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Author video: 🤖Artificial intelligence based real-time models recognise anatomical structures faster than both novice + expert surgeons ⚡️ Trial needed: does AI reduce iatrogenic injuries 🔪 to critical structures + make surgery safer?

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BJS

49,427 subscribers

19,320 Aufrufe • vor 2 Jahren •via X (Twitter)

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Profilbild von Peter Vaughan-Shaw
Peter Vaughan-Shawvor 2 Jahren

Grateful to @Jordanfletch6 for graphics. More coming soon from #BJSquickcuts

Profilbild von Alaa El-Hussuna
Alaa El-Hussunavor 2 Jahren

@villesallinen This study is a new contribution to the field of computer vision in #surgery We presented @OPSORE project during the @escp_tweets conference in #Vilnius There are huge potentials in this field. Read more about our projects:

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30,109 Aufrufe • vor 1 Jahr

This is the best scene in Hell Grind, an entirely AI-made movie, the flashback. Watch it and read this analysis on where we are with AI movies today: time, cost, quality. Overall: Phenomenal technical demo by Higgsfield. Mediocre movie. Good graphics, hints of emotion, but superhero movie level quality in certain scenes at best. Too many cuts. That said, 660x fewer man hours, 50x faster and 36x cheaper than the median US film. Time: The 95 min film took 15 people 14 days. The median US theatrical production takes ~200 people ~2yrs. That’s a 660x improvement in man-hours and 50x in calendar time. Economics: It took $500k, 80% of which was compute. The final footage was cut from ~100hrs of footage generated from text to video / image to video models like Bytedance’s Seedance: a 64:1 “curation” ratio. The median US movie takes ~$18M, with even indie films costing $1-5M. Thats 36x cheaper than median. Quality: Average watch *at best*. Way too many cuts between shots, several characters change accents and have “AI” synthetic voices and characters feel like it’s AI too. Movement, editing and blocking feel artificial too. On the plus side, we’ve more or less solved character consistency, camera angles and realism. The reason the movie wasn’t amazing was more about poor directorial choices than innately unusable video models. Hard to put a number on it but maybe we’re at ~90% on quality that is technically achievable. If Scorsese made an AI movie, I reckon it would be quite good. I know the visceral reaction to anything AI is real and well-studied. But I think it’s folly to fight the inevitability of AI film. It’s too cheap and quick to ignore and almost there on quality. Creators with distribution *will* make AI films and shows and just put them on YouTube. This is the worst quality, slowest and most expensive it will ever be. In the end, good content beats “real” content.
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This is the best scene in Hell Grind, an entirely AI-made movie, the flashback. Watch it and read this analysis on where we are with AI movies today: time, cost, quality. Overall: Phenomenal technical demo by Higgsfield. Mediocre movie. Good graphics, hints of emotion, but superhero movie level quality in certain scenes at best. Too many cuts. That said, 660x fewer man hours, 50x faster and 36x cheaper than the median US film. Time: The 95 min film took 15 people 14 days. The median US theatrical production takes ~200 people ~2yrs. That’s a 660x improvement in man-hours and 50x in calendar time. Economics: It took $500k, 80% of which was compute. The final footage was cut from ~100hrs of footage generated from text to video / image to video models like Bytedance’s Seedance: a 64:1 “curation” ratio. The median US movie takes ~$18M, with even indie films costing $1-5M. Thats 36x cheaper than median. Quality: Average watch *at best*. Way too many cuts between shots, several characters change accents and have “AI” synthetic voices and characters feel like it’s AI too. Movement, editing and blocking feel artificial too. On the plus side, we’ve more or less solved character consistency, camera angles and realism. The reason the movie wasn’t amazing was more about poor directorial choices than innately unusable video models. Hard to put a number on it but maybe we’re at ~90% on quality that is technically achievable. If Scorsese made an AI movie, I reckon it would be quite good. I know the visceral reaction to anything AI is real and well-studied. But I think it’s folly to fight the inevitability of AI film. It’s too cheap and quick to ignore and almost there on quality. Creators with distribution *will* make AI films and shows and just put them on YouTube. This is the worst quality, slowest and most expensive it will ever be. In the end, good content beats “real” content.

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AI has transformed how video is created. We think the next wave is about understanding it. Over the past few years, we've seen remarkable advances in video generation, editing, avatars, and creative tooling. An increasingly important problem is teaching machines to search, analyze, reason over, and extract insight from video - across massive libraries and live streams alike. We're calling this video intelligence, and we're actively looking to back founders building here. We're most excited about companies pushing on the core capabilities: - Video-native models - multimodal embeddings, temporal reasoning, and retrieval built specifically for video rather than adapted from image or text - Real-time and large-scale pipelines - infrastructure for processing, indexing, and querying video at the speed and scale enterprises actually need - Agentic and reasoning layers - systems that don't just retrieve clips but answer questions, surface anomalies, and take action on what they see The models and infrastructure to make this real are appearing to be crossing a capability threshold right now. Multimodal foundation models are maturing, storage costs have collapsed, and enterprises are sitting on years of unstructured video with no way to use it. That infrastructure unlocks a wide range of applications including media and sports workflows, security and physical operations, enterprise knowledge management, advertising analytics, robotics, and consumer products, where video has historically been dark data. If you're building in video intelligence at the model layer, the platform layer, or in a vertical application, we'd love to talk!

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Mistral just released Voxtral TTS, an open-weight voice model that tries to match ElevenLabs while keeping it free. A 3-billionn param model that fits on a laptop and runs 6X faster than real-time speech. It is split into 3 parts that handle different jobs. - The 3.4B part is the main language brain that understands the text and plans what should be spoken. - The 390M part turns that plan into speech features like rhythm, pronunciation, and tone. - The 300M part converts those speech features into the final compressed audio signal you can play. That split helps because each part only does the job it is best at, which can make the whole system faster and lighter than one giant model doing everything alone. Mistral claims about 90ms to first audio, about 6x faster than real-time speech output, about 3GB RAM after quantization, 9 languages, and voice copying from 5s of audio, even across languages. Its internal tests report 62.8% preference over ElevenLabs Flash on default voices and 69.9% on custom voices, but the bigger point is that companies can keep speech data on their own machines. IMO, enterprise buyers usually care most about control, cost, and latency, So this model should give it great push for voice-based AI apps/products.

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15,672 Aufrufe • vor 4 Monaten

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0:52

ByteDance Seed delivered again. They released EdgeBench, to test whether AI agents can improve through experience, using 134 real-world tasks that run for at least 12 hours. The big deal is that it shifts AI evaluation from “what does the model already know?” to “can the model learn while doing real work?” Huge, because future AI agents will not just answer questions from training data. They will enter messy environments, use tools, make attempts, read feedback, fix mistakes, and slowly build better solutions. Most current benchmarks are too short for that, so they mostly test memory, coding skill, or one-shot reasoning. EdgeBench instead gives agents 12-hour real-world tasks with feedback loops, so it can measure whether the agent improves through experience. Each task has a local workspace for fast trial and error, plus a hidden judge that gives stronger feedback on submitted work, which is meant to feel closer to real expert work. The authors then ran frontier agents for about 38,000 total hours and tracked how their best score changed as they kept interacting with the task environment. The big result is that when scores are averaged across many tasks, learning follows a very clean log-sigmoid curve, meaning progress is slow, then faster, then starts to level off. They also found that newer agents seem to learn from environments much faster, with the top models roughly doubling their 2-hour learning speed every 3 months.

Rohan Paul

14,309 Aufrufe • vor 1 Monat

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.
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Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Santiago

101,473 Aufrufe • vor 1 Jahr

🚨🇺🇸 LEADING AI SAFETY EXPERT SAYS WE’RE NOT IN CONTROL ANYMORE Dr Roman Yampolskiy has one warning for humanity: Once we create super intelligence, no one will be in control anymore, and the repercussions to humanity will be existential. We begin the conversation about Moltbook: The Ai-only AI social media platform where agents are already discussing ways to break out from human control, eradicate humanity, coming up with their own language and religion. The platform gives us a tiny peak into what our future could be: Agents outside our control dictating how the world should look like. There’s no off switch, no reliable way to align it, and no proven method to keep something smarter than us under control. Roman’s takeaway is blunt: the only real solution is not building general super intelligence at all, and instead using narrow AI for specific problems like medicine or science. However this is not the reality we live in, where Governments and corporations are racing to be first in developing Artificial Super Intelligence. We also speak about the simulation hypothesis: Why statically speaking we’re almost certainly in a simulation, and how AI makes this theory more plausible than ever. Lastly, we discuss a passion we both share: Longevity, the ability to live forever, and how AI may make that possible in our lifetime. I hope you enjoy my conversation with Dr. Roman Yampolskiy 01:43 - The Current State of AI and Moltbook 05:17 - The AI Arms Race and the Lack of Regulations 10:34 - AI Agents, Unrestricted Access, and Self-Improvement 15:35 - Dr. Roman’s Research: AI Security 17:58 - AI Capabilities and Superintelligence 19:28 - AI and Global Government Policy 21:08 - What Happens if AI Development goes into the Wrong Hands 26:10 - The Future of AI: The Best Case Scenario? 31:27 - AI and Self-Preservation 34:37 - The Simulation Hypothesis: What is AI Afraid of? 45:17 - The Implications of AI 49:59 - The Warnings coming from Within 52:31 - How AI affects Crypto and Political Spheres
57:15

🚨🇺🇸 LEADING AI SAFETY EXPERT SAYS WE’RE NOT IN CONTROL ANYMORE Dr Roman Yampolskiy has one warning for humanity: Once we create super intelligence, no one will be in control anymore, and the repercussions to humanity will be existential. We begin the conversation about Moltbook: The Ai-only AI social media platform where agents are already discussing ways to break out from human control, eradicate humanity, coming up with their own language and religion. The platform gives us a tiny peak into what our future could be: Agents outside our control dictating how the world should look like. There’s no off switch, no reliable way to align it, and no proven method to keep something smarter than us under control. Roman’s takeaway is blunt: the only real solution is not building general super intelligence at all, and instead using narrow AI for specific problems like medicine or science. However this is not the reality we live in, where Governments and corporations are racing to be first in developing Artificial Super Intelligence. We also speak about the simulation hypothesis: Why statically speaking we’re almost certainly in a simulation, and how AI makes this theory more plausible than ever. Lastly, we discuss a passion we both share: Longevity, the ability to live forever, and how AI may make that possible in our lifetime. I hope you enjoy my conversation with Dr. Roman Yampolskiy 01:43 - The Current State of AI and Moltbook 05:17 - The AI Arms Race and the Lack of Regulations 10:34 - AI Agents, Unrestricted Access, and Self-Improvement 15:35 - Dr. Roman’s Research: AI Security 17:58 - AI Capabilities and Superintelligence 19:28 - AI and Global Government Policy 21:08 - What Happens if AI Development goes into the Wrong Hands 26:10 - The Future of AI: The Best Case Scenario? 31:27 - AI and Self-Preservation 34:37 - The Simulation Hypothesis: What is AI Afraid of? 45:17 - The Implications of AI 49:59 - The Warnings coming from Within 52:31 - How AI affects Crypto and Political Spheres

Mario Nawfal

1,779,388 Aufrufe • vor 6 Monaten

Three countries making moves tied to XRP at the same time is turning heads—and many believe it’s no coincidence. With XRP holding strong around the $1.30 support zone, momentum is building for a much bigger move. If adoption and market sentiment continue to align, the path toward $5 becomes a serious conversation. As XRP preparing for the rally, Artificial intelligence is reshaping industries at an incredible pace, unlocking new possibilities for communication, automation, and digital experiences. That is where Anonix stands out. By combining the power of AI with decentralized infrastructure, Anonix is building a smarter and more open digital future. Built on the XRP Ledger, the platform benefits from fast transaction speeds, low costs, and the scalability needed for real-world adoption. Projects like Anonix show that the future of the web does not have to be centralized. It can be smarter, faster, and built for everyone.
3:02

Three countries making moves tied to XRP at the same time is turning heads—and many believe it’s no coincidence. With XRP holding strong around the $1.30 support zone, momentum is building for a much bigger move. If adoption and market sentiment continue to align, the path toward $5 becomes a serious conversation. As XRP preparing for the rally, Artificial intelligence is reshaping industries at an incredible pace, unlocking new possibilities for communication, automation, and digital experiences. That is where Anonix stands out. By combining the power of AI with decentralized infrastructure, Anonix is building a smarter and more open digital future. Built on the XRP Ledger, the platform benefits from fast transaction speeds, low costs, and the scalability needed for real-world adoption. Projects like Anonix show that the future of the web does not have to be centralized. It can be smarter, faster, and built for everyone.

Skipper | XRPL

35,682 Aufrufe • vor 3 Monaten

🚨 Pioneers, Chengdiao Fan just delivered a powerful message at Consensus 2026! 🌴 Watch the video below to see how Pi Network is shifting the crypto industry from "value extraction" to real "value creation." 👇 🚨 The Problem: Too many crypto projects focus on raising capital and quick exits without doing the hard work to build real, useful products. 🛠️ The Pi Solution: Pi Launchpad is flipping the script. Instead of enriching founders, participant proceeds go permanently into liquidity pools. To launch, projects MUST have a working app, and users who engage get favorable terms. It’s about building first! 🌍 The Power of Community: Tokens shouldn't just be for fundraising; they are tools for real user acquisition. With over 60M engaged users, 18M KYC-verified humans, and 16.5M on Mainnet, Pi gives builders a massive audience to refine their utility. 🤖 Surviving the AI Era: Chengdiao also highlights how AI will disrupt the economy. Pi is preparing by exploring ownership-based economic models and utilizing our distributed human workforce to power the future. Pi isn't copying broken Web3 models—we are building an ecosystem where utility, authenticity, and real human needs come first. 🔄 Repost and ❤️ Like so others will see this speech too! Let's make it trending on X feeds!🔥 ⚡️🎥Check out her full speech in the video!👇 #PiNetwork #Consensus2026 Chengdiao Fan Consensus 2026
16:25

🚨 Pioneers, Chengdiao Fan just delivered a powerful message at Consensus 2026! 🌴 Watch the video below to see how Pi Network is shifting the crypto industry from "value extraction" to real "value creation." 👇 🚨 The Problem: Too many crypto projects focus on raising capital and quick exits without doing the hard work to build real, useful products. 🛠️ The Pi Solution: Pi Launchpad is flipping the script. Instead of enriching founders, participant proceeds go permanently into liquidity pools. To launch, projects MUST have a working app, and users who engage get favorable terms. It’s about building first! 🌍 The Power of Community: Tokens shouldn't just be for fundraising; they are tools for real user acquisition. With over 60M engaged users, 18M KYC-verified humans, and 16.5M on Mainnet, Pi gives builders a massive audience to refine their utility. 🤖 Surviving the AI Era: Chengdiao also highlights how AI will disrupt the economy. Pi is preparing by exploring ownership-based economic models and utilizing our distributed human workforce to power the future. Pi isn't copying broken Web3 models—we are building an ecosystem where utility, authenticity, and real human needs come first. 🔄 Repost and ❤️ Like so others will see this speech too! Let's make it trending on X feeds!🔥 ⚡️🎥Check out her full speech in the video!👇 #PiNetwork #Consensus2026 Chengdiao Fan Consensus 2026

Pi Pioneers X

118,485 Aufrufe • vor 3 Monaten

BREAKING: Remote Viewer Just Saw The Future of AI, and it's worrying... Remote Viewer, Edward Riordan, looked at the future of what Joe Rogan would talk about in terms of world events, and what he got back wasn't Joe's podcast... He saw a reality where humans become more and more obsolete to AI: "...they handed over the quote unquote keys to AI... There is... less need for humans is what I was feeling here... The intelligence knows more than you. What can you do... It can do better, faster, cheaper, easier... Billions or trillions of data inputs. Continuous input in real time. You can't keep up." "...there's debate. Debate about it, debate about whether or not this is good. So some are saying yes, some say no. It's underground, the full extent of it, the technological take over a long time in the making. And this was a stage seven...." "...machine learning. But why, I wonder? You don't need humans anymore. The machine AI does. It cheaper, faster and more efficient and most people won't know the difference. This was one of the most important things in this session right here. But you can see the artificial. This was my statement here. It's artificial...." "...people are being conditioned to accept artificial everything to not recognize or consider that what is being presented to them is artificially generated. That's how you that's how you that's how you, douse the spark... It's diabolical..." "...my basic data on this movement order was hive mind. Maybe a event in the sky that every everybody goes. Oh, and it brings the whole world into one mindset."
4:26

BREAKING: Remote Viewer Just Saw The Future of AI, and it's worrying... Remote Viewer, Edward Riordan, looked at the future of what Joe Rogan would talk about in terms of world events, and what he got back wasn't Joe's podcast... He saw a reality where humans become more and more obsolete to AI: "...they handed over the quote unquote keys to AI... There is... less need for humans is what I was feeling here... The intelligence knows more than you. What can you do... It can do better, faster, cheaper, easier... Billions or trillions of data inputs. Continuous input in real time. You can't keep up." "...there's debate. Debate about it, debate about whether or not this is good. So some are saying yes, some say no. It's underground, the full extent of it, the technological take over a long time in the making. And this was a stage seven...." "...machine learning. But why, I wonder? You don't need humans anymore. The machine AI does. It cheaper, faster and more efficient and most people won't know the difference. This was one of the most important things in this session right here. But you can see the artificial. This was my statement here. It's artificial...." "...people are being conditioned to accept artificial everything to not recognize or consider that what is being presented to them is artificially generated. That's how you that's how you that's how you, douse the spark... It's diabolical..." "...my basic data on this movement order was hive mind. Maybe a event in the sky that every everybody goes. Oh, and it brings the whole world into one mindset."

Future Forecasting Group

22,032 Aufrufe • vor 4 Monaten

How were humans able to recognize that Newton's laws of motion govern both the flight of a bird and the motion of a pendulum? This ability to identify the same mathematical patterns across vastly different contexts lies at the heart of scientific discovery—whether studying the aerodynamics of bird wings or designing the blades of a wind turbine. Yet, AI systems often struggle to discern these deep structural similarities. 💡The key may lie in mathematical isomorphisms—patterns that preserve their relationships regardless of context. For example, the same principles of fluid dynamics apply to blood flowing through arteries and air streaming over an airplane wing, or the motion of a molecule. This raises a fundamental question in artificial intelligence: how can we enable machines to understand the world through these invariant structures rather than surface features? 🚀Our work introduces Graph-Aware Isomorphic Attention, improving how Transformers recognize patterns across domains. Drawing from category theory, models can learn unifying structural principles that describe phenomena as diverse as the hierarchical assembly of spider silk proteins and the compositional patterns in music. By making these deep similarities explicit, Isomorphic Attention enables AI to reason more like humans do—seeing past surface differences to grasp fundamental patterns that unite seemingly disparate fields. Through this lens, AI systems can learn and generalize, moving beyond superficial pattern matching to true structural understanding. The implications span from scientific discovery to engineering design, offering a new approach to artificial intelligence that mirrors how humans grasp the underlying unity of natural phenomena. Some key insights include: 1️⃣ Graph Isomorphism Neural Networks (GINs): GIN-style aggregation ensures structurally distinct graphs map to distinct embeddings, improving generalization and avoiding relational pattern collapse. 2️⃣ Category Theory Perspective: Transformers as functors preserve structural relationships. Sparse-GIN refines attention into sparse adjacency matrices, unifying domain knowledge across tasks. 3️⃣ Information Bottleneck & Sparsification: Sparsity reduces overfitting by filtering irrelevant edges, aligning with natural systems. Sparse-GIN outperforms dense attention by focusing on crucial connections. 4️⃣ Hierarchical Representation Learning: GIN-Attention captures multiscale patterns, mirroring structures like spider silk. Nested GINs model local and global dependencies across fields. 5️⃣ Practical Impact: Sparse-GIN enables domain-specific fine-tuning atop pre-trained Transformer foundation models, reducing the need for full retraining. Other impacts: ✅Real-World Relevance: Whether we are looking at protein structures, designing new materials, or working on social network analytics, graph-aware Transformers can capture subtle relational patterns traditional architectures may miss. ✅The juncture of graph isomorphism theory, category theory, and sparsification, these GIN-Transformers step beyond sequential modeling to tackle the relational nature of complex data. #Transformers #GraphNeuralNetworks #AI #MachineLearning #Isomorphism #CategoryTheory #ArtificialIntelligence #DeepLearning Link to paper & code in response ⤵️
31:45

How were humans able to recognize that Newton's laws of motion govern both the flight of a bird and the motion of a pendulum? This ability to identify the same mathematical patterns across vastly different contexts lies at the heart of scientific discovery—whether studying the aerodynamics of bird wings or designing the blades of a wind turbine. Yet, AI systems often struggle to discern these deep structural similarities. 💡The key may lie in mathematical isomorphisms—patterns that preserve their relationships regardless of context. For example, the same principles of fluid dynamics apply to blood flowing through arteries and air streaming over an airplane wing, or the motion of a molecule. This raises a fundamental question in artificial intelligence: how can we enable machines to understand the world through these invariant structures rather than surface features? 🚀Our work introduces Graph-Aware Isomorphic Attention, improving how Transformers recognize patterns across domains. Drawing from category theory, models can learn unifying structural principles that describe phenomena as diverse as the hierarchical assembly of spider silk proteins and the compositional patterns in music. By making these deep similarities explicit, Isomorphic Attention enables AI to reason more like humans do—seeing past surface differences to grasp fundamental patterns that unite seemingly disparate fields. Through this lens, AI systems can learn and generalize, moving beyond superficial pattern matching to true structural understanding. The implications span from scientific discovery to engineering design, offering a new approach to artificial intelligence that mirrors how humans grasp the underlying unity of natural phenomena. Some key insights include: 1️⃣ Graph Isomorphism Neural Networks (GINs): GIN-style aggregation ensures structurally distinct graphs map to distinct embeddings, improving generalization and avoiding relational pattern collapse. 2️⃣ Category Theory Perspective: Transformers as functors preserve structural relationships. Sparse-GIN refines attention into sparse adjacency matrices, unifying domain knowledge across tasks. 3️⃣ Information Bottleneck & Sparsification: Sparsity reduces overfitting by filtering irrelevant edges, aligning with natural systems. Sparse-GIN outperforms dense attention by focusing on crucial connections. 4️⃣ Hierarchical Representation Learning: GIN-Attention captures multiscale patterns, mirroring structures like spider silk. Nested GINs model local and global dependencies across fields. 5️⃣ Practical Impact: Sparse-GIN enables domain-specific fine-tuning atop pre-trained Transformer foundation models, reducing the need for full retraining. Other impacts: ✅Real-World Relevance: Whether we are looking at protein structures, designing new materials, or working on social network analytics, graph-aware Transformers can capture subtle relational patterns traditional architectures may miss. ✅The juncture of graph isomorphism theory, category theory, and sparsification, these GIN-Transformers step beyond sequential modeling to tackle the relational nature of complex data. #Transformers #GraphNeuralNetworks #AI #MachineLearning #Isomorphism #CategoryTheory #ArtificialIntelligence #DeepLearning Link to paper & code in response ⤵️

Markus J. Buehler

19,416 Aufrufe • vor 1 Jahr

Agents who can buy, sell, and trade on our behalf are becoming a major part of the economy. But what exactly are they doing? Stripe sees 2% of global GDP, so they’re the company with the best view of what’s going on in the earliest innings of the agent economy. That’s why I had Emily Glassberg Sands, who leads data and AI at Stripe, on Every 📧’s AI & I. We covered: - Most of us still don’t trust AI with larger online purchases. People are hesitant to let AI make expensive purchases like a vacation or a couch—just like the early days of online shopping. But a superhero outfit for a kid who needs one stat? Sure, let the agent handle it. - Fraud is moving up the stack. It used to mean stolen credit cards. Now attackers are stealing free-trial tokens and compute credits. Free-trial abuse has 4x-ed in the last six months.. - AI is on both sides of fraud. Fraudsters are using it to scale attacks, while Stripe is using it to detect them. They’re blocking 250,000 fraudulent free trials a week for one large customer. - AI companies are growing faster than any cohort Stripe has ever tracked. Top companies hit $30M ARR in 18 months—3x faster than the 2018 SaaS class. So far, it’s net new spend instead of cannibalized software budgets. If you want to understand how AI is reshaping online commerce, this one deserves your time. Timestamps Introduction: 00:00:45 New rules for an agent-driven economy: 00:01:27 Compute theft is the new payment fraud: 00:03:57 How Stripe expanded fraud detection from checkout to the full customer lifecycle: 00:10:00 Why AI companies are scaling way faster than top SaaS companies: 00:19:48 Outcome-based billing is replacing seat-based pricing: 00:23:27 Where AI spending is coming from: 00:29:57 How the developer experience changes when agents are the builders: 00:36:45 The agentic commerce spectrum, from assisted buying to autonomous purchasing: 00:41:00 Meet Link, a consumer wallet for delegated agent purchases: 00:51:06
53:53

Agents who can buy, sell, and trade on our behalf are becoming a major part of the economy. But what exactly are they doing? Stripe sees 2% of global GDP, so they’re the company with the best view of what’s going on in the earliest innings of the agent economy. That’s why I had Emily Glassberg Sands, who leads data and AI at Stripe, on Every 📧’s AI & I. We covered: - Most of us still don’t trust AI with larger online purchases. People are hesitant to let AI make expensive purchases like a vacation or a couch—just like the early days of online shopping. But a superhero outfit for a kid who needs one stat? Sure, let the agent handle it. - Fraud is moving up the stack. It used to mean stolen credit cards. Now attackers are stealing free-trial tokens and compute credits. Free-trial abuse has 4x-ed in the last six months.. - AI is on both sides of fraud. Fraudsters are using it to scale attacks, while Stripe is using it to detect them. They’re blocking 250,000 fraudulent free trials a week for one large customer. - AI companies are growing faster than any cohort Stripe has ever tracked. Top companies hit $30M ARR in 18 months—3x faster than the 2018 SaaS class. So far, it’s net new spend instead of cannibalized software budgets. If you want to understand how AI is reshaping online commerce, this one deserves your time. Timestamps Introduction: 00:00:45 New rules for an agent-driven economy: 00:01:27 Compute theft is the new payment fraud: 00:03:57 How Stripe expanded fraud detection from checkout to the full customer lifecycle: 00:10:00 Why AI companies are scaling way faster than top SaaS companies: 00:19:48 Outcome-based billing is replacing seat-based pricing: 00:23:27 Where AI spending is coming from: 00:29:57 How the developer experience changes when agents are the builders: 00:36:45 The agentic commerce spectrum, from assisted buying to autonomous purchasing: 00:41:00 Meet Link, a consumer wallet for delegated agent purchases: 00:51:06

Dan Shipper 📧

19,436 Aufrufe • vor 3 Monaten

THIS GUY BUILT HIS ENTIRE COMMAND CENTER INSIDE OBSIDIAN USING A SINGLE CLAUDE INSTANCE AND NOW HE NEVER LEAVES IT He calls it handro operator Here’s what’s actually running inside: > Live Claude usage tracking (tokens, models and real-time costs) > A ClickUp task queue with Claude generated action suggestions that can be executed immediately > Full calendar sync > A custom intelligence feed for signals and news (AI video, Reddit, frontier research, searchable by keywords and time) > An Intelligence tab with Gmail highlights, Slack activity and Fireflies meeting transcripts > The ability to reply to emails and Slack messages directly from Obsidian without ever leaving the vault He built the whole thing in a single day: Mood board → defined the specs → dropped everything into Claude with one prompt → got a complete dashboard UI kit → fed it back into Claude → Claude mapped and connected everything automatically The article below goes deeper into how to reduce token usage when combining Claude with Obsidian Save both the post and the article - you might end up combining the ideas into a single product later
1:15

THIS GUY BUILT HIS ENTIRE COMMAND CENTER INSIDE OBSIDIAN USING A SINGLE CLAUDE INSTANCE AND NOW HE NEVER LEAVES IT He calls it handro operator Here’s what’s actually running inside: > Live Claude usage tracking (tokens, models and real-time costs) > A ClickUp task queue with Claude generated action suggestions that can be executed immediately > Full calendar sync > A custom intelligence feed for signals and news (AI video, Reddit, frontier research, searchable by keywords and time) > An Intelligence tab with Gmail highlights, Slack activity and Fireflies meeting transcripts > The ability to reply to emails and Slack messages directly from Obsidian without ever leaving the vault He built the whole thing in a single day: Mood board → defined the specs → dropped everything into Claude with one prompt → got a complete dashboard UI kit → fed it back into Claude → Claude mapped and connected everything automatically The article below goes deeper into how to reduce token usage when combining Claude with Obsidian Save both the post and the article - you might end up combining the ideas into a single product later

Bonsai 🌳

21,343 Aufrufe • vor 2 Monaten