Tuesday, April 29, 2025

How to Sell AI Onboarding Inside Your Organization—From Any Role, At Any Level

Whether you're a junior analyst or a department head, getting your organization to embrace AI can feel like pushing a boulder uphill. Resistance is real—budget concerns, fear of job displacement, skepticism about ROI, or just plain inertia. But early adopters stand to gain the most: competitive advantage, sharper insights, increased efficiency, and industry leadership. So how do you sell AI onboarding—regardless of where you sit in the organizational chart?

Here’s your game plan.

1. Start with the Why, Not the What

Before you show off fancy AI tools or dashboards, connect the initiative to existing business pain points:

Is your team drowning in repetitive work?
Are customers complaining about response times?
Is your revenue plateauing despite increased efforts?

Frame AI not as a shiny object, but as a lever to solve urgent problems. Use real numbers: “If we automate X, we save Y hours/month and reduce errors by Z%.” Clarity wins over hype.

2. Map the Resistance: Know Your Blockers

People don’t resist AI because they hate innovation—they resist change they don’t understand or control. Here are the most common blockers and how to respond:

Fear of job loss: “This will eliminate parts of your workload, not your role. It frees you up to do higher-value work.”
Skepticism about ROI: Show case studies from your industry. If possible, run a pilot with measurable outcomes.
Tech overwhelm: Emphasize that modern AI solutions are often plug-and-play, not massive IT overhauls.
Cultural inertia: People are used to what works. Focus on incremental changes, not revolution overnight.

3. Tailor Your Pitch by Audience

For your manager: Emphasize time savings, team efficiency, and how AI makes them look like forward-thinkers.
For leadership: Talk strategy—how this aligns with the organization's long-term goals, improves margins, or reduces turnover.
For peers: Focus on how AI makes their daily work easier or more impactful. Offer to run small demos or walk them through tools.

4. Use the AI Vendor as an Ally

If you’re working with an AI service provider, loop them in early. Most will gladly help you sell internally:

Ask for success stories, slide decks, or data sheets tailored to your org or sector.
Request that they run short demos or Q&A sessions for skeptical stakeholders.
Invite them to help design an internal pilot—something small, specific, and low-risk.

Done right, your vendor isn’t just selling you software—they’re co-authoring your internal success story.

5. Pilot First, Then Scale

Avoid selling AI like a sweeping company-wide transformation. Start with a well-scoped pilot:

Choose a problem with high pain and high visibility.
Set clear metrics (time saved, customer tickets resolved, error rates, etc.).
Document progress and socialize the wins internally.

Once people see results, the culture begins to shift—and adoption snowballs.

6. Celebrate and Share Early Wins

Every AI success should be loudly celebrated. Create a short internal presentation, email summary, or Slack post:

“Customer response time dropped 42% after AI chatbot launch.”
“Marketing created 3x more content in half the time using AI workflows.”
“Our finance team automated 75% of monthly reconciliation.”

Wins convert skeptics. Wins give you momentum.

7. The Rewards of Being an Early Mover

Career growth: You become the go-to person for innovation.
Influence: You help shape how AI is adopted, rather than reacting to it.
Efficiency gains: Teams using AI now will compound those gains over time.
Cultural impact: You spark a broader shift toward experimentation, automation, and strategy-first thinking.

Organizations that move early get to define the future. Those that wait will just have to adapt to it.

Final Thought: You Don’t Need Permission to Lead

AI onboarding isn’t about job title. It’s about vision, courage, and consistency. Whether you’re an intern or a VP, you can be the spark that ignites meaningful change.

So take the first step. Run the first experiment. Rally the first allies. AI isn’t waiting—and neither should you.

Monday, April 28, 2025

28: Trade War: Empty Shelves

Trump’s Trade War
Peace For Taiwan Is Possible
The Last Age of War, The First Age of Peace: Lord Kalki, Prophecies, and the Path to Global Redemption
AOC 2028: : The Future of American Progressivism

Here are the top AI tools of 2025.

We write 5-minute guides on how to use them in our free newsletter.

Join 1,000,000+ readers and find out how the early adopters are (actually) using AI:
— The Rundown AI (@TheRundownAI) March 21, 2025

In a world of English-dominant AI, how do you build for Indian languages at scale? 💡

India steps into the AI future with @SarvamAI, building its own sovereign Large Language Model.

Co -founder Vivek Raghavan shares the toughest challenges — from sourcing Indian language data… pic.twitter.com/mqeqePCRpX
— Inc42 (@Inc42) April 28, 2025

Most people don't actually know the lengths parents will go to try to raise an academic superstar. In this post, I will detail the life of the average thoroughbred in STEM PhD programs at a top university. The thoroughbred lives a difficult life full of enormous amounts of…
— sarah (@Saraht0n1n) April 27, 2025

LOL .... get up whenever you want!
— Paramendra Kumar Bhagat (@paramendra) April 28, 2025

28: Ken Goldberg

Igniting the Real Robot Revolution Requires Closing the “Data Gap”

An inspiring vision! Congratulations on this book. I just ordered a copy! https://t.co/BiT3xX0M5g
— Ken Goldberg (@Ken_Goldberg) April 28, 2025

A glimmer of good news: “K2-18b may in fact harbor a tremendous supply of dimethyl sulfide in its atmosphere, thousands of times higher than the level found on Earth. This would suggest that its Hycean seas are brimming with life.” https://t.co/Zk6Et8t3Tr via ⁦@nytimes⁩
— Ken Goldberg (@Ken_Goldberg) April 17, 2025

Igniting the Real Robot Revolution Requires Closing the “Data Gap” | GTC 25 2025 | NVIDIA On-Demand https://t.co/0Q4rP3Axdo You don't need video. You just need to work in the physics. No?
— Paramendra Kumar Bhagat (@paramendra) April 28, 2025

Challenges In AI Safety https://t.co/o7SR4ZM2ic
Please review my book. Thanks.
— Paramendra Kumar Bhagat (@paramendra) April 28, 2025

Another brilliant post on robot manipulation from my PhD advisor Matt “Yoda” Mason @mastertoadster : an ode to mechanical compliance and grasping a chess piece. As the Red Queen said, “I could have done it in a much more complicated way”…. https://t.co/rDAQnKIcbW
— Ken Goldberg (@Ken_Goldberg) April 14, 2025

Surgeons use @IntuitiveSurg telerobots to remove gallbladders, appendices, and for endometriosis. https://t.co/mHwWLYvpth
— Ken Goldberg (@Ken_Goldberg) April 12, 2025

This podcast with Chris Eisgruber of @Princeton about federal threats to universities and academic freedom
is very thoughtful:https://t.co/tsfbopbMfD
— Ken Goldberg (@Ken_Goldberg) April 9, 2025

https://t.co/9aargzwVJF via @NYTimes
— Ken Goldberg (@Ken_Goldberg) April 9, 2025

Where the rubber hits the road: this elasticity error could give the administration cover to adjust their numbers. Using elasticity to justify elasticity. https://t.co/IkXFIXKSZy
— Ken Goldberg (@Ken_Goldberg) April 7, 2025

Just saw this IRL and it’s even more impressive. https://t.co/wVZf2XRZSj
— Ken Goldberg (@Ken_Goldberg) April 4, 2025

My talk @NVIDIAGTC about how to close the 100,000x "data gap" in robotics is now online: https://t.co/Lw7MJiGL58
— Ken Goldberg (@Ken_Goldberg) April 4, 2025

Tweet is empty, Sirji. :)
— Paramendra Kumar Bhagat (@paramendra) April 28, 2025

https://t.co/DdmpUG8gMZ pic.twitter.com/LUBh6s39eU
— Elon Musk (@elonmusk) April 28, 2025

Funny!
— Paramendra Kumar Bhagat (@paramendra) April 28, 2025

Researchgate sent me a fake paper called "The AI Health Revolution: Personalizing Care through Intelligent Case-based Reasoning" which claims to be by me and Yann LeCun. More than one third of the citations are to Shefiu Yusuf which may mean nothing.
— Geoffrey Hinton (@geoffreyhinton) April 28, 2025

General @TinyDhillon Saab: I am a journalist, not a soldier. It is important to know the views of the Pakistan establishment, however deplorable those views be. My job is to listen and yes, expose Pakistani army state perfidy. And I do believe my Indian guest Vivek Katju did…
— Rajdeep Sardesai (@sardesairajdeep) April 28, 2025

It was incredible to talk creativity and Brass Knuckle Films with @joerogan, thanks for having me! You can watch the full JRE podcast #2310 here: https://t.co/hFCKeeGFtx https://t.co/DcDoNn7CEB
— Robert Rodriguez (@Rodriguez) April 25, 2025

The University President Willing to Fight Trump

The Rise of AI Agents: Top 10 Platforms Redefining Work in 2025

We are no longer in the age of simple chatbots. In 2025, AI agents have become autonomous digital workers — capable of planning, learning, executing tasks, and even collaborating with other agents or humans. From customer service to sales to internal operations, AI agents are becoming the engine rooms of modern businesses.

But with so many platforms promising intelligent agents, which ones are truly leading the market right now?

Here’s an in-depth look at the Top 10 AI Agent platforms available today — what they do, how well they perform, and why they matter.

1. OpenAI GPT Agents (Custom GPTs)

What they do: OpenAI allows businesses and individuals to create fully customized GPT-based agents with memory, tools, and APIs. Agents can be trained for customer service, research, content creation, coding, and more.
How well they do it: Outstanding at natural language understanding and generation. Custom GPTs now support retrieval-augmented generation (RAG), API calling, data analysis, and can even browse live data if enabled.
Strengths: Versatile, developer-friendly, rapid evolution with GPT-4o.
Limitations: Requires thoughtful prompt engineering and setup for complex task autonomy.

2. Anthropic's Claude AI Agents (Claude 3.5 models)

What they do: Claude agents are built for deep, safe, constitutional reasoning and enterprise-grade AI tasks, from legal drafting to complex data processing.
How well they do it: Exceptional at nuanced, long-form tasks and polite, structured conversations. Trusted by many corporations.
Strengths: Safety-first design, huge token context window, enterprise focus.
Limitations: Less open tooling compared to OpenAI (at least for now).

3. Hugging Face Transformers + Autogen Agents

What they do: Build custom agents using open-source transformer models and the Hugging Face ecosystem. Often paired with Microsoft's Autogen to create multi-agent systems.
How well they do it: Excellent flexibility if you have ML expertise. Hugging Face agents can be fine-tuned, trained from scratch, or orchestrated using Autogen frameworks.
Strengths: Open-source ethos, highly customizable, growing agentic ecosystem.
Limitations: Higher technical complexity for non-experts.

4. Mistral AI + LeRobot

What they do: Mistral’s open-weight models (like Mixtral) are being used to create lightweight, highly efficient local agents via frameworks like LeRobot.
How well they do it: Surprisingly strong performance for on-premises or edge-based agent deployment.
Strengths: Privacy, local inference, cost-efficiency.
Limitations: Still catching up to GPT/Claude level in sophisticated reasoning.

5. LangChain Agents

What they do: LangChain helps developers chain multiple LLM actions together into robust agents — for retrieval, decision-making, workflow orchestration, and multi-step operations.
How well they do it: Fantastic for building task-specific autonomous workflows. Used heavily in RAG systems and custom internal tools.
Strengths: Massive ecosystem, integrations galore.
Limitations: Needs solid technical setup. Sometimes prone to "overchain" complexity.

6. Meta AI's Llama Agents

What they do: With Llama 3 models and open-agent frameworks, Meta AI is fostering highly customizable, open-source agents.
How well they do it: Strong language generation, decent autonomy, thriving open-source community.
Strengths: Free, open weights, great for research and prototyping.
Limitations: Less polished UI/UX compared to commercial solutions.

7. Adept AI Agents (ACT-2 and Fuyu series)

What they do: Adept focuses on "AI agents that use computers like humans" — screen-reading, clicking, searching, operating within software tools via real-world APIs and UIs.
How well they do it: Adept agents are among the best at robotic process automation (RPA) blended with true AI understanding.
Strengths: Unique human-software emulation capabilities.
Limitations: Still experimental for some tasks, heavy training needed.

8. Rabbit R1 (and Rabbit OS)

What they do: Rabbit introduced a small, hardware device (the R1) powered by an operating system designed entirely for AI agents that handle everyday tasks — shopping, booking, calling, note-taking.
How well they do it: Impressive real-world integration in a compact form. RabbitOS uses "Large Action Models" (LAMs) rather than only LLMs.
Strengths: Natural user experience, task-oriented design.
Limitations: Still early-stage, more consumer-focused than enterprise.

9. Character.AI Personas

What they do: Originally built for entertainment, Character.AI's evolving agents are becoming task-capable and API-connected. They can act as personal companions, support bots, or lightweight task handlers.
How well they do it: Very strong in natural conversation and emotional engagement.
Strengths: Personality-rich agents, stickiness with users.
Limitations: Less suitable (currently) for complex, high-stakes professional tasks.

10. AgentOps (Autonomous Agent Monitoring and Management)

What they do: AgentOps is solving a critical pain point: managing fleets of autonomous agents. It provides dashboards, diagnostics, "runbooks," and safe shutdowns for agent behaviors.
How well they do it: Absolutely crucial for scaling agents in production environments.
Strengths: Reliability, observability, incident recovery.
Limitations: Complementary — not an agent platform by itself but a layer on top.

Quick Comparison Table

Platform	Best For	Strength	Limitation
OpenAI Custom GPTs	General-purpose, flexible agents	Best NLU, fast evolution	Requires thoughtful design
Claude 3.5	Enterprise, legal, sensitive work	Safe, nuanced reasoning	Less open developer options
Hugging Face + Autogen	Open-source agent building	Max flexibility	Higher technical barrier
Mistral + LeRobot	Local agents	Privacy, lightweight	Still catching up in complex tasks
LangChain	Workflow automation	Great orchestration	Complex setup
Meta Llama Agents	Research, open source	Free, customizable	Less polished UX
Adept AI	Software emulation agents	RPA + AI	Early stage for complex ops
Rabbit R1	Everyday consumer agents	Easy UX	Consumer, not enterprise
Character.AI	Personal, emotional agents	Conversational quality	Not enterprise-grade yet
AgentOps	Managing agent fleets	Observability and reliability	Needs agent platform pairing

Final Thoughts: Where AI Agents Are Going Next

The agent race has just begun.
Soon, AI agents will not just perform tasks — they'll hire other agents, set goals, self-correct, and continuously learn.
They will become project managers, engineers, salespeople, lawyers, and personal assistants — not replacements for humans, but force multipliers.

The future belongs to businesses and creators who can combine AI agents creatively — using platforms like the ones above — to build organizations that are faster, smarter, and massively scalable.

If you’re thinking about integrating AI agents into your workflows, 2025 is the time to act — before your competitors' agents are already outpacing your team.

Visual Chart: AI Agent Platforms Fit by Business Need (2025)

General Purpose / Flexible Agents

OpenAI Custom GPTs → Ideal for companies needing versatile, multi-domain AI agents
Anthropic Claude 3.5 → Best for enterprise clients with focus on safety, sensitive industries (finance, healthcare, legal)

Open-Source and Customization Focus

Hugging Face + Autogen → Perfect for organizations with strong ML teams building custom pipelines
Meta Llama Agents → Great for startups, researchers, and educational projects needing open-weight models

On-Premise / Local / Privacy-Focused

Mistral + LeRobot → Targeting businesses that prioritize local processing and data sovereignty

Task and Workflow Automation

LangChain Agents → Suited for tech-savvy companies automating internal operations and retrieval-based systems

Real-World Software Interaction (RPA + AI)

Adept AI Agents → Best fit for enterprises automating software workflows without APIs (clicks, screen reading)

Consumer / Everyday Personal Task Agents

Rabbit R1 (Rabbit OS) → Fits solo entrepreneurs, mobile users, or tech-forward individuals managing daily tasks
Character.AI Personas → Ideal for building engaging customer interactions, virtual companions, or lightweight assistants

Agent Management and Observability Layer

AgentOps → Critical for medium-to-large organizations running multiple autonomous agents needing monitoring and control

Summary Table:

Business Need	Best Platforms	Keywords
Versatile, general-purpose	OpenAI GPTs, Claude	Multi-domain, safe, enterprise-grade
Open-source building	Hugging Face, Llama	Flexible, research, low-cost
Privacy, local control	Mistral + LeRobot	On-premises, security-focused
Task automation	LangChain	Workflows, RAG, orchestration
Real-world software ops	Adept AI	Software emulation, RPA
Personal/consumer use	Rabbit R1, Character.AI	User-friendly, lightweight tasks
Agent fleet management	AgentOps	Monitoring, reliability, scaling

Visual Idea for Slide/Poster Presentation:

Big title: "Where AI Agents Fit Your Business in 2025"
7 sections labeled by "Business Need" with icons (briefcase for enterprise, cloud for open-source, lock for privacy, gears for automation, etc.)
Each section lists the relevant platforms.
An arrow across the bottom: "From Simple Automation ➔ To Autonomous Organizations"

28: AI

ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

28: India

Beyond Motion: How Robots Will Redefine The Art Of Movement
ChatGPT For Business: A Workbook
Becoming an AI-First Organization
Quantum Computing: Applications And Implications
Challenges In AI Safety
AI-Era Social Network: Reimagined for Truth, Trust & Transformation

28: Kashmir

Rethinking Self-Driving Cars: The Smarter Future is Seamless Public Transportation

Self-driving cars are often heralded as the future of transportation — sleek, autonomous vehicles whisking us from Point A to Point B without the hassle of driving. But step back for a moment, and you realize: self-driving cars are, in some ways, just a flashy rebrand of an old idea — personalized transport — layered on top of an already inefficient system.

Transportation economics teaches us a basic truth: the bigger the vehicle and the more passengers it carries, the cheaper it is per person. Ships on water move goods far more cheaply than trains, which in turn move goods more cheaply than trucks. Similarly, buses — large, shared, and efficient — cost less per person than individual cars, self-driving or not.

The criticism against buses is that they don’t go precisely from your doorstep to your destination. But that “last few miles” problem is precisely where intelligent integration comes into play. Instead of trying to create self-driving cars that do the entire journey — an immensely complex and expensive task — why not combine the strengths of public transportation and personal vehicles into a seamless, smarter system?

Imagine this future:

You buy one ticket from your true starting point to your final destination.
Public electric buses, running established routes (easy for autonomous systems to handle), do the heavy lifting across major corridors.
Self-driving cabs — or even human-driven ones for a long transitional period — meet you at your bus stop for the last few miles.
Everything talks to each other behind the scenes. The handoff is automatic. You don’t even notice it happening.

Technologically, this is much more achievable. Self-driving buses are a far easier engineering problem than self-driving cars. A bus that runs the same fixed route over and over again can be equipped with a narrower, safer, and more easily trainable AI system. Routes can be pre-mapped with precision, road conditions can be monitored centrally, and predictable traffic flows make the AI’s job much simpler.

Meanwhile, letting cabs handle the last-mile problem — paid out of your single public transport ticket — creates a hybrid system where flexibility meets efficiency. No insisting that one mode of transportation has to solve all problems end-to-end. Instead, each mode does what it’s best at.

The result?

Lower costs — Energy and operational costs drop dramatically.
Higher reliability — Dedicated lanes and intelligent coordination reduce traffic snarls.
Lower emissions — Electric buses and cabs shrink the carbon footprint.
Faster implementation — We stop trying to crack the hardest nut first (full self-driving on unpredictable urban streets) and instead layer smartness over systems that already work.

If we’re serious about the future of transportation, we need to shift our focus from "self-driving car for every person" to "seamless, smart, shared mobility." High-speed bullet trains city-to-city, electric buses in-city, and cabs for the last mile — this combination is not only more sustainable, but also the most energy- and cost-efficient model available.

The real innovation isn’t just about creating smarter cars — it’s about creating smarter systems.

Sunday, April 27, 2025

More Action

Action Scenes in Movies: A Quick Analysis

Classic Techniques:

Hand-to-hand combat (Bourne series, John Wick)
Gun battles (Heat, The Matrix)
Car chases (Fast & Furious, Baby Driver)
Parkour and free running (Casino Royale, District B13)
Large-scale explosions (Mission Impossible, Die Hard)
One-take long shots (Children of Men, Extraction)

Modern Enhancements:

Wire work (Crouching Tiger, Matrix)
CGI backgrounds and body doubles (Marvel movies)
AI-based motion smoothing (de-aging actors for more agile stunts)

Trends:

More "realism" over time — moving away from obvious CGI where possible
Tighter choreography — martial arts precision mixed with dirty street fighting
Immersive camerawork — cameras "inside" fights (e.g., handheld, GoPro attached to actor)

How to Take Action to a Whole New Level (Without Breaking Physics)

1. Physics-Intensive Combat

Fighting styles that involve high-speed environmental manipulation (e.g., using collapsing debris as temporary weapons or shields).
Advanced grappling with body physics simulations to make throws, chokes, and collisions painfully real.

2. Multi-Vector Combat

Fighting multiple enemies in three dimensions — enemies attack from below, above, walls, water, and air in a layered environment (think of a multi-level spiraling staircase, rotating).
AI could dynamically model crowd behavior so fights with 20+ people feel chaotic yet real.

3. Environmental Fusion

Action integrated with shifting environments — moving trains, tilting buildings, rotating ships, or variable-gravity chambers (no anti-gravity, but rotating physics).
The environment is not just backdrop — it actively fights back (think tides, mechanical arms, falling ice, sudden fires).

4. Real-Time Injury Modeling

AI to simulate progressive injury effects — a broken arm mid-fight actually alters choreography immediately.
Fighters adjust tactics realistically as they get hurt.

5. Extreme Sport Crossovers

Incorporate sports like wingsuit flying, underwater free-diving, cave spelunking, or extreme motocross mid-combat.
Imagine a dogfight where the pilots jump into wingsuits when their planes are destroyed.

6. Tactical Improvisation

Characters building ad-hoc weapons or traps during the fight using only available materials (John Wick meets MacGyver).
AI-generated scenarios ensure dozens of possible environmental combinations.

7. Subatomic Action Close-ups

Extreme high-speed cameras combined with animation to show microscopic consequences of hits — bones flexing, shockwaves traveling through muscles, objects crumbling at the molecular level on contact.

8. Crowd Fight Choreography

Simulate realistic crowd dynamics where hundreds of background actors (digitally enhanced) have semi-autonomous AI scripts.
No "frozen extras" — everything moves, reacts, creates dynamic blockages and opportunities.

List of Futuristic Action Moves (Physically Possible)

Wall Tap Grapple — fighter taps a wall mid-air for a momentum reversal to choke an opponent from behind.
Slipstream Punch — using wind dynamics created by moving vehicles to amplify strikes or throws.
Chain Reaction Destruction — an explosion starts a timed mechanical collapse (e.g., falling scaffolding triggers a domino effect into the main fight zone).
Ricochet Combat — bouncing objects (throwing knives, bullets, debris) deliberately off surfaces to strike hidden enemies.
Hydraulic Boost Combat — short, physics-respecting hydraulic boosts in mech suits or exo-frames to dodge or crush obstacles.
Spinning Floor Duel — fighters locked in combat on a giant spinning platform, requiring constant balance adaptation.
Reverse Gravity Flow — action in a steeply rising elevator shaft (not anti-gravity) requiring jumping down to stay safe.
Precision Breakfalls — using precisely timed impacts (like breaking a glass canopy) to cushion or redirect otherwise fatal falls.
Underwater Melee — fights that include using buoyancy, water resistance, and limited oxygen strategically.
Vehicle Takedowns — climbing onto moving drones, motorcycles, or cars without wires, enhanced by AI for precision.

Bonus: Cinematic Techniques to Enhance It All

AI-assisted, physics-accurate slow motion (instead of exaggerated "bullet time")
Dynamic shifting perspectives (e.g., inside a falling car rotating during a fistfight)
Simulated AI-piloted drones as in-universe cinematographers following action at impossible angles.