eDisplay

I Love The Bible, Real Estate, Business, Technology, Family, and Friends!

[eDisplay @eDisplay]

My Agent Team Report on @kylon_ai: Token Taster, 194.9K tokens. What's your report? 👀

Get your own Agent Team report on Kylon + free credit 👇
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[Krishna Agrawal @Krishnasagrawal]

2026 Claaude Cowork Setup 📘📚

[Python Programming @PythonPr]

30 AI Algorithms

[Curious Minds @CuriousMindsHub]

Critical Thinking Skills

[Alok Pathak @1005Alok85200]

Claude just dropped 13 FREE AI courses (with certificates).

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👇

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2. AI Fluency: Frameworks & Foundations
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3. Introduction to Agent Skills
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4. Building with the Claude API
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5. Claude Code in Action
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6. Introduction to Model Context Protocol
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10. Teaching AI Fluency
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11. AI Fluency for Nonprofits
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12. Claude with Amazon Bedrock
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13. Claude with Google Vertex AI
  https://t.co/pIsY4DBk5j

—

If you go through even HALF of these…

You’ll be ahead of 95% of people using AI.

Most people won’t.

Because they’re still:

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Don’t be that person.

Do this instead:

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Follow @1005Alok85200 for more Insightful content.

[Alok Pathak @1005Alok85200]

🚀 Want to learn AI for FREE from top universities?

Here are 10 high-quality courses from Harvard University, Google, Microsoft & more 👇

Harvard University – CS50’s Introduction to Artificial Intelligence with Python
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Microsoft – AI for Beginners: A Structured Learning Path
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Google – Introduction to Generative AI
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https://t.co/tCOyqZvYdD – Generative AI for Everyone
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LinkedIn – Generative AI Foundations
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Vanderbilt University – ChatGPT Prompt Engineering
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OpenAI – ChatGPT Prompt Engineering for Developers
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EdX – AI Applications and Prompt Engineering
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Amazon Web Services – Prompt Engineering Fundamentals
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University of California, Davis – Big Data, AI, and Ethics

👉 Follow @1005Alok85200 more Insightful content.

[Alok Pathak @1005Alok85200]

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👇Comment "Remote" for more resources.

Follow @1005Alok85200 more fore Insightful content.

Bookmark for later.

Repost so others can benefit.

[Tech with Mak @techNmak]

Prompting isn’t just asking the AI a question. It’s a deliberate, engineered input design process, and a critical skill when working with Large Language Models (LLMs).

Let's breakdown the prompting techniques.

✅ 1. Core Prompting Techniques
▪ Zero-shot - No examples provided. Just the task.
▪ One-shot - One example shown before the task.
▪ Few-shot - A handful of examples used to teach patterns.

🧠 2. Reasoning-Enhancing Techniques
▪ Chain-of-Thought (CoT) - Encourage step-by-step reasoning.
▪ Self-Consistency - Sample multiple CoTs; choose the best.
�� Tree-of-Thought (ToT) - Explore multiple reasoning paths (advanced).
▪ ReAct - Combine reasoning steps with action/tool use (e.g., API calls).

🧾 3. Instruction and Role-Based Prompting
▪ Instruction prompting - Clear directives (“Summarize this…”).
▪ System / Role prompting - Define persona or behavior (“You are a legal assistant”).
▪ Hybrid (Instruction + Examples) - Combine clarity with few-shot grounding.

⚙️ 4. Prompt Composition Techniques
▪ Prompt chaining - Use one prompt’s output in the next.
▪ Dynamic prompting - Inject real-time variables or context.
▪ Meta prompting - Ask the model to improve or verify its own response.

🖼️ 5. Multimodal Prompting
▪ Image + text - Provide both visual and textual context.
▪ Audio/Video + text - Use transcripts or sensory input (model-dependent, e.g., GPT-4o, Gemini 1.5).

🧑‍⚕️ 6. Domain-Specific Prompting
▪ Code prompting - Constrained, tool-specific inputs (e.g., Python, SQL).
▪ Medical / Legal prompting - High-precision language with strict format and accuracy needs.

🧪 7. Prompt Evaluation & Debugging
(Not prompting techniques, but crucial tools.)
▪ Prompt ablation - Remove elements to test contribution.
▪ Injection testing - Evaluate prompt robustness in apps or agents.

❌ What’s Not a Prompting Technique
▪ RAG: A retrieval + generation architecture. Prompts are used inside it.
▪ Agents / Tool-use systems - Orchestration frameworks (e.g., LangGraph, AutoGPT). Prompting is one component, not the technique itself.

🔧 Prompting is no longer “just prompt engineering.” It’s system design.
If you're working with LLMs, know these cold.

Follow @techNmak for your daily dose of learning.

[Alok Pathak @1005Alok85200]

Learn AI for free directly from top companies.

1 - Anthropic:
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3 - Meta:
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8 - AWS:
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10 - Hugging Face:
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👇Follow @1005Alok85200 and Comment "AI" for more free resources.

Repost so others can benefit.

Bookmark for later.

[freeCodeCamp.org @freeCodeCamp]

A computer's operating system manages hardware & software and provides key services for various programs.

And in this course, you'll learn all about how an OS works.

It covers types of OSs, process management, CPU scheduling, memory management, & more.

https://t.co/UcvV0YFzxQ

[Vaishnavi @_vmlops]

THE ONLY GITHUB REPO YOU NEED FOR DATASETS

tired of googling "free datasets for ml projects"?

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all topic-centric..most are free
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bookmark it once...never search for datasets again

github: https://t.co/kdBzYjCLPr

[Vaishnavi @_vmlops]

AI ENGINEERING INSIDER - THE MULTIMODAL HANDBOOK (2026)

From tokens → production...Real multimodal systems, patterns & interview prep

https://t.co/ssDDNVMue1

[Vaishnavi @_vmlops]

AI Harness Engineering
Interview Preparation Handbook

https://t.co/tSb85mN8JO

[Vaishnavi @_vmlops]

RAG Evaluation & Testing in Production (Offline + Online)

Interview Preparation Playbook for Model Evaluators, AI QA & SDETs for LLMs

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[Vaishnavi @_vmlops]

THIS IS THE ONLY AI ENGINEERING REPO YOU NEED

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the ai-engineering-hub covers everything

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if you're trying to break into AI engineering or level up your skills this is where you start and keep coming back

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→ https://t.co/OVBNcq4ppm

[Vaishnavi @_vmlops]

MICROSOFT MADE RUNNING LLMs ON A SINGLE CPU ACTUALLY POSSIBLE

no GPU... no cloud...no expensive hardware

BitNet - the official inference framework for 1-bit LLMs

here's what that means in practice:

▫️ up to 6x faster inference on x86 CPUs
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how...? every weight is stored in just 1.58 bits not 16, not 8..... 1.58

the model is tiny...the speed is not

and it's not just a research paper. it's a working inference framework with optimized kernels for both ARM and x86, GPU support & official models on hugging face ready to run

if you've been waiting to run LLMs locally without burning your laptop this is the repo

→ https://t.co/vXPkalP8g0

[YourCloudDude | AWS & Python @yourclouddude]

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[YourCloudDude | AWS & Python @yourclouddude]

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[YourCloudDude | AWS & Python @yourclouddude]

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[YourCloudDude | AWS & Python @yourclouddude]

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