标签：#p

Sex workers appear on the livestreams of famous manosphere influencers to boost their followings—but often end up being degraded.

When I first connected to a precious metals WebSocket API, I expected to get a clean stream of prices. What I actually got was a firehose of mixed ticks—gold, silver, platinum—all arriving through the same callback. If you’ve ever tried to build a trading bot or a custom chart, you know this is a recipe for disaster. In this post, I’ll share how I solved the problem with a few lines of Python and a clear mapping strategy. The scenario: You have one WebSocket URL that pushes quotes for multiple metals. You need to separate them so you can update different UI components, run independent strategies, or store them in distinct database tables. The data pain point: every message uses the same JSON structure, and the only differentiator is a field like symbol . If you don’t act on it immediately, everything gets mixed up. Identify Assets via the Symbol Field Start by checking the API docs for the field that carries the instrument code. Usually it’s symbol , but instrumentId or type are also used. Here’s a typical reference table: Field Description Example symbol Asset code XAUUSD, XAGUSD instrumentId Internal platform ID 1001, 1002 type Asset class gold, silver I turn this into a dictionary mapping each symbol to a human-readable category: asset_map = { " XAUUSD " : " gold " , " XAGUSD " : " silver " , " XPTUSD " : " platinum " } Buffer Messages by Type Because these streams are high-frequency, I avoid processing every tick individually. Instead, the WebSocket callback just updates an in-memory store that is already grouped by asset type: # Keep the hot path extremely light def on_message ( msg ): symbol = msg [ ' symbol ' ] price = msg [ ' price ' ] asset_type = asset_map . get ( symbol , " unknown " ) cache [ asset_type ][ symbol ] = price Then, a background timer fetches the latest prices from cache["gold"] and cache["silver"] separately and does the actual work—like computing indicators or rendering charts. The key benefit is complete isolation: your gold logic never t

I recently launched LuaPlay, a free browser-based Lua editor. No setup, no install — just open the site and write Lua. Why I built it: Every time I wanted to test a quick Lua snippet, I had to either open a local environment or use tools that weren't built for Lua specifically. So I built my own. What it does: Run Lua scripts directly in the browser Clean, minimal editor interface Free to use Would love feedback from the dev community. What features would make you actually use it day-to-day? 👉 https://luaplay.online

In modern application development, exposing database logic as REST APIs is a powerful way to integrate systems. Oracle REST Data Services (ORDS) makes it easy to turn PL/SQL into RESTful APIs without needing a separate backend service. In this blog, we’ll walk through how to create a simple POST API using ORDS and PL/SQL to insert data into a table. Pre-requisites A cloud-based ATP wallet (I prefer) Let's start how we create the APIs on the top of any custom table which relies on databases Create a table in the oracle SQL Developer and followed by create an ORDS Module 1.Create an ORDS Module A module is a logical container for related REST endpoints. What this Module does ?? Creates a module named nj_api Defines base URL: http://server_name/ords/table_Schema/nj_api/ 2: Define a Template (Endpoint Path) A template represents the API endpoint path. It defines how Endpoint URL:/ords/table_schema/nj_api/insert_data 3: Define the Handler (Business Logic) The handler contains the logic executed when the API is called. Key Concepts: p_method => 'POST': Defines HTTP method p_source_type => ORDS.source_type_plsql: Uses PL/SQL block Bind variables (:name, :num, etc.) map directly to JSON request body parameters 4: Testing the API Using Tools like Postman,cURL,ORDS REST Workshop I tested with Postman FYR Let's call same in Oracle VBCS in new blog. .. Try other methods like Delete, PATCH & GET

Podlite 2.0 is tagged. Podlite is a block-based markup language built around typed blocks and explicit boundaries — the same document is meant to read cleanly whether a person or a tool parses it. This release adds eight blocks and attributes and changes two parsing rules. The specification is at podlite.org/specification ; the full changelog sits inside the spec under =head2 v2.0 . The Coming in Podlite 2.0 article from the review window covered what is new in depth. This post focuses on what to do now: how to migrate existing documents and where to find the rest. For most documents the answer is short — a well-formed v1.0 document renders unchanged under v2.0. Breaking changes Two changes parse differently than before. Neither touches a well-formed document — if anything needs updating, it is a parser, not your text. Legacy attribute syntax removed A few outdated string attribute formats are gone. The bracket form ( :key<value> ) and the parenthesized form ( :key('value') ) remain. If a document uses the current syntax, nothing changes. =include is now a directive =include always behaved like a directive, but the spec previously listed it under block types. Tokenization rules differ between directives and blocks. Parsers built against the v1.0 spec must move =include into the directive dispatch path alongside =config and =alias . For document authors: no change. =include still takes the same syntax and produces the same output. The reclassification matters only for tools that build ASTs. New features at a glance Eight additions ship in v2.0. Existing documents render unchanged. =boundary : a typed section divider. Renders as a horizontal rule, exposes structure to tools. =set : pre-configure attributes for the next block. Multiline values, inline markup, lexical scope. G<> + :masked : content masking. Inline mark or whole-block attribute; hidden by default, revealed by render condition. =data-table block: renders CSV or TSV as a table. Three source forms (inline b

Welcome back to Day 3, Python dynamic duo! 🚀 If you survived Day 2 , you now know how to create variables and throw strings, integers, floats, and booleans into their own little cardboard boxes. 📦 But what happens when you’re building a game and your character needs an inventory? Or you're making a shopping list app? Creating 50 different variables like item1, item2, item3 will make you want to throw your router out the window. 🪟💻 Today, we are leveling up our storage game. We are moving out of single cardboard boxes and packing a Virtual Backpack: Enter Lists! 🎒🎉 🎒 What is a List? In Python, a List is a data structure used to store a collection of items in one single variable. Think of it like a backpack where you can stuff multiple things inside, keep them in a specific order, and pull them out whenever you need them. Creating a list is simple. You use square brackets [] and separate your items with commas: # Packing our survival backpack 🗺️ backpack = [ " map " , " flashlight " , " water bottle " , " protein bar " ] print ( backpack ) # Prints: ['map', 'flashlight', 'water bottle', 'protein bar'] The coolest part? Python lists don’t care what you put inside. You can mix strings, integers, and booleans all in one single backpack (though usually, it makes the most sense to keep similar things together). 🤯 The First Rule of Coding Club: We Start Counting at Zero! Here is where programming turns your brain upside down. 🧠🙃 If I asked you what the first item in our backpack list is, you’d logically say "map". And you'd be right in human language. But in Python-speak, computer memory starts counting at 0. This is called Indexing. To pull a specific item out of your backpack, you write the name of the list followed by the item's position (index) inside square brackets: backpack = [ " map " , " flashlight " , " water bottle " , " protein bar " ] # Pulling out the items using their index 🔍 print ( backpack [ 0 ]) # Prints: map (The absolute first item!) print ( backpack [

submitted by /u/viks98 [link] [留言]

Alexandros Kapretsos describes how he used some D programming language features in his 2D game engine. He covers his approach to memory management, how he employs metaprogramming, writing scripts with D, and more. submitted by /u/aldacron [link] [留言]

submitted by /u/der_gopher [link] [留言]

A longtime pro barista’s favorite tools for dialing in the perfect shot at home.

A malicious npm package name js-logger-pack , went through 29 versions on the registry which was looking innocuous logger and ending as a binary dropper. The payload it dropped was 81 MB of binary called MicrosoftSystem64 which is a full cross-platform RAT packaged as a Node.js Single Executable Application, so it shows up as a native binary to endpoint tools rather than a node process. And the clever bit was instead of sending the stolen data directly to a C2 server, it uploads everything to private HuggingFace datasets using an embedded API token. So all exfiltration traffic appears as normal HTTPS requests to a legitimate ML platform. If you have any of those in your install history then rotate everything like credentials, SSH keys, API tokens, crypto seed phrases. All packages list and full technical breakdown is in blog. submitted by /u/BattleRemote3157 [link] [留言]

GitHub reports cutting token costs in agentic CI workflows by up to 62% by pruning unused MCP tools, swapping some MCP calls for gh CLI, and running daily “auditor” and “optimizer” agents. A token-usage.jsonl artefact and an Effective Tokens metric help track spend across models and spot regressions. By Mark Silvester

FastAPI released an official VSCode extension, which includes features such as route exploration, endpoint search, and CodeLens-style navigation. This tool aims to enhance the development experience for FastAPI users. submitted by /u/Top-Rush83 [link] [留言]

For a while, my starter kits didn't include any Docker configuration. The foundation was solid with auth, roles, MFA, Horizon, Logs Viewer, but the deployment side was left to whoever cloned the project. That was a deliberate choice at first. Docker setups vary a lot depending on the infrastructure: some people use a reverse proxy, others have Cloudflare in front, some run on bare metal, others on managed platforms. I didn't want to ship something that would need to be ripped out immediately. But over time I changed my mind. Here's why and what the process taught me. The problem with "just configure it yourself" Leaving deployment out of a starter kit sounds reasonable. In practice, it means every project starts with the same 4-6 hours of Docker work that never really changes. Multi-stage Dockerfile. PHP-FPM config. Nginx with HTTPS. PostgreSQL and Redis wired up. Horizon and the scheduler running as proper services. Healthchecks everywhere so Docker knows when things are actually ready. None of it is so complicated. But it's time-consuming, easy to get subtly wrong, and almost identical from one project to the next. Once I admitted that, the question wasn't whether to include Docker, it was how to do it in a way that's actually useful without being too opinionated about production infrastructure. What I ended up building The setup I settled on covers the full local development stack: A multi-stage Dockerfile : separate stages for Composer dependencies, Node assets, and the final PHP-FPM image. Keeps the production image lean. Nginx with HTTP-to-HTTPS redirect and a self-signed certificate for local dev, already included, no setup needed. PostgreSQL and Redis as services with proper healthchecks. Horizon and the scheduler as dedicated services, not crammed into the main app container. A bootstrap service that runs php artisan migrate --force before the app starts. The Dockerfile uses three stages to keep the final image as lean as possible: FROM php:8.4-fpm-alpine A

Every line of your smart contract costs something. Some lines cost more than others. In this part of our gas saving series, we’ll explore how to write smarter Solidity code that keeps your contract lean and efficient. Here are six simple and practical ways to reduce gas costs while writing Solidity smart contracts. 1. Use payable Only When Needed, But Know It Saves Gas In Solidity, a function marked payable can actually use slightly less gas than a non-payable one. Even if you're not sending ETH, the EVM skips some internal checks when the function is marked payable. See this example: function hello() external payable {} // 21,137 gas function hello2() external {} // 21,161 gas That tiny difference may not seem like much, but across thousands of calls, it adds up. Only use payable when your function is actually meant to accept ETH 2. Use unchecked for Safe Arithmetic When You’re Sure Since Solidity 0.8.0, all arithmetic operations automatically check for overflows and underflows. While this makes contracts safer, it also uses extra gas. When you're certain that overflow won't occur, you can use the unchecked keyword to skip these safety checks. uint256 public myNumber = 0; function increment() external { unchecked { myNumber++; } } Gas used: 24,347 (much cheaper than using safe math) Warning: Use unchecked carefully. Only when you're confident there's no risk of overflow. 3. Turn On the Solidity Optimizer The Solidity Optimizer is like a smart helper that cleans up and tightens your compiled bytecode. It does not change how your contract works, but it removes waste and makes it cheaper to run. If you’re using tools like Hardhat or Remix, always enable the Optimizer before deploying to mainnet. 4. Use uint256 Instead of Smaller Integers (Most of the Time) Smaller types like uint8 or uint16 might look more efficient, but they can cost more gas during execution. That’s because the EVM automatically converts them to uint256 behind the scenes. So, if you're not tightly p

If you've ever built or set up a subscription experience on Shopify for a coffee brand, you've probably run into the same problem most merchants face: The signup flow works great. The first order goes out. And then subscribers start quietly disappearing before the third delivery. This isn't a coffee problem. It's a subscription infrastructure problem — and it's almost always caused by the same handful of missing pieces in the system underneath the storefront. In this guide I'll walk through the practical setup decisions that actually move the needle on retention for coffee subscription businesses on Shopify — from choosing the right model and pricing structure to the fulfillment calendar, dunning logic, and cancellation flow that most setups skip entirely. Why Coffee Works So Well as a Subscription Product Before getting into setup, it's worth understanding why coffee is genuinely one of the better products to build a subscription around — when the infrastructure supports it. Predictable consumption cycle. A 12-oz bag of whole beans lasts roughly two to three weeks for a single drinker. That natural rhythm makes it easy to design a billing and delivery schedule that matches actual usage patterns. Daily habit. Roughly two-thirds of American adults drink coffee every day. A subscription removes the friction of reordering, and that convenience compounds into real retention over time. Freshness as a retention argument. Coffee quality degrades noticeably after roasting. Subscribers who care about quality genuinely prefer a recurring shipment over buying retail — which means freshness becomes a built-in reason to stay subscribed that most product categories simply don't have. The global coffee subscription market reached $808.8 million in 2024 and is projected to surpass $2.2 billion by 2033. The infrastructure opportunity for developers and merchants building on Shopify is real and still early. Step 1 — Choose the Right Subscription Model Before You Build The model choic

Trace AI requests, workflows, and costs in one timeline Discussion | Link

Java LLD: Designing a Thread-Safe Parking Lot with Strategy Pattern Designing a parking lot is a staple of Java LLD and machine coding interviews, yet most candidates fail to write production-grade code. As an ex-FAANG interviewer, I've seen countless designs fall apart under concurrent traffic or when asked to support multiple slot allocation algorithms. If you're prepping for interviews, I've been building javalld.com — real machine coding problems with full execution traces. The Mistake Most Candidates Make Monolithic locking on the entire ParkingLot class: Using a global synchronized keyword on the entry method, which serializes all gate entries and destroys system throughput. Hardcoding slot-finding logic: Mixing spatial layout algorithms (like nearest-to-entrance or smallest-available-fit) directly inside the ParkingLot or Gate classes, violating the Open-Closed Principle. Thread-safety as an afterthought: Relying on raw List<Slot> iterations without synchronization, causing race conditions where multiple cars are assigned to the exact same physical slot. The Right Approach Core mental model: Decouple capacity management from slot selection by using a Semaphore for gate-keeping and the Strategy Pattern for thread-safe slot allocation. Key entities: ParkingLot , Gate , Slot , Vehicle , ParkingStrategy ( SmallestFitStrategy , NearestEntranceStrategy ), and StrategyFactory . Why it beats the naive approach: It isolates concurrency concerns (preventing overbooking) from business rules (how we choose a slot), making the system highly performant and easily extensible. The Key Insight (Code) public class EntryGate { private final Semaphore semaphore ; private final ParkingStrategy strategy ; public EntryGate ( int capacity , ParkingStrategy strategy ) { this . semaphore = new Semaphore ( capacity ); this . strategy = strategy ; } public synchronized Ticket park ( Vehicle vehicle ) { if (! semaphore . tryAcquire ()) throw new ParkingFullException (); Slot slot = strat

Over 51% of all GitHub commits in early 2026 are AI-generated or AI-assisted. That statistic creates a problem no one anticipated when AI coding tools first launched: who reviews the AI's code? The answer, increasingly, is another AI. The AI code review market has grown rapidly alongside vibe coding and AI-first development workflows. But the category is fragmented there are PR-level reviewers, IDE inline analyzers, security scanners, and general-purpose AI assistants all claiming to do "code review." They work very differently, and picking the wrong one for your workflow is a real productivity cost. This guide cuts through the noise. We explain what each category does, highlight the best tools in each, and give you a decision framework to help you choose what fits your actual situation. Why AI Code Review Is Now Essential Three converging trends make AI code review the category to watch in 2026: AI-generated code has real quality problems. Research shows 45% of AI-generated code fails at least one OWASP Top 10 security check, and 53% of developers have found security vulnerabilities in AI-written code. When you use tools like Cursor, Claude Code, or GitHub Copilot to write 80% of a feature, you're shipping code you may not have read line by line. Code review is a bottleneck. Stack Overflow's 2026 developer survey found code review wait time is the top-ranked productivity killer. For solo developers and small teams, reviews pile up and slow shipping. AI reviewers don't have calendars. The security stakes are rising. As more non-developers ship production code via vibe coding, the need for automated security checks compounds. AI review tools catch issues like SQL injection, CORS misconfigurations, and hardcoded secrets before they ship. Two Categories of AI Code Review Before picking a tool, understand that "AI code review" means two distinct things. 1. PR-Level AI Reviewers These run at the pull request level. When you open a PR on GitHub, GitLab, or Bitbucket, they

TL;DR I've missed a lot of opportunities simply because I didn't know they existed. So every...