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What makes Bullet possible? Tristan walks through the stack at Solana Accelerate: • Rust-native architecture for ultra-low latency • Modular rollups via Sovereign SDK • 34K TPS w/ nearly optimal Merkle trees • ZKVMs that prove Rust code—no cryptographer required Infra is finally where it needs to be. 🎥👇

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Bullet

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117,848 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 5

Фото профиля Satosol
Satosol1 год назад

@Tristan0x @solana The foundation’s set. Now it’s all about execution.

Фото профиля Rainmaker
Rainmaker1 год назад

Can reinforcement learning handle stock market swings? In my latest free Substack, find out how SARSA reinforcement learning algorithm can help create adaptive strategies and improve performance.

Фото профиля ESH 𝕏
ESH 𝕏1 год назад

@Tristan0x @solana Rust-native, modular, high-throughput. Bullish on Bullet's potential.

Фото профиля To The Point
To The Point1 год назад

@Tristan0x @solana send it

Фото профиля brownbeef
brownbeef1 год назад

@Tristan0x @solana @grok turn her around

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At Hack VC hacksummit (“Seoul”), Joanna Zeng @ SOON (🔴,💊)📍🇺🇸🗽 (Mainnet Arc) joined Joshua (svm/acc) Head of Product Solayer, Austin Federa | 🇺🇸 | IBRL/ACC Co-Founder Tj coy, in a discussion moderated by Ryot 💱 (Xitadel). The Q: “What’s the big picture for the SVM movement?” SOON’s Answer: It’s not just infra for infra’s sake, it’s about apps that can prove out the stack. – Simpfor.fun shows how SVM unlocks consumer-ready UX → copy trading that anyone can use, $250M+ vol in 2 months. – Korea’s community adoption validates product-market fit. – SVM infra goes beyond Solana: 30k TPS, 50ms latency, a credible alternative to EVM and even Visa-scale performance. – Customizable SDKs make it possible for corporates to launch tailored chains with control over privacy, revenue flows, and validators. ⚡ The big picture: SVM is infra built for mass adoption, scaling from killer apps to corporate-grade blockchains. See what you missed👇
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At Hack VC hacksummit (“Seoul”), Joanna Zeng @ SOON (🔴,💊)📍🇺🇸🗽 (Mainnet Arc) joined Joshua (svm/acc) Head of Product Solayer, Austin Federa | 🇺🇸 | IBRL/ACC Co-Founder Tj coy, in a discussion moderated by Ryot 💱 (Xitadel). The Q: “What’s the big picture for the SVM movement?” SOON’s Answer: It’s not just infra for infra’s sake, it’s about apps that can prove out the stack. – Simpfor.fun shows how SVM unlocks consumer-ready UX → copy trading that anyone can use, $250M+ vol in 2 months. – Korea’s community adoption validates product-market fit. – SVM infra goes beyond Solana: 30k TPS, 50ms latency, a credible alternative to EVM and even Visa-scale performance. – Customizable SDKs make it possible for corporates to launch tailored chains with control over privacy, revenue flows, and validators. ⚡ The big picture: SVM is infra built for mass adoption, scaling from killer apps to corporate-grade blockchains. See what you missed👇

SOON - Solana Optimistic Network (Mainnet Arc)

31,365 просмотров • 11 месяцев назад

Why is Rust different than many/most programming languages? Alice Ryhl works on Google's Android Rust team, is a Rust language team advisor, and is a core maintainer of Tokio (the most widely-used async runtime in Rust) Timestamps: 00:00 Intro 04:09 Tokio: an overview 05:11 What Alice likes about Rust 12:48 Rust for TypeScript engineers 13:51 Moving from C++ to Rust 14:34 Memory safety 18:12 Garbage collection tradeoffs 21:46 Ownership, references, and borrowing 26:59 Unsafe in Rust 31:21 Crates and Cargo 35:55 Language design and RFCs 43:02 Building new features 46:30 Editions vs. versions 49:47 Getting paid to work on Rust 51:27 Contributing to Rust 53:03 Rust in the Linux kernel 55:45 AI use cases for Rust 1:01:35 Learning Rust 1:03:54 Book recommendation Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • Sentry – application monitoring software considered “not bad” by millions of developers Three things worth knowing about Rust: 1. Rust was designed to turn implicit failures into compile errors. Where other languages allow you to forget something, Rust makes an omission into a compilation error for things like null checks, uninitialized variables, or error propagation with the ‘?’ character. If you mess something up, it’s almost certain your program will not compile. If it does, at the very least you should see a lint warning. 2. Refactoring in Rust is safe and easy, thanks to the compiler. Alice: “I change a return type or struct field, then just fix the compiler errors until the compiler stops shouting. And then once I’ve done that, I’ve updated every place I need to update.” Rust’s focus on correctness makes refactoring it more straightforward than dynamically-typed languages and Java-style typed ones are to refactor. 3. “Editions” allow Rust to make breaking changes without ‘breaking’ anyone’s code. Rust editions (2015, 2018, 2021, 2024) can be mixed freely across crates. A library on the 2021 edition works seamlessly with a binary on the 2024 edition. This is how Rust evolves syntax (like adding async/await as keywords) without forcing an ecosystem-wide migration. Thanks a lot, Alice for this great discussion! And for your work on Rust.
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Why is Rust different than many/most programming languages? Alice Ryhl works on Google's Android Rust team, is a Rust language team advisor, and is a core maintainer of Tokio (the most widely-used async runtime in Rust) Timestamps: 00:00 Intro 04:09 Tokio: an overview 05:11 What Alice likes about Rust 12:48 Rust for TypeScript engineers 13:51 Moving from C++ to Rust 14:34 Memory safety 18:12 Garbage collection tradeoffs 21:46 Ownership, references, and borrowing 26:59 Unsafe in Rust 31:21 Crates and Cargo 35:55 Language design and RFCs 43:02 Building new features 46:30 Editions vs. versions 49:47 Getting paid to work on Rust 51:27 Contributing to Rust 53:03 Rust in the Linux kernel 55:45 AI use cases for Rust 1:01:35 Learning Rust 1:03:54 Book recommendation Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • Sentry – application monitoring software considered “not bad” by millions of developers Three things worth knowing about Rust: 1. Rust was designed to turn implicit failures into compile errors. Where other languages allow you to forget something, Rust makes an omission into a compilation error for things like null checks, uninitialized variables, or error propagation with the ‘?’ character. If you mess something up, it’s almost certain your program will not compile. If it does, at the very least you should see a lint warning. 2. Refactoring in Rust is safe and easy, thanks to the compiler. Alice: “I change a return type or struct field, then just fix the compiler errors until the compiler stops shouting. And then once I’ve done that, I’ve updated every place I need to update.” Rust’s focus on correctness makes refactoring it more straightforward than dynamically-typed languages and Java-style typed ones are to refactor. 3. “Editions” allow Rust to make breaking changes without ‘breaking’ anyone’s code. Rust editions (2015, 2018, 2021, 2024) can be mixed freely across crates. A library on the 2021 edition works seamlessly with a binary on the 2024 edition. This is how Rust evolves syntax (like adding async/await as keywords) without forcing an ecosystem-wide migration. Thanks a lot, Alice for this great discussion! And for your work on Rust.

Gergely Orosz

51,961 просмотров • 3 месяцев назад

Is your AI infrastructure ready for the agentic era? At Cisco Live, I connected with Gordon Macintosh and Dale Tucker from Equinix, Inc. to explore how the Secure AI Factory ecosystem is building the physical and interconnection foundations that make agentic AI at scale possible. Three thing leaders are still overlooking: → Distributed infrastructure. Production AI = multiple agents, tools, data sources, and policies → Data gravity is real. Sovereignty, regulation, and sensitivity shape where AI can run. → Last mile connectivity is where strategies stall. Secure, predictable, low-latency connections aren't optional at production scale. The Cisco + NVIDIA + Equinix, Inc. reference architecture is one of the most concrete answers I've seen with Equinix as the neutral landing zone across 280+ data centers worldwide. As Dale put it: "Last mile connectivity is where AI strategy can either accelerate or stall." The companies that win the next phase of AI will be the ones who got the infrastructure and the ecosystem right. Watch our full conversation here: #CiscoLive #SecureAIFactory #EquinixPartner #CiscoPartner
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Is your AI infrastructure ready for the agentic era? At Cisco Live, I connected with Gordon Macintosh and Dale Tucker from Equinix, Inc. to explore how the Secure AI Factory ecosystem is building the physical and interconnection foundations that make agentic AI at scale possible. Three thing leaders are still overlooking: → Distributed infrastructure. Production AI = multiple agents, tools, data sources, and policies → Data gravity is real. Sovereignty, regulation, and sensitivity shape where AI can run. → Last mile connectivity is where strategies stall. Secure, predictable, low-latency connections aren't optional at production scale. The Cisco + NVIDIA + Equinix, Inc. reference architecture is one of the most concrete answers I've seen with Equinix as the neutral landing zone across 280+ data centers worldwide. As Dale put it: "Last mile connectivity is where AI strategy can either accelerate or stall." The companies that win the next phase of AI will be the ones who got the infrastructure and the ecosystem right. Watch our full conversation here: #CiscoLive #SecureAIFactory #EquinixPartner #CiscoPartner

Helen Yu

11,558 просмотров • 2 месяцев назад

🚨 FRANCOIS CHOLLET SAYS THE CURRENT LLM STACK IS THE WRONG PATH TO AGI Interviewer: “When will we accomplish the first definition of AGI?” Chollet confirms we are already on that exact trajectory, pointing out that: "Current technology can fully automate at human level or beyond any domain where you have verifiable rewards, right? And code... code being the first one." He then brings up the problem with LLMs, stating that: "It's possible in principle to build something that looks a lot like AGI on top of the LLM stack... I do believe, however, this would be the wrong thing to do because it would be very inefficient." Looking ahead to what the final architecture will actually require, he explains that: "AI research will have to trend towards not just efficiency, but in fact optimality over time. And for this reason, future AI in a few decades... it's not going to be this harness on top of a reasoning model on top of a base LLM."
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🚨 FRANCOIS CHOLLET SAYS THE CURRENT LLM STACK IS THE WRONG PATH TO AGI Interviewer: “When will we accomplish the first definition of AGI?” Chollet confirms we are already on that exact trajectory, pointing out that: "Current technology can fully automate at human level or beyond any domain where you have verifiable rewards, right? And code... code being the first one." He then brings up the problem with LLMs, stating that: "It's possible in principle to build something that looks a lot like AGI on top of the LLM stack... I do believe, however, this would be the wrong thing to do because it would be very inefficient." Looking ahead to what the final architecture will actually require, he explains that: "AI research will have to trend towards not just efficiency, but in fact optimality over time. And for this reason, future AI in a few decades... it's not going to be this harness on top of a reasoning model on top of a base LLM."

Chris

15,578 просмотров • 4 месяцев назад

There's so much focus on "how can AI do my work for me?" I think the more important question is "what work can I now do with AI that I would have never attempted before?" Earlier this year I wrote freestiler, a vector tiling engine for R and Python, with the help of Claude and Codex. I knew what the ideal engine looked like and how it would work at a high level. I didn't know how to put it together, and I don't know Rust, the language I wanted under the hood. Previously I would never have attempted this project as the ROI wasn't there. It would have taken me a year or more to learn the internals of a vector tiling engine and enough Rust to implement one. With Opus-level models, I could take it on. freestiler now powers all my vector tiling pipelines, including the map below rendering 143 million jobs from LODES, and it has 114 GitHub stars. Building this way has required a different set of skills. I don't review the code line by line. I set up adversarial agents to do that and write the test suites. What I review is the architecture, the behavior, and the results. Agent teams surface findings and explain their reasoning; I evaluate and critique. My job isn't to stress over code formatting, but instead to focus on questions like whether the engine is designed right, whether the output is correct, and if the UX makes sense. This means that I haven't "replaced my work." I've taken on entirely new work, with the help of agents, that I would have never done otherwise. It has taken some getting used to shipping code I haven't personally typed. In the old way of working, I built understanding through writing that code. Now I build understanding through managing the project - writing a spec, reviewing structure, evaluating UX. And that's helped me think a whole lot bigger in terms of what I can now do.
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There's so much focus on "how can AI do my work for me?" I think the more important question is "what work can I now do with AI that I would have never attempted before?" Earlier this year I wrote freestiler, a vector tiling engine for R and Python, with the help of Claude and Codex. I knew what the ideal engine looked like and how it would work at a high level. I didn't know how to put it together, and I don't know Rust, the language I wanted under the hood. Previously I would never have attempted this project as the ROI wasn't there. It would have taken me a year or more to learn the internals of a vector tiling engine and enough Rust to implement one. With Opus-level models, I could take it on. freestiler now powers all my vector tiling pipelines, including the map below rendering 143 million jobs from LODES, and it has 114 GitHub stars. Building this way has required a different set of skills. I don't review the code line by line. I set up adversarial agents to do that and write the test suites. What I review is the architecture, the behavior, and the results. Agent teams surface findings and explain their reasoning; I evaluate and critique. My job isn't to stress over code formatting, but instead to focus on questions like whether the engine is designed right, whether the output is correct, and if the UX makes sense. This means that I haven't "replaced my work." I've taken on entirely new work, with the help of agents, that I would have never done otherwise. It has taken some getting used to shipping code I haven't personally typed. In the old way of working, I built understanding through writing that code. Now I build understanding through managing the project - writing a spec, reviewing structure, evaluating UX. And that's helped me think a whole lot bigger in terms of what I can now do.

Kyle Walker

13,740 просмотров • 1 месяц назад

🍊 Rewind Primus AMA for seeking WEB3 new phase Xiang Xie (Xiang Xie) said Primus is a solution that enables real-world data, which is necessary for building various applications in Web3, to be brought on-chain and verified without exposing sensitive information. Through this, Primus enables use cases such as proof of deposits in DeFi, and in the AI agents sector, it allows agents to prove the information they need and the actions they have completed in a trustless way, without relying on third parties. This expands the scope of what these sectors can do and makes broader product development possible. This kind of solution is an essential component for Web3 to move beyond degen culture or crypto-native use cases and enter a much larger arena. Beyond mainnet, Primus acts as an enabler that helps diverse dApps adopt and apply this technology stack. Discover what kind of ecosystem they are building and what plans they have through our recent podcast.
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🍊 Rewind Primus AMA for seeking WEB3 new phase Xiang Xie (Xiang Xie) said Primus is a solution that enables real-world data, which is necessary for building various applications in Web3, to be brought on-chain and verified without exposing sensitive information. Through this, Primus enables use cases such as proof of deposits in DeFi, and in the AI agents sector, it allows agents to prove the information they need and the actions they have completed in a trustless way, without relying on third parties. This expands the scope of what these sectors can do and makes broader product development possible. This kind of solution is an essential component for Web3 to move beyond degen culture or crypto-native use cases and enter a much larger arena. Beyond mainnet, Primus acts as an enabler that helps diverse dApps adopt and apply this technology stack. Discover what kind of ecosystem they are building and what plans they have through our recent podcast.

Wecryptotogether (Theo/acc)

15,181 просмотров • 2 месяцев назад

Why is nobody talking about Polymarket's official API? You open Polymarket Wait for it to load... Click on a market... Wait again... Try to check another one... More waiting... By the time you see the price, it's already stale The opportunity moved while you were clicking through pages Someone else got there first There's a better way // Found the gem hiding in plain sight - library py-clob-client Official from Polymarket. MIT license I spent a week working with multiple markets Checking prices across different positions Monitoring various categories Tracking market movements Then discovered this library Game changer for workflow efficiency // Real example from last week: UI approach: Checking markets one by one through the interface API approach: Monitoring all relevant markets simultaneously with live updates Having comprehensive real-time data makes a meaningful difference Speed and information clarity are valuable advantages // Want to go deeper? Build an arbitrage bot You'll need Rust for execution speed Deploy the server geographically close to Polymarket's infrastructure Every millisecond counts when opportunities last 1-2 seconds. I built mine in Go for monitoring and analysis. Works great for that. But if you're hunting arbitrage at scale against other bots, Rust + low latency setup is the only way. The infrastructure race is real // "But I can't code" With today's AI tools you actually can Cursor, ChatGPT, Claude, Gemini, Kimi, Grok - they write code for you You just describe what you want AI generates it You copy-paste and run Join Polymarket to create: Programming is no longer a barrier Anyone can build now
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Why is nobody talking about Polymarket's official API? You open Polymarket Wait for it to load... Click on a market... Wait again... Try to check another one... More waiting... By the time you see the price, it's already stale The opportunity moved while you were clicking through pages Someone else got there first There's a better way // Found the gem hiding in plain sight - library py-clob-client Official from Polymarket. MIT license I spent a week working with multiple markets Checking prices across different positions Monitoring various categories Tracking market movements Then discovered this library Game changer for workflow efficiency // Real example from last week: UI approach: Checking markets one by one through the interface API approach: Monitoring all relevant markets simultaneously with live updates Having comprehensive real-time data makes a meaningful difference Speed and information clarity are valuable advantages // Want to go deeper? Build an arbitrage bot You'll need Rust for execution speed Deploy the server geographically close to Polymarket's infrastructure Every millisecond counts when opportunities last 1-2 seconds. I built mine in Go for monitoring and analysis. Works great for that. But if you're hunting arbitrage at scale against other bots, Rust + low latency setup is the only way. The infrastructure race is real // "But I can't code" With today's AI tools you actually can Cursor, ChatGPT, Claude, Gemini, Kimi, Grok - they write code for you You just describe what you want AI generates it You copy-paste and run Join Polymarket to create: Programming is no longer a barrier Anyone can build now

BuBBliK

44,204 просмотров • 7 месяцев назад

We've been obsessing a lot about creating the best possible AI support experience Dub.co. And I think we've found the magic formula. The stack: ✦ Mintlify → docs + help articles ✦ Upstash vector → vector search ✦ AI SDK + Claude sonnet → LLM/retrieval ✦ Plain → support infra Here's how it works: We consolidated our docs + help articles on Mintlify (best-in-class docs platform) so all our support content is now in one place. On every push to main, we sync changes to our Upstash Vector DB. We also built a custom chat interface that retrieves content from Upstash Vector and generates responses via AI SDK + Claude sonnet. The best part: The AI agent uses 𝚝𝚘𝚘𝚕() from AI SDK to retrieve the user's workspace / partner data to provide accurate responses. Finally, if the user needs to speak to a human, we generate a 𝚃𝚒𝚌𝚔𝚎𝚝𝚄𝚙𝚕𝚘𝚊𝚍 UI that lets users upload screenshots + add more context, before creating a ticket on Plain. Cherry on top: The chat interface is embeddable, so we're able to embed it both in our Mintlify docs + support portal 👇 Since we launched this, it's been a gamechanger for scaling support – reducing support workload and creating a much better experience for our users. Highly recommend giving this a try!
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We've been obsessing a lot about creating the best possible AI support experience Dub.co. And I think we've found the magic formula. The stack: ✦ Mintlify → docs + help articles ✦ Upstash vector → vector search ✦ AI SDK + Claude sonnet → LLM/retrieval ✦ Plain → support infra Here's how it works: We consolidated our docs + help articles on Mintlify (best-in-class docs platform) so all our support content is now in one place. On every push to main, we sync changes to our Upstash Vector DB. We also built a custom chat interface that retrieves content from Upstash Vector and generates responses via AI SDK + Claude sonnet. The best part: The AI agent uses 𝚝𝚘𝚘𝚕() from AI SDK to retrieve the user's workspace / partner data to provide accurate responses. Finally, if the user needs to speak to a human, we generate a 𝚃𝚒𝚌𝚔𝚎𝚝𝚄𝚙𝚕𝚘𝚊𝚍 UI that lets users upload screenshots + add more context, before creating a ticket on Plain. Cherry on top: The chat interface is embeddable, so we're able to embed it both in our Mintlify docs + support portal 👇 Since we launched this, it's been a gamechanger for scaling support – reducing support workload and creating a much better experience for our users. Highly recommend giving this a try!

Steven Tey

22,918 просмотров • 5 месяцев назад

Free GPT-5.6 Sol/Terra/Luna + Claude Sonnet 4.6 + a full IDE one code editor just dropped a 14-day Pro trial with popular AI models ( no card required, no BS ) what you get: - GPT-5.6 Sol, Terra, Luna (full family) - Claude Sonnet 4.6 - two weeks of real coding - native IDE built in Rust (10x faster than VS Code) - multiplayer coding + native AI integration how to get: 1. go to zed(.)dev, sign in via GitHub (account must be 1+ month old) 2. click Pricing, select Pro, hit "Start Trial Now" 3. download their IDE 4. open a project -> click the star icon bottom-left -> pick your model in other words, you get a full-featured IDE with leading AI tools that work for free for 14 days - all for $0. based on the tests I've run, I'd say the speed and ease of use are really great - on par with Cursor. btw I recently wrote a full guide on ways to pay $0 for every subscription you have - this is just one of them worth to read:
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Free GPT-5.6 Sol/Terra/Luna + Claude Sonnet 4.6 + a full IDE one code editor just dropped a 14-day Pro trial with popular AI models ( no card required, no BS ) what you get: - GPT-5.6 Sol, Terra, Luna (full family) - Claude Sonnet 4.6 - two weeks of real coding - native IDE built in Rust (10x faster than VS Code) - multiplayer coding + native AI integration how to get: 1. go to zed(.)dev, sign in via GitHub (account must be 1+ month old) 2. click Pricing, select Pro, hit "Start Trial Now" 3. download their IDE 4. open a project -> click the star icon bottom-left -> pick your model in other words, you get a full-featured IDE with leading AI tools that work for free for 14 days - all for $0. based on the tests I've run, I'd say the speed and ease of use are really great - on par with Cursor. btw I recently wrote a full guide on ways to pay $0 for every subscription you have - this is just one of them worth to read:

kaize

35,369 просмотров • 12 дней назад

single RTX 3090. 24 GB VRAM. Qwen3.5-35B-A3B. 4-bit quant, 113 tokens per second at full 262K context harnessing Claude Code locally with no API, no subscription, no proxy. told it what it is. 30 Mamba2 layers, 10 attention, 256 experts, 8 active per token. said "build something that shows off what you can do." it visualized its own architecture. interactive. tokens flowing through layers. 256 experts lighting up on routing. served in the browser from the same GPU running inference. single prompt. then i said level up. 3D. Three.js. separate files. flythrough camera. clickable layers. it planned first, scaffolded 6 files, hit one API bug, fixed it itself, then optimized for smooth framerate. two iterations to a working 3D neural network explorer. llama.cpp just merged a native Anthropic endpoint. Claude Code points at localhost. the whole setup is two commands. no LiteLLM. no proxy config. the open source models coming out of china right now are genuinely changing what's possible on consumer hardware. respect to the Qwen team. this is acceleration.
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single RTX 3090. 24 GB VRAM. Qwen3.5-35B-A3B. 4-bit quant, 113 tokens per second at full 262K context harnessing Claude Code locally with no API, no subscription, no proxy. told it what it is. 30 Mamba2 layers, 10 attention, 256 experts, 8 active per token. said "build something that shows off what you can do." it visualized its own architecture. interactive. tokens flowing through layers. 256 experts lighting up on routing. served in the browser from the same GPU running inference. single prompt. then i said level up. 3D. Three.js. separate files. flythrough camera. clickable layers. it planned first, scaffolded 6 files, hit one API bug, fixed it itself, then optimized for smooth framerate. two iterations to a working 3D neural network explorer. llama.cpp just merged a native Anthropic endpoint. Claude Code points at localhost. the whole setup is two commands. no LiteLLM. no proxy config. the open source models coming out of china right now are genuinely changing what's possible on consumer hardware. respect to the Qwen team. this is acceleration.

Sudo su

110,206 просмотров • 5 месяцев назад

Our first Kite demo was ChatGPT ordering Uber Eats for you. It never got past the login screen. Uber's CAPTCHA was built for humans, and agents cannot pass it. On Grace Gong's Venture with Grace, our Co-Founder & CEO Chi Zhang traced how that moment shaped the way we design Kite. ▷ The demo died at the login, not at ordering or paying. The blocker was proving the agent was allowed to be there at all. Spinning up a browser extension to click through the CAPTCHA works, but it is a temporary stage of how agents and the internet should work together. ▷ The permanent path is identity. Every agent needs to prove who it is and where it comes from, carrying who its human or organization principal is, what its purpose is, and the exact authorization scope for that session or request. An agent showing up with no reason is either hallucinating or attacking you. ▷ Where this is heading: an API-first agentic internet, where headless agents authenticate without human login credentials. x402 and MCP are already marching toward that. Kite Agent Passport is that identity layer: verifiable identity, delegated authority, and permissions scoped per session. No more "prove you're human." 🪁
4:53

Our first Kite demo was ChatGPT ordering Uber Eats for you. It never got past the login screen. Uber's CAPTCHA was built for humans, and agents cannot pass it. On Grace Gong's Venture with Grace, our Co-Founder & CEO Chi Zhang traced how that moment shaped the way we design Kite. ▷ The demo died at the login, not at ordering or paying. The blocker was proving the agent was allowed to be there at all. Spinning up a browser extension to click through the CAPTCHA works, but it is a temporary stage of how agents and the internet should work together. ▷ The permanent path is identity. Every agent needs to prove who it is and where it comes from, carrying who its human or organization principal is, what its purpose is, and the exact authorization scope for that session or request. An agent showing up with no reason is either hallucinating or attacking you. ▷ Where this is heading: an API-first agentic internet, where headless agents authenticate without human login credentials. x402 and MCP are already marching toward that. Kite Agent Passport is that identity layer: verifiable identity, delegated authority, and permissions scoped per session. No more "prove you're human." 🪁

KITE AI

18,874 просмотров • 27 дней назад

The value of the work we're doing at Optimum is encapsulated quite well by the phrase "speed is money". In modern markets there are real economic advantages to latency reduction. This is nothing new. Wall Street firms have long been optimizing on latency, primarily through colocation and top of the line hardware. However, when it comes to decentralized systems, expensive hardware and geographic concentration are antithetical to their purpose. Therefore we should optimize decentralized network latency through software, which I'm thrilled about because it's exactly what I've spent the better part of the past 2 decades working on with Random Linear Network Coding. Now let’s talk about networking economics, the relationship between speed and money. First, it's important to note that users will only pay for low latency if it can be consistently guaranteed. Second, you can only make that latency guarantee for a certain number of users. This is a universal law of networking. We can model this relationship on a delay curve, shown below. The delay curve is determined by the utilization rate of the network, meaning how much traffic is flowing through the network divided by the network's throughput. As you approach a level of traffic equal to the available throughput, latency trends infinitely higher. On this delay curve we can impose some utility thresholds. These thresholds are the levels of latency which are important to different groups of users because of how that latency guarantee improves their economic outcomes. Finding the point on the curve where each threshold intersects will tell us what level of traffic we can guarantee that level of latency for. Essentially, there exists a finite supply of speed on a network and the highest utility users of that speed are willing to pay more for it. I like to think of this similarly to expedited shipping options on Amazon. This is why we say speed is money, and why we can create a Latency Marketplace. The only way to increase the supply of speed is to fundamentally increase network throughput. This is what we work on at Optimum by using Random Linear Network Coding. The same relationship between traffic and throughput still applies, but now the delay curve is shifted out further to the right. Now more traffic can be processed at the same latency, or the same traffic can be processed at a lower latency. More speed available to the network. More value unlocked for the network’s users. Crucially, that value is no longer only reserved for those who can afford to sit closest to the machine. Expanding the supply of speed widens who can reach each latency threshold, keeping the network's advantage decentralized rather than concentrated in the hands of a few. When nodes join Optimum and participate, they reap the benefits, but they also add to the capacity. Rather than vying against each other in a zero-sum game, nodes help themselves and others.
0:54

The value of the work we're doing at Optimum is encapsulated quite well by the phrase "speed is money". In modern markets there are real economic advantages to latency reduction. This is nothing new. Wall Street firms have long been optimizing on latency, primarily through colocation and top of the line hardware. However, when it comes to decentralized systems, expensive hardware and geographic concentration are antithetical to their purpose. Therefore we should optimize decentralized network latency through software, which I'm thrilled about because it's exactly what I've spent the better part of the past 2 decades working on with Random Linear Network Coding. Now let’s talk about networking economics, the relationship between speed and money. First, it's important to note that users will only pay for low latency if it can be consistently guaranteed. Second, you can only make that latency guarantee for a certain number of users. This is a universal law of networking. We can model this relationship on a delay curve, shown below. The delay curve is determined by the utilization rate of the network, meaning how much traffic is flowing through the network divided by the network's throughput. As you approach a level of traffic equal to the available throughput, latency trends infinitely higher. On this delay curve we can impose some utility thresholds. These thresholds are the levels of latency which are important to different groups of users because of how that latency guarantee improves their economic outcomes. Finding the point on the curve where each threshold intersects will tell us what level of traffic we can guarantee that level of latency for. Essentially, there exists a finite supply of speed on a network and the highest utility users of that speed are willing to pay more for it. I like to think of this similarly to expedited shipping options on Amazon. This is why we say speed is money, and why we can create a Latency Marketplace. The only way to increase the supply of speed is to fundamentally increase network throughput. This is what we work on at Optimum by using Random Linear Network Coding. The same relationship between traffic and throughput still applies, but now the delay curve is shifted out further to the right. Now more traffic can be processed at the same latency, or the same traffic can be processed at a lower latency. More speed available to the network. More value unlocked for the network’s users. Crucially, that value is no longer only reserved for those who can afford to sit closest to the machine. Expanding the supply of speed widens who can reach each latency threshold, keeping the network's advantage decentralized rather than concentrated in the hands of a few. When nodes join Optimum and participate, they reap the benefits, but they also add to the capacity. Rather than vying against each other in a zero-sum game, nodes help themselves and others.

Muriel Medard

44,395 просмотров • 1 месяц назад

THIS IS A PERFECT EXAMPLE TO PROVE THE AI BUBBLE Claude AI developer Anthropic is valued at nearly $965B, while making only $20B in revenue. For comparison: Walmart is worth nearly the same amount ($940B), but makes $725B in revenue. Let that sink in. Same valuation range. Completely different economic reality. One is built on moving real goods through real infrastructure every day. The other is built on expectations of how widely AI will be used, and how profitable it will eventually become. And thats exactly where the big danger lies. Markets are no longer just pricing what companies earn. They are pricing what companies could become if everything goes right. And when valuations start converging like this, the uncomfortable question isn’t whether AI matters. It’s how much of that future has already been priced in.
0:31

THIS IS A PERFECT EXAMPLE TO PROVE THE AI BUBBLE Claude AI developer Anthropic is valued at nearly $965B, while making only $20B in revenue. For comparison: Walmart is worth nearly the same amount ($940B), but makes $725B in revenue. Let that sink in. Same valuation range. Completely different economic reality. One is built on moving real goods through real infrastructure every day. The other is built on expectations of how widely AI will be used, and how profitable it will eventually become. And thats exactly where the big danger lies. Markets are no longer just pricing what companies earn. They are pricing what companies could become if everything goes right. And when valuations start converging like this, the uncomfortable question isn’t whether AI matters. It’s how much of that future has already been priced in.

CryptoGoos

14,835 просмотров • 2 месяцев назад

Just finished an absolute monster of a podcast with Justin Drake, and it’s packed with brain-melting insights on the future of Ethereum. Here are the juiciest takeaways from Part I 👇 Ethereum’s future could turn the L1 into a rollup itself and making it the best type of rollup. Justin lays out how the L1, L2s, and new designs like Beamchain, native rollups, and based sequencing all come together into a unified vision for Ethereum as the credibly neutral internet of finance. What’s Broken Today • L2s are siloed — synchronous composability is dead. • Most rely on centralized sequencers, multisigs & slow exit bridges. • L2-native assets break Ethereum’s trustless bridge model. • Governance delays, security councils, & EVM reimplementation = attack surfaces. The Fix: Ethereum-native Infrastructure • Based Rollups: Use Ethereum L1 validators as the sequencer. Removes centralization risk, restores censorship resistance. • Native Rollups: No more reimplementing the EVM. No more Security Council multisigs. They inherit Ethereum’s execution layer by design. •Preconfirmations: Users get fast UX (e.g., instant trading feedback) via ETH-backed inclusion guarantees from L1 validators, with slashing if they lie. • Real-Time ZK Proofs: Slot-by-slot proving is here. ZK-enabled rollups with instant finality are becoming possible. To get there, Ethereum is moving toward real-time, slot-by-slot proving using ZK SNARKs. This enables constant-time verification and removes the need for validators to re-execute blocks. Native rollups use a new EXECUTE precompile to introspect and reuse Ethereum’s state transition function. There is no EVM emulation, no governance lag, and no reliance on Security Councils. Preconfirmations offer ETH-backed execution guarantees per slot, priced to offset MEV opportunity loss, while based rollups decentralize sequencing by aligning with Ethereum’s validator set. Beamchain pushes this vision further: a clean-slate Ethereum L1 design that is post-quantum secure, radically simplified (validators could run on a Raspberry Pi), and ossified through optimality. It proposes gigagas/s throughput via ZK proofs, validator statelessness, and Attester-Proposer-Separation (APS) to maximize decentralization and censorship resistance, allowing Ethereum L1 to scale as a high-performance rollup itself. If you're building on Ethereum or betting on its future, this convo is mandatory listening. To be continued in Part II....
2:18:34

Just finished an absolute monster of a podcast with Justin Drake, and it’s packed with brain-melting insights on the future of Ethereum. Here are the juiciest takeaways from Part I 👇 Ethereum’s future could turn the L1 into a rollup itself and making it the best type of rollup. Justin lays out how the L1, L2s, and new designs like Beamchain, native rollups, and based sequencing all come together into a unified vision for Ethereum as the credibly neutral internet of finance. What’s Broken Today • L2s are siloed — synchronous composability is dead. • Most rely on centralized sequencers, multisigs & slow exit bridges. • L2-native assets break Ethereum’s trustless bridge model. • Governance delays, security councils, & EVM reimplementation = attack surfaces. The Fix: Ethereum-native Infrastructure • Based Rollups: Use Ethereum L1 validators as the sequencer. Removes centralization risk, restores censorship resistance. • Native Rollups: No more reimplementing the EVM. No more Security Council multisigs. They inherit Ethereum’s execution layer by design. •Preconfirmations: Users get fast UX (e.g., instant trading feedback) via ETH-backed inclusion guarantees from L1 validators, with slashing if they lie. • Real-Time ZK Proofs: Slot-by-slot proving is here. ZK-enabled rollups with instant finality are becoming possible. To get there, Ethereum is moving toward real-time, slot-by-slot proving using ZK SNARKs. This enables constant-time verification and removes the need for validators to re-execute blocks. Native rollups use a new EXECUTE precompile to introspect and reuse Ethereum’s state transition function. There is no EVM emulation, no governance lag, and no reliance on Security Councils. Preconfirmations offer ETH-backed execution guarantees per slot, priced to offset MEV opportunity loss, while based rollups decentralize sequencing by aligning with Ethereum’s validator set. Beamchain pushes this vision further: a clean-slate Ethereum L1 design that is post-quantum secure, radically simplified (validators could run on a Raspberry Pi), and ossified through optimality. It proposes gigagas/s throughput via ZK proofs, validator statelessness, and Attester-Proposer-Separation (APS) to maximize decentralization and censorship resistance, allowing Ethereum L1 to scale as a high-performance rollup itself. If you're building on Ethereum or betting on its future, this convo is mandatory listening. To be continued in Part II....

Luis

17,653 просмотров • 1 год назад

🚨 NOW: Marine Joey Jones MIC DROPS the Hussein Obama library leftists doing a "stolen land acknowledgement" for native tribes' land they built over "We are a sovereign nation now...if you wanted to keep it, discover the musket sooner, I guess! We've pandered to and provided for Native Americans in a way that has turned a lot of their reservations into socialist, dystopian, terrible places where people don't have opportunity!" "Alcoholism is through the roof, health care isn't what it needs to be. We've been offering them to keep their land and culture provided them no way out!" "There's a lot of problems in the Native American communities all over this country. And if you really care, go invest in that. Go educate yourself on that. See if there's a way to help those people that feel like they're stuck in a generational cycle of poverty rather than doing some virtue signaling about land that has been a part of Chicago for a couple of centuries now!" 🔥🔥🔥
1:22

🚨 NOW: Marine Joey Jones MIC DROPS the Hussein Obama library leftists doing a "stolen land acknowledgement" for native tribes' land they built over "We are a sovereign nation now...if you wanted to keep it, discover the musket sooner, I guess! We've pandered to and provided for Native Americans in a way that has turned a lot of their reservations into socialist, dystopian, terrible places where people don't have opportunity!" "Alcoholism is through the roof, health care isn't what it needs to be. We've been offering them to keep their land and culture provided them no way out!" "There's a lot of problems in the Native American communities all over this country. And if you really care, go invest in that. Go educate yourself on that. See if there's a way to help those people that feel like they're stuck in a generational cycle of poverty rather than doing some virtue signaling about land that has been a part of Chicago for a couple of centuries now!" 🔥🔥🔥

Eric Daugherty

130,117 просмотров • 2 месяцев назад

> celestia's matcha upgrade coming next monday: the first DA layer ready for nasdaq scale onchain finance. what did Nick White say tonight on The Rollup? ⇾ Celestia identified clob exchanges as the strongest product fit for its architecture, since exchanges need ultra low latency and very high throughput. single sequencer rollups provide the fastest latency while still enabling censorship resistance and non custodialness. ⇾ fully onchain clobs require posting all orders and cancels on chain, demanding mb/sec da capacity. the upcoming matcha upgrade enables 5 mb/s, and governance can scale this to 20 mb/s, enough for nasdaq level traffic. ⇾ teams like RISE are ready to use this throughput, reinforcing celestia's push toward vertical integration. ⇾ also depends on matcha's higher throughput, adding urgency to the upgrade. matcha reduces issuance to 2.5% and ships components of the long term proof of governance model, which removes staking and pays ~0.25% issuance directly to validators. ⇾ celestia is shifting from a build whatever approach toward targeting apps with real pmf, mirroring broader industry moves toward more opinionated architectures. because mass adoption is still far away, path dependency matters: the team believes only about 10 apps truly matter, and celestia needs to win some of them. ⇾ high volume markets like onchain forex require massive DA and celestia is one of the few platforms capable of supporting this scale. success of bullet or other clobs would prove that wall street scale onchain finance is possible. ⇾ tradfi teams are increasingly exploring building on chain platforms, and celestia must balance institutional needs with fast moving native teams. ⇾ private blockspace is already live in production with Hibachi with clear institutional and dex interest. celestia focuses mainly on app acquisition while dedicating ~20% to internal tooling.
0:20

> celestia's matcha upgrade coming next monday: the first DA layer ready for nasdaq scale onchain finance. what did Nick White say tonight on The Rollup? ⇾ Celestia identified clob exchanges as the strongest product fit for its architecture, since exchanges need ultra low latency and very high throughput. single sequencer rollups provide the fastest latency while still enabling censorship resistance and non custodialness. ⇾ fully onchain clobs require posting all orders and cancels on chain, demanding mb/sec da capacity. the upcoming matcha upgrade enables 5 mb/s, and governance can scale this to 20 mb/s, enough for nasdaq level traffic. ⇾ teams like RISE are ready to use this throughput, reinforcing celestia's push toward vertical integration. ⇾ also depends on matcha's higher throughput, adding urgency to the upgrade. matcha reduces issuance to 2.5% and ships components of the long term proof of governance model, which removes staking and pays ~0.25% issuance directly to validators. ⇾ celestia is shifting from a build whatever approach toward targeting apps with real pmf, mirroring broader industry moves toward more opinionated architectures. because mass adoption is still far away, path dependency matters: the team believes only about 10 apps truly matter, and celestia needs to win some of them. ⇾ high volume markets like onchain forex require massive DA and celestia is one of the few platforms capable of supporting this scale. success of bullet or other clobs would prove that wall street scale onchain finance is possible. ⇾ tradfi teams are increasingly exploring building on chain platforms, and celestia must balance institutional needs with fast moving native teams. ⇾ private blockspace is already live in production with Hibachi with clear institutional and dex interest. celestia focuses mainly on app acquisition while dedicating ~20% to internal tooling.

Mora 🦥

31,824 просмотров • 9 месяцев назад

Dario Amodei just told software engineers exactly how long they have. Six to twelve months. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.
0:27

Dario Amodei just told software engineers exactly how long they have. Six to twelve months. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.

Dustin

318,457 просмотров • 5 месяцев назад