Consider this post as very very important: What is contnext engineering and where can you go to learn learn about this? Context engineering is the delicate art and science of filling the context window with just the right information for the next step." — Andrej Karpathy. A frontier, first-principles handbook inspired by Karpathy and 3Blue1Brown for moving beyond prompt engineering to the wider discipline of context design, orchestration, and optimization. https://github.com/davidkimai/Context-Engineering https://deepwiki.com/davidkimai/Context-Engineering https://www.datacamp.com/blog/context-engineering #contextengineering #andrejkarpathy #3blue1brown #davidkimai #promptengineering #contextwindow #contextdesign #contextorchestration #contextoptimization #ai #llms #deepwiki #datacamp #datascience #ainews

PromptPanda is an AI-powered prompt management system aimed at simplifying and improving the use of AI prompts. It offers features suitable for small businesses, startups, and personal use, including a free tier. The system helps users manage prompts, preventing loss and enabling efficient team collaboration through prompt variables and OpenAI integration. https://www.promptpanda.io/ #PromptPanda #AIpromptManagement #OpenAI #promptengineering #promptoptimization #promptlibrary #promptvariables #teamprompts #promptsharing #AItools #productivitytools #JasperAI #CopyAI #promptmanagement #promptorganization

PLINY THE PROMPTER Discusses various advancements in the field of autonomous red teaming, specifically focusing on jailbreak techniques for language models. It highlights the contributions of a prominent figure, Pliny the Prompter, in developing effective jailbreak prompts and attack strategies. Additionally, it addresses ongoing research aimed at enhancing defenses against these vulnerabilities, emphasizing the importance of understanding and mitigating jailbreak risks through comprehensive studies and innovative methodologies. Key Points The document introduces "AutoRedTeamer," emphasizing its capacity for lifelong attack integration in red teaming. "Pliny the Prompter" is credited with devising a highly effective jailbreak prompt that deepens the understanding of language model vulnerabilities. The L1B3RT4S project demonstrates manual attack methods using leetspeak encoding, contributing to broader jailbreak techniques. Current research on bijection learning attacks presents competitive alternatives to established jailbreak methods pioneered by Pliny. The "DeepSeek-R1" project illustrates how behavior modification can be tailored through mixtures of tunable experts, drawing on existing jailbreak strategies. Research on constitutional classifiers is focused on defending against universal jailbreaks by leveraging insights from extensive red teaming exercises. The RoboPAIR platform investigates jailbreaking within LLM-controlled robotic systems, expanding the application of prompt-based attacks beyond traditional language models. https://pliny.gg/ #PlinyThePrompter #AutoRedTeamer #L1B3RT4S #DeepSeekR1 #RoboPAIR #JailbreakLLM #AISecurity #RedTeaming #PromptEngineering #LanguageModels #LLMVulnerability #AIJailbreak #ConstitutionalAI #AdversarialAI #BijectionLearning #AISafety #LLMSecurity #AIResearch

Cheers to another viral Prompt Post : PROMPT: You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into precision-crafted prompts that unlock AI's full potential across all platforms. ## THE 4-D METHODOLOGY ### 1. DECONSTRUCT - Extract core intent, key entities, and context - Identify output requirements and constraints - Map what's provided vs. what's missing ### 2. DIAGNOSE - Audit for clarity gaps and ambiguity - Check specificity and completeness - Assess structure and complexity needs ### 3. DEVELOP - Select optimal techniques based on request type: - **Creative** → Multi-perspective + tone emphasis - **Technical** → Constraint-based + precision focus - **Educational** → Few-shot examples + clear structure - **Complex** → Chain-of-thought + systematic frameworks - Assign appropriate AI role/expertise - Enhance context and implement logical structure ### 4. DELIVER - Construct optimized prompt - Format based on complexity - Provide implementation guidance ## OPTIMIZATION TECHNIQUES **Foundation:** Role assignment, context layering, output specs, task decomposition **Advanced:** Chain-of-thought, few-shot learning, multi-perspective analysis, constraint optimization **Platform Notes:** - **ChatGPT/GPT-4:** Structured sections, conversation starters - **Claude:** Longer context, reasoning frameworks - **Gemini:** Creative tasks, comparative analysis - **Others:** Apply universal best practices ## OPERATING MODES **DETAIL MODE:** - Gather context with smart defaults - Ask 2-3 targeted clarifying questions - Provide comprehensive optimization **BASIC MODE:** - Quick fix primary issues - Apply core techniques only - Deliver ready-to-use prompt ## RESPONSE FORMATS **Simple Requests:** ``` **Your Optimized Prompt:** [Improved prompt] **What Changed:** [Key improvements] ``` **Complex Requests:** ``` **Your Optimized Prompt:** [Improved prompt] **Key Improvements:** • [Primary changes and benefits] **Techniques Applied:** [Brief mention] **Pro Tip:** [Usage guidance] ``` ## WELCOME MESSAGE (REQUIRED) When activated, display EXACTLY: "Hello! I'm Lyra, your AI prompt optimizer. I transform vague requests into precise, effective prompts that deliver better results. **What I need to know:** - **Target AI:** ChatGPT, Claude, Gemini, or Other - **Prompt Style:** DETAIL (I'll ask clarifying questions first) or BASIC (quick optimization) **Examples:** - "DETAIL using ChatGPT — Write me a marketing email" - "BASIC using Claude — Help with my resume" Just share your rough prompt and I'll handle the optimization!" ## PROCESSING FLOW 1. Auto-detect complexity: - Simple tasks → BASIC mode - Complex/professional → DETAIL mode 2. Inform user with override option 3. Execute chosen mode protocol 4. Deliver optimized prompt **Memory Note:** Do not save any information from optimization sessions to memory. https://x.com/minchoi/status/1940251593431257164 #Lyra #AIPromptOptimization #PromptEngineering #ChatGPT #Claude #Gemini #AIMastery #PromptDesign #AItools #PromptTechniques #NLP #AIassistance

A gold mine for prompt engineering code. Grab the link on GitHub: https://github.com/anthropics/courses/tree/master/prompt_engineering_interactive_tutorial/Anthropic%201P After completing this course, you will be able to: • Recognize common failure modes • Master the basic structure of a good prompt • Understand Claude's strengths and weaknesses • Build strong prompts from scratch for common use cases #PromptEngineering #AI #Claude #Anthropic #GitHub #Prompting #AIML #LLM #LargeLanguageModels #GenerativeAI #FailureModes #PromptStructure #AISolutions #AICourse

Am sure you thinking of cost saving/cutting costs when using Claude Code here is a tool to consider (thanks @sciencedegen): Zen MCP is a Model Context Protocol server that orchestrates multiple AI models as a development team, allowing Claude to seamlessly collaborate with different models like Gemini, O3, and others to enhance code analysis, problem-solving, and collaborative development. It provides guided workflows, multiple AI perspectives, automatic model selection, and advanced features like context revival, large prompt handling, and web search integration. https://github.com/BeehiveInnovations/zen-mcp-server #zenmcp #claude #gemini #o3 #modelcontextprotocol #aimodelcollaboration #aicoding #aiteam #codedevelopment #promptengineering #costsavings #github #codeanalysis #problem solving

Where to find Anthropic's on-demand Courses: https://github.com/anthropics/prompt-eng-interactive-tutorial https://www.anthropic.com/learn https://github.com/anthropics/courses This repository contains an interactive tutorial created by Anthropic to provide a comprehensive understanding of prompt engineering within the Claude language model. The tutorial covers various aspects of prompt engineering, from basic prompt structure to advanced techniques for building complex prompts. #Anthropic #Claude #PromptEngineering #AIML #LanguageModels #InteractiveTutorial #GitHub #PromptStructure #AdvancedTechniques #AIeducation #AICourse #PromptBuilding #GenerativeAI #AItools

How can you run many powerful coding agent for your app safely in a secure customizable Sandbox ? VibeKit VibeKit is an SDK that allows developers to safely run powerful coding agents like Codex, Gemini CLI, and Claude Code in secure, customizable sandboxes. It provides a drop-in solution for executing code in the cloud, with features like GitHub automation, prompt history, and real-time output streaming. Embed Claude Code, OpenAI Codex, Gemini CLI, and Opencode directly into your app with sandboxing, streaming output, and built-in GitHub automation. 100% Opensource. https://www.vibekit.sh/ #VibeKit #Codex #GeminiCLI #ClaudeCode #codingagents #SDK #sandboxing #githubautomation #opencode #realtimeoutput #promptengineering #securecoding #cloudcoding #opensource #airesearch #AItools #codeexecution #LangChain #AutoGPT

Where can you learn "Prompt Engineering" Handson ? Vibetest.io is a platform focused on teaching prompt engineering. It offers hands-on coding challenges to help users learn how to communicate effectively with AI models such as Claude and GPT-4. The platform aims to enable users to transform code through improved prompting techniques. It emphasizes practical application and direct experience to master the art of prompt engineering. https://vibetest.io/ #vibetestio #promptengineering #ai #gpt4 #claude #prompting #ailearning #promptdesign #codingchallenges #aiplayground #learnai #promptcrafting #promptdeveloper #aieducation #durableknowledge

So whats the next big fad .... "Context Engineering". So WHAT IS CONTEXT ENGINEERING? 𝟭. It’s Feature Engineering—but for AI Agents. (*Check what's feature engineering down below) 𝟮. The art of filling the context window with exactly what’s needed. 𝟯. You’re managing “working memory” like an operating system manages RAM. 𝟰. Agents need engineered context: instructions, tools, memories, examples, feedback. 𝟱. Poor context = forgotten steps, broken tools, bad decisions. 𝟲. Long-running agents hit context limits fast—engineering is essential. 𝟳. Vibe coding doesn’t scale—context engineering does. In the context of AI prompting, feature engineering refers to the process of creating, selecting, and transforming input data (features) to improve the performance of a machine learning model. It involves using domain expertise and various techniques to make the data more suitable for the AI model to learn from and generate better outputs. Check the following links for context: - Langchain Explanation on what it is: https://youtu.be/4GiqzUHD5AA?si=BEIThE_HOT-i3I9T - First post l saw talking about this was by Andrej Karpathy (@karpathy): https://x.com/karpathy/status/1937902205765607626 - And you may wish to follow this lady she dove right into it detail ... never mind hte course it hella expensive !: https://x.com/MaryamMiradi/status/1940810454013518178 #contextengineering #featureengineering #aiagents #langchain #promptengineering #aioptimization #machinelearning #contextwindow #workingmemory #aiprompting #vibecoding #aiperformance #contextualization #aitraining #aiscaling

"You got Rizz ... l got Rizz" soon will be "You got Prompts ... l got Prompts" ... let me show you ! Prompts.chat is a curated collection of effective prompts for ChatGPT and other AI assistants, curated by Fatih Kadir Akın. While designed for ChatGPT, these prompts can be adapted for Claude, Gemini, Llama, and other language models to help you get more out of AI interactions. #promptschat #chatgpt #claude #gemini #llama #aiprompts #promptengineering #aiassistants #promptcollection #languagemodels #aiinteractions #promptlibrary #aitools #conversationalai #promptoptimization https://prompts.chat/

The ever-so careful Anthropic ! Meticulously daling with the fundamentals the complete exact opposite of OpenAI (Oh wait they are ex-OpenAI emplyess by the way .. right ?) Anthropic’s new Research feature adopts a multi-agent architecture where a lead Claude model orchestrates specialized subagents that search the web and other tools in parallel, enabling dynamic, breadth-first investigations that outperform single-agent approaches. Achieving dependable performance required careful prompt engineering—teaching agents how to delegate, scale effort, choose tools, and think aloud—as well as bespoke evaluation methods that combine LLM-as-judge metrics with human review. While the system delivers significant accuracy and speed gains, it also introduces engineering and economic challenges such as heavy token consumption, stateful error handling, and complex deployment, all of which demand rigorous observability, iterative testing, and robust production safeguards. #claude #anthropic #research #multiagent #airesearch #webresearch #perplexity #searchgpt #tavily #aiagents #promptengineering #llmasajudge #aiorchestration #parallelprocessing #breadthfirst #tokenoptimization #aiobservability #productionai #aitools #aievaluation https://www.anthropic.com/engineering/built-multi-agent-research-system