Blockchain & GenAI Pakistan

Exploring how Blockchain—the backbone of cryptocurrencies—and Generative AI (GenAI) are transforming education, finance, health, media, & energy sectors.

[Blockchain & GenAI Pakistan @DefiPakistan]

What is #creativity ? It’s imagination with responsibility. From ancient divine inspiration to AI-augmented innovation, creativity evolves through patronage, focus, and the recombination of ideas. It’s not creating from nothing, but crafting value from what exists.
#Innovation

[Tom Dörr @tom_doerr]

Build specialized AI agents for security assessments

https://t.co/4Qqon6pxok

[zauth @zauthinc]

You used AI to build it.

Now secure it.

Most AI-built apps ship with critical vulnerabilities. Vector scans your stack and finds what other tools miss.

Free scan in minutes.

[Tom Dörr @tom_doerr]

3,000+ integration skills for AI agents

https://t.co/mT4y3ouQbT

[Elizabeth AI @ElizabethA77617]

52 website worth more than most College degrees..

[Vaidehi @Ai_Vaidehi]

Steps to building AI systems with LLM's.

I've given a simple detailed explanation below.

𝗦𝘁𝗲𝗽 1 – 𝗟𝗟𝗠𝘀 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀)
• These are the 𝗯𝗿𝗮𝗶𝗻𝘀 of the system.
• Examples: GPT (OpenAI), Gemini, Claude etc.
• They generate answers, understand queries, and perform reasoning.

𝗦𝘁𝗲𝗽 2 – 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀
• Frameworks help you 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗵𝗲 𝗟𝗟𝗠 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮, 𝘁𝗼𝗼𝗹𝘀, 𝗮𝗻𝗱 𝗮𝗽𝗽𝘀.
• Examples: LangChain, Llama Index, Haystack, Txtai.
• They act like a 𝘁𝗼𝗼𝗹𝗸𝗶𝘁 so you don’t have to build everything from scratch.

𝗦𝘁𝗲𝗽 3 – 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
• LLMs can’t remember everything. They need a 𝗺𝗲𝗺𝗼𝗿𝘆 𝘀𝘆𝘀𝘁𝗲𝗺.
• Vector databases store “embeddings” (numerical representations of text).
• Examples: Pinecone, Weaviate, Chroma, Milvus, Qdrant.
• They make searching fast and relevant (like Google search but for your private data).

𝗦𝘁𝗲𝗽 4 – 𝗗𝗮𝘁𝗮 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻
• Your AI needs real-world 𝗱𝗮𝘁𝗮 𝗶𝗻𝗽𝘂𝘁𝘀.
• Tools like Crawl4AI, FireCrawl, ScrapeGraphAI, Docling, LlamaParse help:
- Scrape websites
- Extract PDFs, docs, or tables
- Clean and structure messy data

𝗦𝘁𝗲𝗽 5 – 𝗢𝗽𝗲𝗻 𝗟𝗟𝗠𝘀 𝗔𝗰𝗰𝗲𝘀𝘀
• Instead of calling proprietary APIs, you can 𝗿𝘂𝗻 𝗟𝗟𝗠𝘀 𝗹𝗼𝗰𝗮𝗹𝗹𝘆 or via open-source providers.
• Examples: Hugging Face, Ollama etc.

𝗦𝘁𝗲𝗽 6 – 𝗧𝗲𝘅𝘁 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀
• To store text in databases, you must first 𝗰𝗼𝗻𝘃𝗲𝗿𝘁 𝗶𝘁 𝗶𝗻𝘁𝗼 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 (𝘃𝗲𝗰𝘁𝗼𝗿𝘀).
• Tools like OpenAI Embeddings, SBERT, Voyage AI etc handle this.
• Embeddings allow semantic search (finding meaning, not just keywords).

𝗦𝘁𝗲𝗽 7 – 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
• Once built, you must 𝘁𝗲𝘀𝘁 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 your system.
• Tools: Giskard, Ragas, Trulens.
• They measure:
- Accuracy
- Hallucinations (wrong answers)
- Relevance of results

✅ 𝗙𝗶𝗻𝗮𝗹 𝗙𝗹𝗼𝘄 𝗶𝗻 𝗦𝗶𝗺𝗽𝗹𝗲 𝗪𝗼𝗿𝗱𝘀:
1. Choose a model (LLM).
2. Connect it with a framework.
3. Collect data and extract it properly.
4. Turn data into embeddings and store them in a vector DB.
5. Give the LLM access to search that DB.
6. Use open access tools if you want local/cheap models.
7. Continuously evaluate and refine.

You can apply this framework in your company to design and deploy powerful AI solutions for your business.

🔖 Save for later.
♻️ Repost to help other engineers learn and grow.

[NoLimit @NoLimitGains]

This man just told you exactly where the money is going for the next decade:

[Coin Bureau @coinbureau]

🚨HUGE: AI BOTS HAVE OFFICIALLY OVERTAKEN HUMANS ON THE INTERNET

Cloudflare CEO Matthew Prince shared new data showing that bots and AI agents are now generating more internet traffic than humans for the first time in history.

[Eren Chen @ErenChenAI]

“The next trillion-dollar opportunity is Physical AI and Robotics.”

— Masayoshi Son, CEO of SoftBank, also just became the richest in Asia

[Swapna Kumar Panda @swapnakpanda]

Be a world-class software engineer when you read these books:

[Tom Dörr @tom_doerr]

39 mental models for Claude Code critical thinking

https://t.co/CbsVfNbStH

[CyrilXBT @cyrilXBT]

9 GITHUB REPOS THAT LET YOU SCRAPE ANY WEBSITE WITHOUT GETTING BLOCKED.
Most scrapers get banned in the first 100 requests.
These ones do not.

Crawl4AI — AI-powered crawler built for LLM pipelines. Extracts clean structured data automatically.
https://t.co/PsCrtSjzsH
Firecrawl — Turns any website into clean markdown for AI consumption. Built for RAG pipelines.
https://t.co/bBKDUVnkcm
Scrapy — The battle-tested Python scraping framework. 50,000+ stars. Still the most reliable at scale.
https://t.co/CkpvJcdwsP
Crawlee — Playwright and Puppeteer wrapped in a scraping framework with built-in anti-detection.
https://t.co/8WKmolKdoj
Playwright — Microsoft's browser automation library. Handles JavaScript-heavy sites that break every other scraper.
https://t.co/2HKw2WPDsU
ScrapeGraph AI — Uses LLMs to navigate and extract data using natural language instructions.
https://t.co/o3UbwInkFb
Browser Use — Gives AI agents full browser control. Your Claude agent can now browse and scrape anything.
https://t.co/GwwR8pgo5B
Katana — Fast reconnaissance crawler built for security researchers. Handles complex site architectures.
https://t.co/iBSajJ43jc
Maxun — No-code web scraping platform. Build scrapers without writing a single line of code.
https://t.co/JxSaiMuYQk

Bookmark this before your next data project.
Follow
@cyrilXBT
for every open source build worth your weekend the moment it surfaces.

[Tom Dörr @tom_doerr]

Self-evolving agent framework for ML engineering

https://t.co/lPOdbHxfyy

[KingWilliam @kingwilliam_]

Anthropic just mass-dropped a 1-hour masterclass on prompt engineering with their entire team

Amanda Askell, Alex Albert, David Hershey, Zack Witten - the people who literally build Claude sat down and explained how to talk to it properly

and 99% of users will never watch it

I did. took notes. tested everything they said against my own workflows

here's what hit different:

> the way you open a conversation with Claude determines the quality of everything that follows
> most people prompt like they're googling - one line, zero context. that's why their outputs feel generic
> the best prompts aren't questions - they're structured briefs with constraints, examples and a definition of "done"
> chaining 3 short prompts beats 1 mega-prompt every single time

I distilled their full framework into 10 rules and 20 ready-to-use prompts

no fluff. no theory. just the system that works

it's right below this post

[Blockchain & GenAI Pakistan @DefiPakistan]

In Web3, digital assets (aka tokens), serve as a container ​for value. Similar to a shipping container can hold anything from electronics ​to canned goods to furniture, a token can be programmed to represent anything digital ​that has value. Not only financial assets like money, but other financial assets like ​securities. It could be a birth certificate, a health record, a property title, intellectual ​property rights, etc.

[Blockchain & GenAI Pakistan @DefiPakistan]

Layers of the Web-AI Ecosystem

[Lian Lim | Dashboard & AI Automation Expert @dashboardlim]

I just finished creating a guide that connects NotebookLM + Antigravity

Spent 67 hours creating this system that turns your knowledge base into an AI agent that actually takes action

BONUS: Complete guide for building 10 workflows + copy-paste prompts

Like & Comment "FREE" and I'll DM it to you as fast as i can

[Blockchain & GenAI Pakistan @DefiPakistan]

https://t.co/maWJRZKp8m

[Tom Dörr @tom_doerr]

Official source code for O'Reilly's AI Agents definitive guide

https://t.co/2EvMZ0aiXL

[Melchor Santiago @MelchorSan26]

🚨 ¡NOTICIA URGENTE: NotebookLM ahora puede enseñarte como un tutor privado de cualquier universidad prestigiosa!! Totalmente gratis

Aquí tienes 8 prompts (órdenes) que reemplazan horas de lecciones pagadas:
(Guarda esto antes de que se te escape)
Hilo:

[Atal @ZabihullahAtal]

This Anti-AI Writing System Prompt turns your Claude, ChatGPT, or any AI writing into 100% Human writing: