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[Adnan @Adnan_Ai2]

🚨 BREAKING: CLAUDE CAN NOW BUILD YOU AN ULTRA-PREMIUM WEBSITE IN JUST 30 MINUTES.

COMPLETELY FREE.

HERE ARE 8 PROMPTS TO HELP YOU BUILD YOUR DREAM WEBSITE:

[Akshat Shrivastava @Akshat_World]

At 3Cr, your life changes dramatically.

1) Setup a tax base abroad. Move your liquid money. And, start paying 0% capital gains.

2) This move comes with frictions. Setting up a tax base, paying money upfront, 250K LRS etc whatnot.

Most people give up here.
And, accept their fate.

But: if you power through this phase, new opportunities open up like never before.

3) Example: if you have a UAE tax base. You can easily invest across: US equities, derivatives, SEA equities, you name it.

It takes 1 min to move 1Mn$, legally. Friction is 0.

4) No nonsense of dealing with currency depreciation, unreliable laws, filing 100 forms for every little move.

You can focus your mental energy growing your wealth. Not on compliances.

5) On top of this: new income streams open up. For eg. Wheel strategy -- you sell Puts 15% OTM. Once you get the stocks, you sell call 15% OTM.

This is a solid active investing strategy with 0% tax.

****
Is the pain worth it? 100% yes. Numbers prove it.

3CR.
Let's say this grows at 13% with 0% capital gains.

₹3 Cr × (1.13)^30 = ₹3 Cr × 39.12 = ₹117.36 Cr
Total gain = ₹114.36 Cr

If you pay 12.5% (and other BS taxes), you will end up paying: 14.30Cr for a passive investment strategy.

If you undertake active investing, this number would be much higher.

Now you decide if paying X lakhs/year as the setup fee is worth it.

****

No Indian fund manager/CA will tell you this. Because if they tell you this, you will leave them & the system, lol 😅

[Selina @selinaai_]

During a job interview, if they ask: "What's your biggest weakness?"

USE THE GOLDEN RESPONSE.

[oblivion @0blvi0n]

@Livpure_India No one contacted me, even when I call your team, there was no reply

[oblivion @0blvi0n]

@Livpure_India
I am writing to express my extreme dissatisfaction with the service I received today regarding the return of my damaged RO filter (Ticket No: 4781273094003).
I have submitted all the required documentation with pics and videos.But I was asked for a pickup invoice

[oblivion @0blvi0n]

@Livpure_India @Livpure_India terrible service...I called your customer care team again. He just said he will transfer the call to seniors...I am still on hold....

You don't reply on mail..you don't reply on call...idk what the heck you guys do at all

[oblivion @0blvi0n]

@Livpure_India
Please see your broken RO system.

And I have already mailed your team 5 times and called your customer care number which is of literally no use, as it got connected 1 out of 5 times where he said ..mail the issue

[oblivion @0blvi0n]

@Livpure_India Lodged a grievance in consumer forum anyway

[Ryan @WritesToProfit]

I’m 24 years old & just 2 years ago I was delivering Amazon packages.

• No business experience
• No marketing background
• No industry connections

But I was still able to hit $20k/mo within 60 days.

[Akshat Shrivastava @Akshat_World]

1) If you have 0 interest in learning finance. Keep adding Land + Gold, whenever you get a chance.

2) If some interest in finance, buy Index. Index beats most of the actively managed Mutual Funds and PMS.

3) If some interest in finance: use 200DMA (single technical indicator). Buy firms where operating profits are expanding. And, the stock trades below 200DMA.

It takes 5 mins to learn.

4) If more interest in finance. Learn diversification, portfolio construction etc. Spend time on this skill.

5) Even more interest in finance/investing? join my investment community.

I build a portfolio LIVE in front of people. And, put real money on the line. I don't simply yap and go away like trolls.

Here is the performance since I started last year (in USD terms). In INR, this is more than 50%

[Anush Lakshmanan @anush100]

A cautionary tale about @urbancompany_uc and how a publicly listed company handles damage claims.

Booked their technician (Sanju Kumar) to install a Luminous inverter at my home in Bangalore. Routine job. Should've been 2 hours.

Invoice no: UCIC260004101947
Complaint no: 69f362690db9530026fe28d7

Instead of a standard install, he opened the main switchboard and worked with MAINS LIVE violating the most basic electrical safety rule. Phase-neutral cross-connection caused a ~280V surge through my home wiring.

Within minutes:
• Geyser circuit dead - Approximation given is around 12,000 to fix
• 55" Smart TV motherboard fried, panel and screen damaged - looking at full loss ( Rs 48,000)
• Apple TV completely destroyed (₹14,900 replacement)
• Home electrical wiring compromised (Assessing currently)

The moment Sanju Kumar realized he'd damaged the geyser, he packed up and LEFT mid-job. Inverter still uninstalled. No fix. No apology. No accountability.

Had to hire a different electrician the next day to fix the wiring he broke out of my own pocket.

Filed formal complaint with UC. Specifically requested a SENIOR technician for independent damage assessment.

UC's response? They sent SANJU KUMAR BACK. The same person who caused the damage. To "take photos" of his own work and walk away.

He showed up, photographed the damage he caused, left without resolution. No claims process opened. No callback.

When we asked UC for a complaint ID, it took multiple follow-ups and a manual download of a tax invoice to extract any documentation. They were not proactive in issuing one almost as if to avoid creating a paper trail.

3 sleepless nights. ₹40,000+ in documented damages. Complete radio silence from UC corporate after that.

@urbancompany_uc @abhirajbhal @raghavchandra @varunkhaitan — your company is publicly listed now. Retail and institutional shareholders are watching how customer grievances are handled.

Is THIS the customer experience model investors signed up for?

1. Technician violates basic electrical safety
2. Damages multiple appliances and home wiring
3. Walks off mid-job when he realizes his mistake
4. Same technician sent back to "investigate" his own damage
5. Customer chases for documentation that should've been auto-generated
6. Then gets ghosted

Listed companies are held to a higher standard of corporate governance, customer protection, and grievance redressal. SEBI compliance and investor disclosures aren't just about quarterly numbers they extend to how the brand treats its customers.

This isn't how a publicly listed company should operate.

Asking for:
1. Independent senior technician (not Sanju Kumar) to assess damage
2. Compensation for documented damages: ₹90,000+
3. Formal acknowledgment of complaint 69f362690db9530026fe28d7
4. Process review — no customer should have to extract their own complaint ID
5. Public clarification on UC's damage claims SOP

Have all invoices, technician's diagnosis report confirming overvoltage cause, photos, timestamps, and WhatsApp records.

please be careful. If a clearly documented case is handled this way, imagine the cases without paper trails.

Consumer forum filing prepared if there's no response in 48 hours.

[Avid @Av1dlive]

Andrej Karpathy : 10x engineers are normal. real agentic engineers are 100x

this guy just shipped the playbook to become 100x

context engineering. tool design. orchestrator-subagent. evals. the harness mindset.

watch & bookmark it for this weekend

[Himanshu Kumar @codewithimanshu]

Andrej Karpathy just sat down and built GPT from scratch, line by line, in 2 hours.

For Free. From the man who co-founded OpenAI.

This video is enough to become an AI engineer.

Bookmark it. Watch it tonight. Build your own GPT this week.

$5,000. $15,000. $40,000.

That's what bootcamps charge to teach less than what's in this 2-hour video.

This video fixes that this week.

Follow @codewithimanshu for more high-signal AI content that actually moves your engineering career forward.

↓

Karpathy doesn't explain GPT. He builds it.

Live. From "Attention is All You Need" the original paper. To the same architecture powering GPT-5.

Founding member of OpenAI in 2015. Senior Director of AI at Tesla. Now running Eureka Labs.

He's not teaching you how to use GPT. He's teaching you how it actually works at the source code level.

Most engineers will never understand transformers this deeply. The ones who do build the next generation of AI products.

Follow @codewithimanshu for breakdowns of every must-watch AI lecture worth your time.

↓

Here's what gets built in 2 hours. No fluff.

Tokenization and data loading.

The foundation of every modern LLM. Train/val splits done right. Batch loaders that don't break in production.

Most tutorials skip this. You can't ship anything serious without it.

The bigram baseline.

The simplest possible language model. Karpathy builds it first because it teaches you what every fancier model is actually trying to improve.

Once you understand bigrams, transformers become obvious. Skip this and the rest never clicks.

Follow @codewithimanshu for daily breakdowns of what AI engineers actually need to know.

↓

Self-attention. From scratch. Live.

This is the section that should have its own course.

Karpathy builds self-attention in 4 versions:

> Version 1: averaging past context with for loops
> Version 2: matrix multiply as weighted aggregation
> Version 3: adding softmax
> Version 4: full self-attention

Each version teaches you why the next one exists. Why attention works. Why matrix math replaces explicit loops. Why scaling matters.

You'll never look at "attention is all you need" the same way again.

Follow @codewithimanshu for production transformer breakdowns weekly.

↓

The 6 attention notes that change everything.

Karpathy drops 6 insights most engineers never hear:

> Attention as communication between tokens
> Attention has no notion of space, operates over sets
> No communication across batch dimension
> Encoder blocks vs decoder blocks
> Attention vs self-attention vs cross-attention
> Why we divide by sqrt(head_size)

Each one of these explains a different failure mode in production AI systems.

Most "AI engineers" can't answer these. The ones who can charge $300K.

Follow @codewithimanshu for the engineering insights that turn into job offers.

↓

Building the full transformer block.

Single self-attention head. Then multi-headed self-attention.

Feedforward layers. Residual connections. LayerNorm.

Each piece added with the reason it exists. Why residuals stop the model from collapsing. Why LayerNorm replaced BatchNorm. Why dropout matters at scale.

This is the architectural understanding that lets you debug any modern AI system.

Once you've built one transformer by hand, every paper you read becomes 10x clearer.

Follow @codewithimanshu for transformer architecture content every week.

↓

Scaling up to a real model.

Karpathy goes from baseline to a working GPT.

Hyperparameters. Dropout. Model dimensions. The exact tradeoffs every production model makes.

By the end you have a Shakespeare-generating language model running on your machine. From scratch. Built by you. Understood by you.

That's not a tutorial. That's an architectural unlock.

Follow @codewithimanshu for production model scaling breakdowns.

↓

Encoder vs decoder vs both.

The architecture choice that defines every modern AI product.

Why GPT is decoder-only. Why BERT is encoder-only. Why translation models use both.

Once you understand this, you can read any AI paper and immediately know what kind of system you're looking at.

This is the difference between someone who follows AI hype and someone who builds it.

Follow @codewithimanshu for AI architecture deep dives weekly.

↓

NanoGPT walkthrough.

Karpathy ends with a quick walk through nanoGPT. The repo every serious AI engineer has cloned at least once.

Batched multi-headed self-attention. Production-grade code. The clean version of everything you just built.

This is the bridge from "I built a toy GPT" to "I can read and modify production AI code."

Follow @codewithimanshu for repos every AI engineer should know.

↓

ChatGPT, pretraining, finetuning, RLHF.

The video closes with the full lineage. From your toy GPT to ChatGPT.

What changes when you scale up. Why RLHF matters. The exact path from research model to product.

You finish the video understanding the entire stack from raw paper to deployed product.

Most "AI experts" can't draw this map. After 2 hours, you can.

↓

What you'll be able to do after this.

Read "Attention is All You Need" and understand every line.
Debug attention layers when they break in production.
Build a custom language model on your own dataset.
Modify transformer architectures for specific use cases.
Have technical conversations with AI engineers without faking it.
Train a GPT on any data you want. Shakespeare. Code. Your own writing.

That's not "AI literacy." That's the foundation of an AI engineering career.

The kind of foundation that turns into senior roles and consulting contracts most people will never access.

↓

2 hours. Free. From the engineer who built it.

You'll spend longer in meetings this week and learn nothing.

This compounds for the rest of your career.

People who watch it can build GPT from scratch by Friday.
People who skip it stay confused about why their prompts fail in production.

Save the video. Watch it this week. Build something with the knowledge by the weekend.

Follow @codewithimanshu for more high-signal AI content from the people actually building the future.

[Vaishnavi @_vmlops]

ANDREJ KARPATHY JUST DROPPED THE MOST IMPORTANT PYTHON FILE FOR ANYONE LEARNING LLMs

He wrote https://t.co/WMajAg17sX a complete GPT from scratch... no libraries... no frameworks... pure python

the entire algorithm fits in one file

the only imports are math, random & os

here's what's inside:

▫️a from-scratch autograd engine (yes, he built backprop by hand).
▫️a full transformer token embeddings, positional embeddings, multi-head attention, MLP, RMSNorm
▫️Adam optimizer implemented from scratch
▫️training loop + inference in under 200 lines

the comment at the top says it all:

"this file is the complete algorithm.
everything else is just efficiency."

and the community went insane 5000+ stars, ports in rust, go, OCaml, julia, CUDA & JS already exist

if you've ever used transformers without really understanding what's happening underneath this is your weekend project

read it once...you'll never look at LLMs the same way

→https://t.co/2MbPGJJeJ1

[Rahul @sairahul1]

MICROSOFT OPEN SOURCED A 7B PARAMETER MODEL THAT TRANSCRIBES 60 MINUTES OF AUDIO IN A SINGLE PASS

and it's completely free

VIBEVOICE ASR no chunking, no context loss, full speaker diarization baked in

not just speech to text..not a basic wrapper

who spoke, when they spoke, exactly what they said..all in one shot

and it handles the hard stuff too..50+ languages, custom hotwords, long form audio that breaks every other tool

the model doesn't know what "context window" means apparently

Available on macOS and Windows right now.

Free to use. Free to fine tune. Free to build on.

[oblivion @0blvi0n]

@GaryStoneSWS Hey gary..didn't get it. Can u send it again

[oblivion @0blvi0n]

@BoucherNicolas AI

[Brij Pandey @LearnWithBrij]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/YkuDFVmW9e

2. LLMs from Scratch: https://t.co/u3kSz5SGuJ

3. Agentic AI Overview (Stanford): https://t.co/W6rzVHGSgC

4. Building and Evaluating Agents: https://t.co/sEl8vVax3F

5. Building Effective Agents: https://t.co/c7fD4aWFYO

6. Building Agents with MCP: https://t.co/GlMdR6htgA

7. Building an Agent from Scratch: https://t.co/kUQ9jPuI0R

8. Philo Agents: https://t.co/8JHvqw0DKn

🗂️ Repos

1. GenAI Agents: https://t.co/cyHPvOAjlK

2. Microsoft's AI Agents for Beginners: https://t.co/zFJAN74JQe

3. Prompt Engineering Guide: https://t.co/liUshX2XsP

4. Hands-On Large Language Models: https://t.co/TXFhbiboZY

5. AI Agents for Beginners: https://t.co/zFJAN74JQe

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/lkXP6itwK0

8. Hands-On AI Engineering:https://t.co/zB8EEctE4Y

9. Awesome Generative AI Guide: https://t.co/lF7CuIQHRw

10. Designing Machine Learning Systems: https://t.co/XlYUZYOoVi

11. Machine Learning for Beginners from Microsoft: https://t.co/hF5UzZoMJB

12. LLM Course: https://t.co/4tLAwy8fOQ

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/0OEKVLgF34

2. Google's Agent Companion: https://t.co/r0Dxe4VvDO

3. Building Effective Agents by Anthropic: https://t.co/I0ZyuwiOS3.

4. Claude Code Best Agentic Coding practices: https://t.co/HIBC2TwwAP

5. OpenAI's Practical Guide to Building Agents: https://t.co/1I8n0wnjHQ

📚Books:

1. Understanding Deep Learning: https://t.co/XEzhyAcWbq

2. Building an LLM from Scratch: https://t.co/4sZmBnHPEg

3. The LLM Engineering Handbook: https://t.co/IkAYNFkVNI

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/KsFnET47hx

5. Building Applications with AI Agents - Michael Albada: https://t.co/lJhMLtsLql

6. AI Agents with MCP - Kyle Stratis: https://t.co/C2lhD8uTDL

7. AI Engineering: https://t.co/34EyUiIVMv

📜 Papers

1. ReAct: https://t.co/kfQ8tWysne

2. Generative Agents: https://t.co/wbfqXq8KZK.

3. Toolformer: https://t.co/OQ7m49YWls

4. Chain-of-Thought Prompting: https://t.co/XeNgLQdTIL.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/tUZyPEGhni

2. MCP with Anthropic: https://t.co/wx1DAIWis0

3. Building Vector Databases with Pinecone: https://t.co/8XsQzDstTB

4. Vector Databases from Embeddings to Apps: https://t.co/9n6DvZGTMN

5. Agent Memory: https://t.co/OxFAaM0fp7

Repost for your network ♻️