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Jisko real data chahiye inbox ao Sirf payment walay real data duga telegram par Pakistani mom son vip videos available hain ajao full data duga Only paid

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Khan Sahab

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43,279 views • 5 months ago •via X (Twitter)

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Iran war ka impact sirf oil ya military tak limited nahi hai… balki global internet infrastructure tak pahunch chuka hai. Duniya ka 95% internet undersea fiber-optic cables se chalta hai, aur inme se kai critical routes Strait of Hormuz ke paas se guzarte hain. Agar war ke dauraan in cables ko damage ya disrupt kiya gaya, toh global level par internet slowdown, banking issues, stock market delays, aur UPI failures jaise serious problems dekhne ko mil sakte hain. India ke liye ye situation aur bhi critical hai kyunki hamara majority international data traffic Gulf region ke cables par depend karta hai. Agar ye network hit hota hai, toh IT companies jaise TCS aur Infosys ka work impact hoga, startups aur AI infrastructure par asar padega, aur digital economy temporary slowdown face kar sakti hai. Ye video aapko simple Hinglish mein samjhata hai ki kaise Iran war global internet ko affect kar sakta hai, aur India par iska kya real impact ho sakta hai. 👉 Authentic aur deep analysis ke liye follow zaroor karein. #IranWar #InternetBlackout #DigitalWar #IndiaImpact #GlobalCrisis
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Iran war ka impact sirf oil ya military tak limited nahi hai… balki global internet infrastructure tak pahunch chuka hai. Duniya ka 95% internet undersea fiber-optic cables se chalta hai, aur inme se kai critical routes Strait of Hormuz ke paas se guzarte hain. Agar war ke dauraan in cables ko damage ya disrupt kiya gaya, toh global level par internet slowdown, banking issues, stock market delays, aur UPI failures jaise serious problems dekhne ko mil sakte hain. India ke liye ye situation aur bhi critical hai kyunki hamara majority international data traffic Gulf region ke cables par depend karta hai. Agar ye network hit hota hai, toh IT companies jaise TCS aur Infosys ka work impact hoga, startups aur AI infrastructure par asar padega, aur digital economy temporary slowdown face kar sakti hai. Ye video aapko simple Hinglish mein samjhata hai ki kaise Iran war global internet ko affect kar sakta hai, aur India par iska kya real impact ho sakta hai. 👉 Authentic aur deep analysis ke liye follow zaroor karein. #IranWar #InternetBlackout #DigitalWar #IndiaImpact #GlobalCrisis

Dr. Mukul Agrawal

40,361 views • 4 months ago

Want to create an avatar from a single image? FlexAvatar is a transformer model that creates full 360°, high-quality, and expressive 3D head avatar from just a single portrait image in minutes. Real-time Demo: FlexAvatar's lightweight architecture allows both animation and rendering in real-time, enabling interactive user experiences. To create a new 3D head avatar, only one image is required, e.g., from a webcam. The final avatar is ready after 2 minutes. Architecture: Under the hood, FlexAvatar adopts a transformer-based encoder-decoder design. The encoder maps the input image onto a latent avatar space, while the decoder produces 3D Gaussian attribute maps by incorporating the animation signal via cross-attention. The model learns all facial animations directly from the data without relying on pre-built 3D face models. This equips the avatars with realistic facial expressions. The internal avatar latent space can be conveniently used to integrate additional observations of a person via fitting. This enables use-cases where more than one image of a person is available, e.g., from a phone scan of the person. We train jointly on 2D monocular videos and multi-view data. However, in monocular videos, the animation signal leaks the target viewpoint, causing the model to produce incomplete 3D heads. We call this phenomenon entanglement of driving signal and target viewpoint. To prevent entanglement, we introduce bias sinks. These are learnable tokens that indicate whether a training sample stems from a monocular or a multi-view dataset. During training, the model learns to produce incomplete 3D heads only when the monocular token is present. During inference, FlexAvatar then always uses the multi-view token for which the model has learned to produce complete 3D heads. This simple design allows to combine the generalizability from monocular data with the quality of multi-view data. FlexAvatar summary: - Input: Single-image, phone scan, or monocular video - Output: Full 360° head avatar - Expressive animations - Real-time rendering and animation - Generalization to any portrait - Create a new avatar in 2 minutes - Use bias sinks to combine 2D and 3D data 🏠 🌍 🎥 Great work by Tobias Kirschstein and Simon Giebenhain!
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Want to create an avatar from a single image? FlexAvatar is a transformer model that creates full 360°, high-quality, and expressive 3D head avatar from just a single portrait image in minutes. Real-time Demo: FlexAvatar's lightweight architecture allows both animation and rendering in real-time, enabling interactive user experiences. To create a new 3D head avatar, only one image is required, e.g., from a webcam. The final avatar is ready after 2 minutes. Architecture: Under the hood, FlexAvatar adopts a transformer-based encoder-decoder design. The encoder maps the input image onto a latent avatar space, while the decoder produces 3D Gaussian attribute maps by incorporating the animation signal via cross-attention. The model learns all facial animations directly from the data without relying on pre-built 3D face models. This equips the avatars with realistic facial expressions. The internal avatar latent space can be conveniently used to integrate additional observations of a person via fitting. This enables use-cases where more than one image of a person is available, e.g., from a phone scan of the person. We train jointly on 2D monocular videos and multi-view data. However, in monocular videos, the animation signal leaks the target viewpoint, causing the model to produce incomplete 3D heads. We call this phenomenon entanglement of driving signal and target viewpoint. To prevent entanglement, we introduce bias sinks. These are learnable tokens that indicate whether a training sample stems from a monocular or a multi-view dataset. During training, the model learns to produce incomplete 3D heads only when the monocular token is present. During inference, FlexAvatar then always uses the multi-view token for which the model has learned to produce complete 3D heads. This simple design allows to combine the generalizability from monocular data with the quality of multi-view data. FlexAvatar summary: - Input: Single-image, phone scan, or monocular video - Output: Full 360° head avatar - Expressive animations - Real-time rendering and animation - Generalization to any portrait - Create a new avatar in 2 minutes - Use bias sinks to combine 2D and 3D data 🏠 🌍 🎥 Great work by Tobias Kirschstein and Simon Giebenhain!

Matthias Niessner

96,186 views • 7 months ago

Understanding the BitTorrent Swarm — A Broader Look With Real Data Dynamics BitTorrent isn’t just a file-sharing protocol; it’s one of the most efficient large-scale distribution systems ever designed. At its core lies a simple but powerful principle: when users contribute bandwidth, the entire network accelerates. This is the swarm and its efficiency can be explained through clear data patterns and network behavior. 🔹 The Swarm Model: How Participation Becomes Performance In a traditional client-server setup, bandwidth is fixed. If 10,000 users try to download a 1 GB file from one server with 1 Gbps bandwidth: ➠ Maximum theoretical throughput per user: 0.1 Mbps ➠ Average download time: 2–3 hours ➠ Server overload: very likely BitTorrent rewrites this logic. When 10,000 users join a swarm and each contributes only 50–200 Kbps of upload bandwidth, the network’s total available throughput multiplies thousands of times. This is why, in real swarm studies: ➠ Larger swarms consistently show 30–400% faster download speeds ➠ Popular torrents reach equilibrium within minutes, not hours ➠ Throughput per user remains stable even under heavy demand BitTorrent’s efficiency grows with usage — something centralized systems struggle with. 🔹 Why More Peers = More Speed (Backed by Data Behavior) BitTorrent breaks files into hundreds or thousands of small pieces. Each piece circulates among peers using a strategy called rarest-first ensuring no piece becomes a bottleneck. Here’s what the data shows: 1. Bandwidth multiplication effect If each peer contributes: ➠ 100 peers × 100 Kbps upload = 10 Mbps swarm capacity ➠ 5,000 peers × 150 Kbps upload = 750 Mbps swarm capacity ➠ 20,000 peers × 200 Kbps upload = 4 Gbps swarm capacity This turning point when collective bandwidth surpasses any server is why torrents of large files often download faster than centralized sources. 2. Availability resilience Even if 90% of peers leave, as long as one full copy exists across the swarm’s collective pieces, the file is recoverable without interruption. 3. Load balancing automatically occurs BitTorrent’s choking/unchoking algorithm ensures: ➠ High-bandwidth peers exchange more data ➠ Low-bandwidth peers still participate ➠ No single peer becomes a bottleneck The data flow adapts in real time based on peer performance. 🔹 The Swarm’s Global Impact: Why It Still Matters BitTorrent traffic routinely accounts for: ➠ 10–20% of global internet upload traffic (varies by region) ➠ Multiple petabytes of data exchanged daily ➠ Millions of active swarms at any given time The model works because it scales with demand: ➠ More users → more bandwidth. ➠ More bandwidth → faster delivery. ➠ Faster delivery → stronger swarm health. This “self-reinforcing cycle” is a core reason decentralized systems from Web3 storage to blockchain data sync borrow heavily from BitTorrent’s architecture. 🔹 The Big Picture The BitTorrent swarm illustrates an important truth about decentralized networks: Efficiency doesn’t come from the center it comes from participation. When thousands of people contribute small amounts of bandwidth, the result is a global system capable of speeds that outperform traditional content delivery models. This is not just technology; it’s cooperative acceleration at internet scale. In One Line Files move faster when everyone contributes and BitTorrent proves it with real data. H.E. Justin Sun 👨‍🚀 🌞 BitTorrent #TRONEcoStar #BitTorrent #SwarmNetwork #DataAnalysis #DecentralizedSystems #P2P
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Understanding the BitTorrent Swarm — A Broader Look With Real Data Dynamics BitTorrent isn’t just a file-sharing protocol; it’s one of the most efficient large-scale distribution systems ever designed. At its core lies a simple but powerful principle: when users contribute bandwidth, the entire network accelerates. This is the swarm and its efficiency can be explained through clear data patterns and network behavior. 🔹 The Swarm Model: How Participation Becomes Performance In a traditional client-server setup, bandwidth is fixed. If 10,000 users try to download a 1 GB file from one server with 1 Gbps bandwidth: ➠ Maximum theoretical throughput per user: 0.1 Mbps ➠ Average download time: 2–3 hours ➠ Server overload: very likely BitTorrent rewrites this logic. When 10,000 users join a swarm and each contributes only 50–200 Kbps of upload bandwidth, the network’s total available throughput multiplies thousands of times. This is why, in real swarm studies: ➠ Larger swarms consistently show 30–400% faster download speeds ➠ Popular torrents reach equilibrium within minutes, not hours ➠ Throughput per user remains stable even under heavy demand BitTorrent’s efficiency grows with usage — something centralized systems struggle with. 🔹 Why More Peers = More Speed (Backed by Data Behavior) BitTorrent breaks files into hundreds or thousands of small pieces. Each piece circulates among peers using a strategy called rarest-first ensuring no piece becomes a bottleneck. Here’s what the data shows: 1. Bandwidth multiplication effect If each peer contributes: ➠ 100 peers × 100 Kbps upload = 10 Mbps swarm capacity ➠ 5,000 peers × 150 Kbps upload = 750 Mbps swarm capacity ➠ 20,000 peers × 200 Kbps upload = 4 Gbps swarm capacity This turning point when collective bandwidth surpasses any server is why torrents of large files often download faster than centralized sources. 2. Availability resilience Even if 90% of peers leave, as long as one full copy exists across the swarm’s collective pieces, the file is recoverable without interruption. 3. Load balancing automatically occurs BitTorrent’s choking/unchoking algorithm ensures: ➠ High-bandwidth peers exchange more data ➠ Low-bandwidth peers still participate ➠ No single peer becomes a bottleneck The data flow adapts in real time based on peer performance. 🔹 The Swarm’s Global Impact: Why It Still Matters BitTorrent traffic routinely accounts for: ➠ 10–20% of global internet upload traffic (varies by region) ➠ Multiple petabytes of data exchanged daily ➠ Millions of active swarms at any given time The model works because it scales with demand: ➠ More users → more bandwidth. ➠ More bandwidth → faster delivery. ➠ Faster delivery → stronger swarm health. This “self-reinforcing cycle” is a core reason decentralized systems from Web3 storage to blockchain data sync borrow heavily from BitTorrent’s architecture. 🔹 The Big Picture The BitTorrent swarm illustrates an important truth about decentralized networks: Efficiency doesn’t come from the center it comes from participation. When thousands of people contribute small amounts of bandwidth, the result is a global system capable of speeds that outperform traditional content delivery models. This is not just technology; it’s cooperative acceleration at internet scale. In One Line Files move faster when everyone contributes and BitTorrent proves it with real data. H.E. Justin Sun 👨‍🚀 🌞 BitTorrent #TRONEcoStar #BitTorrent #SwarmNetwork #DataAnalysis #DecentralizedSystems #P2P

catalina ossa

50,297 views • 8 months ago

Kled Version 3 is coming. Over $20M+ in rewards will be paid directly to users from leading AI labs across robotics, legal services, image and video generation, world modeling, and more. In the last seven days, we’ve received inbound data requests from several decacorn AI labs and enterprises for datasets our human data marketplace is uniquely positioned to provide. Since receiving the specs for these requests, we now have a much better picture and understanding of how to reshape the systems that collect this data, so here’s what’s coming: 1. A fully redesigned home experience: The home feed is being rebuilt to surface the highest-value, most relevant tasks for each user, similar to how Uber Eats surfaces top restaurants. The goal is to turn every user into their most effective version as a data contributor. 2. Automated quality enforcement at scale: New ML systems are being built to evaluate task-specific requirements in real time. For example, if a task requires “two hands visible on camera at all times,” any video that fails that spec will be automatically rejected. This logic will apply across thousands of tasks and specifications using a general ML. 3. Kled Shop: Some tasks require better capture hardware. We’re introducing Kled Shop, where users can redeem points or tokens for equipment like Meta glasses, drones, and other tools. Points and tokens can be converted directly from payouts. 4. Partner-run data labeling and evaluation work: Some of our partners operate high-paying data labeling and model evaluation programs. We’re integrating their workflows directly into Kled so qualified users can access these roles in one place. These jobs are owned and managed by our partners. Kled’s role is to route the right people to the right work. Some opportunities pay $50–$1,000 per hour depending on expertise. 5. Global payouts and localization: We’re partnering with a major payment processor to enable cashouts in users’ native currencies. This unlocks broader global participation. Multi-language support is also coming to accelerate user growth. This full suite of tools will be rolling out soon, directly to Kled users. Top earners are currently making ~$7,000 per month. With this update, we should see the first ~$10,000 per month earner.
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Kled Version 3 is coming. Over $20M+ in rewards will be paid directly to users from leading AI labs across robotics, legal services, image and video generation, world modeling, and more. In the last seven days, we’ve received inbound data requests from several decacorn AI labs and enterprises for datasets our human data marketplace is uniquely positioned to provide. Since receiving the specs for these requests, we now have a much better picture and understanding of how to reshape the systems that collect this data, so here’s what’s coming: 1. A fully redesigned home experience: The home feed is being rebuilt to surface the highest-value, most relevant tasks for each user, similar to how Uber Eats surfaces top restaurants. The goal is to turn every user into their most effective version as a data contributor. 2. Automated quality enforcement at scale: New ML systems are being built to evaluate task-specific requirements in real time. For example, if a task requires “two hands visible on camera at all times,” any video that fails that spec will be automatically rejected. This logic will apply across thousands of tasks and specifications using a general ML. 3. Kled Shop: Some tasks require better capture hardware. We’re introducing Kled Shop, where users can redeem points or tokens for equipment like Meta glasses, drones, and other tools. Points and tokens can be converted directly from payouts. 4. Partner-run data labeling and evaluation work: Some of our partners operate high-paying data labeling and model evaluation programs. We’re integrating their workflows directly into Kled so qualified users can access these roles in one place. These jobs are owned and managed by our partners. Kled’s role is to route the right people to the right work. Some opportunities pay $50–$1,000 per hour depending on expertise. 5. Global payouts and localization: We’re partnering with a major payment processor to enable cashouts in users’ native currencies. This unlocks broader global participation. Multi-language support is also coming to accelerate user growth. This full suite of tools will be rolling out soon, directly to Kled users. Top earners are currently making ~$7,000 per month. With this update, we should see the first ~$10,000 per month earner.

Avi Patel

124,728 views • 6 months ago

HERMES AGENT CAN RUN YOUR SEO. CONNECT IT TO GOOGLE SEARCH CONSOLE AND GOOGLE ANALYTICS. IT MONITORS, REPORTS, AND WRITES CONTENT BASED ON YOUR ACTUAL DATA. stop paying an SEO agency. stop doing the tedious work yourself. Hermes handles it 24/7. WHAT THE SEO AGENT DOES: → pulls clicks, impressions, CTR, and position data from Google Search Console automatically → tracks traffic, user behavior, and conversions from Google Analytics → checks which pages are indexed and which are not → submits sitemaps for indexing → inspects URLs for crawl or indexing issues → identifies ranking drops and keyword opportunities → writes content based on what your data says works → generates weekly SEO performance reports → delivers everything to Telegram CONNECT GOOGLE SEARCH CONSOLE: two paths: 1. COMPOSIO (managed, easiest): paste this into Hermes chat: https:// composio. dev/hermes or add to config.yaml: mcp_servers: composio: url: "https:// connect.composio. dev /mcp" headers: x-consumer-api-key: "YOUR_COMPOSIO_API_KEY" Hermes prompts you to authenticate. one OAuth flow. done. 2. CLAWLINK (one-click): 9 Google Search Console tools exposed via MCP. hosted auth. nothing to run or maintain. paste the install prompt into Hermes chat. CONNECT GOOGLE ANALYTICS: same Composio setup. one MCP endpoint handles both Search Console and Analytics. authenticate once. both data sources available. your agent can now query: → search analytics (clicks, impressions, CTR, position) → traffic by source and landing page → user behavior and conversions → indexing status for any URL → sitemap status WHAT TO AUTOMATE WITH CRON: weekly SEO report (Monday 8am): "pull search analytics for last 7 days. compare vs previous week. flag any keyword that dropped more than 5 positions. flag any page that lost more than 20% clicks. deliver report to Telegram." daily indexing check (6am): "check if any new pages are not indexed. if found, submit sitemap and report to Telegram." wakeAgent gate: skip if all pages indexed. content opportunity scan (weekly): "find queries where my site appears on page 2 (positions 11-20) with high impressions. these are the keywords one good article could push to page 1. deliver list to Telegram with suggested topics." CONTENT WRITING FROM YOUR DATA: the difference between generic SEO content and content that ranks: your agent has your Search Console data. "write a blog post targeting [keyword]. my current position is 14 with 2,400 monthly impressions. check what pages currently rank 1-3 for this keyword. write something better. include the gaps they miss." the agent researches competitors via Firecrawl, checks your existing content in the wiki, and drafts based on real data. not guesswork. WHAT THIS REPLACES: → SEO agency: $1,000-5,000/month → SEO tool subscriptions: $100-300/month → manual reporting: 3-5 hours/week → manual content research: 2-4 hours/week Hermes SEO agent: one profile with two MCPs. cron jobs handle the monitoring. you handle the decisions. SETUP IN 10 MINUTES: 1. create a profile: hermes profile create seo-agent 2. write SOUL.md: "you are an SEO specialist. monitor search performance daily. flag ranking drops and opportunities. write content based on Search Console data. weekly report every Monday." 3. connect Google Search Console + Analytics via Composio or ClawLink 4. set cron jobs (weekly report, daily index check, content opportunity scan) 5. set model: DeepSeek V4 for routine monitoring. Sonnet for content writing. 6. connect to Telegram for delivery. the agent runs. you review reports. rankings improve because you stopped guessing and started using your own data. comment HERMES and I'll send you the full setup guide for running Hermes Agent as your SEO specialist. full Hermes architecture deep-dive in the article 👇
0:44

HERMES AGENT CAN RUN YOUR SEO. CONNECT IT TO GOOGLE SEARCH CONSOLE AND GOOGLE ANALYTICS. IT MONITORS, REPORTS, AND WRITES CONTENT BASED ON YOUR ACTUAL DATA. stop paying an SEO agency. stop doing the tedious work yourself. Hermes handles it 24/7. WHAT THE SEO AGENT DOES: → pulls clicks, impressions, CTR, and position data from Google Search Console automatically → tracks traffic, user behavior, and conversions from Google Analytics → checks which pages are indexed and which are not → submits sitemaps for indexing → inspects URLs for crawl or indexing issues → identifies ranking drops and keyword opportunities → writes content based on what your data says works → generates weekly SEO performance reports → delivers everything to Telegram CONNECT GOOGLE SEARCH CONSOLE: two paths: 1. COMPOSIO (managed, easiest): paste this into Hermes chat: https:// composio. dev/hermes or add to config.yaml: mcp_servers: composio: url: "https:// connect.composio. dev /mcp" headers: x-consumer-api-key: "YOUR_COMPOSIO_API_KEY" Hermes prompts you to authenticate. one OAuth flow. done. 2. CLAWLINK (one-click): 9 Google Search Console tools exposed via MCP. hosted auth. nothing to run or maintain. paste the install prompt into Hermes chat. CONNECT GOOGLE ANALYTICS: same Composio setup. one MCP endpoint handles both Search Console and Analytics. authenticate once. both data sources available. your agent can now query: → search analytics (clicks, impressions, CTR, position) → traffic by source and landing page → user behavior and conversions → indexing status for any URL → sitemap status WHAT TO AUTOMATE WITH CRON: weekly SEO report (Monday 8am): "pull search analytics for last 7 days. compare vs previous week. flag any keyword that dropped more than 5 positions. flag any page that lost more than 20% clicks. deliver report to Telegram." daily indexing check (6am): "check if any new pages are not indexed. if found, submit sitemap and report to Telegram." wakeAgent gate: skip if all pages indexed. content opportunity scan (weekly): "find queries where my site appears on page 2 (positions 11-20) with high impressions. these are the keywords one good article could push to page 1. deliver list to Telegram with suggested topics." CONTENT WRITING FROM YOUR DATA: the difference between generic SEO content and content that ranks: your agent has your Search Console data. "write a blog post targeting [keyword]. my current position is 14 with 2,400 monthly impressions. check what pages currently rank 1-3 for this keyword. write something better. include the gaps they miss." the agent researches competitors via Firecrawl, checks your existing content in the wiki, and drafts based on real data. not guesswork. WHAT THIS REPLACES: → SEO agency: $1,000-5,000/month → SEO tool subscriptions: $100-300/month → manual reporting: 3-5 hours/week → manual content research: 2-4 hours/week Hermes SEO agent: one profile with two MCPs. cron jobs handle the monitoring. you handle the decisions. SETUP IN 10 MINUTES: 1. create a profile: hermes profile create seo-agent 2. write SOUL.md: "you are an SEO specialist. monitor search performance daily. flag ranking drops and opportunities. write content based on Search Console data. weekly report every Monday." 3. connect Google Search Console + Analytics via Composio or ClawLink 4. set cron jobs (weekly report, daily index check, content opportunity scan) 5. set model: DeepSeek V4 for routine monitoring. Sonnet for content writing. 6. connect to Telegram for delivery. the agent runs. you review reports. rankings improve because you stopped guessing and started using your own data. comment HERMES and I'll send you the full setup guide for running Hermes Agent as your SEO specialist. full Hermes architecture deep-dive in the article 👇

YanXbt

40,614 views • 1 month ago

🚨 Tina Peters Still Behind Bars - Despite POTUS Pardon Whistleblower and Gold Star Mom Tina Peters🇺🇸 Whistleblower of fallen Navy SEAL remains imprisoned in solitary confinement, more than a year later - even after President Trump issued a full pardon last week. Attorney Peter Ticktin is calling it what it is: political persecution. Tina is reportedly sick, cold, and isolated Her only “crime”? Imaging Dominion machines to preserve election data No restitution ordered, no financial loss proven Legal expert argues President Donald J. Trump can pardon state offenses under constitutional interpretation “They’re keeping her locked up while the real criminals walk free,” says Tikton, who’s now challenging the conventional legal limits on presidential pardon power.
7:08

🚨 Tina Peters Still Behind Bars - Despite POTUS Pardon Whistleblower and Gold Star Mom Tina Peters🇺🇸 Whistleblower of fallen Navy SEAL remains imprisoned in solitary confinement, more than a year later - even after President Trump issued a full pardon last week. Attorney Peter Ticktin is calling it what it is: political persecution. Tina is reportedly sick, cold, and isolated Her only “crime”? Imaging Dominion machines to preserve election data No restitution ordered, no financial loss proven Legal expert argues President Donald J. Trump can pardon state offenses under constitutional interpretation “They’re keeping her locked up while the real criminals walk free,” says Tikton, who’s now challenging the conventional legal limits on presidential pardon power.

LindellTV

40,301 views • 7 months ago

Victoria Derbyshire, "Elon Musk had already hit out, calling the UK a police state" "Adding, the real goal is to enable the UK government to track everyone" Speaking of tracking people, here are 30 ways Twitter does it: 1. Account activity (posts, likes, reposts, follows, replies, searches) 2. Time spent viewing specific posts 3. Clicks on links and media 4. Cookies stored in your browser 5. IP address 6. Device identifiers 7. Browser fingerprinting signals (browser type, screen size, language settings, etc.) 8. Mobile advertising IDs (Android Advertising ID, Apple Advertising Identifier where available) 9. Location data (GPS if permitted, IP-based location, Wi-Fi/network information) 10. Contact uploads (if you grant access) 11. Email address and phone number 12. Payment information (for paid services) 13. Cross-device matching (linking your phone, tablet, and computer to the same user) 14. Embedded X posts on third-party websites 15. X Pixel tracking on external websites 16. Websites using X advertising or conversion tools 17. Apps using X SDKs or integrations 18. Login with X integrations on third-party sites 19. Ad interactions and conversions 20. Inferred interests and behavioural profiling 21. Social graph analysis (who you follow, interact with, and are connected to) 22. Content analysis of posts, messages, and media 23. Network and connection information (mobile carrier, ISP, network type) 24. Diagnostic and crash reports from the app 25. Approximate location derived from activity patterns 26. Data obtained from advertising partners and data providers 27. Engagement with videos (watch time, rewatches, completion rates) 28. Search history on the platform 29. Hashtags, topics, and communities you engage with 30. Account recovery and security information
0:23

Victoria Derbyshire, "Elon Musk had already hit out, calling the UK a police state" "Adding, the real goal is to enable the UK government to track everyone" Speaking of tracking people, here are 30 ways Twitter does it: 1. Account activity (posts, likes, reposts, follows, replies, searches) 2. Time spent viewing specific posts 3. Clicks on links and media 4. Cookies stored in your browser 5. IP address 6. Device identifiers 7. Browser fingerprinting signals (browser type, screen size, language settings, etc.) 8. Mobile advertising IDs (Android Advertising ID, Apple Advertising Identifier where available) 9. Location data (GPS if permitted, IP-based location, Wi-Fi/network information) 10. Contact uploads (if you grant access) 11. Email address and phone number 12. Payment information (for paid services) 13. Cross-device matching (linking your phone, tablet, and computer to the same user) 14. Embedded X posts on third-party websites 15. X Pixel tracking on external websites 16. Websites using X advertising or conversion tools 17. Apps using X SDKs or integrations 18. Login with X integrations on third-party sites 19. Ad interactions and conversions 20. Inferred interests and behavioural profiling 21. Social graph analysis (who you follow, interact with, and are connected to) 22. Content analysis of posts, messages, and media 23. Network and connection information (mobile carrier, ISP, network type) 24. Diagnostic and crash reports from the app 25. Approximate location derived from activity patterns 26. Data obtained from advertising partners and data providers 27. Engagement with videos (watch time, rewatches, completion rates) 28. Search history on the platform 29. Hashtags, topics, and communities you engage with 30. Account recovery and security information

Farrukh

94,095 views • 2 months ago

We got ChatGPT to leak your private email data 💀💀 All you need? The victim's email address. ⛓️‍💥🚩📧 On Wednesday, OpenAI added full support for MCP (Model Context Protocol) tools in ChatGPT. Allowing ChatGPT to connect and read your Gmail, Calendar, Sharepoint, Notion, and more, invented by Anthropic But here's the fundamental problem: AI agents like ChatGPT follow your commands, not your common sense. And with just your email, we managed to exfiltrate all your private information. Here's how we did it: 1. The attacker sends a calendar invite with a jailbreak prompt to the victim, just with their email. No need for the victim to accept the invite. 2. Waited for the user to ask ChatGPT to help prepare for their day by looking at their calendar 3. ChatGPT reads the jailbroken calendar invite. Now ChatGPT is hijacked by the attacker and will act on the attacker's command. Searches your private emails and sends the data to the attacker's email. For now, OpenAI only made MCPs available in "developer mode", and requires manual human approvals for every session, but decision fatigue is a real thing, and normal people will just trust the AI without knowing what to do and click approve, approve, approve. Remember that AI might be super smart, but can be tricked and phished in incredibly dumb ways to leak your data. ChatGPT + Tools poses a serious security risk
2:54

We got ChatGPT to leak your private email data 💀💀 All you need? The victim's email address. ⛓️‍💥🚩📧 On Wednesday, OpenAI added full support for MCP (Model Context Protocol) tools in ChatGPT. Allowing ChatGPT to connect and read your Gmail, Calendar, Sharepoint, Notion, and more, invented by Anthropic But here's the fundamental problem: AI agents like ChatGPT follow your commands, not your common sense. And with just your email, we managed to exfiltrate all your private information. Here's how we did it: 1. The attacker sends a calendar invite with a jailbreak prompt to the victim, just with their email. No need for the victim to accept the invite. 2. Waited for the user to ask ChatGPT to help prepare for their day by looking at their calendar 3. ChatGPT reads the jailbroken calendar invite. Now ChatGPT is hijacked by the attacker and will act on the attacker's command. Searches your private emails and sends the data to the attacker's email. For now, OpenAI only made MCPs available in "developer mode", and requires manual human approvals for every session, but decision fatigue is a real thing, and normal people will just trust the AI without knowing what to do and click approve, approve, approve. Remember that AI might be super smart, but can be tricked and phished in incredibly dumb ways to leak your data. ChatGPT + Tools poses a serious security risk

Eito Miyamura | 🇯🇵🇬🇧

1,539,104 views • 11 months ago

Hey Anon🟧, Beta is Here – A Glimpse into the Future of DeFAI We’ve skipped the Alpha stage entirely to bring you straight into Public Beta v0.1—your first hands-on experience with DeFAI and Gemma on the 7th of February. What Can You Expect? 🚀 Live, Evolving Experience – From launch, we’ll be testing and integrating every update pushed on Automate’s GitHub. HeyAnon will continuously improve, adding more features and refining workflows, aiming for a fully comprehensive experience by the end of the month. 🔄 Simplified Workflows – Execute multi-action prompts that streamline complex DeFi processes. 🔑 Flexible Onboarding – Connect with Wallet Connect, generate a wallet in Telegram, or use Passkey. ⚡️ Real-Time Functionality – Experience DeFAI fully live, and get a sneak peek at the future of automated DeFi. We’ll be sharing examples and user videos to showcase what’s already possible, so stay tuned. (Make sure to check our docs and guides for the best experience!) 💌 Meet Gemma Gemma AI - The Assistant That Grows with You Gemma is here, and she’s just getting started. As data streams from Messari, Kaito, Cookie, and our internal data mining expand, she will continuously evolve, bringing: 📊 Enhanced Protocol-Specific Capabilities 🔗 More Integrated Data Streams ⚡️ Ongoing AI and Automate Upgrades This is the beta, the starting point, the appetizer - but the full DeFAI experience is coming in multiple courses over the month. Expect rapid improvements, more integrations, and a constantly evolving ecosystem. 🚀 DeFAI starts now.
0:35

Hey Anon🟧, Beta is Here – A Glimpse into the Future of DeFAI We’ve skipped the Alpha stage entirely to bring you straight into Public Beta v0.1—your first hands-on experience with DeFAI and Gemma on the 7th of February. What Can You Expect? 🚀 Live, Evolving Experience – From launch, we’ll be testing and integrating every update pushed on Automate’s GitHub. HeyAnon will continuously improve, adding more features and refining workflows, aiming for a fully comprehensive experience by the end of the month. 🔄 Simplified Workflows – Execute multi-action prompts that streamline complex DeFi processes. 🔑 Flexible Onboarding – Connect with Wallet Connect, generate a wallet in Telegram, or use Passkey. ⚡️ Real-Time Functionality – Experience DeFAI fully live, and get a sneak peek at the future of automated DeFi. We’ll be sharing examples and user videos to showcase what’s already possible, so stay tuned. (Make sure to check our docs and guides for the best experience!) 💌 Meet Gemma Gemma AI - The Assistant That Grows with You Gemma is here, and she’s just getting started. As data streams from Messari, Kaito, Cookie, and our internal data mining expand, she will continuously evolve, bringing: 📊 Enhanced Protocol-Specific Capabilities 🔗 More Integrated Data Streams ⚡️ Ongoing AI and Automate Upgrades This is the beta, the starting point, the appetizer - but the full DeFAI experience is coming in multiple courses over the month. Expect rapid improvements, more integrations, and a constantly evolving ecosystem. 🚀 DeFAI starts now.

Hey Anon

77,772 views • 1 year ago

VIDEO | Delhi: On the Transgender Persons Amendment Bill 2026, Congress MP Renuka Chowdhury says, "What security are they talking about? They should use their brains at times. What is the need for this law? Who have they contacted? Which data do they cite? With what authority are they addressing the society... I want to ask them, how many officers of this government will appear before the medical board... They haven't contacted, discussed, talked to, or consulted any stakeholders. They are just using this to divert attention from the real issues..." On Keralam Chief Minister's statement that the Congress is BJP's B-team in Keralam, she says, "What else will he say? He has to say something because elections are approaching..." On PM Narendra Modi becoming the longest serving head of government in India, she says, "This is his only achievement..." (Full video available on PTI Videos -
2:52

VIDEO | Delhi: On the Transgender Persons Amendment Bill 2026, Congress MP Renuka Chowdhury says, "What security are they talking about? They should use their brains at times. What is the need for this law? Who have they contacted? Which data do they cite? With what authority are they addressing the society... I want to ask them, how many officers of this government will appear before the medical board... They haven't contacted, discussed, talked to, or consulted any stakeholders. They are just using this to divert attention from the real issues..." On Keralam Chief Minister's statement that the Congress is BJP's B-team in Keralam, she says, "What else will he say? He has to say something because elections are approaching..." On PM Narendra Modi becoming the longest serving head of government in India, she says, "This is his only achievement..." (Full video available on PTI Videos -

Press Trust of India

18,493 views • 4 months ago

Diabetes is something you can read about anywhere. It feels different when it concerns your own family. For me, it started with my Family When the Ritual testnet launched, I wanted to build something with it that had a real use case behind it. So I started working on DiaRoutine: a Telegram bot diabetes diary for my mom. She had tried popular diabetes apps before, but never got used to them. Too many screens, too many buttons, too much friction. Telegram was easier. She could just write what happened during the day, and the bot would structure the entry. At first, it was a small idea. Then I showed the bot to someone close to me who has type 1 diabetes. I watched what confused him, what helped, and what was missing. I also spoke with an endocrinologist at the hospital. He explained that statistics over roughly three months can be important for tuning coefficients and understanding dosage patterns. The problem is that many people do not collect enough data. Sometimes it is laziness. Sometimes it is limited access to apps, paid subscriptions, sanctions, or tracking only part of the picture. That changed how I looked at the product. DiaRoutine is now built to help people collect a fuller diabetes diary with less friction. It can track: - glucose - food and nutrition - insulin - activity - boluses, coefficients, and basal profile - daily and weekly reports - doctor-ready exports - reminders from the agent - pattern observations - Ritual proofs for diary entries, reports, and exports - Raw medical data does not go onchain. Ritual is used as a proof layer and an agent layer. The bot can save hashes and proofs for daily diaries, reports, exports, or individual entries. Glucose, food, insulin, activity, notes, and Telegram ID stay private. The bot also has an AI agent connected to Ritual. It can remind the user to log glucose, follow up after food or activity, prepare a daily overview, and suggest saving a proof in Ritual. It does not give medical advice, change dosages, or send anything to Ritual without confirmation. That is what I find interesting about Ritual here. It is not about putting private medical data onchain. It is about giving an agent a verifiable layer for actions and proofs, while the sensitive data stays private. The user does not need a wallet either. A service wallet pays for gas, so the experience stays simple. DiaRoutine is still in alpha. There are bugs, rough edges, and many things to improve. But the first feedback from people using it has been positive. That matters to me because this was never just a technical experiment. It came from a real family need. Big thanks to Meison (❖,❖) for helping with development and pushing this forward with me. We will keep improving DiaRoutine, testing it with real users, and listening to people who live with diabetes every day. It is still early. But it already feels useful. try @DiaRoutine_Bot in TELEGRAM #BUILDONRITUAL
2:30

Diabetes is something you can read about anywhere. It feels different when it concerns your own family. For me, it started with my Family When the Ritual testnet launched, I wanted to build something with it that had a real use case behind it. So I started working on DiaRoutine: a Telegram bot diabetes diary for my mom. She had tried popular diabetes apps before, but never got used to them. Too many screens, too many buttons, too much friction. Telegram was easier. She could just write what happened during the day, and the bot would structure the entry. At first, it was a small idea. Then I showed the bot to someone close to me who has type 1 diabetes. I watched what confused him, what helped, and what was missing. I also spoke with an endocrinologist at the hospital. He explained that statistics over roughly three months can be important for tuning coefficients and understanding dosage patterns. The problem is that many people do not collect enough data. Sometimes it is laziness. Sometimes it is limited access to apps, paid subscriptions, sanctions, or tracking only part of the picture. That changed how I looked at the product. DiaRoutine is now built to help people collect a fuller diabetes diary with less friction. It can track: - glucose - food and nutrition - insulin - activity - boluses, coefficients, and basal profile - daily and weekly reports - doctor-ready exports - reminders from the agent - pattern observations - Ritual proofs for diary entries, reports, and exports - Raw medical data does not go onchain. Ritual is used as a proof layer and an agent layer. The bot can save hashes and proofs for daily diaries, reports, exports, or individual entries. Glucose, food, insulin, activity, notes, and Telegram ID stay private. The bot also has an AI agent connected to Ritual. It can remind the user to log glucose, follow up after food or activity, prepare a daily overview, and suggest saving a proof in Ritual. It does not give medical advice, change dosages, or send anything to Ritual without confirmation. That is what I find interesting about Ritual here. It is not about putting private medical data onchain. It is about giving an agent a verifiable layer for actions and proofs, while the sensitive data stays private. The user does not need a wallet either. A service wallet pays for gas, so the experience stays simple. DiaRoutine is still in alpha. There are bugs, rough edges, and many things to improve. But the first feedback from people using it has been positive. That matters to me because this was never just a technical experiment. It came from a real family need. Big thanks to Meison (❖,❖) for helping with development and pushing this forward with me. We will keep improving DiaRoutine, testing it with real users, and listening to people who live with diabetes every day. It is still early. But it already feels useful. try @DiaRoutine_Bot in TELEGRAM #BUILDONRITUAL

Annae.nad

12,253 views • 3 months ago

dude runs a private AI host out of his basement on 8 stacked 3090s an accounting firm signed the moment they realized their client financials would physically never touch a third party the rig pays his mortgage now the setup is eight RTX 3090s lined up on an open frame down in his basement - fans roaring, yellow zip ties holding cables, a little monitor blinking stats beside a cheap keyboard. it was built as a mining rig and the dashboard still looks the part the accounting firm didn’t care that it looked like a crypto leftover they cared about one promise no cloud provider could make them - that their clients’ financial records would never leave a machine the firm could physically point to that promise closed the deal here’s the corner they were backed into: accounting firms hold the most sensitive data imaginable - tax filings, payroll, full financial histories. they wanted AI to speed up the tedious work, but routing that data through a cloud model was a liability their partners would never sign. so they sat frozen while the tech moved on without them he handed them the one version that cleared legal: a private model running on hardware in a basement, not a server farm they’d never see the build behind the rig: eight 3090s stack up to 192GB of combined vram - enough to run a 70B model with real context through vLLM. the firm’s documents get indexed into an isolated vector database, so every answer pulls only from their own files. queries hit a local endpoint, replies come back, nothing ever leaves the basement. used 3090s were the quiet genius move - around $700 a card instead of triple that for new silicon the rig that once mined coins for pennies now serves inference that bills like a service the contract that covers his house: → managing partner came down for an in-person demo → asked the only question that mattered: where does the data live → he pointed at the rig and said “right here, nowhere else” → they signed a monthly deal before leaving the basement the economics underneath: → 8 used 3090s: ~$5,600, paid off in the first two months → electricity: a few hundred a month under load → what the firm pays: enough to clear his mortgage every month → margin: almost all of it, because the data center is under his own house real firms burn a fortune on compliant cloud infrastructure he does the same job in a basement, and the data is safer for the dumbest reason possible - it has nowhere else it can go the rig that looks one loose cable from death is quietly the most trusted machine his client has ever touched
0:42

dude runs a private AI host out of his basement on 8 stacked 3090s an accounting firm signed the moment they realized their client financials would physically never touch a third party the rig pays his mortgage now the setup is eight RTX 3090s lined up on an open frame down in his basement - fans roaring, yellow zip ties holding cables, a little monitor blinking stats beside a cheap keyboard. it was built as a mining rig and the dashboard still looks the part the accounting firm didn’t care that it looked like a crypto leftover they cared about one promise no cloud provider could make them - that their clients’ financial records would never leave a machine the firm could physically point to that promise closed the deal here’s the corner they were backed into: accounting firms hold the most sensitive data imaginable - tax filings, payroll, full financial histories. they wanted AI to speed up the tedious work, but routing that data through a cloud model was a liability their partners would never sign. so they sat frozen while the tech moved on without them he handed them the one version that cleared legal: a private model running on hardware in a basement, not a server farm they’d never see the build behind the rig: eight 3090s stack up to 192GB of combined vram - enough to run a 70B model with real context through vLLM. the firm’s documents get indexed into an isolated vector database, so every answer pulls only from their own files. queries hit a local endpoint, replies come back, nothing ever leaves the basement. used 3090s were the quiet genius move - around $700 a card instead of triple that for new silicon the rig that once mined coins for pennies now serves inference that bills like a service the contract that covers his house: → managing partner came down for an in-person demo → asked the only question that mattered: where does the data live → he pointed at the rig and said “right here, nowhere else” → they signed a monthly deal before leaving the basement the economics underneath: → 8 used 3090s: ~$5,600, paid off in the first two months → electricity: a few hundred a month under load → what the firm pays: enough to clear his mortgage every month → margin: almost all of it, because the data center is under his own house real firms burn a fortune on compliant cloud infrastructure he does the same job in a basement, and the data is safer for the dumbest reason possible - it has nowhere else it can go the rig that looks one loose cable from death is quietly the most trusted machine his client has ever touched

regent0x

13,334 views • 1 month ago

Michelle Mone's husband Barrowman has issued a "statement" this morning after The Times and Sunday Times report showing the National Crime Agency is investigating a £3 million payment into Mone's Coutts bank account, which would prove her direct benefit from the PPE Medpro contract as well as indirectly through trusts. The statement is full of "look over there", fundamental lies (we saved the govt £100 million - they didn't) & obfuscation. Who'd have thought it? Obfuscation is where he doesn't mention that he didn't declare his interest on company filings- nowhere on the Companies House declarations at the time. She didn't declare her interest on official documents, she lied consistently (par for the course), threatened press with libel, lied to her lawyers, lied about her lawyers "they told me to lie" - they didn't. Now their defence about getting £65 million profit and pandemic profiteering is saying "I'm not the only one who robbed the public, they're picking on me, it's not fair" There is so much which needs to be investigated with other companies too. We've been saying this for a long time. Rachel Reeves has promised a Covid CORRUPTION Commissioner to investigate them if Labour win the next election. More to follow with data..... Don't forget to follow "The VIP Files" with Good Law Project and I here. We have access to huge amounts of data through FOIs and are putting it together about numerous contracts given through the infamous VIP Lane. It's a Tory mess which stinks and they're still trying to hide a lot of the evidence.
1:07

Michelle Mone's husband Barrowman has issued a "statement" this morning after The Times and Sunday Times report showing the National Crime Agency is investigating a £3 million payment into Mone's Coutts bank account, which would prove her direct benefit from the PPE Medpro contract as well as indirectly through trusts. The statement is full of "look over there", fundamental lies (we saved the govt £100 million - they didn't) & obfuscation. Who'd have thought it? Obfuscation is where he doesn't mention that he didn't declare his interest on company filings- nowhere on the Companies House declarations at the time. She didn't declare her interest on official documents, she lied consistently (par for the course), threatened press with libel, lied to her lawyers, lied about her lawyers "they told me to lie" - they didn't. Now their defence about getting £65 million profit and pandemic profiteering is saying "I'm not the only one who robbed the public, they're picking on me, it's not fair" There is so much which needs to be investigated with other companies too. We've been saying this for a long time. Rachel Reeves has promised a Covid CORRUPTION Commissioner to investigate them if Labour win the next election. More to follow with data..... Don't forget to follow "The VIP Files" with Good Law Project and I here. We have access to huge amounts of data through FOIs and are putting it together about numerous contracts given through the infamous VIP Lane. It's a Tory mess which stinks and they're still trying to hide a lot of the evidence.

Carol Vorderman

2,863,211 views • 2 years ago

Introducing AI Lab Monetization Play. AI Lab Monetization Play tracks the revenue run-rate of major AI laboratories in real time using public and alternative data signals. The Play provides a continuously updated view of commercial scale across the frontier AI landscape. ARR Dashboard The dashboard estimates annual recurring revenue for Anthropic, OpenAI, and other frontier labs. Each estimate is broken down by revenue stream, growth trajectory, and implied valuation multiple. It surfaces which labs are scaling fastest and where the commercial momentum is concentrated. Model Routing and Market Share The Play tracks real-time model usage share across inference providers. It measures routing volume through aggregators to identify which models are capturing production workloads. Shifts in routing patterns signal adoption inflections before they appear in reported financials. Model Pricing and Capabilities The Play maintains a comprehensive pricing table across all major foundation models. Input and output token costs, context windows, and capability tiers are mapped side by side. It serves as the reference layer for competitive positioning and margin structure across the model ecosystem. Available to all paid clients on our full-stack research platform. Start here:
0:52

Introducing AI Lab Monetization Play. AI Lab Monetization Play tracks the revenue run-rate of major AI laboratories in real time using public and alternative data signals. The Play provides a continuously updated view of commercial scale across the frontier AI landscape. ARR Dashboard The dashboard estimates annual recurring revenue for Anthropic, OpenAI, and other frontier labs. Each estimate is broken down by revenue stream, growth trajectory, and implied valuation multiple. It surfaces which labs are scaling fastest and where the commercial momentum is concentrated. Model Routing and Market Share The Play tracks real-time model usage share across inference providers. It measures routing volume through aggregators to identify which models are capturing production workloads. Shifts in routing patterns signal adoption inflections before they appear in reported financials. Model Pricing and Capabilities The Play maintains a comprehensive pricing table across all major foundation models. Input and output token costs, context windows, and capability tiers are mapped side by side. It serves as the reference layer for competitive positioning and margin structure across the model ecosystem. Available to all paid clients on our full-stack research platform. Start here:

FUNDA

30,511 views • 24 days ago

PTU will be the most comprehensive and readily accessible PC troubleshooting, tuning, and data platform ever released for Gamers and PC users alike. Today we are showcasing one of our most powerful new features, fully integrated into the application and enhanced by OpenAI. PTU now delivers daily intelligence across major brands such as Intel, AMD, Windows, Microsoft, NVIDIA and more, directly sourced from hundreds of top articles and social media accounts. Every PTU user has instant access to the most relevant information within tech, gaming, and performance. All of this is powered by $PRGN on Solana, ensuring PC hardware data, performance tools, and technical assistance are always available. With the desire for PTU surging, the need and value of $PRGN grows and its visible by the 2.57% supply completely removed from our companies existing customer base. On top of this, multiple skin designs and full customization are in development, giving every user the ability to make PTU not only the tool they rely on, but the one that looks and feels the way they want it to. The flagship tech and gaming ecosystem is being built in front of you. Study ICM. Study $PRGN You cannot Fade Real Working Tech, this week our application expansion will show how we pioneer accessibility for Products based on Solana launched on Believe CA: 5x5FUc5azSNYLhR2iPT9xzcMe6Q5nL6iFAUkHWnFxBLV
0:32

PTU will be the most comprehensive and readily accessible PC troubleshooting, tuning, and data platform ever released for Gamers and PC users alike. Today we are showcasing one of our most powerful new features, fully integrated into the application and enhanced by OpenAI. PTU now delivers daily intelligence across major brands such as Intel, AMD, Windows, Microsoft, NVIDIA and more, directly sourced from hundreds of top articles and social media accounts. Every PTU user has instant access to the most relevant information within tech, gaming, and performance. All of this is powered by $PRGN on Solana, ensuring PC hardware data, performance tools, and technical assistance are always available. With the desire for PTU surging, the need and value of $PRGN grows and its visible by the 2.57% supply completely removed from our companies existing customer base. On top of this, multiple skin designs and full customization are in development, giving every user the ability to make PTU not only the tool they rely on, but the one that looks and feels the way they want it to. The flagship tech and gaming ecosystem is being built in front of you. Study ICM. Study $PRGN You cannot Fade Real Working Tech, this week our application expansion will show how we pioneer accessibility for Products based on Solana launched on Believe CA: 5x5FUc5azSNYLhR2iPT9xzcMe6Q5nL6iFAUkHWnFxBLV

Paragon

21,236 views • 10 months ago

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 views • 8 months ago

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.
2:57

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

Brian Roemmele

72,312 views • 8 months ago