MazenNasser

[Brij Pandey @LearnWithBrij]

8 Types of LLMs for Next Generation AI Agents

[Python Crafted @PythonCrafted]

Master Docker concepts and crack your devops interview . #docker #devops

[Python Programming @PythonPr]

Machine Learning Cheat Sheet: Essential Formulas, Algorithms, and ML Math Notes ✍️

[Dhanian 🗯️ @e_opore]

Web and API Security Architecture

[Tibetan Frontline @justice4_tibet]

China's helicopter reached the spot where the glacier had broken,

and the water released from it had caused massive destruction in Nepal. This event is called a GLOF.

Glacial Lake Outburst Flood (#GLOF) can occur when the dam of a lake held

back by a glacier suddenly bursts.
releasing millions of cubic meters of trapped water.

China | Tibet| Nepal

#NepalFlood

[Surajit @surajit_ghosh2]

This is the moment devastating flash flood hit the Nepal–Tibet border area

[. @AbwAlqhyty]

منقول

هذا الفيديو هو الأكثر رعباً الذي شاهدته على الإطلاق عن الانهيار الجليدي في نيبال...

[saif Ali🖊️ @saif_ali56236]

مشاهد مرعبة وجديدة للفيضان لم تشاهدها من قبل.

[ابو يوسف الزبير @ES188E]

وصل مروحية #الصين إلى المكان الذي انهار فيه الجليدي وتسبب الماء المنبعث منه في تدمير هائل في #نيبال. تُسمى هذه الحادثة #GLOF

[Just My Thoughts @DoubleTroubl001]

तिब्बत/नेपाल से सामने आया यह वीडियो शायद प्राकृतिक आपदा की सबसे भयावह तस्वीरों में से एक है।

जलवायु परिवर्तन कोई भविष्य की चेतावनी नहीं, आज की हकीकत है। इसकी कीमत आम इंसान चुका रहा है, जबकि दुनिया के अरबपति और क्रोनी पूंजीपति अब भी मुनाफे में व्यस्त हैं।

प्रकृति से छेड़छाड़ का खामियाजा तो हम जैसे लोगों को ही भुगतना पड़ेगा।
#नेपाल_त्रासदी

[Surajit @surajit_ghosh2]

A terrifying scene captured from a mountainside shows the Trishuli River raging through the Nepal–Tibet region as flash floods tear through the area

[noha @noha_Bdn]

شباب من حاصبيا غلّطوا بالطريق وفاتوا ع كفرتبنيت...حجم الدمار ببكي
ويلك تضحك عليهم وويلك تبكي عالمنظر

[bintjbeil.org @bintjbeilnews]

🚨📹لحظة قيام دبابة إسرائيلية بإطلاق قذيفة في وادي السلوقي بالتزامن مع تحرّك آليات إسرائيلية!

[bintjbeil.org @bintjbeilnews]

🚨📹 "ضيَّعوا الطريق" ووجدوا أنفسهم في منطقة خطرة جدًا.. رحلة ضياع لشابين تنتهي قرب آليات الاحتلا. ل الإسرائيلي في كفرتبنيت قبل أن يعودوا أدراجهم!

[Akshay 🚀 @akshay_pachaar]

https://t.co/LZBmYCeFnK

[Avi Chawla @_avichawla]

CPU vs GPU vs TPU vs NPU vs LPU, explained visually:

(bookmark this)

5 hardware architectures power AI today.

Each one makes a fundamentally different tradeoff between flexibility, parallelism, and memory access.

> CPU

It is built for general-purpose computing. A few powerful cores handle complex logic, branching, and system-level tasks.

It has deep cache hierarchies and off-chip main memory (DRAM). It's great for operating systems, databases, and decision-heavy code, but not that great for repetitive math like matrix multiplications.

> GPU

Instead of a few powerful cores, GPUs spread work across thousands of smaller cores that all execute the same instruction on different data.

This is why GPUs dominate AI training. The parallelism maps directly to the kind of math neural networks need.

> TPU

They go one step further with specialization.

The core compute unit is a grid of multiply-accumulate (MAC) units where data flows through in a wave pattern.

Weights enter from one side, activations from the other, and partial results propagate without going back to memory each time.

The entire execution is compiler-controlled, not hardware-scheduled. Google designed TPUs specifically for neural network workloads.

> NPU

This is an edge-optimized variant.

The architecture is built around a Neural Compute Engine packed with MAC arrays and on-chip SRAM, but instead of high-bandwidth memory (HBM), NPUs use low-power system memory.

The design goal is to run inference at single-digit watt power budgets, like smartphones, wearables, and IoT devices.

Apple Neural Engine and Intel's NPU follow this pattern.

> LPU (Language Processing Unit)

This is the newest entrant, by Groq.

The architecture removes off-chip memory from the critical path entirely. All weight storage lives in on-chip SRAM.

Execution is fully deterministic and compiler-scheduled, which means zero cache misses and zero runtime scheduling overhead.

The tradeoff is that it provides limited memory per chip, which means you need hundreds of chips linked together to serve a single large model. But the latency advantage is real.

AI compute has evolved from general-purpose flexibility (CPU) to extreme specialization (LPU). Each step trades some level of generality for efficiency.

The visual below maps the internal architecture of all five side by side.

To dive deeper into GPU specifically, Akshay wrote a detailed article on it.

It builds up from first principles why memory and compute compete, why that gap exists in the hardware, and what makes a workload memory-bound in the first place.

Read it below.

[Shalini Goyal @goyalshaliniuk]

Your All-in-One Cheatsheet to Become AI-Ready in 2026

AI isn’t just another tech wave - it’s becoming the foundation of how products are built, how businesses operate, and how careers grow.

To stay ahead, you don’t need to know everything, but you do need the right map.

This cheatsheet brings together the core concepts, essential tools, and future-ready skills you should master to become AI-ready in 2026:

🔹 Foundational AI Concepts
Understand the fundamentals - AI, ML, Deep Learning, Neural Networks, and how training differs from inference. These basics help you make sense of every advanced system built today.

🔹 Agentic AI (The Next Big Leap)
Agents that plan, act, self-correct, and collaborate are becoming the new standard. Learn components like tools, memory, planning, and multi-agent systems - the building blocks of next-gen AI workflows.

🔹 Machine Learning Building Blocks
Study core learning types (supervised, unsupervised, reinforcement), model evaluation metrics, overfitting, feature engineering, and more - everything needed to reason about models deeply.

🔹 Generative AI Terminologies
LLMs, Vision Models, Diffusion, Multimodal AI, Embeddings, Hallucinations - the vocabulary that powers modern AI products. These terms define how today’s AI creates, understands, and reasons.

🔹 AI Systems & Architecture
From RAG and vector databases to fine-tuning and model deployment - this section shows how real-world AI systems are designed and shipped into production.

🔹 Must-Know AI Tools for 2026
A curated list of the most important platforms across:
✔️ LLM Providers
✔️ AI Assistants
✔️ Image Generation Tools
✔️ Agentic AI Builders
✔️ Automation Platforms
✔️ Browsing & Scraping Tools

These will power the next generation of no-code and full-stack AI solutions.

🔹 ML, Data Engineering & RAG Tools
Your quick overview of essential frameworks (TensorFlow, PyTorch), data tools (Pandas, DuckDB), vector DBs (Pinecone, Weaviate), and retrieval engines (Elastic, Qdrant).

2026 will belong to those who understand how AI works, not just how to use it.
This cheatsheet gives you a clear, structured starting point.

[Larry Dean @larry_mcwethy]

lips 👀

Nepal Floods 2026 Explained: What Caused the Deadly Flash Flood?

[Total @meeet__usman]

Do you want to create this app from scratch? EXCEL PROJECT TRACKER !