标签：#ev

Last week I was doing a routine check on a CyberPanel server and noticed something that didn't add...

A suite of developer tools you run with the curl you already have: UUIDs, hashes, JSON End-points, JWTs, JSON, QR, File Transfers and more. Nothing to install, and no signup required. "curl curlhub.sh" to see list of commands or visit https://curlhub.sh to view all tools full details and soon contribute. root@web01:~# curl curlhub.sh curlhub.sh — CLI-first developer utilities Zero-setup tools you curl straight from the terminal. Generators /uuid RFC 4122 v4 UUID. /pass High-entropy password / API key / token. CLI: server-side CSPRNG over TLS; web: client-side. Never logged. ( https ) Utilities /qr ANSI/UTF-8 QR code from ?data. Nothing stored server-side; use HTTPS or pipe stdin for sensitive payloads ( a ?data = value appears in request URLs ) . Encoding /b64 Base64 encode / decode. (https) /hash MD5 / SHA-1 / SHA-256 / SHA-512 of input. (https) Developer /json Validate + prettify + colorize JSON. (https) /jwt Decode & pretty-print JWT header/payload. No verify; token never logged. ( https ) /status Explain an HTTP status code + troubleshooting. /cron Translate a cron expression to English + next run times. /ua Parse the User-Agent you sent (browser / OS / engine / device). /headers Echo the request headers you sent (+ the edge view). Your own request, not a remote audit. /hook Webhook inspector: mint a temp endpoint, inspect incoming HTTP. (https) Text & Logs /md Render Markdown to colorized ANSI (safe, bounded parser). (https) /p Pastebin: pipe text/logs to a short URL (plaintext in terminal, highlighted on web). (https) Network /cidr Subnet / CIDR math: range, netmask, broadcast, host count. /ip Your public IP (thin). Geo/ASN live at worldip.io. /whois Domain registration / registrar / dates (public OSINT; rate-limited + cached ) . Security /ssl Decode a PEM certificate or CSR you paste (expiry, issuer, SANs, key). No outbound connection. (https) File Transfer /u Ephemeral file drop (<=100 MB). ANSI QR; auto-purge after 1 download or TTL. ( https ) Docs /man Com

HashiCorp has introduced tfpolicy, a new HCL-based policy-as-code framework for Terraform, now available in public beta within HCP Terraform. It is designed to simplify and modernize infrastructure governance by integrating policy creation and enforcement directly into Terraform workflows, eliminating the need for separate tools and languages. By Sergio De Simone

Look at chemistry of batteries and motors shows big opportunity for recycling.

One hundred days ago, I had no idea this challenge would become one of the most rewarding technical journeys I've taken. There were days when everything clicked, and there were days when nothing compiled. I celebrated successful deployments, stared at cryptic errors for hours, rewrote programs that weren't good enough, and learned that understanding Solana has far less to do with memorizing APIs than changing how you think about state, ownership, and security. Looking back now, I realize I didn't just complete 100 challenges; I built a completely different mental model of software. Where I started I came in with a biochemistry degree I never used, a self-taught engineering career I built in public, and a GitHub full of Rust and Python work. I co-maintain statix, a Nix linter that ended up in the canonical NixOS/nixpkgs upstream. I had shipped real software before. What I did not have was any intuition for how Solana actually works. I knew the buzzwords. I had the mental model of "it's fast and cheap." I did not know what an account was. I did not know what a program was. I did not know why those two things were different. That gap, between knowing the words and understanding the model, is what these 100 days actually closed. What I expected I expected Solana to feel like a database with extra steps. In Web2, you have a server that holds state. You call an API. The server reads from the database, does something, writes back. I expected a blockchain to be the same thing, just slower and decentralized. The first thing that broke that model: accounts. On Solana, an account is not a row in your database. It is the database. Every piece of state, your wallet balance, a token you hold, the program you deployed, is an account. Programs are accounts. Data is accounts. Everything is an account. That sounds obvious written down. It took me until around Day 15 to actually feel it, when I was staring at a getAccountInfo call wondering why the program I deployed was also an accou

--- title : " Perfect Landing — Soul & Spoon (Comfort Food Edition) — Research Summary" published : false tags : [ " frontend" , " html" , " css" , " javascript" , " accessibility" , " performance" , " react" , " svelte" ] cover_image : " https://images.unsplash.com/photo-1543353071-087092ec393a?q=80&w=1600&auto=format&fit=crop&ixlib=rb-4.0.3&s=3" canonical_url : " " series : " " --- Perfect Landing — Soul & Spoon Research Summary A concise, ready-to-paste DEV post that summarizes the research, design decisions, technical choices, and next steps for the Soul & Spoon landing-page project — a warm, accessible, performance-minded single-page site celebrating soul food. What I Built Soul & Spoon — Comfort Food Landing Page is a single-page landing site that showcases soul-food plates with a polished, modern frontend. The static prototype includes: Hero with a full-bleed background and clear CTAs. Featured dishes section with responsive cards and descriptive copy. Gallery of soul-food plates with a keyboard-accessible lightbox. Contact form with client-side validation and toast feedback. Responsive, accessible, and performant implementation using semantic HTML, picture / srcset /WebP, lazy loading, and minimal JavaScript. Research Summary and Rationale Design goals Evoke warmth and comfort through color, rounded shapes, and soft shadows. Prioritize readability and hierarchy for quick scanning on mobile and desktop. Keep interactions simple and predictable: smooth scroll, accessible modal, and unobtrusive toast notifications. Accessibility findings Semantic elements ( header , main , section , figure , figcaption , footer ) improve screen-reader navigation and SEO. Keyboard operability is essential: gallery images must be focusable and open via Enter/Space; Escape should close the lightbox. ARIA attributes ( aria-hidden , aria-expanded , role="dialog" ) plus focus management significantly improve modal usability. Performance findings Images dominate page weight; srcset an

I run a content pipeline that picks trending topics and publishes articles automatically. Last week I found out it had published the same story three times. Not the same title — the same exact topic, reworded each time. My dedup check was supposed to stop that. It didn't. Here's why, and how I killed the check. The Bug My pipeline had a similarity gate. Every candidate title got compared against the last 30 published titles, and anything scoring 0.58 or higher was rejected. Straightforward, right? from difflib import SequenceMatcher def jaccard_bigram ( a : str , b : str ) -> float : def bigrams ( s : str ) -> set [ str ]: return { s [ i : i + 2 ] for i in range ( len ( s ) - 1 )} x , y = bigrams ( a ), bigrams ( b ) return len ( x & y ) / len ( x | y ) if ( x | y ) else 1.0 def similarity ( a : str , b : str ) -> float : return max ( SequenceMatcher ( None , a , b ). ratio (), jaccard_bigram ( a , b )) THRESHOLD = 0.58 Here's the pair that slipped through. The candidate: 中国军队国际形象网宣片《当红》 And a title I had already published: 《当红》网宣片刷屏，普通人看到的中国军人是什么样 Same film. Same topic. Third time it was being covered. Watch what the algorithm did: candidate = " 中国军队国际形象网宣片《当红》 " published = " 《当红》网宣片刷屏，普通人看到的中国军人是什么样 " print ( similarity ( candidate , published )) # SequenceMatcher: 0.187 # jaccard bigram: 0.185 # max: 0.187 < 0.58 -> PASSED 0.187. The gate let it through with a five-fold margin to spare. Why It Failed The name 当红 is the same in both titles. That is the whole topic. But the algorithm does not care about that. SequenceMatcher matches in order. In the published title, 当红 sits at position zero. In the candidate, it is at the end. Reordered tokens break the match, so the ratio collapses to the shared fragments — 网宣片 plus the generic words around it. The bigram fallback does not save you either. Jaccard over character bigrams measures surface overlap, not meaning. Five shared bigrams out of twenty-seven total. 0.185. It "proves" the titles are unrelated because most of

The leading U.S. automakers are mentioning EVs on their investor calls at pre-pandemic rates, according to new data from TechCrunch and Hudson Labs.

Dropbox has integrated Model Context Protocol (MCP) with its internal knowledge platform, Dash, to surface security design context during AI assisted code reviews. The system retrieves threat models and security requirements for pull requests, helping reviewers validate implementation against design intent. An InfoQ Q&A explores the architecture and key lessons learned. By Leela Kumili

Its latest Pro headset can mix between four inputs and has improved wireless connectivity over the originals.

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

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

Slate's anti-truck EV is poised to deliver on all of its promises.

The refurbished railway station in London I entered last week had none of the usual signs of being a tattoo studio. The walls were devoid of any art, and there were no reclining chairs or massage tables in the room. The only tattoo gun on the premises sat in a display case instead of buzzing […]

The company’s first headphones look very stylish and have great tactile features, but underwhelm on sound quality.