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Intro If you work with real-time financial tick data via WebSocket long connections, you’ve probably run into frustrating hidden issues: Connections show as online, but data stops flowing. Adding/removing trading symbols triggers connection storms. Minor network glitches break your data pipeline without obvious error logs. Today I’ll share a practical Python solution built around single-connection dynamic subscription and heartbeat timeout detection . We use alltick api as our example throughout this post. I’ll cover real production pain points, architecture, full runnable code, common bugs and optimization results. All code can be directly used in your projects. Real Production Pain Points & Background My team maintains real-time data pipelines for stocks, forex and cryptocurrencies. A core requirement is to add or remove monitored trading symbols on demand. At first, we created one independent WebSocket connection for each symbol. This simple approach caused a lot of problems in production: Bulk symbol updates create frequent connection creation and destruction, leading to reconnection storms and high network load. Ghost subscriptions: Data keeps coming even after you send an unsubscribe request. False-alive sockets: The connection status stays connected, but tick data stops completely. Your analysis logic runs on invalid data. We refactored the architecture to use one single persistent WebSocket connection for all symbols, plus an independent thread for heartbeat monitoring. After the upgrade, all hidden connection issues are gone, and the system runs stably under high-frequency data traffic. 4 Common WebSocket Issues in Fintech Let’s break down the most frequent problems when using WebSocket for financial streaming. 1. Connection Flood & Reconnection Storms Rebuilding connections every time you update symbols causes endless handshakes and closures. It wastes system resources and makes the entire data stream unstable. 2. Undetectable False-Alive Sockets Network j

There are already many posts about what agent skills are and how to create your own, so in this post I want to dive into the various skills I use to assist in development. The Skills Grill Me Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me". I start every larger task with this excellent skill created by Matt Pocock. I either start this with an already prepared PRD / detailed task description or use it for discovery purposes. The agent will then ask many questions to align language and functional requirements, so fewer hallucinations happen in follow up requests. You should be well equipped to answer the agent's question or the grill me session can go on for a long time. I had it ask me way over 50 questions when not answering detailed enough. As a little extra I added an extra request to the skill to prompt me if I want to create the PRD when the alignment phase is over, this leads us to the next skill. To PRD Turn the current conversation context into a PRD. Use when user wants to create a PRD from the current context. This will simply take the current conversation and creates a PRD out of it, we do this to summarize the conversation so we can easily start a new context window with all information present To Issue Break a plan, spec, or PRD into independently-grabbable GitHub issues using tracer-bullet vertical slices. Use when user wants to convert a plan into issues, create implementation tickets, or break down work into issues. Another excellent skill by Matt Pocock. I modified the skill slightly to use the GitHub MCP to create issues based on a PRD or planning session. But I often found that letting an agent implement those tasks it resulted in a large amount of code and that is why I added the to tasks skill To Task Break down a single GitHub issue into a sequential list of small i

If you've ever needed a list of local businesses - every dentist in Manchester, every plumber in Leeds - with their contact details , you've probably hit the same wall I did: Google's Places API is rate-limited, costs money once you scale, and annoyingly doesn't return email addresses at all. Copy-pasting from Maps by hand is soul-destroying past the first ten rows. Most "scrapers" give you the name and a phone number, then stop right where the value starts: the email . This guide shows a practical way to pull structured business data straight from Google Maps and auto-enrich each result with emails, extra phones, and social links - no Google API key, exportable to JSON/CSV/Excel, and callable from code. What you actually get per business { "name" : "Ringway Dental - Cheadle" , "address" : "187 Finney Ln, Heald Green, Cheadle SK8 3PX" , "phone" : "0161 437 2029" , "website" : "https://www.ringwaydental.com/" , "rating" : 5 , "reviewsCount" : 598 , "category" : "Dental clinic" , "lat" : 53.37 , "lng" : -2.22 , "emails" : [ "reception@ringwaydental.com" ], // ← enriched from the website "socialLinks" : { "facebook" : "..." , "instagram" : "..." }, "extraPhones" : [ "..." ] } The first block (name → coordinates) comes from Maps. The emails / socialLinks / extraPhones are the bit that makes a list actually usable for outreach - they're crawled from each business's own website. The fast way: a ready-made Actor Rather than build and babysit the scraping yourself, I packaged this as an Apify Actor: Google Maps Scraper . You give it search terms + locations; it returns enriched rows. Input: { "searchTerms" : [ "dentists" ], "locations" : [ "Manchester, UK" ], "maxPlacesPerSearch" : 50 , "scrapeContacts" : true , "relatedEmailsOnly" : true } That's it. scrapeContacts: true turns on the website crawl for emails/socials; relatedEmailsOnly keeps only emails that belong to the business's own domain (so you don't get random gmail noise). Call it from code (Python) Every Apify Act

In a bid to dismiss a lawsuit over xAI’s polluting gas turbines, the Justice Department claimed the company is integral to military operations—including the Iran War.

As the rest of the country celebrated the USA's first World Cup win and the New York Knicks championship, Anthropic spent its weekend fighting the Trump administration over its latest model release. At 5:21 PM on Friday, the company received a US export control directive to suspend access to its Mythos 5 and Fable 5 […]

I asked AI to write a commit message for me last week The code change was simple. A bug fix One line,...

Run your brand shoots from start to finish Discussion | Link

This week's Java roundup for June 8th, 2026, features news highlighting: the GA release of A2A Java SDK 1.0; an update on Jakarta EE 12; point releases of Micrometer Metrics and Micrometer Tracing; maintenance releases of GraalVM Native Build Tools and OpenXava; the second release candidate of Gradle 9.6; and the first milestone release of Eclipse JNoSQL 1.2. By Michael Redlich

You’ve built something with Playwright. It works perfectly in your local environment. CI is green....

Convert your TikTok followers into paying customers Discussion | Link

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

Anthropic leaders flew to Washington, DC, to meet with White House officials on Monday. After high-level talks, they're still split on the risk Claude Fable 5 presents.

Class, Record, and Struct serve as blueprints for creating new objects. Classes Classes are the foundational building blocks of Object-Oriented Programming (OOP). Blueprint and instance // blueprint public class Person { public int ID { get ; set ; } public string Name { get ; set ; } } // instance var p = new Person { ID = 1 , Name = "Mirza" } Inspection Printing the class instance in the console will just display it's name: var p1 = new Person { ID = 1 , Name = "Mirza" }; Console . WriteLine ( p1 ); // Person To print the actual, we'd need print each property explicitly: Console . WriteLine ( p1 . ID ); // 1 Console . WriteLine ( p1 . Name ); // "Mirza" Mutation C# classes are mutable, meaning the originally set values can be altered: var p1 = new Person { ID = 1 , Name = "Mirza" }; Console . WriteLine ( p1 . Name ); // "Mirza" p1 . Name = "Armin" ; Console . WriteLine ( p1 . Name ); // "Armin" That said, this can be tweaked by changing the accessor the class property. public class Person { public int ID { get ; set ; } public string Name { get ; init ; } // <-- } This time around if we try to change the value of the Name property after initialization, the C# compiler will start to complain. var p1 = new Person { ID = 1 , Name = "Mirza" }; p1 . Name = "Edis" ; // ❌ The init accessor allows you to create properties that can only be assigned a value during object initialization. Memory location Classes are reference types and are allocated on the heap. If we create two distinct class objects and assign one to the other, both objects will point to the exact same reference in memory: var p1 = new Person { ID = 1 , Name = "Mirza" }; var p2 = new Person { ID = 2 , Name = "Mirza" }; p1 = p2 ; p1 . Name = "Sead" ; Console . WriteLine ( p1 . Name ); // Sead Console . WriteLine ( p2 . Name ); // Sead If we change a value in one of the objects, it will be reflected in both. Equality Two class instances (objects) aren't equal even if they share the same values: var p1 = new P

In sandbox runtimes, "isolation" is the core requirement. qwrap (based on bwrap user namespace) and Container (podman/docker) are two mainstream backends. They solve the same problem — running code in a restricted environment — but take completely different paths. This article uses extensive analogies to help you understand the similarities and differences. Building Intuition: Two Ways to "Lock the Door" Imagine you need to confine someone you don't fully trust in a room to do work: qwrap approach : In your existing house, you put up a partition to wall off a corner, leaving only a small window to pass materials through. The walls are still the original walls, the floor is still the original floor, but the person can only see what's inside the partition. Container approach : You build a shipping container with its own independent power, water, and ventilation systems. Put the person inside, close the door. They feel like they're in a complete little house, completely unaware of what's outside. This is the most fundamental difference: qwrap is lightweight view isolation, Container is complete environment encapsulation . What is qwrap (bwrap user namespace) qwrap uses bubblewrap (bwrap) under the hood, a sandboxing tool that leverages Linux user namespaces. How it Works Host filesystem ├── /usr/bin/python3 ← Host's Python ├── /home/user/project/ ← User project └── /tmp/secrets/ ← Sensitive files qwrap sandbox view (what the process sees) ├── /usr/bin/python3 ← bind-mounted in, read-only ├── /workspace/ ← Only the project directory is exposed └── (/tmp/secrets/ doesn't exist) ← Completely invisible Key mechanisms: User Namespace : The process thinks it's root, but actually maps to an unprivileged user on the host Mount Namespace : Only bind-mounts necessary directories in, everything else is invisible No images, no layers, no network namespace (unless explicitly configured) Analogy: VPN Split Tunneling qwrap is like split-tunneling rules on your phone — you're not wrap

When you double-click WP Maintenance Manager, it opens a browser tab — and the entire UI lives inside that tab. No native window is created. It's an unusual structure for a first-time user, and the natural question is: "why a browser?" That choice was an intentional design decision when building a Python desktop application. Here's the comparison that led to it, and the side effects of the choice. Four realistic options For a WordPress maintenance automation tool, four implementation styles were practical: Approach UI Distribution size Dev cost Per-OS extra work Native (Swift / WPF) OS-native windows Small–medium High (separate impl per OS) Heavy PyQt / PySide Qt widgets Medium (~80 MB) Medium Light Electron Chromium-embedded web UI Large (~150 MB+) Medium Light Local Flask + system browser System browser tab Small (~50 MB) Medium Light PyQt was a serious early candidate. A Python-only stack is appealing, but widget styling drifts subtly between OSes, Qt's layout system demands constant attention, and resolving Qt plugins under PyInstaller is fiddly. Dev velocity was not where it needed to be. Electron is the industry-standard choice for cross-platform UI, with the big benefit that HTML/CSS-based UIs are quick to write. But the distribution is well over 100 MB, and memory consumption is heavy. For a tool that often runs in the background, that overhead is too much to justify. Why local Flask + browser won The final structure was Flask (Python's lightweight web framework) + the system browser for UI. The decision rested on three axes: 1. The backend had to be Python anyway SSH connections via fabric / paramiko , browser automation via playwright , encryption via cryptography — every library at the core of WordPress maintenance lives in the Python ecosystem. Writing the backend in another language wasn't really an option. If Python is already required on the backend, putting the UI in Python too keeps distribution simple. 2. HTML/CSS/JS makes UI iteration fast Flask r

Most teams I talk to have "evals." I ask them where the evals run. The answer is almost always the same: a notebook, a dashboard, a spreadsheet someone updates after a bad week. That is not an eval suite. That is a museum. Here is the opinion I will defend for the rest of this post: if your agent's quality checks cannot block a merge, they are decorative. The entire value of an eval is that it stops a regression before it reaches a user. A score you read on Monday about a deploy you shipped Friday is a postmortem, not a gate. We gate code with unit tests. We gate APIs with contract tests. We gate infra with terraform plan . Then we take the single most non-deterministic component in the stack — an LLM agent that can silently change behavior when a vendor ships a new checkpoint — and we let it through on vibes. That asymmetry is the actual bug. Why "run it locally and eyeball it" rots The failure isn't that engineers are lazy. It's that manual eval runs degrade under exactly the conditions where you need them most: Prompt edits look harmless. You reword one line of a system prompt to fix a tone complaint and tank tool-selection accuracy on three unrelated tasks. Nobody re-ran the full set because it's a 40-minute slog. The model moves under you. You pinned gpt-4o , but gpt-4o is not a constant — providers roll checkpoints. Your prompt is identical and your behavior shifted anyway. Dependencies leak. A retrieval index gets re-embedded, a tool's API changes a field name, and the agent starts confidently citing stale data. None of that shows up in a code diff. Every one of these passes code review. Every one of these is caught by a regression suite that runs on the PR. The fix is boring and it works: treat agent behavior like any other thing you'd protect with CI. The two halves you actually need A CI gate for agents needs two things, and people consistently build only one. The first is a scorer : something that takes the agent's output for a fixed set of inputs and ret

Spotify Wrapped for Claude, Codex & a Public leaderboard. Discussion | Link

I reopened a file I had already fixed that morning. Not metaphorically. I literally re-fixed a bug I had closed four hours earlier, because between the fix and the reopen, my brain had quietly deleted the entire afternoon. That is the ADHD tax most productivity advice never names: it is not that you cannot focus, it is that the working model of what you were doing does not survive the gap between sessions. CLAUDE.md is the cheapest fix I have found for that specific failure. This is the companion to my Claude Code ADHD workflow ; that post is the full system, this one zooms all the way in on the single file doing most of the work. What Is CLAUDE.md, Actually? CLAUDE.md is a Markdown file that Claude Code reads automatically at the start of every session. You do not paste it. You do not remind Claude it exists. It just gets read, every time, before the first line of work. There are two places it lives: ./CLAUDE.md at a project root holds rules for that project: the tech stack, the conventions, the gotchas. ~/.claude/CLAUDE.md holds your global rules: things true across everything you build (your voice, your defaults, the things you never want re-litigated). For a neurotypical developer this is a convenience. For an ADHD developer it is a prosthetic. The difference is what the file is replacing. Why CLAUDE.md Is External Working Memory for ADHD Brains Working memory is the mental scratchpad that holds "what I am doing right now and the three things I just decided about it." ADHD shrinks that scratchpad and makes it leaky. Every interruption, a Slack ping, a stray thought, a context-switch to email, knocks items off it. When you return, the scratchpad is blank and you rebuild it from scratch. The American Psychological Association puts the rebuild cost at roughly 23 minutes per context switch for a typical brain. For an ADHD brain that involuntarily switches more often and rebuilds slower, the real cost is higher and it compounds. Ten switches a day is not ten minutes

At Inithouse — a studio running parallel product experiments — we built Be Recommended , a tool that checks how visible your brand is across ChatGPT, Perplexity, Claude, and Gemini. The idea sounded simple: query multiple AI models, score the results, show a report. It was not simple. Here are four technical mistakes we made shipping v1 — and the fixes that actually survived production. Mistake 1: Rate Limiting Was an Afterthought We treated rate limits as edge cases. They were not. Every AI provider has different rate-limit headers, different backoff expectations, and different definitions of "too many requests." Our first architecture just retried on 429. That turned a rate limit into a cascade — one provider throttling triggered a retry storm that cascaded to the others. The fix: Per-provider circuit breakers with exponential backoff. Each provider gets its own state machine. When a circuit opens, we serve cached results for that provider and mark the score as "partial" in the UI. Users see real data, not a spinner that never resolves. At Audit Vibe Coding — another tool in our portfolio focused on code quality audits — we observed the same pattern in a different domain: external API dependencies need isolation. The lesson transferred directly. Mistake 2: The Caching Strategy Was Too Naive Our first cache key was query + model . That breaks immediately — AI model responses drift over time, and a cached result from two weeks ago is misleading. We also had no invalidation strategy beyond TTL. The fix: Cache by query + model + week_number . Weekly invalidation with stale-while-revalidate: serve the cached score instantly, trigger a background refresh, update the display when new data arrives. Users get instant feedback and fresh data within the same session. We measured the impact across our portfolio: stale-while-revalidate cut perceived load time from 8+ seconds to under 1 second for returning visitors. The background refresh means scores stay current without the

Yesterday, as I was working on a CORS configuration, AI generated a block of code for me: const allowedOrigins = [ process . env . FRONTEND_URL || " http://localhost:3000 " , process . env . ADMIN_URL || " http://localhost:3001 " , ]. filter ( Boolean ); I was wondering... why use .filter(Boolean) here? 🤔 The fallbacks already guarantee strings. So I hovered on the variable. The type definition read: const allowedOrigins : string [] Fine. Made sense. But then I got curious. What if I removed the hardcoded fallbacks? const allowedOrigins = [ process . env . FRONTEND_URL , process . env . ADMIN_URL , ]. filter ( Boolean ); My type definition changed to: const allowedOrigins : ( string | undefined )[] I was shocked. I just filtered the array. How can TypeScript still think there's an undefined in there? First: What Does .filter(Boolean) Even Do? Boolean used as a filter function removes any falsy value from an array: false null undefined 0 "" NaN So: [ " https://app.com " , "" , undefined ]. filter ( Boolean ) // Result: ["https://app.com"] At runtime, this works exactly as you'd expect. No undefined survives. So why does TypeScript disagree? 🤷‍♀️ The Real Answer: TypeScript Doesn't Run Your Code TypeScript is a transpiler. It doesn't execute .filter(Boolean) — it only looks at types. When it sees this: array . filter ( Boolean ) It knows the callback returns a boolean . But it doesn't know what that means for the type of the elements that survive. It can't infer "if Boolean(x) is true, then x must be a string." So the undefined stays in the type — even though it'll never actually be there at runtime. That's the gap: your runtime behavior is correct, but your types are lying. The Fix: Type Predicates TypeScript lets you close that gap with a type predicate — a way of explicitly telling the compiler what a filter function guarantees: const allowedOrigins = [ process . env . FRONTEND_URL , process . env . ADMIN_URL , ]. filter (( origin ): origin is string => Boolean ( o