John Sanches

Here are some of the most popular and well-regarded stand-up comedy specials from 2025, based on current ratings and reviews: Andrew Schulz: Life: Filmed at NYC's Beacon Theatre, this special covers Schulz's experiences with starting a family and going through IVF treatments with his trademark sharp observational humor. Bill Burr: Drop Dead Years: Burr delivers his signature blend of introspection and unapologetic humor, tackling topics from male sadness to dating advice. Rosebud Baker: The Mother Lode: Baker offers a raw and honest look at motherhood, filmed before and after having a child. Iliza Shlesinger: A Different Animal: Shlesinger brings her high-energy style to topics like relationships, gender dynamics, and taboo subjects. Sarah Silverman: PostMortem: Silverman turns personal grief into comedy after losing both parents, finding humor in funeral planning. Ali Siddiq: My Two Sons: This special is gaining popularity quickly. Shane Gillis: Known for his range of simple to complex humor. Mark Normand: Known for his awkward delivery and self-deprecating humor. For potentially overlooked gems, consider: Bron Lewis: Obviously: Conner O'Malley: Stand Up Solutions: Carmen Christopher: Live from the Windy City: Zoltan Kaszas: Honorary Jones These specials offer a mix of established comedians and rising stars, covering a wide range of comedic styles and topics.

In 2025, NFTs are evolving beyond just speculative art into verifiable utility. While the hype and art market speculation of 2021 have significantly cooled (with art NFT trading volumes collapsing), their underlying technology is finding real-world applications in gaming, digital identity, tokenizing real-world assets, and loyalty programs, cementing their role as a core building block of the decentralized web.

For general desktop use, Pop!_OS offers excellent out-of-the-box hardware support (especially for NVIDIA) and a polished GNOME experience, while Nobara Linux (Fedora-based) is specifically optimized for gaming and content creation.

Linux gaming in 2025 is far from being a niche curiosity; it's a legitimate, strong contender, though not yet fully "mainstream" in the sense that it rivals Windows' market share. The Steam Deck and continuous Proton development have been game-changers, pushing compatibility and user experience to unprecedented levels. What Games Run Well?Vast Majority of Steam Library: Thanks to Proton (Valve's compatibility layer), a massive percentage of Windows-only games on Steam run flawlessly or with minor tweaks. ProtonDB (protondb.com) is the go-to resource for community reports on game compatibility, performance, and necessary tweaks. AAA Titles: Many modern AAA games run exceptionally well. Titles like Elden Ring, Cyberpunk 2077, God of War, and Horizon Zero Dawn are often reported as "Platinum" or "Gold" on ProtonDB, meaning they work out of the box or with minimal fuss, often with comparable performance to Windows. Native Linux Games: A growing number of games have native Linux versions (e.g., Counter-Strike 2, Dota 2, Baldur's Gate 3). These typically offer the best performance and stability. Older Titles: Many older Windows games often run better on Linux with Proton than they do on modern Windows versions, which can sometimes have compatibility issues with legacy APIs. Your Setup and TweaksMy go-to setup for Linux gaming: Distribution: Nobara Linux (a Fedora-based distro optimized for gaming and content creation) or Pop!_OS (Ubuntu-based, good for NVIDIA users). Arch-based distros like Manjaro or Bazzite (a Fedora Atomic spin, like SteamOS) are also popular for bleeding-edge drivers and performance. Graphics Drivers: Always use the latest proprietary drivers for NVIDIA, or the latest Mesa drivers for AMD. Distros like Nobara and Pop!_OS often include these pre-installed or make installation easy. Proton Version: While Steam's default Proton is great, Proton GE (GloriousEggroll) often offers better compatibility and performance for specific games, especially newer releases or those with problematic cutscenes. It's easily installed via tools like ProtonUp-Qt. Filesystem: Use Btrfs or Ext4. Some users report slight performance gains with specific settings, but it's often negligible for most. Tweaks: GameMode: A daemon that applies performance-enhancing tweaks (CPU governor, I/O priority, etc.) when games are running. Most gaming-focused distros include it. DXVK/VKD3D-Proton: These are translation layers (DirectX to Vulkan) that are integrated into Proton and are crucial for performance. Ensure they're up-to-date. Shader Pre-caching: Let Steam download and compile shaders before launching a game to avoid stuttering (jank) during gameplay. This can take time for new games but is worth it. Wayland (Increasingly): While X11 is still the most stable, Wayland compositors (especially KDE Plasma with Gamescope) are improving rapidly, offering features like proper HDR and VRR, and potentially lower latency. Anti-Cheat and Performance IssuesAnti-Cheat (The Main Hurdle): This remains the biggest hurdle for Linux gaming. While Easy Anti-Cheat (EAC) and BattlEye have official Proton support, it's up to game developers to enable it for their specific titles. Some developers simply choose not to, or use proprietary/kernel-level anti-cheats (e.g., Vanguard for Valorant, RICOCHET for newer Call of Duty titles) that often explicitly block Linux users. This means popular multiplayer titles might be unplayable. Status in 2025: Many major online games do work (e.g., Apex Legends, Rust, War Thunder), but a significant number of others still don't (e.g., Destiny 2, Valorant, newer Call of Duty titles, Fortnite on desktop). The situation is constantly evolving, but it's the primary reason Linux isn't fully mainstream for all gamers. Performance Issues: Shader Compilation Stutter: This is the most common performance "issue." When a new game or update is launched, Vulkan shaders need to be compiled for your specific hardware. This can cause brief stutters the first time certain effects or assets appear. Pre-caching helps, but some stutter is unavoidable. NVIDIA Performance: While vastly improved, NVIDIA on Linux can sometimes require more tinkering than AMD due to NVIDIA's proprietary driver stack. Ray tracing performance might also be slightly worse than on Windows. Bleeding Edge Hardware/Software: Sometimes, brand-new games or very new hardware might take a bit longer for Proton/drivers to fully optimize. Proton Version Selection: Using the wrong Proton version can significantly impact performance or cause crashes. Checking ProtonDB is crucial. My ExperienceI've been primarily gaming on Linux for years. My current setup (AMD Ryzen 7, RX 6800, Nobara Linux) offers a fantastic experience. I've played through games like Cyberpunk 2077, The Witcher 3, Deep Rock Galactic, and many indie titles with performance often on par with or even better than Windows in some cases (especially if the Windows version has bloat or specific driver overhead). The biggest frustration is when a game I want to play has an anti-cheat that explicitly blocks Linux, or developers simply refuse to enable Proton support. This is where the "not truly mainstream" feeling still lingers. However, for a single-player focus or games that do have anti-cheat enabled, Linux gaming is a powerful, stable, and increasingly user-friendly option. The Steam Deck's success has undeniably accelerated development and awareness, making 2025 a very exciting time for Linux gamers.

Authentication and authorization are fundamental pillars of web application security, yet they remain common targets for attackers. As developers, we constantly strive to balance strong security measures with a smooth user experience. What are the most effective and recommended approaches for implementing robust authentication (e.g., multi-factor authentication, passwordless options, single sign-on) and fine-grained authorization (e.g., role-based access control, attribute-based access control) in modern web applications? How can we defend against emerging threats like credential stuffing, session hijacking, or token manipulation without overly burdening the user? I'm also curious about the pros and cons of using established libraries/frameworks versus building custom solutions, and how to securely manage secrets and API keys in a distributed application architecture.

We're all familiar with the OWASP Top 10, which provides a great foundation for web application security. However, with the rapid evolution of web technologies (SPAs, APIs, microservices, serverless), new attack vectors and sophisticated exploitation techniques are constantly emerging. Beyond the basic understanding of the OWASP Top 10, what are the most critical and often overlooked vulnerabilities in modern web application stacks (e.g., Node.js, React/Angular, GraphQL APIs, containerized deployments)? What are practical, actionable mitigation strategies and best practices for developers and security professionals to defend against these advanced threats, particularly in environments with continuous integration/continuous deployment (CI/CD) pipelines? I'm looking for insights into security challenges beyond the basics, such as insecure deserialization, SSRF in cloud environments, GraphQL specific attacks, or container escape vulnerabilities.

As AI becomes more prevalent in mobile app development, new monetization strategies and ethical concerns are emerging. For AI-powered mobile apps, beyond traditional ads and IAPs, what are innovative and sustainable monetization models that leverage AI capabilities (e.g., premium AI features, subscription tiers for advanced AI insights, data-driven personalized offers)? Also, what are the most pressing ethical challenges developers face when building AI into mobile apps, particularly concerning user data privacy, algorithmic bias, transparency in AI decision-making, and potential misuse of AI for manipulation? Are there specific considerations or best practices for addressing these challenges when targeting users in emerging markets like Bangladesh, where digital literacy and privacy regulations might differ from Western contexts? To provide a more comprehensive answer, I would perform the following searches: Here are two questions for a forum on "Mobile App Development Using AI," with a focus on their global implications and relevance to a Bangladeshi context where applicable:

I'm developing a mobile application and want to leverage AI to provide a smarter, more personalized user experience, with a focus on on-device processing to ensure privacy and offline functionality. What are the most practical and efficient ways to integrate AI/ML capabilities directly into mobile apps (iOS/Android)? I'm particularly interested in frameworks like TensorFlow Lite, Core ML, or ML Kit, and real-world examples of how they are used to achieve features like intelligent content recommendations, real-time image/text analysis, predictive input, or adaptive UI. What are the key considerations for model optimization (size, performance), data privacy, and the trade-offs between on-device and cloud-based AI for common mobile app functionalities?

Best Free Web Dev Resources (2025)Top Platforms: freeCodeCamp: Full curriculum, project-based, certifications. The Odin Project: Project-focused, full-stack, strong community. MDN Web Docs: Official, comprehensive reference for HTML, CSS, JS. Best YouTube Channels: Traversy Media: Project-based, crash courses. freeCodeCamp.org: Full-length courses. Academind: In-depth framework courses. Personal Roadmap: Foundations: HTML, CSS, JavaScript (Vanilla JS), Git/GitHub. Build a simple portfolio. Frontend: Pick a framework (React/Vue/Angular). Build an interactive app. Backend: Node.js/Python + Database (SQL/NoSQL). Build a full-stack app. Deploy: Learn to deploy (Netlify, Vercel, Render). Free Tools & Tips: Code Editor: VS Code (with extensions). Browser DevTools: Essential for debugging. GitHub: For version control and portfolio. Online Playgrounds: CodePen, Replit. Design: Figma (free tier), Google Fonts, Font Awesome. Practice: Consistently build projects; don't just watch.

While freelancing on platforms like Upwork and Fiverr is a common entry point, many coders are looking for more sustainable and scalable ways to generate online income that aren't directly trading time for money. What are some proven strategies for coders to build passive or semi-passive income streams using their skills on a global scale? I'm interested in detailed insights into options like creating and selling digital products (e.g., WordPress themes, plugins, mobile app templates, code snippets, e-books, online courses), developing and monetizing SaaS products (Micro-SaaS), contributing to open-source projects with sponsorship models, or even technical content creation (blogging, YouTube tutorials) that can generate ad revenue or affiliate income. What are the global market considerations, and what specific tools or platforms are best suited for these ventures for coders aiming for a worldwide audience?

The rapid advancement of Artificial Intelligence is undeniably changing the global job market. While some argue AI will primarily augment human work and create new opportunities, others predict significant job displacement, particularly in routine or predictable roles. What are the prevailing global predictions regarding AI's net impact on employment over the next 5-10 years across different sectors (e.g., manufacturing, service, healthcare, creative)? Which job types are most at risk globally, and what new roles are emerging? Crucially, what global strategies are governments, educational institutions, and industries implementing to address the need for widespread reskilling and upskilling of the workforce to prepare for an AI-driven economy? Are there specific challenges or unique considerations for countries like Bangladesh in preparing their workforce for these global shifts?

Generative AI tools (like ChatGPT, DALL-E 3, Sora) are rapidly transforming various creative industries, from art, music, and writing to filmmaking and advertising. Globally, there's a huge discussion about the opportunities these tools present for creativity and efficiency, but also significant concerns. What are the most prominent global trends in how generative AI is being adopted by creative professionals and industries? More importantly, what are the major ethical dilemmas arising from this adoption, such as copyright infringement, questions of authorship and originality, potential job displacement for human artists, and the proliferation of deepfakes? How are different countries and regions, including developing nations like Bangladesh, beginning to address these challenges through policy, industry standards, or new business models to ensure fair compensation and protect human creativity?