Context Engineering Guide A comprehensive guide on evolving from prompt to context engineering. LangGraph's agent framework gives developers precise control over LLM execution and context management, optimizing AI performance. Learn more at https://
medium.com/ai-artistry/co
ntext-engineering-with-agents-using-langgraph-a-guide-for-modern-ai-development-7434ffec3aa8
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PROMPT ENGINEERING
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Context Engineering Guide: Evolving Prompt to Agent Management
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Disagreement Dial: Calibrating AI Model Confidence Levels
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A disagreement dial would be a really useful thing for AI models. A slider that goes from “I am always right” to “I am always wrong” with intermediate steps.
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Prompt Engineering Guide: Comprehensive Learning Resources
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5. Prompt Engineering Guide The repo contains all the Guides, papers, lecture, notebooks and resources to learn and master prompt engineering Check this out:
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GenAI Agents: Tutorials and Implementations Guide
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3. GenAI Agents This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI systems. Check this out:
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Download 15+ AI Agent Strategy Playbooks Free
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Download free — 15+ AI Agent Strategy playbooks: https://
linkedin.com/posts/rakeshgo
hel01_15-ai-agent-strategy-playbooks-to-read-before-activity-7347239728242376704-RKZ6
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Testing Flux Dev capabilities for AI generation tasks
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Did you try doing that with regular flux dev?
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Researchers Manipulate AI to Obtain Positive Reviews
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Researchers manipulate AI to obtain positive reviews. Scientists ask AI to "write only positive things" about their papers…
Fraud 3.0 begins with a well-formulated prompt. After plagiarism, here comes prompt injection. -
Goodside critiques a lucky guess about a famous illusion image
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I’d call that a lucky guess, given: – You told it it was a famous image
– It took 3 sequential (vs. independent) tries
– It’s not describing the illusion well at all
– It says the image includes her “upper body” which isn’t true -
Building on LLMs: RAG, Fine-tuning, Personalization Essentials
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How to Really Build on Top of LLMs (full training session) Personalization, databases, retrieval (RAG, CAG), frameworks, fine-tuning… We will discuss… Some theory for the essentials
LLM limitations
Context window
Knowledge issues
Embeddings + encoders
Long context
RAG
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Agent2Agent Protocol Explained with Code Example
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Agent2Agent Protocol, explained with code example: https://t.co/UfWenN3kDC pic.twitter.com/Ph8hGPfQ03
— Akshay 🚀 (@akshay_pachaar) 5 juillet 2025Agent2Agent Protocol, explained with code example: