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Productivity News
Medium
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Mac Mini M4 vs. M4 Pro: Don’t Overpay for What You Don’t Need in 2025
- The Mac Mini M4 is perfect for most users, ideal for development, office tasks, light video editing, and casual content creation.
- The Mac Mini M4 Pro is priced significantly higher at €1,600, offering additional power with a 12-core CPU, 19-core GPU, and faster 200GB/s memory bandwidth.
- The M4 Pro comes with 24GB RAM and 512GB SSD by default, while the M4 has 16GB RAM and 256GB SSD.
- The M4 Pro excels in resource-intensive tasks, making it a solid choice for power users.
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Medium
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The Rise of the Artificial Project Manager: How AI is Transforming the Future of Work
- The rise of the Artificial Project Manager is transforming the future of work.
- AI is augmenting human leadership, automating and optimizing project management.
- The Artificial Project Manager is reshaping how work gets delivered by focusing on repetitive, administrative tasks.
- By embracing AI thoughtfully, project teams can deliver projects faster and smarter while focusing on what matters most.
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Medium
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Noah Samson 6 Figure Business Roadmap
- Noah Samson 6 Figure Business Roadmap is a step-by-step roadmap for first-time entrepreneurs to launch profitable online agencies within 30 days.
- Noah Samson provides insights on business scaling, wealth creation, and market mastery in his Medium articles.
- The roadmap helps entrepreneurs discover the benefits of online business, choose the right niche and online business model, succeed in their online business, set goals, generate traffic, and market their businesses.
- Trending niches covered by Noah Samson include beating recessions and capitalizing on black swan events.
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Medium
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AI Tools Revolutionizing Developer Skills and Productivity
- Artificial intelligence in software development has undergone a transformative journey.
- Advancements in computational power enabled the growth of machine learning techniques.
- The arrival of Large Language Models like GPT-3 has redefined roles in software development.
- AI tools are revolutionizing developer skills and productivity.
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Medium
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AI and Energy: Powering a Smarter, Faster, Greener Future (and Why It Matters)
- A significant chunk of our electricity still comes from fossil fuels, contributing to the growing challenge of electronic waste and increasing greenhouse gas emissions.
- AI can be an optimization engine for energy systems, analyzing the supply and demand on power grids and enabling more efficient distribution.
- AI algorithms can forecast the peak times for renewable energy sources like solar and wind, reducing reliance on traditional, less sustainable sources.
- The future of AI and energy requires responsible innovation and a commitment to building a cleaner, more efficient, and more resilient energy future.
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Dev
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Finding the Best AI Coding Assistant: From Pure Vibe to Practical Power
- AI coding assistants vary in capabilities, from basic pattern completion tools to sophisticated reasoning engines.
- The pricing of these AI tools ranges from free options to premium tools that may not always deliver superior performance.
- Choosing the right AI coding assistant involves considering factors like reliability, contextual awareness, and cleanup required after use.
- There's a distinction between general intelligence AI models like ChatGPT and specialized coding tools like GitHub Copilot.
- The size of the context window in AI coding tools can impact their effectiveness in handling complex coding tasks and understanding code relationships.
- Larger context windows allow AI models to process more information but can lead to challenges like the 'Lost in the Middle' phenomenon.
- Effective context management is crucial for optimizing the use of large context windows in AI coding assistants.
- Incorporating benchmarks and real-world testing can help determine the optimal context size for specific AI models and use cases.
- Balancing big context windows with control is essential to prevent AI models from getting overwhelmed by irrelevant information.
- Further exploration in Part 2 will delve into the key capabilities that differentiate effective AI coding partners, such as code understanding and debugging intelligence.
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Towards Data Science
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How to Level Up Your Technical Skills in This AI Era
- AI-assisted coding tools like Cursor, V0, and Lovable have reduced the entry barriers by enabling faster development of dashboards, pipelines, and apps.
- While AI tools enhance productivity, relying too heavily on them may hinder the development of deep technical understanding and problem-solving skills.
- Engaging in 'vibe coding' for quick code generation can lead to complex issues and technical debt, emphasizing the importance of building a strong mental model.
- Using AI tools judiciously, such as selecting suggestions before implementing them, can mitigate errors and enhance code quality.
- Contributing to open source projects is highlighted as a valuable way to enhance technical skills, learn best practices, and interact with a community of developers.
- Open source contributions offer benefits like enhancing version control proficiency, networking, and portfolio development for career advancement.
- Choosing open source projects based on personal interest, utilizing features you need, and starting with familiar technologies can enhance motivation and learning.
- Practical steps for contributing to open source include selecting relevant features, setting up the local environment, starting with small tasks, and learning by doing.
- Navigating the codebase, writing clear commit messages, and engaging in the review process are essential components of successful open source contribution.
- Despite initial challenges, persistence in open source contribution can lead to valuable learning experiences, networking opportunities, and personal growth.
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Medium
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The quick discovery that made me earn the power of AI Efficiency.
- A person shares their experience of utilizing AI efficiency in their work.
- They successfully setup an auto responder with MailChimp and Klaviyo and built a chat box AI analytics+A/B optimization.
- They used AI language model to draft and send replies, build buyer personas, and automate email campaigns.
- The person helped service-based brands integrate AI into their business operations to improve email marketing.
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24 Likes
Dev
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How Modern Engineering Teams Gain Clarity with Entelligence AI
- Modern engineering teams face challenges such as limited visibility into blockers and reliance on outdated metrics like lines of code and pull request counts.
- Entelligence AI addresses these issues by providing real-time actionable insights and a comprehensive view of team progress.
- Traditional metrics are becoming irrelevant in the era of AI-assisted development, leading to misguidance in performance evaluations.
- Common productivity limitations include lack of real-time metrics, manual reporting processes, and inconsistent code review practices.
- Entelligence AI offers a platform that emphasizes outcome-driven insights through advanced analytics and AI technology.
- Key features include sprint assessments, performance reviews, and overall code overview metrics to enhance team performance.
- Setup of Entelligence AI involves connecting with GitHub, defining roles, and customizing dashboards with key metrics.
- After implementing Entelligence AI, teams experienced improvements in sprint completion rate, reduced meeting time, and enhanced review quality consistency.
- The key wins post-implementation included improved sprint planning, data-driven performance reviews, reduced meeting time, and over 100 hours saved monthly.
- Entelligence AI shifts the focus from outdated metrics to meaningful, real-time insights, empowering teams to identify bottlenecks, enhance collaboration, and drive innovation.
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Dev
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From Code Monkey to Code Whisperer: How AI Copilots are Transforming Our Dev Lives
- AI copilots, powered by algorithms and machine learning, are revolutionizing the developer experience by assisting in coding tasks.
- These AI tools like GitHub Copilot, TabNine, Kite, and IntelliCode enhance productivity by suggesting code snippets and functions in real-time.
- AI copilots enable rapid coding, learning from the developer's style and project structure to provide personalized assistance over time.
- They facilitate exploration of new languages and frameworks, assist in bug detection, code refactoring, documentation, and testing.
- Despite their benefits, AI copilots are tools that complement human creativity, problem-solving skills, and architectural understanding.
- Suggestions for maximizing AI copilot efficiency include starting small, reviewing suggestions, customization, continuous learning, and experimentation.
- As AI technology advances, the future promises even more sophisticated AI copilot features, potentially transforming the coding landscape.
- Embracing AI copilots can lead to improved coding practices, faster development, and enhanced problem-solving abilities for developers.
- The collaboration between human developers and AI copilots heralds a new era of coding efficiency and innovation in the tech industry.
- Incorporating AI copilots can be the start of a valuable partnership in streamlining code creation and fostering developer growth and creativity.
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Dev
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431
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Why Convert Excel to PDF? Key Benefits for Business and Personal Use
- Converting Excel files to PDF provides several key benefits for both business and personal use.
- PDF ensures that what you see is what others get, as the format remains fixed regardless of the version, device, or settings.
- PDFs offer a more professional presentation compared to raw spreadsheets, making them ideal for project proposals or reports.
- Converting to PDF provides security and protection for sensitive information, making it read-only by default and allowing for password protection and restricted printing.
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25 Likes
Dev
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Bringing Python 3.10’s Pattern Matching to Python 3.7+ with Patterna
- Python 3.10 introduced structural pattern matching for concise, expressive code, but older Python versions lacked this feature until Patterna.
- Patterna is an open-source library that brings Python 3.10's pattern-matching syntax to Python 3.7+ using a simple @match decorator.
- Pattern matching simplifies code by directly matching on values, lists, dicts, and custom objects, improving readability and reducing boilerplate.
- It allows matching sequences like user input lists and dictionaries like API responses or JSON data with named fields.
- Patterna provides near-Python-3.10 syntax compatibility, making it a powerful tool for handling complex inputs and nested data structures.
- The library introduces @match decorator that allows defining functions with case patterns and blocks similar to Python 3.10's match/case statements.
- Examples include matching simple values, sequences, dictionaries (API responses), custom classes, user input routing, and data validation, all leading to cleaner code.
- Patterna is installed using pip and supports matching on literals, lists, tuples, dicts, custom classes, wildcards, and variable captures.
- By emulating Python's pattern matching behavior, Patterna makes code more declarative and manageable, providing a significant upgrade for Python 3.7-3.9.
- Efficient use cases of Patterna include API response handling, user command routing, configuration validation, object dispatch, and data validation.
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Medium
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202
Does AI Make Us Dumber? YES, Unless… (A Cognitive Science-Based Perspective)
- The article explores the impact of AI on human cognition, suggesting that whether AI makes us dumber is still uncertain in our complex world.
- By redistributing mental energy through offloading tasks to AI, individuals can decide what to automate and what to retain for human insights.
- The concept of cognitive load, which measures mental capacity demands, plays a significant role in understanding the interaction between humans and AI.
- Through a real-world example of language communication, the article showcases how AI can transform intrinsic and extraneous cognitive loads.
- The author argues that historical technological advancements have redefined what is considered intrinsic versus extraneous, shaping our perception of problem characteristics.
- The potential of AI in handling cognitive loads raises questions about redefining human roles and the necessity of balancing intrinsic and extraneous loads for optimal cognitive performance.
- The article emphasizes the importance of utilizing AI to enhance human capabilities in problem-solving, ethical judgment, and context-building.
- Using AI as a tool to complement human cognition rather than replace it is highlighted as crucial to prevent the fear of intelligence decline.
- The need to adopt new mindsets towards problem-solving and productivity, with AI as a transformative tool, is underscored for navigating the evolving landscape of AI integration.
- The author suggests that embracing AI while focusing on refining problem-framing skills can lead to enhanced problem discovery and cognitive development.
- The conclusion encourages readers to consider investing cognitive resources efficiently, either in traditional tasks or in exploring new approaches supported by AI, to adapt to the changing cognitive demands of the future.
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Department of Product
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Deep: How are companies pricing their AI features?
- A recent survey found that 90% of mid-sized SaaS companies expect their business model to undergo changes due to the impact of AI.
- Companies have been releasing new AI features and determining their pricing strategies.
- Different pricing models for AI features are being adopted, including outcome-based pricing, bundled value, credits, free offerings, and add-ons.
- A breakdown of how 25+ companies price their AI features and a discussion on the future of outcome-based pricing for SaaS companies is provided.
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ADPList’s Newsletter
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Proven design process for the real world
- In this subscriber-only edition, Ted Goas shares his playbook for navigating complex design projects, highlighting real-world challenges and trade-offs.
- The article delves into how design processes can appear neat in theory but often become messy in practice, emphasizing the importance of practical solutions.
- Ted Goas, a seasoned product designer, outlines his approach to discovery, definition, and development stages in design projects.
- He discusses the need for a flexible framework to address ambitious product challenges and shares insights into his design process.
- The article illustrates Dialpad's adaptation of the product design process into a triple diamond model, showcasing the practical application of design frameworks.
- Ted emphasizes the significance of documenting one's design process to ensure consistency and avoid overlooking key elements that may impact designs.
- The discovery phase is highlighted as crucial for setting project direction by defining problem statements, gathering evidence, and aligning team expectations.
- Various activities such as observations, proto-personas, journey mapping, and service blueprints are recommended during the discovery phase to enhance understanding and clarity.
- Contextual inquiry is mentioned as a valuable tool for gaining insights into user behaviors and motivations, essential for designing user-centric products.
- Ted's approach to design process is likened to a toolbox, serving as a reference guide to select appropriate tools and methodologies based on project requirements.
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