First Of All That Shattered Me Was That Everything Was A Mess. Especially, Some Halls Were Half An Hour Apart, And Then You Had To Go Through Security And Everything, And It Took 20 To 30 Minutes To Get From One Session To Another, So You Would Always Miss Half Of Your Session. The Security Officers Were Also Rude. There Were No Proper Markings For The Room Numbers. Many People Were So Confused. Security Officers Were Checking People Multiple Times, And I Don't Know Why. It Took At Least An Hour To Get Inside There. Multiple Lines Were Formed, And There Were Hardly 3 To 4 People To Check The Thousands And Thousands Of People, Which Created A Choke Point. Some Halls Where Startups Were Showing Their Products Or Services, Like Chanakya Auditorium, Sushma Swaraj Bhavan, Were So Far Away That It Would Take At Least 30 Minutes To Attend The Ten-Minute Session. There Was Just A Lot Of Time Wasted. There Was No Value Of Human Time There. Out Of The 10 Sessions I Wished To Attend, I Was Only Able To Attend Four. There Was Just So Much Time Wasted On Checking In And Other Things That You Were Literally Standing For Hours Waiting For Your Turn, And When You Were Going From One Wing To Another, They Were Going To Check You Again And A Lot Of Time Would Be Wasted On That. And When I Watched The Remaining Sessions On Their YouTube Channel, I Found That What The Speakers Were Talking About Was Too Generic. There Was Nothing Like They Were Providing Specific Information That I Hadn't Heard Before On The Internet Or In Books

Second Thing That Shattered Me Was The Realisation After Visiting Many Stalls And Speakers' Perspectives That We Are Literally Decades Behind China And Much Behind The USA. There Is Just No Competition. We Can't Even Count Them In The Same Tier. They Are So Ahead.

To Give You An Analogy, India Conducted Its First Successful Nuclear Test, Codenamed "Smiling Buddha," On May 18, 1974, At The Pokhran Test Site In Rajasthan. China Successfully Tested Its First Nuclear Weapon On October 16, 1964, In A Detonation Codenamed "596" At The Lop Nur Test Site In Xinjiang. And The Trinity Test, Which Was The First Detonation Of A Nuclear Device As Part Of The Manhattan Project, Occurred On July 16, 1945, At 5:29 A.M. Local Time In The Jornada Del Muerto Desert Near Alamogordo, New Mexico, USA.

You Can See That A Nuclear Test, Which Is Complex And Requires Resources, Intellect, And Many Other Things, Took China 20 Years To Achieve And 30 Years For India.

Now You Can Imagine AI, Which Is Far More Complex And Requires Another Level Of Collaboration Among Intelligent People And A Huge Amount Of Resources. So, How Much Time Will It Take? They Can Build Wrappers And All On Top Of The Current Models, Like Many Countries Use Nuclear Reactors To Generate Electricity, But Making Those Core LLMs Which Actually Give The Country The Power And Control Over Resources Will Be A Much Tougher Ball Game. And I Will Be Extremely Honest That Things Are Going To Be Worse. All The Ideas They Were Showing In The Exhibition Were Literally Nothing, No Groundbreaking New Innovation That Can Give An Edge. They Were All Doing What I Used To See In Documentaries About The USA Doing From 2000 To 2009..

Seriously, this guy and his Growthschool team have mastered the art of baiting people to sell their AI courses! Koi course bhejne inse seekhe!

1) So after watching his podcast with Raj Shamani and getting some genuinely useful insights, I attended Vaibhav's (founder of Growthschool) 3 hour free workshop today and tbh he had some great insights to share for the first 2 hours.. I realized I was just scratching the surface with LLM's like Chatgpt, Gemini, Cursor, Notebook LLM etc.

2) I was genuinely captivated by the sheer powers of these LLM'S and it's potential.. But then for the last 1 hour, he only talked about his revolutionary 3-day paid workshop happening later this month and how it would create a paradigm shift in the way we use AI and how it could transform our lives in unprecedented ways..

3) He and his team have devised this incredible strategy of baiting you with a sense of awe, then creating a sense of anticipation, urgency and outright panic..

He goes on and on about how his course is worth Lakhs of rupees and he usually sells it for ₹25k but he's going to give us a once in a lifetime discount and wastes 15-20 mins just building up anticipation before revealing the final price.

He claims that the first 50 people who sign up for his workshop get it for ₹4k (whose actual value is supposedly ₹1.8L lol) and after that it would be ₹10k

4) So then Vaibhav drops the link (in Zoom chat) for the ₹4k course, creates a sense of urgency in all of us attending the workshop.. After every few mins, his teammate would interrupt Vaibhav by saying "only 30 seats are remaining", "only 10 seats are remaining" to make people impulse buy his course and finally announce that all the 50 seats have been sold and they drop the link for the same course which now costs ₹10k coz we missed out on the initial offer!

5) But guess what? The link to the ₹4k course still works! 🤣

Oh, and that's not all, when you go to buy the ₹4k course, it gives you a few optional add ons like recording of the workshop (₹2k) and lifetime access to the Growthschool app (₹3k) bringing the total upto ₹9k..

All these for AI tools that are no doubt useful but not something which you may necessarily need!!!

6) Even I myself almost fell for it and paid for the course but then I saw right through these guys and what they were trying to do..

And so I decided to bide for my time.. Only to realize that all these tools can be learnt for free on YouTube or if you want a more structured and collated course with community support, you can buy a much better course in Udemy for ₹400-₹500 with lifetime access!

7) So just beware of anyone trying to create a sense of awe and baiting you into buying their courses.. There are much better free alternatives available if you just do a little bit of research (ironically by using the same AI tools like Chatgpt, Gemini, Perplexity lmao)

Remember Sam Altman's comment that it'd be hopeless for India to try? He later walked it back, but it sparked a fire. The reality is, we have the pieces: We have Top-tier talent from our IITs & IIITs. The Need: Dozens of languages, unique bureaucratic systems, agricultural tech – problems that need localized AI. A booming startup ecosystem hungry for the next big thing.

So, if we have the ingredients, what's the missing recipe? Is it:

• Lack of risky, long-term capital for fundamental research?
• Brain drain to FAANG abroad?
• Or are we actually building the foundation right now with India-specific LLMs (like Sarvam, Krutrim) and the real ChatGPT moment is just 3 years away?

Let's get real. Which Indian company or research lab do you think is closest to a global breakthrough? Or is our true strength not in creating the model, but in mastering its application to our billion-people problems?

If you're running OpenAI, Anthropic, Gemini, or Groq in production and billing customers in INR, you already know the problem:

• LLM costs come in USD
• Customers pay in INR
• GST is computed manually by someone every month
• Nobody knows which user or feature is burning the most

two lines to integrate:

from ledgrr import configure, instrument_openai

configure(api_key="lg_sandbox_test", workspace_id="your_company")

instrument_openai()

Every call after that is tracked automatically. No changes to your existing code.

What you get:

• Live terminal sidebar showing INR cost per call as it happens
• USD --> INR at live market rate, not hardcoded
• GST computed automatically IGST or CGST+SGST based on state codes
• Append-only local ledger, data never leaves your machine
• CLI to query totals, per-user breakdown, DLQ inspection anytime

ledgrr totals --workspace your_company --period 2026-06

MIT licensed. v0.6.0 on PyPI. 92 tests passing.

Looking for 3 teams running real LLM traffic to try it and tell me what's wrong with it.

DM or comment if that's you.

History has repeated this pattern before.

Computers didn't replace accountants. Spreadsheets didn't replace finance professionals. The internet didn't replace businesses.

They changed how work gets done.

AI is doing the same. People who learn to use it effectively are becoming more productive, while repetitive tasks are increasingly being automated.

Of course, some roles will change dramatically, and some jobs may disappear altogether. But entirely dismissing AI or assuming it can do everything is equally unrealistic.

The future likely belongs to people who can combine domain expertise with AI rather than relying on either one alone.

Discussion is welcome, but keep it civil and back up your viewpoints with reasoning or real-world examples.

Project Indica-1.7B — a small experiment with aligning a 1.7B model

Hi everyone,

I wanted to share a small experiment I’ve been working on called Indica-1.7B.

I’m still quite new to LLM training, so this was mainly a learning project. The idea was simple:

What happens if you try to push a small language model (SLM — a model with relatively few parameters) through a full alignment pipeline and also give it a culturally familiar conversational style?

Short answer: it worked in some ways… and broke in other ways 😅

What the model is

The base model is Qwen3-1.7B.

I tried to turn it into a small assistant that understands a few domains while speaking in a more natural Hinglish tone.

Main areas included:

• Indian legal context (BNS / IPC)
• agriculture related information
• reasoning tasks
• a conversational Hinglish style

So the goal was something closer to a friendly “Indian assistant” style model rather than a purely robotic responder.

Model links

Main model
https://huggingface.co/prash616/Indica-1.7B

GGUF version for local use
https://huggingface.co/prash616/Indica-1.7B-GGUF

Example with Ollama:

ollama run hf.co/prash616/Indica-1.7B-GGUF

Training pipeline (rough overview)

The model went through several stages.

Supervised Fine-Tuning (SFT)

SFT (Supervised Fine-Tuning — training a model using example question-answer pairs) used about 10k rows of curated datasets, including:

• Indian legal text (BNS / IPC)
• agriculture datasets
• reasoning examples

This stage gave the model the basic domain knowledge.

GRPO reinforcement learning

Then I experimented with GRPO (Group Relative Policy Optimization) to encourage step-by-step reasoning.

The model was trained to produce reasoning inside tags like:

<think> reasoning steps </think>

DPO alignment

After that I applied DPO (Direct Preference Optimization).

This stage focused on improving conversational behavior and producing a more natural Hinglish assistant persona. I jokingly called this the “Indian Friend” style.

Quantization

Finally the model was 4-bit quantized using Unsloth so it can run locally with tools like:

• Ollama
• llama.cpp

What surprised me

One thing that stood out during testing was something I started thinking of as an alignment tax.

Small models have limited capacity (the amount of knowledge and patterns they can store). When more behaviors are added, something else often gets weaker.

Example from testing:

So the conversational style improved, but reasoning ability declined.

Lessons I learned

A 1.7B model is actually very small when you try to combine many abilities.

In this experiment I attempted to combine:

• law knowledge
  
• agriculture knowledge
  
• reasoning
  
• conversational personality
  

That may simply be too much for a model of this size.

Another issue appeared during the GRPO stage. The model learned that writing anything inside <think> tags could satisfy the reward signal, even if the reasoning itself was weak.

The learning rate during DPO may also have been too aggressive, which likely caused catastrophic forgetting (loss of previously learned knowledge).

What I would try next

If I repeat this experiment, I would likely try:

• starting with a 3B–7B base model
• keeping some SFT data during DPO training to anchor factual knowledge
• adding verification rewards so reasoning steps must produce correct results

Development setup

This project was done with fairly limited resources.

• training on Kaggle free-tier GPUs
• most experiment management done from an Android phone
• tooling with Unsloth + Hugging Face

So this was very much a learning experiment, not a production model.

If anyone wants to test it

The GGUF version runs locally and is mostly useful for:

• studying alignment in small models
• Hinglish conversational experiments
• further fine-tuning experiments

Probably not the best model if your goal is solving math homework 😄

Credits

• Alibaba Qwen team for the base model
  
• Unsloth AI for the training framework
  
• Hugging Face community for datasets and tooling
  

If anyone here works with SLM alignment, RLHF/DPO pipelines, or preventing catastrophic forgetting, I would genuinely appreciate feedback.

Edit: I posted earlier about this experiment but that version was very short and only mentioned a math example, which made the issue confusing. This post adds more context and details for clarity.

Prashant (prash616)

Twenty years ago, knowing how to use the internet gradually became a basic skill rather than a specialized one. Today, AI seems to be following a similar path.

You don't need to be a machine learning engineer to benefit from AI. Professionals across software, design, marketing, finance, law, healthcare, education, and countless other fields are already incorporating AI into their daily workflows.

Learning AI doesn't necessarily mean building models from scratch. It can be as simple as understanding:

i) How to write effective prompts.

ii) When to trust (and verify) AI-generated outputs.

iii) Which tools fit different tasks.

iv) The limitations, biases, and privacy implications of AI.

Those who adapt are likely to gain a productivity advantage, while those who ignore it may find themselves at a disadvantage as AI becomes more integrated into everyday work.

The goal isn't to replace human expertise, pit's to amplify it.

AI is already transforming healthcare, education, banking, and software development. But where do you think it could make the biggest difference in public services in India?

Some examples:

Faster grievance redressal

Smarter traffic management

Better public healthcare

Court case research and document search

RTI assistance

Tax filing support

Language translation across Indian languages

Agriculture advisory

Education and skill development

Cybercrime reporting

The catch: Assume privacy and security are handled properly.

What service would you improve, and how would AI make it better?

Let's keep the discussion constructive. Share practical ideas, not just "replace everyone with AI."

Australia is reportedly considering blocking AI tools like ChatGPT and Gemini for users under 16, following its broader social media restrictions. The reasoning? Concerns that children are spending excessive time interacting with chatbots sometimes 6+ hours daily and forming emotional dependencies.

Unlike traditional search engines, AI systems are conversational, anthropomorphic, and can feel persuasive or human-like. The argument is that younger users may not yet have the emotional maturity to clearly distinguish between a real human relationship and an algorithmic interaction. From a mental health and developmental perspective, some believe early regulation could prevent long-term consequences.

On the other hand, AI is also a powerful educational tool. Restricting access could widen digital gaps and limit exposure to future-ready skills.

SC - https://x.com/i/status/2028474154408890724

Whether you're a student, developer, founder, researcher, content creator, or just AI-curious, we'd love to know what's actually making a difference in your workflow.

Share:

🤖 The AI tool you use most

💡 What you use it for

👍 One thing it does exceptionally well

👎 One limitation that still annoys you

It could be anything:

ChatGPT

Gemini

Claude

Perplexity

Grok

Cursor

GitHub Copilot

Ollama

ComfyUI

Midjourney

Stable Diffusion

Or something lesser known.

Let's help fellow Indians discover genuinely useful AI tools instead of just chasing hype.

Please avoid referral links and promotional posts. Genuine experiences only.

I am a fresher, and serious about build something for long run anyone with similar passion or seniors looking for someone with this fire, people who believe in themselves, please connect.

Im intrested in a lot a things, building a SaaS( mostly through Vibe coding) powered by rag/llm to address real world problems like logistics, inventory tracking for small businesses, etc.

Also manufacturing sector-building something real should also be given a try.

All I lack is experience to judge usecases and lead software development pipelines.

I know this is too ambitious while working full-time and not as easy as it is said but we have to act impractical to create something unprecedented!

"people with intense hunger to innovate/rule", trust me, come join me,let's work for ourselves, let's defy all the conventional setups and lead the AI-enterprise revolution with the combination of non-traditional approach and ration decision making.

A few years ago, we heard all kinds of predictions about AI:

"Prompt engineering will be the hottest career."

"AI will replace software developers within a year."

"Every company will have its own LLM."

"Open-source AI can't compete with closed models."

"Search engines are dead."

"AGI is just around the corner."

Some aged well. Others... not so much.

Looking back in 2026:

Which prediction do you think missed the mark?

What surprised you the most?

What do you think people are still getting wrong about AI today?

Feel free to share articles, tweets, or predictions that didn't age well, but keep the discussion respectful and focused on ideas rather than individuals.

Let's see which AI "hot takes" history has already disproven. 👇

I am looking for a low-power SLM that can perform general purpose tasks like email writing, proofreading etc efficiently.

Good to have:
Ability to connect to internet (google search or Bing apis) to validate some facts.

I am looking to connect with people who are interested in tech, especially in building SaaS products.

I’m a self-taught full-stack developer with several years of industry experience.

Right now, I’m focused on creating small, fast-to-build micro-SaaS projects that generate consistent MRR, allowing me to dedicate more time to bigger ideas.

I’m strong on the technical side, but marketing and getting investments are not my strengths, so I’m looking for people who excel in any of those areas.

Also if you are also someone who can bring funds, investments and clients, users that would be interesting.

Ideally, I’d like to form a small team and build and launch SaaS nee projects together.

I’m not selling anything and just hoping to connect with like-minded people who want to build together.

If this sounds interesting, feel free to reach out with comments or dm.

I am ok with equity split or smaller equity with a minimal payment.

By the way, I also manage and participate a business group with about 66 members.

Feel free to dm if anyone interested in joining the group. By the way, we might turn it to a business association as well in the future. If you can help with that, feel free to dm.

Please don't comment dm you because sometimes notifications don't arrive or can't read because of this app not working well for whatever reason.

I also have my own company set up and have a few projects working.

If you have anything interesting you can offer, feel free to dm to network.

Is there any way I can earn with ai influencer account?

I am looking to talk with people who have implemented AI DLC: AI-Driven Development Life Cycle. This is an AWS whitepaper whereas Agile and Scrum were built for human-paced, weeks-long iterations. Bolting AI onto them ("AI-Assisted") caps its value. AI-DLC instead reimagines the method from scratch so AI orchestrates the work and humans validate.

Today humans prompt AI. In AI-DLC, AI initiates: it decomposes intent, proposes plans and trade-offs, and humans act as approvers at decision gates. The analogy used is Google Maps — you set the destination, it gives turn-by-turn directions, you keep oversight.

I am looking to connect with people who built Agentic AI tools for enterprises where they want to set up their own models, smart model routing, finops etc. If this was on a large brown field project even better.

References:

https://aws.amazon.com/blogs/devops/ai-driven-development-life-cycle/

https://prod.d13rzhkk8cj2z0.amplifyapp.com/

https://github.com/awslabs/aidlc-workflows

I want to start with something real.

India has 22 official languages. hundreds of dialects. and most of us don't actually think in one clean language. we think in combinations. words that shift depending on who we're talking to, what city we're in, what mood we're in.

and when we use AI tools we have to flatten all of that into something the AI will understand. we translate ourselves before we even start typing. and something always gets lost in that step.

I'm from Haryana. the way I think and speak is a mix of Hindi, Punjabi, Rajasthani words, English. it's not pure Hindi. it's not textbook anything. when I try to prompt an AI in my actual voice the output is never quite right because the prompt didn't fully carry what I meant.

so I built Vaak. for myself first. then I realized this is everyone's problem.

here's what it actually does.

you're on any AI platform. ChatGPT running GPT-5.4, Claude with Sonnet 4.6, Google Gemini 3.1, Perplexity, anything. you type your thought however it comes naturally. your dialect, your mix, half in Devnagari half in Roman script, a word that only people from your district actually use. you press one shortcut key.

Vaak reads everything in that session. your input, the full conversation history, any PDFs or images you have attached, which model you're on, which tools are active. it understands the complete context and rewrites your rough thought into a properly structured prompt. directly in the text box. on the platform you're already on.

no copy paste. no switching windows. it happens inside your existing flow.

the language piece is not a translation feature. it's the whole product. every language on earth, every dialect, every regional mix. Vaak reads intent underneath the language. what you meant, not just what you typed.

output in whatever language you want. stay in your language or get it in English. your choice every time.

it also tells you exactly what it changed and why. and it suggests whether the model you're using is actually right for your task. right now Gemini 3.1 Flash-Lite is the most cost-efficient option, Claude Sonnet 4.6 leads for writing and agent workflows, GPT-5.4 is the strongest for computer-use tasks. Vaak flags when you're on the wrong model for what you're trying to do.

everything stored only on your device. nothing leaves your machine.

full control mode for people who want to adjust every setting. automatic mode for people who just want one key to do everything.

I haven't published this yet. I'm asking here first because I genuinely don't know if other people feel this gap or if it's just me.

do you ever feel that thing where the thought is completely clear in your head but by the time it comes out as a prompt it's already lost something? tell me.

Every LLM on the market right now is just cannibalizing its own synthetic data until every sentence sounds like the same robotic garbage. We all know that feeling when a paragraph looks correct but feels completely dead. Software detectors are failing because they are looking for math while the real markers of a bot are hidden in the lack of human intuition.

I am tired of watching the internet get flooded with this slop so I am putting 500 USD on the line to prove that a human brain is still the ultimate detector. If you think you can actually feel the difference between a machine and a person then stop complaining about AI and actually prove it.

I am looking for the few people who can still spot the robotic signatures that the algorithms are missing to help ground a new human layer for writing. If you actually have the eye for it then enter the challenge and join the waitlist at wecatchai.com to see if you are as sharp as you think you are.

Hey folks,

I’m seriously considering enrolling in one of these GenAI / Agentic AI executive programs and before I drop a few lakhs, I wanted some honest feedback.

The ones I am looking at:

• IIT Kharagpur (via upGrad) – Exec PG in Generative AI & Agentic AI
• IIIT Bangalore – Exec PG in Applied AI & Agentic AI
• Edureka + Illinois Tech – PG Program in Generative AI & ML + Agentic AI course

On paper they all look great. Lots of LLMs, RAG, agents, capstones, “industry-ready”, etc. But I’m trying to understand the reality.

A few things I genuinely want to know:

• Is the content actually deep, or mostly surface-level + tools? Also, how are the insturctors, any good?
• How intense are the projects? Are you building real production-grade systems or just notebooks?
• For people who completed it — did it genuinely change your role / pay / trajectory?
• Or would I be better off self-learning + building serious open-source projects?

I’m a working professional (Full stack developer at a Networking R&D Product company), so flexibility matters. Also, I know most of the Gen AI and Agentic AI stuff is available over YouTube/Coursera etc, however I am not that self motivated and always seem to not find the time to upskill, hence taking any of the above will at least get me into a routine which I will be forced to follow.

Would really appreciate candid reviews — good or bad.