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🔥 SandeepVashishtha / Eventra - Eventra is a comprehensive event management system that empo

GitHub热门项目 | Eventra is a comprehensive event management system that empowers organizers to create, manage, and track events seamlessly. Built with a modern tech stack featuring React frontend and Spring Boot backend, Eventra provides everything needed to run successful events from creation to post-event analytics. | Stars: 106 | 3 stars today | 语言: JavaScript

2026-05-31 原文 →
AI 资讯

How I Built Hidden Collector Game in Unity

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

2026-05-31 原文 →
AI 资讯

Moving Beyond the Context Window: The Agentic Memory Architecture

I’ve spent a lot of time lately thinking about why some LLM agents feel "intelligent" while others just feel like chatbots with a slightly better prompt. It almost always comes down to how the system handles memory. When we treat the context window as the only place for state, we hit a ceiling very quickly. To build an actual agent, we have to move away from "one big prompt" and toward a layered memory architecture. Agentic Memory can be categorized in 4 layers by their function: Working Memory: The current context window. It's our RAM—fast, essential, but wiped clean after every session. Semantic Memory: The Vector DB or knowledge base. This is where the "world rules" and global conventions live. It’s the reference manual the agent checks to stay aligned. Procedural Memory: The "how-to" layer. Instead of stuffing every tool description into the prompt, the agent maintains a lean index of skills and pulls in the full implementation only when a specific task triggers it. This keeps the context window clean. Episodic Memory: This is the hardest part. It's the ability to distill a past interaction into a reusable insight. The real engineering challenge here isn't storage—it's the "forgetting" logic. Deciding what is noise and what is a core pattern is where most frameworks still struggle. Depending on the use case, the architecture changes: Reflex Agents: Just Working Memory. Support Agents: Working + Procedural. Coding Agents: The full stack. The gap between a demo and a production-ready agent is usually the distance between simple RAG and a functioning episodic memory. The ability to compress experience into a usable state is still a significant hurdle. Which of these layers are you currently implementing, and how are you handling the "forgetting" logic in your episodic memory?

2026-05-31 原文 →
AI 资讯

🚀 Building an open-source email blast tool — free, self-hosted, no Mailchimp needed. Looking for contributors to help add: 📊 Open & click tracking 🐳 Docker support All issues are open. Jump in 👇 https://github.com/nikhilt101/email-blast-tool

GitHub - nikhilt101/email-blast-tool: Open source HTML email sender tool using CSV/XLSX + Gmail SMTP · GitHub Open source HTML email sender tool using CSV/XLSX + Gmail SMTP - nikhilt101/email-blast-tool github.com

2026-05-31 原文 →
AI 资讯

Progressive Distillation

Now that almost everyone has thought about or is actively integrating AI workflows into their projects, some might ask is this all worth the cost? Many think the current economics of the AI space don't scale and that there will be upward price movement. Others still might not be comfortable with sending their data to remote services for processing. Then there is the crowd that wants to deploy models in small spaces with limited compute. Are there ways we can deploy small models locally and run at a lower cost? Yes with Knowledge Distillation . Knowledge distillation can get a bad rap due to it's questionable use in training some Large Language Models (LLMs). But it's a perfectly valid way to transfer performance from a larger model to a smaller one. Especially when both models are yours and/or open. This article will explore progressive distillation which is a technique to incrementally transfer knowledge from a series of larger teacher models into a smaller student. Install dependencies Install txtai and all dependencies. pip install txtai [ pipeline - train ] datasets Setup the Training Pipeline The first step we need to do is setup up the training pipeline. We'll use the Hugging Face Training framework to build a series of models. The following code establishes a train method, test method and loads the classification training data. from datasets import load_dataset from transformers import AutoModelForSequenceClassification , AutoTokenizer from txtai.pipeline import HFTrainer , Labels def train ( teacher , student , distillation , ** kwargs ): trainer = HFTrainer () model = AutoModelForSequenceClassification . from_pretrained ( student , trust_remote_code = True ) tokenizer = AutoTokenizer . from_pretrained ( student , trust_remote_code = True ) return trainer ( ( model , tokenizer ), ds [ " train " ], columns = ( " sentence " , " label " ), maxlength = maxlength , teacher = teacher , distillation = distillation , ** kwargs ) def test ( model ): labels = Labels (

2026-05-31 原文 →
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