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The Enhanced Games — a singular sporting competition where a majority of the athletes were on performance enhancing drugs — may herald a new business model that the tech industry is ready to embrace.

As part of my game development journey, I recently created Hidden Collector , a Unity-based game where players explore levels and collect hidden items while progressing through different challenges. This project started as a way for me to improve my Unity and C# skills, but it quickly became an opportunity to learn about game design, UI systems, audio management, scene transitions, and player experience. What I Worked On While building Hidden Collector, I implemented: Player movement and interactions Collectible item systems Multiple game levels UI menus and game screens Audio and sound effects Progress tracking Game flow and scene management Challenges During Development One of the biggest challenges was making different game systems work together smoothly. Something as simple as collecting an item often required updates to UI elements, game state management, and progression systems. Debugging these interactions taught me a lot about organizing Unity projects and writing maintainable code. What I Learned This project helped me gain experience with: Unity Engine C# scripting Game architecture UI implementation Audio management Debugging and testing Most importantly, I learned that building complete projects teaches far more than following tutorials. Play the Game You can try Hidden Collector here: https://sinxcos07.itch.io/hiddencollector Screenshots What's Next? I'm continuing to improve my game development skills by building new projects, experimenting with different mechanics, and learning more about creating engaging player experiences. If you try the game, I'd love to hear your feedback. By Suryansh Sinha (sinxcos07) Connect With Me GitHub: https://github.com/sinxcos07 LinkedIn: https://www.linkedin.com/in/suryansh-sinha/ Play Hidden Collector: https://sinxcos07.itch.io/hiddencollector

This is a submission for the Hermes Agent Challenge : Write About Hermes Agent What if the biggest limitation in AI today isn't reasoning, model size, or context windows? What if it's memory? Every morning, millions of people open ChatGPT, Claude, Gemini, or another AI assistant and start a conversation. The AI seems intelligent. It writes code. It explains concepts. It helps brainstorm ideas. It can even help design an entire software architecture. Then the conversation ends. Tomorrow? It remembers nothing. Imagine hiring a senior engineer who forgets everything at the end of every workday. Every morning you would need to explain: What your company does How your product works Which technologies you use Why certain decisions were made What happened yesterday Nobody would call that employee productive. Yet this is exactly how most AI systems operate. And it reveals something important: Most AI agents aren't actually learning from experience. They're simply reasoning over whatever context happens to be available right now. That distinction may define the future of agentic AI. Because the next generation of AI won't just need better reasoning. It will need memory. And that's where Hermes Agent becomes interesting. The Strange Reality of Modern AI The public perception of AI often looks like this: User → AI → Intelligence But the reality is closer to this: User → Context Window → AI → Response The AI only knows what exists inside its current context. Once that context disappears, so does most of its understanding. This is why many AI experiences feel surprisingly repetitive. You spend 30 minutes explaining your project. The AI finally understands your goals. The answers become better. The recommendations become more relevant. Then the session ends. The next conversation starts from scratch. Not because the model isn't powerful. But because the knowledge never became persistent. Context Windows Are Not Memory A context window is not memory. It is temporary working space.

This is a submission for the Hermes Agent Challenge : Write About Hermes Agent Most AI agents have a dirty secret: they forget everything the moment the session ends. You explain your project once. Then again next time. And again. The agent never gets better at your workflow — it just stays a general-purpose tool that happens to be smart. Hermes Agent is built differently. It ships with two systems that together form something closer to a genuine long-term memory: a Skills System and a Persistent Memory layer. This post digs into how they actually work — not the marketing summary, but the mechanics. The Problem With Stateless Agents Before getting into Hermes, it's worth understanding what problem this solves. Standard LLM-based agents operate inside a context window. Everything the agent knows during a session lives in that window. When the session ends, it's gone. The next time you open a conversation, you're talking to an agent with no memory of you, your codebase, your preferences, or the workflows you've developed together. Some tools patch this with naive "memory" — they dump a text blob of past conversations into the system prompt. This works up to a point, but it's not selective, it gets expensive as context grows, and it doesn't help the agent get better at tasks — just recall facts. Hermes takes a different approach with two distinct systems serving different purposes. System 1: The Skills System (Procedural Memory) Skills in Hermes aren't plugins you install. They're on-demand knowledge documents — markdown files the agent loads when it needs them, and more importantly, creates on its own when it discovers something worth remembering. The SKILL.md Format Every skill is a structured markdown file with a YAML frontmatter header: --- name : deploy-runbook description : Our deployment runbook — services, rollback, Slack channels version : 1.0.0 metadata : hermes : tags : [ deployment , runbook , internal ] requires_toolsets : [ terminal ] --- # Deploy Runbook

Despite headwinds from the current administration, automakers continue to release well-equipped EVs with bigger battery packs and increasingly faster charging speeds. For those who want to travel further between plugging in, the future is still bright, just slightly tinted. But there haven't been many sedans starting around or below $50,000, as crossover SUVs have largely […]

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As temperatures rise, some creatures pick fights while others struggle to learn.

This is a submission for the [GitHub Finish-Up-A-Thon Challenge] What I Built LeadBotX is an AI-powered lead generation platform prototype designed to simulate how modern businesses can automatically discover, filter, and manage high-quality leads using intelligent automation workflows. This project is not just a simple frontend demo — it represents a revived college-level concept that I initially started earlier but left incomplete due to time constraints and complexity. For this challenge, I revisited the idea and transformed it into a fully structured SaaS-style frontend system with improved UI, better workflow representation, and a more realistic product-like experience. The main goal of LeadBotX is to visually demonstrate how an AI-based lead generation system works end-to-end in a real-world SaaS environment. Tech Stack This project was built using modern frontend technologies: React.js → Component-based UI structure CSS3 → Custom responsive styling system AOS (Animate On Scroll) → Smooth scroll animations Lucide Icons & React Icons → UI iconography GitHub Copilot → Assisted in code generation, debugging, and UI improvements Demo Source Code: https://github.com/Khushisingh-dev/LeadBotX Production Landing Page: https://lead-bot-x.vercel.app/ Before (Initial Version)- After (Final Version)- The Comeback Story This project originally started as a college-level concept during my development learning phase. At that time, I built only a basic structure and initial UI, but I was unable to complete it due to time limitations and complexity of the idea. It remained an unfinished project for a long time. When I came across this challenge, I decided to revisit LeadBotX and transform it into something more meaningful and complete. Instead of just polishing the UI, I focused on: Rebuilding the structure into a proper SaaS layout Improving workflow clarity and user journey Enhancing UI/UX consistency across all sections Making the product feel like a real-world AI tool prot