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Entire DMK camp (from top leadership to low level cadres) ridicule Thalaivar K.Annamalai as ‘Goat’ as his parents own goat farm. Event 1 (Before Election) - DMK Minister Dr. T R B Rajaa claims after the elections they were planning to serve biryani here in Coimbatore, now with Annamalai as the candidate they will serve tasty Goat biryani after winning the elections with huge margin. Event 2 (After the result) - DMK cadres parades a goat with Annamalai’s photo garlanded our it’s neck. Event 3 (After the result) - DMK cadres serve Goat biryani in Coimbatore, they have two goats for symbolic reason. The photograph gets posted in their news channel’s X account. Event 4 (After the result) - DMK cadres garlands a goat with Annamalai photo, slaughter the same, sends a symbolic message as they are beheading the man and pour the blood on his photograph. Ladies and Gentlemen, that’s DMK for you…

Entire DMK camp (from top leadership to low level cadres) ridicule Thalaivar K.Annamalai as ‘Goat’ as his parents own goat farm. Event 1 (Before Election) - DMK Minister Dr. T R B Rajaa claims after the elections they were planning to serve biryani here in Coimbatore, now with Annamalai as the candidate they will serve tasty Goat biryani after winning the elections with huge margin. Event 2 (After the result) - DMK cadres parades a goat with Annamalai’s photo garlanded our it’s neck. Event 3 (After the result) - DMK cadres serve Goat biryani in Coimbatore, they have two goats for symbolic reason. The photograph gets posted in their news channel’s X account. Event 4 (After the result) - DMK cadres garlands a goat with Annamalai photo, slaughter the same, sends a symbolic message as they are beheading the man and pour the blood on his photograph. Ladies and Gentlemen, that’s DMK for you…

Arvinth Easwaran

24,391 просмотров • 2 лет назад

🚨NEW INVESTIGATION: A Fortune 500 company reportedly cancelled a Hindu American Foundation training session after employees circulated its Wikipedia page. That page tells readers HAF aligns with Hindu nationalism, threatens academic freedom, and has been accused of acting as a foreign agent. We traced who built that narrative. The same handful of accounts kept appearing across HAF, its critics, activist groups, and key public figures—building an interconnected narrative that now feeds Google and AI systems. Full investigation in thread. Receipts 👇

🚨NEW INVESTIGATION: A Fortune 500 company reportedly cancelled a Hindu American Foundation training session after employees circulated its Wikipedia page. That page tells readers HAF aligns with Hindu nationalism, threatens academic freedom, and has been accused of acting as a foreign agent. We traced who built that narrative. The same handful of accounts kept appearing across HAF, its critics, activist groups, and key public figures—building an interconnected narrative that now feeds Google and AI systems. Full investigation in thread. Receipts 👇

NPOV

392,831 просмотров • 3 месяцев назад

Coordinated Inauthentic Behavior (CIB) is when multiple accounts work together to push a narrative or target someone while hiding that they are acting as a group. These campaigns use fake identities, shared content, or synchronized posting to make manufactured criticism look organic. On April 26 a hashtag none of us had seen before, #fake_journalism, flooded Pakistani Twitter with attacks on journalist Matiullah Jan. It looked coordinated from the first hour, so we saved every tweet inside a 58-minute window and went through the accounts one by one. What we found is significant evidence of coordination and inauthenticity. Multiple handles were created on the same two days in February and stayed dormant until that night. Multiple tweets closed with the same three demands in the same order. The same graphic was uploaded individually by account after account. #SochVideos #MatiullahJan #FakeJournalism #Journalism #PressFreedom #Pakistan

Coordinated Inauthentic Behavior (CIB) is when multiple accounts work together to push a narrative or target someone while hiding that they are acting as a group. These campaigns use fake identities, shared content, or synchronized posting to make manufactured criticism look organic. On April 26 a hashtag none of us had seen before, #fake_journalism, flooded Pakistani Twitter with attacks on journalist Matiullah Jan. It looked coordinated from the first hour, so we saved every tweet inside a 58-minute window and went through the accounts one by one. What we found is significant evidence of coordination and inauthenticity. Multiple handles were created on the same two days in February and stayed dormant until that night. Multiple tweets closed with the same three demands in the same order. The same graphic was uploaded individually by account after account. #SochVideos #MatiullahJan #FakeJournalism #Journalism #PressFreedom #Pakistan

Soch سوچ

13,832 просмотров • 4 месяцев назад

New York native James Nicholas hit Thursday's opening tee shot at the U.S. Open at Shinnecock Hills, but it was far from ceremonial. Nicholas took the early lead with two birdies in his first six holes, and he steadied the ship after a missed 1-foot bogey putt at the seventh hole to sign for a 1-over 71, just three strokes back of early leader Sam Stevens. Nicholas competed on the Korn Ferry Tour last week in Amarillo, Texas, before an adventurous trip to Long Island that included a 4-hour evening drive from Amarillo to Albuquerque. Nicholas, who played collegiate football at Yale before turning his full-time attention to golf, was greeted by numerous fans and family members after signing his card Thursday. One fan who wasn't on-site but followed along: former New York Jets quarterback Joe Namath, whose Jets team physician was Nicholas' grandpa. The elder Nicholas performed four knee operations that prolonged Namath's career. After a strong start at Shinnecock, Nicholas could have a prolonged stay at the U.S. Open as well.

New York native James Nicholas hit Thursday's opening tee shot at the U.S. Open at Shinnecock Hills, but it was far from ceremonial. Nicholas took the early lead with two birdies in his first six holes, and he steadied the ship after a missed 1-foot bogey putt at the seventh hole to sign for a 1-over 71, just three strokes back of early leader Sam Stevens. Nicholas competed on the Korn Ferry Tour last week in Amarillo, Texas, before an adventurous trip to Long Island that included a 4-hour evening drive from Amarillo to Albuquerque. Nicholas, who played collegiate football at Yale before turning his full-time attention to golf, was greeted by numerous fans and family members after signing his card Thursday. One fan who wasn't on-site but followed along: former New York Jets quarterback Joe Namath, whose Jets team physician was Nicholas' grandpa. The elder Nicholas performed four knee operations that prolonged Namath's career. After a strong start at Shinnecock, Nicholas could have a prolonged stay at the U.S. Open as well.

PGA Tour

255,876 просмотров • 3 месяцев назад

WILD FOOTAGE 🔴 Orthodox Jewish passengers were violently choked, assaulted, and threatened with death on a crowded New York City subway train after a group of attackers deliberately targeted them in public. According to a firsthand account, the attackers began hurling curses at the victims at the station, then boarded the train with them and escalated the violence onboard. The group choked the victims, beat them, and threatened to kill them as the train continued along the route. “It happened about 1 hour ago on the 3 line,” the witness said. “We transferred from the 4 to the 3 at Franklin Station. They were already there, started cursing, got on the train with us, and followed us toward Kingston. We got off 1 stop before.” The attack unfolded in front of a packed train, with Orthodox Jews singled out, physically assaulted and threatened with murder, while other passengers looked on.

WILD FOOTAGE 🔴 Orthodox Jewish passengers were violently choked, assaulted, and threatened with death on a crowded New York City subway train after a group of attackers deliberately targeted them in public. According to a firsthand account, the attackers began hurling curses at the victims at the station, then boarded the train with them and escalated the violence onboard. The group choked the victims, beat them, and threatened to kill them as the train continued along the route. “It happened about 1 hour ago on the 3 line,” the witness said. “We transferred from the 4 to the 3 at Franklin Station. They were already there, started cursing, got on the train with us, and followed us toward Kingston. We got off 1 stop before.” The attack unfolded in front of a packed train, with Orthodox Jews singled out, physically assaulted and threatened with murder, while other passengers looked on.

Open Source Intel

1,117,446 просмотров • 9 месяцев назад

This guy turned Grok Bot into an MM system and it made $1,335 a day The result is already +$65,425 PnL with only $40 per average trade while repeating the same setup 9 times every hour across BTC "Up or Down" markets Six AI agents work together as one algorithm: 1. They try to build Up + Down < 100¢ The bot accumulates both outcomes at different moments as BTC moves For example: Down at 38¢ + Up later at 56¢ = 94¢ total cost (6¢ locked on every complete set) The two entries do not need to happen at the same time 2. Order Flow tells the agents which side to accumulate next The model tracks imbalance inside the book: OBI = (B - A) / (B + A) When buying pressure shifts toward one outcome, its fair probability changes and the bot adjusts which side it wants to accumulate This trader’s Polymarket account: GoodFallen 10,322 predictions with a 50% win rate Follow this account and trade on Polymarket through this bot: Most profitable trades: $974 → $2,525 (+$1,551) $723 → $1,923 (+$1,200) $1,270 → $2,323 (+$1,053) The bot uses changes in Order Flow and BTC price to keep rebuilding the same position before resolution

This guy turned Grok Bot into an MM system and it made $1,335 a day The result is already +$65,425 PnL with only $40 per average trade while repeating the same setup 9 times every hour across BTC "Up or Down" markets Six AI agents work together as one algorithm: 1. They try to build Up + Down < 100¢ The bot accumulates both outcomes at different moments as BTC moves For example: Down at 38¢ + Up later at 56¢ = 94¢ total cost (6¢ locked on every complete set) The two entries do not need to happen at the same time 2. Order Flow tells the agents which side to accumulate next The model tracks imbalance inside the book: OBI = (B - A) / (B + A) When buying pressure shifts toward one outcome, its fair probability changes and the bot adjusts which side it wants to accumulate This trader’s Polymarket account: GoodFallen 10,322 predictions with a 50% win rate Follow this account and trade on Polymarket through this bot: Most profitable trades: $974 → $2,525 (+$1,551) $723 → $1,923 (+$1,200) $1,270 → $2,323 (+$1,053) The bot uses changes in Order Flow and BTC price to keep rebuilding the same position before resolution

Daniro

19,789 просмотров • 19 дней назад

HERMES DESKTOP APP JUST GOT A NEW ULTRA MODE FOR DEEPSEEK V4 PRO – AND IT COSTS ABOUT $1 A DAY TO RUN If you're using Hermes desktop app, you're probably already running DeepSeek – it's one of the top models available. The update: → DeepSeek V4 Pro now has thinking mode, fast mode, and a brand new ultra mode → There's also an extra high mode, but the real upgrade is ultra The actual cost: roughly $1/day for a normal workflow. That's about $20-25/month – basically the same as before, just with a significantly faster mode unlocked. The verdict after a full day of testing: it flies through tool calling. Noticeably faster, same price range. If you're already paying for DeepSeek through Hermes, this is a free upgrade sitting in your settings right now. Update Hermes, grab the DeepSeek API if you don't have it yet, and try V4 Pro on ultra. Bookmark this, you'll want to come back to it later.

SCOTTY BEAM

24,032 просмотров • 2 месяцев назад

This guy made Grok Bot harvest BTC volatility and it made $2,701 a day The result is already +$40,522 PnL after 4,127 predictions while repeating the same setup 11 times every hour across BTC “Up or Down” markets Six AI agents work together as one algorithm: 1. They use BTC volatility to build Up + Down below 100¢ The bot does not need both outcomes at a good price simultaneously It can accumulate one side first and wait for BTC to move before buying the opposite side For example: Up at 34¢ + Down later at 58¢ = 92¢ total cost (8¢ locked on every complete set) 2. Inventory Rotation decides how much gets paired The system continuously compares both inventories: Paired = min(Qup, Qdown) Everything that can be matched becomes a complete set Any remaining imbalance stays directional only while the agents still see an edge This trader’s Polymarket account: AdanaKebab 4,127 predictions with a 52% win rate Most profitable trades: $759 → $2,880 (+$2,121) $1,121 → $3,120 (+$1,999) $1,441 → $3,420 (+$1,979) The system keeps rotating between directional exposure and paired inventory as BTC moves

This guy made Grok Bot harvest BTC volatility and it made $2,701 a day The result is already +$40,522 PnL after 4,127 predictions while repeating the same setup 11 times every hour across BTC “Up or Down” markets Six AI agents work together as one algorithm: 1. They use BTC volatility to build Up + Down below 100¢ The bot does not need both outcomes at a good price simultaneously It can accumulate one side first and wait for BTC to move before buying the opposite side For example: Up at 34¢ + Down later at 58¢ = 92¢ total cost (8¢ locked on every complete set) 2. Inventory Rotation decides how much gets paired The system continuously compares both inventories: Paired = min(Qup, Qdown) Everything that can be matched becomes a complete set Any remaining imbalance stays directional only while the agents still see an edge This trader’s Polymarket account: AdanaKebab 4,127 predictions with a 52% win rate Most profitable trades: $759 → $2,880 (+$2,121) $1,121 → $3,120 (+$1,999) $1,441 → $3,420 (+$1,979) The system keeps rotating between directional exposure and paired inventory as BTC moves

Daniro

98,692 просмотров • 17 дней назад

A 15-YEAR-OLD HIGH SCHOOL STUDENT FOUND A WAY TO SPOT NEW COMPANIES BEFORE MAJOR INVESTORS DID The interviewer walked into the house as Eric finished a business call and asked the person to call back in 30 minutes. Eric was 15. During lockdown, Eric Zhu joined Discord servers where high school students discussed startups and venture deals. He was the only middle school student in those chats. Eric joined a young company as one of its first employees. He then launched his own startup, sold it, and began building Aviato. Investors searched databases for new companies. A startup usually appeared only after its first funding rounds and visible growth. By then, dozens of other funds had seen it. Eric started hunting for signals that appeared before a company entered a database. The Aviato team gathered early signals about new projects and organized them into a search engine for investors. A fund could discover a company before its name started showing up in every venture database. Eric used the same approach as cofounder of his own fund. His team invested at an early stage and took equity. If a larger company acquired the startup or it went public, the fund made money on its stake. At 15, Eric was the cofounder of a fund with roughly $20 million. He also raised more than $1 million for Aviato. To stay near his customers, Eric moved to San Francisco and rented a house with office space for $8,000 a month. He hated the city. The funds had millions for deals. Eric sold them something scarcer: time before everyone else found the same company.

A 15-YEAR-OLD HIGH SCHOOL STUDENT FOUND A WAY TO SPOT NEW COMPANIES BEFORE MAJOR INVESTORS DID The interviewer walked into the house as Eric finished a business call and asked the person to call back in 30 minutes. Eric was 15. During lockdown, Eric Zhu joined Discord servers where high school students discussed startups and venture deals. He was the only middle school student in those chats. Eric joined a young company as one of its first employees. He then launched his own startup, sold it, and began building Aviato. Investors searched databases for new companies. A startup usually appeared only after its first funding rounds and visible growth. By then, dozens of other funds had seen it. Eric started hunting for signals that appeared before a company entered a database. The Aviato team gathered early signals about new projects and organized them into a search engine for investors. A fund could discover a company before its name started showing up in every venture database. Eric used the same approach as cofounder of his own fund. His team invested at an early stage and took equity. If a larger company acquired the startup or it went public, the fund made money on its stake. At 15, Eric was the cofounder of a fund with roughly $20 million. He also raised more than $1 million for Aviato. To stay near his customers, Eric moved to San Francisco and rented a house with office space for $8,000 a month. He hated the city. The funds had millions for deals. Eric sold them something scarcer: time before everyone else found the same company.

Blaze

14,265 просмотров • 1 месяц назад

My AI made Shopify pages are getting better and better everyday Here’s an example of a Shopify section I built with the reference page and the result on my store 👇 Guide: To make good AI landing pages, you need to use the same method as making good AI UGC or AI product images you have to take something that's already good as a reference for Claude/gemini to analyze and adapt for your product/brand for now you can't adapt full landing pages to your product because the Claude context gets bloated fast and you get poor output. Listicles are the only kind of page you can "one shot" with Claude. But Product pages are another story Analyze an existing page and adapting it to your product section by section is the way to go. The results are 10x better. here's an example (on the video): 1- I found this good product section from im8's product page. I screen-recorded the section on both desktop and mobile, going through the animations to capture the dynamism that a screenshot wouldn’t show. Then I asked Claude Web or Gemini to analyze the recording and produce a very detailed report. Full prompt is on my TG channel, it's quite long. 2- then ask Claude (inside your Shopify brand project folder): "I have a detailed UI/UX specification document for a product page section I want to adapt to my product. Recreate this exactly on Shopify as a section template and adapt it to [name of the product] using the brand guidelines" (paste the result prompt from step 1 ). 3- you will have your section ready after 3-4 minutes. you'll probably have to change a few things. spacing, small visual bugs, price not appearing correctly. it will take you 5 minutes maximum. 4- then you can ask Claude to make 4 different variations of the section using different designs and pick the best one using this prompt: "Create 4 design variations of this section. Keep the content and layout structure identical across all, only vary the visual treatment (color usage, typography hierarchy, spacing, component styling). I will choose the one I like the most." after that you have a pretty good section, and you can do the process again for all sections of the page. The less complex the section, the faster the process will be. note: I know the AI result is not perfect, but it's pretty impressive imo and it will only get better.

Olivier

84,679 просмотров • 6 месяцев назад

I just built a plugin with Claude Fable 5 that turns Claude Code into a $5,000/mo SEO consultant 🤯 9 skills, one plugin: it connects straight to your Search Console + GA4 data, finds the wins, ships the fixes, and renders a live SEO dashboard that looks like a $200/mo SaaS product. All inside Claude Code. Perfect for DTC brands and agencies sitting on months of Search Console data nobody has time to read. Right now, you probably can't answer: Which keywords are sitting on page 2, one title tag away from page 1, Which pages are bleeding traffic to redirect chains and broken canonicals, Which blog posts rank for commercial terms but never link to a product page. This plugin answers all of it from your live data, then ships the fixes: → Finds your page-2 keywords and ships the fix: new title, headings, content, paste-ready → Clusters every query into a hub-and-spoke content map with the gaps flagged → Drafts posts from your actual search data, not guesses → Writes dev tickets for redirect chains and slow pages, ranked by traffic at risk → Builds the internal links between your blog and your money pages → Flags toxic backlinks and ranks outreach targets → Drops a Monday report with 3 priorities before the client even asks → Renders it all as a one-file HTML dashboard with a 0-100 SEO health score No dashboard staring. No CSV archaeology. No $5K/mo retainer for a PDF. What you get: → Page-2 keywords moved to page 1 → A content calendar that fills itself from data → Dev tickets that write themselves → A live SEO dashboard on command Built 100% in Claude Code with Claude Fable 5. I put the entire build into a step-by-step Playbook: all 8 workflow prompts (including the dashboard), how to turn them into a plugin, and the full Google setup (Including the 2 landmines Google doesn't tell you about). Want access for free? > Like this post > Comment "SEO" And I'll send it over (must be following so I can DM)

I just built a plugin with Claude Fable 5 that turns Claude Code into a $5,000/mo SEO consultant 🤯 9 skills, one plugin: it connects straight to your Search Console + GA4 data, finds the wins, ships the fixes, and renders a live SEO dashboard that looks like a $200/mo SaaS product. All inside Claude Code. Perfect for DTC brands and agencies sitting on months of Search Console data nobody has time to read. Right now, you probably can't answer: Which keywords are sitting on page 2, one title tag away from page 1, Which pages are bleeding traffic to redirect chains and broken canonicals, Which blog posts rank for commercial terms but never link to a product page. This plugin answers all of it from your live data, then ships the fixes: → Finds your page-2 keywords and ships the fix: new title, headings, content, paste-ready → Clusters every query into a hub-and-spoke content map with the gaps flagged → Drafts posts from your actual search data, not guesses → Writes dev tickets for redirect chains and slow pages, ranked by traffic at risk → Builds the internal links between your blog and your money pages → Flags toxic backlinks and ranks outreach targets → Drops a Monday report with 3 priorities before the client even asks → Renders it all as a one-file HTML dashboard with a 0-100 SEO health score No dashboard staring. No CSV archaeology. No $5K/mo retainer for a PDF. What you get: → Page-2 keywords moved to page 1 → A content calendar that fills itself from data → Dev tickets that write themselves → A live SEO dashboard on command Built 100% in Claude Code with Claude Fable 5. I put the entire build into a step-by-step Playbook: all 8 workflow prompts (including the dashboard), how to turn them into a plugin, and the full Google setup (Including the 2 landmines Google doesn't tell you about). Want access for free? > Like this post > Comment "SEO" And I'll send it over (must be following so I can DM)

Mike Futia

80,646 просмотров • 3 месяцев назад

New body-physics test for the best AI video tools: The newly released Grok Imagine 1.5, the king Seedance 2.0, Google’s “revolutionary super-duper” Gemini Omni Flash, and the old-timer Kling 3.0 Pro. This time I tested how each model handles realistic running motion, body movement, fabric physics, and natural secondary motion. And the result was much less obvious than I expected. Believe it or not, I can’t call Seedance the clear winner this time. Each model got several attempts. - Grok Imagine 1.5: The new version is finally available on the official Grok website, so I could properly test it. Honestly, I didn’t notice a massive improvement over the previous version. Still, the result was acceptable. It produced the most cartoonish image, and the woman runs as if she’s wearing heels, but the body physics were decent enough. It also understood the instructions quickly and followed them correctly. - Kling 3.0 Pro: The old man decided to test my patience. It repeatedly blocked a simple running scene as adult content, then misunderstood the instructions several times. The successful result has the most realistic lighting and frame rate, but the actual body physics look strange. It almost feels like loose foam padding is bouncing inside the leggings. There are also several visible artifacts and unnatural movements. - Gemini Omni Flash: As usual, it gave me that strange slow-motion, low-FPS look that Google models seem to love. But it didn’t censor anything, understood the instructions immediately, and produced a beautiful, realistic result. Surprisingly, this is the output I liked the most in this test. - Seedance 2.0: Seedance also blocked a couple of generations, just like Kling, but eventually produced a strong result. It delivered the most beautiful and visually appealing footage, but I honestly expected better physics. The video looks great, yet I can’t confidently call it the most physically accurate result. - My ranking: 1. Gemini Omni Flash — not perfect, but the best overall result for me 2. Seedance 2.0 — visually stunning, but I preferred Omni’s physics 3. Grok Imagine 1.5 — cartoonish look and strange running, but still acceptable 4. Kling 3.0 Pro — the longest wait, the strangest physics, and the most inconsistent result Do you agree with my ranking? #AIVideo

New body-physics test for the best AI video tools: The newly released Grok Imagine 1.5, the king Seedance 2.0, Google’s “revolutionary super-duper” Gemini Omni Flash, and the old-timer Kling 3.0 Pro. This time I tested how each model handles realistic running motion, body movement, fabric physics, and natural secondary motion. And the result was much less obvious than I expected. Believe it or not, I can’t call Seedance the clear winner this time. Each model got several attempts. - Grok Imagine 1.5: The new version is finally available on the official Grok website, so I could properly test it. Honestly, I didn’t notice a massive improvement over the previous version. Still, the result was acceptable. It produced the most cartoonish image, and the woman runs as if she’s wearing heels, but the body physics were decent enough. It also understood the instructions quickly and followed them correctly. - Kling 3.0 Pro: The old man decided to test my patience. It repeatedly blocked a simple running scene as adult content, then misunderstood the instructions several times. The successful result has the most realistic lighting and frame rate, but the actual body physics look strange. It almost feels like loose foam padding is bouncing inside the leggings. There are also several visible artifacts and unnatural movements. - Gemini Omni Flash: As usual, it gave me that strange slow-motion, low-FPS look that Google models seem to love. But it didn’t censor anything, understood the instructions immediately, and produced a beautiful, realistic result. Surprisingly, this is the output I liked the most in this test. - Seedance 2.0: Seedance also blocked a couple of generations, just like Kling, but eventually produced a strong result. It delivered the most beautiful and visually appealing footage, but I honestly expected better physics. The video looks great, yet I can’t confidently call it the most physically accurate result. - My ranking: 1. Gemini Omni Flash — not perfect, but the best overall result for me 2. Seedance 2.0 — visually stunning, but I preferred Omni’s physics 3. Grok Imagine 1.5 — cartoonish look and strange running, but still acceptable 4. Kling 3.0 Pro — the longest wait, the strangest physics, and the most inconsistent result Do you agree with my ranking? #AIVideo

Alpha Mom

156,463 просмотров • 3 месяцев назад

🚨 JUST IN: Fabrizio Romano: • Carlos Baleba transfer confirmed as “Here We Go”: Deal fully agreed between Manchester United and Brighton after an improved second bid (the first was rejected). • Transfer fee: £65 million fixed + £5 million in add-ons, plus a sell-on clause. • Contract details: Baleba will sign a 5-year deal with a 1-year option (same structure as Andrey Santos). • United not finished in the market: The club is actively looking for a left-back, and movement in that position is expected. • Joshua Zirkzee situation: Italian clubs have made calls; Aston Villa also contacted United (exploratory only - no bids or formal negotiations yet). • Possible further action: If Zirkzee leaves before the window closes, United would monitor a new striker, though this is not guaranteed.

🚨 JUST IN: Fabrizio Romano: • Carlos Baleba transfer confirmed as “Here We Go”: Deal fully agreed between Manchester United and Brighton after an improved second bid (the first was rejected). • Transfer fee: £65 million fixed + £5 million in add-ons, plus a sell-on clause. • Contract details: Baleba will sign a 5-year deal with a 1-year option (same structure as Andrey Santos). • United not finished in the market: The club is actively looking for a left-back, and movement in that position is expected. • Joshua Zirkzee situation: Italian clubs have made calls; Aston Villa also contacted United (exploratory only - no bids or formal negotiations yet). • Possible further action: If Zirkzee leaves before the window closes, United would monitor a new striker, though this is not guaranteed.

UtdXclusive

558,138 просмотров • 1 месяц назад

JUST IN 🚨🚨🚨 "I'm sending these 3 whores home. 4 hour minimum." - Boston Police Officer John O'Keefe to ATF Agent Brian Higgins on December 18, 2021. I have just uncovered text messages making it clear that John O'Keefe and Brian Higgins were involved with hiring prostitutes in the months before John's death on January 29, 2022. Upon doing so, as I was reviewing the text messages on live stream and found that specific line, I threw a water bottle at the wall and lost my last semblance of faith in the now-collapsing edifice America once called a justice system. We will rebuild, but first we must reflect. Of note: the source file containing these messages is incredibly difficult to read but, after about an hour, I was able to put together the big secret (and I am horrified). Also, Higgins --out of no where-- told John that a Boston Cop had been "hit by a truck" earlier in that same text message conversation. I also found text where John and Higgins were talking about "bars over charging for drinks" before John outright mentioned buying prostitutes. Source regarding "whores" page 9 out of 12, text message 10 - Source regarding Higgins mentioning a Boston Cop who was hit by a truck (page 7-8 out of 12, text messages 15, 14 and 13) -

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JUST IN 🚨🚨🚨 "I'm sending these 3 whores home. 4 hour minimum." - Boston Police Officer John O'Keefe to ATF Agent Brian Higgins on December 18, 2021. I have just uncovered text messages making it clear that John O'Keefe and Brian Higgins were involved with hiring prostitutes in the months before John's death on January 29, 2022. Upon doing so, as I was reviewing the text messages on live stream and found that specific line, I threw a water bottle at the wall and lost my last semblance of faith in the now-collapsing edifice America once called a justice system. We will rebuild, but first we must reflect. Of note: the source file containing these messages is incredibly difficult to read but, after about an hour, I was able to put together the big secret (and I am horrified). Also, Higgins --out of no where-- told John that a Boston Cop had been "hit by a truck" earlier in that same text message conversation. I also found text where John and Higgins were talking about "bars over charging for drinks" before John outright mentioned buying prostitutes. Source regarding "whores" page 9 out of 12, text message 10 - Source regarding Higgins mentioning a Boston Cop who was hit by a truck (page 7-8 out of 12, text messages 15, 14 and 13) -

Grant Smith Ellis

25,302 просмотров • 7 месяцев назад

🌟Introducing Lovense Lush 4 With rapid charging, powerful vibrations, and customizable LED lights, the future of pleasure is here! 🌈 Grab your light🌟: 💦Intense G-spot vibrations Up to 7,000 vibrations/min on your G-Spot. Orgasm is just a little cake walk. ⚡Rapid charging 5 mins for 1 hour of play. Enjoy uninterrupted fun with up to 350 mins on a full charge. 🌈New LED Tail Light 7 vibrant colors to choose from! Have fun changing the color you like with just a fingertip on the Lovense Remote App. 📱Remote Control Support Join the wireless long-distance control adventure. We have endless pattern modes for you to choose from. #Lovense #Lush4 #eggvibrator #gspotvibrator #remotecontrol #longdistancecontrol #LovenseLush4

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🌟Introducing Lovense Lush 4 With rapid charging, powerful vibrations, and customizable LED lights, the future of pleasure is here! 🌈 Grab your light🌟: 💦Intense G-spot vibrations Up to 7,000 vibrations/min on your G-Spot. Orgasm is just a little cake walk. ⚡Rapid charging 5 mins for 1 hour of play. Enjoy uninterrupted fun with up to 350 mins on a full charge. 🌈New LED Tail Light 7 vibrant colors to choose from! Have fun changing the color you like with just a fingertip on the Lovense Remote App. 📱Remote Control Support Join the wireless long-distance control adventure. We have endless pattern modes for you to choose from. #Lovense #Lush4 #eggvibrator #gspotvibrator #remotecontrol #longdistancecontrol #LovenseLush4

LovenseOfficial

50,862 просмотров • 1 год назад

This intern 6x'd her company's website traffic with an AI content machine called BlogEO. The big f*cking problem - Blog had almost no real measurement - Insights lived in ~5 places (GSC, Semrush, PostHog, Sanity, prior run snapshots) so nobody joined the data - ~70% of traffic came from 14 posts; ~50% from just 5 - Top SEO posts were often invisible to LLMs (AEO gap) Step 1: Run the AI audit - Pulls those five sources into one view - Content hygiene scan first: broken links, positioning drift, deprecated products - Broken-link fixes use Browserbase's fetch API to find the right replacement - Broken links + missing SEO fields can auto-publish (no human gate) - Scores every post into a ranking / opportunity queue - Flags the top ~15 for deeper fact-check + SEO verification - Drafts surgical edits (small, intentional) and stores each suggestion - Edits stay small on purpose. Most posts were handwritten; don’t paste AI voice over human voice - High-leverage changes: SEO title, meta description, swap a link - 28-day cooldown after changes so GSC has time to catch up (no thrashing the same post) Step 1b: Score post opportunities • Unit of opportunity = clicks • Click recovery: if clicks fell hard vs the last ~28 days, that lost volume is opportunity. Real click loss beats any estimate and jumps the queue. • CTR gap: compare your CTR at a given position to what page-one / peers get at that same spot. Gap × impressions ≈ clicks you’re leaving on the table. (Alex’s example: position 9 averages ~5% CTR; you’re at 1% → the 4-pt delta is the opportunity.) • Rank upside: if you’re on page 2/3, estimate clicks if you moved to page 1 / to the average for that better position. (Her example: post at 9.4 with a pink-dot underperform → ~7,400 click opportunity if it hit the average for that position.) • Queue, then spend: every post gets a cheap score; only the top ~15 get the expensive fact-check / deep SEO pass (token control). Near-invisible / irrelevant queries don’t score high on purpose. Step 3: Avoid AI slop - New posts aren’t a firehose ideas surface as drafts, ~once a week - Ignore / skip is a first-class option if the query isn’t worth owning - Quality gate before anything ships - Slack card shows who approved and who published — accountability stays with a person - Human pride > “we shipped another AI blog” Step 4: Run the full blog machine - Weekly cadence in Slack: Monday = content generation, Tuesday = audit (plus on-demand “audit this page now”) - Agent (BB) has skills: audit, strategy, writing, generate...always drafts suggestions first - Agent has no write path to live content until a human clicks - Slack cards: Approve / Edit / Skip / Discard → only then does it hit the CMS - Approved edits + outcomes land in internal DBs (including what never shipped) - Near-miss queries (show up in search, no targeted page) feed the generator side The results Search impressions: +5.8x Page-one queries: +9.8x Avg blog position: page 2 → page 1 Massive shoutout to harsehaj ⋆˙⟡ for the amazing internship project & masterclass in AI-powered AEO/SEO!

This intern 6x'd her company's website traffic with an AI content machine called BlogEO. The big f*cking problem - Blog had almost no real measurement - Insights lived in ~5 places (GSC, Semrush, PostHog, Sanity, prior run snapshots) so nobody joined the data - ~70% of traffic came from 14 posts; ~50% from just 5 - Top SEO posts were often invisible to LLMs (AEO gap) Step 1: Run the AI audit - Pulls those five sources into one view - Content hygiene scan first: broken links, positioning drift, deprecated products - Broken-link fixes use Browserbase's fetch API to find the right replacement - Broken links + missing SEO fields can auto-publish (no human gate) - Scores every post into a ranking / opportunity queue - Flags the top ~15 for deeper fact-check + SEO verification - Drafts surgical edits (small, intentional) and stores each suggestion - Edits stay small on purpose. Most posts were handwritten; don’t paste AI voice over human voice - High-leverage changes: SEO title, meta description, swap a link - 28-day cooldown after changes so GSC has time to catch up (no thrashing the same post) Step 1b: Score post opportunities • Unit of opportunity = clicks • Click recovery: if clicks fell hard vs the last ~28 days, that lost volume is opportunity. Real click loss beats any estimate and jumps the queue. • CTR gap: compare your CTR at a given position to what page-one / peers get at that same spot. Gap × impressions ≈ clicks you’re leaving on the table. (Alex’s example: position 9 averages ~5% CTR; you’re at 1% → the 4-pt delta is the opportunity.) • Rank upside: if you’re on page 2/3, estimate clicks if you moved to page 1 / to the average for that better position. (Her example: post at 9.4 with a pink-dot underperform → ~7,400 click opportunity if it hit the average for that position.) • Queue, then spend: every post gets a cheap score; only the top ~15 get the expensive fact-check / deep SEO pass (token control). Near-invisible / irrelevant queries don’t score high on purpose. Step 3: Avoid AI slop - New posts aren’t a firehose ideas surface as drafts, ~once a week - Ignore / skip is a first-class option if the query isn’t worth owning - Quality gate before anything ships - Slack card shows who approved and who published — accountability stays with a person - Human pride > “we shipped another AI blog” Step 4: Run the full blog machine - Weekly cadence in Slack: Monday = content generation, Tuesday = audit (plus on-demand “audit this page now”) - Agent (BB) has skills: audit, strategy, writing, generate...always drafts suggestions first - Agent has no write path to live content until a human clicks - Slack cards: Approve / Edit / Skip / Discard → only then does it hit the CMS - Approved edits + outcomes land in internal DBs (including what never shipped) - Near-miss queries (show up in search, no targeted page) feed the generator side The results Search impressions: +5.8x Page-one queries: +9.8x Avg blog position: page 2 → page 1 Massive shoutout to harsehaj ⋆˙⟡ for the amazing internship project & masterclass in AI-powered AEO/SEO!

Alex Lieberman

33,210 просмотров • 16 дней назад