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Nike, the global sportswear giant, is facing mounting pressure over human rights concerns in its supply chain. As reported by Reuters and other sources, the company is grappling with allegations of wage theft, forced labor, and inadequate worker prot

Helping a client sell TOX-3, a Tesla stock prediction tool with ~95% accuracy on UP/DOWN calls 📈 Clean dashboard + full source code, API & ML model. One-time $2K, lifetime rights, no subs. DM for demo & details! (Plus if anyone wanna give it a ha

Navigating AI's Legal Challenges in India AI's rapid evolution is transforming industries and posing legal challenges. Key issues include: 1. Data Privacy: AI relies on vast personal datasets. Ensuring compliance with privacy laws is crucial to pre

→ It's not just about bat and ball 🌚💀 . It's big business. 1. Broadcasting Rights: The golden goose • TV deals worth billions. Ka-ching! 2. Sponsorships: Logos everywhere • From stadiums to stumps, everything's for sale 3. Ticket Sales: Pa

🚀 Can AI Make Moral Decisions? The Ethics of Machine Judgment 🤖⚖ AI is reshaping decision-making in healthcare, finance, and law. But should machines decide who gets a loan, medical care, or even freedom? 🔥 The Ethical Dilemma ✅ AI processes data

🧠💀 The AI That Failed With Confidence (aka why ChatGPT is the overconfident friend we all have) So this guy uses ChatGPT to write his law exam. The answers? 🔥 Clean formatting 📚 Long paragraphs 🧑‍⚖️ Fancy citations 📖 Phrases like “According t

Why Math's & Psychology Are the Only Two Subjects You Need to Master if you want to be in the top 1% in any field. Math's is the game of numbers, the technical aspects that define the structure and strategy of what you do. Psychology, on the other

While We Were Scrolling-Or Atleast I was-May Day 2025 Was On Fire (Literally and Metaphorically) So, while most of us were debating whether pineapple belongs on pizza or doom-scrolling through social media… the world? It was out there. Marching. Sho

3 ways how most AI systems are built. I’ve explained each one step-by-step. 1) 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽) 1. 𝗦𝗲𝘁 𝘁𝗮𝘀𝗸 – Decide what problem the model should solve. 2. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗱𝗮𝘁𝗮 – Gather lots of example