🚨BREAKING: CLAUDE IS COMING TO YOUR IPHONE. iOS 27 could let you pick Claude instead of only using ChatGPT for Apple Intelligence. That means Claude, Gemini, and ChatGPT could all live inside your iPhone. Here is the full breakdown.🧵👇🏼

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CES 2026 just started, and we’re already seeing incredible tech. Here're 20 most impressive reveals of CES 2026 so far: 1. Halliday Glasses: 3.5-inch AI smart glasses

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🚨 BREAKING: Claude can now perform stock market research like a top-tier consulting firm — for free. Here are 10 Claude prompts that replace $100K/year stock analysts. (Save this for later) 📌

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ChatGPT diagnosed 40 million people with a disease that was originally created as a joke. Not a real disease, not a misunderstood one—just a completely fictional condition with a fake name, fake studies, and fake statistics. And it told patients to see a specialist. The disease is called Bixonimania. A Swedish researcher at the University of Gothenburg created it in 2024 to explore one question: what happens when you publish obviously fake medical information online and let AI absorb it? She deliberately chose the name bixonimania because it sounded ridiculous — bixon is a nonsense word, and mania is a psychiatric term that no legitimate eye condition would ever use. She uploaded two papers to a preprint server. Both were obviously fraudulent. AI-generated images of patients with dark circles gave the fake research a veneer of plausibility. Then she waited. She did not have to wait long. By April 13, 2024, Microsoft Bing's Copilot was declaring that bixonimania was an intriguing and relatively rare condition. On the same day, Google's Gemini was informing users that bixonimania was caused by excessive blue light exposure and advising them to visit an ophthalmologist. Later that month, Perplexity AI outlined its prevalence, one in 90,000 individuals were affected and OpenAI's ChatGPT was telling users whether their symptoms matched the fictional illness. One in 90,000. A precise statistic. For a disease that does not exist. Every red flag was visible. The name was absurd. The papers were crude. The condition made no scientific sense. None of the AI systems flagged any of it. They read the fake papers. They absorbed the fake statistics. They presented both to patients with clinical authority and zero hesitation. Then it got worse. Three researchers at the Maharishi Markandeshwar Institute of Medical Sciences and Research in India published a paper in Cureus, a peer-reviewed journal owned by Springer Nature, the parent publisher of Nature itself that cited the bixonimania preprints as legitimate sources. A real peer-reviewed paper. In a Springer Nature journal. Citing a fictional disease as established medical fact. Passing editorial review. Entering the permanent scientific record. It was only retracted after the hoax became public. Nature published a full investigation of the experiment. Alex Ruani, a health-misinformation researcher at University College London, called it a masterclass in how misinformation operates. Here is the scale of what this means. More than 40 million people turn to ChatGPT every day for health information, according to OpenAI's own analysis. ECRI, a US patient-safety nonprofit has named chatbot misuse the number-one health technology hazard of 2026. ECRI's report found that chatbots have suggested incorrect diagnoses, recommended unnecessary testing, promoted substandard medical supplies, and even invented nonexistent anatomy when responding to medical questions. Number one. Out of every health technology hazard that exists in 2026. An April 2026 study published in BMJ Open found that nearly half of the answers provided by leading AI chatbots to common health questions contain misleading or problematic information. Nearly half. Of all health answers. From the tools 40 million people use every day. Here is the line from the researcher that cuts through everything. The Bixonimania case is striking precisely because it was engineered to be so obviously fake. The real question it raises is: what is passing through the same systems that is not nearly so easy to spot? The experiment used a ridiculous name. Fraudulent papers. Visible red flags at every level. It was designed to be caught. It was not caught. The AI that told patients about Bixonimania is the same AI they asked about their chest pain, their medication, their child's symptoms, and their cancer screening schedule. 40 million people. Every day. And nobody is telling them that nearly half of what comes back may be wrong. Source: Osmanovic Thunström · University of Gothenburg · Nature · April 2026 · Link in the (comments)

My Uber driver asked what I do for work. “Software.” “Nice. Can you take a look at something?” At the next red light, he handed me his phone. Terminal open. Claude running. Green P&L everywhere. $6,200. He drives Uber 4 days a week and makes around $1,100. He has a 2-year-old daughter. I asked: “Where did you learn this?” He smiled. “From one of your articles.” Three months ago he read my post about tracking profitable wallets. He didn’t understand most of it. So he opened Claude and said: “Explain this like I’m five.” That single prompt changed everything. Over 200 messages later—written between rides, parked at gas stations, and waiting for passengers—he had built a complete workflow. The first lesson Claude gave him: 87% of wallets lose money. Don’t follow the 87%. He started analyzing top-performing wallets, filtering for consistency, risk-adjusted returns, and crypto-only activity. With a small bankroll funded from Uber tips, he gradually built a simple automated strategy. The results over the last 45 days: → 480 trades → 91.3% win rate → $6,200 profit His favorite trade came when several large traders positioned around the same macro event within minutes of each other. The setup paid off in a big way. The funny part? While he was dropping off a passenger, the passenger tipped him $5. At the same time, the bot generated far more than that. His wife later found the Telegram alerts on his phone and thought he was messaging someone. Instead, it was trade notifications. I asked him: “How long until you stop driving Uber?” He looked at me in the mirror and laughed. “I’m not quitting.” “Uber is just my cover story.” The difference wasn’t coding skills. It was curiosity, consistency, and the willingness to learn. Sometimes all it takes is an idea, the right tools, and a little time each day. What do you think?

A 33-year-old nerd just turned $1,000 into $946,207 trading Bitcoin, using a trick he stole from hurricane forecasts. No finance degree. No trading desk. Just a trick every meteorologist uses and every trader forgets. The trick: meteorologists never forecast tomorrow with a single model. They run 31 and count the votes. He aimed exactly that same trick at Bitcoin. A Claude agent reads every 5-minute BTC market and feeds it into MiroFish, a simulation that runs 31 model paths and only fires when 28 of them agree. Below 26 votes, it kills the trade. The agent system's coverage speed is way faster than any elite trading team's. They gather data 24/7 and run simulations with that data in the MiroFish engine, completely autonomously. Every trade is a perfect cycle. Every dollar earned is pure exploitation of market inefficiency. That's the whole edge. Not a prediction. A quorum. Sizes with Kelly and hits a button. Most signals never pass the vote, so most days it stays still. He spent years learning that certainty is a scam and consensus is the edge. You only need Claude laptop 1 hour/day. Giving This Free for 24 hours. To get it: 1. Comment the word 'Claude' 2. Like and Retweet this post 3. Follow me @marryevan999

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🚨 BREAKING: AI is creating a new kind of technical debt. Not code debt. Verification debt. AI ships code in minutes. Reviewers can't scale to match. AWS CTO Werner Vogels calls it **verification debt** — and your team is probably accumulating it whether you've named it or not. That's why @Test_Sprite caught my attention. Other verification tools read code and guess. TestSprite opens your app and uses it. It explores real workflows, validates actual behavior, and helps catch the bugs that slip past AI-generated PRs and rubber-stamped reviews. As AI-generated code becomes the norm, this feels increasingly important.

Other verification tools read your code and guess. @Test_Sprite opens your app and uses it. That's a bigger difference than it sounds. The bugs users actually care about aren't hidden in a diff. They're hidden in broken checkout flows. Failed logins. Missing data. Workflows that look fine in a PR but break in production. What caught my attention about TestSprite is that it starts with the product, not the code. It launches the application, explores real user journeys, and builds tests around actual behavior. That's a fundamentally different approach to software validation.

Brands are paying for influence. Too often they're getting inflated numbers instead. @nikitabier

Professionals won’t tell you this 👀 They use these daily. 🪄⚡ 1. Ideas 🧠 - YOU - Claude - ChatGPT - Perplexity - Bing Chat 2. Presentation - Prezi - Pitch - PopAi - Slides AI - Slidebean 3. Website - Dora - Wegic - 10Web - Framer - Durable 4. Writing - Rytr - Jasper - Copy AI - Textblaze - Writesonic 5. AI Models - RenderNet - Glambase App - Luma AI - Sora (OpenAI) - Leonardo AI 6. Meeting - Tldv - Krisp - Otter - Avoma - Fireflies 7. Chatbots - Poe - Claude - Gemini - ChatGPT - HuggingChat 7. Automation - ClickUp - Drift - Outreach - Emplifi - Phrasee 8. UI/UX - Uizard - Visily - Khroma - Galileo AI - VisualEyes 9. Image - Stylar - Freepik - Phygital - StockIMG - Bing Create 10. Video - Pictory - HeyGen - Nullface - Decohere - Synthesia 11. Design - Looka - Clipdrop - Autodraw - Vance AI - Designs AI 12. Marketing - AdCopy - Predis AI - Howler AI - Bardeen AI - AdCreative 13. Twitter - Typefully - Postwise - Metricool - Tribescaler - TweetHunter AI updates you shouldn’t miss 👀 Follow @tec_safwan for more.👇🔰

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13 Powerful AI Tools that can save 30 hours in every week: 1. ChatGPT.com (solve any problem) 2. PicWish.com (remove backgrounds) 3. Perplexity.ai (research anything) 4. Suno.ai (compose music) 5. Canva.com (design graphics) 6. ElevenLabs.io (clone voices) 7. Grammarly.com (perfect writing) 8. Luma.ai (create 3D models) 9. RecCloud.com (summarize YouTube) 10. Runway.ml (edit videos) 11. Descript.com (edit podcasts) 12. Syllaby.io (create faceless videos) 13. skysnail.io (create viral thumbnails) Don’t lose this list, it could be incredibly helpful.

Professionals won’t tell you this 👀 They use these daily. 🪄⚡ 1. Ideas 🧠 - YOU - Claude - ChatGPT - Perplexity - Bing Chat 2. Presentation - Prezi - Pitch - PopAi - Slides AI - Slidebean 3. Website - Dora - Wegic - 10Web - Framer - Durable 4. Writing - Rytr - Jasper - Copy AI - Textblaze - Writesonic 5. AI Models - RenderNet - Glambase App - Luma AI - Sora (OpenAI) - Leonardo AI 6. Meeting - Tldv - Krisp - Otter - Avoma - Fireflies 7. Chatbots - Poe - Claude - Gemini - ChatGPT - HuggingChat 7. Automation - ClickUp - Drift - Outreach - Emplifi - Phrasee 8. UI/UX - Uizard - Visily - Khroma - Galileo AI - VisualEyes 9. Image - Stylar - Freepik - Phygital - StockIMG - Bing Create 10. Video - Pictory - HeyGen - Nullface - Decohere - Synthesia 11. Design - Looka - Clipdrop - Autodraw - Vance AI - Designs AI 12. Marketing - AdCopy - Predis AI - Howler AI - Bardeen AI - AdCreative 13. Twitter - Typefully - Postwise - Metricool - Tribescaler - TweetHunter AI updates you shouldn’t miss 👀 Follow @Onil_Coder for more.👇🔰

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I’ve tested a lot of AI writing tools. Most help you type faster. @typelessdotcom helps you stop typing. 🎙️ Talk once. ✍️ Get polished emails, Slack replies, docs, and notes. It keeps the context, removes the fluff, and preserves your voice. One of the few AI tools that actually changed my workflo Try ir yourself: typeless.com/?via=aryan-rak… #Typeless #typelessdotcom