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Hello I am curious about the election criteria for ICML financial aid. If anyone have been granted financial aid would you mind sharing your profile. Somehow being a black woman ( 2 underrepresented groups) with one paper accepted at the main conference and two papers accepted at different workshops is not enough to get financial aid. Are they solely for oral papers perhaps ? Last year a colleague (white man) with one spotlight paper did not get it neither. Maybe you need to belong to all minority groups ( half black half Latina Native American woman lgbtq+ and so on so far and what have you) with at least 3 oral papers 💀💀 to get any sort of help? The selection process and criteria should have more transparency cause chances are people in those committees are just giving the money to their own student and postdocs. submitted by /u/DazzlingPin3965 [link] [留言]

I'm running into an issue with an ablation study for a paper I'm preparing. I trained a model. The model achieved my best result, and I saved the trained checkpoint ( .pth file). Now my supervisor wants me to perform an ablation study by removing components and how it impacts the accuracy. My concern is that if I retrain from scratch, the accuracies will not exactly match the original run due to randomness, different seeds, etc. is there any way i can do the ablation study without retraining? I'd appreciate hearing how others have handled this situation in publications or thesis work. please help me out submitted by /u/Plane_Stick8394 [link] [留言]

Hackers are convincing Meta’s AI support chatbot to let them take over other peoples’ accounts: A video posted on X showed the step-by-step process to hack someone’s Instagram account. The hacker allegedly used a VPN to spoof the targets’ presumed location to avoid triggering Instagram’s automated account protections. Then, the hacker opened a chat with Meta AI Support Assistant and asked the bot to add a new email address to the target’s account. The chatbot can be seen sending a verification code to the email address provided by the hacker; the hacker then shares the verification code with the chatbot, which prompts the chatbot to show a button to “Reset Password.” The hacker enters a new password and takes over the victim’s account...

Your Claude AI Video Editor for Premiere Pro Discussion | Link

Most days in her chambers, Judge Maritza Braswell, a federal magistrate judge in Colorado, sifts through stacks of documents written by people without a lawyer. Many of them can’t afford to hire a lawyer, and others have cases too weak or too small to interest one. She reads each one carefully, mindful of how daunting…

Here is the situation of the website. If someone in Canada enters www.example.com they are redirected to www.example.com/ca and ALL the mention of "USA" is hidden and replaced with "Canada"! For example in Canada, instead of people seeing "Home Improvement in the USA and Canada", people in Canada just see "Home improvement in Canada", and vice versa; someone in USA and everywhere other than Canada on the globe does NOT see Canada on the website pages . My question is: Shouldn't a website have unified info and list BOTH USA and Canada, because with current situation someone accessing the homepage in Canada would NOT know that the company can also do Home improvement in the USA and vice versa . Even for AIs, I asked Chatgpt where is the company located and did NOT see Canada. P.S. The only mention of both countries is in the contact page. submitted by /u/RadiantQuests [link] [留言]

Samsung will roll out an update for its Health app before launching its new Galaxy Watches.

The speakers discuss the architectural challenges of executing safe data deletion across distributed datastores. Balancing durability, availability & correctness, they explain how to orchestrate multi-system deletion propagation without impacting live traffic. They share lessons on controlling tombstone accumulation, building continuous audit loops, and gaining trust with a centralized platform. By Vidhya Arvind, Shawn Liu

I tagged v0.9.0 of LaraFoundry this week: billing. Except the honest headline is that I shipped the billing seam , not billing. The free core now has the whole shape of a subscription system, a payment-gateway contract, a driver manager, a real access gate over subscription columns, and it cannot take a single cent. That is on purpose, and the reason is the most interesting part of the phase. LaraFoundry is a SaaS core I'm extracting in public from a live CRM, one module at a time. The deal I made with myself early on: the core is free and stays free for everything except money. Auth, multi-tenancy, RBAC, the admin console, the activity log, i18n, files: all free. The day a business wants to charge its customers , that is the paid part. So billing could not just be "another module." It had to split cleanly down a line, with the free side carrying real, useful structure and the paid side carrying the parts that actually move money. The donor habit I would not carry Here is what the original CRM did when a company "paid" for its subscription: // TODO: real payment gateway integration // TEMPORARY: every payment is successful, for testing $paymentStatus = 'success' ; That success was hardcoded. There was no Stripe, no Paddle, no gateway at all. "Paying" wrote a row into a company_payments table and flipped the subscription date forward. For a CRM I run myself, with one real user, that was fine: I never needed the real thing, so the placeholder sat there indefinitely. The moment this becomes a reusable core, that placeholder is poison. A success that is always true is worse than no gateway, because it looks like billing works. So the rule for this phase was simple: the fake gateway does not get extracted. Whatever stands in its place has to be honest about the fact that it takes no money. What the seam actually is The free core ships a PaymentGatewayInterface : subscribe, cancel, refund, status, and verify a webhook. It describes only the mechanics of moving money. It d

Marketing wants an A/B landing page by Friday. Product wants to gracefully deprecate a legacy API without breaking old mobile clients. Growth wants ten thousand short links, and Ops does not want ten thousand Nginx edits. At some point, a single return 301 in your CDN stops being a configuration problem and becomes a routing product . Someone has to answer, on every HTTP request: Given this host, path, query, and method—where does this visitor go, and with which status code? I built that answer as a pipeline at LinkShift . Not a pile of special cases, but a fixed sequence of steps that runs the exact same way for real visitors and for the tools you use to test rules before rollout. Here is how I designed a deterministic redirect engine, the pipeline that powers it, and the edge cases that kept me up at night so they don't have to keep you up. Why "Usually Works" Is Not Enough A redirect engine fails quietly. The browser follows a broken 302 and nobody files a ticket. The damage shows up days later in analytics: wrong campaign, wrong locale, or an infinite loop that only appears when two rules on the same host point at each other. What I wanted early on was boring, bulletproof reliability: Same inputs → same decision. Two engineers simulating the same request against the same rule set should get the same target URL. Same resolution logic everywhere. Matching and destination resolution must not diverge between the "Test Rule" button in the dashboard and an actual click on a custom domain. Guards before cleverness. Rate limits and access checks run before anyone evaluates a ternary conditional in a destination string. Expressiveness is easy. Ordering is what saves you in production. The Pipeline, Told as a Story Picture a request hitting a hostname. Before the engine asks "which rule wins?", the request walks through a strict corridor of gates. Only then does it enter the rule loop. Gate 1: The Host Has to Exist If the hostname does not resolve to a domain or LinkShift

Welcome to our weekly digest, where we unpack the latest in account and chain abstraction and the broader infrastructure shaping Ethereum. This week: Base ships its first independent upgrade with a TEE+ZK multiproof system and confirms native AA is next; Biconomy turns the ERC-8211 smart batching standard into a TypeScript SDK; LI.FI launches an enterprise intents engine for stablecoin and RWA flows; and Vitalik steps back from technical essays to write fiction. Base Launches Azul, Bringing Multiproofs to Coinbase's L2 Biconomy Ships Smart Batching SDK for ERC-8211 LI.FI Launches Intents Engine for Enterprise Cross-Chain Flows Vitalik Pivots to Fiction and Floats a "Trust Dependency" Framework Please fasten your belts! Base Launches Azul, Bringing Multiproofs to Coinbase’s L2 Base activated Azul on mainnet on May 28, its first network upgrade built entirely on its own stack. The headline feature is a multiproof system that pairs Trusted Execution Environment (TEE) proofs with Zero-Knowledge (ZK) proofs, advancing the Coinbase-incubated L2 toward Stage 2 decentralization. Either proof type can finalize a withdrawal independently, but when both agree, finality drops to as little as one day, far faster than the typical multi-day optimistic rollup wait. Crucially, permissionless ZK proofs can override permissioned TEE proofs if the two conflict, a design Base says meaningfully improves censorship resistance. The upgrade also makes base reth the sole execution client and introduces a new consensus client, phasing out older software. Node operators must migrate to the new stack to stay in sync. For AA and chain abstraction builders, the more important signal is what comes next: Base confirmed its end-of-June upgrade will include native account abstraction, an enshrined token standard and Flashblock Access Lists. The largest L2 by activity moving toward native AA is a meaningful pull on the whole ecosystem. Biconomy Ships Smart Batching SDK for ERC-8211 Biconomy released t

Introduction: The Controversial Rise of AI in Software Development The software development industry is at a crossroads. On one side, the rapid advancement of AI tools promises to revolutionize coding, automate repetitive tasks, and accelerate project timelines. On the other, a growing chorus of experts, led by figures like George Hotz , warns that the integration of AI agents into software development could become "one of the most costly mistakes in the field’s history." This bold prediction isn’t just hyperbole—it’s a call to scrutinize the mechanisms by which AI adoption could deform the very foundation of software engineering. At the heart of this debate are three critical failure points: over-reliance on AI without human oversight , insufficient real-world testing , and misalignment between AI capabilities and software development demands . Each of these factors acts as a stressor on the system, threatening to heat up development costs, expand systemic vulnerabilities, and ultimately break the delicate balance between innovation and reliability. Consider the causal chain: over-reliance on AI leads to a degradation of human expertise , as developers become less engaged in problem-solving. This, in turn, creates a feedback loop where AI-generated code, lacking nuanced understanding, introduces errors that go unnoticed. Without proper oversight , these errors propagate through systems, causing observable effects like reduced software quality and increased maintenance costs. Similarly, insufficient testing of AI agents in real-world scenarios means their failure modes remain unknown until they’re deployed at scale, risking systemic collapse in critical applications. The stakes are high. If unchecked, AI integration could lead to a loss of institutional knowledge , escalating development costs , and vulnerabilities in critical systems . The question isn’t whether AI has a role in software development—it’s how to implement it without deforming the field’s core princi

As part of the GitHub Copilot Challenge, I revisited one of my older cybersecurity notes on Authentication Failures and transformed it into a clear, structured, and SOC‑focused write‑up. This challenge helped me improve my technical writing, organise my thoughts, and explain concepts in a more human, readable way. * BEFORE GITHUB SCREENSHOTS: * AFTER GITHUB SCREENSHOTS: What I Improved I rewrote my entire explanation of authentication failures, focusing on: Token leakage Weak or missing MFA Poor session management Brute force & credential stuffing Misconfigured OAuth / SSO I also added SOC detection examples to make the content more practical and relevant for blue‑team work. How GitHub Copilot Helped GitHub Copilot supported me by: Suggesting clearer explanations Expanding short bullet points into meaningful content Helping me structure the write‑up Improving readability and flow Encouraging a more human, natural tone GitHub Repository Here is the updated write‑up in my repo: https://github.com/sujalavnelavai/Cybersecurity-Notes/blob/main/OWASP-Authentication-Failures/README.md Final Thoughts This challenge helped me understand authentication failures more deeply from a SOC and IAM perspective. It also improved my documentation skills — something extremely important for cybersecurity roles. I’m proud of the transformation and excited to continue building my cybersecurity learning notes.

Let me tell you exactly what happened the first time I tried to set up Lambda manually. Four hours. IAM trust policies I didn't fully understand, ARNs copy-pasted into the wrong fields, an API Gateway that was technically configured but somehow not routing anything correctly, and a deploy that failed with an error message pointing me nowhere useful. I hadn't written a single line of actual business logic yet. That's when someone on my team mentioned the Serverless Framework. My first reaction was honestly skepticism — another abstraction layer sounded like another thing to learn and eventually fight with. I was wrong about that. This isn't a "look how clean this tool is" post. It's more like: here's what I actually did to get a Postgres-backed CRUD API running on Lambda, step by step, including the parts that tripped me up. What the Framework Is Actually Doing Under the Hood Worth knowing before you start: the Serverless Framework isn't magic. It's generating CloudFormation templates and submitting them to AWS on your behalf. Your Lambda functions, API Gateway routes, CloudWatch log groups — all of it gets provisioned from a single config file. It works with other providers too, but the AWS integration is where it really earns its keep. The console clicking and manual ARN-wiring that burns time at the start of every serverless project? Gone. Same deploy workflow whether you're building a REST API, an event processor, or a cron job. Once you've done it once, the second project takes a fraction of the time. What You're Building Four live endpoints backed by PostgreSQL. A Users table. Create, read, update, delete — nothing exotic, but a real enough foundation that you can extend it into something actual once this guide is done. You'll need an AWS account, the AWS CLI installed, and the Serverless Framework installed before starting. That's it. Step 1: Sort Out Your AWS Credentials Run this to create both config files in one go: bash cat << EOF > ~/.aws/credentials [def

Bilingual post · Post bilíngue Jump to: English · Português English {#english} Navigating the Reorganized CrabPascal Docs Phase 1 of this blog series covered fundamentals — CLI, lexer, sprints, and how to contribute. If you read Contributing to CrabPascal: Get Involved , you already know where the repo lives and how to open a PR. Phase 2 is different: we walk the Mintlify documentation site the way a new teammate would, mapping each article to the pages you actually need in daily work. The live docs live at crabpascal.mintlify.app . The source is under mintlify/ in the Bitbucket repo. Think of Mintlify as the canonical handbook; Dev.to posts are the guided tour. Why the docs were reorganized CrabPascal grew from a v1.5 experimental interpreter (October 2025) to v2.22.0 with sprint-driven releases, Horse E2E tests, and honest build-exe . Old folders like documentation/00-INICIO/ still exist in git history, but Mintlify groups content by job to be done : You want to… Start here Install and run Quickstart See current capabilities Project status Understand architecture Architecture Follow releases Changelog Historical reports from v1.5–v2.8 remain under Histórico with archive banners — useful for context, not for "what works today." The home page is your compass Open the Mintlify index . It shows v2.22.0 at a glance: real diagnostic spans, System.* RTL, Unicode strings, dual-mode run/build, and Horse HTTP parity. Four cards link to quickstart, changelog, sprint roadmap, and technical-debt backlog. Essential CLI commands are listed once: crab-pascal check programa.dpr # diagnostics with line/column crab-pascal run programa.dpr # interpreter / runtime crab-pascal build-exe programa.dpr # native binary via C toolchain Bookmark this page. When someone asks "is feature X done?", check status + changelog before diving into Rust sources. The documentation map: question-driven navigation The page essentials/documentation-map is the "I want X, go to Y" cheat sheet. It answers sc

Internal note to the team, we need to improve test coverage and keep shipping, which means we should treat AI as a helper in the workflow, not as a replacement for testing discipline. AI-assisted development changes the shape of our risk. It can produce more code faster, but it also increases the chance that small logic mistakes, brittle selectors, and shallow test cases slip through review. The answer is not to add more manual checking everywhere. The answer is to be more deliberate about what we review, what we automate, and where we let AI help. What changes when AI writes part of the code The first thing that changes is review. When a developer uses AI to draft a feature, a test, or a refactor, the reviewer is no longer only checking intent and style. The reviewer also needs to check whether the generated code matches the product rule, whether it introduced a hidden dependency, and whether it quietly weakened coverage. That does not mean every AI-assisted change deserves extra ceremony. It means our review checklist should shift from "does this look correct" to "what did the model assume, and did we verify those assumptions?" That is especially important for test code, because generated tests often look plausible even when they do not prove much. Coverage should move from volume to signal AI tends to produce more test cases, but more cases are not the same as better coverage. If a generated test suite repeats the same happy path under slightly different names, the team gets a false sense of safety. Coverage should answer a more practical question, where are we most likely to break the user experience, and where will a test actually catch it? For chat and other AI features, prompt-by-prompt manual checks are a trap. They do not scale, and they encourage a habit of eyeballing output instead of verifying behavior. A better pattern is to build assertions around expected properties, create eval sets for representative prompts, and add regression coverage for failure

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

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/).