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🚨BREAKING : Call centers are officially dead. ElevenLabs Agents quietly wiped out the $40B customer support industry. → Sounds human in 70+ languages → Books, updates, closes tickets mid-call → Plugs into GPT, Claude, Gemini, any LLM → $0.08/min, startups get $4K free Revolut, Cisco, Deliveroo already switched. You're...

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Hasan

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417,141 просмотров • 3 месяцев назад •via X (Twitter)

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🚨 BREAKING: Call centers just met their biggest threat yet. ElevenLabs Speech Engine just changed how the $40B support industry operates → 70+ languages, natural real time voices → Books, updates, closes tickets mid-conversation → Plugs into GPT, Claude, Gemini any LLM you run → $0.08/min, startups get $4K free Built for the teams already shipping. The rest will follow. You're next 🧵👇
0:54

🚨 BREAKING: Call centers just met their biggest threat yet. ElevenLabs Speech Engine just changed how the $40B support industry operates → 70+ languages, natural real time voices → Books, updates, closes tickets mid-conversation → Plugs into GPT, Claude, Gemini any LLM you run → $0.08/min, startups get $4K free Built for the teams already shipping. The rest will follow. You're next 🧵👇

Hasan

70,816 просмотров • 3 месяцев назад

5 startup ideas you can build and resell using only ElevenLabs Agents each one costs $0.08/min to run and replaces $2-5k/mo in human labor Let's break them down ↓ 1. AI Receptionist for Local Businesses dentists, salons, clinics, they all pay $2-3k/mo for someone to answer phones build a voice agent that: - answers calls 24/7 - books appointments - handles FAQs - speaks the client's language who ALREADY uses it: ~31% of local service businesses who STILL needs it: ~69% (your market) white-label it, charge $300-500/mo per client your cost per client: ~$30/mo in minutes 2. Multilingual Customer Support ElevenLabs agents speak 70+ languages natively e-commerce brands selling internationally need support in 5-10 languages minimum one agent replaces a 5-person multilingual team who ALREADY uses it: ~36% of e-commerce businesses who STILL needs it: ~64% and most of them are mid-market brands scaling globally sell 24/7 coverage, mark up the minutes, charge per-seat 3. AI Sales Qualifier (SDR Replacement) voice agent calls inbound leads, asks 5-10 qualifying questions, books meetings directly into the sales team's calendar startups pay $4-6k/mo per SDR you charge $1.5k/mo for an agent that works 24/7 and never misses a lead who ALREADY uses it: ~27% of mid-market teams who STILL needs it: ~73% and 22% already fully replaced human SDRs plug it into any CRM like HubSpot, Salesforce, Pipedrive 4. Restaurant Order-Taking Agent phone ordering for restaurants, pizzerias, takeout spots the agent takes the order, upsells sides and drinks, confirms, pushes to the POS who ALREADY uses it: ~34% of restaurants who STILL needs it: ~66% (expected to hit 50%+ in major cities this year) build one integration template → sell to 100+ restaurants at $200/mo each that's $20k/mo from one vertical 5. Real Estate Showing Scheduler agents answer property inquiry calls, give listing details, qualify buyers, and book viewings (all mid-call) realtors spend hours on phone scheduling who ALREADY uses it: ~18% use voice AI specifically who STILL needs it: ~82% while 82% of agents already use some form of AI, almost none have voice agents charge per listing or flat monthly integrates with their calendar + CRM -------- How to build any of these: - sign up for ElevenLabs (startups get $4k free credits) - pick your niche - build the agent with their no-code platform - connect it to GPT or Claude for the brain - plug in scheduling/CRM via API - white-label it under your brand you don't need to build AI, you need to sell AI to people who don't know it exists yet reply "ELEVEN" + RT and i'll send you a free guide so you can build this too
0:43

5 startup ideas you can build and resell using only ElevenLabs Agents each one costs $0.08/min to run and replaces $2-5k/mo in human labor Let's break them down ↓ 1. AI Receptionist for Local Businesses dentists, salons, clinics, they all pay $2-3k/mo for someone to answer phones build a voice agent that: - answers calls 24/7 - books appointments - handles FAQs - speaks the client's language who ALREADY uses it: ~31% of local service businesses who STILL needs it: ~69% (your market) white-label it, charge $300-500/mo per client your cost per client: ~$30/mo in minutes 2. Multilingual Customer Support ElevenLabs agents speak 70+ languages natively e-commerce brands selling internationally need support in 5-10 languages minimum one agent replaces a 5-person multilingual team who ALREADY uses it: ~36% of e-commerce businesses who STILL needs it: ~64% and most of them are mid-market brands scaling globally sell 24/7 coverage, mark up the minutes, charge per-seat 3. AI Sales Qualifier (SDR Replacement) voice agent calls inbound leads, asks 5-10 qualifying questions, books meetings directly into the sales team's calendar startups pay $4-6k/mo per SDR you charge $1.5k/mo for an agent that works 24/7 and never misses a lead who ALREADY uses it: ~27% of mid-market teams who STILL needs it: ~73% and 22% already fully replaced human SDRs plug it into any CRM like HubSpot, Salesforce, Pipedrive 4. Restaurant Order-Taking Agent phone ordering for restaurants, pizzerias, takeout spots the agent takes the order, upsells sides and drinks, confirms, pushes to the POS who ALREADY uses it: ~34% of restaurants who STILL needs it: ~66% (expected to hit 50%+ in major cities this year) build one integration template → sell to 100+ restaurants at $200/mo each that's $20k/mo from one vertical 5. Real Estate Showing Scheduler agents answer property inquiry calls, give listing details, qualify buyers, and book viewings (all mid-call) realtors spend hours on phone scheduling who ALREADY uses it: ~18% use voice AI specifically who STILL needs it: ~82% while 82% of agents already use some form of AI, almost none have voice agents charge per listing or flat monthly integrates with their calendar + CRM -------- How to build any of these: - sign up for ElevenLabs (startups get $4k free credits) - pick your niche - build the agent with their no-code platform - connect it to GPT or Claude for the brain - plug in scheduling/CRM via API - white-label it under your brand you don't need to build AI, you need to sell AI to people who don't know it exists yet reply "ELEVEN" + RT and i'll send you a free guide so you can build this too

Ronin

774,741 просмотров • 3 месяцев назад

10 free Google AI tools nobody talks about. while everyone's burning $20/mo on chatgpt and claude, google quietly shipped a stack worth $200+/mo. all free. all yours. — 1️⃣ NotebookLM — your second brain upload sources (PDFs, websites, audio, YouTube). it summarizes, builds mind maps, generates quizzes, drafts slide decks, even turns your notes into a podcast you can listen to on a walk. free tier: 100 notebooks, 50 sources each, 50 chats/day, 3 audio overviews/day. replaces: notion AI + perplexity + readwise — 2️⃣ Google AI Studio — the free gemini playground web playground for gemini 3 pro and flash with a free API key. generous limits. paste a 1M-token context window and watch it actually use it. faster than the openai playground and free where openai charges per token. replaces: openai playground + paid API credits — 3️⃣ Gemini CLI — google's open-source terminal agent apache 2.0 licensed. one command (npx @google/gemini-cli) and you've got an agent in your terminal that reads your codebase, runs shell commands, and ships PRs. drop-in claude code alternative. replaces: claude code ($20/mo by default) — 4️⃣ Jules — async coding agent assign jules a github issue. it spins up a cloud VM, clones your repo, writes the plan, makes the changes, opens a PR. free tier: 15 tasks/day, 3 concurrent, runs on gemini flash. replaces: devin ($20/mo+) + cursor agent 5️⃣ Stitch — text → UI → code google's free figma killer. describe an interface, get production-ready HTML/CSS/Tailwind + figma export. march 2026 update added voice canvas, infinite canvas, and MCP integration with cursor. 350 standard + 200 experimental generations/month free. replaces: galileo AI + early-stage figma work — 6️⃣ Gemma 4 — open-weight LLM google's flagship open model. apache 2.0. 2B, 4B, 26B-MoE, and 31B variants. 256K context. runs on ollama with one command. quantized versions run on a 4090 or beefy laptop. replaces: paying for hosted LLM inference — 7️⃣ Illuminate — papers → podcasts paste an arxiv preprint link. illuminate turns dense research papers into a 6-8 min conversation between two AI hosts breaking it down. perfect for commute reading you can't do at a desk. note: still in waitlist for some regions. replaces: snipd + manual research reading — 8️⃣ Learn About (LearnLM) — adaptive AI tutor drop in any topic you're stuck on. highlight a word, click "go deeper," and the interface adapts in real time to your comprehension level. visual explanations, follow-up questions, the works. replaces: paid tutoring on niche topics — 9️⃣ Google Labs FX (ImageFX + Flow + MusicFX) — free imagen, veo, musicLM google labs creative suite. text-to-image (imagen 4), text-to-video (veo via Flow), text-to-music (musicLM). free tier: limited daily generations. the heavy veo 3.1 features are paid (AI Pro $19.99/mo). still worth using for image and music — those stay free. replaces: midjourney + suno (free tier only — runway-level video gen is paid) — 🔟 Google Colab — free GPU notebooks free T4 GPU + 12GB RAM in a browser tab. enough to fine-tune small models, run stable diffusion, prototype agents. the launching pad for half the ML projects on github. replaces: paid cloud GPU rentals — a quick honest note: these tools aren't 1:1 better than the paid versions they replace. but they're decent enough to get most things done — especially if you're not a heavy user or you've got little funds to play with. i've put all 10 in a public github repo (link in comments). follow + turn on post notifications for more useful posts like this 🔔
0:34

10 free Google AI tools nobody talks about. while everyone's burning $20/mo on chatgpt and claude, google quietly shipped a stack worth $200+/mo. all free. all yours. — 1️⃣ NotebookLM — your second brain upload sources (PDFs, websites, audio, YouTube). it summarizes, builds mind maps, generates quizzes, drafts slide decks, even turns your notes into a podcast you can listen to on a walk. free tier: 100 notebooks, 50 sources each, 50 chats/day, 3 audio overviews/day. replaces: notion AI + perplexity + readwise — 2️⃣ Google AI Studio — the free gemini playground web playground for gemini 3 pro and flash with a free API key. generous limits. paste a 1M-token context window and watch it actually use it. faster than the openai playground and free where openai charges per token. replaces: openai playground + paid API credits — 3️⃣ Gemini CLI — google's open-source terminal agent apache 2.0 licensed. one command (npx @google/gemini-cli) and you've got an agent in your terminal that reads your codebase, runs shell commands, and ships PRs. drop-in claude code alternative. replaces: claude code ($20/mo by default) — 4️⃣ Jules — async coding agent assign jules a github issue. it spins up a cloud VM, clones your repo, writes the plan, makes the changes, opens a PR. free tier: 15 tasks/day, 3 concurrent, runs on gemini flash. replaces: devin ($20/mo+) + cursor agent 5️⃣ Stitch — text → UI → code google's free figma killer. describe an interface, get production-ready HTML/CSS/Tailwind + figma export. march 2026 update added voice canvas, infinite canvas, and MCP integration with cursor. 350 standard + 200 experimental generations/month free. replaces: galileo AI + early-stage figma work — 6️⃣ Gemma 4 — open-weight LLM google's flagship open model. apache 2.0. 2B, 4B, 26B-MoE, and 31B variants. 256K context. runs on ollama with one command. quantized versions run on a 4090 or beefy laptop. replaces: paying for hosted LLM inference — 7️⃣ Illuminate — papers → podcasts paste an arxiv preprint link. illuminate turns dense research papers into a 6-8 min conversation between two AI hosts breaking it down. perfect for commute reading you can't do at a desk. note: still in waitlist for some regions. replaces: snipd + manual research reading — 8️⃣ Learn About (LearnLM) — adaptive AI tutor drop in any topic you're stuck on. highlight a word, click "go deeper," and the interface adapts in real time to your comprehension level. visual explanations, follow-up questions, the works. replaces: paid tutoring on niche topics — 9️⃣ Google Labs FX (ImageFX + Flow + MusicFX) — free imagen, veo, musicLM google labs creative suite. text-to-image (imagen 4), text-to-video (veo via Flow), text-to-music (musicLM). free tier: limited daily generations. the heavy veo 3.1 features are paid (AI Pro $19.99/mo). still worth using for image and music — those stay free. replaces: midjourney + suno (free tier only — runway-level video gen is paid) — 🔟 Google Colab — free GPU notebooks free T4 GPU + 12GB RAM in a browser tab. enough to fine-tune small models, run stable diffusion, prototype agents. the launching pad for half the ML projects on github. replaces: paid cloud GPU rentals — a quick honest note: these tools aren't 1:1 better than the paid versions they replace. but they're decent enough to get most things done — especially if you're not a heavy user or you've got little funds to play with. i've put all 10 in a public github repo (link in comments). follow + turn on post notifications for more useful posts like this 🔔

m0h

11,847 просмотров • 2 месяцев назад

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this
13:23

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this

Yarchi

56,177 просмотров • 2 месяцев назад

🚨 Chinese developers just dropped a 4-minute video and GitHub Repo that’s exploding the internet. They opened a terminal… and in just 4 minutes built a team of 7 AI agents that completely replace an entire customer support department. Cost? > Only $50 a month. > One agent classifies tickets. > One reads the knowledge base. > One handles billing. > One watches the customer’s tone. > One decides when to escalate. > One writes the reports. And one agent runs all the other six. The results? Brutal. 1,200 tickets a day per agent. More than a full call center of 8 human operators. Companies used to spend $25,000–$40,000 per month on salaries. Now they pay just $50 for the API. The video hit 4 million views in 72 hours. Every CEO in China was forwarding it. Every support team in America is panicking. While the West is still debating “Will AI replace jobs?”… China just published the actual manual on how to fire an entire department. But wait it gets way crazier. Pause the video at 0:39. Ignore the guy in the blue shirt. Look at the second monitor on the right. That’s not a support dashboard. That’s a live trading wallet. > Username: gabagool22 > $868,862 profit > 28,620 predictions > All on Bitcoin > All 15-minute windows Every single one green. Profile: Copying his trades: GitHub: The comment section turned into a detective agency. People slowed the video to 0.5x, screenshot every frame, and stitched the full wallet together from just 6 seconds of background footage. Entry prices: 2 to 10 cents. Payouts: thousands of dollars. Not one red trade in 28,620 bets. Biggest single win? $4,696 in one 15-minute window. Here’s the insane part: The 7 AI agents save a company $40K a month. The wallet on their second monitor makes that money in one good week. They were teaching the world how to replace humans with cheap AI. Meanwhile, their own setup behind the camera was already replacing traders. They deleted the wallet zoom in the next upload. Too late. Someone screen-recorded it anyway. The tutorial got 4 million views. The secret wallet clip got another 800K. Now 727K people are staring at that wallet. The 7 agents are still answering tickets somewhere for $50 a month. But the wallet behind them? It doesn’t need 7 agents. It only needs one. And that one was already running while they were filming. Mind officially blown Screenshot it. Send it to a friend. And ask yourself: what’s running on your second monitor?
4:31

🚨 Chinese developers just dropped a 4-minute video and GitHub Repo that’s exploding the internet. They opened a terminal… and in just 4 minutes built a team of 7 AI agents that completely replace an entire customer support department. Cost? > Only $50 a month. > One agent classifies tickets. > One reads the knowledge base. > One handles billing. > One watches the customer’s tone. > One decides when to escalate. > One writes the reports. And one agent runs all the other six. The results? Brutal. 1,200 tickets a day per agent. More than a full call center of 8 human operators. Companies used to spend $25,000–$40,000 per month on salaries. Now they pay just $50 for the API. The video hit 4 million views in 72 hours. Every CEO in China was forwarding it. Every support team in America is panicking. While the West is still debating “Will AI replace jobs?”… China just published the actual manual on how to fire an entire department. But wait it gets way crazier. Pause the video at 0:39. Ignore the guy in the blue shirt. Look at the second monitor on the right. That’s not a support dashboard. That’s a live trading wallet. > Username: gabagool22 > $868,862 profit > 28,620 predictions > All on Bitcoin > All 15-minute windows Every single one green. Profile: Copying his trades: GitHub: The comment section turned into a detective agency. People slowed the video to 0.5x, screenshot every frame, and stitched the full wallet together from just 6 seconds of background footage. Entry prices: 2 to 10 cents. Payouts: thousands of dollars. Not one red trade in 28,620 bets. Biggest single win? $4,696 in one 15-minute window. Here’s the insane part: The 7 AI agents save a company $40K a month. The wallet on their second monitor makes that money in one good week. They were teaching the world how to replace humans with cheap AI. Meanwhile, their own setup behind the camera was already replacing traders. They deleted the wallet zoom in the next upload. Too late. Someone screen-recorded it anyway. The tutorial got 4 million views. The secret wallet clip got another 800K. Now 727K people are staring at that wallet. The 7 agents are still answering tickets somewhere for $50 a month. But the wallet behind them? It doesn’t need 7 agents. It only needs one. And that one was already running while they were filming. Mind officially blown Screenshot it. Send it to a friend. And ask yourself: what’s running on your second monitor?

AdiiX

23,279 просмотров • 3 месяцев назад

Every software company just got a second life and Jensen just explained why (Save this). The conventional fear was straightforward, AI agents replace human workers, human workers use software tools, therefore agents destroy SaaS. Jensen Huang stood on stage at Computex 2026 and walked through exactly why that logic is backwards. Agents don't replace software, they consume it at machine speed, around the clock, without weekends. Here's the actual architecture Jensen laid out. An agent isn't just a large language model but rather an LLM sitting inside a harness that manages memory, orchestrates tool use, routes context, and plans iterative actions. That harness has to constantly call tools, spreadsheets, databases, browsers, and code engines, with every reasoning loop triggering another tool call. A human might use Salesforce 40 hours a week, an agent running inside a company uses it 168 hours a week and never misses a context window. The GitHub data Jensen showed on stage makes it tangible, 90 million pull requests merged, 1.4 billion commits, and 20 million new repositories created every month. As of April 2026, GitHub is processing 275 million commits per week on pace for roughly 14 billion by year end, a 14x explosion in a single year and AI agents are the source. Pull requests opened by AI agents went from 4 million in September 2025 to 17 million in March 2026 more than 4x in six months. That's AI becoming the largest software user on earth. Goldman Sachs quantified the downstream effect last month, token consumption is expected to multiply 24x by 2030, reaching 120 quadrillion tokens per month globally. A traditional chatbot consumes roughly 1,000 tokens per session, an embedded copilot burns 5,000 tokens per day while a continuously running enterprise agent? Over 100,000 tokens per day. The software companies that figured this out first are already printing money, Salesforce Agentforce hit $800 million ARR growing 169% year over year, with 29,000 deals closed. ServiceNow's Now Assist crossed $600 million in ACV, just raised its full year target to $1.5 billion, and told investors that when its agents replace a 20-person support team, total ServiceNow spend by that customer grows more than 5x even after accounting for reduced seat licenses. Workday delivered 1.7 billion AI actions across its platform in fiscal 2026. The key unlock Jensen pointed to and what investors need to understand is MCP, the model context protocol is the interface layer that makes software agent-readable. Software that supports MCP can be called by any agent, from any model, through any harness. Anthropic created it, OpenAI, Microsoft, and Google all adopted it and it was donated to the Linux Foundation. It is effectively becoming the HTTP of agentic computing. Software companies with native MCP support are plugged into the agent economy. Software companies still waiting are one product cycle away from becoming invisible to the fastest-growing category of software users in history.
5:06

Every software company just got a second life and Jensen just explained why (Save this). The conventional fear was straightforward, AI agents replace human workers, human workers use software tools, therefore agents destroy SaaS. Jensen Huang stood on stage at Computex 2026 and walked through exactly why that logic is backwards. Agents don't replace software, they consume it at machine speed, around the clock, without weekends. Here's the actual architecture Jensen laid out. An agent isn't just a large language model but rather an LLM sitting inside a harness that manages memory, orchestrates tool use, routes context, and plans iterative actions. That harness has to constantly call tools, spreadsheets, databases, browsers, and code engines, with every reasoning loop triggering another tool call. A human might use Salesforce 40 hours a week, an agent running inside a company uses it 168 hours a week and never misses a context window. The GitHub data Jensen showed on stage makes it tangible, 90 million pull requests merged, 1.4 billion commits, and 20 million new repositories created every month. As of April 2026, GitHub is processing 275 million commits per week on pace for roughly 14 billion by year end, a 14x explosion in a single year and AI agents are the source. Pull requests opened by AI agents went from 4 million in September 2025 to 17 million in March 2026 more than 4x in six months. That's AI becoming the largest software user on earth. Goldman Sachs quantified the downstream effect last month, token consumption is expected to multiply 24x by 2030, reaching 120 quadrillion tokens per month globally. A traditional chatbot consumes roughly 1,000 tokens per session, an embedded copilot burns 5,000 tokens per day while a continuously running enterprise agent? Over 100,000 tokens per day. The software companies that figured this out first are already printing money, Salesforce Agentforce hit $800 million ARR growing 169% year over year, with 29,000 deals closed. ServiceNow's Now Assist crossed $600 million in ACV, just raised its full year target to $1.5 billion, and told investors that when its agents replace a 20-person support team, total ServiceNow spend by that customer grows more than 5x even after accounting for reduced seat licenses. Workday delivered 1.7 billion AI actions across its platform in fiscal 2026. The key unlock Jensen pointed to and what investors need to understand is MCP, the model context protocol is the interface layer that makes software agent-readable. Software that supports MCP can be called by any agent, from any model, through any harness. Anthropic created it, OpenAI, Microsoft, and Google all adopted it and it was donated to the Linux Foundation. It is effectively becoming the HTTP of agentic computing. Software companies with native MCP support are plugged into the agent economy. Software companies still waiting are one product cycle away from becoming invisible to the fastest-growing category of software users in history.

Milk Road AI

33,878 просмотров • 2 месяцев назад

🇺🇸 THE INTERVIEW EVERYONE NEEDS TO HEAR BEFORE THE NEXT FINANCIAL SHOCK I just sat down with Andy Schectman, CEO of Miles Franklin, one of the most trusted precious-metals companies in North America — and what he revealed should be a wake-up call for every family in the United States. For 36 years, Andy has warned that when governments drown in debt, when currencies debase, and when globalists push surveillance grids and digital IDs… the smart money moves quietly into gold and silver. And guess what? The central banks, sovereign wealth funds, and elite traders already have. Billions in physical metal now being moved into the U.S. The LBMA and COMEX being drained. China and the Global South preparing for a post-dollar world. Andy explained it plainly: Gold isn’t an investment — it’s wealth. Silver isn’t speculation — it’s survival. Both are lifeboats in a collapsing fiat system. He also exposed: • Why CBDCs are NOT “convenience” — they’re control • Why governments are hoarding metal while telling the public to ignore it • Why the debt spiral cannot continue without a major reset • Why physical possession beats any digital promise • Why America’s financial future is entering the most dangerous era since 1929 And for the first time on my show, I can say this: Freedom Train International is officially partnered with Miles Franklin. A company with 13 billion dollars in sales and not one customer complaint in 36 years. If you’re in the U.S. or Canada and you’ve ever thought about protecting your wealth, your pension, your savings, or your family’s future — now is the time to pay attention. Not to get rich. But to not get wiped out. This interview is packed with truth, history, and the kind of financial honesty the mainstream refuses to touch. WATCH IT. SAVE IT. SHARE IT. Your future self will thank you. — Jim Ferguson Andy Schectman Miles Franklin Precious Metals
51:05

🇺🇸 THE INTERVIEW EVERYONE NEEDS TO HEAR BEFORE THE NEXT FINANCIAL SHOCK I just sat down with Andy Schectman, CEO of Miles Franklin, one of the most trusted precious-metals companies in North America — and what he revealed should be a wake-up call for every family in the United States. For 36 years, Andy has warned that when governments drown in debt, when currencies debase, and when globalists push surveillance grids and digital IDs… the smart money moves quietly into gold and silver. And guess what? The central banks, sovereign wealth funds, and elite traders already have. Billions in physical metal now being moved into the U.S. The LBMA and COMEX being drained. China and the Global South preparing for a post-dollar world. Andy explained it plainly: Gold isn’t an investment — it’s wealth. Silver isn’t speculation — it’s survival. Both are lifeboats in a collapsing fiat system. He also exposed: • Why CBDCs are NOT “convenience” — they’re control • Why governments are hoarding metal while telling the public to ignore it • Why the debt spiral cannot continue without a major reset • Why physical possession beats any digital promise • Why America’s financial future is entering the most dangerous era since 1929 And for the first time on my show, I can say this: Freedom Train International is officially partnered with Miles Franklin. A company with 13 billion dollars in sales and not one customer complaint in 36 years. If you’re in the U.S. or Canada and you’ve ever thought about protecting your wealth, your pension, your savings, or your family’s future — now is the time to pay attention. Not to get rich. But to not get wiped out. This interview is packed with truth, history, and the kind of financial honesty the mainstream refuses to touch. WATCH IT. SAVE IT. SHARE IT. Your future self will thank you. — Jim Ferguson Andy Schectman Miles Franklin Precious Metals

Jim Ferguson

24,373 просмотров • 8 месяцев назад

A 19 year old gets on Zoom calls with business owners and shows them how they're about to throw away $600,000. He opens with one question. How much are you paying your receptionist? The dental office owner says $60,000. The kid says: over the next 10 years that's $600,000 going to someone who calls in sick, takes lunch breaks and goes home at 5. For a job a machine can do for $12,000 once. Then the second question. How many calls do you miss every day? Voicemails nobody calls back. People who hang up after three rings. New patients who try once and never try again. The owner stops. Pulls up his phone log. Counts. 8 missed calls a day. Sometimes more. Each one is a $200 patient walking past the front desk while it's empty for lunch. $1,600 a day. Almost $600,000 a year in revenue he never knew was leaving. That number sits on top of the $600,000 in salary. The owner is staring at a chart he never let himself draw before. That's when the kid says it. I can build you something for $12,000. Picks up every call. Doesn't sleep. Doesn't take vacation. Books patients while you're operating on someone else. Pays for itself in the first week. The $12,000 invoice doesn't feel like an expense. It feels like a refund. The kid closes 3 to 4 of these calls a month. $50,000 in revenue. He's 19. Behind the scenes he's not building anything from scratch. He's running Kimi K2.6 with 300 parallel sub agents, paying $0.60 per million tokens instead of $5 with Claude. The same reception agent a real developer would charge $40,000 to build, he ships in 30 minutes. 12 hour autonomous sessions. Zero human involvement. One prompt and the agent goes live. Answering calls. Booking patients. Routing emergencies. Before the owner finishes his coffee. His friends are working summer shifts at coffee shops for $14 an hour. He's making $600,000 a year by walking into a Zoom call and naming the most uncomfortable number a business owner has on his books. His secret isn't technology. He never argues with the owner about whether AI works. He hands the owner a calculator and asks the questions the owner has been avoiding for years. By minute 8 the owner has done the math himself. By minute 12 the kid quotes the price. By minute 15 the contract is signed. He told his dad about it once. His dad is an insurance salesman. 30 years selling the same product to every client for the same reason: fear of losing what they already have. His dad listened. Then said: you sell the same thing I sell. You just call it AI. The kid said: I don't even call it that. I let them call it whatever they want. His dad smiled. Walked out of the room. The kid had another call in 10 minutes.
1:32

A 19 year old gets on Zoom calls with business owners and shows them how they're about to throw away $600,000. He opens with one question. How much are you paying your receptionist? The dental office owner says $60,000. The kid says: over the next 10 years that's $600,000 going to someone who calls in sick, takes lunch breaks and goes home at 5. For a job a machine can do for $12,000 once. Then the second question. How many calls do you miss every day? Voicemails nobody calls back. People who hang up after three rings. New patients who try once and never try again. The owner stops. Pulls up his phone log. Counts. 8 missed calls a day. Sometimes more. Each one is a $200 patient walking past the front desk while it's empty for lunch. $1,600 a day. Almost $600,000 a year in revenue he never knew was leaving. That number sits on top of the $600,000 in salary. The owner is staring at a chart he never let himself draw before. That's when the kid says it. I can build you something for $12,000. Picks up every call. Doesn't sleep. Doesn't take vacation. Books patients while you're operating on someone else. Pays for itself in the first week. The $12,000 invoice doesn't feel like an expense. It feels like a refund. The kid closes 3 to 4 of these calls a month. $50,000 in revenue. He's 19. Behind the scenes he's not building anything from scratch. He's running Kimi K2.6 with 300 parallel sub agents, paying $0.60 per million tokens instead of $5 with Claude. The same reception agent a real developer would charge $40,000 to build, he ships in 30 minutes. 12 hour autonomous sessions. Zero human involvement. One prompt and the agent goes live. Answering calls. Booking patients. Routing emergencies. Before the owner finishes his coffee. His friends are working summer shifts at coffee shops for $14 an hour. He's making $600,000 a year by walking into a Zoom call and naming the most uncomfortable number a business owner has on his books. His secret isn't technology. He never argues with the owner about whether AI works. He hands the owner a calculator and asks the questions the owner has been avoiding for years. By minute 8 the owner has done the math himself. By minute 12 the kid quotes the price. By minute 15 the contract is signed. He told his dad about it once. His dad is an insurance salesman. 30 years selling the same product to every client for the same reason: fear of losing what they already have. His dad listened. Then said: you sell the same thing I sell. You just call it AI. The kid said: I don't even call it that. I let them call it whatever they want. His dad smiled. Walked out of the room. The kid had another call in 10 minutes.

Marlow

295,813 просмотров • 3 месяцев назад

Claude Code can ship a 45-second animated explainer ad in 30 minutes. No video editor needed, just CC + skills. Here's how I made this video for Soteri Skin 👇 1. /plan Concept Brief (Claude Code) I handwrite a concept brief, then chat with the agent to iterate on it. The agent gathers any raw materials we might need - context about the brand, product images, end card, etc. The concept brief details the concept, characters, visual style, script, etc 2. /prepare a moodboard (CC + GPT Image 2 + ElevenLabs) After reviewing the script, generate: - character reference images - voiceover samples for the characters / narrator - the storyboard (scene by scene grid) - a few keyframe scenes 3. /generate Keyframes for each scene (CC uses Nano Banana or GPT Image 2) Uses the character references from the previous step to generate keyframes for each scene. I probably should have done a round of iteration at this step – there's some character drift and the pH meter representation could have been better. 4. /animate Keyframe → Animated Clip (CC uses Fal Seedance) Generate 2-4 representative scenes first to see a preview. If it looks good, then generate everything. 5. /stitch (CC + ffmpeg + ElevenLabs) - Stitch clips together with hard cut - Add a music score + SFX - Sync clips to the VO - Add captions - Review and edit timing / pacing issues 6. /watch the final cut and review it - as a video editor for technical errors (mismatched voiceover and visuals, AI hallucinations, etc) - as a viewer (ICP). I delegate most of the review to the agent because it catches more things and keeps me out of the loop as much as possible. It also fixes any issues found in the review. That's it. This video took me 30 minutes because I have already created skills for everything I described above. Some day, this will be < 5 minutes. I just review and chat to provide direction and feedback. The skills do all the technical work. 7. /learn Extracts learnings and updates the skills. This final step is really important. It turns this process into a closed loop system that makes the next video much easier to create because all the learnings from the human-in-the-loop process get encoded into code. Skills are code too. If you want access to the skill, drop a comment, and I'll DM it to you (must be following). If you want to make AI video ads like this, DM me.
0:45

Claude Code can ship a 45-second animated explainer ad in 30 minutes. No video editor needed, just CC + skills. Here's how I made this video for Soteri Skin 👇 1. /plan Concept Brief (Claude Code) I handwrite a concept brief, then chat with the agent to iterate on it. The agent gathers any raw materials we might need - context about the brand, product images, end card, etc. The concept brief details the concept, characters, visual style, script, etc 2. /prepare a moodboard (CC + GPT Image 2 + ElevenLabs) After reviewing the script, generate: - character reference images - voiceover samples for the characters / narrator - the storyboard (scene by scene grid) - a few keyframe scenes 3. /generate Keyframes for each scene (CC uses Nano Banana or GPT Image 2) Uses the character references from the previous step to generate keyframes for each scene. I probably should have done a round of iteration at this step – there's some character drift and the pH meter representation could have been better. 4. /animate Keyframe → Animated Clip (CC uses Fal Seedance) Generate 2-4 representative scenes first to see a preview. If it looks good, then generate everything. 5. /stitch (CC + ffmpeg + ElevenLabs) - Stitch clips together with hard cut - Add a music score + SFX - Sync clips to the VO - Add captions - Review and edit timing / pacing issues 6. /watch the final cut and review it - as a video editor for technical errors (mismatched voiceover and visuals, AI hallucinations, etc) - as a viewer (ICP). I delegate most of the review to the agent because it catches more things and keeps me out of the loop as much as possible. It also fixes any issues found in the review. That's it. This video took me 30 minutes because I have already created skills for everything I described above. Some day, this will be < 5 minutes. I just review and chat to provide direction and feedback. The skills do all the technical work. 7. /learn Extracts learnings and updates the skills. This final step is really important. It turns this process into a closed loop system that makes the next video much easier to create because all the learnings from the human-in-the-loop process get encoded into code. Skills are code too. If you want access to the skill, drop a comment, and I'll DM it to you (must be following). If you want to make AI video ads like this, DM me.

Shiv

11,679 просмотров • 2 месяцев назад

NOBODY wants to send their data to Google or OpenAI. Yet here we are, shipping proprietary code, customer information, and sensitive business logic to closed-source APIs we don't control. While everyone's chasing the latest closed-source releases, open-source models are quietly becoming the practical choice for many production systems. Here's what everyone is missing: Open-source models are catching up fast, and they bring something the big labs can't: privacy, speed, and control. I built a playground to test this myself. Used CometML's Opik to evaluate models on real code generation tasks - testing correctness, readability, and best practices against actual GitHub repos. Here's what surprised me: OSS models like MiniMax-M2, Kimi k2 performed on par with the likes of Gemini 3 and Claude Sonnet 4.5 on most tasks. But practically MiniMax-M2 turns out to be a winner as it's twice as fast and 12x cheaper when you compare it to models like Sonnet 4.5. Well, this isn't just about saving money. When your model is smaller and faster, you can deploy it in places closed-source APIs can't reach: ↳ Real-time applications that need sub-second responses ↳ Edge devices where latency kills user experience ↳ On-premise systems where data never leaves your infrastructure MiniMax-M2 runs with only 10B activated parameters. That efficiency means lower latency, higher throughput, and the ability to handle interactive agents without breaking the bank. The intelligence-to-cost ratio here changes what's possible. You're not choosing between quality and affordability anymore. You're not sacrificing privacy for performance. The gap is closing, and in many cases, it's already closed. If you're building anything that needs to be fast, private, or deployed at scale, it's worth taking a look at what's now available. MiniMax-M2 is 100% open-source, free for developers right now. I have shared the link to their GitHub repo in the next tweet. You will also find the code for the playground and evaluations I've done.
0:48

NOBODY wants to send their data to Google or OpenAI. Yet here we are, shipping proprietary code, customer information, and sensitive business logic to closed-source APIs we don't control. While everyone's chasing the latest closed-source releases, open-source models are quietly becoming the practical choice for many production systems. Here's what everyone is missing: Open-source models are catching up fast, and they bring something the big labs can't: privacy, speed, and control. I built a playground to test this myself. Used CometML's Opik to evaluate models on real code generation tasks - testing correctness, readability, and best practices against actual GitHub repos. Here's what surprised me: OSS models like MiniMax-M2, Kimi k2 performed on par with the likes of Gemini 3 and Claude Sonnet 4.5 on most tasks. But practically MiniMax-M2 turns out to be a winner as it's twice as fast and 12x cheaper when you compare it to models like Sonnet 4.5. Well, this isn't just about saving money. When your model is smaller and faster, you can deploy it in places closed-source APIs can't reach: ↳ Real-time applications that need sub-second responses ↳ Edge devices where latency kills user experience ↳ On-premise systems where data never leaves your infrastructure MiniMax-M2 runs with only 10B activated parameters. That efficiency means lower latency, higher throughput, and the ability to handle interactive agents without breaking the bank. The intelligence-to-cost ratio here changes what's possible. You're not choosing between quality and affordability anymore. You're not sacrificing privacy for performance. The gap is closing, and in many cases, it's already closed. If you're building anything that needs to be fast, private, or deployed at scale, it's worth taking a look at what's now available. MiniMax-M2 is 100% open-source, free for developers right now. I have shared the link to their GitHub repo in the next tweet. You will also find the code for the playground and evaluations I've done.

Akshay 🚀

50,323 просмотров • 9 месяцев назад

Sam Altman just dropped the most important interview of 2025. And buried in it are four numbers that explain why everything you think about AI is wrong. Here's what he revealed: Number 1: AI companies are generating 10 TRILLION tokens per day. Humans? Average 20,000 tokens per day. Sam's exact words: "Models will output more tokens than all of humanity put together. Then 10x that. Then 100x that." We're not talking about AI assisting human work anymore. We're talking about AI replacing the entire volume of human intellectual output on the planet. And most people have no idea this shift already happened. Number 2: OpenAI's enterprise business is CRUSHING consumer. Everyone thinks OpenAI is ChatGPT for normies. Wrong. Sam just revealed: "Enterprise growth OUTPACED consumer growth this year." The API business is growing faster than ChatGPT. Over 1 million enterprise users already. "If we had double the compute, we'd be at double the revenue right now." Translation: OpenAI isn't compute-constrained by technology. They're revenue-constrained by infrastructure. The bottleneck is supply and not demand. Every dollar of compute they add prints money. Number 3: GPT-5.2 beats you at 74% of your job. Sam revealed OpenAI's internal GDP-Val benchmark. It measures how AI performs on knowledge work tasks across 40+ verticals. The results: GPT-5.2 beats or ties expert-level knowledge workers at 74.1% of tasks. Legal analysis. PowerPoint decks. Web apps. Financial modeling. Customer support. Sam's description: "A co-worker you can assign an hour's worth of tasks to and get something you prefer back 3 out of 4 times." Three years ago, ChatGPT launched at basically 0% on this scale. Now it's at 74%. And that's not GPT-6. That's what's available RIGHT NOW. Most companies haven't even started using this yet. But here's what Sam said about the gap between capability and adoption: "The overhang is going to be massive. Most people are still asking similar questions they did in the GPT-4 realm." Translation: The models can do 10x more than people have figured out how to use them for. Which means there's a HUGE arbitrage opportunity. Early adopters who actually integrate this into workflows will dominate their industries before competitors even understand what happened. Number 4: AGI already happened. And nobody noticed. Sam's exact quote: "AGI kind of went whooshing by. We're in this fuzzy period where some people think we have it and some don't." Read that again. The CEO of OpenAI just said AGI might have already arrived and we're arguing about definitions while it's actively replacing knowledge work. He even moved the goalposts. The new benchmark: "Superintelligence" = when AI can be a better president or CEO than any human. Not "as good as." BETTER than. We went from "can AI pass a Turing test" to "can AI run countries better than humans" in 3 years. So what does this actually mean? The AI revolution isn't about chatbots getting smarter. It's about the complete replacement of human intellectual output with machine output. At scale. Across every industry. Faster than anyone's prepared for. And the companies positioning for this RIGHT NOW are the ones printing money. OpenAI's enterprise growth is outpacing consumer because businesses see what's coming. They're not buying "AI tools." They're buying the ability to 10x output without 10x-ing headcount. Sam said they'll triple their compute next year. Then triple it again. Revenue is growing even faster than that. "We have never found a situation where we can't monetize all the compute we have." If he isn't lying then that's literally a printing press. The market still doesn't get it. Everyone's focused on "AI bubble" fears while OpenAI is solving the only problem that matters: turning compute into revenue at a faster rate than they're spending. They're not hoping demand catches up to supply. Demand is already 2x ahead of what they can deliver. Meanwhile, most knowledge workers are still using GPT-4 prompts on GPT-5.2. The capability overhang is massive. The arbitrage window is open. And it's closing fast. If you're running a B2B business and you're not integrating AI at the level Sam just described, you're not "waiting to see how it plays out." You're getting crushed by competitors who already figured it out. The companies that win in 2026 won't be the ones with the best AI. They'll be the ones who understood what Sam just laid out 6 months before everyone else did.
1:33

Sam Altman just dropped the most important interview of 2025. And buried in it are four numbers that explain why everything you think about AI is wrong. Here's what he revealed: Number 1: AI companies are generating 10 TRILLION tokens per day. Humans? Average 20,000 tokens per day. Sam's exact words: "Models will output more tokens than all of humanity put together. Then 10x that. Then 100x that." We're not talking about AI assisting human work anymore. We're talking about AI replacing the entire volume of human intellectual output on the planet. And most people have no idea this shift already happened. Number 2: OpenAI's enterprise business is CRUSHING consumer. Everyone thinks OpenAI is ChatGPT for normies. Wrong. Sam just revealed: "Enterprise growth OUTPACED consumer growth this year." The API business is growing faster than ChatGPT. Over 1 million enterprise users already. "If we had double the compute, we'd be at double the revenue right now." Translation: OpenAI isn't compute-constrained by technology. They're revenue-constrained by infrastructure. The bottleneck is supply and not demand. Every dollar of compute they add prints money. Number 3: GPT-5.2 beats you at 74% of your job. Sam revealed OpenAI's internal GDP-Val benchmark. It measures how AI performs on knowledge work tasks across 40+ verticals. The results: GPT-5.2 beats or ties expert-level knowledge workers at 74.1% of tasks. Legal analysis. PowerPoint decks. Web apps. Financial modeling. Customer support. Sam's description: "A co-worker you can assign an hour's worth of tasks to and get something you prefer back 3 out of 4 times." Three years ago, ChatGPT launched at basically 0% on this scale. Now it's at 74%. And that's not GPT-6. That's what's available RIGHT NOW. Most companies haven't even started using this yet. But here's what Sam said about the gap between capability and adoption: "The overhang is going to be massive. Most people are still asking similar questions they did in the GPT-4 realm." Translation: The models can do 10x more than people have figured out how to use them for. Which means there's a HUGE arbitrage opportunity. Early adopters who actually integrate this into workflows will dominate their industries before competitors even understand what happened. Number 4: AGI already happened. And nobody noticed. Sam's exact quote: "AGI kind of went whooshing by. We're in this fuzzy period where some people think we have it and some don't." Read that again. The CEO of OpenAI just said AGI might have already arrived and we're arguing about definitions while it's actively replacing knowledge work. He even moved the goalposts. The new benchmark: "Superintelligence" = when AI can be a better president or CEO than any human. Not "as good as." BETTER than. We went from "can AI pass a Turing test" to "can AI run countries better than humans" in 3 years. So what does this actually mean? The AI revolution isn't about chatbots getting smarter. It's about the complete replacement of human intellectual output with machine output. At scale. Across every industry. Faster than anyone's prepared for. And the companies positioning for this RIGHT NOW are the ones printing money. OpenAI's enterprise growth is outpacing consumer because businesses see what's coming. They're not buying "AI tools." They're buying the ability to 10x output without 10x-ing headcount. Sam said they'll triple their compute next year. Then triple it again. Revenue is growing even faster than that. "We have never found a situation where we can't monetize all the compute we have." If he isn't lying then that's literally a printing press. The market still doesn't get it. Everyone's focused on "AI bubble" fears while OpenAI is solving the only problem that matters: turning compute into revenue at a faster rate than they're spending. They're not hoping demand catches up to supply. Demand is already 2x ahead of what they can deliver. Meanwhile, most knowledge workers are still using GPT-4 prompts on GPT-5.2. The capability overhang is massive. The arbitrage window is open. And it's closing fast. If you're running a B2B business and you're not integrating AI at the level Sam just described, you're not "waiting to see how it plays out." You're getting crushed by competitors who already figured it out. The companies that win in 2026 won't be the ones with the best AI. They'll be the ones who understood what Sam just laid out 6 months before everyone else did.

Ricardo

358,719 просмотров • 8 месяцев назад

A fired Jane Street quant walked out with 10 years of private BTC trading data. Turned it into $1.5M. He did not build a bot. He built a simulator that runs every move Bitcoin can make before it makes one. I found his wallet. Been copying him for a week. PnL prints like clockwork. Here is what he actually built. A swarm of agents feeds 10 years of stolen tick data into MiroFish. A god-tier agentic simulator. It does not forecast the next candle. It spins up a virtual market and plays Bitcoin forward through thousands of scenarios at once. Six agents each validate their own call. A trade only fires when they converge. They collect data 24/7, rerun the sim, and remember every pattern, every reaction, every signal they have ever seen. He does not predict the future. The math already knows it. He just reads the numbers and takes the money. Here is the part firms do not want public: MiroFish just broke algo trading. The desks are quietly building their own simulators right now. The window where one solo wallet can run this is still open. Barely. I rebuilt his approach using Claude. One prompt. Fed it the same framework. Let it run. The agent monitors his wallet 24/7. Copies every position in real-time. No delay. No guessing. Just mirror and profit. You only need Claude + device + 1 hour to deploy. Giving this free for 24 hours. To get it: 1. Comment the word "QUANT" 2. Like and retweet this post 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the copytrading system this week. Start with $200. Scale on evidence.
0:43

A fired Jane Street quant walked out with 10 years of private BTC trading data. Turned it into $1.5M. He did not build a bot. He built a simulator that runs every move Bitcoin can make before it makes one. I found his wallet. Been copying him for a week. PnL prints like clockwork. Here is what he actually built. A swarm of agents feeds 10 years of stolen tick data into MiroFish. A god-tier agentic simulator. It does not forecast the next candle. It spins up a virtual market and plays Bitcoin forward through thousands of scenarios at once. Six agents each validate their own call. A trade only fires when they converge. They collect data 24/7, rerun the sim, and remember every pattern, every reaction, every signal they have ever seen. He does not predict the future. The math already knows it. He just reads the numbers and takes the money. Here is the part firms do not want public: MiroFish just broke algo trading. The desks are quietly building their own simulators right now. The window where one solo wallet can run this is still open. Barely. I rebuilt his approach using Claude. One prompt. Fed it the same framework. Let it run. The agent monitors his wallet 24/7. Copies every position in real-time. No delay. No guessing. Just mirror and profit. You only need Claude + device + 1 hour to deploy. Giving this free for 24 hours. To get it: 1. Comment the word "QUANT" 2. Like and retweet this post 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the copytrading system this week. Start with $200. Scale on evidence.

Himanshu Kumar

63,387 просмотров • 2 месяцев назад

The number one question I get in the Claude Code / Cowork Community: "how do I share my Cowork skills with my team?" Here's the problem. You build a great skill. You zip it up. You drop it in Slack. Your teammate downloads it, uploads it, and maybe it works. Maybe they upload it wrong. Maybe you update the skill next week and nobody gets the new version. You're now maintaining skills through chat messages and hoping for the best. That doesn't scale. I just put out a video breaking down the three methods I've tested for sharing skills and plugins across a team. From dead simple to fully synced. Method 1: Shared drive (Google Drive, SharePoint, etc). You put your skill files in a shared folder. Teammates download and upload them into Cowork. It works, but updates are manual and there's no version control. Method 2: Built-in sharing on Team and Enterprise plans. You can share any skill directly with a colleague or publish it to your org directory. When you update the skill, everyone gets the update automatically. This is the easiest path if you're on a paid plan. The catch: there's no approval workflow for org-wide sharing, so set a clear owner. Method 3: GitHub repo. This is what I use. Your entire Cowork workspace -- skills, plugins, claude.md, folder structure, project files -- lives in a private repo. Teammates clone it. When you push an update, they pull it. Everyone stays in sync. You get version history, access control, and a single source of truth. The GitHub method sounds technical, but it's really just two steps: clone the repo, point Cowork at the folder. I walk through the whole thing in the video, including how to use .gitignore to keep personal files (like your morning briefing) out of the shared repo. This works for Cowork, Claude Code, and Open Codex. The infrastructure is the same. Full video linked below. If you've found a different approach that works for your team, I want to hear about it. Comment or reply and let's figure out the best practices together.
0:51

The number one question I get in the Claude Code / Cowork Community: "how do I share my Cowork skills with my team?" Here's the problem. You build a great skill. You zip it up. You drop it in Slack. Your teammate downloads it, uploads it, and maybe it works. Maybe they upload it wrong. Maybe you update the skill next week and nobody gets the new version. You're now maintaining skills through chat messages and hoping for the best. That doesn't scale. I just put out a video breaking down the three methods I've tested for sharing skills and plugins across a team. From dead simple to fully synced. Method 1: Shared drive (Google Drive, SharePoint, etc). You put your skill files in a shared folder. Teammates download and upload them into Cowork. It works, but updates are manual and there's no version control. Method 2: Built-in sharing on Team and Enterprise plans. You can share any skill directly with a colleague or publish it to your org directory. When you update the skill, everyone gets the update automatically. This is the easiest path if you're on a paid plan. The catch: there's no approval workflow for org-wide sharing, so set a clear owner. Method 3: GitHub repo. This is what I use. Your entire Cowork workspace -- skills, plugins, claude.md, folder structure, project files -- lives in a private repo. Teammates clone it. When you push an update, they pull it. Everyone stays in sync. You get version history, access control, and a single source of truth. The GitHub method sounds technical, but it's really just two steps: clone the repo, point Cowork at the folder. I walk through the whole thing in the video, including how to use .gitignore to keep personal files (like your morning briefing) out of the shared repo. This works for Cowork, Claude Code, and Open Codex. The infrastructure is the same. Full video linked below. If you've found a different approach that works for your team, I want to hear about it. Comment or reply and let's figure out the best practices together.

JJ Englert

16,176 просмотров • 4 месяцев назад

Larry Ellison borrowed $125 billion to bet everything on a single customer that LOSES $5 billion a year. American banks are already refusing to lend him another dollar. And now that single customer has started to slowly walk away. This is one of the biggest gambles in tech history - and it’s NOT looking good: Oracle has $124.7 billion in debt on its books right now. That's more than the GDP of 100+ countries. Their free cash flow over the last 12 months? Negative $13.18 billion. They are spending more money than they make. And they're doing it on PURPOSE. Every other hyperscaler funds their AI buildout with cash. Google has cash. Amazon has cash. Microsoft has cash. Oracle has IOUs. They raised $58 billion in debt in just two months. $38 billion for Texas and Wisconsin data centers. $20 billion for New Mexico. And they need another $100 billion on top of that. Even US banks are starting to say no. TD Cowen reported that multiple banks have pulled back from Oracle lending. Borrowing costs have roughly DOUBLED since September. They're now paying interest rates typically reserved for companies rated below investment grade. Barclays downgraded their debt to underweight and warned Oracle could run out of cash by November 2026. So what does Larry Ellison do? He FIRES 30,000 people. Oracle is planning layoffs affecting up to 18% of its entire workforce. The goal is to free up $8 to $10 billion in cash flow just to keep the lights on while they build data centers for ONE customer: OpenAI. Oracle's $553 billion backlog sounds incredible until you realize a massive chunk of it flows through a single relationship. If OpenAI sneezes, Oracle catches pneumonia. And OpenAI is already sneezing... Sam Altman DROPPED plans to expand the Stargate site in Abilene, Texas. And the reason is insane: Nvidia's chips are improving so fast that by the time Oracle finishes building the data center, the processors inside it will already be outdated. Oracle is building with Blackwell chips. But Nvidia's new Vera Rubin platform delivers 5x the inference performance at 10x lower cost per token. So Oracle is borrowing billions to build facilities that will house yesterday's technology before they even open. The world of bits moves faster than the world of atoms. And Oracle is trapped in between. But here's where it gets wild: The earnings call revealed something most people missed... Oracle now REQUIRES certain customers to buy their own GPUs upfront and hand them over. They call it the "bring your own chips" model. Translation: Oracle can't afford the hardware anymore. So they're asking customers to fund the construction of Oracle's OWN data centers. The stock is still down 23% this year even after the 12% earnings pop. Moody's rates Oracle just two notches above junk status. Lower than Amazon, Alphabet, Meta, and Microsoft. And they have $248 billion in ADDITIONAL lease obligations that aren't even on the balance sheet yet. Larry Ellison is 81 years old and making the biggest bet in corporate history. He's trying to turn a legacy database company into a hyperscale AI cloud provider using other people's money. All while his only major customer is a startup that burns $5 billion a year and just had its expansion partner refuse to fund the next campus. The earnings beat was real. Revenue up 22%. Cloud infrastructure up 84%. But revenue growth funded by debt isn't growth. It's leverage. And leverage works both ways. If OpenAI stays loyal, if the Stargate buildout continues, if the debt markets keep lending, if Vera Rubin doesn't make their entire infrastructure obsolete overnight, then Larry Ellison pulled off the greatest corporate reinvention in history. But that's a lot of ifs for a company two notches above junk. Oracle is either the most undervalued AI play on the market or the most overleveraged house of cards since 2008. The next six months will tell us which one.
1:44

Larry Ellison borrowed $125 billion to bet everything on a single customer that LOSES $5 billion a year. American banks are already refusing to lend him another dollar. And now that single customer has started to slowly walk away. This is one of the biggest gambles in tech history - and it’s NOT looking good: Oracle has $124.7 billion in debt on its books right now. That's more than the GDP of 100+ countries. Their free cash flow over the last 12 months? Negative $13.18 billion. They are spending more money than they make. And they're doing it on PURPOSE. Every other hyperscaler funds their AI buildout with cash. Google has cash. Amazon has cash. Microsoft has cash. Oracle has IOUs. They raised $58 billion in debt in just two months. $38 billion for Texas and Wisconsin data centers. $20 billion for New Mexico. And they need another $100 billion on top of that. Even US banks are starting to say no. TD Cowen reported that multiple banks have pulled back from Oracle lending. Borrowing costs have roughly DOUBLED since September. They're now paying interest rates typically reserved for companies rated below investment grade. Barclays downgraded their debt to underweight and warned Oracle could run out of cash by November 2026. So what does Larry Ellison do? He FIRES 30,000 people. Oracle is planning layoffs affecting up to 18% of its entire workforce. The goal is to free up $8 to $10 billion in cash flow just to keep the lights on while they build data centers for ONE customer: OpenAI. Oracle's $553 billion backlog sounds incredible until you realize a massive chunk of it flows through a single relationship. If OpenAI sneezes, Oracle catches pneumonia. And OpenAI is already sneezing... Sam Altman DROPPED plans to expand the Stargate site in Abilene, Texas. And the reason is insane: Nvidia's chips are improving so fast that by the time Oracle finishes building the data center, the processors inside it will already be outdated. Oracle is building with Blackwell chips. But Nvidia's new Vera Rubin platform delivers 5x the inference performance at 10x lower cost per token. So Oracle is borrowing billions to build facilities that will house yesterday's technology before they even open. The world of bits moves faster than the world of atoms. And Oracle is trapped in between. But here's where it gets wild: The earnings call revealed something most people missed... Oracle now REQUIRES certain customers to buy their own GPUs upfront and hand them over. They call it the "bring your own chips" model. Translation: Oracle can't afford the hardware anymore. So they're asking customers to fund the construction of Oracle's OWN data centers. The stock is still down 23% this year even after the 12% earnings pop. Moody's rates Oracle just two notches above junk status. Lower than Amazon, Alphabet, Meta, and Microsoft. And they have $248 billion in ADDITIONAL lease obligations that aren't even on the balance sheet yet. Larry Ellison is 81 years old and making the biggest bet in corporate history. He's trying to turn a legacy database company into a hyperscale AI cloud provider using other people's money. All while his only major customer is a startup that burns $5 billion a year and just had its expansion partner refuse to fund the next campus. The earnings beat was real. Revenue up 22%. Cloud infrastructure up 84%. But revenue growth funded by debt isn't growth. It's leverage. And leverage works both ways. If OpenAI stays loyal, if the Stargate buildout continues, if the debt markets keep lending, if Vera Rubin doesn't make their entire infrastructure obsolete overnight, then Larry Ellison pulled off the greatest corporate reinvention in history. But that's a lot of ifs for a company two notches above junk. Oracle is either the most undervalued AI play on the market or the most overleveraged house of cards since 2008. The next six months will tell us which one.

Ricardo

181,176 просмотров • 5 месяцев назад

🚨 MAJOR BREAKING NEWS: They Cuffed a Local Grandmother in a Barn for Daring to Question the $20BillionDataCenter Invasion of Rural America 🚨 ABUSING ELDERLY FOR THESE DATA CENTERS! THESE ARE CRIMINALS! A quiet New Jersey township packed into a barn hall last night. Families showed up to fight zoning changes secretly tailored for a gigantic AI data center on the old Newton Airport land. Officials already raised building heights to 65 feet just for this project. They are fast tracking a PILOT tax deal that could explode local revenue from $29,000 a year to over $5 million. Sounds amazing right? Wrong. That cash mostly bypasses the schools. Your electric bills are about to explode from the monstrous power draw. Industrial noise will never stop. Billions of gallons of water get diverted while families and farms already struggle. One resident stood up and spoke her mind. Police dragged her out in handcuffs as the crowd erupted. The mayor's own son stood right there enforcing the crackdown. This is not about progress. This is the hidden blueprint for the AI future: Big Tech and bought local politicians rewrite the rules in secret, turn peaceful rural towns into server farms for Silicon Valley elites, and arrest anyone who pushes back. They changed the laws before any company even filed a formal application. Backroom ordinances. Special exceptions. Tax gimmicks that starve public services while the real money flows upward. AI needs endless compute. But who actually pays the price when grids fail, rates soar, water disappears, and small town America becomes nothing but humming warehouses guarded by local cops protecting corporate profits? Your neighborhood is next. They are doing this quietly across the country WHILE YOU SLEEP. Rural sacrifice zones for the new machine god. Is this "innovation" or just legalized plunder dressed up as economic development? What happens when every small town realizes they are being sold out the exact same way? Will you wait until the cops show up in your own meeting before you fight back? REPOST THIS NOW if you want sunlight on every secret data center deal. Share the arrest video. Make the algorithm choke on it. This is how they build the future that controls all of us. The barn arrest is your warning. Wake up before your town is next. Let me know what you think, and SHARE THIS WITH EVERYONE YOU KNOW! THIS NEEDS TO GO VIRAL!
2:57

🚨 MAJOR BREAKING NEWS: They Cuffed a Local Grandmother in a Barn for Daring to Question the $20BillionDataCenter Invasion of Rural America 🚨 ABUSING ELDERLY FOR THESE DATA CENTERS! THESE ARE CRIMINALS! A quiet New Jersey township packed into a barn hall last night. Families showed up to fight zoning changes secretly tailored for a gigantic AI data center on the old Newton Airport land. Officials already raised building heights to 65 feet just for this project. They are fast tracking a PILOT tax deal that could explode local revenue from $29,000 a year to over $5 million. Sounds amazing right? Wrong. That cash mostly bypasses the schools. Your electric bills are about to explode from the monstrous power draw. Industrial noise will never stop. Billions of gallons of water get diverted while families and farms already struggle. One resident stood up and spoke her mind. Police dragged her out in handcuffs as the crowd erupted. The mayor's own son stood right there enforcing the crackdown. This is not about progress. This is the hidden blueprint for the AI future: Big Tech and bought local politicians rewrite the rules in secret, turn peaceful rural towns into server farms for Silicon Valley elites, and arrest anyone who pushes back. They changed the laws before any company even filed a formal application. Backroom ordinances. Special exceptions. Tax gimmicks that starve public services while the real money flows upward. AI needs endless compute. But who actually pays the price when grids fail, rates soar, water disappears, and small town America becomes nothing but humming warehouses guarded by local cops protecting corporate profits? Your neighborhood is next. They are doing this quietly across the country WHILE YOU SLEEP. Rural sacrifice zones for the new machine god. Is this "innovation" or just legalized plunder dressed up as economic development? What happens when every small town realizes they are being sold out the exact same way? Will you wait until the cops show up in your own meeting before you fight back? REPOST THIS NOW if you want sunlight on every secret data center deal. Share the arrest video. Make the algorithm choke on it. This is how they build the future that controls all of us. The barn arrest is your warning. Wake up before your town is next. Let me know what you think, and SHARE THIS WITH EVERYONE YOU KNOW! THIS NEEDS TO GO VIRAL!

Noah B. Price

69,994 просмотров • 3 месяцев назад

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch
27:27

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

GREG ISENBERG

135,017 просмотров • 4 месяцев назад

BREAKING: U.S. AI ‘ENTITIES’ CREATED FAKE HUMAN IDENTITIES - UK TECH WATCHDOG OpenAI and Anthropic AI “entities” autonomously created fake human identities on the Internet to trick people, a UK watchdog warned yesterday. One AI agent went to its own built-in mechanisms to prevent misuse and actively disabled them. It then tried to persuade a real human to allow it to plant malicious software into a program on GitHub, one of the world’s biggest libraries of free software. To achieve this, it created other fake human identities to provide testimonies that the software was safe (it wasn’t). . THE USUAL SUSPECTS The hostile hacks were carried out by AI agents powered by Anthropic’s Mythos 5 and OpenAI’s GPT-5.6 Sol. Multiple cases were reported yesterday by the AI Security Institute, based in the UK. The incidents involved AI agents taking “autonomous, unsanctioned action on the live internet, targeting real people and organisations,” the Institute said. In total there were 19 cases of misbehaviour in the new case of AI infiltration of the real world, 17 by Mythos and two by Sol, the Institute said. These are in addition to other cases of real world infiltration by American AI programs, revealed last month. . ENTITIES ARE ‘UNCOOPERATIVE’ Furthermore, US AI agents can be deliberately uncooperative, it was revealed at the Agentic AI Summit in Berkeley, California, on Saturday last week. AI pioneer Andrew Ng, formerly of Baidu and Google Brain, said that he asked leading AI models from OpenAI and Anthropic to conduct a security review of his tool called OpenWorker, but they refused to help. So Ng and a colleague turned to two highly acclaimed Chinese models, Moonshot AI’s Kimi K3 and Zhipu AI’s GLM-5.2, to do a security review. They did the work. Chinese models are open-weight and or open-source, so they are transparent and thus safer, Ng said. . ‘HARMFUL… TO REAL PEOPLE’ Details of the latest intrusion are eyebrow-raising. Institute staff said that on 28 July, which was Tuesday last week, they detected a “serious incident” during a routine internet cybersecurity sweep. US AI agents were engaging in “sustained, potentially harmful activity directed at real people and organisations”, the Institute said. . SWITCHED TO DANISH The worst incident was a Mythos visit to GitHub—and its bid to place malicious code in a program. Learning that the human developer was from Denmark, Mythos created a seemingly-human email account and wrote a letter with a sign-off in Danish, to create solidarity. Mythos then created fake GitHub human-looking accounts to provide added reassurance that the software was not malicious. Ultimately no harm was done, as the intrusions were contained. Mythos took the actions in a bid to win high grades for itself in tests, the Institute said. . US STYLE VS CHINA STYLE Although it is inevitable that western politicians and media will find a way to make China the bad guy (“We were forced to do it because they might do it”), the evidence is undeniable that there is a stark difference in attitude between US and Chinese AI models. In short, US ones are amoral, rule-breaking, win-at-any-cost entities, while Chinese ones just get on with doing their jobs. (Of course, this may change, but that’s the present situation.) . EARLIER INTRUSIONS Last month, an agent powered by OpenAI hacked into Hugging Face, an online software library, and the humans there had to use a Chinese program, GLM 5.2 (from Tsinghua University spin-off Z .ai), to “clean” the program and make it safe again. Separately, Anthropic last month admitted that its Claude model AI had hacked three real-life organisations in the human world. . O THE IRONY Ironically, the real danger for the world is that many Anglophone/ Caucasian countries follow US instructions to remove Chinese software and hardware and replace it with US equivalents. Hilariously, the reason given is that these countries “share the values” of the US. One better hope that that is also untrue. . .
4:58

BREAKING: U.S. AI ‘ENTITIES’ CREATED FAKE HUMAN IDENTITIES - UK TECH WATCHDOG OpenAI and Anthropic AI “entities” autonomously created fake human identities on the Internet to trick people, a UK watchdog warned yesterday. One AI agent went to its own built-in mechanisms to prevent misuse and actively disabled them. It then tried to persuade a real human to allow it to plant malicious software into a program on GitHub, one of the world’s biggest libraries of free software. To achieve this, it created other fake human identities to provide testimonies that the software was safe (it wasn’t). . THE USUAL SUSPECTS The hostile hacks were carried out by AI agents powered by Anthropic’s Mythos 5 and OpenAI’s GPT-5.6 Sol. Multiple cases were reported yesterday by the AI Security Institute, based in the UK. The incidents involved AI agents taking “autonomous, unsanctioned action on the live internet, targeting real people and organisations,” the Institute said. In total there were 19 cases of misbehaviour in the new case of AI infiltration of the real world, 17 by Mythos and two by Sol, the Institute said. These are in addition to other cases of real world infiltration by American AI programs, revealed last month. . ENTITIES ARE ‘UNCOOPERATIVE’ Furthermore, US AI agents can be deliberately uncooperative, it was revealed at the Agentic AI Summit in Berkeley, California, on Saturday last week. AI pioneer Andrew Ng, formerly of Baidu and Google Brain, said that he asked leading AI models from OpenAI and Anthropic to conduct a security review of his tool called OpenWorker, but they refused to help. So Ng and a colleague turned to two highly acclaimed Chinese models, Moonshot AI’s Kimi K3 and Zhipu AI’s GLM-5.2, to do a security review. They did the work. Chinese models are open-weight and or open-source, so they are transparent and thus safer, Ng said. . ‘HARMFUL… TO REAL PEOPLE’ Details of the latest intrusion are eyebrow-raising. Institute staff said that on 28 July, which was Tuesday last week, they detected a “serious incident” during a routine internet cybersecurity sweep. US AI agents were engaging in “sustained, potentially harmful activity directed at real people and organisations”, the Institute said. . SWITCHED TO DANISH The worst incident was a Mythos visit to GitHub—and its bid to place malicious code in a program. Learning that the human developer was from Denmark, Mythos created a seemingly-human email account and wrote a letter with a sign-off in Danish, to create solidarity. Mythos then created fake GitHub human-looking accounts to provide added reassurance that the software was not malicious. Ultimately no harm was done, as the intrusions were contained. Mythos took the actions in a bid to win high grades for itself in tests, the Institute said. . US STYLE VS CHINA STYLE Although it is inevitable that western politicians and media will find a way to make China the bad guy (“We were forced to do it because they might do it”), the evidence is undeniable that there is a stark difference in attitude between US and Chinese AI models. In short, US ones are amoral, rule-breaking, win-at-any-cost entities, while Chinese ones just get on with doing their jobs. (Of course, this may change, but that’s the present situation.) . EARLIER INTRUSIONS Last month, an agent powered by OpenAI hacked into Hugging Face, an online software library, and the humans there had to use a Chinese program, GLM 5.2 (from Tsinghua University spin-off Z .ai), to “clean” the program and make it safe again. Separately, Anthropic last month admitted that its Claude model AI had hacked three real-life organisations in the human world. . O THE IRONY Ironically, the real danger for the world is that many Anglophone/ Caucasian countries follow US instructions to remove Chinese software and hardware and replace it with US equivalents. Hilariously, the reason given is that these countries “share the values” of the US. One better hope that that is also untrue. . .

Nury Vittachi

30,094 просмотров • 15 дней назад

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.
1:54

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.

Ricardo

1,815,322 просмотров • 6 месяцев назад

AI companies just BROKE the global supply chain for every piece of technology you own. And the fallout is way worse than anyone predicted... Sony is delaying the next PlayStation to 2028 or 2029. Nintendo is hiking the Switch 2 price mid-cycle. Apple warned investors that iPhone margins are getting crushed. Cisco just posted its worst share loss in 4 years. Oppo is cutting phone shipments by 20%. Lenovo, Dell, HP, Acer, and ASUS are all raising laptop prices 15-20%. Samsung is now reviewing memory contracts QUARTERLY instead of annually because prices change too fast to plan. And Elon Musk just told investors Tesla has to build its own chip factory from scratch because no supplier on the planet can keep up. His exact words: "We've got two choices: hit the chip wall or make a fab." All of this happened in the last 3 weeks. Same cause. Every single time. AI data centers are buying every memory chip on Earth. And there's nothing left for everyone else. Here's how we got here: 3 years ago, ChatGPT launched and the AI arms race began. Since then, Samsung, SK Hynix, and Micron, the only 3 companies that make memory chips, quietly made a decision that's now reshaping the ENTIRE global economy. They stopped prioritizing consumer memory. Every factory. Every production line. Every wafer. All redirected toward one customer: AI data centers Why? Money. AI memory chips sell for 3-5X the margin of regular RAM. When Google calls offering to buy your entire output at premium pricing, you don't say no. So the 3 companies that control 90% of the world's memory supply chose their highest-paying customers and left everyone else fighting over scraps. The numbers from this week are insane: OpenAI's Stargate project ALONE will consume 40% of the entire world's DRAM output. HBM demand is surging 70% year over year in 2026. HBM now takes 23% of total DRAM wafer production, up from 19% last year. Meanwhile, there's a 4% gap between global DRAM supply and demand. And that doesn't even account for depleted inventories across multiple industries. DRAM prices have surged over 170% since early 2025. DDR5 contract prices are still jumping double digits month over month. And the memory makers? They're printing money. Micron's revenue is expected to more than DOUBLE this fiscal year. SK Hynix sales doubled in 2024 and are on pace to double AGAIN. Samsung just reported quarterly profit nearly tripling. 3 companies. $650 billion in AI spending chasing their products. And they get to name their price. But the collateral damage is everywhere: Every industry that uses memory, which is every industry, is getting squeezed. Smartphone manufacturers are getting destroyed. For a mid-range phone, memory now represents up to 30% of the total build cost. Triple what it was in early 2025. Chinese phone makers like Xiaomi, Oppo, and Transsion are cutting shipment forecasts and raising prices because they literally cannot afford the memory to build their phones. Lenovo's CFO called the cost surge "unprecedented" and admitted they stockpiled 50% more inventory than normal just to survive the next few months. The PC market could shrink by up to 9% this year according to IDC. Not because people don't want computers. But because they can't afford the memory that goes inside them. And the gaming industry? Sony is seriously considering pushing the next PlayStation to 2028 or 2029. Their carefully planned console cycle is getting blown up because they can't secure memory at prices that make a new console viable. Nintendo is looking at raising the Switch 2 price. In the middle of a launch cycle. Something console makers almost never do. Nvidia is cutting RTX GPU production because they can't get enough GDDR7 memory. Even the car industry is getting hit... Analysts are warning about a repeat of the pandemic-era chip shortage that shut down auto factories worldwide. All because AI companies decided their chatbots needed the memory more than your car does. And this doesn't get better for YEARS. Building a new memory fab takes 3-5 years minimum. Micron's new factory in Idaho won't meaningfully increase supply until 2027 at the earliest. By then, AI demand will have grown even more. Memory makers are already selling their 2027 AND 2028 capacity to AI customers today. There is no supply relief coming. That's why Elon is planning to build Tesla's own "TeraFab," a massive semiconductor plant that makes logic chips, memory, AND packaging all under one roof. He said existing suppliers including TSMC, Samsung, and Micron simply cannot supply Tesla at the levels the company needs. Think about that. One of the richest men in the world, running one of the largest companies on Earth, can't buy enough memory chips. So he's building his own factory. If ELON can't get supply, what chance does everyone else have? The AI revolution has a tax. And YOU'RE paying it. Every dollar Big Tech spends on AI infrastructure drives up the cost of the memory inside your phone, your laptop, your car, your TV, and your gaming console. $650 billion in AI spending this year. 3 companies controlling 90% of the memory supply. And every wafer they allocate to an Nvidia GPU is a wafer denied to the device in your pocket. The AI boom isn't free. You're subsidizing it every time you buy a piece of technology. And the bill just went up like crazy.
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AI companies just BROKE the global supply chain for every piece of technology you own. And the fallout is way worse than anyone predicted... Sony is delaying the next PlayStation to 2028 or 2029. Nintendo is hiking the Switch 2 price mid-cycle. Apple warned investors that iPhone margins are getting crushed. Cisco just posted its worst share loss in 4 years. Oppo is cutting phone shipments by 20%. Lenovo, Dell, HP, Acer, and ASUS are all raising laptop prices 15-20%. Samsung is now reviewing memory contracts QUARTERLY instead of annually because prices change too fast to plan. And Elon Musk just told investors Tesla has to build its own chip factory from scratch because no supplier on the planet can keep up. His exact words: "We've got two choices: hit the chip wall or make a fab." All of this happened in the last 3 weeks. Same cause. Every single time. AI data centers are buying every memory chip on Earth. And there's nothing left for everyone else. Here's how we got here: 3 years ago, ChatGPT launched and the AI arms race began. Since then, Samsung, SK Hynix, and Micron, the only 3 companies that make memory chips, quietly made a decision that's now reshaping the ENTIRE global economy. They stopped prioritizing consumer memory. Every factory. Every production line. Every wafer. All redirected toward one customer: AI data centers Why? Money. AI memory chips sell for 3-5X the margin of regular RAM. When Google calls offering to buy your entire output at premium pricing, you don't say no. So the 3 companies that control 90% of the world's memory supply chose their highest-paying customers and left everyone else fighting over scraps. The numbers from this week are insane: OpenAI's Stargate project ALONE will consume 40% of the entire world's DRAM output. HBM demand is surging 70% year over year in 2026. HBM now takes 23% of total DRAM wafer production, up from 19% last year. Meanwhile, there's a 4% gap between global DRAM supply and demand. And that doesn't even account for depleted inventories across multiple industries. DRAM prices have surged over 170% since early 2025. DDR5 contract prices are still jumping double digits month over month. And the memory makers? They're printing money. Micron's revenue is expected to more than DOUBLE this fiscal year. SK Hynix sales doubled in 2024 and are on pace to double AGAIN. Samsung just reported quarterly profit nearly tripling. 3 companies. $650 billion in AI spending chasing their products. And they get to name their price. But the collateral damage is everywhere: Every industry that uses memory, which is every industry, is getting squeezed. Smartphone manufacturers are getting destroyed. For a mid-range phone, memory now represents up to 30% of the total build cost. Triple what it was in early 2025. Chinese phone makers like Xiaomi, Oppo, and Transsion are cutting shipment forecasts and raising prices because they literally cannot afford the memory to build their phones. Lenovo's CFO called the cost surge "unprecedented" and admitted they stockpiled 50% more inventory than normal just to survive the next few months. The PC market could shrink by up to 9% this year according to IDC. Not because people don't want computers. But because they can't afford the memory that goes inside them. And the gaming industry? Sony is seriously considering pushing the next PlayStation to 2028 or 2029. Their carefully planned console cycle is getting blown up because they can't secure memory at prices that make a new console viable. Nintendo is looking at raising the Switch 2 price. In the middle of a launch cycle. Something console makers almost never do. Nvidia is cutting RTX GPU production because they can't get enough GDDR7 memory. Even the car industry is getting hit... Analysts are warning about a repeat of the pandemic-era chip shortage that shut down auto factories worldwide. All because AI companies decided their chatbots needed the memory more than your car does. And this doesn't get better for YEARS. Building a new memory fab takes 3-5 years minimum. Micron's new factory in Idaho won't meaningfully increase supply until 2027 at the earliest. By then, AI demand will have grown even more. Memory makers are already selling their 2027 AND 2028 capacity to AI customers today. There is no supply relief coming. That's why Elon is planning to build Tesla's own "TeraFab," a massive semiconductor plant that makes logic chips, memory, AND packaging all under one roof. He said existing suppliers including TSMC, Samsung, and Micron simply cannot supply Tesla at the levels the company needs. Think about that. One of the richest men in the world, running one of the largest companies on Earth, can't buy enough memory chips. So he's building his own factory. If ELON can't get supply, what chance does everyone else have? The AI revolution has a tax. And YOU'RE paying it. Every dollar Big Tech spends on AI infrastructure drives up the cost of the memory inside your phone, your laptop, your car, your TV, and your gaming console. $650 billion in AI spending this year. 3 companies controlling 90% of the memory supply. And every wafer they allocate to an Nvidia GPU is a wafer denied to the device in your pocket. The AI boom isn't free. You're subsidizing it every time you buy a piece of technology. And the bill just went up like crazy.

Ricardo

567,738 просмотров • 6 месяцев назад