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A few months ago, I didn't expect that I'd be spending my days debugging authentication flows, opening pull requests, analyzing backend architectures, and building a Bitcoin education platform during a hackathon. Yet here we are. What started as curiosity about Bitcoin turned into one of the most intense learning experiences I've had as a builder, and honestly, I wouldn't trade it for anything. This is the story of how I joined Hack4Freedom Lagos 2026, helped build BitPath, contributed to open source, discovered OpenCode, and learned that software engineering is often just solving one problem after another until things somehow start working. How I Ended Up Building in Bitcoin My interest in Bitcoin didn't start from price charts or trading. What attracted me was the builder ecosystem around it. I've contributed to open source before, so I already appreciated the value of collaborative software development. But what stood out about Bitcoin was how deeply open source is woven into the culture. In many ecosystems, open source feels like an option. In Bitcoin, it feels like a foundation. Everywhere I looked, people were building in public, contributing to projects, improving documentation, reviewing code, and helping newcomers find their footing. That environment made me want to participate more deeply. When the opportunity came to join the Hack4Freedom Lagos 2026 hackathon, I said yes. The Project: BitPath Our team worked on BitPath, an AI-powered learn-and-earn platform designed to make Bitcoin education more accessible. The idea was simple: Instead of overwhelming learners with technical concepts, BitPath uses conversational learning experiences, AI tutoring, quizzes, progress tracking, and rewards to help users learn Bitcoin and financial literacy in a more engaging way. Our stack looked something like this: Frontend Next.js TypeScript Tailwind CSS Zustand Backend NestJS PostgreSQL Redis Queue processing Additional Services Google OAuth OpenAI APIs Lightning Network

A copy of Super Mario Bros., still in the box and sealed with its original sticker, just sold at Heritage Auctions for $3 million. That absolutely crushes the previous record of $2 million, also for a copy of Super Mario Bros., in 2021. That sale also came hot on the heels of a controversial auction […]

Six months ago, I could tell you which model to use for almost any job, and I would have said it with confidence. Today I hedge, and so does almost everyone I talk to who builds with these tools. The reason is simple. The ground keeps moving under us. Models get smarter on a schedule no one can forecast, and they get cheaper to run on a second schedule that is just as hard to predict. Both curves are bending at once, and they point in directions that change how I build and how I think you should build too. I spend my days crafting development, content and productivity workflows that lean on these models. I wire up agents, route tasks, and watch the bills. So this is not a far-off observation for me. It is the thing I am living with week to week, and it has forced me to rethink habits I held for years. This is not the usual story about a single breakthrough. It is four shifts happening together. Frontier performance is climbing past what most of us guessed was possible this year. Small models are getting good enough to run on a phone or a thirty-five dollar computer. The smart move has stopped being "pick the best model" and started being "build a system that picks for you." And a coding startup with a rocket company behind it is showing what happens when product, data, and compute sit under one roof. Let me take these one at a time. Then I want to show you what they add up to, because the sum is bigger than the parts. Performance Is Outrunning the Forecasts Start at the top of the market, where the most capable models live. Anthropic now ships a tier above its Opus line. The Fable and Mythos family is a class of model built for problems that smaller systems still fumble: long chains of reasoning, deep code work, research that needs to hold many threads at once. Claude Fable 5 carries extra safety work so it can go out to the public. A more powerful sibling, used inside a small set of trusted partners, stays behind tighter controls. The names are not the point. The p

Working with complex JSON payloads can quickly become a nightmare. You end up chaining .map() , .filter() , and .reduce() calls across multiple lines just to pull out a few nested values. Add optional chaining to avoid crashes and the code becomes nearly unreadable. There is a cleaner way - JSONata . It is a compact, purpose-built query and transformation language for JSON data. Think of it as XPath for XML, but designed from the ground up to work with JSON objects and arrays. What is JSONata? JSONata is an open-source project originally created by Andrew Coleman at IBM. It gives developers a declarative syntax to extract and reshape JSON data without writing procedural JavaScript loops. Where vanilla JS might take 15 lines, a JSONata expression often takes one. It is available as an npm package and integrates naturally into Node.js and TypeScript projects. Simple Path Navigation The foundation of JSONata is its dot-notation path traversal. Given a nested JSON object, you simply trace the path to the value you need: customer.address.city This returns the city value without any need for null checks or defensive coding. JSONata handles missing properties gracefully by returning undefined rather than throwing errors. Automatic Array Mapping When JSONata encounters an array during path traversal, it automatically maps across all items. There is no need to write an explicit .map() call: customer.orders.product This returns an array of all product names from every order in one clean expression. Inline Filtering You can filter arrays directly using bracket notation with a condition: customer.orders[price > 1000].product This returns only the products from orders where the price exceeds 1000. No .filter() callback required. Built-in Aggregation Functions JSONata ships with a solid set of built-in functions for math, strings, and arrays. Aggregating a set of values is straightforward: $sum(customer.orders.price) Other useful functions include $count() , $average() , $string(

Sliding-Window Spend Guard for AI Agents: Catch the $47K Loop Per-Call Caps Miss A sliding-window spend guard sums what your agent has spent over the last N minutes and refuses the next call before it dispatches — which is the thing a per-call cap can't do. A per-call cap asks "is this one call too expensive?" The runaway loops that empty a budget are built from calls that each pass that check. The damage lives in the sum, not in any single call. In short: a sliding-window spend guard tracks a trailing window of calls and blocks the next one when cumulative spend or a repeated near-identical call breaches a per-window rule. In my run it stopped an Analyzer-Verifier ping-pong at call 12, $45.80 in, after a naive per-call $5 cap let all 12 through. Stdlib, keyless, runs in seconds. AI disclosure: I wrote window_guard.py with AI assistance and ran it myself before publishing. Every number in the output blocks below is pasted from a real run of that script on a fixture I'll show you. The $47K incident is someone else's, and I link the postmortem next to it. I label which is which. A $47K agent loop where every single call was fine In November 2025 a team woke up to a $47,000 bill from a single agent deployment. Four LangChain agents, talking to each other over A2A, and two of them — an Analyzer and a Verifier — got into a ping-pong. Analyzer hands work to Verifier, Verifier kicks it back, repeat. For 264 hours. The cost didn't spike. It escalated , week over week: $127, then $891, then $6,240, then $18,400. The author of the postmortem, Gabriel Anhaia, describes the root cause in a way I keep coming back to: the dashboard was green for eleven days, and there was no step cap, no per-conversation USD budget, no orchestrator deciding when the work was done ( dev.to/gabrielanhaia, Nov 2025 ). The dashboard showed the number. It just showed it after each call, never before the next one. A follow-up teardown by the Waxell team sharpened the line into the title of their piece:

From Confused to Confident: How I Finally Mastered GitHub Copilot in Every Situation I still remember the afternoon I rage-closed VS Code because Copilot kept suggesting the wrong function signatures — again . I had been treating it like a magic oracle, typing vague comments and expecting perfect code to rain down from the AI heavens. Spoiler: that's not how it works. After weeks of trial, error, and a few embarrassing pull request reviews, I cracked the code (pun intended). Here's everything I wish someone had told me about using GitHub Copilot accurately — across Chat , Plan , and Agent modes. 🧠 First, Understand What Copilot Actually Is Before diving into tips, let's reset expectations. GitHub Copilot is not a search engine. It's not Stack Overflow with a fancy UI. It's a context-aware AI assistant trained on massive amounts of code. That means: The quality of your output depends directly on the quality of your input . It works best when it has rich context — open files, good comments, clear naming. It can be wrong. Confidently wrong. Always review what it generates. With that mindset locked in, let's explore each mode. 💬 Copilot Chat: Your Pair Programmer in the Sidebar The first time I opened Copilot Chat, I typed: "fix my code." It stared back at me, basically confused. Of course it was — I hadn't told it which code, what was broken, or what I expected. Tips for Accurate Chat Usage 1. Be specific and contextual. Instead of: "Why isn't this working?" Try: "This useEffect hook in React runs on every render instead of only when userId changes. Here's the code: [paste snippet]. What's wrong?" The more context you give, the more surgical the answer. 2. Use slash commands to guide intent. Copilot Chat supports built-in commands that dramatically improve accuracy: /explain → Explains selected code in plain English /fix → Suggests a fix for a highlighted bug /tests → Generates unit tests for selected code /doc → Writes documentation for a function or class These aren'

Extract any website's design DNA Discussion | Link

There's nothing new or surprising, but it's still an entertaining film from one of our greatest directors.

Your private AI memory for ChatGPT, Claude, Gemini and Grok Discussion | Link

It's not clear which states are involved, but they're asking about everything from OpenAI's ad policies to its handling of health data.

Microsoft is preparing to lay off a significant chunk of its Xbox division and is reevaluating the plans for its next-generation Project Helix console. It's apparently also considering dramatically restructuring its relationship with Xbox, and hasn't ruled out spinning it off into a separate company. A new report from The Information suggests that Microsoft has […]

GitHub热门项目 | An open source collection of animated, interactive & fully customizable React components for building memorable websites. | Stars: 41,003 | 91 stars today | 语言: JavaScript

I’ve struggled with insomnia since I was very young. Like many chronic overthinkers, I tend to fall asleep best when my mind is occupied by something else, such as podcasts, YouTube compilations, or my personal favorite: rain sounds. But earbuds can be uncomfortable, and playing audio out loud isn’t exactly considerate when I’m staying at […]

If you’re planning on traveling anytime soon, Bose’s second-generation QuietComfort Ultra headphones are a great companion for long flights and train rides. Not only do they offer excellent noise cancellation, but they also retain the foldable design of their predecessor, making them easy to pack in a carry-on. They’re even easier to recommend today, now […]

GSoC 2026 | CircuitVerse × Canvas LMS LTI 1.3 Integration If Week 1 was about getting familiar with the codebase and understanding what needed to be built, Week 2 was about learning the hard way that writing code is only half the job. The other half — the messier, more humbling half — is getting that code accepted by the people who actually maintain the project. This week was full of detours, rejected pull requests, reviewer feedback that stung a little, and a surprisingly frustrating fight with a two-letter word in Ruby. But by the end of it, I had something real to show: a clean, reviewed, and submitted change to CircuitVerse that lays the foundation for the entire LTI 1.3 integration. Let me walk you through it. A Quick Refresher: What Are We Building? CircuitVerse is an open-source platform where students can build and simulate digital circuits right in their browser. The project I'm working on aims to connect CircuitVerse with Canvas, one of the most widely used Learning Management Systems (LMS) in universities around the world. The technology that makes this connection possible is called LTI — Learning Tools Interoperability. Think of it as a universal plug that lets any educational tool (like CircuitVerse) slot into any LMS (like Canvas) so that students can log in once, get assignments, submit work, and have their grades flow back automatically — all without leaving their course page. There are two versions of this plug: LTI 1.1 , which is old and uses a simpler (but outdated) security mechanism, and LTI 1.3 , which is newer, more secure, and what Canvas actually recommends today. My job is to bring CircuitVerse fully up to LTI 1.3 standards. Monday–Tuesday: A Pull Request That Taught Me to Read Diffs I started the week with what I thought was a solid pull request (PR) — a fix for a bug in CircuitVerse's existing LTI 1.1 grade passback feature. "Grade passback" is the process where CircuitVerse sends a student's score back to Canvas after they complete an as

本記事は、AtCoder Beginner Contest 462 (ABC462) に参加した際の、A〜E問題の復習と解答の備忘録です。コンテスト中に考えた解法の方針や、提出したPythonのコードについて整理しています。 A - Secret Numbers / 実行時間制限: 2 sec / メモリ制限: 1024 MiB / Difficulty: None 配点 : 100 点 問題文 英小文字と数字のみからなる文字列 $S$ が与えられます。 $S$ から数字である文字だけを取り出し、元の順序のまま並べた文字列を求めてください。 制約 $S$ は英小文字と数字のみからなる長さ 1 以上 50 以下の文字列 自分の解答の方針 一文字づつ数字かどうかを判定し、数字のみを配列に入れて出力する。 提出の時には数字かどうかの判定は0-9のどれかに含まれているかを調べたが、解説ではPythonは isdigit() で数値かどうかを調べられるらしい。 提出したコード S = list ( input ()) T = [] for i in range ( len ( S )): if S [ i ] in [ " 1 " , " 2 " , " 3 " , " 4 " , " 5 " , " 6 " , " 7 " , " 8 " , " 9 " , " 0 " ]: T . append ( S [ i ]) print ( "" . join ( T )) B - Gift / 実行時間制限: 2 sec / メモリ制限: 1024 MiB / Difficulty: None 配点 : 200 点 問題文 人 1 から人 $N$ の $N$ 人がギフトを送り合いました。 人 $i$ は人 $A_{i,1}, A_{i,2}, \dots, A_{i,K_i}$ の $K_i$ 人にギフトを送りました。 $i=1,2,\dots,N$ に対し、人 $i$ にギフトを送った人を全て求めてください。 制約 $2 \le N \le 100$ $1 \le K_i \le N-1$ $1 \le A_{i,1} < A_{i,2} < \dots < A_{i,K_i} \le N$ $A_{i,j} \neq i$ 入力される値は全て整数 自分の解答の方針 辞書に人 $i$ と、その人にギフトを送った人の番号をリストとして持つことを考える。 入力で受け取った、ギフトを送った人と送られた人すべてに対して辞書に登録し、結果を出力する。 提出したコード N = int ( input ()) dct = dict () for i in range ( N ): dct [ i + 1 ] = [] for i in range ( N ): A = list ( map ( int , input (). split ())) for j in range ( 1 , A [ 0 ] + 1 ): dct [ A [ j ]]. append ( i + 1 ) for i in range ( N ): print ( " " . join ([ str ( len ( dct [ i + 1 ]))] + list ( map ( str , dct [ i + 1 ])))) C - Not Covered Points / 実行時間制限: 2 sec / メモリ制限: 1024 MiB / Difficulty: None 配点 : 300 点 問題文 2 次元平面上に点 1 から点 $N$ の $N$ 個の点があります。点 $i$ $(1 \le i \le N)$ の座標は $(X_i, Y_i)$ です。ここで、 $X, Y$ はそれぞれ $(1,2,\dots,N)$ の順列であることが保証されます。 左下の頂点を $(0,0)$ 、右上の頂点を $(X_i, Y_i)$ とする $x$ 軸に平行な辺と $y$ 軸に平行な辺のみからなる長方形の内部（辺上を含まない）に点 1 から点 $N$ までの $N$ 個の点をどれも含まないような $i$ の個数を求めてください。 制約 $1 \le N \le 3 \times 10^5$ $1 \le X_i, Y_i \le N$ $X, Y$ はそれぞれ $(1,2,\dots,N)$ の順列 入力される値は全て整数 自分の解答の方針 端から考えたいので、初めに $X$ についてソートする。 $X$ が小さい順に見ていっ​​たとき、現在の点が作る長方形の内部にほかの点が含まれるかどうかは、、これまでに走査した（$X$座標が自身より小さい）点の中に、自身より $Y$ 座標

If you've ever tried building a sports betting application, odds tracker, arbitrage scanner, value betting tool, or sports analytics dashboard, you've probably experienced the same thing: You start with the exciting part. The idea. The algorithm. The UI. The business logic. And then reality hits. The Hidden Problem Nobody Talks About Most developers assume the hardest part of a betting-related project is the prediction model or arbitrage logic. In practice, the real challenge is data infrastructure. Before your project can calculate anything, you need: Live events Accurate odds Multiple bookmakers Consistent market structures Historical updates Reliable refresh rates And suddenly your "weekend project" turns into a full-time data engineering job. The Scraping Trap Most developers begin by scraping bookmaker websites. At first it seems simple: Open DevTools Find the API request Parse the response Save the data Done, right? Not quite. Within a few weeks you'll likely encounter: Changed endpoints Rate limits Cloudflare protection Different JSON formats Missing markets Broken parsers Increased maintenance costs Instead of improving your product, you're fixing scrapers. Again. And again. And again. Every Bookmaker Speaks a Different Language Let's say you want to compare odds from five sportsbooks. You quickly discover that every provider structures data differently. One bookmaker might return: { "home" : "Liverpool" , "away" : "Arsenal" } Another might return: { "team1" : "Liverpool" , "team2" : "Arsenal" } A third one could use: { "participants" : [ "Liverpool" , "Arsenal" ] } Now multiply that problem across: dozens of bookmakers hundreds of leagues thousands of events You end up spending more time normalizing data than building features. Real-Time Data Changes Everything Many projects work perfectly during testing. Then live data arrives. Odds can move multiple times within a minute. If your system refreshes too slowly: arbitrage opportunities disappear alerts become

What This Repository Is A complete, structured, beginner-friendly Node.js learning path. 30 sessions. Each session has Clear learning objectives Step-by-step explanations Working code examples Practice exercises Interview questions Summary of key points No fluff. No assumptions. Just code. The Complete Curriculum Phase 1 - Node.js Fundamentals (Sessions 1-5) Session Topic What You Will Build 01 Introduction to Node.js Your first Node.js program 02 Project Setup and npm package.json, node_modules 03 How Node.js Works Event loop, blocking vs non-blocking 04 Modules and Imports Your first custom module 05 File System Module Read, write, update, delete files Sample code from Session 05 const fs = require ( " fs " ); // Create a file fs . writeFileSync ( " student.txt " , " Welcome To Node.js " ); // Read the file const data = fs . readFileSync ( " student.txt " , " utf8 " ); console . log ( data ); // Welcome To Node.js // Append to file fs . appendFileSync ( " student.txt " , " \n New line added " ); // Delete file fs . unlinkSync ( " student.txt " ); Phase 2 - Core Modules (Sessions 6-10) Session Topic What You Will Build 06 Path Module Cross-platform file paths 07 OS Module System information 08 Events and EventEmitter Custom event handling 09 HTTP Module Create a server 10 Multi-Route Server Multiple routes, JSON responses Sample code from Session 10 const http = require ( " http " ); const server = http . createServer (( req , res ) => { if ( req . url === " / " ) { res . end ( " Home Page " ); } else if ( req . url === " /about " ) { res . end ( " About Page " ); } else if ( req . url === " /products " ) { res . setHeader ( " Content-Type " , " application/json " ); res . end ( JSON . stringify ([{ id : 1 , name : " Laptop " }])); } else { res . statusCode = 404 ; res . end ( " Page Not Found " ); } }); server . listen ( 3000 ); Phase 3 - Building REST APIs (Sessions 11-15) Session Topic What You Will Build 11 CRUD with Dummy Data Complete REST API using array 12

Your API hands out 50 records at a time across 400 pages. You need all of them. You do not need them all at once. Here's a very familiar situation that shows up constantly on the backend. Some API returns data in pages, 50 or 100 records at a time, and you need to walk every page: sync them to your database, export them to a file, run a report. The endpoint gives you a cursor or a page number and you keep asking until there's nothing left. The way most of us write it the first time looks like this: async function getAllRecords () { const all = []; let cursor = 0 ; while ( cursor !== null ) { const { records , nextCursor } = await fetchPage ( cursor ); all . push (... records ); cursor = nextCursor ; } return all ; } const everything = await getAllRecords (); for ( const record of everything ) { process ( record ); } It works. At four hundred records it's fine. The trouble starts when the dataset grows, and it has three separate problems hiding in it. It holds the entire dataset in memory before you touch a single record. It's all or nothing: if page 380 fails, you've thrown away the 19,000 records you already fetched . And it's eager. You can't start processing record one until the very last page has landed , even if all you wanted was the first ten. There's a shape in JavaScript built for exactly this, and if you read the first two posts in this series you already have both halves of it. Two ideas you've already seen In the CSV post , we pulled rows out of a huge file one at a time with a generator, so the file never fully loaded into memory. Lazy. Pull-based. You ask for the next row, you get the next row, nothing more. In the async/await post , we saw that a generator can pause at a yield and resume later.A generator can hold its place across an asynchronous gap. Put those together. A generator that pulls data lazily, and can pause to await something between pulls. That's an async generator, and it's the natural tool for walking a paginated API. You pull records

Reflecting on two major technical challenges from my backend engineering internship, focusing on fault tolerance, infrastructure, and distributed architectures. Introduction As I wrap up my HNG internship, I’ve been reflecting on the gap between code that "works on my machine" and code that survives in production. Here is a look at two tasks from Stage 9—one solo, one team-based—that completely changed how I approach backend engineering and infrastructure. The Individual Task: Background Job Scheduler What it was For my individual Stage 9 task, I built a distributed background job scheduler backed by PostgreSQL and a FastAPI backend, featuring a vanilla HTML/CSS/JS frontend. It manages async tasks (like a mock email sending queue) using a MinHeap priority queue, Directed Acyclic Graph (DAG) dependency resolution, a Dead-Letter Queue (DLQ), and a real-time Server-Sent Events (SSE) dashboard. The problem it was solving Heavy asynchronous tasks—like email generation or batch processing—cannot block the main API thread. The system needed to successfully queue, prioritize, retry on failure, and track every job entirely independently from the standard request-response cycle. How I approached it I built the core logic from the ground up: a MinHeap and an alternative Timing Wheel algorithm for scheduling, a worker engine featuring a 3-attempt backoff sequence (1s, 5s, 25s with jitter), a DAG dependency checker, and a starvation daemon to prevent tasks from hanging. Once the CRUD API and SSE streaming were hooked up, I containerized the entire application with Docker and wrote my deploy scripts. I thought I was done. What actually broke and how I fixed it The application code took hours. The deployment took a full day of non-stop debugging across multiple cloud providers. Oracle Cloud was out of capacity on every free tier shape, and GCP demanded upfront payment. I finally got a t3.micro running on AWS, but that’s when the real DevOps nightmare began: The SSL Chicken-and-Egg