eDisplay @eDisplay

2026 Claaude Cowork Setup 📘📚

Krishna Agrawal @Krishnasagrawal

2 months ago

This feels like a real shift in AI architecture. Not another LLM tweak a system built specifically for memory. If this works as promised (persistent, proactive recall, near-zero hallucinations), it changes how apps actually remember users. Signed up. engramme.com

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30 AI Algorithms

Critical Thinking Skills

Claude just dropped 13 FREE AI courses (with certificates). No $500 course needed. No “guru” required. Just real skills — straight from Anthropic. Here’s the full list: 👇 1. Claude 101   lnkd.in/gCPUQsRg 2. AI Fluency: Frameworks & Foundations   lnkd.in/gS6ceZ_M 3. Introduction to Agent Skills   lnkd.in/g_wWNiEb 4. Building with the Claude API   lnkd.in/gDr5K_B4 5. Claude Code in Action   lnkd.in/g9wWZbK9 6. Introduction to Model Context Protocol   lnkd.in/gAj5HqMY 7. MCP: Advanced Topics   lnkd.in/g3eDwBFY 8. AI Fluency for Students   lnkd.in/gKKujHGG 9. AI Fluency for Educators   lnkd.in/gVcKnuhA 10. Teaching AI Fluency   lnkd.in/g9P4gJFM 11. AI Fluency for Nonprofits   lnkd.in/gpsm_BVf 12. Claude with Amazon Bedrock   lnkd.in/gbfPjSFt 13. Claude with Google Vertex AI   lnkd.in/gvVgB4Ub — 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: • Watching random YouTube videos • Buying overpriced courses • “Learning AI” without actually building Don’t be that person. Do this instead: 1. Save this post (you’ll come back to it) 2. Pick 1 course → start today 3. Share it with someone who needs this Free. Practical. No excuses. Follow @1005Alok85200 for more Insightful content.

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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.

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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. freecodecamp.org/news/learn-abo…

THE ONLY GITHUB REPO YOU NEED FOR DATASETS tired of googling "free datasets for ml projects"? awesome-public-datasets has everything agriculture, biology, climate, finance, government, health, nlp & more all topic-centric..most are free curated from blogs, research papers, and community responses bookmark it once...never search for datasets again github: github.com/awesomedata/aw…

AI ENGINEERING INSIDER - THE MULTIMODAL HANDBOOK (2026) From tokens → production...Real multimodal systems, patterns & interview prep drive.google.com/file/d/1VY8iYG…

AI Harness Engineering Interview Preparation Handbook drive.google.com/file/d/1PcF8rS…

RAG Evaluation & Testing in Production (Offline + Online) Interview Preparation Playbook for Model Evaluators, AI QA & SDETs for LLMs drive.google.com/file/d/1nvKRSs…

THIS IS THE ONLY AI ENGINEERING REPO YOU NEED 93+ production-ready projects all in one place the ai-engineering-hub covers everything RAG pipelines, AI agents, LLM fine-tuning, MCP integrations, voice bots, multimodal apps not just theory actual working code you can run, adapt & ship beginner...? start with a simple RAG or local ChatGPT clone intermediate..? build agentic workflows with CrewAI and LangGraph advanced...? fine-tune DeepSeek, build reasoning models, deploy production systems and it keeps growing new projects drop regularly if you're trying to break into AI engineering or level up your skills this is where you start and keep coming back open source...free...no gatekeeping → github.com/patchy631/ai-e…

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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 ▫️ up to 82% less energy consumption ▫️ a 100B parameter model running on a single CPU at human reading speed (5-7 tokens/sec) 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 → github.com/microsoft/BitN…

Python simplified for you 🚀

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Get hired in PYTHON by Building 25+ Adv. PYTHON Projects with this one resource 👇 yourclouddude.gumroad.com/l/wjhhqs

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Get AWS Certified with ease with this one Organized SYSTEM 👇

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Machine Learning notes drive.google.com/file/d/1HSDp8x…

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