Abhinav

Tech enthusiast | Python geek | Javascript nerd | UI/UX beginner

[ADG PI - INDIAN ARMY @adgpi]

Operation Sindoor

Pakistan's Bid to Escalate Negated- Proportionate Response by India.

https://t.co/E6e65goX9R

#OperationSindoor
@DefenceMinIndia
@SpokespersonMoD
@HQ_IDS_India

[Krishna @Atheist_Krishna]

Pakistan's defence system activated after #OperationSindoor

[ADG PI - INDIAN ARMY @adgpi]

"प्रहाराय सन्निहिताः, जयाय प्रशिक्षिताः"
Ready to Strike, Trained to Win.

#IndianArmy

[Riley Brown @rileybrown]

Struggle to keep up with all the most powerful AI tools?

Well here is a comprehensive overview:

Sections:
1: AI Tools
2: AI Automations & AI Agents
3: VibeCoding

The Full Mindmap down below 🧵 ⬇️

TIMESTAMPS

A INTRO
00:00 Introduction

B CHAT TOOLS
01:50 ChatTools
01:50 Getting Started with Chat AI Tools
02:27 Exploring Chat GPT's Capabilities
04:11 Creating Custom ChatGPT Projects
07:54 Creating Files (PDF) ChatGPT Projects
11:44 Upload image and Search the web on ChatGPT
15:14 Looking at Google Gemini AI Chat Tool
16:36 Perplexity is another great tool
18:10 This AI Chat Model Can Analyze Videos (Most underrated AI use cases @OfficialLoganK you guys need to hype this up more)
20:16 Recapping Chat Tools

C CREATIVE TOOLS
21:05 Introduction to AI Image Models
22:19 Using Chat GPT-4o for Image Editing
22:30 Using gpt4o images for Business: Repaint Houses
26:36 Edit Parts of an image gpt4o
27:55 Midjourney
28:23 Midjourney is better for exploration
31:25 Midjourney Editor is fun to use

D: CREATING VIDEOS
36:29 Image to Video with Kling and Runway
38:52 Gen4 Turbo
41:22 Kling is Superior
42:00 gpt4o Ghibli Image + Video + Sound
44:58 Ghibli time
49:15 ElevenLabs Sound Effect for pressing button
50:00 Generating Voice for Voice Over
51:04 Getting Music
52:07 Video Editing These together
53:47 Finished the Video - Final Results
55:02 Video Tool Recap
56:27 AI Avatars with Heygen
57:42 Example of good avatar content ROWAN CHUNG
58:43 Pause and Reflect before Automations
59:14 Recap of section 1

E: VIBE FLOWS (AUTOMATIONS AND AGENTS)
59:34 (add here - explanation of workflow automation)
59:34 Zapier Automation
01:04:24 Adding Step in Automation
01:09:45 Stacks, Automations, NOW AGENTS
01:10:29 Deep Research Agent
01:13:11 What is Manus? Let's ask Claude to Diagram it
01:16:25 Prompting Manus
01:18:20 The Future of Agents will look like Online Poker in 2010
01:19:15 WHAT DO WE DO WHEN AGENTS ARE WORKING?
01:19:56 Greg's Theory of "Vibe Marketing"
01:20:44 Manus and Deep Research are done Agenting, let's see what they did
01:23:32 Ok Lets Reflect and Move to VIBE CODING

F: VIBE CODING
01:25:35 VIBE CODING!
01:25:48 Landing Page with Sound, Images, and Video on v0
01:29:07 Generating Video for v0 site
01:31:31 Adding Video and AUDIO to v0 site (ElevenLabs)
01:33:36 Using API's in your Vibe Code Apps (Power Ups)
01:35:26 What makes the best App Idea
01:37:41 Build a simple app with an API with Cursor
01:42:57 Diagraming how the API key works in the Bill Splitter
01:43:25 Structured vs Unstructured text to text API's
01:44:41 Building a mobile app using built in API's from your phone
01:47:31 Creating native share features on ios app
01:49:21 What we talked about in this video (Where to Find the MindMap)

Tools mentioned:
1:ChatGPT, Google Gemini, Anthropic Claude, Perplexity, Grok, GPT‑4o Images / DALL·E 3, MidJourney, Cling 2.0, Krea AI, Runway Gen‑4, Google Veo, Luma Labs, Pika Labs, Heygen, Suno, ElevenLabs
2: Zapier,n8n, Manus, Deep Research, Canva, CapCut, Premiere Pro
3: Cursor, V0, OpenAI API, Replicate, Vercel, Replit, Firebase, Supabase, Claude 3, DeepSeek, Grok, Llama

[Radical Watch @RadicalWatchOrg]

In a recent tweet, ArfaArfa Khanum Sherwani, a journalist known for her Anti-Modi views, declared her receipt of JOHN S KNIGHT JOURNALISM FELLOWSHIP. This fellowship will take her to Stanford University. But what's the real story behind this fellowship. Let's see:

THREAD

[ThinkFi @ThinkFi_net]

Surprising #election interference revealed

KKR won the #IPL2024 by 8 wickets, and while tweets were pouring after #IPLFinal, we found some strange Japanese hashtags riding on IPL hashtag.
Turned out, it was a bot army which has also been disrupting #IndianElections2024

Thread:

[Abhinav @abhinav_py]

@reason @threadreaderapp unroll

[DisInfo Lab @DisinfoLab]

Sudden spike in news about ‘bad’ Indian medicine – from US, Gambia, Indonesia, Sri Lanka - and emergence of Indian origin American ‘hero’ #DineshThakur.

Story of biggest assault on #IndianPharma, aided and abetted by Indian media, particularly, The Wire.

A Thread:

[OSINTवा @OSINTWa_com]

The three credible institutions referred to in the #WaPo story on #Pegasus incidentally are all funded by Soros : Amnesty, Access Now, and OCCRP. And of course Washington Post.

You can’t make this shitt up.

Congratulations to the brilliant Gerry Shih and team on yet another feat!

[DisInfo Lab @DisinfoLab]

An #ISIS module was busted by the NIA, headed by Saquib Nachan. Many would not know that Saquib goes back to the 1990s with Human rights ‘activist’ Bhajan Bhinder.

A trans-national - trans-generational strategy of bleeding India by thousand cuts.

A Thread: 🧵

[DisInfo Lab @DisinfoLab]

In a long English word salad, a quarter dozen WaPo journalists in a hit piece have tried painting us somewhere between RW & RAW (no pun intended).

To do this, they recycled 2-year-old Pakistani propaganda, making it ‘ANONYMOUS.’

We dissect the ‘exposé’.

A Thread:

[DisInfo Lab @DisinfoLab]

Financial Times (FT) quoting anonymous sources reported that US thwarted an attack by India on Khalistani loudmouth Gurpatwant Singh Pannun who as per Indian media’s anonymous sources is a US Asset

But there's more to mix of Pannun/ #Khalistan-Role of UK establishment & BBC.

🧵

[Alex Xu @alexxubyte]

What happens when you type a URL into a browser? The method to download the high-resolution PDF is available at the end.

Let’s look at the process step by step.

Step 1: The user enters a URL (bytebytego .com) into the browser and hits Enter. The first thing we need to do is to translate the URL to an IP address. The mapping is usually stored in a cache, so the browser looks for the IP address in multiple layers of cache: the browser cache, OS cache, local cache, and ISP cache. If the browser couldn’t find the mapping in the cache, it will ask the DNS (Domain Name System) resolver to resolve it.

Step 2: If the IP address cannot be found at any of the caches, the browser goes to DNS servers to do a recursive DNS lookup until the IP address is found.

Step 3: Now that we have the IP address of the server, the browser sends an HTTP request to the server. For secure access of server resources, we should always use HTTPS. It first establishes a TCP connection with the server via TCP 3-way handshake. Then it sends the public key to the client. The client uses the public key to encrypt the session key and sends to the server. The server uses the private key to decrypt the session key. The client and server can now exchange encrypted data using the session key.

Step 4: The server processes the request and sends back the response. For a successful response, the status code is 200. There are 3 parts in the response: HTML, CSS and Javascript. The browser parses HTML and generates DOM tree. It also parses CSS and generates CSSOM tree. It then combines DOM tree and CSSOM tree to render tree. The browser renders the content and display to the user.

–
Subscribe to our newsletter to download the 𝐡𝐢𝐠𝐡-𝐫𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐏𝐃𝐅. After signing up, find the download link on the success page: https://t.co/VvkjFvSWBf

[Johnson Olubodun @uijohnson]

Try these websites for device mockup👇

1. Angle - https://t.co/IFbFJML7zd
2. Cleanmockup - https://t.co/lAfovwL2Kp
3. Device Frames - https://t.co/ktmbiwUwD1
4. Mockuuups - https://t.co/cNn5W8YZCH
5. Animockup - https://t.co/vCsCuUp96d

#uidesign #uxdesign #uiux #uidesigner #ux

[Santiago @svpino]

A 2-minute introduction to the fundamental building block behind Large Language Models:

Text Embeddings

(This is the most helpful explanation you'll read online today. I promise.)

The Internet is mainly text.

For centuries, we've captured most of our knowledge using words, but there's one problem:

Neural networks hate text.

Judging by how good language models are today, this might not be obvious, but turning words into numbers is more complex than you think.

Imagine a 4-word vocabulary: King, Queen, Prince, and Princess.

The most straightforward approach to converting our vocabulary into numbers is to use consecutive values:

• King → 1
• Queen → 2
• Prince → 3
• Princess → 4

Unfortunately, neural networks tend to see what's not there. Is a Princess four times as important as a King? Of course not, but the values say otherwise: Princess is "worth" 4 while a King is "worth" 1.

We don't know how a neural network will interpret this, so we need a better representation.

Instead of using numerical values, we can use vectors. We call this particular representation "one-hot encoding," where we use ones and zeros to differentiate each word:

• King → [1, 0, 0, 0]
• Queen → [0, 1, 0, 0]
• Prince → [0, 0, 1, 0]
• Princess → [0, 0, 0, 1]

This encoding fixes the problem of a network misinterpreting ordinal values but introduces a new one:

According to the Oxford English Dictionary, there are 171,476 words in use. We certainly don't want to deal with large vectors with mostly zeroes.

Here is where the idea of "word embeddings" enters the picture.

We know that the words King and Queen are related, just like Prince and Princess are. Word embeddings have a simple characteristic: related words should be close to each other, while words with different meanings should lie far away.

The attached image is a two-dimensional chart where I placed the words from our vocabulary.

Look at the image and something critical will become apparent:

King and Queen are close to each other, just like the words Prince and Princess are. This encoding captures a crucial characteristic of our language: related concepts stay together!

And this is just the beginning.

Notice what happens when we move on the horizontal axis from left to right: we go from masculine (King and Prince) to feminine (Queen and Princess). Our embedding encodes the concept of "gender"!

And if we move on the vertical axis, we go from a Prince to a King and from a Princess to a Queen. Our embedding also encodes the concept of "age"!

We can derive the new vectors from the coordinates of our chart:

• King → [3, 1]
• Queen → [3, 2]
• Prince → [1, 1]
• Princess → [1, 2]

The first component represents the concept of "age": King and Queen have a value of 3, indicating they are older than Prince and Princess with a value of 1.

The second component represents the concept of "gender": King and Prince have a value of 1, indicating male, while Queen and Princess have a value of 2, indicating female.

I used two dimensions for this example because we only have four words, but using more would allow us to represent other practical concepts besides gender and age.

For instance, GPT3 uses 12,288 dimensions to encode their vocabulary. That's a lot!

Text Embeddings are the backbone of some of the most impressive generative AI models we use today.

I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me @svpino so you don't miss what comes next.

[Adrian @adriankuleszo]

How to improve your UI designs 👇

[David Herbert👨🏽‍💻🚀 @DaveyHert]

A practical thread on getting into technical writing.

- What it is?
- Types of technical writers.
- How to get started?
- Things to know?
- Developing & structuring outline
- Platforms to kickstart?
- Where to find gigs?
- Demystifying some myths.
- Tools & Resources

Retweet!🧵

[Sharyph @sharyph_]

Data Analysis & Data Science Tools you have never heard off,

But that can save your money and time.

[Code Geek @codek_tv]

JavaScript String Methods You Should Know | JavaScript Tutorial
☞ https://t.co/85WtFCEUea
#javascript #programming
Photo From : CodingTute

[Adrian @adriankuleszo]

Amazing site with my favorite fonts👇