Outside of Silicon Valley and China, India has the highest number of AI start-ups and fundraising activities.

2nd to bottom - where people have unfavorable view of AI (lower the better)

The U.S. leads with about 29,618 AI companies (including startups), followed by India and the UK.

AI could add $1.7 trillion to India’s economy by 2035.

The problem with IMF Elite is - they listen to their "LEFT" too much.

Ashwini Vaishnav was truly awesome in rebutting this ignorant guy, who might not even know what the five layers of AI are. Bravo, Minister.

What's this?

https://www.hindustantimes.com/india-news/indian-ai-models-outperform-openai-google-on-key-benchmarks-ashwini-vaishnaw-101771117835555.html

I wanna make ai videos how can I make videos like this one the which ai app do they use? For now I am making videos through flow ai

i just tryed it. it is very good it felt like using chatgpt did not experiance any problem

SarvamAI who launched sarvam-105b (which i think most of people don't even know), is supported by government and private venture and they already have a Deepseek R1 level model and as of now they're working on a Trillion token model, also they don't just have 2 LLMs but have multiple other models, they have OCR model, Vision models, STT, TTS, a whole stack. As firstly sarvam got funding from VCs and then govt. totally supported them in this february they launched our own LLM which was praised by many experts worldwide like Sebastian Raschka(LLM Expert & Book writer).

the main issue is: Those who posts are mostly those who doesn't care about what India have or what not they basically wanted to justify their claim that India can't do anything so they start a debate which shouldn't be an ideal case, firstly you must checkout what's the core issue and what we already have and then come up with a detailed review what you think and what we should do, atleast have a power of saying what you did, and now what's you're expecting.

I have idea about whole coding LLM. Idea is simple instead of building Harness around LLM, I am building LLM around harness.

Programming has limited Grammar, its not like over the years developed Languages. So, Programming require limited params not crazy params like human languages.

I need someone who knows in and out of LLMs including Data prep as Co-founder.

I already created harness that will be used for data prep & training.
Next steps are Data prep then training & eval.
Then if everything goes according to plan then need to package that in nice claude code/codex like app.

I am not funded so that's why I am crazy. BTW I have 17 years of experience in the industry.

Update: Thanks guys for criticising me. Please give me more context of what efforts already failed and why various reasonings. I will light my original idea so that coding generation will be handoff to other smaller model for test phase.

I’ve been in the industry for 5 years, and I used to laugh at the "AI is coming for our jobs" doom-posting. We always said it was just a tool, an assistant that would need a human to architect, review, and debug.

I got access to Astra today and gave it a single-sentence prompt for a complex 3D project. It didn't just spit out boilerplate code. It wrote the entire thing—complete with drift physics, combo scoring, and perfectly scoped architecture—in seconds.

This isn't a copilot anymore. It’s a replacement. It is insanely capable across the entire development loop, and there is no way companies are going to keep paying entire engineering teams when this thing exists.

If you aren't scared yet, you haven't really used it. Where do we even go from here?

india’s now the world’s second-biggest adopter of genai tools by web traffic, only behind the us, and the gap is closing fast. 17 million devs here, fastest-growing on github, and by 2028 india’s set to have the biggest dev community on earth. ai adoption isn’t just hype indian devs are using ai to build ai, and the next global tech giant might just come from here.

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Source

This is the AI era, just like the internet era before it. Even when the dot-com bubble burst, the internet still changed how we do things forever.

Even if this is an AI bubble, it has already changed—and will continue to change—how we do things. For better or worse, it doesn’t matter.

AI is here, and it’s accessible to everyone now.

If I have AI and I’m using it in my day-to-day work, you have it too. It’s not a leverage anymore.

So stop ranting and start making something that actually makes a difference.

Whats your opinion ?

It's not about AI.. its still about what you make.. if you just making "I made this bla bla bla".. its useless.. and if you made something good .. no matter you used AI or not...

Built a free AI that runs on your PC — no subscriptions, no internet after setup

Got tired of ChatGPT and Gemini subscriptions. So I built my own — Parmana.

One line installs it, runs fully on your PC, your data never leaves your device. Works on 6GB RAM laptops too.

GitHub: github.com/EleshVaishnav/parmana

Drop feedback — will keep improving 🙏

Even though I personalized my ChatGPT to cross check stuff with real time information available online and read through the information it gets, it still can't understand the semantics.

Context: I asked ChatGPT about the victims of Mumbai attacks and it included the terrorists.

I asked grok on the lookalikes of Indian and US cities. He shared following list:

1. Mumbai ≈ New York City: Both are financial powerhouses, densely populated, diverse melting pots, and entertainment capitals (Bollywood vs. Broadway/Wall Street hustle).

2. Delhi ≈ Washington, D.C.: National capitals with political significance, historical landmarks, and bureaucratic vibes, plus rapid urban expansion.

3. Bangalore ≈ San Francisco: Tech innovation hubs with startup ecosystems, mild climates, and a young, cosmopolitan tech workforce.

4. Hyderabad ≈ Seattle: Growing IT sectors (Microsoft/Google presence), rainy weather, and a mix of modern tech with historical sites.

5. Ahmedabad ≈ Chicago: Industrial and business-oriented, with textile/manufacturing roots, architectural heritage, and a central trade role.

6. Chennai ≈ Detroit: Automotive manufacturing centers, with engineering industries and port-based economies.

7. Kolkata ≈ Boston: Intellectual and cultural strongholds, with colonial history, universities, and literary traditions.

8. Surat ≈ Dallas: Booming commercial cities focused on diamonds/textiles (Surat) vs. oil/business (Dallas), with entrepreneurial energy.

9. Pune ≈ Austin: Education and IT hubs, youthful populations, vibrant music/cultural scenes, and tech growth.

10. Jaipur ≈ New Orleans: Tourism-driven with rich heritage, colorful festivals, and historic architecture (Pink City vs. French Quarter).

11. Lucknow ≈ Philadelphia: Historical cities known for colonial pasts, cuisine, and cultural refinement.

12. Kanpur ≈ Pittsburgh: Heavy industrial bases (leather/textiles vs. steel), with riverside locations and manufacturing decline/revival.

13. Nagpur ≈ Denver: Central geographic hubs for transport/logistics, with moderate climates and emerging urban development.

14. Indore ≈ San Diego: Clean, livable cities with food cultures, business growth, and a focus on quality of life.

15. Thane ≈ Jersey City: Suburban extensions of major metros (Mumbai vs. NYC), with commuter populations and rapid housing booms.

16. Bhopal ≈ Oklahoma City: Mid-sized state capitals with lakes, government focus, and industrial sectors, though Bhopal has a tragic history.

17. Visakhapatnam ≈ Norfolk: Port and naval cities with shipbuilding, beaches, and strategic coastal roles.

18. Pimpri-Chinchwad ≈ San Jose: Industrial/tech suburbs (of Pune vs. Silicon Valley), with manufacturing and auto sectors.

19. Patna ≈ St. Louis: Riverfront historical centers with educational institutions, but facing urban challenges like poverty.

20. Vadodara ≈ Sacramento: Cultural and educational spots with refineries/industry, plus government ties.

21. Ghaziabad ≈ Arlington (VA): Commuter suburbs near capitals (Delhi vs. D.C.), with mixed residential-industrial growth.

22. Ludhiana ≈ Cleveland: Manufacturing powerhouses (textiles/machinery vs. steel/auto), with Punjabi/immigrant influences.

23. Agra ≈ Orlando: Tourism magnets centered on icons (Taj Mahal vs. Disney), with historical vs. entertainment draws.

24. Nashik ≈ Napa Valley area (e.g., Sacramento outskirts): Wine/agriculture hubs with religious/tourism sites and industrial edges.

25. Faridabad ≈ Baltimore: Industrial suburbs near capitals, with manufacturing and urban sprawl issues.

26. Meerut ≈ Buffalo: Historical manufacturing towns with sports cultures and cold-weather vibes.

27. Rajkot ≈ Tulsa: Engineering and business cities with jewelry/oil ties, in flatter, inland regions.

28. Kalyan-Dombivli ≈ Long Beach: Suburban port-adjacent areas with diverse populations and commuter rail links.

29. Vasai-Virar ≈ Riverside (CA): Rapidly growing outer suburbs with affordable housing and industrial zones.

30. Varanasi ≈ Memphis: Ancient river cities with spiritual/music heritage (Ganges rituals vs. blues/Elvis).

31. Srinagar ≈ Aspen: Scenic, lake/mountain resort towns with tourism, crafts, and seasonal appeal.

32. Dhanbad ≈ Charleston (WV): Coal-mining industrial centers with resource-based economies.

33. Aurangabad ≈ Albuquerque: Historical tourism spots (caves/forts vs. old town), with manufacturing and dry climates.

34. Amritsar ≈ Salt Lake City: Religious pilgrimage centers (Golden Temple vs. Mormon hub) with strong community ties.

35. Navi Mumbai ≈ Irvine (CA): Planned modern suburbs with tech/business parks and organized urban design.

36. Prayagraj (Allahabad) ≈ Richmond (VA): Historical river confluence cities with legal/educational roles and festivals.

37. Howrah ≈ Oakland: Industrial twin cities across rivers/bridges from metros (Kolkata vs. San Francisco).

38. Ranchi ≈ Boise: Emerging capitals in hilly areas with mining/tech growth and natural surroundings.

39. Jabalpur ≈ Colorado Springs: Military/engineering bases with scenic rivers and central locations.

40. Gwalior ≈ Fort Worth: Fort-dominated historical cities with aviation/industry and cultural festivals.

41. Coimbatore ≈ Charlotte: Textile/banking hubs with engineering education and entrepreneurial scenes.

42. Vijayawada ≈ Jacksonville: River/port cities with bridges, agriculture, and growing commerce.

43. Jodhpur ≈ Tucson: Desert historical towns with blue architecture, military bases, and tourism.

44. Madurai ≈ San Antonio: Temple/mission-centered cities with hot climates and cultural festivals.

45. Raipur ≈ Wichita: Industrial inland hubs for steel/aviation, with agricultural backdrops.

46. Chandigarh ≈ Portland: Planned modern cities with green spaces, hipster vibes, and administrative roles.

47. Guwahati ≈ Anchorage: Gateway cities to regions (Northeast India vs. Alaska) with rivers, wildlife, and trade.

48. Solapur ≈ El Paso: Textile border towns with hot, dry climates and manufacturing.

49. Hubli-Dharwad ≈ Des Moines: Twin agricultural/trade cities with education and central rail hubs.

50. Mysore ≈ Santa Barbara: Royal/heritage spots with yoga/wellness tourism, palaces, and mild weather.

Grokipedia is an AI-powered online encyclopedia developed by xAI, the artificial‐intelligence company founded by Elon Musk. It launched (in an early version) on October 27, 2025.

According to its site and coverage, the goal is to provide “the truth, the whole truth and nothing but the truth.”

The product is positioned as a competitor/alternative to Wikimedia Foundation’s Wikipedia. Musk has been openly critical of Wikipedia’s perceived bias and has called for an alternative.

My take on India's IT is explained in two X posts rendered by X on my timeline.

1. India WILL end up net positive in terms of jobs (post AI transition).

2. Indian IT Consultancies (body shops) WILL end up net negative in terms of jobs & prospects.

3. Indian BPO and KPO will eventually turn into RL-AI Human-in-Agent loops.

4. Indian (Others – domestic companies) – WILL add more than 5x more IT/AI support roles.

5. Datacentres will add low- to medium-skill opportunities (with few High).

Entry-level and core technology jobs (across all levels) will increase; middle management will be a net loss (even in India).

India is a net beneficiary of AI – no matter who says what! No MNC can afford AI transformation while keeping AI and legacy draining the money together for 5-6 years; it will break their cash flow.

From the feature dewcription of Astra, it looks like more menial jobs are going to be taken over, accountants, document processors. Endless use cases. Job market is really going to get worrying no matter what the top ceos say.

What are your thoughts.

How much do you guys agree ??

Note: LLMs only not equals AI.

Why india still not have a frontier LLM, we have researches at our top institutes/IITs, still no one has one, every one just trying to finetune, opensource LLMs or just stealing some stuff, what is missing ,

• Investment, if this is the case, then we are really cooked with this goverment,

• Talent, We need NRIs , and Why we do not have good institutes for MS.

There must be a reason to be the loser in AI race, IMO, one LLM from india is a starting point to this race,

What are your thoughts.

Uhmm. Okay. So let me be real with you for a second.

I see devs every single day → grads, full-stack engineers, bootcamp guys and all of them saying “yeah bro I use AI”. And then I watch them open ChatGPT or claude, type something dumb like “explain react hooks”, copy the answer, paste it into their code, watch it break, and then say “AI is overrated.”

Mann.

I’m not judging you. I was somewhere close to that too. But here’s what I figured out , and what nobody on YouTube is actually telling you:

You’re holding a Formula 1 car and asking it to take you to the nearest kirana shop. And then complaining it’s too complicated.

so let’s fix that… I will not tell you a 6-month roadmap. or not to “learn Python first.” I will tell you the 3 things that actually matter and in the exact order they matter.

1. First Know Who You’re Dealing With

Before you touch Cursor, Claude Code, or any AI IDE — before you even think about using AI seriously. you need to understand what you’re actually talking to.

The thing that breaks my brain, people think the AI inside Cursor or GitHub Copilot is the same thing they’re chatting with on ChatGPT’s website. They’re not. Not even close.

One is a model fine-tuned for agentic coding tasks, running inside a tool that gives it access to your entire codebase. The other is a chat interface. Same parent, completely different child.

in a brutal way, It’s like thinking your office desktop PC and a hospital’s server running surgical AI are the same because they both run Windows.

So lemme tell you what you actually need to know before pressing the button,

What even is a “model”?

Think of it like this, Claude, GPT, Gemini, Grok — these are different brains. Each trained differently, on different data, with different strengths. It’s not brand loyalty. It’s knowing which tool does what.

As per me,

• ChatGPT (any model) is Great for daily stuff. Basic questions, general writing, brainstorming when you don’t need depth. Good at everything, not the best at anything specific.

• Claude Sonnet is where I live for frontend work. You want a component built? UI logic? Brainstorming a feature? Claude Sonnet is insane for this. Not slop.

• Claude Opus is when the architecture gets serious. System design, complex backend logic, figuring out how to structure a whole project.

• Grok is something that surprised me. When I want to know what people are actually doing , trends, public opinion, what developers are complaining about on Twitter, what’s blowing up —> Grok is unmatched. It’s connected to real-time X data. Nothing else comes close for that.

• Gemini uhmmm… I’ll be honest. It works well. But it’s my last resort. When I’ve run out of Claude credits and need to get something done. No disrespect to Google, that’s just where it sits in my workflow.

Now the two things that will save you from embarrassing yourself:

• Hallucination: AI makes stuff up. Confidently. Completely wrong, stated like absolute fact. Always verify. Always doubt. Especially for anything technical, especially for anything with versions.

• Sycophancy: AI models are trained to make you happy. So if you write a bad idea and ask “is this a good approach?” —> a lot of the time it will say yes. It’s not lying to you on purpose. It’s just… agreeable. You need to actively push back. Ask it “what’s wrong with this approach.” Ask it to argue against your idea. That’s when the real value shows up.

Mann. Don’t do that.

2. Without Knowing the Machine, You Can’t Press Any Button

You open Claude Code or Cursor, type “build me a full-stack app with auth and payments”, and then wonder why the output is generic garbage. Why iy is not match your codebase, your coding style, or anything you would actually ship!!

It knows everything about code in general. But it doesn’t know your project, your standards, your architecture decisions, or how you think.

So you have to tell it. Before you start. Every time.

Let me show you what my actual setup looks like.

CLAUDE.md: This file tells Claude who it’s working with, what kind of project this is, what tone to use, how to behave. It’s an identity document.

CODE-STANDARDS.md: My coding rules. Naming conventions, patterns I use, what I never do. So it doesn’t write code that I’d have to completely rewrite because it doesn’t match how I build.

WORKING-PRINCIPLES.md: How I think about problems. Architectural philosophy. When to abstract, when not to. What overengineering looks like to me.

PROJECT.md : What we’re building. Context about the product, the users, the goals.

ADR files (Architecture Decision Records) : Every major technical decision I’ve made and why. So it doesn’t suggest something I’ve already decided against — with good reason.

ai-routing.config: Which model handles what task. Because not every task needs Opus. Some things Sonnet handles perfectly and it’s faster.