Teaching Python Podcast’s Post

Dan Roam’s The Back of the Napkin highlights the power of visual thinking: "There is no more powerful way to prove that we know something well than to draw a simple picture of it. And there is no more powerful way to see hidden solutions than to pick up a pen and draw out the pieces of our problem." This idea aligns with Harvard University Graduate School of Education’s Visible Thinking framework, which emphasizes making students’ thinking processes visible through routines, documentation, and reflection. Whether we’re clarifying ideas or solving problems, visual thinking is a powerful way to deepen understanding and communicate insights. But how does this evolve in the age of AI tools like Napkin.ai? Does Napkin.ai Take Away the Thinking? In many ways, it does. Traditional visual thinking encourages us to wrestle with ideas on paper, sketching our way toward clarity. Tools like Napkin.ai automate the drawing, skipping over the tactile process of creating visuals by hand. This could seem like a loss—especially if you value the reflective thinking that comes with pen and paper. And a bit of me is sad about eliminating this process all together, but the beauty of this tool also excites me. Napkin.ai doesn’t eliminate thinking all together—but it does change the direction. Automating visuals with AI shifts the focus from metacognitive processing to conceptual clarity and understanding. Instead of developing and creating, you’re analyzing and verifying: “What relationships am I trying to show?” “Does this graph represent my data or ideas accurately?” “What story is this visual telling?” Napkin.ai speeds up the process, taking you straight to: analyzing and refining your ideas. For example, I used Napkin.ai to create a graphic for Teaching Python Podcast, showing the value we provide to our listeners. The tool generated a quick, clean visual, allowing me to immediately focus on whether the representation aligned with our message. I think the key to using AI is balance. Tools like Napkin.ai are at their best when paired with thinking practices: That means in the educational setting: *Use routines to help students structure their ideas before visualizing them. *Use AI to simplify or enhance the process of documenting thoughts in a visual format. * Have students analyze the visuals to ensure they accurately convey their thinking. The challenge (and opportunity) lies in adapting these tools to deepen, rather than bypass, our understanding. #VisualThinking #NapkinAI #TeachingPython #HarvardVisibleThinking #EdTech #Creativity #ProblemSolving

#Week 6 #Learning at #Growjunction Topic :“Technology 101 for Product Managers Part-2” led by the exceptional mentorship of Kunal Parekh. Let us deep dive into the key takeaways: 1. Agile Principles Walkthrough & PRD References: - Explored Agile methodologies meticulously, dissecting their implementation nuances in real-world scenarios such as Scrum, Kanban and Lean. - Unveiled the art of crafting Product Requirement Documents (PRDs), serving as the basic of product development journeys with elements like User Stories, Acceptance Criteria, Epics and Features. 2. Basics of SQL & Introduction to Prototyping Tools: - Equipped ourselves with the bedrock of SQL, enabling seamless data analysis and manipulation to drive data-led decisions using Data Manipulation Language (DML), Data Definition Language (DDL), and SQL clauses like SELECT, FROM and WHERE. - Embarked on a journey into the insights of prototyping and wireframing tools like Figma, Draw.io, mastering the art of translating ideas into tangible product designs through Wireframes, Mockups and UI/UX Design. 3. Basic of System Design: - Delved into the intricate world of system design, unraveling the architecture blueprints that underpin scalable and robust products with concepts such as Scalability, Reliability, Availability, Load Balancing, Microservices Architecture and RESTful APIs. - Explored strategies to architect systems that are not only efficient but also adaptable to evolving user needs and technological landscapes using Architecture Patterns like Client-Server, Peer-to-Peer and Model-View-Controller (MVC). 4. Introduction to Generative AI & Prompt Engineering: - Took a fascinating deep dive into Generative AI, unlocking its transformative potential to generate new data, text, images, and even music autonomously with techniques like Deep Learning, Natural Language Processing (NLP), Neural Networks, and Language Models such as GPT (Generative Pre-trained Transformer). - Empowering natural language generation systems to craft compelling narratives with precision through techniques like Zero-Shot Learning, One-Shot Learning, Few-Shot Learning and consideration of AI Kunal Parekh is a remarkable mentor, guiding us towards becoming technically adept product leaders poised to drive innovation and impact! #growthschool #growjunction# #productmanagement #learningjourney #EmpathyDriven #IterateAndInnovate #productinnovation #agilemethodologies #sqlmastery #prototyping #systemdesign #generativeai #techstrategy #continuouslearning

Conversational Generative AI Prompt Engineering for Beginners Hello everyone! Let's dive into the fascinating world of prompt engineering for conversational AI. Here is a comprehensive introduction to get you started on your AI prompt engineering journey. What is Prompt Engineering? Prompt engineering is the art & science of crafting effective inputs to get the best outputs from AI language models like ChatGPT, Claude, & Gemini. It's a crucial skill in the age of AI. Why is Prompt Engineering Important? Improved Accuracy: Well-crafted prompts lead to more accurate & relevant responses. Enhanced Efficiency: Good prompts save time by getting the desired output faster. Versatility: Mastering prompt engineering allows you to use AI for a wide range of tasks. Key Principles of Prompt Engineering Let's dive into five essential principles to guide your prompt engineering: Be Specific: The more details you provide, the better the AI can understand & respond to your needs. Instead of asking "Tell me about cars," try "Explain the basic components of an electric car's drivetrain." Use Clear Instructions: Tell the AI exactly what you want it to do. For example: "Summarize this in 5 bullet points" "Explain this concept as if I'm a 10-year-old" "Write a pros & cons list for this topic" Provide Context: Give background information or set a scenario to help the AI understand the broader picture. For instance, "Imagine you're a financial advisor speaking to a client who's new to investing. Explain the concept of diversification." Specify the Output Format: If you have a preferred format for the response, mention it explicitly. For example, "Provide your answer in a table with two columns: 'Advantage' & 'Disadvantage'." Use Examples: If you're looking for a specific type of response, provide an example. This technique, known as "few-shot learning," can significantly improve the AI's understanding of your request. Advanced Techniques As you become more comfortable with basic prompt engineering, you can explore advanced techniques: Chain-of-Thought Prompting: Guide the AI through a step-by-step reasoning process for complex problems. Role-Playing: Assign a specific role to the AI, like "Act as a senior software engineer reviewing this code." Iterative Refinement: Start with a basic prompt & gradually refine it based on the AI's responses. Common Pitfalls to Avoid Ambiguity: Vague prompts often lead to unhelpful responses. Overcomplication: Sometimes, simpler prompts work better. Find the right balance. Neglecting to Fact-Check: Always verify important information provided by AI. Remember, practice makes perfect. Experiment with different phrasings & approaches to see what works best for your needs. Don't be afraid to iterate & refine your prompts. That's it! You're now equipped with the knowledge to start your journey into AI prompt engineering. As you practice, you'll develop an intuition for crafting effective prompts. Happy prompting!

Top 40 AI 💮Tools Categorized to Enhance Productivity in Research - Discover the most effective AI tools designed to boost your productivity and streamline your research process. Here are the top 40 AI tools: 🟠Writing Tools ◾️Notion AI - Enhances note-taking and organization. ◾️Monica Ai Manages research contacts. ◾️ChatGPT - Assists in drafting and brainstorming. ◾️QuillBot - Paraphrasing and grammar checking. ◾️Jenni AI - Drafts research paper sections. ◾️Text FX - Improves text formatting and layout. 🔴Coding Tools ◾️Phind - Finds relevant code snippets. ◾️Safurai - Optimizes and refines code. ◾️Replit - Supports collaborative coding. ◾️MarsAI - Tools for machine learning tasks. ◾️Codeium - Assists in writing and debugging code. 🟣Drawing Tools ◾️Adobe Firefly - Creates high-quality illustrations. ◾️Leonardo.Ai - Detailed and realistic drawings. ◾️Playground AI - Digital art creation. ◾️SeaArt - Artistic visual representations. ◾️Midjourney - Generates creative artwork. 🔵Visualization Tools ◾️Brighteye - Enhances visual focus. ◾️Lucidpic - Generates images for visualization. ◾️DALL-E2 - Creates visual content from text prompts. ◾️Synthesys AI Studio - Generates realistic images. 🟤Necessary Tools ◾️Syntheyes - Automates repetitive tasks. ◾️Audior - Converts text to audio. ◾️Taskade - Project management and task lists. ◾️Magical - Automates formatting tasks. ◾️GitMind - Creates mind maps and diagrams. 🟡Similarity Check ◾️Turnitin - Robust plagiarism detection. ◾️Grammarly - Grammar and plagiarism checking. ◾️iThenticate - Advanced plagiarism checking. ◾️Paperpal - Comprehensive language and plagiarism checks. 🔵Paraphrasing Tools ◾️QuillBot - Advanced paraphrasing capabilities. ◾️Paraphrase Online - Effective rephrasing. ◾️SpinBot - Automated paraphrasing. ◾️Paraphraser.io - Multi-language support. ◾️WordAi - Human-like paraphrasing. 🟡Finding Articles ◾️Google Scholar - Comprehensive academic search. ◾️Semantic Scholar - AI-driven search features. ◾️Microsoft Academic - Detailed search results. ◾️pubmedia - Biomedical references. ◾️ResearchGate - Wide range of research articles. Explore these tools and enhance your research productivity! Follow me on LinkedIn: @engrAbdinasir Visit my website: www.abdinasirhirsi.com

Let's dive into the fascinating world of prompt engineering for conversational AI. Here is a comprehensive introduction to get you started on your AI prompt engineering journey. What is Prompt Engineering? Prompt engineering is the art & science of crafting effective inputs to get the best outputs from AI language models like ChatGPT, Claude, & Gemini. It's a crucial skill in the age of AI.

3 Resources to Jumpstart Your Generative AI Journey: Generative AI for Beginners Curriculum Build Generative AI Apps Code-First With Azure AI Responsible AI Resources For Developers Welcome to the fifth post in my This Week in AI News series! Today, I want to talk about Generative AI For Beginners – from prompt engineering to fine tuning. Let’s dive in! I was out on vacation so this post and the next are being published together today instead of on their regular cadence. They are related though, so hope you find them useful. Skilling Up on Generative AI By now you’ve probably asked ChatGPT to answer a question, or asked Bing Create to generate an image, or asked GitHub Copilot to explain some code to you. These are all examples of generative AI experiences driven by large language models (LLMs). But what exactly does an LLM do? And how does a generative AI solution differ from traditional AI/ML applications that we’ve built before? What are the fundamental concepts to know, and how can you apply these concepts to build real-world applications? These are some of the questions we tried to answer in the Generative AI For Beginners curriculum that was released as an open-source series of 12 lessons (v1) last October. Each lesson covers a core topic and provides an assignment to help reinforce the ideas you learned. Since the launch, ~25K users have starred the repo, with many users also contributing issues, translations, fixes and suggestions to help us improve this. It is now the #1 “generative-ai” repo on GitHub. Today, I’ll focus on the lesson I contributed to this curriculum – the chapter on Prompt Engineering Fundamentals – that sets the stage for the rest of the course. What Is Prompt Engineering? Generative AI applications differ from traditional AI experiences in many ways – but a key aspect is their use of Large Language Models (LLM) to understand natural language. This allows users to engage with the applications using text inputs (or “prompts”) in a conversational manner (“chat”) that feels natural. Understanding how prompts work is critical to building reliable applications that deliver quality responses to the user. The visual below shows how these concepts are connected – but what exactly does prompt engineering mean, and why do we need it? Review: Illustrated Guide The lesson is broken down into these sections: Core Concepts – explains key terms including tokenization, that are critical to prompt usage. Core Challenges – tells you why prompt design is critical to getting better quality responses. GitHub Copilot – provides a real-world case study that illustrates prompt engineering examples. Prompt Construction – describes the components of a prompt, and how complex prompts are designed. Primary Content – highlights a number of prompt design patterns that improve quality of responses. Advanced Techniques – shows how to improve quality

RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning As large language models are increasingly used as agents, grounding them in feedback is critical for iterative task success, especially in code synthesis. Researchers have proposed an end-to-end reinforcement learning method, RLEF (Reinforcement Learning from Execution Feedback), which enables language models to improve code iteratively by utilizing feedback from code execution. What’s new: The authors benchmark their approach on competitive programming tasks, achieving new state-of-the-art results while reducing the number of samples needed by a factor of 10 compared to previous models. This method applies to both small (8B) and large (70B) models. The work is led by Jonas Gehring and colleagues at Meta AI. How it works: The proposed method focuses on grounding models through feedback in iterative code generation tasks: • The authors frame code synthesis as an iterative task where models generate code, receive execution feedback, and modify the code based on this feedback. • A reinforcement learning loop optimizes models based on a reward signal, with the final solution evaluated against held-out test cases. • The model is fine-tuned using Proximal Policy Optimization (PPO), with a penalty for invalid code. Results: The method achieves notable improvements on benchmarks: • The 70B model, after RLEF training, surpasses previous state-of-the-art systems (such as AlphaCodium and MapCoder) on CodeContests with fewer samples. • The models generalize well to additional benchmarks (HumanEval+ and MBPP+), achieving improved solve rates across multi-turn scenarios. • RLEF-trained models consistently make fewer errors initially and effectively repair faulty code through multiple iterations. Why it matters: By using reinforcement learning to ground LLMs in execution feedback, the proposed method allows for significant improvements in code synthesis, reducing inference costs and improving accuracy in iterative tasks. The approach highlights the importance of feedback-driven refinement for AI models performing complex tasks. We’re thinking: This study offers a promising path toward using LLMs for more complex problem-solving by equipping them to learn from their own mistakes through reinforcement learning, potentially transforming how AI assists in software development. For more information check-out the paper: https://lnkd.in/euT2E_dk

AI is playing an increasing role in my design work through the tools and processes that make me more efficient and productive. But I’m also thinking about how a deeper understanding of AI’s capabilities and mechanisms can help me be a better designer of useful, human-centred products. I’ve learned a huge amount about AI from reading and exploring, but nothing beats getting hands on and learning by doing. So, I set myself the challenge of building a useful and fully functioning AI-powered product, even though my coding skills are at a basic level (to be kind). Here’s what I learned. https://lnkd.in/gy6HGyX6

Visual SKETCHPAD: Sketching as a Visual Chain of Thought Reasoning for Multimodal Language Models Humans draw to facilitate reasoning: we draw auxiliary lines when solving geometry problems; we mark and circle when reasoning on maps. However, such actions are missing in current multimodal language models. In this paper, authors introduce SKETCHPAD, a framework that provides multimodal LMs with the tools necessary to generate intermediate sketches to reason over tasks 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 𝗼𝗳 𝗦𝗞𝗘𝗧𝗖𝗛𝗣𝗔𝗗 - SKETCHPAD agent solves tasks by engaging in an iterative interaction process with an environment - Given a multimodal query q that includes both visual and textual components, the model generates a series of thoughts, actions, and observations to gather the information needed to answer the query. At each time step t, the model performs three key steps: i) Thought: The model analyzes the current context ct, which includes the query, previous thoughts, actions, and observations, to generate a thought plan pt for the next action ii) Action: Based on the thought plan, the model executes an action at, which can manipulate both visual and textual content iii) Observation: Based on the action at, SKETCHPAD’s environment returns a new observation 𝗦𝗸𝗲𝘁𝗰𝗵𝗶𝗻𝗴 𝘃𝗶𝗮 𝗖𝗼𝗱𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 - SKETCHPAD enables LMs to sketch through code generation. The LM is provided, through a prompt, with a detailed description of the available tools that can generate multimodal content - LM generates Python code in a code block, using the provided tools, which, when executed, generates new image and text outputs 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗦𝗞𝗘𝗧𝗖𝗛𝗣𝗔𝗗 𝘄𝗼𝗿𝗸 - Dense information like depth and segmentation cannot be described easily through language. SKETCHPAD provides LMs the opportunity to use visualization techniques that are direct, efficient, and informational. - in SKETCHPAD, multimodal LMs can plan and reason based on the intermediate visual artifacts they created, as compared to a predefined plan by either humans or code - plans of multimodal LMs are similar to human plans, and therefore likely benefit from the fact that the underlying LMs have seen data with similar reasoning patterns during pretraining 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 - Across all four categories of mathematical tasks(geometry, mathematical functions, graph algorithms, strategy games), SKETCHPAD consistently improves the baseline GPT-4o performance, yielding an average gain of 12.7% - shows consistent improvements across all seven types of computer vision tasks(depth, spatial reasoning, jigsaw, visual and semantic correspondence, MMVP, V*Bench benchmarks). For example, GPT-4o, augmented with SKETCHPAD, sees 14.3% improvement on V*Bench, 12.1%, and 9.7% improvements on BLINK’s depth and semantic correspondence tasks, setting a new SOTA across all tasks 𝗕𝗹𝗼𝗴: https://lnkd.in/ew4FUkfH 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/e6Dma-KD #generativeai

• Researchers at McGill University have initiated the "The Lives of Literary Characters" project, utilizing AI and crowdsourcing techniques to gain insights into storytelling. • Examination of character interactions in narratives can reveal the complexities of storytelling across various cultures. • Individuals can participate in the project as citizen scientists, providing input on character interactions in contemporary fiction. Source: [https://lnkd.in/eqjbAqHN] —————— P.S.: This project shows how combining human-centered research with advanced tech can help understand complex cultural stories. It highlights the need for AI that can handle lots of character data effectively. #TechNews #Productivity #OpenAI #Courses #Learning #Charts #Insights #Analysis #Data #Coding #Enhancement #Tips #DataSecurity #WebDesign #Development #Computer #Programming #Developer #WebDevelopment #Programmer #Robotics #ComputerScience #ArtificialIntelligence #AI #MachineLearning #DataScience #BigData #NeuralNetworks #SoftwareDevelopment #Automation #DataVisualization #Python #DeepLearning #Analytics #DataAnalysis #MachineLearningAlgorithms #AICommunity #MachineLearningEngineer #MachineLearningTraining #AIInnovation #CyberSecurity #NLP #LLM #TextProcessing #NaturalLanguageProcessing #LanguageModel #GPT #Bard #TextAnalytics #SpeechRecognition #Chatbots #PythonCoding #CodingLife #ProgrammingLife #SoftwareEngineer #Code #DataAnalytics #DataMining #Statistics #DigitalTransformation #DataScientist #ComputationalScience #QuantitativeAnalysis #BusinessGrowth #Entrepreneurship #Startup #Innovation #DigitalMarketing #SocialMedia #Marketing #Branding #Economics #Fintech #Security #CloudComputing #Blockchain #IoT #VR #AR #AIForGood #SustainableTech #EdTech #HealthTech