GitHub热门项目 | The Parity Polkadot Blockchain SDK | Stars: 2,789 | 0 stars today | 语言: Rust

GitHub热门项目 | Official repo for spec & SDK of MCP Apps protocol - standard for UIs embedded AI chatbots, served by MCP servers | Stars: 2,652 | 8 stars today | 语言: TypeScript

GitHub热门项目 | Auto-complete every Discord Quest in seconds. Paste one script, get all rewards. Resilient module loader, auto-claim, adaptive speed. No installs, no tokens, no dependencies. | Stars: 344 | 14 stars today | 语言: JavaScript

GitHub热门项目 | Low-code programming for event-driven applications | Stars: 23,474 | 4 stars today | 语言: JavaScript

GitHub热门项目 | State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server! | Stars: 16,221 | 1 star today | 语言: JavaScript

GitHub热门项目 | My blog website. | Stars: 302 | 37 stars today | 语言: JavaScript

GitHub热门项目 | Trail of Bits Claude Code skills for security research, vulnerability detection, and audit workflows | Stars: 6,363 | 18 stars today | 语言: Python

GitHub热门项目 | 🎯 All you need. Nothing you don't. Open source project management that works for you, not against you. | Stars: 4,677 | 188 stars today | 语言: TypeScript

GitHub热门项目 | Reverse Engineering / Authorized Penetration Testing / Security Research Skill Router Pack AI-powered routing + On-demand toolchain bootstrapping + Self-evolving knowledge base Supports Claude Code, Kiro, Cursor, Cline, and other AI coding clients 逆向/渗透/安全技能路由包 - AI 自动路由 + 按需自举工具链 + 自动进化经验库 | 支持 Claude Code / Kiro / Cursor / Cline 等代码 AI 客户端 | Stars: 10,112 | 612 stars today | 语言: PowerShell

The hot new roguelite twin-stick shooter lives up to the hype.

The sunny 2002 platformer was the direct follow-up to Super Mario 64.

Today is the day the EU's Right to Repair rules come into force that will, eventually, make it much easier to fix your broken gear.

This is a submission for DEV's Summer Bug Smash: Clear the Lineup powered by Sentry . You are migrating a 50,000-message Slack workspace to Zulip. Somewhere around message 31,000 the import dies with KeyError: 'ts' . Annoying, but here is the uncomfortable part: that is the lucky outcome. The unlucky one is "ts": "NaN" , where nothing dies, nothing warns, and your company's message history quietly comes out in the wrong order. TL;DR: Zulip's Slack importer used float(message["ts"]) unguarded, both as a sort key and as date_sent . One message with a missing or malformed ts aborted the entire import; a non-finite value like "NaN" did not even raise, it silently broke the sort. My fix ( zulip/zulip#39813 ) skips such messages with a warning and requires ts to parse to a finite float via math.isfinite . The regression test fails with KeyError: 'ts' on the old code. Project Overview Zulip is an open-source team chat server (Django/Python, ~25k stars) with an unusually strict engineering culture: near-total backend test coverage, strict mypy, and a commit discipline of "each commit is a minimal coherent idea". The code I touched lives in zerver/data_import/ : the subsystem that converts exports from Slack, Microsoft Teams, and Mattermost into Zulip's format. This subsystem has one property that should shape every line in it: the input is another tool's output. Import is a long batch process over data of arbitrary quality, and the admin running the migration has no way to "fix" what Slack's export tool produced. A pipeline that dies on record 31,207 of 50,000 is strictly worse than one that skips record 31,207 with a warning. Bug Fix or Performance Improvement get_messages_iterator() in zerver/data_import/slack.py streams every message of the export, sorting each day's messages by timestamp: yield from sorted ( messages_for_one_day , key = get_timestamp_from_message ) where the sort key was simply: def get_timestamp_from_message ( message : ZerverFieldsT ) -> float : retur

ImgProxy is an open-source image-processing server — resize, convert, and transform images on the fly via URL parameters, ideal as a caching layer in front of a CDN or web app. This guide builds ImgProxy from source on Ubuntu, runs it as a systemd service behind Nginx with TLS, walks through its URL processing options, and secures it with signed URLs. Prerequisites: an Ubuntu server, a domain A record (e.g. imgproxy.example.com ), non-root sudo user. Install ImgProxy ImgProxy uses libvips for image processing; this builds it from source with Go. $ sudo add-apt-repository ppa:dhor/myway $ sudo apt update $ sudo apt install libvips-dev -y $ sudo snap install --classic --channel = latest/stable go $ git clone https://github.com/imgproxy/imgproxy.git $ cd imgproxy $ sudo CGO_LDFLAGS_ALLOW = "-s|-w" go build -o /usr/local/bin/imgproxy Create the environment config: $ sudo touch /usr/local/bin/imgproxy.env $ sudo nano /usr/local/bin/imgproxy.env IMGPROXY_BIND = :8080 IMGPROXY_NETWORK = tcp IMGPROXY_READ_TIMEOUT = 10 IMGPROXY_WRITE_TIMEOUT = 10 IMGPROXY_WORKERS = 2 IMGPROXY_REQUESTS_QUEUE_SIZE = 0 IMGPROXY_QUALITY = 100 IMGPROXY_PREFERRED_FORMATS = webp,jpeg,png,gif,avif IMGPROXY_LOG_FORMAT = "pretty" IMGPROXY_LOG_LEVEL = "INFO" IMGPROXY_WATERMARK_URL = https://example.com/watermark.png IMGPROXY_WATERMARK_OPACITY = 1 Key settings: IMGPROXY_WORKERS should be ~2× your vCPU count; IMGPROXY_REQUESTS_QUEUE_SIZE=0 means unlimited queueing; IMGPROXY_WATERMARK_URL points at whatever image you want overlaid when watermarking is enabled. Point ImgProxy at the config and test: $ export IMGPROXY_ENV_LOCAL_FILE_PATH = /usr/local/bin/imgproxy.env $ cd $ imgproxy WARNING [2024-05-28T00:40:42Z] No keys defined, so signature checking is disabled WARNING [2024-05-28T00:40:42Z] No salts defined, so signature checking is disabled INFO [2024-05-28T00:40:42Z] Starting server at :8080 Stop it with Ctrl+C once verified, then set it up as a service. Run ImgProxy as a systemd Service $ sudo useradd

A few days ago I fixed update_article , one of the tools in this repo's MCP server, because it had a nasty shape: it took a bare integer article_id , PUT whatever fields you gave it straight to the DEV.to API, and if the id was wrong or hallucinated, it would silently overwrite a live published post with nothing left behind to show it had happened. The fix added a fetch-before-write diff and a JSONL audit log: _ARTICLE_UPDATE_LOG = " logs/article_updates.jsonl " def _log_article_update ( article_id , before , fields_changed , after ): os . makedirs ( os . path . dirname ( _ARTICLE_UPDATE_LOG ), exist_ok = True ) entry = { " article_id " : article_id , " fields_changed " : sorted ( fields_changed ), " url " : after . get ( " url " )} for field in fields_changed : entry [ f " { field } _before " ] = before . get ( field ) entry [ f " { field } _after " ] = after . get ( field ) with open ( _ARTICLE_UPDATE_LOG , " a " ) as f : f . write ( json . dumps ( entry ) + " \n " ) The whole point of that function is durability. "Zero trace" was the bug; a JSONL file that records before/after state on every write was the fix. I verified the logging logic itself with an offline unit test against fake before/after states and moved on, same as the diff field. What I never checked is whether logs/article_updates.jsonl outlives the process that writes it. Checking whether the trace actually exists anywhere logs/ isn't in .gitignore — I checked, it's not there. So nothing is actively hiding it. But not-hidden isn't the same as tracked: $ git log --all --oneline -- 'logs/*' $ ls logs/ ls: cannot access 'logs/': No such file or directory Empty output from the first command, across every branch and every commit this repo has ever had (50 commits, not a shallow clone — git rev-parse --is-shallow-repository is false ). Nothing has ever touched logs/ . The directory doesn't even exist right now. Not because anything deleted it — because nothing has ever run update_article in an environment

code-server is the open-source project that runs full VS Code including extensions, integrated terminal, Git, IntelliSense — on a remote server, accessible from any browser. This guide deploys it on Ubuntu 24.04 with Docker Compose, fronted by Traefik for automatic HTTPS. Prerequisites: an Ubuntu 24.04 server (1GB RAM / 2 vCPU minimum), a domain A record (e.g. code.example.com ), Docker and Docker Compose installed. Set Up the Project $ mkdir -p ~/vscode-server/ { project,config,local,letsencrypt } $ cd ~/vscode-server project — your editable workspace config — code-server settings/extensions local — user-specific data letsencrypt — Traefik's ACME certificate storage Find your UID/GID and add yourself to the docker group: $ id $USER $ sudo usermod -aG docker $USER Write the Compose File $ nano docker-compose.yml services : code-server : image : codercom/code-server:latest container_name : code-server user : " UID:GID" # Replace with your user's UID and GID environment : - PASSWORD=SECURE_PASSWORD # Replace with a strong password - DOCKER_USER=LINUXUSER # Replace with your username volumes : - ./project:/home/coder/project - ./config:/home/coder/.config - ./local:/home/coder/.local networks : - internal restart : unless-stopped labels : - " traefik.enable=true" - " traefik.http.routers.code-server.rule=Host(`CODE.EXAMPLE.COM`)" # Replace with your domain name - " traefik.http.routers.code-server.entrypoints=websecure" - " traefik.http.routers.code-server.tls.certresolver=myresolver" - " traefik.http.services.code-server.loadbalancer.server.port=8080" traefik : image : traefik:latest container_name : traefik ports : - " 80:80" - " 443:443" volumes : - /var/run/docker.sock:/var/run/docker.sock:ro - ./letsencrypt:/letsencrypt command : - " --providers.docker=true" - " --providers.docker.exposedbydefault=false" - " --providers.docker.network=internal" - " --entrypoints.web.address=:80" - " --entrypoints.websecure.address=:443" - " --entrypoints.web.http.redirections.entr

Gradio is a Python library for wrapping any ML model in a web interface, ready to deploy and scale as an app. This guide builds a GFPGAN-powered face-restoration demo with Gradio on Ubuntu 22.04, runs it as a systemd service, and exposes it through Nginx with TLS. Prerequisites: a GPU-enabled Ubuntu 22.04 server, a domain A record (e.g. gradio.example.com ), non-root sudo access, Nginx installed. Set Up the Server 1. Install dependencies: $ pip3 install realesrgan gfpgan basicsr gradio realesrgan — background restoration gfpgan — face restoration basicsr — provides RRDBNet , the super-resolution architecture GFPGAN relies on gradio — the web interface 2. GFPGAN's pandas dependency needs jinja2 >= 3.1.2: $ pip show jinja2 Upgrade if it's older: $ pip install --upgrade jinja2 3. Create the project directory: $ sudo mkdir -p /opt/gradio-webapp/ $ sudo chown -R : $( id -gn ) /opt/gradio-webapp/ $ sudo chmod -R 775 /opt/gradio-webapp/ Build the Gradio App Uploads a face image and returns two enhanced outputs. $ cd /opt/gradio-webapp/ $ nano app.py import gradio as gr from gfpgan import GFPGANer from basicsr.archs.rrdbnet_arch import RRDBNet from realesrgan import RealESRGANer import numpy as np import cv2 import requests def enhance_image ( input_image ): arch = ' clean ' model_name = ' GFPGANv1.4 ' gfpgan_checkpoint = ' https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth ' realersgan_checkpoint = ' https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth ' rrdbnet = RRDBNet ( num_in_ch = 3 , num_out_ch = 3 , num_feat = 64 , num_block = 23 , num_grow_ch = 32 , scale = 2 ) bg_upsampler = RealESRGANer ( scale = 2 , model_path = realersgan_checkpoint , model = rrdbnet , tile = 400 , tile_pad = 10 , pre_pad = 0 , half = True ) restorer = GFPGANer ( model_path = gfpgan_checkpoint , upscale = 2 , arch = arch , channel_multiplier = 2 , bg_upsampler = bg_upsampler ) input_image = input_image . astype ( np . uint8 ) cropped_fa

Metabase is an open-source BI tool for building charts and dashboards over MySQL, PostgreSQL, MongoDB, Redshift, and more. This guide deploys Metabase on Kubernetes, loads the Sakila sample dataset into MySQL, builds a dashboard, and secures it behind Nginx Ingress with cert-manager TLS. Prerequisites: a Kubernetes cluster with kubectl / helm configured, a Linux workstation, a reachable MySQL server, and a domain name. Load the Sakila Sample Database Sakila models a DVD rental store — films, actors, inventory, rentals. $ sudo apt install zip -y $ wget https://downloads.mysql.com/docs/sakila-db.zip $ unzip sakila-db.zip Connect to your MySQL server (replace host/port/user): $ mysql -h <HOST_ENDPOINT> -P <DATABASE_PORT> -u <ADMIN_USER> -p mysql > CREATE DATABASE sakila ; mysql > SOURCE sakila - db / sakila - schema . sql ; mysql > SOURCE sakila - db / sakila - data . sql ; Deploy Metabase $ nano metabase.yaml apiVersion : apps/v1 kind : Deployment metadata : name : metabase spec : selector : matchLabels : app : metabase replicas : 1 template : metadata : labels : app : metabase spec : containers : - name : metabase image : metabase/metabase:latest ports : - containerPort : 3000 protocol : TCP --- apiVersion : v1 kind : Service metadata : name : metabase-svc spec : type : LoadBalancer selector : app : metabase ports : - name : http port : 8080 targetPort : 3000 Your cloud provider may need a provider-specific LoadBalancer annotation here (e.g. to set the listener protocol) — check its Kubernetes docs if the default doesn't work. $ kubectl apply -f metabase.yaml $ kubectl get deployments $ kubectl get services Wait for metabase-svc to get an EXTERNAL-IP (can take a few minutes), then visit http://<external-ip>:8080 to confirm the Metabase welcome page loads. Connect Metabase to the Database Let's get started → pick language. Enter your name, email, company, and a password. Select your use case. Database engine: MySQL . Set a display name, then host/port/database/user/pa

Nginx UI is an open-source web GUI for managing Nginx, single-node or clustered, with real-time stats, automatic Let's Encrypt TLS, performance monitoring, and even LLM-assisted config editing. This guide installs it on Ubuntu 24.04, puts it behind a reverse proxy with TLS, sets up ACME certificate management, and creates a virtual host through the dashboard. Prerequisites: an Ubuntu 24.04 server, non-root sudo user, a domain A record (e.g. nginx-ui.example.com ), Docker installed if you choose that install path. $ sudo apt update $ sudo apt install nginx -y Pick one of the two install methods below. Option A: Install via Script Runs Nginx UI as a system service, managing the host's Nginx directly. $ curl -O https://cloud.nginxui.com/install.sh $ sudo bash install.sh install $ nginx-ui --version $ sudo systemctl start nginx-ui $ sudo systemctl status nginx-ui Option B: Install via Docker Runs as a container — you won't be able to edit the host's Nginx configs directly through it. $ mkdir -p ~/nginx-ui-docker $ cd ~/nginx-ui-docker $ sudo mkdir -p /opt/nginx-ui/nginx $ sudo mkdir -p /opt/nginx-ui/config $ sudo mkdir -p /opt/nginx-ui/www $ nano docker-compose.yml version : ' 3.8' services : nginx-ui : image : uozi/nginx-ui:latest container_name : nginx-ui restart : always environment : - TZ=UTC volumes : - /opt/nginx-ui/nginx:/etc/nginx - /opt/nginx-ui/config:/etc/nginx-ui - /opt/nginx-ui/www:/var/www ports : - " 127.0.0.1:9000:80" networks : - nginx-ui-net networks : nginx-ui-net : driver : bridge $ sudo docker compose up -d $ sudo docker compose ps $ curl -X GET http://localhost:9000 Configure the Reverse Proxy Nginx UI listens on localhost:9000 . Put it behind a public vhost with WebSocket support (needed for live stats/terminal): $ cd /etc/nginx/sites-available $ sudo nano nginx-ui.conf map $http_upgrade $connection_upgrade { default upgrade ; '' close ; } server { listen 80 ; listen [::]:80 ; server_name nginx-ui.example.com ; location / { proxy_set_header Host $

Kubernetes makes it easy to define CPU and memory requests for our applications. But there is a problem: How do we know whether those resource requests are actually correct? If an application requests: yaml resources: requests: cpu: "1000m" memory: "1Gi" but normally consumes only a few millicores of CPU and a few megabytes of memory, we may be reserving significantly more cluster capacity than the workload actually needs. I wanted to understand how Kubernetes cost optimization platforms detect this situation, so I built a small hands-on lab using: Google Kubernetes Engine (GKE) CAST AI Kubernetes Docker FastAPI Google Artifact Registry The goal wasn't simply to install CAST AI. I wanted to observe the complete process: Deploy workload ↓ Observe resource usage ↓ Compare requests vs usage ↓ Identify over-provisioning ↓ Generate recommendation ↓ Apply rightsizing ↓ Verify from Kubernetes Architecture The lab architecture was intentionally simple. FastAPI Coffee API | v Docker Image | v Google Artifact Registry | v GKE Cluster | v Kubernetes Deployment | +----------------+ | | v v Pod #1 Pod #2 | | +-------+--------+ | v ClusterIP Service + | v CAST AI | +-------+-------+ | | v v Cost Monitoring Workload Optimization 1. Building a Small Test Application I created a very small FastAPI application for the experiment. from fastapi import FastAPI import socket import os import time app = FastAPI() @app.get("/") def home(): return { "message": "Coffee Shop API", "hostname": socket.gethostname(), "pod": os.getenv("HOSTNAME"), "time": time.time() } @app.get("/coffee") def coffee(): return { "coffee": "Cappuccino", "price": 120 } The hostname in the response was useful later because I could see which Kubernetes Pod handled each request. 2. Containerizing the API The application was packaged using Docker. FROM python:3.12-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY app.py . EXPOSE 8000 CMD ["uvicorn", "app:app", "--host", "0