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Every dApp using AI today calls out to OpenAI or Anthropic. That single API call is a centralised control point. Somnia put LLM inference inside consensus instead, running it across validators. Here's the full conversation which paul.somi had with Ruthy | Surgence Labs 👇🏻

Surgence Labs

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Every share card a user posts is a billboard the protocol never paid for. Build something the community feels proud to share, and the brand wins every single time it shows up. Here's Ruthy | Surgence Labs on the marketing principle hiding inside launches we execute for our partners 👇🏻

Surgence Labs

17,594 views • 3 months ago

LFG! Nous Research's Hermes Agent is live on the Umbrel App Store! It's an AI agent that gets smarter the longer it runs, remembers stuff with persistent memory across sessions, learns your workflow, and autonomously creates its own reusable skills from experience. And it's sandboxed like every Umbrel app so it can't touch anything it's not supposed to. 1-click install. Login with ChatGPT, use an OpenRouter/OpenAI/Anthropic API key, or point it to a local Ollama model running on the same Umbrel.

LFG! Nous Research's Hermes Agent is live on the Umbrel App Store! It's an AI agent that gets smarter the longer it runs, remembers stuff with persistent memory across sessions, learns your workflow, and autonomously creates its own reusable skills from experience. And it's sandboxed like every Umbrel app so it can't touch anything it's not supposed to. 1-click install. Login with ChatGPT, use an OpenRouter/OpenAI/Anthropic API key, or point it to a local Ollama model running on the same Umbrel.

Umbrel ☂️

29,882 views • 4 months ago

Anthropic won't like this open-source repo. It is going to cost LLM providers a lot of money. Every CI run of an AI app today sends real requests to providers like OpenAI or Anthropic. Like any other LLM call, this too gets billed at actual API rates. So for teams with high commit volumes, this accumulates into a meaningful chunk of API spend. One common hack devs use is that instead of invoking the LLM API, the test calls a fake local server that speaks the same API and returns a dummy response. The catch is that the dummy response is a copy of what the provider returned on the day it was saved, and providers keep adding fields and changing types. So the tests keep passing against a schema that's no longer valid, while the real integration breaks in production. A smart approach is now actually implemented in CopilotKit🪁's recently open-sourced aimock project. Every day, the repo's own CI sends a handful of requests to the real API and the same requests to the fake server, then compares both against the official client library's type definitions. Those are the only real API calls in the whole setup, and they run on the repo's own keys, not in anyone else's CI. A single team can push hundreds of commits a day, and thousands of teams are already doing that with coding agents. All of those runs stay offline, because one repo checks against the real API on everyone's behalf. When a check fails, a coding agent updates aimock's built-in response schema, the full test suite has to pass, and a patch version ships to npm. By simply upgrading the package, the corrected schema gets reflected in every project using it. The capability is not just limited to a single provider. The same server works for Claude, OpenAI, Gemini, Bedrock, Azure, Ollama, plus MCP tools, A2A agents, AG-UI event streams, vector DBs like Pinecone and Qdrant, and search, speech, image, and video endpoints. Here's the repo: (don't forget to star it ⭐) That said, mocking your API calls is one thing. AI engineers should also know how to test agents properly in the first place, which several teams still skip. I wrote a full walkthrough on that, covering build, testing, evals, tracing, and deployment. Read it below.

Anthropic won't like this open-source repo. It is going to cost LLM providers a lot of money. Every CI run of an AI app today sends real requests to providers like OpenAI or Anthropic. Like any other LLM call, this too gets billed at actual API rates. So for teams with high commit volumes, this accumulates into a meaningful chunk of API spend. One common hack devs use is that instead of invoking the LLM API, the test calls a fake local server that speaks the same API and returns a dummy response. The catch is that the dummy response is a copy of what the provider returned on the day it was saved, and providers keep adding fields and changing types. So the tests keep passing against a schema that's no longer valid, while the real integration breaks in production. A smart approach is now actually implemented in CopilotKit🪁's recently open-sourced aimock project. Every day, the repo's own CI sends a handful of requests to the real API and the same requests to the fake server, then compares both against the official client library's type definitions. Those are the only real API calls in the whole setup, and they run on the repo's own keys, not in anyone else's CI. A single team can push hundreds of commits a day, and thousands of teams are already doing that with coding agents. All of those runs stay offline, because one repo checks against the real API on everyone's behalf. When a check fails, a coding agent updates aimock's built-in response schema, the full test suite has to pass, and a patch version ships to npm. By simply upgrading the package, the corrected schema gets reflected in every project using it. The capability is not just limited to a single provider. The same server works for Claude, OpenAI, Gemini, Bedrock, Azure, Ollama, plus MCP tools, A2A agents, AG-UI event streams, vector DBs like Pinecone and Qdrant, and search, speech, image, and video endpoints. Here's the repo: (don't forget to star it ⭐) That said, mocking your API calls is one thing. AI engineers should also know how to test agents properly in the first place, which several teams still skip. I wrote a full walkthrough on that, covering build, testing, evals, tracing, and deployment. Read it below.

Akshay 🚀

62,821 views • 15 days ago

I used to dread heavy testing days because every API call felt like watching money disappear in real time. Found a backend built to run flat instead of per call, made specifically for agent work. Pointed my Claude Code setup at it and had it running in under 5 minutes. Here's the full run:

I used to dread heavy testing days because every API call felt like watching money disappear in real time. Found a backend built to run flat instead of per call, made specifically for agent work. Pointed my Claude Code setup at it and had it running in under 5 minutes. Here's the full run:

Z-Coder

60,855 views • 2 months ago

SF Compute CEO evan conrad on his contrarian thesis that OpenAI and Anthropic won't eat the world, everyone will become OpenAI and Anthropic instead: "Our impression is not that OpenAI and Anthropic will eat the world, it's that everyone will look like OpenAI and Anthropic. That's simply the way that companies work now." "Every single company in the world that is of meaningful scale will look like a frontier lab. There is this belief that the large labs have won it all, and they've got this compounding advantage. The default assumption of that is assuming nobody else will do anything." "The bottleneck was there's 100 people, and they know how to train the models. Or you thought, the problem is compute. The problem of compute is credit. And OpenAI and Anthropic are not unique to having credit. In fact, they don't have as good credit as other people." "If you assume the problem is not the researchers, that that problem's basically gone away the moment you got to auto research, then what you should assume is that pretty much every other company is going to swap the way they work to look like a frontier lab. It won't be OpenAI and Anthropic take the whole world. It's that everyone's OpenAI and Anthropic now." San Francisco Compute

SF Compute CEO evan conrad on his contrarian thesis that OpenAI and Anthropic won't eat the world, everyone will become OpenAI and Anthropic instead: "Our impression is not that OpenAI and Anthropic will eat the world, it's that everyone will look like OpenAI and Anthropic. That's simply the way that companies work now." "Every single company in the world that is of meaningful scale will look like a frontier lab. There is this belief that the large labs have won it all, and they've got this compounding advantage. The default assumption of that is assuming nobody else will do anything." "The bottleneck was there's 100 people, and they know how to train the models. Or you thought, the problem is compute. The problem of compute is credit. And OpenAI and Anthropic are not unique to having credit. In fact, they don't have as good credit as other people." "If you assume the problem is not the researchers, that that problem's basically gone away the moment you got to auto research, then what you should assume is that pretty much every other company is going to swap the way they work to look like a frontier lab. It won't be OpenAI and Anthropic take the whole world. It's that everyone's OpenAI and Anthropic now." San Francisco Compute

MTS

133,799 views • 6 days ago

With companies like SpaceX, Anthropic, and OpenAI potentially heading toward IPOs, investors are already debating which one will be the biggest winner. My view is that trying to pick a single champion is a mistake. SpaceX gives you exposure to Elon Musk and the success of Starlink, which is already a highly profitable business. Anthropic has impressed me personally after rolling out Claude across my organization, it quickly became a tool I use every day. And OpenAI remains one of the most important players in the space. The reality is nobody knows how this race will play out once these companies are public. That’s why I’d rather own all of them on a diversified basis instead of trying to predict a winner. When a sector is moving this fast, the smarter bet is often on the entire trend, not a single stock.

With companies like SpaceX, Anthropic, and OpenAI potentially heading toward IPOs, investors are already debating which one will be the biggest winner. My view is that trying to pick a single champion is a mistake. SpaceX gives you exposure to Elon Musk and the success of Starlink, which is already a highly profitable business. Anthropic has impressed me personally after rolling out Claude across my organization, it quickly became a tool I use every day. And OpenAI remains one of the most important players in the space. The reality is nobody knows how this race will play out once these companies are public. That’s why I’d rather own all of them on a diversified basis instead of trying to predict a winner. When a sector is moving this fast, the smarter bet is often on the entire trend, not a single stock.

Kevin O'Leary aka Mr. Wonderful

34,397 views • 2 months ago

Today we're launching Mars, a new kind of X-ray system from Lumafield. It bolts onto an existing conveyor and inspects every single unit at full production speed—up to 2.5 feet per second—seeing inside parts and using AI to analyze what it finds. Here's why that matters…🧵

Today we're launching Mars, a new kind of X-ray system from Lumafield. It bolts onto an existing conveyor and inspects every single unit at full production speed—up to 2.5 feet per second—seeing inside parts and using AI to analyze what it finds. Here's why that matters…🧵

Jon Bruner

697,543 views • 1 month ago

🇮🇱 🇮🇷 Israeli opposition leader Yair Lapid calls Netanyahu's Iran campaign a failure: "After six weeks of dead and wounded and running to shelters, it turned out that Netanyahu is incapable of winning any campaign." When even the Israeli opposition, which supported the war from the beginning with "rare consensus," is calling it a loss, that tells you how the mood has shifted inside Israel.

🇮🇱 🇮🇷 Israeli opposition leader Yair Lapid calls Netanyahu's Iran campaign a failure: "After six weeks of dead and wounded and running to shelters, it turned out that Netanyahu is incapable of winning any campaign." When even the Israeli opposition, which supported the war from the beginning with "rare consensus," is calling it a loss, that tells you how the mood has shifted inside Israel.

Mario Nawfal

146,730 views • 4 months ago

$PLTR is one of the cleanest ways to play the AI cycle because it benefits from the entire infrastructure buildout without financing the data centers, chips, memory, power or networking underneath it. Palantir is also emerging as one of the biggest sovereign AI winners by building the control layer that determines what AI understands, which proprietary data it can access and what decisions or actions it can influence inside an organization. OpenAI and Anthropic create the intelligence while Palantir connects it to permissions, workflows and real operating systems where measurable economic value is produced.

$PLTR is one of the cleanest ways to play the AI cycle because it benefits from the entire infrastructure buildout without financing the data centers, chips, memory, power or networking underneath it. Palantir is also emerging as one of the biggest sovereign AI winners by building the control layer that determines what AI understands, which proprietary data it can access and what decisions or actions it can influence inside an organization. OpenAI and Anthropic create the intelligence while Palantir connects it to permissions, workflows and real operating systems where measurable economic value is produced.

Shay Boloor

200,100 views • 28 days ago

Firecrawl open-sourced "web-agent," a new framework that allows developers to build AI agents capable of autonomously executing "search-scrape-interact" loops on the live web. By running a single command, developers can scaffold a complete project with streaming UI, API, or library templates. The framework uses a "Plan-Act" mechanism and can spin up parallel sub-agents to process multiple websites simultaneously. It is entirely model-agnostic, allowing you to plug in OpenAI, Anthropic (with recent builds defaulting to Claude Opus 4.7), or your own self-hosted models.

Firecrawl open-sourced "web-agent," a new framework that allows developers to build AI agents capable of autonomously executing "search-scrape-interact" loops on the live web. By running a single command, developers can scaffold a complete project with streaming UI, API, or library templates. The framework uses a "Plan-Act" mechanism and can spin up parallel sub-agents to process multiple websites simultaneously. It is entirely model-agnostic, allowing you to plug in OpenAI, Anthropic (with recent builds defaulting to Claude Opus 4.7), or your own self-hosted models.

Wes Roth

53,653 views • 4 months ago

Elon Musk on how his actions unintentionally accelerated AI: “I guess the only thing we can try to do is minimize the probability of a bad outcome. For the longest time, I declined to participate in AI. I created OpenAI essentially as a counterweight to Google because, at the time, they were more or less a monopoly on AI. Then Anthropic spun out of OpenAI. If anything, it seems like my actions have really accelerated AI. I wasn’t aiming to accelerate AI, but creating OpenAI, and then having Anthropic spin out of it, resulted in knock-on effects that accelerated AI, which wasn’t really my intention. It just seems like all roads lead to the acceleration of AI. So then I’m like, okay, you can either be sad about it or join the club.”

Elon Musk on how his actions unintentionally accelerated AI: “I guess the only thing we can try to do is minimize the probability of a bad outcome. For the longest time, I declined to participate in AI. I created OpenAI essentially as a counterweight to Google because, at the time, they were more or less a monopoly on AI. Then Anthropic spun out of OpenAI. If anything, it seems like my actions have really accelerated AI. I wasn’t aiming to accelerate AI, but creating OpenAI, and then having Anthropic spin out of it, resulted in knock-on effects that accelerated AI, which wasn’t really my intention. It just seems like all roads lead to the acceleration of AI. So then I’m like, okay, you can either be sad about it or join the club.”

DogeDesigner

35,071 views • 1 month ago

My husband was one of the service members put at risk by the signal chats that Sen. Mullin is now LYING about in this video he filmed instead of returning my calls. So here's a snippet of my convo with his office today. I meant every word of this. I expect a call, Senator.

My husband was one of the service members put at risk by the signal chats that Sen. Mullin is now LYING about in this video he filmed instead of returning my calls. So here's a snippet of my convo with his office today. I meant every word of this. I expect a call, Senator.

Kendall Brown

4,456,468 views • 1 year ago

OpenAI just admitted Anthropic is KILLING their business. Their own applications chief told employees it was a "code red." Said Anthropic was a "wake-up call." Then admitted OpenAI had been "spreading efforts across too many apps" and it was "slowing them down." This is an internal confession. Here's why Anthropic is eating up OpenAI: 12 months ago, OpenAI owned 50% of all enterprise AI spending. Today it's just 27%. Anthropic went from nearly ZERO to winning 70% of every first-time enterprise AI deal. Seven out of ten companies buying AI tools for the first time are choosing Claude over ChatGPT. A year ago, one in 25 businesses on Ramp paid for Anthropic. Today it's one in four. OpenAI just had its biggest single-month adoption decline ever recorded. And Anthropic literally charges MORE than OpenAI for roughly the same performance. And businesses are STILL choosing them. In enterprise software, that never happens. The cheaper product usually wins. But Claude became something OpenAI never figured out how to be: Cool. Celebrities publicly switched to Claude. Senators are tweeting about using it. Engineers are shipping entire products with Claude Code in hours that used to take weeks. It started to became an identity signal. Like blue bubble vs green bubble in iMessage. Choosing Claude says something about you now. Meanwhile OpenAI went the opposite direction: They took the Pentagon contract that Anthropic refused. Greg Brockman donated $25 million to fund wars. ChatGPT uninstalls jumped 295% in a single day. Reddit posts saying "Cancel and Delete ChatGPT" got 30,000 upvotes. Anthropic said no to mass surveillance and autonomous weapons. Got blacklisted by the Pentagon. Trump called them a "Radical Left AI company." And their downloads went to #1 on the App Store the next day. Turns out refusing to build weapons is good marketing. But the real damage isn't consumer downloads. It's the MONEY. Claude Code hit $2.5 billion in annual revenue in six months. OpenAI's competing product Codex just barely crossed $1 billion. And Anthropic literally cannot meet demand. They're turning away paying customers because they don't have enough compute to serve them. A company REJECTING revenue because it's growing too fast. While OpenAI scrambles to consolidate. Last week OpenAI announced they're merging ChatGPT, Codex, and their browser into one "superapp." But what this really means: "We launched too many products, none of them worked well enough alone, so now we're cramming everything together and hoping it sticks." And remember their video tool Sora? Launched standalone. Hit #1 on the App Store. Usage flatlined within weeks. Now they're forced to shut it down. Their browser Atlas? Still hasn't launched publicly. Their IPO? Polymarket odds dropped from 55% to 35%. OpenAI has 900 million users. Anthropic has maybe 10 million daily actives. But here's the thing... OpenAI won the consumer war. ChatGPT is where your mom asks about recipes and your cousin makes memes. Anthropic won the war that actually MATTERS. The developers. The engineers. The enterprises writing 7 figure checks. OpenAI built the biggest chatbot on Earth. Anthropic built the tool that companies can't stop paying for. This is Yahoo vs Google all over again. Yahoo had the users. Google had the product. And we all know how that ended. OpenAI has 12 months to prove the superapp works, land the IPO, and stop the enterprise bleeding. If they can't, the most valuable startup in history becomes the most cautionary tale in tech. 900 million users don't mean anything if the people who actually pay are walking out the door. What do you think?

OpenAI just admitted Anthropic is KILLING their business. Their own applications chief told employees it was a "code red." Said Anthropic was a "wake-up call." Then admitted OpenAI had been "spreading efforts across too many apps" and it was "slowing them down." This is an internal confession. Here's why Anthropic is eating up OpenAI: 12 months ago, OpenAI owned 50% of all enterprise AI spending. Today it's just 27%. Anthropic went from nearly ZERO to winning 70% of every first-time enterprise AI deal. Seven out of ten companies buying AI tools for the first time are choosing Claude over ChatGPT. A year ago, one in 25 businesses on Ramp paid for Anthropic. Today it's one in four. OpenAI just had its biggest single-month adoption decline ever recorded. And Anthropic literally charges MORE than OpenAI for roughly the same performance. And businesses are STILL choosing them. In enterprise software, that never happens. The cheaper product usually wins. But Claude became something OpenAI never figured out how to be: Cool. Celebrities publicly switched to Claude. Senators are tweeting about using it. Engineers are shipping entire products with Claude Code in hours that used to take weeks. It started to became an identity signal. Like blue bubble vs green bubble in iMessage. Choosing Claude says something about you now. Meanwhile OpenAI went the opposite direction: They took the Pentagon contract that Anthropic refused. Greg Brockman donated $25 million to fund wars. ChatGPT uninstalls jumped 295% in a single day. Reddit posts saying "Cancel and Delete ChatGPT" got 30,000 upvotes. Anthropic said no to mass surveillance and autonomous weapons. Got blacklisted by the Pentagon. Trump called them a "Radical Left AI company." And their downloads went to #1 on the App Store the next day. Turns out refusing to build weapons is good marketing. But the real damage isn't consumer downloads. It's the MONEY. Claude Code hit $2.5 billion in annual revenue in six months. OpenAI's competing product Codex just barely crossed $1 billion. And Anthropic literally cannot meet demand. They're turning away paying customers because they don't have enough compute to serve them. A company REJECTING revenue because it's growing too fast. While OpenAI scrambles to consolidate. Last week OpenAI announced they're merging ChatGPT, Codex, and their browser into one "superapp." But what this really means: "We launched too many products, none of them worked well enough alone, so now we're cramming everything together and hoping it sticks." And remember their video tool Sora? Launched standalone. Hit #1 on the App Store. Usage flatlined within weeks. Now they're forced to shut it down. Their browser Atlas? Still hasn't launched publicly. Their IPO? Polymarket odds dropped from 55% to 35%. OpenAI has 900 million users. Anthropic has maybe 10 million daily actives. But here's the thing... OpenAI won the consumer war. ChatGPT is where your mom asks about recipes and your cousin makes memes. Anthropic won the war that actually MATTERS. The developers. The engineers. The enterprises writing 7 figure checks. OpenAI built the biggest chatbot on Earth. Anthropic built the tool that companies can't stop paying for. This is Yahoo vs Google all over again. Yahoo had the users. Google had the product. And we all know how that ended. OpenAI has 12 months to prove the superapp works, land the IPO, and stop the enterprise bleeding. If they can't, the most valuable startup in history becomes the most cautionary tale in tech. 900 million users don't mean anything if the people who actually pay are walking out the door. What do you think?

Ricardo

35,020 views • 5 months ago

A researcher went on vacation and left a model training. When they came back, it had suddenly solved modular arithmetic perfectly - not by memorizing answers, but by reinventing trigonometric identities from scratch. Nobody told it to. This is grokking - the moment an LLM stops memorizing and starts understanding. Anthropic found the same phenomenon inside Claude Haiku: a six-dimensional manifold tracking character count, with attention heads that detect exactly how close Claude is to a line break. 35 minutes. Bookmark & watch today. The only AI model anyone fully understands - and what it reveals about every LLM nobody does.

A researcher went on vacation and left a model training. When they came back, it had suddenly solved modular arithmetic perfectly - not by memorizing answers, but by reinventing trigonometric identities from scratch. Nobody told it to. This is grokking - the moment an LLM stops memorizing and starts understanding. Anthropic found the same phenomenon inside Claude Haiku: a six-dimensional manifold tracking character count, with attention heads that detect exactly how close Claude is to a line break. 35 minutes. Bookmark & watch today. The only AI model anyone fully understands - and what it reveals about every LLM nobody does.

Eron

36,272 views • 6 days ago

$Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?$

Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?

Ricardo

93,276 views • 1 month ago

.carried_no_interest says Manus is one of the most underrated AI products today, and that it was a genius acquisition by Zuck: “There are simply things that you can do with Manus that no other inference provider or product can do. I would reveal them, but that’s alpha for me.” “What Zuck realized is — 'I don’t have to care about inference or who’s giving it to me, because [Manus] figured out some really special stuff around how an LLM processes data in and around a web browser.'” “And now he’s already integrating it with Ads Manager. It’s a great bet.”

.carried_no_interest says Manus is one of the most underrated AI products today, and that it was a genius acquisition by Zuck: “There are simply things that you can do with Manus that no other inference provider or product can do. I would reveal them, but that’s alpha for me.” “What Zuck realized is — 'I don’t have to care about inference or who’s giving it to me, because [Manus] figured out some really special stuff around how an LLM processes data in and around a web browser.'” “And now he’s already integrating it with Ads Manager. It’s a great bet.”

TBPN

37,131 views • 5 months ago

OpenAI's Swarm Web Extractor This can autonomously search the web, map entire websites, and extract data. This is built on top of OpenAI’s new multi-agent framework Swarm, Serp AI and Firecrawl API. - Swarm is a lightweight and experimental framework introduced by OpenAI to develop multi-agent systems. - Serp API A is a real-time API that allows users to access Google search results. - Firecrawl API turns entire websites into clean, LLM-ready markdown or structured data. Scrape, crawl and extract the web with a single API. Video credits: Eric Ciarla #webextraction #llms #nlproc #swarm #multiagents

OpenAI's Swarm Web Extractor This can autonomously search the web, map entire websites, and extract data. This is built on top of OpenAI’s new multi-agent framework Swarm, Serp AI and Firecrawl API. - Swarm is a lightweight and experimental framework introduced by OpenAI to develop multi-agent systems. - Serp API A is a real-time API that allows users to access Google search results. - Firecrawl API turns entire websites into clean, LLM-ready markdown or structured data. Scrape, crawl and extract the web with a single API. Video credits: Eric Ciarla #webextraction #llms #nlproc #swarm #multiagents

Kalyan KS

174,419 views • 1 year ago

Q: Is it your posture to put more restrictions on ICE as a condition for funding DHS? JEFFRIES: Renee Good should be alive today. There was no justification for that killing. Clearly there are common sense measures that need to put in place so that ICE can conduct itself in a manner at least consistent with every other law enforcement agency in the US. That's not too much to ask. Instead, they're running around out of control.

Q: Is it your posture to put more restrictions on ICE as a condition for funding DHS? JEFFRIES: Renee Good should be alive today. There was no justification for that killing. Clearly there are common sense measures that need to put in place so that ICE can conduct itself in a manner at least consistent with every other law enforcement agency in the US. That's not too much to ask. Instead, they're running around out of control.

Aaron Rupar

70,253 views • 7 months ago

New course: Efficient Inference with SGLang: Text and Image Generation, built in partnership with LMSys LMSYS Org and RadixArk RadixArk, and taught by Richard Chen Richard Chen, a Member of Technical Staff at RadixArk. Running LLMs in production is expensive, and much of that cost comes from redundant computation. This short course teaches you to eliminate that waste using SGLang, an open-source inference framework that caches computation already done and reuses it across future requests. When ten users share the same system prompt, SGLang processes it once, not ten times. The speedups compound quickly, especially when there's a lot of shared context across requests. Skills you'll gain: - Implement a KV cache from scratch to eliminate redundant computation within a single request - Scale caching across users and requests with RadixAttention, so shared context is only processed once - Accelerate image generation with diffusion models using SGLang's caching and multi-GPU parallelism Join and learn to make LLM inference faster and more cost-efficient at scale!

New course: Efficient Inference with SGLang: Text and Image Generation, built in partnership with LMSys LMSYS Org and RadixArk RadixArk, and taught by Richard Chen Richard Chen, a Member of Technical Staff at RadixArk. Running LLMs in production is expensive, and much of that cost comes from redundant computation. This short course teaches you to eliminate that waste using SGLang, an open-source inference framework that caches computation already done and reuses it across future requests. When ten users share the same system prompt, SGLang processes it once, not ten times. The speedups compound quickly, especially when there's a lot of shared context across requests. Skills you'll gain: - Implement a KV cache from scratch to eliminate redundant computation within a single request - Scale caching across users and requests with RadixAttention, so shared context is only processed once - Accelerate image generation with diffusion models using SGLang's caching and multi-GPU parallelism Join and learn to make LLM inference faster and more cost-efficient at scale!

Andrew Ng

101,794 views • 4 months ago