param range those papers operate in — "neither small nor an LLM" fits that. The 40–200× speedup is consistent with removing autoregression, not with any exotic mechanism."

Niels Rogge

19,452 görüntüleme • 3 gün önce