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Article URL: https://www.washingtonpost.com/technology/2026/06/26/openai-says-us-government-will-vet-users-its-latest-ai-model/ Comments URL: https://news.ycombinator.com/item?id=48690101 Points: 44 # Comments: 21

Originally published at malaymehta.com The Interview Trap If you look at most system design tutorials, you get an extreme use case. Design Twitter. Design YouTube. Scale it to a billion users. Draw boxes on a whiteboard for 45 minutes. Do you think your app will be used by a billion users on day one? The answer is almost always no. But the tutorials don't teach you what to do when you have 500 users, unclear requirements, a team of four, and a quarter to ship something that works. Real system design is nothing like a whiteboard interview. You don't get clean requirements, you don't design from scratch, and nobody asks you to handle a billion requests per second on day one. Real System Design Starts with Questions, Not Diagrams The very first thing that matters in system design is something most tutorials skip entirely: unclear and chaotic requirements. In the real world, requirements don't come as a clean problem statement. They come from non-technical business teams, and you need to navigate through cross-questions to get all the clarity you need. Ask as many questions as possible. Understand your functional and non-functional requirements. Which features need to be synchronous and which can be async? What are the read and write load patterns? What is the maximum and average number of concurrent users right now? What does authentication look like? Do you need role-based access control? These questions drive your choices. You don't always need an axe where a knife will do. Being minimalist with a reasonable growth prediction and a 3, 6, 9 month plan will take you in the right direction. There will be things the situation demands immediately but would take more time than expected. Taking a predictable hit now and fixing it at the right future time without missing that balance is truly important. Weighing what will be expensive to change later, in terms of dollar cost or human effort, is how real architectural decisions get made. Pushing Back on Bad Requirements Many

How I built an open-source Slack assistant with persistent semantic memory, powered by any LLM and Mem0's managed memory layer — no vector database required. The problem with Slack bots and memory Most AI Slack bots have the memory of a goldfish. Every conversation starts from scratch. You ask it about your sprint goals, it gives a great answer, then three days later you ask a follow-up and it has no idea what you're talking about. You end up re-explaining context constantly. The commercial solution to this is Claude Tag — a Slack integration that maintains genuine conversational continuity. But it's tied to one provider and not open-source. slacktag-oss is our attempt to replicate that experience: a Slack bot with real, semantic, persistent memory that works with any LLM — including ones running entirely on your laptop. What I built A Python Slack bot with: Socket Mode for local dev (no public URL needed), HTTP-ready for prod LangChain to abstract LLM calls across any OpenAI-compatible endpoint Mem0 managed cloud for semantic memory — no Qdrant, no Pinecone, no infra to run Three memory scopes: per-channel, per-thread, per-DM Built-in !clear and !memory commands A clean, extensible architecture you can fork and build on Architecture Before diving into code, here's the full request lifecycle: ┌─────────────────────────────────────────────────────────────┐ │ Slack │ │ @mention in channel ──┐ │ │ DM to bot ──┼──► Slack Events API │ │ Thread reply ──┘ │ │ └───────────────────────────────────│─────────────────────────┘ │ (Socket Mode / HTTP) ▼ ┌─────────────────────────────────────────────────────────────┐ │ slack-bolt (Python) │ │ bot.py ──► router.py ──► handler.py │ │ │ │ │ ┌───────────────┤ │ │ │ │ │ │ ▼ ▼ │ │ Mem0 Client LangChain │ │ (managed) ChatOpenAI │ └────────────────────────────────────────────────────────────-┘ │ ▼ ┌───────────────────────┐ │ Mem0 Managed Cloud │ │ Vector Embeddings │ │ Entity Extraction │ │ Deduplication │ └───────────────────────┘ The ke

Malware authors don't make our job easy. Every time we think we've figured out their tricks, they layer on another obfuscation technique, another anti-debugging check, another sandbox evasion. Over the past few weeks, I've been deep in the trenches with some particularly stubborn samples — the kind that detect your debugger, hide their strings behind XOR encoding, and hollow out legitimate processes to hide their payload. This article walks through my hands-on exploration of these techniques. We'll look at how malware detects analysis tools, how it obfuscates its strings, how it unpacks itself in memory, and most importantly — how we can bypass these defenses to see what the malware is actually trying to do. The tools we'll use: x64dbg/x32dbg for dynamic analysis and patching IDA Pro for static disassembly REMnux (Linux toolkit) for string deobfuscation FLOSS, XORSearch, bbcrack for automated string decoding Scylla & OllyDumpEx for dumping unpacked payloads Process Hacker for memory forensics Problem Statement Modern malware is rarely "what you see is what you get." A single executable might be: Packed — the actual malicious code is compressed/encrypted and only revealed at runtime Anti-debug aware — it checks for debuggers and changes behavior or terminates Sandbox-aware — it detects virtualized environments and refuses to execute its payload String-obfuscated — URLs, registry keys, and IOCs are encoded to evade signature detection Process-injecting — it hollows out a legitimate process (like explorer.exe ) and runs its code there Our goal: peel back these layers and extract the real payload for analysis. Exercise 1: Bypassing Debugger Detection in getdown.exe What I Found The first sample, getdown.exe , refused to show any network activity when run inside a debugger. Outside the debugger, it connected to 1.234.27.146:80 . Classic anti-debugging behavior. The Detection Mechanism Using x64dbg, I searched for intermodular calls and immediately spotted IsDebuggerPrese

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One thing I've noticed after using AI for development over the past year is this: The code it generates is usually correct. The architecture slowly isn't. That doesn't happen because AI writes bad code. It happens because architecture rarely erodes all at once. Imagine a modular application with clear boundaries. The billing module talks to the orders module through its public interface. Authentication is isolated. Notifications are independent. Everything is predictable. Now imagine hundreds of AI-assisted commits over the next few months. One suggestion imports an internal class because it already exists. Another bypasses a service layer because it's shorter. A helper gets copied into another module. A database query is duplicated instead of reused. None of those changes are catastrophic. In fact, every pull request probably gets approved. The application still builds. The tests still pass. Customers never notice. Until one day, making a simple change requires touching five different modules because everything has quietly become connected. That's architecture debt. And unlike a failing test, it doesn't show up immediately. One thing I've realized is that our current tooling doesn't really watch for this. Unit tests verify behavior. Integration tests verify interactions. Linters enforce style. Static analysis finds bugs. All of those are important. But none of them are asking questions like: Should this module depend on that one? Did someone bypass a defined boundary? Are we introducing new architectural coupling? Is the overall architecture getting healthier or worse over time? Those questions usually get answered during code review. Or worse, during a production incident. The interesting part is that AI isn't really the problem. If anything, it's doing exactly what we ask it to do. It optimizes for solving the problem in front of it. Architecture, on the other hand, is about protecting the system as a whole. Those are different goals. As AI makes us write code fa

Preface Link to the repository containing all examples Back when I was at my first class of Data Structures and Algorithms, I started to solve competitive programming questions in judges like CodeForces, I didn't know why my code was slower if my solution was efficient (at least in theory), my professor explain to the class the reason why our programs were slow, because of I/O is an expensive task for computers. To start working with the examples in this article just clone the repository and play with it as long the article explains how to run the code. git clone https://github.com/MiztonCodes/OptimizeIO.git Why std::cout and std::cin are slow? In C++ by default, both std::cin and std::cout streams are synchronized with standard C scanf and printf streams and at the same time every single call to std::cin flushes the std::cout buffer, because std::cin before reading input, wants to output any pending prompt to the user, which can cause performance issues because reading and writing are expensive tasks due to the need to make calls to the operating system, the solution is to disable all of this synchronizations and manually flushing the output buffer whenever it is needed. Note: For convenience in all examples I'll use #include<bits/stdc++.h> header to simplify the imports, this header includes everything we'll need and this header only works on GCC compiler. What is input and output? When you run your program, it is like an isolated box inside your computer, ready to do some work, the input process involves moving raw data from an external source like a keyboard, a file or even the network to your program (reading) , the output process involves moving data from your program to an external destination like a monitor, the console, a file, the network or even an external device (writing) , for both processes your program does not read or write directly to the input and output sources, because each source has its own way to transfer data, C++ provides a uniform interfac

J'utilise la dictée vocale tous les jours depuis six mois. Pas pour taper moins vite. Pour penser plus vite quand je vibe-code avec Claude Code et Cursor. Pis j'ai fini par construire mon propre outil parce que les outils existants me tapaient sur les nerfs d'une façon très précise. Le problème réel Quand tu travailles en tech au Québec, tes phrases ressemblent à ça : "OK fa que je fais un useState pour le component pis je passe le handler en props" Ça, c'est une phrase normale. Personne en tech QC ne parle autrement. Pas parce qu'on est négligents avec la langue. Parce que le vocabulaire technique vient de l'anglais et qu'on le soude naturellement au français au fil de la pensée. Ça s'appelle le code-switching. Et c'est là que la plupart des outils de dictée craquent. Ce que les outils mainstream font mal Dragon NaturallySpeaking Dragon, c'est le vieux standard. Médical, juridique, corporate. Ça coûte environ 500$ en une shot. C'est lourd à installer et à entraîner. Et sa gestion du français québécois avec des termes tech intercalés... c'est en gros zéro. "useState" devient "usé état". "Fa que" devient "faque" parfois, "fake" d'autres fois. C'est aléatoire. T'as intérêt à corriger après chaque phrase. Wispr Flow Wispr Flow est plus moderne. UX propre, cross-platform, et leur gestion du français s'est améliorée. Leur plan Pro coûte 15$/mois, soit environ 144$/an. Mais il y a un problème structurel que leur propre doc admet : la détection de langue se fait par session, pas par mot. Autrement dit : Wispr détecte la langue une fois au début de la session. Si tu commences en français, il reste en mode français jusqu'à la fin. Les mots anglais qui arrivent dans la phrase, il tente de les translittérer en français. "Handler" peut devenir "andler" ou "ender", "props" survit parfois, parfois pas. C'est variable. Pour une phrase de temps en temps avec un mot anglais, ça passe. Pour un vibe-coder québécois qui switch constamment dans la même phrase, ça ne passe pas. Pourquoi

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DISCLAIMER : NO AI OR LLMS WERE USED IN WRITING THIS ARTICLE . EVERY SINGLE WORD WAS TYPED MANUALLY USING A KEYBOARD I HAVE A COMPLETE UNEDITED VIDEO OF ME WRITING THE ARTICLE HERE: https://www.youtube.com/watch?v=6j1dbN64LyI submitted by /u/Inevitable_Back3319 [link] [留言]