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this is huge news from Cursor, they've pulled of the impossible and turned their ai-wrapper into a in-destructible moat sam altman literally called it cursor's ai agent harness is available for anyone to build on, which means ai models are now a commodity > 1-time install and now anyone...

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950,452 görüntüleme • 4 ay önce •via X (Twitter)

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You can now delegate tasks to Cursor directly from Notion. It's built on the Cursor SDK, so every cloud agent runs on the same models, harness, and runtime that power Cursor. @​Cursor on any spec or assign it a task to open a PR your whole team can review.
0:37

You can now delegate tasks to Cursor directly from Notion. It's built on the Cursor SDK, so every cloud agent runs on the same models, harness, and runtime that power Cursor. @​Cursor on any spec or assign it a task to open a PR your whole team can review.

Cursor

322,004 görüntüleme • 2 ay önce

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.
3:19

The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.

Vikram M

21,463 görüntüleme • 2 ay önce

introducing OpenBench v1, an open framework for measuring AI performance and efficiency for your codebase and use case. Companies are realizing that you can't simply tokenmaxx, and are frantically looking for better ways to use and measure AI use and efficiency. One approach is evidenced by the rise in model routers: OpenRouter, Cloudflare, Databricks, Vercel, and now Ramp to name a few. But they'll also need the ability to evaluate how agents are performing in their actual use cases and codebase. A good example is DoorDash's recent evals on their codebase and prs. This will become even more important as companies explore different model + harness combinations. OpenBench will be built to this direction, focusing on the cross between correctness vs efficiency in both token use and latency. Starting off, OpenBench makes it easy for anyone to add to the task set, and run a variety of harness + models. The framework also makes it easy to add any custom harness variant, for example I've been testing codex variants with ablations against the stock codex harness. OpenBench will also have tooling to help with discovering/adding reliable tasks from your repo. Today I have integrated harnesses: codex, claude, cursor, devin, grok build, pi, and eval'ed them for different models like gpt 5.6-sol, GLM 5.2, Kimi K3, Grok 4.5, etc. measuring correctness %, token use (in/out/cache), and latency.
0:20

introducing OpenBench v1, an open framework for measuring AI performance and efficiency for your codebase and use case. Companies are realizing that you can't simply tokenmaxx, and are frantically looking for better ways to use and measure AI use and efficiency. One approach is evidenced by the rise in model routers: OpenRouter, Cloudflare, Databricks, Vercel, and now Ramp to name a few. But they'll also need the ability to evaluate how agents are performing in their actual use cases and codebase. A good example is DoorDash's recent evals on their codebase and prs. This will become even more important as companies explore different model + harness combinations. OpenBench will be built to this direction, focusing on the cross between correctness vs efficiency in both token use and latency. Starting off, OpenBench makes it easy for anyone to add to the task set, and run a variety of harness + models. The framework also makes it easy to add any custom harness variant, for example I've been testing codex variants with ablations against the stock codex harness. OpenBench will also have tooling to help with discovering/adding reliable tasks from your repo. Today I have integrated harnesses: codex, claude, cursor, devin, grok build, pi, and eval'ed them for different models like gpt 5.6-sol, GLM 5.2, Kimi K3, Grok 4.5, etc. measuring correctness %, token use (in/out/cache), and latency.

Matthew Lam

123,006 görüntüleme • 1 ay önce

Elon Musk just made one if the biggest moves in taking over the programming industry “SpaceX just bought Cursor for $60 billion. Do you realize how big this is? SpaceX went public — the biggest IPO in history. $75 billion raised, almost a $2 trillion valuation and the first thing to do with that money? Buy the most popular AI coding tool on the planet. Here's why that changes everything. Elon now owns 3 layers: the compute, Colossus data centers, the models, Grok through xAI, and now the tool that developers actually use every day. It's the full stack. And here's what makes Cursor different from Claude Code or Codex. Cursor is model agnostic. You can run Claude in it, GPT, Gemini, whatever model you want. It's not locked to any one company, and now it has SpaceX's resources behind it. Cursor said they were bottlenecked by compute. Well, that bottleneck has just been removed. $4 billion in annual revenue, over half the Fortune 500 already uses it, and now it's backed by a $2 trillion company. OpenAI has Codex, Anthropic has Claude Code, and now Elon has Cursor.” Let me break this down in simple terms Elon Musk now controls more of the full AI picture: - Massive computers, power (data centers like Colossus) - Smart AI models (Grok from xAI) - The actual tool millions of developers use every day (Cursor) For every day users this means Faster and smarter apps and websites in the future. More developers using powerful AI tools means new apps, games, websites, and features get built quicker and cheaper. This means better video games, smoother streaming, smarter phone apps and better programs For Developers they can describe what they want in plain English (“make a feature that does X”) and the AI handles more of the heavy lifting
2:13

Elon Musk just made one if the biggest moves in taking over the programming industry “SpaceX just bought Cursor for $60 billion. Do you realize how big this is? SpaceX went public — the biggest IPO in history. $75 billion raised, almost a $2 trillion valuation and the first thing to do with that money? Buy the most popular AI coding tool on the planet. Here's why that changes everything. Elon now owns 3 layers: the compute, Colossus data centers, the models, Grok through xAI, and now the tool that developers actually use every day. It's the full stack. And here's what makes Cursor different from Claude Code or Codex. Cursor is model agnostic. You can run Claude in it, GPT, Gemini, whatever model you want. It's not locked to any one company, and now it has SpaceX's resources behind it. Cursor said they were bottlenecked by compute. Well, that bottleneck has just been removed. $4 billion in annual revenue, over half the Fortune 500 already uses it, and now it's backed by a $2 trillion company. OpenAI has Codex, Anthropic has Claude Code, and now Elon has Cursor.” Let me break this down in simple terms Elon Musk now controls more of the full AI picture: - Massive computers, power (data centers like Colossus) - Smart AI models (Grok from xAI) - The actual tool millions of developers use every day (Cursor) For every day users this means Faster and smarter apps and websites in the future. More developers using powerful AI tools means new apps, games, websites, and features get built quicker and cheaper. This means better video games, smoother streaming, smarter phone apps and better programs For Developers they can describe what they want in plain English (“make a feature that does X”) and the AI handles more of the heavy lifting

Wall Street Apes

213,601 görüntüleme • 2 ay önce

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.
11:39

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

Santiago

155,647 görüntüleme • 1 yıl önce

Anthropic Technical Staff Jess Yan says the harness and the model cannot be pulled apart without giving up performance, though she flags her own bias. "I think I am quite biased but I also think that it is impossible to get the maximum possible performance without tying together the harness and the model." "Now the components of the harness and maybe the thickness of the harness will change over time as models get more and more capable." "However, when we test our models and when we are assessing their performance we always have to test it in conjunction with a harness." "And are we going to test it with all the different harnesses of the world? We're going to select the harnesses that we have built." "And so there is an aspect of the necessity of building models is that you have to be testing them with harnesses, and that sort of keeps them paired together."
0:46

Anthropic Technical Staff Jess Yan says the harness and the model cannot be pulled apart without giving up performance, though she flags her own bias. "I think I am quite biased but I also think that it is impossible to get the maximum possible performance without tying together the harness and the model." "Now the components of the harness and maybe the thickness of the harness will change over time as models get more and more capable." "However, when we test our models and when we are assessing their performance we always have to test it in conjunction with a harness." "And are we going to test it with all the different harnesses of the world? We're going to select the harnesses that we have built." "And so there is an aspect of the necessity of building models is that you have to be testing them with harnesses, and that sort of keeps them paired together."

Fireside Alpha

584,074 görüntüleme • 1 ay önce

$60 BILLION FROM 4 MIT STUDENTS In 2022, four MIT classmates started Cursor. Four years later, SpaceX just completed a $60 BILLION acquisition of their company. This might be one of the craziest startup speedruns ever. - Cursor reached ~$2.6B in annualized enterprise revenue - It went from a VS Code fork to one of the biggest AI coding tools - SpaceX gets Cursor's talent, developer distribution and coding ecosystem - Cursor gets massive SpaceXAI compute and is already helping build Grok 4 people. 4 years. $60 billion. AI is creating giants faster than we've ever seen before.
1:56

$60 BILLION FROM 4 MIT STUDENTS In 2022, four MIT classmates started Cursor. Four years later, SpaceX just completed a $60 BILLION acquisition of their company. This might be one of the craziest startup speedruns ever. - Cursor reached ~$2.6B in annualized enterprise revenue - It went from a VS Code fork to one of the biggest AI coding tools - SpaceX gets Cursor's talent, developer distribution and coding ecosystem - Cursor gets massive SpaceXAI compute and is already helping build Grok 4 people. 4 years. $60 billion. AI is creating giants faster than we've ever seen before.

BuBBliK

46,105 görüntüleme • 21 gün önce

The new SpaceX-Cursor model is going to be insane. It is being trained from scratch, unlike previous Composer models, which were built off Kimi. This new model is Elon’s attempt to get back in the AI race, and he might rejoin as the frontrunner. Composer 2.5 is already a world-class model, only losing to models like GPT-5.5, Opus 4.8, and Fable 5. Grok Composer 3, with SpaceX’s massive compute and xAI’s team, could become the best AI model on the planet. Elon is back.
5:01

The new SpaceX-Cursor model is going to be insane. It is being trained from scratch, unlike previous Composer models, which were built off Kimi. This new model is Elon’s attempt to get back in the AI race, and he might rejoin as the frontrunner. Composer 2.5 is already a world-class model, only losing to models like GPT-5.5, Opus 4.8, and Fable 5. Grok Composer 3, with SpaceX’s massive compute and xAI’s team, could become the best AI model on the planet. Elon is back.

Ali Romman

119,837 görüntüleme • 2 ay önce

WTFFF, this is absolutely insane 😳 i replaced my $200/mo Claude plan for deepseek harness and it costs nothing deepseek dropped their agent harness 2 days ago. 99.7K stars already everything is a plugin. MIT. FREE and you can run it for $0; free models, no credit card here is how you set it up: 1. install node.js 2. run npx deepseek-ai/dsh web 3. open 4. settings → add custom provider → free model (any free model provider) done. full agent harness for $0 dev preview, expect breaking changes try it before they add upgrades
0:41

WTFFF, this is absolutely insane 😳 i replaced my $200/mo Claude plan for deepseek harness and it costs nothing deepseek dropped their agent harness 2 days ago. 99.7K stars already everything is a plugin. MIT. FREE and you can run it for $0; free models, no credit card here is how you set it up: 1. install node.js 2. run npx deepseek-ai/dsh web 3. open 4. settings → add custom provider → free model (any free model provider) done. full agent harness for $0 dev preview, expect breaking changes try it before they add upgrades

painn

193,177 görüntüleme • 21 gün önce

Enterprise AI is in a wildly paradoxical state 🤔, and we’re reaching the inflection point that will resolve it. 💥 Enterprises want to differentiate with AI, yet rent the same intelligence as their competitors. Their workflows and expertise are highly specialized, yet they rely on generic models built to be good at everything. They worry about AI costs, yet pay premium prices for massive models where only a fraction (1%) of the intelligence is relevant to their task. 💸 And they demand control and sovereignty, yet rent the intelligence becoming core to their business. This is not a sustainable equilibrium. The next era of enterprise AI is specialized intelligence companies build, own, and compound. And we are at the inflection point of this transition. That’s the bet we made when we started Oumi two years ago. Today we’re closing the loop: Oumi can now not only automatically build your specialized AI models, but also deploy them into production, learn from their production experience, and continuously improve them. The intelligence that your business runs on, becomes your differentiator. Your compounding advantage. The winners of the next AI era will turn their own data, expertise, and experience into specialized intelligence that nobody else can rent. Don’t rent your AI. Build it. Own it. Compound it.
0:43

Enterprise AI is in a wildly paradoxical state 🤔, and we’re reaching the inflection point that will resolve it. 💥 Enterprises want to differentiate with AI, yet rent the same intelligence as their competitors. Their workflows and expertise are highly specialized, yet they rely on generic models built to be good at everything. They worry about AI costs, yet pay premium prices for massive models where only a fraction (1%) of the intelligence is relevant to their task. 💸 And they demand control and sovereignty, yet rent the intelligence becoming core to their business. This is not a sustainable equilibrium. The next era of enterprise AI is specialized intelligence companies build, own, and compound. And we are at the inflection point of this transition. That’s the bet we made when we started Oumi two years ago. Today we’re closing the loop: Oumi can now not only automatically build your specialized AI models, but also deploy them into production, learn from their production experience, and continuously improve them. The intelligence that your business runs on, becomes your differentiator. Your compounding advantage. The winners of the next AI era will turn their own data, expertise, and experience into specialized intelligence that nobody else can rent. Don’t rent your AI. Build it. Own it. Compound it.

Manos Koukoumidis

1,002,111 görüntüleme • 24 gün önce

Cursor x E2B Many of our customers use E2B in their internal development workflows. Some work in regulated industries where code and internal services can’t leave the company. For teams that need control over the execution environment, Cursor’s cloud agents can now use E2B as their machine. With Self-Hosted Machines, Cursor handles the agent loop while E2B runs the code. Teams can give agents bigger machines and start them from custom E2B templates, so their Cursor workloads boot straight into an environment with internal dependencies already in place. Try it:
0:41

Cursor x E2B Many of our customers use E2B in their internal development workflows. Some work in regulated industries where code and internal services can’t leave the company. For teams that need control over the execution environment, Cursor’s cloud agents can now use E2B as their machine. With Self-Hosted Machines, Cursor handles the agent loop while E2B runs the code. Teams can give agents bigger machines and start them from custom E2B templates, so their Cursor workloads boot straight into an environment with internal dependencies already in place. Try it:

Vasek Mlejnsky

71,080 görüntüleme • 2 gün önce

.martin_casado on the culture that makes Cursor iterate faster than any other AI company he's worked with: "A lot of these AI companies I work with are so research-heavy. If you're not working on a new model architecture, it's kind of hard to actually interest the core teams." "Cursor always kept the main thing the main thing, which is changing how you write software engineering. They believed fundamentally it was a product problem, not a model architecture problem." "They hired incredibly well. They set culture incredibly well. The founders probably spend 30, 40% of their time hiring and setting culture. They were very focused on engineering, not research and model architecture." "It's just wild in this space. There's actually not a lot of companies focused on product. They'll focus on services. A lot of the fine-tuning, post-training companies are more service-oriented. Neo labs are research companies. But Cursor said, we are a product company. That's actually a key differentiator in this era." a16z Cursor
1:23

.martin_casado on the culture that makes Cursor iterate faster than any other AI company he's worked with: "A lot of these AI companies I work with are so research-heavy. If you're not working on a new model architecture, it's kind of hard to actually interest the core teams." "Cursor always kept the main thing the main thing, which is changing how you write software engineering. They believed fundamentally it was a product problem, not a model architecture problem." "They hired incredibly well. They set culture incredibly well. The founders probably spend 30, 40% of their time hiring and setting culture. They were very focused on engineering, not research and model architecture." "It's just wild in this space. There's actually not a lot of companies focused on product. They'll focus on services. A lot of the fine-tuning, post-training companies are more service-oriented. Neo labs are research companies. But Cursor said, we are a product company. That's actually a key differentiator in this era." a16z Cursor

MTS

14,592 görüntüleme • 15 gün önce

Sam Altman recently said: “GPT-OSS has strong real-world performance comparable to o4-mini—and you can run it locally on your phone.” Many believed running a 20B-parameter model on mobile devices was still years away. At Nexa AI, we’ve built our foundation on deep on-device AI technology—turning that vision into reality. Today, GPT-OSS is running fully local on mobile devices through our app, Nexa Studio. Real performance on Snapdragon Gen 5: - 17 tokens/sec decoding speed - < 3 seconds Time-to-First-Token Developers can now use NexaSDK to build their own local AI apps powered by GPT-OSS. What this unlocks: - Real reasoning models running locally - The next wave of AI Agent use cases—without cloud limits - True data privacy: your data never leaves your device Appreciation to OpenAI Sam Altman Greg Brockman and the entire leadership team for pushing the boundaries of the open-source AI community. Thanks to Qualcomm for the partnership, and to Manoj Khilnani and Chun-Po (Jerry) Chang for the incredible collaboration.
0:31

Sam Altman recently said: “GPT-OSS has strong real-world performance comparable to o4-mini—and you can run it locally on your phone.” Many believed running a 20B-parameter model on mobile devices was still years away. At Nexa AI, we’ve built our foundation on deep on-device AI technology—turning that vision into reality. Today, GPT-OSS is running fully local on mobile devices through our app, Nexa Studio. Real performance on Snapdragon Gen 5: - 17 tokens/sec decoding speed - < 3 seconds Time-to-First-Token Developers can now use NexaSDK to build their own local AI apps powered by GPT-OSS. What this unlocks: - Real reasoning models running locally - The next wave of AI Agent use cases—without cloud limits - True data privacy: your data never leaves your device Appreciation to OpenAI Sam Altman Greg Brockman and the entire leadership team for pushing the boundaries of the open-source AI community. Thanks to Qualcomm for the partnership, and to Manoj Khilnani and Chun-Po (Jerry) Chang for the incredible collaboration.

NEXA AI

197,453 görüntüleme • 11 ay önce

Sam Altman says AI models are general-purpose reasoning engines with a huge world knowledge built into them The next milestone is letting a model see something totally new once, then use it reliably and learn it faster than humans "that doesn't feel very far away"
0:59

Sam Altman says AI models are general-purpose reasoning engines with a huge world knowledge built into them The next milestone is letting a model see something totally new once, then use it reliably and learn it faster than humans "that doesn't feel very far away"

Haider.

20,500 görüntüleme • 7 ay önce

🎉Introducing PyFlue: The Python-Native Agent Harness Framework.🧰 💡Flue for Python: Fred K. Schott fks CEO of HTML has launched Flue: The Agent Harness Framework for TypeScript. It brings programmable harness right into your agents rather than DIY plumbing. Python ecosystem already has 🦾 powerful AI/ML tools and frameworks and research initiatives but most frameworks asked users to build your own harness. Superagentic AI bringing this concept of Flue to Python 🐍 ecosystem. Here is PyFlue even even better 🤖 Agent = Model 💻 + Harness 🧰 + Memory 🧠 Almost all the feature of Flue plugged with LangChain Deepagents harness built by Harrison Chase and team Viv Sydney Runkle and more coming soon. 👉 Stop building agent loops, start using a harness. 💻 Try PyFlue Now : ⭐️ GitHub: 📚Docs: 📙 Blog Post: #HarnessEngineering #AgentHarnesses
0:39

🎉Introducing PyFlue: The Python-Native Agent Harness Framework.🧰 💡Flue for Python: Fred K. Schott fks CEO of HTML has launched Flue: The Agent Harness Framework for TypeScript. It brings programmable harness right into your agents rather than DIY plumbing. Python ecosystem already has 🦾 powerful AI/ML tools and frameworks and research initiatives but most frameworks asked users to build your own harness. Superagentic AI bringing this concept of Flue to Python 🐍 ecosystem. Here is PyFlue even even better 🤖 Agent = Model 💻 + Harness 🧰 + Memory 🧠 Almost all the feature of Flue plugged with LangChain Deepagents harness built by Harrison Chase and team Viv Sydney Runkle and more coming soon. 👉 Stop building agent loops, start using a harness. 💻 Try PyFlue Now : ⭐️ GitHub: 📚Docs: 📙 Blog Post: #HarnessEngineering #AgentHarnesses

Shashi

69,884 görüntüleme • 4 ay önce

Perplexity CEO Aravind Srinivas on the brutal truth about who actually makes money in AI (and why it's not who you think): Aravind argues that the real value in AI comes from orchestration. He points to products like Codex, Claude Code, and Perplexity Computer: "What is that? It's an orchestration system. It takes a model, pairs it with an agent harness." And what is an agent harness? "The simplest way of describing it is like rules for how the agent loop should run. What are all the skills and sub-agents and connectors and tools it accesses? Without the harness, you don't necessarily capture and convert the intrinsic intelligence in the model into valuable output tokens." This leads to a blunt conclusion about who has a real business in AI, and who doesn't: "If you're literally just a reseller of model tokens, you have no business, because the model will get commoditized. So even if you're a model builder, you don't have a business. As an infra layer, you have some business on serving those output tokens. But as an application layer or model builder, you don't really have a business if you're just a reseller of tokens that come directly out of the model." So where does the value accrue? "You have a business if you know how to take the model, ground it in valuable context, orchestrate it with a really good agent harness, connected to the right set of tools and connectors (whether it's personal connectors or business connectors) and provide the experience to people in one single unified system." Aravind Srinivas then explains Perplexity's specific edge: Beyond orchestrating across tools, files, and connectors, they also orchestrate across models. "That is the differentiation that Anthropic and OpenAI cannot claim, because you wouldn't find GPT-5 inside the Claude Code harness. You wouldn't find Claude Opus inside the Codex harness. These are competing with each other. Whereas you would find both these models inside Perplexity Computer." Why does this matter? Because it all comes down to power. In Aravind's framing, the fundamental cost driver in AI is watts (the one input nobody can subsidize except the government). "Whoever provides the most valuable output tokens with the least amount of power expended to produce them generates the greatest value to the end user, has the most pricing power, has the most value. That is the orchestration problem to solve." His conclusion: "The one single most important metric in AI is token value per watt per user."
2:50

Perplexity CEO Aravind Srinivas on the brutal truth about who actually makes money in AI (and why it's not who you think): Aravind argues that the real value in AI comes from orchestration. He points to products like Codex, Claude Code, and Perplexity Computer: "What is that? It's an orchestration system. It takes a model, pairs it with an agent harness." And what is an agent harness? "The simplest way of describing it is like rules for how the agent loop should run. What are all the skills and sub-agents and connectors and tools it accesses? Without the harness, you don't necessarily capture and convert the intrinsic intelligence in the model into valuable output tokens." This leads to a blunt conclusion about who has a real business in AI, and who doesn't: "If you're literally just a reseller of model tokens, you have no business, because the model will get commoditized. So even if you're a model builder, you don't have a business. As an infra layer, you have some business on serving those output tokens. But as an application layer or model builder, you don't really have a business if you're just a reseller of tokens that come directly out of the model." So where does the value accrue? "You have a business if you know how to take the model, ground it in valuable context, orchestrate it with a really good agent harness, connected to the right set of tools and connectors (whether it's personal connectors or business connectors) and provide the experience to people in one single unified system." Aravind Srinivas then explains Perplexity's specific edge: Beyond orchestrating across tools, files, and connectors, they also orchestrate across models. "That is the differentiation that Anthropic and OpenAI cannot claim, because you wouldn't find GPT-5 inside the Claude Code harness. You wouldn't find Claude Opus inside the Codex harness. These are competing with each other. Whereas you would find both these models inside Perplexity Computer." Why does this matter? Because it all comes down to power. In Aravind's framing, the fundamental cost driver in AI is watts (the one input nobody can subsidize except the government). "Whoever provides the most valuable output tokens with the least amount of power expended to produce them generates the greatest value to the end user, has the most pricing power, has the most value. That is the orchestration problem to solve." His conclusion: "The one single most important metric in AI is token value per watt per user."

Big Brain AI

42,319 görüntüleme • 1 ay önce

We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!
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We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!

Jasper

161,741 görüntüleme • 1 yıl önce