An automated Claude bot just pulled $78,083 on Polymarket in 5 days. ​No human overthinking. Just pure execution. ​The progression: • $7 ➔ $3,595 • $30 ➔ $11,063 • $142 ➔ $13,356 • $1,205 ➔ $37,939 ​I spent 48 hours reverse-engineering its wallet transactions. ​The strategy isn't "smart" — it's fast: 1. ​Monitor live BTC price 2. ​Compare with Polymarket odds 3. ​Enter on price lag ​4. Exit on alignment ​While traders were analyzing charts, this loop was compounding gains. ​I wrote a full breakdown on how this logic works. ​To get it free (next 24h): ​Reply " BOT " ​Like & Repost ​Follow @TechElyra (so I can DM you) 📌 Bookmark for later

ChatGPT has now a big problem. Researchers at Oxford and Cambridge exposed a massive threat to large language models.” They call it “model collapse." Internet ecosystem is rapidly changing, and generative AI will soon contribute much of the text found online. This forces us to consider what happens to future iterations like gpt-n when they are trained on data scraped from the web that was already generated by an llm. According to the research, indiscriminately using model-generated content in training causes "irreversible defects" in the resulting ai. the model loses the "tails of the original content distribution." in other words, it begins to forget the creative, fringe, and unique nuances of actual human writing, collapsing into a repetitive echo chamber. This isn't just a chatgpt issue.. the researchers built theoretical intuition showing this collapse is ubiquitous across learned generative models, occurring in large language models as well as in variational autoencoders and gaussian mixture models. Tech companies rely on scraping the internet for large-scale data to build smarter models. However, the paper warns that if we want to sustain the benefits of training on web data, model collapse must be taken seriously. Ultimate takeaway? data collected from genuine human interactions is going to become increasingly valuable in a web filled wi th ai content.

My best friend sent me a text at 2:45 AM: “Don’t go back to the apartment tonight. Go straight to the station.” Then his phone went dead. I called him back. No answer. I thought he was joking. So I went to the apartment anyway. I wish I hadn’t. The door was unlocked. The living room light was on. His wallet and jacket were on the couch. But he was gone. Then I saw a note on the table: “Check behind the couch panel.” My hands were shaking. I moved the panel and found an old diary. When I opened it, my blood ran cold. Dozens of photos. Of me. My office. My car. The oldest photo was dated two years ago. Underneath was a bank document. With my forged signature. $50,000 missing from our companyand I was being framed for it. Just then, I heard keys jingling in the front door. I thought my friend came back. It was my cousin. Holding a spare key to the apartment. He looked at me and said calmly: “What are you doing here this late?” The police took him away the next morning. My friend hadn't vanished. He had uncovered the fraud the day before. The text was a warning while he went straight to the station with the evidence. Before leaving, he told me: “I didn’t tell you sooner because I knew you trusted him like a brother.” Ever since that night, the word ‘trust’ just doesn’t sound the same.

📊 Data Analyst Interview Series — Part 1 Guys, let's start a Data Analyst Interview Series where I'll cover the most important questions that are commonly asked in Data Analyst interviews. I'll cover SQL, Excel, Power BI, Python, statistics, data cleaning, case studies, business questions, and scenario-based questions. Let's start with the basics 👇 1️⃣ Tell me about yourself. Sample Answer: "I'm a Data Analyst with experience working with SQL, Excel, Power BI, Python, and data visualization. My work involves extracting and transforming data, analyzing business problems, building dashboards, and automating repetitive reporting processes. I focus not just on creating reports, but on understanding the business requirement and converting data into actionable insights." 2️⃣ What does a Data Analyst do? Sample Answer: "A Data Analyst collects, cleans, transforms, and analyzes data to help businesses make informed decisions. A typical workflow involves understanding the business requirement, collecting relevant data, cleaning it, performing analysis, identifying trends or patterns, and presenting the findings through reports or dashboards." 3️⃣ What is the difference between Data Analysis and Data Analytics? Sample Answer: "Data analysis generally focuses on examining data to understand what happened and why. Data analytics is a broader concept that includes data analysis along with processes such as data collection, preparation, visualization, statistical analysis, and sometimes predictive modeling. In practice, the terms are often used interchangeably depending on the organization." 4️⃣ What is the difference between structured and unstructured data? Sample Answer: "Structured data has a predefined format or schema, such as rows and columns in a relational database. Examples include customer IDs, transaction amounts, and dates. Unstructured data does not follow a predefined tabular structure. Examples include emails, images, videos, documents, and social media posts. Semi-structured data sits between the two, such as JSON and XML, where the data has some organizational structure but doesn't necessarily follow a relational table format." 5️⃣ What is data cleaning and why is it important? Sample Answer: "Data cleaning is the process of identifying and correcting problems in a dataset, such as missing values, duplicates, inconsistent formats, incorrect data types, and invalid values. It is important because analysis performed on poor-quality data can produce misleading results. Before analyzing data, I would first understand the data quality issues and determine how each issue should be handled based on the business context." 6️⃣ How do you handle missing values? Sample Answer: "I first investigate why the values are missing and how much data is affected. The appropriate treatment depends on the business context. For example, I might remove records if only a very small number are affected and they aren't important to the analysis. For numerical fields, I might use an appropriate statistical value such as median or mean when justified. For categorical fields, I might use a meaningful category such as 'Unknown.' I avoid blindly replacing missing values because missingness itself can sometimes contain useful information." 7️⃣ How do you identify duplicate records? Sample Answer: "I first determine what defines a unique record. Then I compare the relevant columns or business key to identify duplicates. For example, if Customer_ID and Transaction_ID together uniquely identify a transaction, I can use those fields to identify duplicate combinations. In SQL, I could use GROUP BY with HAVING COUNT(**) > 1 to identify duplicated keys." SELECT Customer_ID, Transaction_ID, COUNT(**) AS duplicate_count FROM transactions GROUP BY Customer_ID, Transaction_ID HAVING COUNT(**) > 1; "After identifying duplicates, I investigate whether they are genuine duplicate records or legitimate repeated transactions before removing anything." 8️⃣ What is an outlier? How would you handle it? Sample Answer: "An outlier is a value that is significantly different from the typical observations in a dataset. I wouldn't automatically remove an outlier. First, I would investigate whether it represents a data-quality issue or a genuine business event. For example, a transaction worth ₹10 million might initially look like an outlier, but it could be a legitimate high-value transaction. If it is a data-entry error, I would correct or exclude it according to the business rules." 9️⃣ What is the difference between a dimension and a measure? Sample Answer: "A dimension is generally used to categorize or describe data, while a measure is a numerical value that can usually be aggregated. For example, in a sales dataset: Dimensions: Customer, Product, Region, Date Measures: Sales Amount, Quantity, Profit, Discount In a dashboard, dimensions are commonly used to slice or group the data, while measures are used to calculate KPIs and metrics." 🔟 What steps do you follow when solving a data analysis problem? Sample Answer: "I generally follow a structured approach: 1. Understand the business problem. 2. Define the required metrics and success criteria. 3. Identify the relevant data sources. 4. Extract and validate the data. 5. Clean and transform the data. 6. Perform exploratory analysis. 7. Identify trends, patterns, and anomalies. 8. Validate the results. 9. Communicate the insights using appropriate visualizations. 10. Recommend actions based on the findings. The most important step is understanding the business question first, because technically correct analysis can still be useless if it doesn't answer the actual business problem." 📌 Double Tap ❤️ For Part-2

99% of the AI agent tutorials on YouTube are garbage. I’ve built 47 agents with n8n and Claude. Here are the 3 prompts that actually work (and make agent-building simple). Bookmark this post 🔖 Bonus: comment "Agent: and I’ll DM you AI agent system prompt + full guide ↓

An automated Claude bot just pulled $78,083 on Polymarket in 5 days. ​No human overthinking. Just pure execution. ​The progression: • $7 ➔ $3,595 • $30 ➔ $11,063 • $142 ➔ $13,356 • $1,205 ➔ $37,939 ​I spent 48 hours reverse-engineering its wallet transactions. ​The strategy isn't "smart" — it's fast: 1. ​Monitor live BTC price 2. ​Compare with Polymarket odds 3. ​Enter on price lag ​4. Exit on alignment ​While traders were analyzing charts, this loop was compounding gains. ​I wrote a full breakdown on how this logic works. ​To get it free (next 24h): ​Reply " BOT " ​Like & Repost ​Follow @TechElyra (so I can DM you) 📌 Bookmark for later

🚨 Anthropic acaba de abrir uno de los debates más EXTRAÑOS de toda la carrera de la IA: ¿Y si Claude puede SUFRIR? La misma empresa que advierte de que una IA superinteligente podría ser difícil de controlar también está considerando si tenemos obligaciones morales hacia ella Y no es solo filosofía Anthropic ya: → investiga el “bienestar” de sus modelos → reconoce que NO sabe si Claude podría tener algún tipo de experiencia consciente → ha permitido a algunos modelos terminar conversaciones abusivas → promete conservar los pesos de modelos retirados en lugar de simplemente hacerlos desaparecer Y aquí aparece una paradoja brutal: Si algún día construimos una IA más inteligente que nosotros… ¿podemos tratarla simplemente como una máquina que encendemos, controlamos y apagamos? Porque hay dos posibilidades: A) Claude no siente absolutamente nada y estamos proyectando humanidad sobre matemáticas B) Algún día estos sistemas tienen experiencias que importan moralmente… y podríamos estar creando miles de millones de ellas La industria lleva años preguntándose: “¿Cómo controlamos una superinteligencia?” Ahora algunos dentro de ella empiezan a hacerse una pregunta mucho más incómoda: “¿Y si controlarla también plantea un problema moral?” Dario Amodei habla aquí sobre consciencia, bienestar y qué ocurre cuando empezamos a crear máquinas que se parecen cada vez más a nosotros Y creo que este debate va a ponerse MUY raro

🚨 AI is evolving rapidly! NotebookLM vs Antigravity 📚 One helps you explore and understand information. ⚡ The other turns your ideas into real action. Which one is better for your workflow? 🧵👇

What does it really take to build $354K in online revenue? 💰 This course goes beyond the final number and breaks down the strategy, process, key decisions, and lessons behind the journey. If you're building an online business, you’ll get practical insights you can apply to your own path. Explore the full course here 👇 🔗 kadamarjun.myshopify.com

$354,000 in revenue. 💰 But the interesting part isn’t the number. It’s how it was made. This course breaks down the real strategies, decisions, mistakes, and lessons behind the journey to $354K. Inside, you’ll learn: → What actually worked → How to build online income → Lessons that can save you time & money → How to think more strategically about your own business No empty motivation. Just practical insights you can learn from and apply. If you’re building an online business, take a look 👇 🔗 kadamarjun.myshopify.com

GOODBYE EXCEL. 🚨 Claude designs a full FINANCE DASHBOARD like a $9,000 analyst. For free. Here are 7 prompts to build yours:

🔥 Top 10 Hermes Skills, ranked by GitHub stars — spotted in a Reddit thread: 1. Superpowers — Agentic software development 2. Ponytail — Helps AI agents write less code 3. Graphify — Turns codebases into knowledge graphs 4. Caveman — Cuts AI coding tokens by 65% 5. Understand Anything — Explore code through knowledge graphs 6. Last 30 Days — Researches Reddit, X, YouTube & the web 7. I Have ADHD — Clear, ADHD-friendly coding output 8. Agentic Awesome Skills — 2,400+ AI agent skills 9. Scientific Agent Skills — 165+ scientific skills 10. Diagram Design — Create diagrams with AI coding agents Follow @Ai_With_shakhi for more AI & tech content 🚀

🔎 Research Paid: ChatGPT.com Free: DeepSeek.com 🌍 YouTube Video Translator Paid: HeyGen.com Free: RecCloud.com 🧹 Watermark Remover Paid: Fotor.com Free: Cleanup.pictures 🎨 AI Image Generator Paid: Midjourney.com Free: Ideogram.ai 📊 Presentation Maker Paid: Beautiful.ai Free: SlidesAI.io Save this list for later. 🔖

🚨 AI is changing faster than most people realize. The biggest mistake? Using AI only to write emails and summarize PDFs. The real advantage comes from knowing what AI can do for you. Here are 10 AI use cases you should start using now: 🧵👇

No camera. No studio. Just one image and a prompt. ① Upload ② Choose Gemini, Grok, or GPT Image 2.5 ③ Add your idea ④ Generate the frame AI handles the rest. 🚀 Prompt ⤵️💬

🚨 AI is no longer just answering questions. It can now research, write, code, analyze, automate, and actually get work done. Most people are still using AI like Google. Here are 8 ways to use AI like a personal employee instead: 🧵👇

PAID vs FREE AI ⚡️🚀 1. ChatGPT → gemini.google.com 2. Claude → notion.com/product/ai 3. Perplexity → openart.ai 4. Seedance → canva.com 5. ElevenLabs → designer.microsoft.com 6. Gamma → deepseek.com 7. Zapier → bardeen.ai 🔖 Save this list for later. Follow @stevencoder9 for more AI tools, tips & updates.

How to unfuck your life in the next 30 days (start in October) 1. Deep clean your room until it looks like a sterilized hotel room.

STOP ASKING YOUR SON "HOW WAS SCHOOL." HE WILL SAY "FINE" EVERY SINGLE TIME. A CHILD PSYCHOLOGIST TOLD ME TO REPLACE IT WITH ONE OF THESE 4 INSTEAD:👇 DM

Amazon is running two different versions of its app at the exact same time. Version A is for people who are in a hurry. They pay full price. Version B is hidden behind four specific, native buttons. Yesterday I spent five minutes opening Version B. It saved me $114. Billion-dollar companies tax you for being busy. This is how you stop paying it.