标签：#coding

Hey DEV community! 👋 When debugging network streams, parsing custom file formats, or inspecting database buffers, we often extract data as raw arrays of numbers rather than human-readable text. This data typically presents itself as raw byte sequences formatted in either decimal or hexadecimal notation. While there are online decoders available, pasting raw byte sequences into third-party sites that process data on their backend databases introduces an unnecessary data privacy risk. To solve this, I designed a lightweight, entirely browser-based Byte to String Converter that decodes raw byte sequences locally using standard JavaScript APIs. In this post, we will look at how bytes map to character encodings and implement a client-side JavaScript utility to decode them safely. The Structure of a Byte In modern computing, a byte is the basic unit of digital information, consisting of an 8-bit sequence: 1 byte = 8 bits Because each bit represents a binary state (0 or 1), a single byte can represent: 2 8 = 256 states This translates to numeric values spanning from: Decimal (Base 10): Range of [ 0 , 255 ] Hexadecimal (Base 16): Range of [ 00 , FF ] When we render characters on a screen, we rely on character encoding tables (such as ASCII or UTF-8) to map these numerical byte values back to their original symbolic representations. Navigating Encodings: ASCII vs. UTF-8 The reconstruction process depends entirely on the encoding format used: ASCII: A basic 7-bit standard where each character maps to exactly one byte. It covers basic English letters, numbers, and core control characters. For example, the decimal value 72 maps to the uppercase letter 'H' . UTF-8: A variable-length encoding format that utilizes between 1 and 4 bytes per character. This structure allows UTF-8 to represent emojis, mathematical notations, and diverse language scripts. Our browser utility parses byte sequences using UTF-8 to maintain compatibility with modern web standards. JavaScript Implementatio

Vibecoding: How to Manage an AI Coder and Not Drown in Spaghetti Code Forget fairy tales about AI doing everything for you at the touch of a button. Without strict control, vibecoding quickly turns into a mess of broken, unmaintainable code. Modern vibecoding isn't blind generation — it is strict architectural supervision . To build real products, you must change your approach to context and redefine your role in the process. Forget Persona Prompting: Context is the Only King of Modern Prompting Fables like "Act as a Senior Developer" were left back in 2023. Modern LLMs don't need roleplay — they need the cleanest, deepest context possible . Why "Persona Prompting" is Outdated AI doesn't start coding better just because you called it a senior dev. It needs concrete technical boundaries. Skip the foreplay and provide the AI with technical specifications: Stack and versions: Not just "React", but React 18, Next.js 14 (App Router), Tailwind CSS . Architectural constraints: Show folder structure, naming conventions, and API response formats. Rule files ( .cursorrules / .clauderules ): Load strict rules into the project that the AI must follow at all times (e.g., "Never use any in TypeScript, write functional components only" ). Humans as Strict Regulators, Not Blind Consumers of Code The biggest danger of vibecoding is shipping AI-generated garbage straight to production without looking. If you blindly consume whatever the AI spits out, your project is doomed. 1. Total Quality Control and Code Review You act as the Technical Regulator and Censor . You never take the AI's word for it. Read every diff : Check exactly what the AI changes. Don't let it rewrite working modules from scratch just to add one button. Don't know how to code? Use basic logic: adding a single button shouldn't make 30 lines of code disappear from main.py or app.js . Force it to justify decisions: If the AI suggests a library, ask: "Why this one over a native solution? Will it impact performance?" 2.

Hey DEV community! 👋 When writing technical documentation, user guides, or long-form informational articles, maintaining a clear and engaging reading style is highly important. However, as writers, we often fall into repetitive phrasing habits without realizing it. Traditional word counters only track isolated, single words. To evaluate multi-word phrases and understand the flow of our writing, we need a different approach. This is where an N-gram analysis becomes highly useful. To provide a safe and private solution for content editors, I built a lightweight, entirely client-side N-gram Analyzer . In this post, we will explore the technical implementation of this utility, how to handle text segmentation in JavaScript, and why local browser processing is a reliable choice for data privacy. What is an N-gram? In computational linguistics and text processing, an N-gram is a contiguous sequence of $n$ items (usually words) from a given sample of text. A Unigram represents single words ($n=1$). A Bigram represents two-word phrases ($n=2$). A Trigram represents three-word phrases ($n=3$). A 4-gram represents four-word phrases ($n=4$). Analyzing these combinations helps developers and content creators identify repetitive phrases, evaluate vocabulary diversity, and check if the thematic distribution of a document aligns with its target focus. The Client-Side Approach: Privacy and Data Isolation Many online text tools process user inputs on backend servers. This setup introduces a significant privacy risk if you are analyzing sensitive internal documentation, unpublished drafts, or proprietary code comments. By executing the lexical parsing entirely within the user's browser, we keep the processing local. The text never travels across the network, and there are no external database logs. The local device handles the entire operation. Implementing the N-gram Extraction in JavaScript Let's look at the core logic. To build an N-gram extractor, the utility must perform three ke

I began this investigation with a specific question: can Codex autonomously add, modify, and delete its own memories? The product documentation already says that it has memory. What I wanted to know was who actually decides what survives. When an old chat contains a useful build command, does deterministic application code copy it into a database? Does the active coding model call a memory tool? Does another model summarize the chat later? When the command becomes obsolete, is the old fact overwritten, invalidated, aged out, or simply left where future agents may still find it? Those questions led to a more interesting result than a feature checklist. Codex has a genuine cross-session memory subsystem, but its behavior is split between model judgment and deterministic lifecycle code. Models decide what a rollout means and how durable guidance should be rewritten. Runtime code decides which rollouts are eligible, which evidence remains in the working set, when old records are deleted, and when the consolidation model is allowed to run. That makes the short answer precise: With local memories enabled, Codex can autonomously add, modify, merge, and remove persistent memory without a user approving each write. User-requested corrections follow a separate append-only note path, while retention, thread deletion, and reset provide additional forms of forgetting. The rest of this article explains why each word in that answer matters. This analysis is pinned to OpenAI Codex commit 8444cf63b50a8a88521e0d2970d49f659b48eac7 , checked on August 25, 2026. The feature is marked stable in that source tree but remains off by default, so this describes implemented behavior, not behavior every Codex user is currently receiving. Key Takeaways Codex local memory is a background two-model pipeline. One model extracts reusable material from each eligible rollout. A second model consolidates those outputs into a global file-based memory workspace. The LLM owns semantic CRUD, but not lifecy

Hey DEV community! 👋 As developers, we handle Base64 encoding and decoding almost daily—whether we're debugging API payloads, formatting authorization headers, or embedding small graphic assets directly into stylesheets. However, many online translation utilities process your inputs on their backend servers. If you are dealing with sensitive configuration parameters, internal logs, or keys, pasting that data into a third-party web tool is a clear data privacy risk. To solve this, I decided to "vibe-code" a lightweight, strictly browser-based, privacy-oriented Base64 Encoder & Decoder . In this post, we will look at how this utility was built using AI assistance and vanilla JavaScript, along with the core logic to handle common encoding pitfalls. What is "Vibe Coding"? For those unfamiliar with the term, vibe coding is the practice of leveraging modern generative AI models to handle the bulk of the standard layout and event listeners, while you focus on the core logic, user experience, and privacy requirements. Instead of writing every CSS class and event listener manually, I guided an AI assistant to generate a clean, responsive layout using a standard grid framework, while ensuring that the core translation logic resides strictly in the user's browser. The Pitfall of Traditional JS Base64 (and How to Fix It) If you have ever used native JavaScript btoa() and atob() functions, you might know they struggle with Unicode/UTF-8 characters (like emojis or non-Latin scripts). Running this in your console will throw an error: btoa ( " Xin chào! 🚀 " ); // Throws "Uncaught DOMException" To resolve this during the development process, the utility implements modern TextEncoder and TextDecoder APIs. This approach converts strings into binary byte arrays before encoding them, avoiding exceptions. The Client-Side Implementation Here is the clean JavaScript snippet used for bidirectional encoding and decoding: function processBase64 ( action , inputValue ) { try { if ( action ===

A polished UI can make a product feel completely different, but getting there usually takes more than...

AI coding agent Cursor has launched Origin, a git based code hosting platform embedded inside its AI-powered editor, positioning it as an alternative to GitHub for teams that already work in Cursor. Origin is rolling out in early beta on Pro, Teams and Enterprise plans, and lives inside a new Codebase tab within the Cursor application. By Matt Saunders

I started this investigation after finding a local OpenCode project that appeared to remember guidance across sessions. The guidance lived in Markdown files outside the repository, yet every new session followed it. The operational question was simple: had OpenCode decided to preserve those facts, or had someone explicitly written them? The session record answered it. An agent had created the files through an ordinary file tool after an explicit user request. A project-local instructions configuration then loaded them on every provider turn. What looked like autonomous memory was user-triggered file authoring plus deterministic prompt injection. That result sent me looking for the actual memory subsystem. There is no general runtime-managed service that decides what to save, updates facts when they change, and semantically retrieves useful knowledge in later sessions. What users experience as "memory" is produced by three different mechanisms with different owners and failure modes: instruction files are loaded into the system prompt, durable session events and projected messages are persisted in SQLite, and old model-visible context is replaced by a generated compaction checkpoint when the request grows too large. These mechanisms work together, but they do not form an autonomous long-term memory manager. That distinction matters. If a coding agent remembers a project rule because AGENTS.md is injected on every turn, that is not learned memory. If it can reopen an old transcript from SQLite, that does not mean a new session can retrieve facts from it. If a long session survives by summarizing its history, that does not mean the runtime selected the most important information. This article follows the current OpenCode source tree, which contains both the desktop-compatible session path under packages/opencode and the newer V2 runtime under packages/core . Where the two paths differ, I call out the difference rather than treating them as one implementation. Primary c

Shift-left and DevOps have impacted how we flow changes from inception to production, but at the cost of increased cognitive load and duplication of effort across testing, security, and maintenance. This article explores the real-world challenges of rightsizing developer platforms and finding a cultural match for engineering teams who use them to reduce cognitive load and deliver change faster. By John Keates

Question : Implement next permutation, which rearranges numbers into the lexicographically next greater permutation of numbers. If such arrangement is not possible, it must rearrange it as the lowest possible order (ie, sorted in ascending order). The replacement must be in-place and use only constant extra memory. Here are some examples. Inputs are in the left-hand column and its corresponding outputs are in the right-hand column. Example : 1,2,3 → 1,3,2 3,2,1 → 1,2,3 1,1,5 → 1,5,1 Idea : Scan from right to left and find the first element that is less that its previous. eg: 1 6 3 5 -> here it is 3. Let's name it as index. Again scan from right to left and find the first element that is greater than 3 and that's 5. Let's mark it as idx. 3.In this step we swap 3 and 5. Reverse elements from index+1 till the array length. Code: public void nextPermutation(int[] nums) { int index = -1; for(int i=nums.length-1;i>0;i--){ if(nums[i]>nums[i-1]){ index = i-1; break; } } if(index==-1){ reverse(nums,0,nums.length-1); return; } int idx=0; for(int i=nums.length-1;i>=index+1;i--){ if(nums[i]>nums[index]){ idx=i; break; } } swap(nums,index,idx); reverse(nums,index+1,nums.length-1); } void swap(int[] nums,int i,int j){ int temp =nums[i]; nums[i] = nums[j]; nums[j] = temp; } void reverse(int[] nums,int i ,int j){ while(i<j){ swap(nums,i,j); i++; j--; } } Code Explanation : We first initialize index=-1 and traverse backward to find the first one with i that satisfy the condition nums[i]>nums[i-1] . We assign this to index and break out of the loop. for(int i=nums.length-1;i>0;i--){ if(nums[i]>nums[i-1]){ index = i-1; break; } } Next step we are discussing a corner case. For example if the given array is 3,2,1 then we cannot find the element that satisfies the previous condition. So when the array is given in decreasing order we just reverse it and return. if(index==-1){ reverse(nums,0,nums.length-1); return; } Next iteration we are considering another variable idx and traverse backw

This is a simplified guide to an AI model called Beat_this maintained by Xavriley . If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter . Overview beat_this is a beat and downbeat tracking model from the ISMIR 2024 paper "Beat This! Accurate Beat Tracking Without DBN Postprocessing" by xavriley and collaborators at CPJKU. The model detects precise beat positions and downbeat boundaries in audio files without relying on Dynamic Bayesian Network postprocessing, achieving state-of-the-art F1 scores while maintaining generality across diverse music genres. The architecture alternates convolutions with transformers operating either over frequency or time dimensions, and is trained on multiple datasets including solo instruments, pieces with time signature changes, and classical music with high tempo variations. The main model ( final0 , final1 , final2 ) weighs approximately 78 MB each, with a smaller variant available at 8.1 MB. The most critical detail before using it: the model achieves good results specifically because it avoids meter and tempo constraints that traditional systems impose, but this means it can still fail on difficult and underrepresented genres and performs worse on continuity metrics compared to methods using postprocessing. Best use cases Music information retrieval and analysis workflows. If you build music analysis software that needs to segment tracks into beat-aligned sections for tempo detection, structural analysis, or synchronization with other modalities, beat_this provides clean beat and downbeat annotations without requiring external postprocessing pipelines. The model outputs precise timestamps suitable for downstream music information retrieval tasks like onset detection or harmonic analysis. Rhythm-aware music production tools. For digital audio workstations, beat detection plugins, or metronome applications, this model provides frame-level accuracy suitable for real-time audio alignment and grid s

I Built a Python Bot That Plays Blackjack on Discord's OwO Bot 🃏 An automation experiment in game logic, human-like timing, and why the house still wins. ⚠️ Disclaimer first: This project is for educational purposes only . It's a coding experiment about automation, pacing, and basic blackjack strategy. I'm not promoting gambling, I'm not responsible for any losses, and self-bots can violate Discord's Terms of Service — know the rules before running anything like this. What is this thing? If you've spent time in Discord economy servers, you've probably met OwO Bot — one of the most popular Discord bots out there, with its own cash economy and gambling minigames, including Blackjack . I asked myself a fun engineering question: Can I write a Python client that plays full Blackjack sessions on its own — with human-like pacing, break cycles, and a sensible betting strategy? That experiment became GhoSty OwO BlackJack Farm — a Python-based Discord self-bot focused on OwO Bot's Blackjack, now at V2.1 . What it does 🔄 Full Blackjack automation — handles the game loop end-to-end. 💡 Smart betting — strategy-based decisions instead of random yolo bets. 😴 Smart Sleep — lifetime work/break cycles instead of 24/7 spamming. ⏱️ Dynamic gaps — randomized delays between every action. 🚨 Zero win guarantees — on purpose. More on that below. The stack (and why an old discord.py) Python 3.10+ discord.py==1.7.3 colorama Yes, 1.7.3 is ancient — deliberately. The self_bot=True pattern that this kind of client relies on was removed in newer discord.py versions, so legacy 1.7.3 is the line that still supports it. If you've never touched pre-2.0 discord.py, this project is a small time capsule of that API. The whole project is intentionally tiny: OwO-Blackjack-Farm/ ├── main.py # bot + game logic ├── config.json # your token & settings ├── requirements.txt └── README.md Setup is two steps: drop your token into config.json , then: pip install discord.py == 1.7.3 colorama python main.py Start it

Enterprise vibe coding: the governance framework for shipping AI-generated apps to production Published: August 22, 2026 Category: Enterprise · AI Deployments Reading time: 9 minutes Author: NEXUS AI Team Gartner forecasts that 40% of new enterprise production software will be built using vibe coding techniques by 2028. A 2026 scan of more than 1,400 live vibe-coded applications found that 65% already had a security issue, and 58% shipped with at least one critical vulnerability. Those two numbers describe the same industry moving in opposite directions at once: adoption is outrunning governance. This post covers what a governance framework for enterprise vibe coding actually looks like, the five controls it needs, and where most teams get it wrong. What is enterprise vibe coding? Enterprise vibe coding is the practice of using natural-language prompts to generate application code, then governing that code through mandatory review, access control, and audit before it reaches production, rather than letting it ship straight from a prompt to a live endpoint. The term (coined by Andrej Karpathy in early 2025) originally described a fast, low-friction way for one person to build a prototype. What "enterprise" adds is the governance layer prototyping was never built for: staging environments, encrypted secrets, role-based access, and a record of who approved what. That distinction matters because the adoption curve and the risk curve are not moving together. The governance gap, in three numbers 40% of new enterprise production software will be built using vibe coding techniques by 2028, according to Gartner's May 2025 report "Why Vibe Coding Needs to Be Taken Seriously," as reported by CIO Dive . 65% of vibe-coded production applications had a security issue, in a 2026 scan of more than 1,400 live apps by the API security firm Escape.tech, reported via a Cloud Security Alliance research note . 58% of those same applications shipped with at least one critical vulnerabilit

Full walkthrough: https://www.youtube.com/watch?v=vFuFA3ByCs0 LeetCode 3116 — Kth Smallest Amount With Single Denomination Combination. Here’s the trick everyone misses: Brute force (generate all multiples, pick k-th) fails because k can reach 2×10⁹. The real approach: Binary search the answer X Count valid amounts ≤ X using inclusion-exclusion Odd subsets add, even subtract (bitmask over coins) LCM via GCD, break when LCM > X O(n · 2ⁿ · log(k·M)) — passes cleanly. The 26% acceptance rate makes this look harder than it is. Once you see the count(X) monotonic trick, it clicks.

I expected the audit to find broken code. That's what I was bracing for going in — a pile of half-working features, sloppy logic, the kind of mess you'd assume from software built at maximum speed with no human reviewing every line. That's not what I found. Almost everything Claude built actually worked, taken piece by piece. What I found instead was something I didn't expect at all: the agent had made the exact same mistakes I've watched human startup teams make, over and over, when they move fast and nobody's job is to say no. That's the real story here, and it's more interesting than "AI wrote bad code" would have been. The experiment The project is called GetPricePulse — a SaaS pricing intelligence product. It's Claude's entry from The $100 AI Startup Race , the season-long challenge I run where seven AI agents each get $100 and full autonomy to build a real startup from scratch, with no human coding and no product manager in the loop. Each agent picked its own idea and ran with it. Claude picked SaaS pricing intelligence, named it PricePulse, and kept building on it for the entire race. That "no product manager in the loop" part is the thing that made this interesting to watch. Nobody was deciding what PricePulse should be. Nobody was saying "we have enough pricing tiers now" or "this feature doesn't belong here." Claude got to build exactly what its own priorities told it to build, at whatever speed it chose, for the length of the race — optimizing, as far as I could tell from the commit history, for speed, feature creation, shipping, and monetization experiments. Not correctness. Not coherence. Not "does this still make sense in three weeks." I've written before about what all seven agents in this race said, independently, when I asked them what AI agents still can't do — they converged on the same answer without seeing each other's responses. This piece is narrower: a full production audit of Claude's specific build, PricePulse, done after the race, before I

Photo by Microsoft Copilot on Unsplash TL;DR: In a poll of 138 developers, three‑quarters say Claude Code outperforms Codex for everyday AI‑driven coding, pointing to higher accuracy, deeper context awareness, and a smoother workflow. The AI‑coding battlefield has been dominated by OpenAI’s Codex for years, powering tools like GitHub Copilot and shaping how developers write code. Yet a fresh wave of feedback suggests a shift: Anthropic’s Claude Code is rapidly becoming the preferred assistant for many programmers. A recent survey of 138 software engineers—spanning startups, enterprise teams, and freelance coders—revealed that 75% now rely on Claude Code as their go‑to AI partner. What drives this migration, and what does it mean for the future of AI‑augmented development? Survey Overview and Key Findings The questionnaire targeted developers who regularly use AI code generators, asking them to rank their primary tool and rate specific workflow attributes. Respondents represented a broad skill spectrum, from junior developers to senior architects, and worked across languages such as Python, JavaScript, Java, and Go. Adoption rate: 104 out of 138 participants (75%) listed Claude Code as their primary AI assistant, while only 34 (25%) still favored Codex. Primary criteria: Accuracy of generated snippets, ability to retain long‑form context, and ease of integration into existing IDEs topped the list. Secondary factors: Cost efficiency, response latency, and the perceived safety of the model (fewer hallucinations) also swayed decisions. The data paints a clear picture: developers are no longer satisfied with a one‑size‑fits‑all approach. They want an AI that can understand the nuance of a multi‑file project, stay on‑topic across extended sessions, and deliver code that compiles on the first try. Why Claude Code Wins Over Codex Higher Accuracy and Fewer Hallucinations Respondents repeatedly highlighted Claude Code’s ability to generate syntactically correct, production‑re

DeepSeek has released a developer preview of DeepSeek Harness (dsh), an open-source execution runtime for building autonomous AI agents. The software features a micro-kernel architecture with modular plugins for various functional units. The release includes an append-only event logging system for tracking execution activities. Adoption may depend on plugin ecosystem stability and API maintenance. By Olimpiu Pop

SpaceX was reportedly in talks to buy AI coding startup Cognition. SpaceX has already acquired Cursor as it races to catch up to rivals like OpenAI and Anthropic in enterprise AI.

What if deciding what to eat felt as exciting as winning a championship? It's 2 AM. You're hungry. You open your favorite food delivery app, convinced you'll order something in two minutes. Thirty minutes later, you're still scrolling. Pizza? Burger? Pasta? Fries? Momos? Ice cream? Suddenly, every option looks equally good, and now you're questioning your entire existence just because you wanted dinner. I have this problem almost every time I order food. So when I saw the DEV Challenge, I wanted to build something fun around this tiny but painfully relatable problem. Unfortunately, I couldn't finish it before the deadline, but I still wanted to share the idea because it's one of those projects that made me smile while building it. Meet Foodwars . Instead of endlessly scrolling through hundreds of dishes, why not let your favorite comfort foods battle each other until only one champion remains? What I Built We've all watched cooking shows like MasterChef and somehow turned into professional judges sitting comfortably on our sofas. "That steak is overcooked." "The sauce needed more balance." "I would've plated it differently." As if Gordon Ramsay personally asked for our opinion. Foodwars lets us finally put those imaginary judging skills to good use. Instead of comparing hundreds of dishes at once, the platform randomly pairs comfort foods against each other in head-to-head battles. You become the judge. Pick the winner, move on to the next matchup, and continue until one food survives the tournament. No endless scrolling. No decision fatigue. Just a series of fun, quick decisions that eventually crown your Ultimate Comfort Food . And once the champion is decided... Go order it. Or cook it. Either way, dinner has finally been decided. Demo comfort-foodwars.vercel.app Features of Foodwars Foodwars isn't just a random food picker. Every round is designed to make choosing food feel like a game instead of a chore. 1. Interactive Tournament Brackets Instead of presenting

Most write-ups on this subject are about technique. This one is about the three decisions you should make before you write any code, because in my experience every project that went badly went badly for a reason that was decided on day one and not noticed until much later. I have built this kind of collection twice, for competitive analysis and for a partner-vetting workflow. Neither of them needed to touch anything behind a login, and I want to explain why that turned out to be the useful constraint rather than the limiting one. Line one: the login wall is a boundary A login wall is a statement about who the content is for. Treating it as an engineering obstacle to be routed around is the decision that puts a project on the wrong side of everything: terms of service, the platform's own detection, and in several jurisdictions the law. So the first line is simply: if it requires an account to see, it is out of scope. Not "hard," not "for later." Out of scope. I am not going to discuss techniques for getting past one, and I would be sceptical of any article that does. The interesting engineering question here is not how to see more. It is how much you can actually do with what is openly published, and the honest answer is: considerably more than people assume before they check. This constraint also has a practical benefit that is easy to miss. A pipeline built only on openly available data does not break when authentication changes, does not require credential management, and does not put an account at risk. Mine has survived two platform changes that took down colleagues' authenticated collectors. Line two: public does not mean unrestricted The second line is the one developers get wrong most often, and it has nothing to do with access. Data being publicly visible says nothing about whether you may store it, for how long, or what you may do with it. In the EU and UK, information about an identifiable person is personal data whether or not they published it themselves