Google Gemini Diffusion : The Future of Smarter, Faster Text Creation
What if the future of text generation wasn't just faster, but smarter and more adaptable? Enter Gemini Diffusion, a new approach that challenges the long-standing dominance of autoregressive models. By using the power of diffusion-based techniques—previously celebrated in image and video generation—this innovative system reimagines how text is created. Imagine crafting entire paragraphs in parallel, refining specific sections without disrupting the rest, and achieving speeds of up to 800 tokens per second. It's not just about efficiency; it's about precision and creative freedom. But with great promise comes great complexity, and Gemini Diffusion's journey is as much about overcoming challenges as it is about innovation.
This overview by Prompt Engineering explores the fantastic potential of Gemini Diffusion, diving into its unique strengths, current limitations, and real-world applications. From collaborative editing to algorithm visualization, the model's versatility hints at a future where text generation tools are faster, more intuitive, and more responsive than ever before. Yet, the road ahead isn't without obstacles—technical hurdles and nuanced challenges still shape its evolution. Whether you're a developer, writer, or simply curious about the next frontier of AI, Gemini Diffusion offers a fascinating glimpse into what's possible when speed meets precision. Could this be the shift that redefines how we create and interact with text? Let's explore. Gemini Diffusion Explained How Diffusion-Based Text Generation Stands Out
Diffusion models, such as Gemini Diffusion, distinguish themselves by generating text in parallel rather than sequentially. Unlike autoregressive models, which produce tokens one at a time to maintain coherence, diffusion models generate all tokens simultaneously. This parallel processing not only accelerates output but also enables iterative refinement, allowing for more controlled and targeted adjustments.
For example, when editing a specific section of a paragraph, Gemini Diffusion can focus on refining that portion without altering the rest of the text. This capability provides greater precision and localized control, making it particularly valuable for tasks that require frequent edits or adjustments, such as collaborative writing or technical documentation. Performance Strengths and Current Limitations
One of the most notable advantages of Gemini Diffusion is its speed. Capable of generating up to 800 tokens per second, it is well-suited for applications that demand rapid output, including web content creation, game script development, and algorithm visualization. This efficiency makes it an attractive option for professionals seeking to streamline their workflows.
However, the model's performance diminishes when tasked with complex reasoning or highly structured outputs. While effective for straightforward prompts, it struggles with nuanced or multi-layered content, highlighting its current limitations in handling sophisticated challenges. These constraints underscore the need for further refinement to expand its applicability to more intricate use cases. What is Gemini Diffusion?
Watch this video on YouTube.
Gain further expertise in AI text generation by checking out these recommendations. Comparing Diffusion Models to Autoregressive Models
Autoregressive models have long been the standard for text generation, producing tokens sequentially to ensure coherence and logical flow. While reliable, this process is inherently slower and less adaptable to iterative changes.
In contrast, diffusion models like Gemini Diffusion generate all tokens simultaneously, offering a significant speed advantage. Additionally, their ability to refine specific sections of text without regenerating the entire output makes them particularly useful for tasks such as collaborative editing, code refinement, and creative writing. This flexibility positions diffusion models as a compelling alternative to traditional approaches, especially for users who prioritize efficiency and precision. Technical Challenges in Training Diffusion Models
Despite their advantages, diffusion models face several technical challenges. Training a large language model like Gemini Diffusion requires substantial computational resources and advanced technical expertise. Moreover, details about the model's architecture, such as its context window size and optimization techniques, remain unclear. This lack of transparency makes it difficult to fully evaluate its capabilities and potential.
These challenges highlight the complexities of developing diffusion-based text generation models. Overcoming these barriers will be essential to unlocking their full potential and making sure their scalability for broader applications. Applications and Real-World Use Cases
Gemini Diffusion has already demonstrated its versatility across a range of creative and technical applications. Some of its notable use cases include: Generating interactive games, such as tic-tac-toe, with dynamic and responsive text-based interactions.
Developing drawing applications and visual tools that integrate text-based instructions or annotations.
Animating algorithms for educational purposes, providing clear and concise textual explanations alongside visual demonstrations.
Editing text or code with precision, allowing localized changes without regenerating the entire content.
These capabilities make Gemini Diffusion particularly valuable for developers, writers, and creators who aim to enhance their productivity. Its combination of speed and precision underscores its potential to redefine workflows in various industries. Historical Context and Unique Challenges in Text Generation
Diffusion models have a well-established history in image and video generation, where they have been used to create high-quality visuals with remarkable detail. However, their application to text generation is relatively new and presents unique challenges. Unlike visual media, text generation requires maintaining grammatical coherence, logical consistency, and contextual relevance—factors that are less critical in image-based tasks.
Earlier efforts, such as Mercury by Inception Labs, laid the groundwork for diffusion-based text generation. Gemini Diffusion builds on these innovations, adapting diffusion techniques to address the complexities of text. This evolution reflects the growing potential of diffusion models to tackle domain-specific challenges, particularly in creative and technical fields. The Future of Diffusion Models in Text Generation
While Gemini Diffusion is not yet a definitive breakthrough, it represents a promising step forward in text generation technology. By addressing the limitations of autoregressive models and using the unique strengths of diffusion, it opens the door to new possibilities in writing, editing, and creative content generation.
As research and development continue, diffusion models could unlock innovative tools for faster, more efficient workflows. Whether you're a developer, writer, or content creator, these advancements may soon redefine how you approach text-based projects. By bridging the gap between speed and precision, Gemini Diffusion paves the way for a new era of text generation technology, offering exciting opportunities for professionals across various domains.
Media Credit: Prompt Engineering Filed Under: AI
Latest Geeky Gadgets Deals
Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Geeky Gadgets
2 days ago
- Geeky Gadgets
Gemini 2.5 Flash Hybrid Reasoning AI Optimized for AI Thinking for Efficiency
What if artificial intelligence could think only when you needed it to? Imagine a tool that seamlessly transitions between complex reasoning and straightforward processing, adapting to your specific needs without wasting resources. Enter Google's Gemini 2.5 Flash, a new AI model that redefines efficiency with its hybrid reasoning capabilities. By allowing developers to toggle between 'thinking' and 'non-thinking' modes, Gemini 2.5 Flash offers a level of control and adaptability that traditional AI systems simply can't match. Whether you're solving intricate problems or managing routine tasks, this innovation promises to deliver precision, scalability, and cost-efficiency—all tailored to your workflow. In this coverage, Prompt Engineering explore how Gemini 2.5 Flash is reshaping the AI landscape with its thinking budget optimization, multimodal processing, and enhanced token capacities. You'll discover how its unique architecture eliminates the need for separate models, streamlining operations while reducing costs. But it's not without its limitations—plateauing performance at higher token usage and capped reasoning budgets raise important questions about its scalability for resource-intensive projects. As we unpack its strengths and challenges, you'll gain a deeper understanding of whether Gemini 2.5 Flash is the right fit for your next AI endeavor. Sometimes, the real innovation lies in knowing when not to think. Gemini 2.5 Flash Overview Understanding Hybrid Reasoning At the core of Gemini 2.5 Flash lies its hybrid reasoning model, a feature that distinguishes it from traditional AI systems. This capability enables you to toggle 'thinking mode' on or off based on the complexity of the task. By managing the 'thinking budget'—the maximum number of tokens allocated for reasoning—you can optimize the model's performance to suit specific use cases. This approach eliminates the need for separate models for reasoning-intensive and simpler tasks, streamlining workflows and reducing operational overhead. Whether you're addressing intricate problem-solving scenarios or routine data processing, the model's adaptability ensures optimal performance. The ability to fine-tune the reasoning process provides a significant advantage, allowing you to allocate resources efficiently while achieving high-quality results. Cost-Efficiency and Competitive Pricing Gemini 2.5 Flash is designed with cost-conscious developers in mind, offering a pricing structure that reflects its focus on affordability and performance. The model's pricing tiers are as follows: Non-thinking mode: $0.60 per million tokens $0.60 per million tokens Thinking mode: $3.50 per million tokens This competitive pricing positions Gemini 2.5 Flash as a cost-effective alternative to other leading AI models, such as OpenAI and DeepSync. By integrating proprietary hardware and software, Google ensures a strong performance-to-cost ratio, making the model an attractive option for projects that require scalability without sacrificing quality. This balance between affordability and capability makes it a practical choice for developers aiming to optimize their resources. Gemini 2.5 Flash Hybrid Reasoning AI Model Watch this video on YouTube. Find more information on Hybrid Reasoning AI by browsing our extensive range of articles, guides and tutorials. Performance and Benchmark Comparisons In benchmark evaluations, Gemini 2.5 Flash ranks second overall on the Chatbot Arena leaderboard, trailing only OpenAI's O4 Mini in specific areas. However, it demonstrates significant improvements over its predecessor, Gemini 2.0 Flash, particularly in academic benchmarks. These advancements highlight the model's enhanced capabilities and its potential to deliver robust performance across various applications. While these results underscore its strengths, it is recommended that you test the model against your internal benchmarks to determine its suitability for your unique requirements. This hands-on evaluation will provide a clearer understanding of how Gemini 2.5 Flash can integrate into your workflows and meet your specific needs. Enhanced Token and Context Window Capabilities One of the standout features of Gemini 2.5 Flash is its enhanced token capacity, which significantly expands its utility for developers. The model supports: Maximum output token length: 65,000 tokens, making it ideal for programming tasks and applications requiring extensive outputs. 65,000 tokens, making it ideal for programming tasks and applications requiring extensive outputs. Context window: 1 million tokens, allowing the processing of large datasets or lengthy documents with ease. These enhancements provide a substantial advantage for handling complex inputs and generating detailed outputs. Whether you're working on data-heavy projects or applications requiring extensive contextual understanding, Gemini 2.5 Flash offers the tools necessary to manage these challenges effectively. Multimodal Processing for Diverse Applications Gemini 2.5 Flash extends its capabilities to multimodal processing, supporting a variety of input types, including video, audio, and images. This versatility makes it a valuable tool for industries such as media analysis, technical documentation, and beyond. However, it is important to note that the model does not include image generation features, which may limit its appeal for creative applications. Despite this limitation, its ability to process diverse input types enhances its utility across a wide range of use cases. Key Limitations to Consider While Gemini 2.5 Flash excels in many areas, it is not without its limitations. These include: Challenges with certain logical deduction tasks and variations of classic reasoning problems. A 'thinking budget' capped at 24,000 tokens, with no clear explanation for this restriction. Performance gains that plateau as token usage increases, indicating diminishing returns for resource-intensive tasks. These constraints highlight areas where the model may fall short, particularly for developers requiring advanced reasoning capabilities or higher token limits. Understanding these limitations is crucial for making informed decisions about the model's applicability to your projects. Strategic Value for Developers Google's Gemini 2.5 Flash reflects a strategic focus on cost optimization, scalability, and accessibility, making advanced AI technology available to a broader audience. Its hybrid reasoning capabilities, enhanced token and context window capacities, and multimodal processing features position it as a versatile and scalable tool for developers. By balancing quality, cost, and latency, the model caters to a wide range of applications, from data analysis to technical problem-solving. For developers seeking practical solutions that combine flexibility, performance, and affordability, Gemini 2.5 Flash offers a compelling option. Its ability to adapt to diverse tasks and optimize resource allocation ensures that it can meet the demands of modern AI challenges effectively. Media Credit: Prompt Engineering Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Geeky Gadgets
11-06-2025
- Geeky Gadgets
Master the Art of Prompt Engineering and Unlock AI's Full Potential
What if mastering a single skill could transform the way you interact with AI, unlocking its full potential to solve problems, generate ideas, and streamline tasks? Welcome to the world of prompt engineering, a discipline that's quickly becoming indispensable in the age of artificial intelligence. Whether you're a curious beginner or a seasoned user, crafting the right prompts can mean the difference between mediocre results and new insights. Think of it as learning to ask the perfect question—one that guides AI to deliver exactly what you need, every time. This how-to, brought to you by Matthew Berman, is your roadmap to mastering this critical skill, from foundational principles to advanced techniques. Matthew Berman uncovers the secrets to creating clear, specific, and relevant prompts that drive consistent and high-quality outputs. You'll also explore advanced strategies, like iterative refinement and contextual framing, that can elevate your AI interactions to new heights. Along the way, we'll tackle common challenges, share practical examples, and reveal tips for optimizing prompts across diverse applications—from content creation to data analysis. By the end, you won't just understand prompt engineering—you'll be equipped to use it as a powerful tool to amplify your work and ideas. So, what makes a prompt truly effective? Let's explore the answer together. Mastering Prompt Engineering Understanding Prompt Engineering and Its Significance Prompt engineering involves designing and refining inputs—referred to as 'prompts'—to guide AI models in generating accurate and relevant outputs. The quality of a prompt directly impacts the AI's performance. For example, a well-constructed prompt can enable an AI to summarize complex topics, generate innovative ideas, or solve technical problems with precision. By mastering this skill, you can unlock the full potential of AI systems across diverse applications, such as content creation, data analysis, and customer support. Effective prompt engineering ensures that the AI delivers outputs that align with your objectives, making it an indispensable tool in using AI technology. Core Principles for Crafting Effective Prompts Creating effective prompts requires adherence to three fundamental principles: clarity, specificity, and relevance. These principles form the foundation of successful prompt engineering. Clarity: A clear prompt eliminates ambiguity, making sure the AI understands your request. For instance, instead of saying, 'Explain this,' specify what 'this' refers to and the type of explanation you require. A clear prompt might be, 'Explain the concept of renewable energy in simple terms.' A clear prompt eliminates ambiguity, making sure the AI understands your request. For instance, instead of saying, 'Explain this,' specify what 'this' refers to and the type of explanation you require. A clear prompt might be, 'Explain the concept of renewable energy in simple terms.' Specificity: Narrowing the scope of your request reduces the likelihood of irrelevant or generic responses. For example, instead of asking, 'Describe renewable energy,' you could say, 'List three advantages of solar energy compared to fossil fuels.' Narrowing the scope of your request reduces the likelihood of irrelevant or generic responses. For example, instead of asking, 'Describe renewable energy,' you could say, 'List three advantages of solar energy compared to fossil fuels.' Relevance: Align your prompt with the AI model's capabilities. Understanding the strengths and limitations of the system is crucial for crafting prompts that yield meaningful results. For example, some models excel at creative writing, while others are better suited for technical analysis. By applying these principles, you can create prompts that are actionable and precise, leading to more effective and reliable outputs. Prompt Engineering Guide : From Beginner to Advanced Watch this video on YouTube. Take a look at other insightful guides from our broad collection that might capture your interest in prompt writing. Advanced Techniques for Refining Prompts Refining prompts is an iterative process that involves testing and improving their effectiveness. Advanced techniques can help you fine-tune prompts for greater precision and relevance, especially when working on complex tasks. Iterative Adjustments: Analyze the AI's initial responses to identify areas for improvement. If the output is too vague, revise the prompt to include more detailed instructions. For example, instead of 'Explain climate change,' you might say, 'Explain the primary causes of climate change and their impact on global ecosystems.' Analyze the AI's initial responses to identify areas for improvement. If the output is too vague, revise the prompt to include more detailed instructions. For example, instead of 'Explain climate change,' you might say, 'Explain the primary causes of climate change and their impact on global ecosystems.' Contextual Framing: Adding context or constraints to your prompt can guide the AI toward more accurate and relevant responses. For instance, specifying 'Assume the audience is unfamiliar with technical jargon' helps the AI tailor its output for a non-technical audience. Adding context or constraints to your prompt can guide the AI toward more accurate and relevant responses. For instance, specifying 'Assume the audience is unfamiliar with technical jargon' helps the AI tailor its output for a non-technical audience. Layered Prompts: For complex tasks, use a series of prompts to guide the AI step by step. For example, start with 'Create an outline for a report on renewable energy,' followed by 'Expand on each section of the outline with detailed explanations.' These techniques allow you to refine prompts systematically, making sure that the AI delivers outputs that meet your expectations. Strategies for Iterative Optimization Prompt optimization is a continuous process that involves experimentation and refinement. A systematic approach can help you develop prompts that consistently deliver high-quality results. Experiment with Variations: Test different phrasing, formats, and structures to determine which version produces the best results. For example, compare the effectiveness of an open-ended question versus a directive statement for the same task. Test different phrasing, formats, and structures to determine which version produces the best results. For example, compare the effectiveness of an open-ended question versus a directive statement for the same task. Maintain a Prompt Log: Keep a record of prompts and their corresponding outputs. This helps you track what works, identify patterns, and build a library of effective prompts for future use. Keep a record of prompts and their corresponding outputs. This helps you track what works, identify patterns, and build a library of effective prompts for future use. Evaluate Outputs: Assess the AI's responses based on criteria such as relevance, coherence, and completeness. For instance, if the goal is to generate a persuasive argument, check whether the output includes logical reasoning, evidence, and a clear conclusion. By following these strategies, you can refine your prompts over time, making sure consistent and reliable performance from the AI. Addressing Common Challenges in Prompt Engineering Even with careful crafting, prompts may sometimes fail to produce satisfactory results. Understanding common challenges and their solutions can help you troubleshoot effectively. Vague or Irrelevant Outputs: Revisit the prompt's clarity and specificity. Ensure the instructions are explicit and provide additional context if needed. For example, instead of 'Describe this topic,' specify, 'Describe the benefits of renewable energy with three examples.' Revisit the prompt's clarity and specificity. Ensure the instructions are explicit and provide additional context if needed. For example, instead of 'Describe this topic,' specify, 'Describe the benefits of renewable energy with three examples.' Overly Generic Responses: Add constraints or request more detail. For instance, instead of 'Explain renewable energy,' you could say, 'Explain renewable energy with a focus on solar and wind power.' Add constraints or request more detail. For instance, instead of 'Explain renewable energy,' you could say, 'Explain renewable energy with a focus on solar and wind power.' Task Complexity: Break down large tasks into smaller, manageable components. For example, instead of asking the AI to 'Write a detailed report,' divide the task into sections, such as 'Create an outline' and 'Expand on each section.' By addressing these challenges systematically, you can refine your prompts to achieve better outcomes and more precise results. Maximizing the Potential of AI Models To fully use AI models, it is essential to align your prompts with the model's strengths. Some models excel at creative tasks, such as storytelling or brainstorming, while others are better suited for analytical or technical challenges. Familiarize yourself with the specific capabilities of the AI system you are using and tailor your prompts accordingly. Additionally, staying informed about advancements in AI technology can help you adapt your prompt engineering techniques. As models evolve, new features and capabilities may become available, offering opportunities to enhance your interactions with AI systems. By combining a deep understanding of the model's capabilities with effective prompt engineering techniques, you can maximize the value of AI in your work and achieve superior outcomes. Media Credit: Matthew Berman Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Geeky Gadgets
04-06-2025
- Geeky Gadgets
Google Gemini Diffusion : The Future of Smarter, Faster Text Creation
What if the future of text generation wasn't just faster, but smarter and more adaptable? Enter Gemini Diffusion, a new approach that challenges the long-standing dominance of autoregressive models. By using the power of diffusion-based techniques—previously celebrated in image and video generation—this innovative system reimagines how text is created. Imagine crafting entire paragraphs in parallel, refining specific sections without disrupting the rest, and achieving speeds of up to 800 tokens per second. It's not just about efficiency; it's about precision and creative freedom. But with great promise comes great complexity, and Gemini Diffusion's journey is as much about overcoming challenges as it is about innovation. This overview by Prompt Engineering explores the fantastic potential of Gemini Diffusion, diving into its unique strengths, current limitations, and real-world applications. From collaborative editing to algorithm visualization, the model's versatility hints at a future where text generation tools are faster, more intuitive, and more responsive than ever before. Yet, the road ahead isn't without obstacles—technical hurdles and nuanced challenges still shape its evolution. Whether you're a developer, writer, or simply curious about the next frontier of AI, Gemini Diffusion offers a fascinating glimpse into what's possible when speed meets precision. Could this be the shift that redefines how we create and interact with text? Let's explore. Gemini Diffusion Explained How Diffusion-Based Text Generation Stands Out Diffusion models, such as Gemini Diffusion, distinguish themselves by generating text in parallel rather than sequentially. Unlike autoregressive models, which produce tokens one at a time to maintain coherence, diffusion models generate all tokens simultaneously. This parallel processing not only accelerates output but also enables iterative refinement, allowing for more controlled and targeted adjustments. For example, when editing a specific section of a paragraph, Gemini Diffusion can focus on refining that portion without altering the rest of the text. This capability provides greater precision and localized control, making it particularly valuable for tasks that require frequent edits or adjustments, such as collaborative writing or technical documentation. Performance Strengths and Current Limitations One of the most notable advantages of Gemini Diffusion is its speed. Capable of generating up to 800 tokens per second, it is well-suited for applications that demand rapid output, including web content creation, game script development, and algorithm visualization. This efficiency makes it an attractive option for professionals seeking to streamline their workflows. However, the model's performance diminishes when tasked with complex reasoning or highly structured outputs. While effective for straightforward prompts, it struggles with nuanced or multi-layered content, highlighting its current limitations in handling sophisticated challenges. These constraints underscore the need for further refinement to expand its applicability to more intricate use cases. What is Gemini Diffusion? Watch this video on YouTube. Gain further expertise in AI text generation by checking out these recommendations. Comparing Diffusion Models to Autoregressive Models Autoregressive models have long been the standard for text generation, producing tokens sequentially to ensure coherence and logical flow. While reliable, this process is inherently slower and less adaptable to iterative changes. In contrast, diffusion models like Gemini Diffusion generate all tokens simultaneously, offering a significant speed advantage. Additionally, their ability to refine specific sections of text without regenerating the entire output makes them particularly useful for tasks such as collaborative editing, code refinement, and creative writing. This flexibility positions diffusion models as a compelling alternative to traditional approaches, especially for users who prioritize efficiency and precision. Technical Challenges in Training Diffusion Models Despite their advantages, diffusion models face several technical challenges. Training a large language model like Gemini Diffusion requires substantial computational resources and advanced technical expertise. Moreover, details about the model's architecture, such as its context window size and optimization techniques, remain unclear. This lack of transparency makes it difficult to fully evaluate its capabilities and potential. These challenges highlight the complexities of developing diffusion-based text generation models. Overcoming these barriers will be essential to unlocking their full potential and making sure their scalability for broader applications. Applications and Real-World Use Cases Gemini Diffusion has already demonstrated its versatility across a range of creative and technical applications. Some of its notable use cases include: Generating interactive games, such as tic-tac-toe, with dynamic and responsive text-based interactions. Developing drawing applications and visual tools that integrate text-based instructions or annotations. Animating algorithms for educational purposes, providing clear and concise textual explanations alongside visual demonstrations. Editing text or code with precision, allowing localized changes without regenerating the entire content. These capabilities make Gemini Diffusion particularly valuable for developers, writers, and creators who aim to enhance their productivity. Its combination of speed and precision underscores its potential to redefine workflows in various industries. Historical Context and Unique Challenges in Text Generation Diffusion models have a well-established history in image and video generation, where they have been used to create high-quality visuals with remarkable detail. However, their application to text generation is relatively new and presents unique challenges. Unlike visual media, text generation requires maintaining grammatical coherence, logical consistency, and contextual relevance—factors that are less critical in image-based tasks. Earlier efforts, such as Mercury by Inception Labs, laid the groundwork for diffusion-based text generation. Gemini Diffusion builds on these innovations, adapting diffusion techniques to address the complexities of text. This evolution reflects the growing potential of diffusion models to tackle domain-specific challenges, particularly in creative and technical fields. The Future of Diffusion Models in Text Generation While Gemini Diffusion is not yet a definitive breakthrough, it represents a promising step forward in text generation technology. By addressing the limitations of autoregressive models and using the unique strengths of diffusion, it opens the door to new possibilities in writing, editing, and creative content generation. As research and development continue, diffusion models could unlock innovative tools for faster, more efficient workflows. Whether you're a developer, writer, or content creator, these advancements may soon redefine how you approach text-based projects. By bridging the gap between speed and precision, Gemini Diffusion paves the way for a new era of text generation technology, offering exciting opportunities for professionals across various domains. Media Credit: Prompt Engineering Filed Under: AI Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
