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In this article, the author explores data poisoning as a threat to machine learning systems, covering techniques such as label flipping, backdoors, clean-label poisoning, and gradient manipulation. The article reviews real-world incidents, discusses the challenges of detecting poisoned data, and presents practical defenses, tools, and operational practices for securing ML training pipelines. By Igor Maljkovic

AWS made Graviton5-powered EC2 M9g and M9gd instances generally available with 192 ARM cores, formally verified VM isolation via the Nitro Isolation Engine, and DDR5-8800 memory. ClickHouse reported 36% better performance with zero code changes. Meta committed tens of millions of cores. On-demand pricing is 9% above Graviton4, translating to roughly 15% better price-performance. By Steef-Jan Wiggers

Originally published at htpbe.tech . The version on htpbe.tech stays in sync with the latest detection algorithm — refer to it for the canonical text. Accounts payable teams are receiving receipts generated by ChatGPT plugins. HR platforms are seeing payslips rendered by Python scripts. Insurance claims contain repair estimates that no shop ever issued. The documents look correct. The logos match. The numbers are plausible. The question is: what can actually be detected, and what cannot? The honest answer requires separating two things that are often confused under the phrase “AI-generated document detection.” Two distinct problems called "AI-generated document detection" When people ask how to detect an AI-generated document, they usually mean one of two distinct things: Content classification asks: was the text in this document written by an AI language model? This is what tools like GPTZero and Turnitin’s AI detector do. They analyze writing style, token probability distributions, and linguistic patterns to estimate whether a human or a model produced the text. Structural forensics asks: was this PDF file generated by a real institutional system, or did it come from a headless browser, a PDF library, or a consumer tool? This is what HTPBE does. It reads the binary structure of the file — producer metadata, xref patterns, font embedding, object numbering — and checks whether those patterns match how legitimate institutional software generates documents. These are not the same problem. A document can contain AI-written text and still come from a real corporate system. A document can contain entirely human-written text and still have been rendered by Puppeteer an hour ago. The structural check and the content check answer different questions. HTPBE does structural forensics. It does not classify text. This article explains what that distinction means in practice, what the structural approach reliably catches, and where its limits are. What structural forensics detec

Every World Cup there's a moment. Some player nobody outside their domestic league had heard of scores an absolute screamer in a knockout match, and by the time they've finished celebrating, their follower count is climbing like a rocket. I always found that fascinating, but I could never see it happening. By the time the "X gained 3M followers!" tweets show up, the surge is already over. So this tournament I built a little tracker that snapshots player follower counts on a schedule and shows me the growth curve in near real-time. Here's how it works. The problem with doing this "properly" My first instinct was the official APIs. That died fast. Instagram's Graph API won't give you follower counts for accounts you don't own. TikTok's Research API is academics-only and takes weeks of applications. X's API now starts at $100/month and climbs steeply from there. I just wanted public follower counts — numbers anyone can see by opening the app. I didn't want a data partnership and a legal review. I ended up using the SociaVault API , which wraps public profile data from each platform behind one key. One request, one credit, JSON back. The shared client Everything runs through one tiny helper: // Node 18+ has fetch built in const API_KEY = process . env . SOCIAVAULT_API_KEY ; const BASE = " https://api.sociavault.com " ; async function sv ( path , params ) { const url = new URL ( BASE + path ); Object . entries ( params ). forEach (([ k , v ]) => url . searchParams . set ( k , v )); const res = await fetch ( url , { headers : { " X-API-Key " : API_KEY } }); if ( ! res . ok ) throw new Error ( ` ${ res . status } ${ await res . text ()} ` ); return res . json (); } Grabbing follower counts across platforms Each platform nests the count slightly differently, so I use fallback chains to stay defensive: async function instagramFollowers ( username ) { const data = await sv ( " /v1/scrape/instagram/profile " , { username }); const p = data . data ?. user ?? data . data ?? data

Big Tech is throwing big money into data center buildouts. As national opposition to the facilities grows, some workers are beginning to question whether it’s worth it.

Por muito tempo eu acreditei que programação e desenvolvimento de software como sinônimos. Na...

Every developer I know has the same problem: knowledge scattered across five places at once. Browser bookmarks they never re-read. Notion docs that become graveyards. Slack threads with critical context that disappear into the archive. README files that contradict each other. Stack Overflow answers bookmarked with zero recall of why. I tried most of the "second brain" setups and none of them stuck until I figured out why they kept failing: generic productivity systems are not built for how developers actually think and work. A developer's knowledge is fundamentally different from a writer's or a manager's. It is: Code-linked (a note about a library is useless without the actual code it explains) Decision-heavy (architecture decisions need context, rationale, and alternatives considered) Debugging-intensive (solutions to bugs need the exact error message, environment, and what you tried) Time-sensitive (that API migration note is only relevant for a 3-month window) Here is the structure that actually worked. The Core Structure 00-Inbox/ 10-Projects/ 20-Areas/ - Language: Python/ - Stack: AWS/ - Domain: Auth/ 30-Resources/ - Libraries/ - Tools/ - Patterns/ 40-Archive/ The key insight: Resources are evergreen, Projects are temporary, Areas are ongoing responsibilities. A note about how JWT works lives in 30-Resources/Domain-Auth/ . A note about implementing JWT for the current sprint lives in 10-Projects/Sprint-42-Auth-Revamp/ . When the sprint is done, the project gets archived. The JWT fundamentals note stays forever. The Templates That Made It Click Architecture Decision Record (ADR) # ADR-042: Use Postgres over DynamoDB for user sessions Status: Accepted | Date: 2026-06-22 ## Context We need session storage that supports complex queries for the audit log feature. ## Decision Postgres with connection pooling via PgBouncer. ## Alternatives Considered - DynamoDB: rejected (query limitations for audit log requirements) - Redis: rejected (not durable enough for complian

1. Introduction Django's ORM is one of its greatest strengths. It abstracts away raw SQL, lets you express database operations in clean Python, and gets you productive fast. But that convenience comes with a hidden cost: if you're not deliberate about how you fetch related objects, you'll silently generate far more queries than you intend — and you won't notice until your app slows to a crawl in production. The most common culprit is the N+1 query problem : a pattern where fetching a list of N objects triggers an additional query for each one, resulting in N+1 total round-trips to the database. At ten rows it's invisible. At ten thousand rows, it's a disaster. Django provides two tools to fix this: select_related and prefetch_related . This article explains how each one works internally, when to use which, and how to combine them effectively — with before/after examples and real query counts throughout. 2. Understanding the N+1 Problem Consider a simple blog with posts and authors. You want to render a list of posts, showing each post's title and its author's name. Models: # models.py from django.db import models class Author ( models . Model ): name : str = models . CharField ( max_length = 100 ) class Post ( models . Model ): title : " str = models.CharField(max_length=200) " author : Author = models . ForeignKey ( Author , on_delete = models . CASCADE , related_name = " posts " , ) The naive approach: # views.py from django.db import connection from .models import Post def list_posts () -> None : posts = Post . objects . all () # Query 1: fetch all posts for post in posts : print ( f " { post . title } by { post . author . name } " ) # ^^^ Query 2, 3, 4, ... N+1: one per post For 100 posts, this produces 101 queries . Django lazily fetches post.author the first time you access it on each object. Each access hits the database separately. You can verify this with django.db.connection.queries (requires DEBUG = True ): from django.db import connection , reset_queries

Whether you're brand new to Power BI or just getting started with data analytics, this guide walks you through everything you need to know about data modeling — from how tables connect, to the schemas that make your reports fast and reliable. What Is Data Modeling in Power BI? Imagine you have three spreadsheets: one with your customers , one with your products , and one with your sales transactions . Individually, each table tells you something. But together, they can tell you which customer bought which product, when, and for how much . That's exactly what data modeling is: the process of organizing your data tables and defining how they relate to each other so Power BI can combine them into meaningful reports and dashboards. A data model in Power BI has three core building blocks: Tables — your data sources (Excel files, databases, CSVs, cloud services, etc.) Relationships — the links between tables that tell Power BI how data connects Measures & Calculations — formulas (in DAX) that compute totals, averages, and other insights A well-designed data model is the difference between a report that loads in seconds and one that takes forever. It's also what keeps your numbers accurate and your dashboards easy to use. Why Does Data Modeling Matter? Here's a simple analogy: a city without roads is just a collection of buildings. Data modeling is the road system that lets you travel between your tables. Without a good data model: Your visuals may show incorrect or duplicated numbers Filters in one chart won't affect another Reports will be slow and hard to maintain With a good data model: Clicking on a customer in one visual automatically filters every other visual Calculations are accurate and consistent You can easily add new data sources without rebuilding everything Types of Tables: Facts vs. Dimensions Before diving into schemas and relationships, you need to understand the two types of tables that make up most Power BI models. Fact Tables A Fact table stores the ev

Claude Guillemot, who founded Ubisoft with his four brothers, has died at the age of 69.

nvidia-peermem "Invalid argument" on Ubuntu — Fix GPUDirect RDMA with DMA-BUF TL;DR: If modprobe nvidia-peermem fails with Invalid argument ( -EINVAL ) on a system using the inbox Ubuntu InfiniBand stack ( rdma-core ), the module is not broken and you do not need it. nvidia-peermem requires an API that only exists in MLNX_OFED. On Hopper/Blackwell GPUs with the NVIDIA open driver, use DMA-BUF instead — it does GPUDirect RDMA natively. The one gotcha: you must enable nvidia-drm modeset=1 . Applies to: Ubuntu 22.04 / 24.04, inbox rdma-core stack, NVIDIA open kernel driver, H100 / H200 / B200, ConnectX-6/7 (or any HCA with ODP support). The symptom $ sudo modprobe nvidia-peermem modprobe: ERROR: could not insert 'nvidia_peermem' : Invalid argument dmesg shows nvidia-peermem loaded but registered nothing, or the load returns -EINVAL . GPUDirect RDMA appears to be unavailable. Why this happens (and why it is not a bug) nvidia-peermem is the legacy path for GPUDirect RDMA. It registers GPU memory with the InfiniBand subsystem through a Mellanox-proprietary kernel API: ib_register_peer_memory_client () That symbol only exists in MLNX_OFED's build of ib_core . It is not in the mainline kernel, and it is not in rdma-core , which is the inbox InfiniBand stack on Ubuntu. If you are on the inbox stack, nvidia-peermem was compiled without that API present, so it can never bind and always returns Invalid argument . No module parameter or config change will fix it, because the thing it needs was never there. Do not install MLNX_OFED just to make nvidia-peermem load. That works, but it is the wrong fix — you would be adding a heavy proprietary stack to revive an obsolete module. There is a native path already in your kernel. The fix: use DMA-BUF On Hopper and newer with the open driver, GPUDirect RDMA works through DMA-BUF , a mainline Linux framework. No external module, no MLNX_OFED. Requirements (check these first) NVIDIA open kernel driver (not the proprietary build) nvidia-drm

Many of those videos were reportedly filmed on “near-perfect copies” of the Polymarket website, while featuring trades and winnings that were not real.

Learn how to organize React projects for scalability, maintainability, and team collaboration. Introduction As React applications grow, one challenge consistently emerges: project organization . A folder structure that works perfectly for a small side project can quickly become a nightmare when multiple developers contribute to a production-scale application. Whether you're a junior React developer preparing for interviews, a mid-level frontend engineer looking to improve code maintainability, or a technical recruiter trying to understand modern React development practices, understanding scalable React folder structures is essential. In this guide, we'll explore the most popular React folder organization strategies, their pros and cons, and the structure many modern engineering teams use to build scalable applications. Why React Folder Structure Matters A well-organized React project provides several benefits: Faster onboarding for new developers Easier code maintenance Better scalability as features grow Reduced code duplication Improved team collaboration Cleaner separation of concerns Many React applications start simple: src/ ├── App.jsx ├── Home.jsx ├── Login.jsx └── Dashboard.jsx This works initially, but as the project grows to dozens or hundreds of components, finding and maintaining files becomes increasingly difficult. Common React Folder Structure Approaches 1. Type-Based Folder Structure One of the earliest and most common approaches is organizing files by their type. src/ ├── components/ ├── pages/ ├── hooks/ ├── services/ ├── utils/ ├── assets/ └── contexts/ Advantages Easy to understand Suitable for small projects Quick setup Disadvantages Components folder can become enormous Related files are spread across multiple directories Harder to maintain in large teams For example, a user profile feature might have files located in: components/UserProfile.jsx hooks/useUser.js services/userService.js pages/ProfilePage.jsx Finding all files related to one feat

If you've worked with Angular Material, you know theming can be one of the trickiest parts of the library — especially after the move to Material 3. Token-based theming, custom palettes, dark mode, component-level overrides... there's a lot going on under the hood. I built a full course to break it all down, and I'm excited to announce it's now completely free . What's in the course Angular Material Theming System is a deep, practical walkthrough of Angular Material's theming API for Material 3. By the end, you'll be able to: Build and customize themes from scratch Apply themes at the application level Override and extend themes for individual components Work confidently with Angular Material's theming tokens and APIs It's 46 lessons and roughly 4.5 hours of content, all hands-on and example-driven. Where to find it 🎥 Watch on YouTube: https://www.youtube.com/playlist?list=PLOjtJUnDeEIyaeUs_jrxylnD2IxSb3Ku7 📝 Read the article version: https://angular-ui.com/courses/angular-material-theming/ 💻 Full source code on GitHub: https://github.com/Angular-UI-com/angular-material-theming If you're building with Angular Material and theming has ever felt like a black box, give it a watch. I'd love to hear your feedback in the comments. If this helped you, consider checking out Angular Material Blocks — a library of pre-built Angular Material + Tailwind components, available via a simple CLI.

Organizing notes by topic sounds logical until you have notes on PostgreSQL in five different folders and cannot find the one that matters for today's problem. The issue is not discipline. The issue is that topic-based organization asks the wrong question. "What is this about?" is useful for libraries. For engineers, the better question is "What am I doing with this?" That is the premise of PARA. PARA is a simple four-bucket system created by Tiago Forte as the organizational backbone of his Building a Second Brain framework. The idea is that all information can be sorted into four categories: Projects, Areas, Resources, and Archives. Each category represents a different level of actionability, and that distinction drives where every note lives. This guide applies PARA to engineering work specifically — codebases, documentation, learning material, and the tension between active project work and long-term reference. The Problem With Topic-Based Organization Most engineers organize knowledge the way they organize code: by domain. databases/ postgresql/ redis/ api/ rest/ graphql/ devops/ kubernetes/ terraform/ That structure makes sense when you are browsing. It breaks down when you need something for a specific task. You remember a useful note about database migration safety, but it could be in databases/postgresql/ , devops/deployments/ , api/versioning/ , or nowhere because you saved it somewhere temporary. Topic folders force you to decide where knowledge belongs before you understand its context. PARA delays that decision — instead of asking what something is about, it asks what you are currently doing with it. The Four Buckets Projects A project is active, time-bound work with a defined outcome. For engineers, projects are things like: Migrate billing service to queue v2 Upgrade PostgreSQL from 14 to 16 Write architecture decision record for auth service redesign Implement rate limiting on public API Publish article about distributed tracing Every project has a c

Sure, anyone can use OpenAI’s chatbot. But with smart engineering, you can get way more interesting results.

A great CLAUDE.md is not the longest one. It is the one where every line changes what Claude does. The whole skill is knowing what belongs in it — and, just as importantly, what does not. The sections that earn their place Start with a one or two line project description and your stack, with version numbers. Claude infers a lot from your code, but it will not guess that you are on Next.js 15 instead of 14, or which ORM you chose. Then a directory map — not every file, just the top-level layout with a note on what each part holds. After that: the build and test commands, the conventions a formatter does not enforce, and critically, the things not to touch. # Project: Acme Dashboard Next.js 15 (App Router), TypeScript, Drizzle ORM, Vitest. ## Structure src/app/ # routes and pages src/lib/ # shared utilities, db client db/migrations/ # generated - never hand-edit ## Commands Build: npm run build Test: npm run test ## Conventions - API routes return { data, error } - never throw to client - Server components by default ## Do not touch - db/migrations/ is generated. Never edit by hand. Every line in that file would cause a mistake if removed. That is the bar. What to leave out This is where most files go wrong. Two kinds of content waste your budget: Personality instructions. "Act as a senior engineer," "think step by step," "be thorough." These feel productive but change nothing — Claude already does them. General advice that does not prevent a specific mistake is pure noise. Rules a tool already enforces. If you have a formatter or linter, do not restate what it enforces. Wire it into a hook instead, and keep CLAUDE.md for what tools cannot enforce. The one-line test For every line, ask: "If I remove this, will Claude make a mistake?" If yes, keep it. If no, delete it. This single question, applied ruthlessly, is the difference between a file Claude follows and one it ignores. A bloated file buries the rules that matter in noise, so Claude cannot tell which line is the

If you use Claude Code, there is one file that quietly shapes every session: CLAUDE.md. Most developers either do not have one or have one that works against them. Here is what it actually is, in plain terms. The file Claude reads every session CLAUDE.md is a markdown file that Claude Code reads at the start of every conversation. Think of it as your project's constitution — the source of truth for how your specific repository works. Because Claude reads it every time, you stop re-explaining your stack, your conventions, and your commands on every task. Why it exists Without a CLAUDE.md, every session starts cold. Claude can read your code, but it cannot infer the things that live outside the code: that you are on Next.js 15 and not 14, that a directory is generated and must never be edited, that your team has a particular commit style. You end up explaining these again and again, slightly differently each time, so the output drifts. CLAUDE.md captures that knowledge once, somewhere Claude always sees it. Where it lives, and how to start Put CLAUDE.md in the root of your project. You do not have to write it from a blank page — the /init command analyses your codebase and generates a starter, detecting your build tools, test framework, and existing patterns: $ claude > /init Treat the result as a foundation, not a finished product. The real value comes from refining it as you learn what Claude gets wrong without guidance. What belongs in it A good CLAUDE.md is short and specific: A one-line stack description, with versions — Claude will not guess Next.js 15 over 14 A directory map — the top-level layout and what each part holds The build and test commands The conventions a newcomer could not infer from the code A "do not touch" section — generated files, migrations, protected paths Here is a compact example: # Project: Acme Dashboard Next.js 15 (App Router), TypeScript, Drizzle ORM, Vitest. ## Structure src/app/ # routes and pages db/migrations/ # generated - never h

You wrote a detailed CLAUDE.md, and Claude Code still gets things wrong — wrong convention, touches files it should not, ignores rules you clearly wrote down. The cause is almost never that the rules are missing. It is that they are buried. The over-specified file problem CLAUDE.md loads into Claude's context every single session, and performance degrades as that context fills. When the file grows too long, something counterintuitive happens: Claude starts ignoring parts of it. The important rules get lost in the noise, and the genuinely critical instructions sit too deep to reliably influence output. A bloated file does not just waste tokens. It actively makes Claude less reliable, because it cannot tell which of your hundred lines is the one that matters. The trap of good intentions It always starts reasonably: "let me put everything relevant in here." But relevant is a low bar. The file grows until it is impossible to scan, full of duplication, and so noisy that the truly important rules carry no weight. More content felt like more control. It was the opposite. The fix: prune ruthlessly Run every line through one question: "If I remove this, will Claude make a mistake?" If the answer is no, the line is noise — delete it. And if something only matters in a specific situation rather than always, it does not belong in the always-loaded file at all. That is what skills and subdirectory CLAUDE.md files are for — they load on demand, only when relevant. Let Claude fetch what it needs Instead of embedding everything, tell Claude how to pull context when it needs it. Rather than pasting an entire API guide into the file: # Wasteful - embeds the whole file every session: @docs/api-guide.md # Better - Claude reads it only when relevant: For Stripe integration work, read docs/stripe-guide.md The second form costs almost nothing until the moment it is needed. The result A pruned CLAUDE.md is often a third of the length and many times more effective. The rules that matter are

Gelişmiş Veri İşleme (Python) Sıralama, Filtreleme ve Arama – Profesyonel Veri Manipülasyonu Rehberi Python’da veri işleme, sadece döngülerden ibaret değildir. Modern Python yaklaşımı; fonksiyonel programlama araçları , yüksek seviyeli built-in fonksiyonlar ve lambda ifadeleri ile daha kısa, daha okunabilir ve daha performanslı çözümler üretmeyi hedefler. Bu bölümde dört kritik alanı derinlemesine inceleyeceğiz: sorted() ile gelişmiş sıralama lambda ile karmaşık veri yapıları üzerinde sıralama filter() ve map() ile fonksiyonel veri dönüşümü any() ve all() ile toplu doğrulama (validation) Her bölümde gerçek dünya senaryoları ve hands-on örnekler olacak. 1. sorted() Fonksiyonu — Gelişmiş Sıralama Motoru 1.1 Temel Yapı sorted ( iterable , key = None , reverse = False ) Parametreler: iterable: Liste, tuple, set vb. key: Sıralama kriteri (fonksiyon) reverse: True → büyükten küçüğe 1.2 Basit Sıralama ```python id="s1" sayilar = [5, 1, 9, 3, 7] sonuc = sorted(sayilar) print(sonuc) --- ## 1.3 Ters Sıralama ```python id="s2" sayilar = [5, 1, 9, 3, 7] print(sorted(sayilar, reverse=True)) 1.4 Tuple Sıralama ```python id="s3" veri = (10, 5, 20, 15) print(sorted(veri)) --- ## 1.5 String Sıralama (ASCII mantığı) ```python id="s4" kelimeler = ["python", "ai", "data", "backend"] print(sorted(kelimeler)) 2. key Parametresi — Sıralamanın Beyni sorted() fonksiyonunun gerçek gücü burada başlar. 2.1 String Uzunluğuna Göre Sıralama ```python id="k1" kelimeler = ["python", "ai", "veri", "makineöğrenmesi"] sonuc = sorted(kelimeler, key=len) print(sonuc) --- ## 2.2 Sayıların Moduna Göre Sıralama ```python id="k2" sayilar = [10, 3, 7, 21, 14, 9] sonuc = sorted(sayilar, key=lambda x: x % 5) print(sonuc) 2.3 Tuple Sıralama (Gerçek Dünya) ```python id="k3" urunler = [ ("Laptop", 45000), ("Mouse", 500), ("Monitör", 12000) ] sonuc = sorted(urunler, key=lambda x: x[1]) print(sonuc) --- ## 2.4 Çok Katmanlı Sıralama Fiyat → sonra isim ```python id="k4" urunler = [ ("Laptop", 45000), ("Mouse", 500),