akmal ahmat

Digital Scholarship Librarian, Open Access Malaysia, Research Support Service, Universiti Sains Malaysia

[CopyRebeldia @CopyRebeldia]

Así se ve un sueldo de 750.000 dólares al año: un tipo en camiseta blanca, un pizarrón y 2 horas y media.

Stanford, CS336. Percy Liang construye un LLM desde cero. Lo que hay debajo de Claude y ChatGPT, y arranca por la parte que todos saltan: el modelo no lee tu texto, lee números.

Anthropic paga ese sueldo a los ingenieros que entienden esa capa.

Lo único que cobra Stanford son 2 horas y media de tu atención.

[Rasidi Zainal @YOEPCD]

2 panggilan telefon yg dirakam pada 17 Januari dan 22 Januari 2016, antara Pengerusi Tabung Haji ketika itu, Abdul Azeez Abdul Rahim, dgn Dzulkifli Ahmad

Klip audio tersebut, yang dikeluarkan oleh SPRM, kini berada dlm simpanan pihak polis.

Kredit : FMT Release Date : 11 Jan 20

[Divya Tripathi @Divya_Tripa23]

2 Minutes Hand Mudras for Healthy Organs 👇

[Kirk Borne @KirkDBorne]

Layers of AI

Infographic Source: https://t.co/BUGU69XSlm

[Shalini Goyal @goyalshaliniuk]

Building a GenAI app?

Don’t just plug in a model - design it to scale, adapt, and evolve.
Here’s your blueprint for future-ready GenAI systems. 👇

1. Modular Architecture
Separate UI, orchestration, models, and storage to swap parts independently. Use LangChain or LlamaIndex to build pipelines.

2. Context Engineering
Layer system prompts, memory, and retrieved knowledge to optimize generation. Use chunking and summarization to stay efficient.

3. Retrieval-Augmented Generation (RAG)
Connect vector DBs like Pinecone or Weaviate and use hybrid search (dense + keyword) for domain-specific relevance.

4. Low-Latency Design
Cut load times and delay using model distillation, quantization, and async I/O.

5. Agent-Based Systems
Use CrewAI, AutoGen, or LangGraph for task decomposition and tool execution via specialized sub-agents.

6. Tool & Plugin Integration
Enable LLMs to run code, hit APIs, or use external tools through OpenAI function-calling or LangChain routing.

7. Streaming & Feedback
Improve experience with real-time streaming via WebSockets and user feedback for continuous refinement.

8. Memory Management
Support both session and long-term memory using Redis, Postgres, or vector DBs for persistence.

9. Smart Deployment
Use K8s or serverless runtimes (like AWS Lambda) to deploy GenAI apps with dynamic scaling.

10. Observability
Track usage, hallucinations, and prompts using tools like LangSmith or WhyLabs for LLM monitoring.

[Explore More In The Post]

Good GenAI apps aren’t just about prompts, they’re engineered for performance, adaptability, and scale.

[Sam Bhagwat @calcsam]

last month we wrote a new agents book: patterns for building ai agents

it has everything you need to take your agents from prototype to production, like agent design patterns, the basics of security, etc

reply to this tweet with BOOK and we'll dm you so you can get a copy

[Sunil Gurjar, CFTe @sunilgurjar01]

22 TECHNICAL ANALYSIS TOPICS YOU NEED TO LEARN.📊🎯

Bookmark it.👇🔖

1. Fibonacci Retracements
2.Breakouts
3.Reversal
4.Elliott Wave
5.Fair Value Gap
6.Candlesticks
7.Heikin Ashli
8.Moon Phases
9.Renko
10.Harmonic Patterns
11. Support and resistance
12.Dynamic Support and resistance
13.Trend lines
14.Gann Angles
15.Momentum Indicators
16.Oscillators
17.Divergence
18.Volume
19. Supply &Demand
20. Market Structure
21.BOS
22.CHOCH

#trading

[Scholarship for PhD @ScholarshipfPhd]

How to Be a Great Storyteller: Master the Art of Captivating Communication

[Mindset Machine @mindsetmachine]

This guy literally explained How to focus and cure your distraction ( in 1 min)

[TheBetterPath @TheBetterPath_]

Bookmark this for later

[Dhanian 🗯️ @e_opore]

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[Shalini Goyal @goyalshaliniuk]

Which AI Agent Framework Should You Choose?

With so many AI agent frameworks available today, the right choice depends on your goals, technical skills, and use case.

Here’s a guide that breaks them into three main categories so you can quickly narrow down your options.

1. General-Purpose Agent Frameworks
Designed for versatility, these frameworks let you build a wide variety of AI agents with tool and memory integration.

LangChain → Perfect for custom LLM applications like chatbots, document Q&A, autonomous agents, or multi-tool workflows.

AgentOps → Focuses on tracking, debugging, and improving deployed agents, ideal for production observability, A/B testing, and prompt tracing.

2. Infrastructure-First Agent Frameworks
Built for enterprise-level AI agent orchestration and multi-agent collaboration.

AutoGen (Microsoft) → Enables human–AI and AI–AI collaboration with memory and tool use, great for enterprise workflows and co-pilot systems.

CrewAI → Lets you assign roles and tasks to multiple agents for project management, research assistance, and task planning.

MetaGPT → Turns product specs into structured multi-agent collaboration for software project generation and dev team coordination.

3. Tool-Integrated Automation Frameworks
Best for those who want fast prototyping or visual, no-code agent building.

SuperAgent → Plug-and-play LLM agent creation for chat assistants, API automation, and customer service.

Flowise → A drag-and-drop visual builder for sales bots, support bots, and lead generation flows.

Autogen Studio (Microsoft) → A UI-based environment for visualizing and testing agent workflows, useful for research experiments and debugging.

If you want full customization and coding flexibility, start with LangChain or CrewAI.
If you need rapid deployment without heavy coding, SuperAgent or Flowise is your friend.

Which of these frameworks would you try first, the flexible all-rounders, the enterprise orchestrators, or the quick-build automation tools? Let’s discuss in the comments!

[Shalini Goyal @goyalshaliniuk]

Imagine building a system that scales effortlessly, never crashes, and handles millions of users seamlessly. Sounds impossible? It’s not - it’s system design.

Every high-performing system follows a set of essential principles that make it secure, scalable, and resilient. Let’s explore them:

1. Observability & Monitoring – Like having a control room for your system, with logging, tracing, and real-time monitoring using tools like Prometheus and OpenTelemetry.

2. Security & Compliance – Protecting data with encryption, API authentication, and zero-trust architecture to keep systems secure.

3. Distributed Systems – The backbone of large-scale applications. Caching, message queues, and leader election mechanisms keep everything running smoothly.

4. High Availability & Fault Tolerance – Backup strategies that ensure systems stay up even when failures happen, using failovers, redundancy, and disaster recovery.

5. Microservices & Architecture – From REST vs. gRPC to service discovery and circuit breakers, these patterns help prevent cascading failures and improve flexibility.

6. Database Design – Choosing between SQL and NoSQL, data partitioning, replication, and consistency trade-offs to optimize performance.

7. Scalability & Performance – Load balancing, caching, and auto-scaling ensure that systems can grow without breaking.

Building a robust system isn’t just about writing code—it’s about designing for scale, security, and reliability. Master these concepts, and you’ll be ready to build systems that can handle anything.

[Explore More In The Post]

Follow me at @goyalshaliniuk for more such information.

[Shalini Goyal @goyalshaliniuk]

Confused by all the different types of AI Agents?
Here’s a simple breakdown of the 5 core types and how they differ! ⬇️

1. Simple Reflex Agents
Work on if-then logic. No memory. Fast, but only suited for basic, predictable environments.

2. Model-Based Reflex Agents
Add memory and a transition model. Can handle sequences and partial observability - ideal for robotics.

3. Goal-Based Agents
Plan actions to reach a goal. Evaluate future states and paths. Used in search, planning, and games.

4. Utility-Based Agents
Go beyond goals, optimize outcomes based on preferences, costs, and satisfaction. Think smarter agents.

5. Learning Agents
Most advanced. Learn from experience and feedback. Adapt strategies, support all learning types, and evolve.

6. Common Building Blocks Across All Agents:
Sensors, effectors, decision logic, performance modules, and interaction with the environment.

This guide can help you understand how autonomous systems make decisions, adapt, and improve from simple bots to super-intelligent agents.

[Bilgin Ibryam @bibryam]

RAG Patterns

more resources: https://t.co/zbl6GZO7rO

[Shalini Goyal @goyalshaliniuk]

Exploring a Career in AI or Data? Start Here.

This guide breaks down 8 high-impact roles in AI & Data - showing you what skills, tools, and knowledge areas matter most for each:

1. Data Analyst – Turn raw data into business insights with stats and Python.
2. ML Engineer – Build predictive systems using modeling tools like Scikit-learn and TensorFlow.
3. AI Specialist – Apply AI in domains like healthcare, finance, and business intelligence.
4. AI Engineer – Use frameworks (PyTorch, Keras) to engineer production-ready AI systems.
5. Data Scientist – Combine stats, programming, and domain expertise for pattern discovery.
6. Agentic AI Expert – Design autonomous agents with LLMs, LangChain, and vector DBs.
7. AI Product Manager – Bridge business and technical strategy with knowledge of MLOps & LLMs.
8. AI Research Scientist – Dive into deep mathematical foundations and push the boundaries of AI.

Note: This is a quick snapshot, not an complete checklist.

Which of these roles are you aiming for in 2025? Let’s discuss!

https://t.co/4aRrBxJbsT
Follow me on Twitter at https://t.co/t0WuiUATeN
Follow me on Instagram at https://t.co/i4lhLXz4BT

[Shalini Goyal @goyalshaliniuk]

AI agents are the future, but how well do you understand them?

This one-page guide breaks down everything you need to know about AI agents: from how they work to where they're used 👇

1. Core Concepts That Power Agents
Understand key ideas like Goal Decomposition, Self-Reflection, Memory, and Multi-Agent Collaboration to grasp how agents reason and act autonomously.

2. Capabilities You Should Expect
Modern AI agents can scrape web data, call APIs, execute code, retrieve documents (RAG), and even handle multi-step planning with tool orchestration.

3. Tools, Frameworks & Libraries
From LangChain and AutoGen to LangGraph and Superagent, these frameworks help developers build, host, and monitor smart agent workflows.

4. Real-World Applications
Think beyond chatbots. Agents are now used for content generation, meeting automation, research, data cleaning, CRM updates, and even test refactoring.

5. Challenges & Risks
AI agents can hallucinate, loop indefinitely, or misuse tools. Watch out for token costs, data privacy risks, and debugging complexity in production.

AI agents are not just a trend, they’re a paradigm shift. If you understand their architecture, tech stack, and risks, you’re already ahead of 90% of the market.

Which of these areas do you want to explore deeper? 👇

[Book Therapy @Book_therapy223]

9 Leadership Styles