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.aeon ⭐'s security software is already running inside companies like SpaceX.. @aaronjmars broke down the security framework: $AEON is an AI framework built for background intelligence, agents that run continuously for weeks or months, self-evolving and adapting to real conditions rather than firing once when a human pushes a... show more

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30,577 просмотров • 7 дней назад •via X (Twitter)

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Lab0 (lab0) is the AI FDE to make Enterprise Software self-serve. They automate the entire post-sales delivery process for software companies, from client process discovery, configuration, & testing to go-live. Six months of deployment, done in ten days. Congrats on the launch, Onkar Borade, Lakshya, and Sujay Srivastava!

Lab0 (lab0) is the AI FDE to make Enterprise Software self-serve. They automate the entire post-sales delivery process for software companies, from client process discovery, configuration, & testing to go-live. Six months of deployment, done in ten days. Congrats on the launch, Onkar Borade, Lakshya, and Sujay Srivastava!

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$This is the biggest irony in tech history. Microsoft beat revenue estimates. Stock plunged 11%, wiped out $400 BILLION in market cap. Salesforce reported growth. Stock fell 5.6%. ServiceNow beat earnings. Stock crashed 11%. SAP beat projections. Stock dropped 16%. Entire software sector entered bear market territory. Down 22% from peak. These are the companies everyone said would WIN from AI. They spent billions BUYING AI companies. ServiceNow: $7.75 billion for Armis. Salesforce: $8 billion for Informatica. They launched AI products. Built AI workflows. Hired AI teams. And the market said: You're all dead. Because investors just realized something nobody wanted to admit: AI doesn't make software companies stronger. AI makes software companies OBSOLETE. Morgan Stanley: "In an environment of heightened investor skepticism, stable growth falls short of shifting the narrative." Good earnings aren't enough anymore. The market is pricing in a world where AI replaces the software these companies sell. ServiceNow CEO tried defending on the earnings call: "AI needs workflow orchestration. ServiceNow is the gateway to this shift." Market response: 11% crash. Because here's what he didn't say: If AI can write code, automate workflows, and generate apps at a fraction of the cost, why would anyone pay $50,000 per year for enterprise software licenses? The per-seat pricing model that made SaaS companies rich is getting murdered by AI efficiency. One AI agent replaces 10 seats. One prompt replaces months of custom development. One LLM call replaces entire software categories. Klarna already proved it. CEO said they pulled Salesforce out of their stack. Built everything themselves using AI. And that's just the beginning. The software apocalypse hit hardest on companies that INVESTED IN AI: Atlassian: down 12.6% Intuit: down 7.8% HubSpot: down 11.5% Zscaler: down 6.3% Meanwhile, the companies ENABLING AI made money: Nvidia: up Semiconductor stocks: surging Memory firms: rallying The divide is brutal. Hardware companies print cash. Software companies get destroyed. Because in an AI-first world, you need GPUs to build the models. But you don't need software subscriptions when the AI builds the software for you. Jim Cramer called it the "P/E multiple compression crisis." Translation: Investors don't care about earnings anymore. They care about whether your business model survives the next 5 years. And right now software business models look doomed. They're literally stuck: If they DON'T invest in AI, they fall behind. If they DO invest in AI, they cannibalize their own products. It's a death spiral with no exit. ServiceNow spent $12 BILLION on acquisitions in 2025 alone. Trying to buy their way into relevance. And yesterday the market cooked them. The craziest thing to me tho... Most software companies beat earnings. Revenue was solid. Growth was fine. But it didn't matter. Because the market stopped pricing software on what it earns TODAY. It's pricing software on what it's worth in a world where AI does the job for free. And in that world these companies are worth nothing. This is the biggest sector repricing since 2008. $500 billion in market value gone in ONE DAY. And it's not stopping. Because every company watching this is thinking the same thing: "If I can replace ServiceNow with 3 AI agents and save $10 million per year, why wouldn't I?" The answer used to be: "Because you need enterprise-grade reliability." But now? AI agents are getting reliable. Fast. Software companies just realized they're competing with open-source models that cost $0.02 per 1,000 tokens. You can't win a pricing war against free. The companies that spent BILLIONS preparing for AI are getting killed BY AI. What an irony.$

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Anthropic admitted they built an AI so capable they were scared to release it and the number that explains why is 250. Anthropic's CFO Krishna Rao described in this clip what happened when they ran Mythos against an open source codebase that a previous frontier model had already analyzed. The prior model found 22 security vulnerabilities, Mythos found 250. In the same codebase, that the previous model had already reviewed and flagged as relatively clean. That number, more than 11 times as many vulnerabilities discovered is not just a benchmark improvement, it is a signal that there is an entire layer of software infrastructure that humanity has been operating under the assumption was secure and that assumption may no longer hold. The UK AI Security Institute independently evaluated Mythos Preview and confirmed what the internal numbers suggested. On expert level capture the flag challenges that no model could complete before April 2025, Mythos succeeded 73% of the time and it became the first model ever to complete a complex end-to-end attack range from start to finish, autonomously, without human guidance. The World Economic Forum called this a new security-driven era for AI, the Governor of the Bank of England publicly warned that Anthropic may have found a way to unlock the entire cyber-risk landscape, and the European Central Bank began quietly contacting financial institutions to assess their security posture. The response from Anthropic is what makes this story genuinely important. Rather than shelving the model or publishing it as a standard API release, Rao described a phased approach restricting access to a controlled group, focusing specifically on how the cyber capabilities can be used defensively rather than offensively and treating that framework as a template for how to release powerful but dangerous models in the future. The broader context makes that framing even more significant. AI generated code is already creating ten times more security vulnerabilities than human-written code, 63% of organizations reported experiencing an AI driven cyberattack in the past 12 months, and traditional signature-based security tools were built for a threat model that no longer describes the attack surface companies are defending against. Mythos represents a genuine leap in what autonomous security reasoning can do and it cuts both ways. The model that can find 250 vulnerabilities in a codebase a prior model rated as mostly clean is also, in the wrong hands, the model that can exploit those 250 vulnerabilities before a human defender has even finished reading the report. Anthropic's phased release strategy is not just a legal or PR decision, it is the most honest signal yet from a frontier lab that safety governance and capability development can no longer be treated as separate workstreams. The question is not whether this technology gets deployed, it is whether the institutions using it defensively stay ahead of the ones who will eventually use it offensively and whether the labs building it can keep those two timelines from inverting.

Anthropic admitted they built an AI so capable they were scared to release it and the number that explains why is 250. Anthropic's CFO Krishna Rao described in this clip what happened when they ran Mythos against an open source codebase that a previous frontier model had already analyzed. The prior model found 22 security vulnerabilities, Mythos found 250. In the same codebase, that the previous model had already reviewed and flagged as relatively clean. That number, more than 11 times as many vulnerabilities discovered is not just a benchmark improvement, it is a signal that there is an entire layer of software infrastructure that humanity has been operating under the assumption was secure and that assumption may no longer hold. The UK AI Security Institute independently evaluated Mythos Preview and confirmed what the internal numbers suggested. On expert level capture the flag challenges that no model could complete before April 2025, Mythos succeeded 73% of the time and it became the first model ever to complete a complex end-to-end attack range from start to finish, autonomously, without human guidance. The World Economic Forum called this a new security-driven era for AI, the Governor of the Bank of England publicly warned that Anthropic may have found a way to unlock the entire cyber-risk landscape, and the European Central Bank began quietly contacting financial institutions to assess their security posture. The response from Anthropic is what makes this story genuinely important. Rather than shelving the model or publishing it as a standard API release, Rao described a phased approach restricting access to a controlled group, focusing specifically on how the cyber capabilities can be used defensively rather than offensively and treating that framework as a template for how to release powerful but dangerous models in the future. The broader context makes that framing even more significant. AI generated code is already creating ten times more security vulnerabilities than human-written code, 63% of organizations reported experiencing an AI driven cyberattack in the past 12 months, and traditional signature-based security tools were built for a threat model that no longer describes the attack surface companies are defending against. Mythos represents a genuine leap in what autonomous security reasoning can do and it cuts both ways. The model that can find 250 vulnerabilities in a codebase a prior model rated as mostly clean is also, in the wrong hands, the model that can exploit those 250 vulnerabilities before a human defender has even finished reading the report. Anthropic's phased release strategy is not just a legal or PR decision, it is the most honest signal yet from a frontier lab that safety governance and capability development can no longer be treated as separate workstreams. The question is not whether this technology gets deployed, it is whether the institutions using it defensively stay ahead of the ones who will eventually use it offensively and whether the labs building it can keep those two timelines from inverting.

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Brad Flora

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Will Lawrence is running a people-powered campaign taking on Big Money interests. He's an organizer ready to champion Medicare for All, protect Social Security and demand accountability for AI companies. He's the exact type of leader the American people deserve in Congress.

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"Epstein receives an email from Bill Gates informing him that he's about to have a meeting with Obama about Ebola..." reports Sayer Ji, revealing a stunning timeline buried in the leaked Epstein files. At the same time, the UN Security Council and the WHO laid down a rigid "public emergency" structure that treated health crises like military security threats - the framework that was later used for COVID-19 lockdowns and mask mandates. Coincidence?

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Marc Benioff just exposed the biggest hypocrisy in the AI boom. The companies building the AI that’s supposed to kill software are some of Salesforce’s largest customers. Benioff: “The AI companies love our products and they can’t buy enough of them. They’re some of our largest customers now: Anthropic, OpenAI, Google, Amazon, you name it.” Let that land. The most advanced AI labs on earth. The companies with more engineering talent and compute than anyone. The ones building the technology that analysts say will make traditional software obsolete. Still buying traditional software. At scale. Benioff: “No one has a company that’s running entirely on a large language model because it’s not real.” Not because they haven’t tried. Because an LLM is not a foundation. It’s a feature. Benioff: “Yeah, Minority Report, I watched the movie. Great guys, fantastic. But I’m in the present-moment reality right now. We’re living in this world. This is 2026.” The analysts writing reports about fully autonomous AI companies have never had to run one. Benioff is running one of the largest enterprise software companies on earth. The gap between those two perspectives is where billions of dollars are being misallocated. Benioff: “How are we doing our financials, our HR, our customer information? How are we doing all of these aspects of our business?” A neural network that hallucinates cannot execute a financial transaction that has to be right every single time. Cannot secure customer data with zero tolerance for error. Cannot provide the determinism that every real business runs on. Benioff: “We need the determinism, and the programmability, and the security, and the sharing.” AI doesn’t replace those requirements. It sits on top of them. Benioff: “I think the software industry is going to be bigger and broader and do more this year than ever before.” The future isn’t AI replacing software. It’s AI making software exponentially more powerful. The smartest people building the future already know this. They’re the ones still buying the software.

Dustin

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.Haseeb Qureshi ＞|＜ calls any team not running its releases through frontier-model security scanning "delinquent": "We are in a world where the previous rules around cybersecurity no longer apply. Because the reality is, if you can find this bug for $2, the team that was building this also could have done security scanning for $2. What were they doing?" "GLM has been out for a while. Kimi obviously more recent, but even just doing this on Opus would have cost probably four or five times that at most. Supposedly somebody ran Claude Code and found this in two minutes." "So what all this tells you is that there are companies that are going to survive this transition, and there are companies that are not. And there's going to be this kind of COVID-like mass inoculation period that we're going through right now in cybersecurity." "It's probably going to be a very centralizing force, because it means there's gonna be more consolidation toward the companies that have good security practices, have enough money to spend real dollars on security engineering, and are gonna be running every release through thousands of dollars of security scanning from frontier-level models." "If you are not doing that now, you are delinquent. You are already behind. You are putting your users and your company at risk." "AI is kind of an everything technology. It's like this background radiation now that is just touching everything that is software based." Dragonfly ＞|＜

.Haseeb Qureshi ＞|＜ calls any team not running its releases through frontier-model security scanning "delinquent": "We are in a world where the previous rules around cybersecurity no longer apply. Because the reality is, if you can find this bug for $2, the team that was building this also could have done security scanning for $2. What were they doing?" "GLM has been out for a while. Kimi obviously more recent, but even just doing this on Opus would have cost probably four or five times that at most. Supposedly somebody ran Claude Code and found this in two minutes." "So what all this tells you is that there are companies that are going to survive this transition, and there are companies that are not. And there's going to be this kind of COVID-like mass inoculation period that we're going through right now in cybersecurity." "It's probably going to be a very centralizing force, because it means there's gonna be more consolidation toward the companies that have good security practices, have enough money to spend real dollars on security engineering, and are gonna be running every release through thousands of dollars of security scanning from frontier-level models." "If you are not doing that now, you are delinquent. You are already behind. You are putting your users and your company at risk." "AI is kind of an everything technology. It's like this background radiation now that is just touching everything that is software based." Dragonfly ＞|＜

MTS

12,086 просмотров • 26 дней назад

Introducing Causa Prima - coming out of stealth today with a $10M raise. I spent 25 years building software that moves money between companies. But every tool I built only ever sat inside one company. AP tools for the buyer. AR tools for the supplier. Nobody built anything for the space between them. The moment a payment had to move between two companies, the software stopped and a human stepped in. Henrik Gebbing , Philip Stanislaus , and I spent too long watching this happen to not do something about it. So we built the solution. Causa Prima is an agent-to-agent network between the buyer and the supplier. AI agents on both sides handle disputes, negotiate payment timing, and capture early pay discounts automatically in real time. We have 3000+ active accounts on the network already. And we’re just getting started.

Introducing Causa Prima - coming out of stealth today with a $10M raise. I spent 25 years building software that moves money between companies. But every tool I built only ever sat inside one company. AP tools for the buyer. AR tools for the supplier. Nobody built anything for the space between them. The moment a payment had to move between two companies, the software stopped and a human stepped in. Henrik Gebbing , Philip Stanislaus , and I spent too long watching this happen to not do something about it. So we built the solution. Causa Prima is an agent-to-agent network between the buyer and the supplier. AI agents on both sides handle disputes, negotiate payment timing, and capture early pay discounts automatically in real time. We have 3000+ active accounts on the network already. And we’re just getting started.

Maex 🦁

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🇮🇷 The son of Shah Reza Pahlavi: "To ensure that Iran does not experience disruption in governance with the fall of the Islamic Republic, two important efforts have been made in recent months - developing a clear plan for managing the country, and identifying and selecting women and men suitable to serve in the transitional framework and implement this plan. The transitional framework under my leadership will be ready to take over the governance of the country the moment the Islamic Republic falls and, in the shortest possible time, establish order, security, freedom, and conditions for the prosperity and flourishing of Iran."

🇮🇷 The son of Shah Reza Pahlavi: "To ensure that Iran does not experience disruption in governance with the fall of the Islamic Republic, two important efforts have been made in recent months - developing a clear plan for managing the country, and identifying and selecting women and men suitable to serve in the transitional framework and implement this plan. The transitional framework under my leadership will be ready to take over the governance of the country the moment the Islamic Republic falls and, in the shortest possible time, establish order, security, freedom, and conditions for the prosperity and flourishing of Iran."

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90,643 просмотров • 5 месяцев назад

10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇

10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇

unicode

30,511 просмотров • 15 дней назад

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)

Rohan Paul

16,584 просмотров • 3 месяцев назад

Palantir CEO Alex Karp goes after AI slop. The fight over AI “slop” is really a fight over whether software is performing or merely pretending. "The appearance of software working is not software working. And the slop that is getting a lot of attention is not only dangerous in terms of the hyperbolic rhetoric, but also in claims like, “There will be no jobs because of the slop,” or that “nothing will work,” while somehow we will have a God-like figure in the name of AI. When, in fact, what actually does work is a platform built by a motley crew of highly technical people who, over 20 years, have been maligned for being right about the nature of having to build Foundry and the nature of having to build Apollo." ---- Software used to fail in blunt ways: a crash, a wrong number, a missing button, a process that simply stopped. Generative systems often fail more seductively, by producing fluent surfaces that look like work until they meet the stubborn world of permissions, edge cases, audit trails, security, accountability, and changing human intent. --- From "Palantir" YT channel, full link in comment.

Palantir CEO Alex Karp goes after AI slop. The fight over AI “slop” is really a fight over whether software is performing or merely pretending. "The appearance of software working is not software working. And the slop that is getting a lot of attention is not only dangerous in terms of the hyperbolic rhetoric, but also in claims like, “There will be no jobs because of the slop,” or that “nothing will work,” while somehow we will have a God-like figure in the name of AI. When, in fact, what actually does work is a platform built by a motley crew of highly technical people who, over 20 years, have been maligned for being right about the nature of having to build Foundry and the nature of having to build Apollo." ---- Software used to fail in blunt ways: a crash, a wrong number, a missing button, a process that simply stopped. Generative systems often fail more seductively, by producing fluent surfaces that look like work until they meet the stubborn world of permissions, edge cases, audit trails, security, accountability, and changing human intent. --- From "Palantir" YT channel, full link in comment.

Rohan Paul

73,728 просмотров • 3 месяцев назад

Just watched Kai-Fu Lee 's interview on the US-China AI landscape. A few takeaways felt especially useful✌️: 1. The US is currently about 15 months ahead of China in frontier AI models. 2. Top US AI companies are like “geniuses trying to win the Nobel Prize,” while Chinese AI companies are more like “a group of smart kids forming a study group.” They still compete, but open source creates more shared learning. 3. China’s AI companies face a real monetization challenge in enterprise software. Many Chinese companies are less used to annual subscriptions or usage-based pricing, and still tend to think of software as project-based work. 4. For the same product, Western, Middle Eastern, and Central Asian companies may be willing to pay around 8x more than Chinese companies. That helps explain why many Chinese AI companies are expanding overseas. 5. China may produce many breakthrough AI consumer apps, because Chinese companies are especially strong at consumer apps, hardware, robotics, embodied AI, and AI-first devices. 6. The US may continue to lead in frontier models, while China may move faster in turning AI into products people actually use. 7. His advice for young people in the age of AI: you don’t necessarily need to become an engineer first. What matters more is accountability, taste, conviction, courage, and foresight. More👇

Just watched Kai-Fu Lee 's interview on the US-China AI landscape. A few takeaways felt especially useful✌️: 1. The US is currently about 15 months ahead of China in frontier AI models. 2. Top US AI companies are like “geniuses trying to win the Nobel Prize,” while Chinese AI companies are more like “a group of smart kids forming a study group.” They still compete, but open source creates more shared learning. 3. China’s AI companies face a real monetization challenge in enterprise software. Many Chinese companies are less used to annual subscriptions or usage-based pricing, and still tend to think of software as project-based work. 4. For the same product, Western, Middle Eastern, and Central Asian companies may be willing to pay around 8x more than Chinese companies. That helps explain why many Chinese AI companies are expanding overseas. 5. China may produce many breakthrough AI consumer apps, because Chinese companies are especially strong at consumer apps, hardware, robotics, embodied AI, and AI-first devices. 6. The US may continue to lead in frontier models, while China may move faster in turning AI into products people actually use. 7. His advice for young people in the age of AI: you don’t necessarily need to become an engineer first. What matters more is accountability, taste, conviction, courage, and foresight. More👇

Mia Bao

44,564 просмотров • 2 месяцев назад

Most of the market is priced for a world that's coming to an end. Traditional software, legacy platforms, much of the SaaS layer of the S&P 500… all built on the assumption that humans will remain the primary customers. AI agents don't buy software the way we do. They don't need dashboards, subscriptions, or logins. And once the buyer changes, so does everything built around them. The investable opportunity ahead is the agentic economy… what gets built for agents as customers. That's where the next wave of capital ends up, and most people aren't positioned for it.

Most of the market is priced for a world that's coming to an end. Traditional software, legacy platforms, much of the SaaS layer of the S&P 500… all built on the assumption that humans will remain the primary customers. AI agents don't buy software the way we do. They don't need dashboards, subscriptions, or logins. And once the buyer changes, so does everything built around them. The investable opportunity ahead is the agentic economy… what gets built for agents as customers. That's where the next wave of capital ends up, and most people aren't positioned for it.

Raoul Pal

214,820 просмотров • 1 месяц назад