I once spent nearly a week trying to fix a web scraper that, on paper, had absolutely no reason to fail. The target website wasn't using aggressive, visible defense walls. My script spaced out requests naturally, rotated common user agents, and used browser automation configured to mimic human interactions down to mouse movements. Yet, the results were an absolute nightmare. Some batches of requests would go through cleanly, while others immediately triggered CAPTCHAs or returned 403 Forbidden errors. Every single time I thought I had patched the logic, the failure rate climbed right back up. Like most developers, my default instinct was to assume the application layer was broken. I went down a rabbit hole optimization sprint checking request headers, browser fingerprints, cookies, and session persistence. Nothing explained the wild inconsistency until I noticed a strange clue: some proxy pools performed beautifully, while others crashed on the exact same codebase. The code wasn’t the issue. The culprit was a fundamental misunderstanding of proxy network architecture. Looking Beyond the IP Address: Enter the ASN For a long time, I treated proxies as interchangeable commodities. An IP address was just an IP address, and if one got blocked, you simply rotated to the next. Modern anti-bot solutions like Cloudflare, Akamai, and PerimeterX don't look at IPs in a vacuum. They analyze network layer characteristics, specifically the ASN (Autonomous System Number). An ASN is a unique identifier assigned to a network operator that defines who owns and routes an IP range. When your scraper hits a website, the target's security system looks up your ASN to check your network identity. If your traffic originates from a commercial hosting provider or data center ASN, it carries an automatic penalty score for sensitive endpoints. To build reliable systems, you have to move past basic rotation and understand the two core proxy frameworks that mask this identity: ISP Proxies and Resi

Dumpert is a Dutch video site I've watched for years, but there's never been an Apple TV app, so I built one. DumpertTV is an unofficial, open-source tvOS client. Here's how it's put together and a few things that were more interesting than I expected. Disclaimer up front: this project is not affiliated with Dumpert or DPG Media B.V. It's an independent app that uses the public Dumpert API. The stack Swift 6 with strict concurrency ( complete mode) across every target SwiftUI for all UI, tvOS 18+ XcodeGen so the .xcodeproj is generated from a project.yml and never committed CloudKit , GroupActivities (SharePlay), Vision , AVKit , Swift Testing One actor for the network, one source of truth for the UI The whole networking layer is an actor . That makes per-request state like ETags and retry bookkeeping thread-safe without a single lock: actor DumpertAPIClient { private var etags : [ URL : String ] = [:] func fetch < T : Decodable > ( _ endpoint : APIEndpoint ) async throws -> T { // Exponential backoff on 5xx + network errors; honours 304 Not Modified. try await fetchWithRetry ( endpoint , attempt : 0 ) } } The UI reads from a single @Observable @MainActor repository injected through the SwiftUI environment — no Combine, no view models competing over the same state: @Observable @MainActor final class VideoRepository { private(set) var hotshiz : [ MediaItem ] = [] let apiClient : APIClientProtocol // protocol-backed for testing } ContentView () . environment ( videoRepository ) Views just read repository.hotshiz ; updates flow automatically. With Swift 6's strict concurrency on, the compiler kept me honest about every actor hop. Things that were trickier than expected tvOS focus + a top tab bar. Getting a Netflix-style hero carousel to behave with the focus engine took real care. It also made automating screenshots interesting — scripted remote input doesn't reliably drive the simulator, so I added #if DEBUG launch-argument hooks to jump straight to any tab/category f

Quantinuum, a quantum computing startup, is losing millions. Investors want in anyway.

With the recent surge in AI-generated content, I built a website called Voloshow that generates images and videos using AI. However, it has been live for almost a month and still hasn’t attracted a single user. I’m not sure what I did wrong. website is called Voloshow (https://voloshow.com/).

Amazon has announced a new version of its fully autonomous warehouse robot, Proteus, that will can interact using language instead of code. The expanded capabilities come as part of a growing pivot toward automation as the e-commerce giant replaces its human workers with robots. Amazon says the AI-powered upgrade means its human employees can assign […]

A video creator known as Doctor Spaghetti has scrutinized hours of Diners, Drive-Ins and Dives to get to the bottom of an explosive culinary conspiracy theory.

SpaceX expects to become a trillion-dollar company when it goes public.

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Automated support that learns from docs + past conversations Discussion | Link

my friend just told me about this and i had to share it immediately cursor is giving students 12 months of pro completely free. no credit card. just verify your .edu email and that’s it you get full access to gpt, claude, gemini… all the models. for a whole year. for free. that’s $240 you just keep in your pocket while everyone else is paying $20 a month wondering why their bank account looks sad takes like 2 minutes. go to cursor.com/students, throw in your .edu, pass the verification, done and if you graduated already, you probably know someone still in college who has no idea this exists. do them a favour link in the comments. seriously just go do it right now submitted by /u/NewMuffin3926 [link] [留言]

Solo dev here. My biggest bottleneck isn't building, it's deciding when something is done. I keep polishing past the point of diminishing returns and delay shipping for weeks over things no user would notice. For those who ship regularly: - What's your actual "ship it" threshold? - Do you use a hard rule (a deadline, a checklist, a launch date you can't move), or is it a feel thing? - Has shipping earlier than felt comfortable ever hurt you? Trying to build a saner habit around this. How do you draw the line? submitted by /u/IcyButterscotch8351 [link] [留言]

ChatGPT introduces a new memory system to better remember preferences, keeping context fresh and relevant across conversations.

Architectural change cases extend architecture decision record (ADR) thinking by evaluating how decisions may evolve over time. Change cases expose hidden assumptions and help teams estimate the reversibility and cost of change. By Pierre Pureur, Kurt Bittner

The app AT4K turns the messy Google TV interface into something closer to the Apple TV interface, making it cleaner, more customizable, and less frustrating to use.

You can get a 2h free trial by solving a proof-of-work captcha when topping up your account for the first time. If you'd like to learn more, an independent interview was posted a couple of weeks ago [1], and the FAQ [2] has a lot of information as well. For the source code sharing, we've talked with lawyers and are inclined to no longer require the NDA/NCC for privacy reasons shared with us before (signing requires identification), but instead use a source-available permissive license that doesn

Hey all, we have a quick update for everyone who participated in the Google I/O Writing...

I wrote a short article about why Pandas is worth learning from a general programming perspective, not just a data science one. A lot of everyday programming work involves tabular data - CSV files, reports, logs, exports, billing data, sales data, inventory data, operational spreadsheets, analytics extracts, etc. You can process that kind of data with loops and dictionaries, SQL, shell tools, or spreadsheets. But Pandas gives Python a very compact and expressive way to do filtering, grouping, aggregation, joins, and reshaping in code. The article uses a small sales/purchases CSV example and compares the Pandas approach with plain Python and spreadsheet-style thinking. I’m curious how other programmers think about this: is Pandas one of the libraries that makes Python worth learning, even for people whose main work is not data science? Or would you usually reach for SQL, spreadsheets, shell tools, or something else? submitted by /u/Horror-Willingness74 [link] [留言]

AWS Face Liveness has native SDKs for iOS and Android. That works well if you are building fully...

Your ambient memory for Claude Discussion | Link