What is the single most influential book every Programmers should read

A deep dive into the “Agentic Revolution” and a step-by-step roadmap to mastery for under $100.Continue reading on Medium »

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Colleagues, our goal is to provide Javascript Full Stack professionals with up-to-date and actionable information to advance your career…Continue reading on Medium »

Welcome to this new Medium post. In this article, I will show you a custom implementation of the LoadLibrary function. Instead of calling…Continue reading on Medium »

Most solo developers don’t fail because they can’t code.Continue reading on Medium »

Most EF Core performance problems don’t start with slow SQL.Continue reading on Medium »

What LLMs actually do, where they work well, and where they failContinue reading on Medium »

making something lastingContinue reading on Medium »

Ada satu momen yang hampir pasti dialami banyak mahasiswa tingkat akhir dan fresh graduate. Momen ketika kita mulai membuka lowongan…Continue reading on Medium »

Everything you need on your toolbelt to create reusable AI capabilitiesContinue reading on Medium »

In a silent room, between keystrokes and coffee, programmers model universes.  Not the tangible universe, but another one: abstract…Continue reading on Medium »

Execution logs are boring until you read them like a crime scene.Continue reading on Medium »

C++ is a powerful, high-performance language that fully supports Object-Oriented Programming (OOP). Unlike procedural programming, which…Continue reading on Medium »

C++ is a powerful, high-performance language that fully supports Object-Oriented Programming (OOP). Unlike procedural programming, which…Continue reading on Medium »

Real-world Python systems that generate money while I sleepContinue reading on Python in Plain English »

Forget copy-pasting, clicking through forms, tell the AI what you want, and watch it happen.Continue reading on Level Up Coding »

For a decade, tech felt unstoppable.Continue reading on Medium »

Sıfırdan Zirveye Uzanan AI YolculuğuContinue reading on Medium »

Teach Your Computer to Detect Diabetes: A Beginner‑Friendly Guide Using Random Forests in PythonContinue reading on Medium »

Hard lessons from turning models into revenue instead of demosContinue reading on CodeToDeploy »

No theory, no fluff — just battle-tested patterns I now use in every project.Continue reading on Write A Catalyst »

Open Twitter.Continue reading on Medium »

The archaeology of understanding production code written by developers who left 3 years agoContinue reading on Medium »

A modern CPU is incredibly fast. It can do billions of operations every second. But most of the time, your code makes it do nothing. When…Continue reading on Medium »

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Same codebase, fewer rewrites, better scaling.Continue reading on Medium »

Same codebase, fewer rewrites, better scaling.Continue reading on Medium »

By Phoebe DuboisContinue reading on Medium »

A long-form essay reflecting on complexity as the core challenge in software development, with observations drawn from maintaining systems over time. It touches on abstraction, testing strategies, refactoring, APIs, tooling, and system design, framed in an intentionally simple and humorous tone. submitted by /u/Digitalunicon [link] [comments]

The next big thing after Claude Code, maybe?Continue reading on Dev Genius »

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Listing my workflow changes and opinionContinue reading on Medium »

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I've been working on a webhook debugging tool and wanted to share some of the non-obvious engineering problems I ran into. These aren't specific to my project—they're patterns that apply to any Node.js service handling real-time streams, user-supplied URLs, or API authentication. 1. SSE connections behind corporate proxies don't work (until you pad them) Server-Sent Events seem simple: open a connection, keep it alive with heartbeats. But many users reported 10+ second delays before seeing any data. The cause: Corporate proxies and Nginx buffer responses until they hit a size threshold (often 4KB). Your initial : connected\n\n message is 13 bytes—nowhere close. The fix: javascript res.setHeader("X-Accel-Buffering", "no"); res.setHeader("Content-Encoding", "identity"); // Disable compression res.write(": connected\n\n"); res.write(`: ${" ".repeat(2048)}\n\n`); // 2KB padding forces flush Also, one setInterval per connection is a memory leak waiting to happen. With 500 connections, you have 500 timers. A single global timer iterating a Set<Response> cut our memory usage by ~40%. 2. String comparison leaks your API key (timing attacks) If you're validating API keys with ===, you're vulnerable. The comparison returns early on the first mismatched character, so an attacker can measure response times to guess the key character-by-character. The fix: crypto.timingSafeEqual ensures constant-time comparison: javascript const safeBuffer = expected.length === provided.length ? provided : Buffer.alloc(expected.length); // Prevent length leaking too if (!timingSafeEqual(expected, safeBuffer)) { /* reject */ } 3. SSRF is harder than you think (IPv6 mapped addresses) We allow users to "replay" webhooks to arbitrary URLs. Classic SSRF vulnerability. The obvious fix is blocking private IPs like 127.0.0.1 and 10.0.0.0/8. The gotcha: ::ffff:127.0.0.1 bypasses naive regex blocklists. It's an IPv4-mapped IPv6 address that resolves to localhost. We had to: Resolve DNS (A + AAAA records) before making the request Normalize IPv6 addresses to IPv4 where applicable Check against a comprehensive blocklist including cloud metadata (169.254.169.254) 4. In-memory rate limiters can OOM your server Most rate limiters use a simple Map<IP, timestamps[]>. A botnet scanning with 100k random IPs will grow that map indefinitely until you crash. The fix: Sliding Window + LRU eviction. We cap at 1,000 entries. When full, the oldest IP is evicted before inserting a new one. Memory stays bounded regardless of attack volume. 5. Searching large datasets without loading them into memory Users can replay webhooks from days ago. Naively loading thousands of events into memory to find one by ID will OOM your container. The fix: Iterative pagination with early exit: ```javascript while (true) { const { items } = await dataset.getData({ limit: 1000, offset, desc: true }); if (items.length === 0) break; const found = items.find((i) => i.id === targetId); if (found) return found; offset += 1000; // Only fetch next chunk if not found } ``` This keeps memory constant regardless of dataset size. 6. Replay retry with exponential backoff (but only for the right errors) When replaying webhooks to a user's server, network blips happen. But blindly retrying every error is dangerous—you don't want to hammer a 404. The pattern: Distinguish transient from permanent errors: ```javascript const RETRYABLE = ["ECONNABORTED", "ECONNRESET", "ETIMEDOUT", "EAI_AGAIN"]; if (attempt >= 3 || !RETRYABLE.includes(error.code)) throw err; const delay = 1000 * Math.pow(2, attempt - 1); // 1s, 2s, 4s await sleep(delay); ``` 7. Header stripping for safe replay If you replay a production webhook to localhost, you probably don't want to forward the Authorization: Bearer prod_secret_key header. We maintain a blocklist of sensitive headers that get stripped automatically: javascript const SENSITIVE = ["authorization", "cookie", "set-cookie", "x-api-key"]; const safeHeaders = Object.fromEntries( Object.entries(original).filter(([k]) => !SENSITIVE.includes(k.toLowerCase())) ); 8. Hot-reloading without losing state Platform-as-a-Service environments treat configs as immutable. But restarting just to rotate an API key drops all SSE connections. We implemented a polling loop that reads config every 5 seconds. The tricky part is reconciliation: If urlCount increases from 3→5: generate 2 new webhook IDs If urlCount decreases from 5→3: don't delete existing IDs (prevents data loss) Auth key changes take effect immediately without restart 9. Self-healing bootstrap for corrupted configs If a user manually edits the JSON config and breaks the syntax, the server shouldn't crash in a loop. The fix: On startup, we detect parse errors and auto-recover: javascript try { config = JSON.parse(await readFile("INPUT.json")); } catch { console.warn("Corrupt config detected. Restoring defaults..."); await rename("INPUT.json", "INPUT.json.bak"); await writeFile("INPUT.json", JSON.stringify(defaults)); config = defaults; } The app always starts, and the user gets a clear warning. TL;DR: The "easy" parts of building a real-time webhook service are actually full of edge cases—especially around proxies, security, and memory management. Happy to discuss any of these patterns in detail. Source code if you want to see the implementations. submitted by /u/ar27111994 [link] [comments]

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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]

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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]