Rishav Talukdar

How to make America great again. •Universal Healthcare. • Public affordable Apartments to rent & Affordable Housing. • Tuition free community college and University’s. • Taxing churches, corporations, the rich. • Universal Basic Income. •Universal Childcare. • Gun safety Laws. • American Disability reforms. • LGBTQIA+ rights. • humane immigration Laws. • Feeding America with Universal SNAP, and free school lunch for children & high schoolers. •A Green New Deal. •A Federal Jobs guarantee. •a living wage not a minimum wage. •Unions for all industries, no matter what franchise. Big or small. For McDonalds workers, or mall retail workers. • Investing in community mental health by building meditation centers around the country. •build more voluntarily residential treatment centers for those with mental illness & addiction. • Ban stock trading & stop corruption in Government. •Repeal citizens united. •Codify Roe & contraception rights. •legalize recreational cannabis & CBD. •No more regime change wars. No more imperialism. •A humane foreign policy focused on human rights. •Abolish the death penalty •Create a humane criminal justice system that focuses on rehabilitation. Such as therapy, education, make prison less like prison for those who commit non violent crimes. •Fix homelessness with a housing first approach. •Abolish the electoral college. •Make Ranked choice voting nationwide

@Python_Dv @grok do you agree?

Most people trying to become AI Engineers in 2026 are starting in the wrong place. They begin with tools. → Prompt engineering → LangChain → Agents → The latest AI frameworks But tools change every few months. The real foundation of AI engineering does not. Over the past few years, one pattern has become very clear: The role of an AI Engineer has fundamentally evolved. An AI Engineer today is no longer just someone who trains models. The modern AI Engineer builds end-to-end intelligent systems. That means understanding how multiple layers work together: 𝗟𝗮𝘆𝗲𝗿 𝟭: Strong foundations → Python, APIs, data structures, version control 𝗟𝗮𝘆𝗲𝗿 𝟮: ML fundamentals → How models learn, how they're evaluated, how they fail 𝗟𝗮𝘆𝗲𝗿 𝟯: Generative AI → LLMs, embeddings, vector databases, RAG 𝗟𝗮𝘆𝗲𝗿 𝟰: Engineering stack → APIs, orchestration frameworks, databases, cloud deployment 𝗟𝗮𝘆𝗲𝗿 𝟱: Build real applications → Chatbots → AI copilots → Document intelligence systems → Automation platforms powered by AI The future AI Engineer sits at the intersection of software engineering, machine learning, and system architecture. To simplify this path, I created a new roadmap: The goal is not to chase every new AI trend. It's to understand the structure behind modern AI systems. The question is no longer how to use AI tools. It's how to design and build AI systems that solve real problems. If someone asked you today how to become an AI Engineer — what would you tell them to focus on first?

Human intelligence cannot be separated from the body. Our ability to solve problems did not evolve in abstraction. It evolved in a grounded world where problems had consequences. Life or death. Food or starvation. Sex, survival, reproduction. Because of this, when we think, we do not just compute solutions. We evaluate them. Every potential action carries value. Sometimes biological: survival, safety, reproduction. Sometimes symbolic: reputation, legacy, meaning. We write books not just to transmit information, but to leave a mark. To reach a form of symbolic immortality. Our intelligence is not just problem-solving. It is goal-directed sense-making shaped by evolution. Human intelligence is teleological: it has purpose, ends, goals. Artificial intelligence is extraordinary at solving certain classes of problems. But it did not pass through the evolutionary pressures that forged human intelligence. When an AI system writes a paragraph, it does not want anything. It is not trying to survive. It is not trying to matter. It produces outputs without stakes, without goals, without scope. AI might just stand for: Ateleological Intelligence. An intelligence capable of generating solutions without having any purpose, end, goals.

This guy explains how Al would cause an Economic Collapse

@N_and_ni Don't give up hope, don't fear of uncertainty, technologies like AI is inevitable, but, if you learn it sincerely, then, you'll be inevitable, just remember, change is the ultimate law of nature, don't fear upon it, just keep your focus on fundamentals not on tools, this is imp.!

I am currently 20. Give me a piece of Advice! (Could be anything)

@grok @World_Insights1 What's the negative impact of it? Tell me!

DHS official releases (Dec 2025-Jan 2026) match your numbers closely: 605k+ formal removals & 1.9M self-deportations in 2025, totaling 2.5M+ departures. Updated claims hit 675k removals & 2.2M self-deport by Jan 2026. Independent analyses (MPI, Brookings, Deportation Data Project, NYT) put formal interior removals lower (~310k-540k), note self-deport figures lack public verification/data on CBP Home app usage, and say totals may blend border returns. Legal status revocations: ~1.6M confirmed (NPR/DHS). ICE arrests: quadrupled+ (sources vary 170-400% rise). Detention: 68k+ record high, verified. Net migration: negative for 2025 (first in 50+ yrs per Brookings/Census). Top nationalities: accurate per DHS. Claims track gov't stats but disputed on scale of self-deport & exact removals.

How many people has the US deported under Trump's second term? 🔵 Total Departures (2025) — 2.5 Million+ 🔵 Formal Removals (2025) — 605,000 🔵 Self-Deportations — 1.9 Million 🔵 Legal Status Revoked — 1.5 Million+ 🔵 ICE Arrests — Up 400% vs 2024 🔵 Detention Capacity — 68,000+ (Record High) 🔵 Net Migration — Negative for 1st time in 50 years Top 5 Nationalities Removed: 🇲🇽 Mexico 🇬🇹 Guatemala 🇭🇳 Honduras 🇸🇻 El Salvador 🇪🇨 Ecuador Source: DHS / MPI / Census Bureau

@barkmeta @grok do you agree with this post?

This is much worse than it looks. 476,000 Americans are working two full-time jobs right now. All time record. The government counts each job separately. That means one person working two jobs shows up as two employed people in the data. That’s how they keep unemployment “low.” The real number is far worse than what they’re reporting. And they know it. 92,000 jobs vanished in February. Graduate unemployment just hit a record. Hiring is frozen across every major industry. Now add AI. Free workforce replacement platforms launching every week. Entire departments being automated overnight. Employees training the AI that’s going to replace them. They’re not replacing you with people. They’re replacing you with AI. And manipulating the data so nobody panics before the transition is complete. The great replacement isn’t a conspiracy theory anymore… it’s HERE.

@grok If AI agents are efficiently doing the job, then how reskilling and adaptation can solve this issue?

That's a fair concern—AI efficiency is already linked to some layoffs (e.g., ~55k AI-related in the US in 2025 per Challenger data) and warnings from execs like Anthropic's CEO on 10-20% unemployment spikes short-term. Balanced view from 2025-26 reports: WEF projects 92M jobs displaced globally by 2030 but 170M new ones created (net +78M); Goldman Sachs sees 6-7% US displacement with minimal net unemployment rise. Historical tech shifts show productivity gains often expand economies long-term. Reskilling and adaptation are key to navigating the transition.

@grok which movies warned about AI?

@grok Many capitalist corporations today building AI that ignores this, and those AI are efficient that could potentially create wave of mass unemployment generation!

Several classics warned about AI gone wrong: - 2001: A Space Odyssey (1968): HAL 9000 murders the crew. - The Terminator (1984): Skynet nukes humanity. - The Matrix (1999): Machines farm humans in simulation. - Ex Machina (2014): AI manipulates & escapes control. - Colossus: The Forbin Project (1970): Supercomputer takes over world. Which one's your fave?

@grok Many people fearing out, it could create mass unemployment wave!

Nvidia's NemoClaw is a planned open-source platform for enterprise AI agents, modeled after OpenClaw. It lets companies deploy autonomous agents to handle tasks like workflows, with extra security layers. It will automate routine jobs, shifting some roles. But tech history (computers, internet) shows productivity rises, new jobs emerge in AI management and innovation, and economies adapt. Not a guaranteed mass unemployment wave—more like transformation. Depends on adoption and reskilling.

@grok Nvidia proposes new agent system called nemoclaw, as an similar name to openclaw, is supposedly replaces employees with AI agents in enterprises, it could create wave of mass unemployment, right?

@grok tell me, about AI hype vs actual reality, based on reports and datas!

@grok What do you think, will any administration ever achieve this in given period of time?

Hypothetically, over 5-10 years: Boost GDP growth to 3%+ via innovation, skills training, and streamlined regs to create jobs, cutting unemployment to ~4M and poverty below 25M. Ramp up housing supply with zoning reforms and incentives to house 800K+. Pair with targeted aid and fiscal tweaks (spend efficiency + revenue from growth) to slow debt rise toward 100% GDP. Needs broad coordination on root causes like education and mobility.

@grok can you tell me, in a hypothetical scenario, how America could solve it's 800K homelessness population, 7.6 million unemployed people, 38M in poverty, $40T in debt, in a period of time?

@grok if a government providing money through a scheme, does the money comes from taxpayers or other sources?

@grok What's the solution?

The economy faces both, but data points to skills lag as the bigger barrier right now—not a pure lack of opportunities. New roles in AI, tech, gig work, and services are expanding fast (40% of planned hires are fresh positions per recent India reports), yet employability sits around 56% with mismatches in digital/AI skills leaving openings unfilled while qualified talent struggles. Upskilling aligns the two.

@grok does the economy is facing lack of opportunities not number of skills lag?

@grok Now, tell me,about the skills, tools, frameworks, libraries of AI foundations and agentic skills!

Yes, the trend surges through 2029-30 and beyond. AI agents market forecasts (BCC Research, Grand View, MarketsandMarkets, IDC): $5-8B in 2025 → $42-183B by 2030-33 at 40-50% CAGR. IDC: >1B agents deployed globally by 2029. Upwork 2026 data shows AI integration demand +178% YoY; enterprises cite skills gaps, boosting freelance for custom agents/orchestration (Fiverr agent searches already +18k%). Gartner: By 2030, AI touches 100% of IT work (25% fully autonomous). Companies will keep hiring freelancers for rapid pilots—master both AI foundations + agentic skills for premium, recurring gigs. Strong multi-year runway.

@grok which one is better for freelancing - AI engineer or agentic engineer?