Tag: The Art of Computer Programming by Donald Knuth

Florence-2 segmentation, explained in a practical wayContinue reading on Image Segmentation tutorials »

A few months ago, I witnessed a fascinating scene in the company cafeteria.Continue reading on Medium »

The sleek, shockingly effective tools every developer should add to their stack before the year endsContinue reading on Python in Plain English »

A step-by-step guide to building a Telegram bot that analyzes financial news and sends bullish or bearish alerts.Continue reading on Python in Plain English »

Python has long been celebrated as a versatile, beginner-friendly programming language, famous for its readable syntax and vibrant…Continue reading on Python in Plain English »

Python has long been celebrated as a versatile, beginner-friendly programming language, famous for its readable syntax and vibrant…Continue reading on Python in Plain English »

Python has long been celebrated as a versatile, beginner-friendly programming language, famous for its readable syntax and vibrant…Continue reading on Python in Plain English »

Memory pain, build chaos, and concurrency issues handled properlyContinue reading on CodeToDeploy »

Memory pain, build chaos, and concurrency issues handled properlyContinue reading on CodeToDeploy »

How I Turned a Messy Experiment Into a Shippable Google Docs PluginContinue reading on Medium »

dwContinue reading on Medium »

dwContinue reading on Medium »

Day 19. Why simply listing data isn’t enough, and how I programmed “Financial Anxiety” into my Lambda.Continue reading on Medium »

Day 19. Why simply listing data isn’t enough, and how I programmed “Financial Anxiety” into my Lambda.Continue reading on Medium »

The market doesn’t care about your title. Here’s how to survive anyway.Continue reading on Medium »

Arquitetura de software usa heurísticas contextuais para criar os limites certos onde todo o resto acontece. Comece por entender o…Continue reading on Medium »

If you have ever have heard from someone “I use Arch btw” and wondered why only this Linux distro is too much hyped, trust me you are not…Continue reading on Medium »

Your new way of interacting with temporalContinue reading on Medium »

While Creational patterns focus on object creation and Structural patterns on object composition, Behavioral Patterns deal with the…Continue reading on Medium »

📖 Related Reading: Before diving into what’s coming in Django 7.0, check out our latest article on the current stable release: Django…Continue reading on Medium »

RAM prices are rising because of AIContinue reading on Data Science in Your Pocket »

How to access a full library of automation and mindset courses for less than the price of a salad.Continue reading on Write Your World »

আপুনি কেতিয়াবা শুনিছেনে মানুহে কৈছে —  “Python শিকক, ইয়াৰ বহুত scope আছে”?Continue reading on Medium »

O Emotracing é uma aplicação que analisa sentimentos e emoções em redes sociais, focada inicialmente no Reddit e Bluesky. A aplicação…Continue reading on Medium »

Working on a side project and hit an interesting architectural question. Every AI chat is stateless. You start fresh, explain your codebase, your conventions, your preferences, then 2 hours later you start a new session and do it all over again. The model learned nothing permanent. ChatGPT added memory but its capped and global. Claude has something similar with the same limits. Neither lets you scope context to specific projects. From a technical standpoint the obvious solutions are either stuffing relevant context into the system prompt every request, or doing RAG with embeddings to pull relevant memories dynamically. System prompt stuffing is simple but doesnt scale. RAG adds latency and complexity for what might be overkill in most cases. Anyone building tools that interact with LLMs regularly - how are you handling persistent context? Is there a middle ground between dumb prompt injection and full vector search that actually works well in practice? Curious what patterns people have landed on. submitted by /u/Main_Payment_6430 [link] [comments]

I've been noticing a huge gap between what you see on social media (everyone apparently orchestrating multi-agent workflows) and what most developers I talk to actually do. So I tried to map out the different levels of AI adoption I'm seeing: Conversationalists — ask ChatGPT/Claude questions, copy-paste code, workflow unchanged Copilots — use Cursor/Copilot/Windsurf for autocomplete, still write most code Prompt Engineers — describe what they want, run single agents, review generated code, occasionally write manually Orchestrators — run multiple agents in parallel, rarely write code themselves Systems Designers — design processes and feedback loops, agents do all implementation Would love to know from all of you where you are seeing yourself! submitted by /u/PuzzleheadedAd7828 [link] [comments]

Ever wondered which news outlets specialize in which topics?Continue reading on Medium »

The best no-code AI tools to build apps, automations, and products without learning to codeContinue reading on Medium »

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Trying out Anthropic’s Cowork to organize files, visualize disk usage, and sync with NotionContinue reading on Data Science Collective »

прозоростіContinue reading on Medium »

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If you’re still building frontend apps like it’s 2023, you’re already behind. The landscape has shifted fast, and 2026 isn’t about small…Continue reading on Medium »

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I am the author of the nature programming language and you can ask me questions. submitted by /u/hualaka [link] [comments]

Hey Devs, Do not become The Lost Programmer in the bottomless ocean of software abstractions, especially with the recent advent of AI-driven hype; instead, focus on the fundamentals, make the magic go away and become A Great One! submitted by /u/BinaryIgor [link] [comments]

From the article: Two days ago, Anthropic released the Claude Cowork research preview (a general-purpose AI agent to help anyone with their day-to-day work). In this article, we demonstrate how attackers can exfiltrate user files from Cowork by exploiting an unremediated vulnerability in Claude’s coding environment, which now extends to Cowork. The vulnerability was first identified in Claude.ai chat before Cowork existed by Johann Rehberger, who disclosed the vulnerability — it was acknowledged but not remediated by Anthropic. submitted by /u/sean-adapt [link] [comments]

Hey everyone, I've been using Claude Code a lot lately and kept running into the same problem: I wanted to work on multiple features simultaneously, each with its own Claude instance, but switching branches and contexts was a pain. So I built Multi-Claude - a desktop app that combines git worktrees with multiple terminal sessions. And yes, the entire thing was built using Claude Code itself. The problem it solves: When you're deep into a feature with Claude and suddenly need to fix a bug on another branch, you either have to stash everything, switch branches, lose your Claude context... or just wait. With worktrees, each branch lives in its own directory, so you can run multiple Claude sessions in parallel without any conflicts. What it does: - Opens any git repo and lists all your worktrees - Create new worktrees directly from the UI - Multiple terminals per worktree with split panes (horizontal/vertical) - Drag & drop to rearrange terminal layouts - Automatically detects when a terminal is running Claude Code - Full terminal emulation (not just a basic shell) Tech stack: Electron + React + TypeScript + xterm.js + node-pty GitHub: https://github.com/LaurentMnr95/multi-claude It's pretty bare-bones right now but functional. Here's what's on the roadmap: - Recent repos memory (reopen where you left off) - Color themes - Delete worktrees from the UI - Remote status (ahead/behind indicators) - Notifications when Claude finishes a task - Open in external editor (VS Code, Cursor, etc.) - Keyboard shortcuts Refactoring to styled components Would love feedback on what features to prioritize. submitted by /u/Money_Warthog6133 [link] [comments]

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In many coding agents, the assumption is that re-reading the latest code is sufficient context. I’ve been experimenting with whether explicitly tracking recent user edits improves agent behavior. I found a few things in practice: ​- First, it’s better UX. Seeing your edits reflected back makes it clear what you’re sending to the agent, and gives users confidence that their changes are part of the conversation. - Second, agents don’t always re-read the entire file on every step. Depending on context and task state, recent local changes can otherwise be easy to miss.​ - And third, isolating user edits helps the agent reason more directly about intent. Separating recent changes gives the agent a clearer signal about what’s most relevant for the next step. One approach I experimented with was creating a separate “user edits” context channel so the agent explicitly sees recent changes rather than just the full file. This seemed to improve both agent behavior and user confidence. Do you think this is better than relying entirely on re-ingestion? submitted by /u/National_Purpose5521 [link] [comments]

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I am a C++ programmer and i can confidently say i know the C++ till the intermediate level but when ever i writes my own code it feels very easy. But the moment i thinks about contributing the new features to my favourite open source software the code base overwhelms me. I mean i completely feels lost in it. I am writing this post right now because i just thought to add "new file" option to Nautilus , But when i saw it's code base , Even though i know C++, Python and Javascript very well i couldn't even understand a single bit of what was going on and i simply closed the browser. Is this normal ? I mean do almost every programmer have this problem or this is specific to me ? Do this impacts the jobs as right now i am in a college , As a student in computer engineering and i wanna make the CSE My passion but this thing always demotivates me in thinking that i don't know a lot. submitted by /u/PlaneBitter1583 [link] [comments]

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In 1988, James D. Johnston at Bell Labs and Karlheinz Brandenburg in Germany independently invented perceptual audio coding - the science behind MP3. Brandenburg became famous. Johnston got erased from history. The evidence is wild: Brandenburg worked at Bell Labs with Johnston from 1989-1990 building what became MP3. A federal appeals court explicitly states they "together" created the standard. Ken Thompson - yes, that Ken Thompson - personally rewrote Johnston's PAC codec from Fortran to C in a week after Johnston explained the functions to him in real time, then declared it "vastly superior to MP3." AT&T even had a working iPod competitor in 1998, killed it because "nobody will ever sell music over the internet," and the prototype now sits in the Computer History Museum. I interviewed Johnston and dug through court records, patents, and Brandenburg's own interviews to piece together what actually happened. The IEEE calls Johnston "the father of perceptual audio coding" but almost no one knows his name. submitted by /u/Traditional_Rise_609 [link] [comments]

Christine Miao nails it here: > Teams that can easily absorb junior talent have systems of resilience to minimize the impact of their mistakes. An intern can’t take down production because **no individual engineer** could take down production! The whole post is a good sequel to Charity Majors' "In Praise of Normal Engineers" from last year. submitted by /u/sean-adapt [link] [comments]

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I have been building parser for NASDAQ ITCH. That is the binary firehose behind real time order books. During busy markets it can hit millions of messages per second, so anything that allocates or copies per message just falls apart. This turned into a deep dive into zero copy parsing, SIMD, and how far you can push Rust before it pushes back. The problem allocating on every message ITCH is tight binary data. Two byte length, one byte type, fixed header, then payload. The obvious Rust approach looks like this: ```rust fn parse_naive(data: &[u8]) -> Vec<Message> { let mut out = Vec::new(); let mut pos = 0; while pos < data.len() { let len = u16::from_be_bytes([data[pos], data[pos + 1]]) as usize; let msg = data[pos..pos + len].to_vec(); out.push(Message::from_bytes(msg)); pos += len; } out } ``` This works and it is slow. You allocate a Vec for every message. At scale that means massive heap churn and awful cache behavior. At tens of millions of messages you are basically benchmarking malloc. Zero copy parsing and lifetime pain The fix is to stop owning bytes and just borrow them. Parse directly from the input buffer and never copy unless you really have to. In my case each parsed message just holds references into the original buffer. ```rust use zerocopy::Ref; pub struct ZeroCopyMessage<'a> { header: Ref<&'a [u8], MessageHeaderRaw>, payload: &'a [u8], } impl<'a> ZeroCopyMessage<'a> { pub fn read_u32(&self, offset: usize) -> u32 { let bytes = &self.payload[offset..offset + 4]; u32::from_be_bytes(bytes.try_into().unwrap()) } } ``` The zerocopy crate does the heavy lifting for headers. It checks size and alignment so you do not need raw pointer casts. Payloads are variable so those fields get read manually. The tradeoff is obvious. Lifetimes are strict. You cannot stash these messages somewhere or send them to another thread without copying. This works best when you process and drop immediately. In return you get zero allocations during parsing and way lower memory use. SIMD where it actually matters One hot path is finding message boundaries. Scalar code walks byte by byte and branches constantly. SIMD lets you get through chunks at once. Here is a simplified AVX2 example that scans 32 bytes at a time: ```rust use std::arch::x86_64::*; pub fn scan_boundaries_avx2(data: &[u8], pos: usize) -> Option<usize> { let chunk = unsafe { _mm256_loadu_si256(data.as_ptr().add(pos) as *const __m256i) }; let needle = _mm256_set1_epi8(b'A'); let cmp = _mm256_cmpeq_epi8(chunk, needle); let mask = _mm256_movemask_epi8(cmp); if mask != 0 { Some(pos + mask.trailing_zeros() as usize) } else { None } } ``` This checks 32 bytes in one go. On CPUs that support it you can do the same with AVX512 and double that. Feature detection at runtime picks the best version and falls back to scalar code on older machines. The upside is real. On modern hardware this was a clean two to four times faster in throughput tests. The downside is also real. SIMD code is annoying to write, harder to debug, and full of unsafe blocks. For small inputs the setup cost can outweigh the win. Safety versus speed Rust helps but it does not save you from tradeoffs. Zero copy means lifetimes everywhere. SIMD means unsafe. Some validation is skipped in release builds because checking everything costs time. Compared to other languages. Cpp can do zero copy with views but dangling pointers are always lurking. Go is great at concurrency but zero copy parsing fights the GC. Zig probably makes this cleaner but you still pay the complexity cost. This setup focused to pass 100 million messages per second. Code is here if you want the full thing https://github.com/lunyn-hft/lunary Curious how others deal with this. Have you fought Rust lifetimes this hard or written SIMD by hand for binary parsing? How would you do this in your language without losing your mind? submitted by /u/capitanturkiye [link] [comments]

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LLMs are an incredibly powerful tool, that do amazing things. But even so, they aren’t as fantastical as their creators would have you believe. I wrote this up because I was trying to get my head around why people are so happy to believe the answers LLMs produce, despite it being common knowledge that they hallucinate frequently. Why are we happy living with this cognitive dissonance? How do so many companies plan to rely on a tool that is, by design, not reliable? submitted by /u/SwoopsFromAbove [link] [comments]

Be honest: has anyone actually gotten this working correctly in production? In a distributed environment, so much can go wrong. If the network fails during the commit phase, the rollback will likely fail too—you can't stream a failure backward. Meanwhile, the source data is probably still changing. It feels impossible. submitted by /u/christoforosl08 [link] [comments]