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GitHub热门项目 | IP addresses break, dial keys instead. Modular networking stack in Rust. | Stars: 9,076 | 326 stars today | 语言: Rust

GitHub热门项目 | A markup-based typesetting system that is powerful and easy to learn. | Stars: 54,316 | 75 stars today | 语言: Rust

GitHub热门项目 | Extremely fast Query Engine for DataFrames, written in Rust | Stars: 38,779 | 17 stars today | 语言: Rust

GitHub热门项目 | Graphs that teach > graphs that impress. Turn any code into an interactive knowledge graph you can explore, search, and ask questions about. Works with Claude Code, Codex, Cursor, Copilot, Gemini CLI, and more. | Stars: 61,090 | 1,222 stars today | 语言: TypeScript

GitHub热门项目 | Focus on what matters instead of fighting with Git. | Stars: 21,588 | 5 stars today | 语言: TypeScript

GitHub热门项目 | NocoBase is an open-source AI + no-code platform for building business systems fast. Instead of generating everything from scratch, AI works on top of production-proven infrastructure and a WYSIWYG no-code interface, so you get both speed and reliability. | Stars: 22,945 | 118 stars today | 语言: TypeScript

GitHub热门项目 | ☕️ Classic, reliable, trusted test framework for Node.js and the browser | Stars: 22,956 | 12 stars today | 语言: JavaScript

GitHub热门项目 | 📈 Get real-time stocks from TradingView | Stars: 3,638 | 67 stars today | 语言: JavaScript

GitHub热门项目 | The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw | Stars: 65,108 | 150 stars today | 语言: Python

Microsoft has finally refreshed its premium Surface Laptop and Surface Pro with new chips, but the update comes with a steep price hike.

The commercial was submitted by the Freedom of the Press Foundation to run during Donald Trump’s UFC event. It criticized the $111 billion merger as a threat to the First Amendment.

Microsoft's new Surface Pro and Surface Laptop are about 50 to 60 percent more expensive than the previous generation. Thanks, AI!

Microsoft is launching new Surface Laptops and a Surface Pro with Qualcomm Snapdragon X2 processors. These are direct follow-ups to the Surface Laptops and Surface Pro from 2024 that launched with Snapdragon X1 chips and jumpstarted Microsoft's Copilot Plus PC initiative with Windows on Arm. The new X2 Surfaces are available today. The Surface Pro […]

Comcast is launching a same-day Xfinity Wi-Fi offering that spares you from waiting for your new router. Starting today, new Xfinity customers in almost 20 markets - including Atlanta, Chicago, Denver, Houston, Nashville, Philadelphia, San Francisco, and more - can receive the Xfinity Gateway equipment they need to get online the same day they order […]

It's been more than a year since Schlage announced its first smart lock to support ultra wideband technology (UWB), but now it's finally almost available to purchase. Starting June 29th, the Schlage Sense Pro deadbolt lock will be available for $399 in the US, allowing customers to unlock their doors by simply approaching them with […]

There is a persistent myth that to build a worthy code assistant, you absolutely must use GPT or Claude. This is false. You don't need a 1-trillion parameter model. You need a small local model and extremely rigorous engineering around it. This is the direction history is taking for companies. As Mark Zuckerberg mentioned, the future isn't a single omniscient model, but "every company having its own specialized AI" . And this specialization necessarily involves fine-tuning and local deployment (or on sovereign servers) to guarantee data security. The thesis behind the construction of Vibrisse Agent can be summed up in one sentence: Small models, Great tools. In this article, I will detail the technical stack and concrete engineering solutions I implemented to tame a local model and make it reliable in production: LangGraph, Ollama, FastAPI, React (no build step, with embedded custom CSS) , all running on a machine with 32 GB of RAM. For the curious who want to run the agent on their machine right now: // MacOs / Linux curl -sSL https://agent.vibrisse-studio.dev/install.sh | bash // Windows irm https://agent.vibrisse-studio.dev/install.ps1 | iex Architecture: Why a State Machine (LangGraph)? At first, when building an LLM application, we tend to think in sequential chains: Input -> Prompt -> Tool -> Output . The problem is that if one node fails, the whole chain stops without us being able to catch the error or understand the context of the crash. That's where LangGraph comes in. Vibrisse's architecture isn't a chain, it's a state machine . Every node in the graph has a very precise responsibility, shares a global conversation state, and uses conditional transitions to move to the next node. I implemented the Supervisor / Worker pattern: The Supervisor analyzes the user's intent. It does nothing else but route. It dispatches the task to specialized Workers (the RAG Worker, the Search Worker, the Ghost Worker...). If a Worker fails or needs more information, it can se

Il y a un mythe persistant selon lequel pour construire un assistant de code digne de ce nom, il faut absolument utiliser GPT ou Claude. C'est faux. Vous n'avez pas besoin d'un modèle à 1 trillion de paramètres. Vous avez besoin d'un modèle local de taille réduite et d'une ingénierie extrêmement rigoureuse autour de lui. C'est d'ailleurs le sens de l'histoire pour les entreprises. Comme l'évoquait Mark Zuckerberg, l'avenir n'est pas à un modèle omniscient unique, mais à "chaque entreprise avec sa propre IA spécialisée" . Et cette spécialisation passe obligatoirement par le fine-tuning et le déploiement local (ou sur serveurs souverains) pour garantir la sécurité des données. La thèse derrière la construction de Vibrisse Agent tient en une phrase : Small models, Great tools. Dans cet article, je vais détailler la stack technique et les solutions d'ingénierie concrètes que j'ai mises en place pour dompter un modèle local et le rendre fiable en production : LangGraph, Ollama, FastAPI, React (sans build step, avec CSS custom embarqué) , le tout tournant sur une machine avec 32 Go de RAM. Pour les curieux qui souhaitent lancer l'agent sur leur machine dès maintenant : // MacOs / Linux curl -sSL https://agent.vibrisse-studio.dev/install.sh | bash // Windows irm https://agent.vibrisse-studio.dev/install.ps1 | iex L'Architecture : Pourquoi une Machine à États (LangGraph) ? Au début, quand on construit une application LLM, on a tendance à penser en chaîne séquentielle : Input -> Prompt -> Outil -> Output . Le problème, c'est que si un nœud échoue, toute la chaîne s'arrête sans qu'on puisse rattraper l'erreur ou comprendre le contexte du plantage. C'est là qu'intervient LangGraph . L'architecture de Vibrisse n'est pas une chaîne, c'est une machine à états . Chaque nœud du graphe a une responsabilité très précise, partage un état global de la conversation, et utilise des transitions conditionnelles pour passer au nœud suivant. J'ai implémenté le pattern Supervisor / Worker : L

If you’ve used S3, MinIO, or any cloud storage API, it’s easy to assume object storage is just a “cloud folder system.” That assumption is wrong — and it leads to confusion when you start working with distributed systems. Object storage is not a file system. It’s closer to a distributed key-value system with strong durability guarantees and a very specific access model . Once you understand that shift, a lot of cloud infrastructure starts to make more sense. The mental model most people start with When people first see object storage, they imagine something like this: /photos/cats.png /photos/dogs.png A hierarchical file system: folders subfolders files inside directories This is how traditional systems like ext4 or NTFS work. But object storage doesn’t actually work this way. The actual model: key → object Object storage is much simpler at its core: key → value Example: key : photos/cats.png value : <binary data> There are no real folders. “folders” are just string prefixes used for organization. That’s it. Why this design exists This model isn’t accidental. It solves real distributed system problems. Traditional file systems struggle when you try to: scale across many machines replicate data reliably handle partial failures coordinate metadata changes at scale Object storage avoids many of these problems by simplifying the model. Instead of supporting complex file operations, it focuses on: store object retrieve object delete object list objects by prefix Nothing more. The most important design choice: immutability In most object storage systems: Objects are not modified in place. If you “update” a file, what actually happens is: upload a new object replace the key pointer old object becomes orphaned (eventually cleaned up) This is a huge shift from file systems. Why this matters Immutability makes distributed systems easier because: no concurrent write conflicts on the same object replication becomes simpler caching becomes safer failure recovery is easier to rea

🚀 Hello, DEV Community! I'm Nader Al Shawki , a final-year AI Engineering student at Al-Razi University, Yemen. This is my first post here, and I'm excited to start sharing my journey with this amazing community. 🎯 Who Am I? I'm passionate about building production-grade AI systems that solve real-world problems. My main areas of focus are: 🖼️ Computer Vision & Deep Learning 🤖 ML Model Deployment (Docker, FastAPI, REST APIs) 🧠 LLMs, RAG, and AI Agents (currently learning) 📊 Data Visualization & Analytics (Power BI) 💡 What I've Built So Far 1. 🍅 Tomato Leaf Disease Detection Platform Tech: YOLOv8, PyTorch, FastAPI, Docker What it does: Detects tomato leaf diseases from images with real-time inference. Containerized with Docker for easy deployment. 2. 🫁 Pneumonia Detection System Tech: PyTorch, CNN Architecture, Medical Imaging What it does: A deep learning model that detects pneumonia from chest X-ray images. 3. 📊 Sales Profit Analysis Dashboard Tech: Power BI, DAX, Data Analysis What it does: Interactive dashboard for tracking sales KPIs. 4. 😀 Face Detection & Emotion Recognition Tech: OpenCV, Deep Learning What it does: Real-time face detection, age estimation, emotion recognition, and gender classification. 5. 🍽️ Restaurant Website Tech: HTML5, CSS3, JavaScript What it does: Fully responsive restaurant website with interactive UI. 🌱 What I'm Currently Learning LLMs (Large Language Models) RAG (Retrieval-Augmented Generation) LangChain & AI Agents Workflow automation with n8n 🔗 Let's Connect 🐙 GitHub: Naderalshawki 💼 LinkedIn: in/nader-al-shawky 📫 Email: naderalshawki@gmail.com Thanks for reading! I'll be posting regularly about AI projects, tutorials, and lessons learned. Stay tuned! 🚀

Every Indian data scientist hits the same wall. You need district-level population data. You go to censusindia.gov.in. You find hundreds of inconsistent Excel files with merged headers, footnote rows, and zero documentation. You spend a full day just loading the data before doing any actual analysis. I fixed that. Once. For everyone. What I built indiaset/census-2011 India's Census 2011 district data, clean, typed, and ready for pandas. 640 districts · 29 columns · 0 missing values Validated against official India total · LGD codes attached Load it in 4 lines from huggingface_hub import hf_hub_download import pandas as pd path = hf_hub_download ( repo_id = " indiaset/census-2011 " , filename = " census_2011_districts_final.parquet " , repo_type = " dataset " ) df = pd . read_parquet ( path ) print ( df . shape ) # (640, 29) What's in it Column Description state_code Census 2011 state code state_name Official state/UT name district_code Census 2011 district code district_name District name as per Census lgd_code LGD permanent district code district_name_lgd District name as per LGD pop_total Total population pop_male Male population pop_female Female population pop_under6_total Children under 6 years pop_sc Scheduled Caste population pop_st Scheduled Tribe population literate_total Literate persons literate_male Literate males literate_female Literate females illiterate_total Illiterate persons workers_total Total workers workers_male Male workers workers_female Female workers non_workers_total Non workers literacy_rate Literate / Total × 100 sex_ratio Females per 1000 males workforce_participation Workers / Total × 100 The validation The most important test - do all 640 district populations sum to India's official total? print ( df [ ' pop_total ' ]. sum ()) # 1210854977 ✅ — exact match, zero discrepancy What the data actually shows Most literate district → Pathanamthitta, Kerala : 88.74% Least literate district → Alirajpur, Madhya Pradesh : 28.77% Literacy gap acro