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Instagram users could soon see more ways to tune their content.

In the landscape of modern programming, delimiters serve as the essential scaffolding that organizes logic and defines structure. Among these, curly braces—often referred to as braces or squiggly brackets—occupy a unique position. While they are ubiquitous, they are frequently the source of developer frustration and logic errors. A common pitfall for many programmers is the tendency to treat all delimiters as interchangeable, leading to a fundamental misunder身 of how a compiler or interpreter parses a script. Confusion often arises when developers conflate the purpose of curly braces with those of parentheses or square brackets. For instance, in many languages, curly braces denote a scope or a code block, whereas square brackets handle indexing. However, the nuances become even more complex when examining specific environments like R, where the semantic meaning of a symbol can shift depending on the context—moving from defining a function to facilitating list extraction. Understanding the specific curly braces semantics is not merely an academic exercise in syntax; it is a practical necessity for writing clean, maintainable code. When a developer understands why a brace is used, they can more easily debug nested structures and communicate intent to their teammates. Grasping these distinctions reduces the cognitive load required to read complex scripts and prevents the subtle bugs that emerge when syntax is used incorrectly. Curly Braces vs. Other Delimiters: Semantic Roles in R and Beyond To master programming syntax, one must move beyond recognizing symbols and begin understanding their semantic intent. While many developers treat curly braces as just another set of punctuation, their role is fundamentally distinct from parentheses and square brackets. Understanding the nuance of curly braces semantics is essential for writing logic that is both functional and readable. The Primary Role: Defining Code Blocks In most procedural and object-oriented languages (such as

Research Context "In advanced network observability, understanding the default behavior of various operating systems is vital for traffic profiling. This article explores the structural differences in ICMP Echo Requests across different OS environments and analyzes how 'Traffic Mimicry' can be used to evaluate the accuracy of Network Intrusion Detection Systems (NIDS)." 1. The Anatomy of an ICMP Signature A standard ICMP Echo Request is not just a simple signal; it carries a specific "fingerprint" based on the operating system that generated it. These fingerprints consist of: Total Packet Size TTL (Time to Live) values Default Payload Content 2. Cross-Platform Discrepancies (Linux vs. Windows) When a system sends a "ping," the default data size ($D$) and the total packet length ($L$) vary significantly between architectures. Feature Linux (Typical) Windows (Typical) Data Size ($D$) 56 Bytes 32 Bytes ICMP Header ($H$) 8 Bytes 8 Bytes Total ICMP Length ($L$) 64 Bytes 40 Bytes Default Payload Timestamp + Data abcdefg... The Linux Signature In most Linux distributions, the ping utility sends 56 bytes of data. When combined with the 8-byte ICMP header, it totals 64 bytes. A key characteristic of Linux ICMP traffic is that the first few bytes of the payload are often occupied by a high-resolution timestamp, used to calculate RTT (Round Trip Time) with microsecond precision. The Windows Signature Windows systems default to a 32-byte data payload. The payload content is static and follows a predictable alphabetical sequence: abcdefghijklmnopqrstuvwabcdefghi. This static nature makes Windows ICMP traffic easily identifiable during deep packet inspection (DPI). 3. The Concept of Traffic Mimicry Traffic Mimicry is a research method used to test the resilience of network filters. By aligning custom communication protocols with the default signatures of a specific OS, researchers can evaluate whether a security appliance is biased toward certain traffic patterns. For example, wh

Research Context When doing low-level network programming in Assembly, you experience firsthand the immense chaos running behind the scenes of operations we solve with a single line in high-level languages (Python, C, etc.). While developing the Nested-ICMP-Communication Analysis project, specifically an Encapsulated ICMP framework, I hit exactly this kind of wall: extracting an IP address from a packet header and printing it to the screen in the correct format. Sounds simple, right? However, when x86 architecture and network protocols are involved, seeing 5.1.168.192 instead of 192.168.1.5 on your terminal is extremely common. So why does this happen, and what kind of algorithm did I develop to overcome this issue during the debugging process? Let's dive into the background. The Endianness Problem in Network Headers When you capture a packet coming over the network and read the source/destination IP address inside the sockaddr_in structure, the data arrives in Network Byte Order (Big-Endian) format. This means the most significant byte is stored at the lowest memory address. However, the x86/x64 processor architectures we use rely on Little-Endian (Host Byte Order). When the processor pulls this 4-byte IP data into a register, the reading direction is effectively reversed for our purposes. The result? A packet that arrives as 192.168.1.5 appears scrambled if we try to naively print it from memory. The inet_ntoa() function in high-level languages handles this conversion in the background. But if you are writing a custom sniffer in pure Assembly, you must do this conversion byte by byte yourself. Debugging Hell: The Problems Encountered While writing this conversion, I encountered a few critical issues that cost me hours in GDB (GNU Debugger): Register Clashes: While separating each octet (byte) of the IP address and converting it to an ASCII character (string), you must use the AX register for division operations (DIV). If you don't carefully manage your remainders

I'm building LOOM — a small open-source language that is a machine-checked trust layer for AI-written code. I don't write it by hand anymore: an organism I built grows it, day and night, on my own machine. This is Day 6, and the whole day went to one thing — WebAssembly . Why this was a real test LOOM already runs three ways: an interpreter, and backends that compile checked code to Python and JavaScript. The thesis is "trust survives translation" — effects and provenance, proven once, hold the same on every target. WebAssembly is the strongest test of that: a low-level stack machine with linear memory, nothing like Python or JS. And there was a constraint. This machine's clang has no wasm target, and I install nothing paid or heavy. So I don't compile to wasm through a toolchain — I emit the wasm bytes myself (LEB128, the type / function / memory / global / export / code sections, the i32 stack machine) and run them through node's built-in WebAssembly . Zero dependencies. From fib to a value runtime, in a day Every step was prototyped and proven (wasm output == interpreter output) before it touched the kernel: The integer core — arithmetic, comparison, if , first-order calls and recursion. fib(10) becomes 61 bytes of real WebAssembly and returns 55, identically on the interpreter, Python, Node and wasm. A value runtime — let and integer lists in a real linear-memory heap (a bump pointer + a $cons cell allocator; head / tail are i32.load , empty is i32.eqz ). A list sums and folds by recursion, inside wasm. Sum types — (variant Tag e) becomes a tagged cell [tag-id | payload] ; match loads the tag, compares, binds the payload, branches. You can watch it: the live playground has a Compile → WAT button and WASM · fib / list-sum / match examples. Type a program, see it become real assembly, in your browser. Honest scope: ints, let , integer lists and sum types compile to wasm today. Records, closures and effects are the next frontiers (closures are the hard one — a func

A quiet experiment in Bluetooth, offline communication, and why we gave up too much when we handed our conversations to the cloud. The last time I was truly unreachable, I was in a place with no cell signal and no Wi-Fi. And I realized something strange: I had no way to send a message to the person sitting three feet away from me — because every app on my phone needed the internet to do it. That felt wrong. We've built the most sophisticated communication technology in human history, and somehow it all routes through a handful of servers in Northern Virginia before reaching someone in the same room. So I built Bluetoosh. The Premise: What If the Network Was Just… You and Me? Bluetoosh is a peer-to-peer messenger that runs entirely over Bluetooth. No internet. No servers. No accounts. No cloud storage. Just two devices, talking to each other the way devices were always capable of doing — directly. You open the app. You see who's nearby. You start a conversation. That's it. No phone number required. No email verification. No terms of service asking you to agree that your metadata might be used for advertising. The only network involved is the six feet of air between you and the other person. Why Bluetooth? Bluetooth is one of the most underrated communication protocols we carry around every day. Your phone already has it. Your laptop has it. Most people use it for headphones and nothing else. But Bluetooth is capable of much more. It can discover nearby devices, establish encrypted connections, and transfer data — all without touching the internet. The range is roughly 10–30 meters in open space. That covers a room, a floor, a campsite. Bluetoosh uses both BLE (Bluetooth Low Energy) and Classic Bluetooth, plus Google Nearby Connections for mesh-style discovery. In practice, this means you can find people around you, chat, share files, and even make voice calls — all completely offline. What It Actually Does Here's what surprised me most while building this: offline co

Oracle is spending billions on data center infrastructure to support AI.

The smart glasses are available in several countries starting today in a variety of color and lens combinations.

The new Meta-branded glasses have the same camera, microphones, and chatbot as the Ray-Bans. They come in three styles, one of which was codesigned with Kylie Jenner.

Smart glasses are hitting the mainstream, so companies are now getting into the accessories game.

Snap's long-awaited smart glasses debut hasn't exactly done wonders for the company's stock.

"Space Launch Complex-6 represents six decades of American innovation."

Qualcomm's CEO said today that the company is working on over 40 new AI hardware designs

For over a decade now, Snap has been working on this device. Now the glasses are finally here. So what stands out on first impression?

Snap CEO Evan Spiegel lays out the company’s vision for its augmented-reality smart glasses, arriving later this year.

Probably wants to prevent hallucinations and factual errors from reaching users, and achieve accuracy on par with deterministic systems.

Indian IT services company HCLTech is investing $150 million in the Bengaluru startup.

While building a web application using React, TypeScript, C++, Emscripten, and Raylib, I ran into two linker-related issues that took far longer to diagnose than they should have. This is a short article on two problems that I have faced, I am sharing this so that developers who are exploring Web Assembly using Emscripten can easily avoid these issues as I'll also cover the workarounds that solved them for me. I'll start with the major one. Link-Time Optimization and EM_JS The problem appears when Link Time Optimization (-flto) is enabled and an EM_JS(ret, name, params, ...) macro function is invoked in one translation unit but called from another. You'll find that the linker complains that the function symbol you're trying to call is undefined. The EM_JS macro defines a C interface to call the JS function. So if you have your EM_JS macros in a js_layer.cpp source file, then you need to wrap the macro invocation as extern "C" { EM_JS ( void , add , ( int a , int b ), { //your JS code; }); } The same applies to the function declaration in js_layer.hpp file. I'll briefly go through the three workarounds or 'solutions' before bringing up the last quirk. The Three Workarounds If performance isn't of any concern Well the first one is obvious, just don't use -flto in your release builds. The downside is that your binaries (the .data and .wasm compiler outputs in this case) will be larger. Without LTO, the linker has fewer opportunities for whole-program optimization and dead-code elimination, which can increase the size of the generated .wasm and potentially reduce performance. Write a wrapper You can simply have a wrapper function in the same translation unit which calls the C function interfacing with the JS function, add() if you take the above example. The wrapper can simply be: // In js_layer.hpp void add_wrapper ( int , int ); // In js_layer.cpp void add_wrapper ( int a , int b ){ add ( a , b ); } Now you can call add_wrapper() from any source file just by including

A complete ML pipeline: engine, backprop, binary format, and a live browser demo. Zero dependencies. Under 200 KB total. If you have built machine-learning projects before, you have probably done it by importing PyTorch, TensorFlow, or scikit-learn and calling .fit() . Those are excellent libraries. This article is about what happens when you deliberately do not use them — when you build every piece of the pipeline yourself, in a language that compiles to WebAssembly, and the result runs live in the browser with no server, no Python, and no cloud bill. Here is the live demo: move four sliders, watch the predicted Iris species update in real time. The model is running entirely inside your browser tab, loaded from a 1.1 KB binary file, powered by ~100 KB of WebAssembly compiled from pure Rust. This is the story of how I built it and why the engineering choices made it work. Why Rust? Why WebAssembly? Why zero dependencies? Three constraints drove every design decision. WASM requires no_std or a carefully limited std . The wasm32-unknown-unknown target has no operating system, no file system, and no libc. A crate that links against rand , ndarray , or any library that makes OS calls will not compile to it without significant plumbing. An engine built from nothing but the Rust standard library compiles cleanly to every target, including WASM. A zero-dependency std -only crate is uniquely auditable. There are no transitive dependency trees to vet, no supply-chain risks, no version conflicts. Every line of code that runs in the user's browser lives in this repository. The deployment story becomes the technical story. A 100 KB WASM blob that runs locally in the browser is not just a cost optimisation — it is a privacy guarantee (user inputs never leave the machine) and a latency guarantee (inference is microseconds, not a round trip to a cloud API). That story is only possible because the engine has no external dependencies that would bloat the binary. The architecture: ei

These display smart glasses can connect to a phone, laptop, or gaming handheld and project the screen to your eyeballs.