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It may become useful to Gen X and Millennials to understand the lingo of gen Z. Rizz – Charisma, especially in flirting. Example : “He’s got mad rizz when he talks to her.” Simp – Someone who does too much for someone they like, often at their own expense. Example : “He buys her gifts every day—such a simp.” Clock it – To notice or recognize something quickly. Example : “Did you clock it? He was staring at you.” Bet – Agreement, “okay” or “yes.” Example : “You coming tonight?” → “Bet.” Cap – A lie, false statement. Example : “He said he met Drake. That’s cap.” No Cap – For real, no lie. Example : “This burger is the best, no cap.” Slay – To do something amazingly well. Example : “She slayed that outfit.” Sus – Suspicious or sketchy. Example : “He left suddenly, that’s sus.” Drip – Cool fashion sense. Example : “Check out his drip.” Mid – Average, unimpressive. Example : “That movie was mid.” Lit – Fun, exciting, excellent. Example : “The concert was lit.” Ghost – To cut off...

News 'She gave it her all': Meta employee says teammate who spent sleepless nights on project got laid off We analyzed the reasons. Why Strong Employees Get Laid Off — And What You Can Do About It Even high-performing employees can be laid off due to factors unrelated to performance. Below are key reasons this happens, along with practical actions tailored to two career stages: 1–3 years experience (early career) 6–10 years experience (mid-to-senior) 👨‍💻 1–3 Years Experience (Early Career) At this stage, your biggest risks are low visibility and replaceability . Your goal is to become noticeable, adaptable, and hard to ignore . 1. Role Redundancy Problem: Your role overlaps with others; easy to cut. What you can do: Learn adjacent skills (backend + frontend, data + product basics) Volunteer for cross-team tasks Avoid being “just another developer” on the team ✅ Goal: Be seen as multi-purpose , not interchangeable 2. Strategic Pivot / Project Cuts Problem: Your project los...

https://www.businesstoday.in/latest/trends/story/you-came-here-as-a-beggar-ai-startup-founder-recalls-humiliation-and-a-strangers-kind-move-532699-2026-05-21 The founder of Vokido recalled that his father once ran a transport business with three trucks and a financially stable life. But disaster struck when all three trucks met with accidents within two years, wiping out the family’s savings and livelihood. A petrol pump owner, a near stranger, was holding an account at the very same bank where he went to ask for loan for engineering. The businessman placed a single, powerful phone call to the manager: “Give this man the loan. His son wants to study.”  That single intervention unlocked an incredible transformation.  Khatri got his engineering degree, moved to the United States to earn an MBA from Duke University, and scaled the heights of the tech world. He went on to build AI products used by tens of millions of people at global giants like Amazon, Careem, and Deliveroo.

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Based on the report’s “mixed picture” and the 75-year baseline projection, here are the estimated scenarios for India avoiding or falling into the middle-income trap. Best Case (Escaping the Trap) Approximate chance: 15-20% · Scenario: India implements radical reforms in land, labor, and capital allocation. The dynamism seen in digital services spreads to manufacturing and traditional sectors. Small firms rapidly scale up, productivity gaps close, and the economy grows at 8-10% consistently for decades. · Outcome: India closes the income gap with the US in 25-35 years (not 75). It becomes a high-income economy by 2050-2060, avoiding the trap entirely. Worst Case (Full Trap) Approximate chance: 30-35% · Scenario: Policy distortions persist, capital/labor remain misallocated, and small, low-productivity firms dominate. Growth stalls in upper-middle income range (~$5,000–$10,000 per capita). Political economy blocks reforms, and populist measures worsen fiscal health. · Outcome: India nev...

By 2026, the artificial intelligence debate has moved beyond novelty and productivity hacks. The real question is no longer whether AI will transform economies, but which regions will absorb the gains, which industries will be restructured first, and which workers will be pushed into transition without a safety net. That shift is already visible. In the U.S., recent reporting has cited estimates that AI is already contributing to measurable monthly job displacement, especially in entry-level white-collar roles, while major AI firms are simultaneously pushing policy ideas such as shorter workweeks, public wealth funds, and new industrial policy frameworks. Critics argue that much of this discourse is less about protecting workers and more about shaping a favorable regulatory environment for frontier AI companies. But the consequences of this transition will not be uniform. India, the Gulf, and the United States are entering the AI era from very different labor-market structures, energ...