标签：#tco

I've been building AI agents for a while now, and streaming responses to a UI has always been the painful part. In previous projects I tried API Gateway streaming, Lambda response streaming, and even AppSync Events via an agent tool call to notify the UI. I also looked at adding my own WebSocket API through API Gateway, which requires managing $connect , $disconnect , and $default routes, storing connection IDs in DynamoDB, and posting messages back through @connections . All of these approaches felt like too much ceremony for what should be simple. That's when I found that AgentCore has built-in WebSocket support. The browser can just connect directly to the agent. No middleman. I built WearCast to demonstrate this functionality. This is an AI agent that helps you pick out what to where based on the weather. I've found this very helpfu when packing for a trip. The code for this application is public and you can find the full implementation on this GitHub repo . The problem with the traditional approach We usually build app applications as we do REST APIs User → REST API → Lambda → AI Service → Lambda → REST API → User Every message makes a round trip through multiple intermediaries. The response waits until the entire generation is complete, and then it all comes back at once. For a chat interface, this feels sluggish. Users stare at a spinner while the model generates hundreds of tokens they could already be reading. Even if you add Server-Sent Events or long-polling, you're still stitching together a real-time experience on top of infrastructure that seemed like overkill. I wanted something better. The architecture Here's what I ended up with: ┌─────────────┐ ┌──────────────────┐ │ React UI │─── JWT ─▶│ API Gateway + │──▶ Lambda (presigned URL) │ (Cognito) │ │ Cognito Auth │ └──────┬──────┘ └──────────────────┘ │ │ WebSocket (SigV4 presigned URL) │ ← No middleman! Direct connection → ▼ ┌──────────────────────────────────┐ ┌────────────────────┐ │ AgentCore Runtim

Link https://leetcode.com/problems/subsets/description/ Problem Given an integer array nums of unique elements, return all possible subsets (the power set). The solution set must not contain duplicate subsets. Return the solution in any order. Example Example 1: Input: nums = [1,2,3] Output: [[],[1],[2],[1,2],[3],[1,3],[2,3],[1,2,3]] Example 2: Input: nums = [0] Output: [[],[0]] Solution First, create a variable subsets, initialized to [[]], as the return value. Loop through nums, and for each element, create new subsets by appending that element to each existing subset. Then, append these new subsets to subsets. Sample code class Solution : def subsets ( self , nums : List [ int ]) -> List [ List [ int ]]: """ 0: [[]] 1: [[]]+[1] -> [[], [1]] 2: [[],[1]] + [2],[1,2] -> [[], [1], [2], [1, 2]] 3: [[], [1], [2], [1, 2]] + [3], [1, 3], [2, 3], [1,2,3] -> [[], [1], [1, 2], [3], [1, 3], [2, 3], [1, 2, 3]] """ subsets = [[]] for num in nums : new_subsets = [ subset + [ num ] for subset in subsets ] subsets += new_subsets return subsets

Everyone who has ever been right about something loud enough to remember it will tell you they called it. The screenshot arrives after the match, after the candle, after the election. And there is no way to know whether it was written on Monday or edited on Friday. This is the quiet rot at the center of most "track records": a prediction you cannot date is not a prediction at all. It is a memory with good lighting. The technical name for the problem is look-ahead . If a forecast can be created, tweaked, or cherry-picked after the outcome is known, then it carries zero information about skill. The only fix is to make the timing of a prediction independently checkable вАФ to prove a document existed in a specific form before a specific moment, without asking anyone to trust you, your server clock, or your database. That is precisely what OpenTimestamps does, using the Bitcoin blockchain as a shared, tamper-evident clock. Why timing is the whole game A forecast is a bet against the future. Its value comes entirely from the fact that the future was unknown when the forecast was fixed. The instant you allow post-hoc editing, every desirable property collapses: calibration becomes meaningless, Brier scores become fiction, and "I predicted this" becomes unfalsifiable. So an honest forecasting system needs one hard guarantee before anything else: this exact text existed at this exact time, and has not changed since. Note what that guarantee does not require. It does not require publishing the forecast publicly in advance (you might want it sealed). It does not require a notary, a lawyer, or a trusted timestamping company that could be subpoenaed, hacked, or simply go out of business. It requires a clock that nobody controls and nobody can wind backward. What "proof of existence" actually means The building block is a cryptographic hash вАФ typically SHA-256. Feed any file into it and you get a 64-character fingerprint. Change a single comma and the fingerprint changes compl

Hello Dev Community! 👋 It is officially Day 127 of my software engineering marathon! Today, I leveled up my asynchronous data pipeline in React.js by tackling a critical production-grade performance problem: avoiding memory leaks and managing component unmounting states using the useEffect Cleanup function alongside the native browser AbortController API ! ⚛️🛡️⚡ Additionally, I integrated a fully responsive async loading engine to drastically improve our overall User Experience (UX). 🛠️ Deconstructing the Day 127 Network Boundary Control As shown inside my refactored workspace code layout across "Screenshot (283)_2.png" and "Screenshot (284)_2.png" , the side-effect layer is now safe from ghost background executions: 1. Ingesting the Abort Signal API Inside the lifecycle layer, before initiating the endpoint call, I instantiated an active execution cancellation anchor on Lines 12-13 inside PostContainer.jsx : javascript const controller = new AbortController(); const signal = controller.signal;

Two Sum Given an array of integers nums and an integer target , return indices of the two numbers such that they add up to target . (You may assume that each input would have exactly one solution, and you may not use the same element twice. You can return the answer in any order.) Python ####Double FOR Loops (Runtime: 2100ms, Memory: 13.3MB) #DECLARE nums: ARRAY of INTEGER #DECLARE target: INTEGER class Solution : def twoSum ( self , nums , target ): Length = len ( nums ) for i in range ( Length - 1 ): for j in range (( i + 1 ), Length ): if nums [ i ] + nums [ j ] == target : return i , j return None ####Hashing Algorithm (Runtime: 0ms, Memory: 12.9MB) #DECLARE nums: ARRAY of INTEGER #DECLARE target: INTEGER class Solution : def twoSum ( self , nums , target ): Seen = {} for i , Value in enumerate ( nums ): if ( target - Value ) in Seen : return Seen [ target - Value ], i Seen [ Value ] = i return None

Are you building a trading dashboard, a market sentiment tracker, or a financial data pipeline in Go? If so, you know that gathering reliable social intelligence and market data is often a complex, messy process. You have to juggle raw HTTP requests, decode deeply nested JSON payloads, and manually handle rate limits. But what if you could access a wealth of crypto and stock social intelligence idiomatically, right where your Go code lives? Enter lunarcrush-go , a powerful, zero-dependency SDK designed to seamlessly integrate the LunarCrush API v4 into your Golang applications. In this article, we will explore why lunarcrush-go is the ultimate tool for developers looking to tap into social and market intelligence, how to get started in under 60 seconds, and why its zero-dependency architecture makes it a robust choice for production workloads. Why LunarCrush? Before diving into the SDK, it is worth understanding what LunarCrush brings to the table. LunarCrush goes beyond traditional price charts. It measures what the internet is actually saying about Bitcoin, Ethereum, Tesla, and thousands of other assets. By analyzing social buzz, creator impact, and overall market sentiment across various platforms, LunarCrush provides a holistic view of the market 1 . Whether you want to know the Galaxy Score of a specific coin, track the hourly social time-series of a stock, or get AI-generated insights on a trending topic, LunarCrush has you covered. Introducing lunarcrush-go The lunarcrush-go library was built with one primary goal: to provide clean, typed, and production-ready access to every LunarCrush endpoint without pulling in a single third-party dependency. It speaks Go natively, meaning you do not have to wrestle with raw JSON or hand-roll your own retry loops. Key Features Here is what makes lunarcrush-go stand out: Complete API Coverage: The SDK supports every LunarCrush endpoint, including Coins, Stocks, Topics, Categories, Creators, Posts, Searches, AI summaries, a

Decentralized Autonomous Organizations are often presented as simple governance systems: token holders create proposals, vote, and execute decisions on-chain. In practice, building a production-grade DAO is far more difficult. A DAO is not only a smart contract. It is a distributed coordination system that combines governance logic, treasury security, token economics, identity, off-chain infrastructure, and human decision-making. A failure in any one of these layers can compromise the entire organization. Below are some of the most important technical problems DAO developers must solve. 1. Governance Attacks Through Borrowed Voting Power Many DAOs calculate voting power based on the number of governance tokens held at a specific moment. This creates a serious attack surface when tokens can be borrowed through lending protocols or flash loans. An attacker may temporarily acquire a large amount of voting power, submit or approve a malicious proposal, and return the borrowed assets shortly afterward. The standard defense is snapshot-based voting power. Instead of checking a user’s current balance, the governance contract reads historical balances from a previous block. function getVotes( address account, uint256 blockNumber ) public view returns (uint256) { return token.getPastVotes(account, blockNumber); } However, snapshots alone do not solve every problem. Developers should also consider proposal delays, minimum token-holding periods, quorum requirements, and vote-delegation risks. 2. Dangerous Proposal Execution The most sensitive part of a DAO is usually the executor. A successful proposal may call arbitrary contracts, transfer treasury assets, upgrade protocols, or change governance parameters. If proposal calldata is incorrectly validated, a governance action can execute unintended operations. A DAO should clearly separate: Proposal creation Voting Proposal queuing Timelock execution Emergency cancellation Using a timelock gives token holders and security teams

The mental model most people have is too simple "Chainlink Functions lets smart contracts call APIs." That's true the same way "Ethereum lets people send money" is true. Technically accurate, misses almost everything that makes the product interesting and almost everything that matters for security. Chainlink Functions is better understood as a decentralized serverless compute platform: arbitrary JavaScript runs across every node in a DON, each node executes independently, OCR aggregates the results, and the aggregated output gets delivered back to the consumer contract through a verified callback. The "API call" is just one of the things that JavaScript can do inside that environment. The DON consensus and the threshold-encrypted secrets model are what make it meaningfully different from a centralized API proxy. This is day 9 of the 28-day Chainlink architecture series. Today covers the full request lifecycle, every contract in the chain, how threshold encryption protects secrets without exposing them to any individual node, and the integration mistakes that come from misunderstanding how billing and callbacks actually work. The four contracts you need to understand Before tracing the full lifecycle, it helps to know exactly which contract does what. FunctionsRouter : the stable, immutable entry point for consumers. Manages subscriptions and authorized consumer contracts. Its interface doesn't change when the underlying implementation upgrades, consumer contracts call sendRequest here and only here. Also handles billing: estimates fulfillment cost at request time and finalizes it at response time. FunctionsCoordinator : the interface between the Router and the DON. Emits the OracleRequest event that DON nodes watch for. Handles fee distribution to transmitters via a fee pool. Inherits from OCR2Base , meaning the full OCR consensus machinery runs here. This contract can be upgraded independently of the Router, which is why the Router exists as a stable facade in fro

디지털 자산과 인공지능 분야는 핵심 기술은 다르지만, 데이터의 진실성, 규제 체계, 지정학적 함의에 대한 공통된 도전에 직면하며 점차 수렴하고 있다. 최근 일련의 사건들은 탈중앙화와 첨단 연산이 약속하는 미래가 인간의 행동, 경제적 유인, 그리고 국가적 목표라는 현실과 충돌하는 중요한 변곡점을 보여준다. 제재 대상 러시아 스테이블코인의 논란 많은 거래량 주장부터 전 미국 대통령이 약세장 속에서 거둔 전례 없는 암호화폐 수익, 그리고 선두 AI 모델을 둘러싼 당혹스러운 "너프(성능 저하)" 논쟁에 이르기까지, 이 모든 이야기는 혁신과 불투명성이 난무하는 디지털 최전선의 모습을 생생하게 그려낸다. 이 글은 겉으로는 서로 달라 보이는 이러한 현상들을 깊이 파고들어, 그 기저의 메커니즘, 기술적 복잡성, 그리고 글로벌 디지털 경제에 미치는 광범위한 영향을 탐색하고자 한다. 우리는 블록체인 분석이 불법 금융 활동 주장에 어떻게 도전하는지, 정치인들이 신생 산업에 관여하며 제기하는 윤리적 및 규제적 난제는 무엇인지, 그리고 복잡한 AI 시스템을 평가하는 미묘한 기술적 문제들을 살펴볼 것이다. 이러한 분석들을 관통하는 공통적인 실마리는 바로 강력한 검증, 투명한 거버넌스, 그리고 정교한 이해가 필수적이라는 점이다. 정보가 쉽게 조작될 수 있고, 진정한 효용성이 복잡성이나 전략적 오도 뒤에 가려지기 쉬운 생태계를 헤쳐나가기 위해서 말이다. 디지털 자산과 AI가 금융, 거버넌스, 그리고 일상생활을 계속해서 재편하는 가운데, 부풀려진 지표 속에서 진정한 활동을, 시스템적 결함 속에서 실제 역량을 식별하는 능력은 투자자, 정책 입안자, 기술자 모두에게 더없이 중요해지고 있다. 지난 10년간 암호화폐와 인공지능 분야는 폭발적인 성장을 거듭하며 각각 변혁적인 잠재력을 제시하는 동시에 새로운 도전 과제들을 안겨줬다. 예를 들어, 스테이블코인은 본래 암호화폐 시장의 변동성을 완화하기 위해 법정화폐나 다른 자산에 가치를 고정하도록 고안되었으나, 글로벌 디지털 금융 인프라의 핵심 구성 요소로 진화했다. 특히 엄격한 금융 제재를 받는 지역에서 국경 간 결제를 촉진하는 그들의 유용성은 양날의 검이 되어, 합법적인 사용자뿐 아니라 전통적인 금융 통제를 우회하려는 이들까지 끌어들이고 있다. 2022년 이후의 지정학적 환경은 경제 제재에 대한 초점을 더욱 강화했고, 제재 대상 기업들은 디지털 자산이 제공하는 대안적 금융 경로를 모색하게 되었다. 동시에 디지털 자산의 주류 금융 및 정치권으로의 통합은 가속화됐다. 한때 틈새 기술적 호기심에 불과했던 암호화폐는 이제 상당한 경제적 힘으로 자리 잡았고, 기관 투자뿐만 아니라 최근 공개된 바와 같이 유명 인사들에게도 막대한 개인 자산을 안겨주고 있다. 이러한 주류화는 필연적으로 암호화폐를 국가 규제 기관의 감시 아래 놓이게 하며, 업계의 종종 자유지상주의적 정신과 국가의 감독, 과세, 소비자 보호 요구 사이에서 긴장을 유발한다. 특히 규제 환경이 아직 형성되는 단계에서 정치인들이 이 신흥 부문에 관여하는 것은 이해 상충과 공직 내 개인적 금전 이득의 윤리적 경계에 대한 복잡한 질문들을 제기한다. 이러한 발전과 병행하여, 인공지능, 특히 대규모 언어 모델(LLM)은 불과 몇 년 전에는 상상할 수 없었던 능력을 보여주며 빠르게 발전했다. 그러나 종종 "블랙박스"처럼 작동하는 이 모델들의 복잡성은 평가, 제어, 그리고 윤리적 배포를 보장하는 데 상당한 난관을 초래한다. "너프" 또는 성능 저하를 둘러싼 논쟁은 AI 시스템의 진정한 능력을 벤치마킹하고 이해하는 데 내재된 어려움을 강조한다. 특히 안전 분류기와 같은 내부 아키텍처 구성 요소가 관찰되는 동작을 크게 바꿀 수 있기 때문이다. 제재 회피, 암호화폐의 정치경제, AI 모델 평가라는 이 세 가지 독특하지만 서로 연결된 서사는 점점 더 디지털화되고 알고리즘에 의해 움직이는 세상에서 투명성, 책임성, 그리고 정확한 평가를 위한 광범위한 노력을 강조한다. 최근의 뉴스들은 디지털 자산과 AI 생태계에 내재된 기술적 복잡성과 분석적 도전 과제들을 심층적으로 보여준다. 제

leetcode.com Problem Statement Given the root of a binary tree, return its preorder traversal. Preorder Traversal follows: Root ↓ Left ↓ Right Brute Force Intuition In an interview, you can explain it like this: Visit the current node first, then recursively traverse the left subtree followed by the right subtree. Recursion naturally follows the preorder sequence. Complexity Time Complexity: O(N) Space Complexity: O(H) Where: N = Number of Nodes H = Height of Tree Recursive Code class Solution { public List < Integer > preorderTraversal ( TreeNode root ) { List < Integer > ans = new ArrayList <>(); preorder ( root , ans ); return ans ; } private void preorder ( TreeNode root , List < Integer > ans ) { if ( root == null ) return ; ans . add ( root . val ); preorder ( root . left , ans ); preorder ( root . right , ans ); } } Moving Towards the Optimal Iterative Approach Instead of recursion, we can use a stack. Since preorder visits: Root ↓ Left ↓ Right we should process the root immediately. To ensure the left subtree is processed first, push the right child before the left child . Pattern Recognition Whenever you see: Preorder Traversal Simulate Recursion Think: Stack Key Observation Stack follows: LIFO To visit: Left First push: Right First ↓ Left Second so that left is popped first. Optimal Java Solution class Solution { public List < Integer > preorderTraversal ( TreeNode root ) { List < Integer > ans = new ArrayList <>(); if ( root == null ) return ans ; Stack < TreeNode > st = new Stack <>(); st . push ( root ); while (! st . isEmpty ()) { TreeNode node = st . pop (); ans . add ( node . val ); if ( node . right != null ) st . push ( node . right ); if ( node . left != null ) st . push ( node . left ); } return ans ; } } Dry Run 1 / \ 2 3 / \ 4 5 Stack: 1 Visit: 1 Push: 3 2 Visit: 2 Push: 5 4 Traversal: 1 ↓ 2 ↓ 4 ↓ 5 ↓ 3 Answer: [1,2,4,5,3] Why Stack Works? A stack processes the most recently added node first. By pushing: Right Child ↓ Left Child the left child

When most people hear Bitcoin , the conversation usually starts with price. But for developers, Bitcoin is much more than a chart. Bitcoin is a distributed system operating without a central authority. It combines networking, cryptography, game theory, economics, and software engineering into a protocol that has remained operational for years while processing value globally. As a software developer, what fascinates me most is not speculation it’s the architecture. Some concepts every developer can appreciate: ⚡ Distributed Consensus Thousands of nodes independently verify the same rules without trusting each other. 🔐 Cryptography in Practice Digital signatures make ownership verifiable without revealing private keys. ⛏️ Proof of Work A mechanism that converts computation into security and coordination. 🌍 Open Source at Global Scale Anyone can inspect the code, run a node, contribute, or build on top of the ecosystem. 📦 Immutability Through Design Data integrity is achieved through incentives, validation rules, and chained blocks. Studying Bitcoin changes how you think about: System reliability Security models Network design Incentive structures Building software that survives failure Whether you plan to build in blockchain or not, Bitcoin is worth studying because it teaches principles that extend far beyond finance. Curious to hear from other developers: What concept in Bitcoin architecture changed the way you think about software systems?

You just got your hands on an AI agent. It writes code, researches things, sends emails, books meetings. You feel like you're holding a chainsaw. But you keep using it to cut butter. The problem nobody talks about The gap between what your agent knows and what it can do is almost always a paywall, a KYC wall, or an API key. Here's what 'just add one data source' actually looks like: Go to the site. Click pricing. Choose a plan. Enter your email. Wait for verification. Click the link. Set a password. Enable 2FA. Download an authenticator app. Scan the QR code. Enter the 6-digit code. Fill in your company name. Add a credit card. Agree to terms. Find the API section. Generate a key. Copy it. Paste it into your code. Realize your agent doesn't know how to use it. Write a wrapper. Test it. Hit the rate limit. Add retry logic. That's one data source . Some workflows need ten. What x402 actually does Your agent hits an endpoint, gets a 402 (Payment Required) response with payment terms, pays a fraction of a cent in USDC or sats, gets the data back. No accounts. No API keys. No subscriptions. No puzzles. No humans in the loop. The concrete version Competitor research workflow: POST /company-info {"domain": "competitor.com"} -- $0.03 Returns: industry, HQ, headcount range, tech stack, social links POST /github-user {"username": "their-cto"} -- $0.002 Returns: repos, commit frequency, stars, languages, last active POST /dns-lookup {"domain": "competitor.com", "type": "MX"} -- $0.001 Returns: mail provider Full competitor profile: under $0.04. Under 3 seconds. Lead enrichment on 500 domains: under $20, done overnight, zero human hours. Setup (one system prompt line) Get a free key first (no wallet, no email): curl -X POST https://api.ideafactorylab.org/proxy/keygen Returns your key and an agent-ready prompt. Then tell your agent: You have a Cinderwright key. POST to https://api.ideafactorylab.org/proxy/do with header X-CW-Key and body {"task": "describe what you need in plain

How to Decompose a Legacy Java Monolith Without Disrupting Business Operations The Java monolithic applications have been supporting businesses for years. In these applications, the entire business logic, presentation layer, and data access layer are bundled into a single unit. These architectures are functional but hard to scale, maintain, and improve due to changing business needs. An expert Java app development company helps growing organizations in addressing this issue through Java modernization services. Instead of developing a whole software application from scratch, firms can transform their software in stages with the right boundaries. The biggest challenge here is to determine where to make those cuts in a bundle. Poorly chosen service boundaries create operational complexity issues and long-term maintenance problems. Understanding how to identify seams in the monolith application helps in achieving modernization successfully. Let's take a look at what contributes to the success of monolith decomposing and how organizations can approach it wisely. Why Organizations Are Modernizing Legacy Java Monoliths The legacy Java monolith applications were built during a time when monolithic architecture was common. They were optimized for easy deployment and centralized management. But today, businesses require flexibility. This is due to challenges such as Slow release cycles Increasing maintenance costs Limited scalability Complex dependency management Difficult onboarding new developers Growing technical debt These issues have increased the demand for software architecture modernization in business sectors. Modern architecture gives the following advantages to the teams: Deploy features independently Scale services individually Improve system resilience Accelerate development cycles Support cloud-native environments The objective of architecture modernization is to create a technical foundation that supports future business growth. Understanding business goals of

After a full week of intensive Bitcoin programming training, the developers at Zone01 Kisumu moved into the most exciting phase of the bootcamp: building real-world solutions powered by Bitcoin, the Lightning Network, and LND. One thing I learned throughout the experience is that the human mind is truly fascinating. The room was filled with innovative ideas, each attempting to solve a different problem. As the saying goes, no idea is a bad idea—every concept had the potential to make an impact. A total of 17 teams were formed, and each team embarked on a 24-hour hackathon journey to transform their ideas into working products. After an intense day of development came the presentation phase, where we had the privilege of showcasing what we had built. Our team developed Kasi , a WhatsApp chatbot that enables Bitcoin transactions directly through WhatsApp. The goal was to make Bitcoin payments more accessible by leveraging a platform that millions of people already use daily. To build Kasi, we integrated the Twilio API for WhatsApp communication and utilized the Bitnob platform to facilitate Bitcoin transactions. Python was used throughout the development process. The project was brought to life by six developers: Claire, Lamka, Ijay, Dishon, Talo, and myself. Beyond the technical implementation, the hackathon strengthened our understanding of collaborative software development. We practiced Git workflows, team coordination, version control, task management, and effective communication under tight deadlines—skills that are just as valuable as writing code. Although we did not finish at the top of the leaderboard, the experience was incredibly rewarding. Every team brought something unique to the table, and the winners fully deserved their recognition. Congratulations to all the teams that participated and showcased their creativity, determination, and technical skills. One moment from the presentation will stay with me for a long time. As we were demonstrating Kasi to

A smart contract does not need an overflow, reentrancy bug, or broken access-control check to lose money. Sometimes, the exploit is hidden inside an ordinary division: uint256 result = amount * rate / SCALE; The expression looks harmless. It may even produce the expected answer in every unit test. But financial smart contracts operate with integer arithmetic. Fractions are discarded, rounding direction changes who receives value, and an error of one unit can be repeated across thousands of transactions. In a financial protocol, rounding is not merely a mathematical implementation detail. Rounding is a value-transfer policy. Every division should therefore answer three questions: Which direction does the calculation round? Which party benefits from that direction? Can the rounding advantage be repeated or amplified? This article examines the most dangerous precision problems in Solidity and the engineering patterns used to prevent them. Solidity Does Not Have Native Fixed-Point Arithmetic Most financial formulas use fractions: interest = principal × rate × time fee = amount × fee percentage shares = assets × total shares ÷ total assets collateral value = token amount × oracle price Solidity primarily performs these calculations with integers. For unsigned integers: uint256 result = 5 / 2; The result is: 2 The fractional component is discarded. For positive values, this behaves like rounding down: 2.5 → 2 This appears insignificant until the result represents: vault shares; debt; collateral; protocol fees; interest; rewards; liquidation bonuses; exchange rates; token prices. The lost fraction does not disappear economically. One party receives less value, while another party retains the remainder. Precision Loss Is Not Always Small Consider a protocol calculating a percentage: function calculateFee( uint256 amount, uint256 feeBps ) public pure returns (uint256) { return amount * feeBps / 10_000; } For a 0.3% fee: amount = 100 feeBps = 30 fee = 100 × 30 ÷ 10,000 fee =

The crypto platform claims you can “pay anyone to do anything,” from quitting a job on camera to getting a memecoin-themed tattoo. But it mostly seems like people trying to scam each other.

You've heard of Bitcoin. You've maybe heard of the Lightning Network. But what exactly is LND, and why should developers care? Let's break it down — technically, but from the ground up. The Problem: Bitcoin is Superb but Slow Bitcoin's base layer — the blockchain itself — is intentionally slow. Every transaction must be broadcast to thousands of nodes, verified, and bundled into a block that gets mined roughly every 10 minutes . The network handles about 7 transactions per second (TPS). Compare that to Visa's ~24,000 TPS and you quickly see the problem. Bitcoin in its raw form isn't built for buying coffee, splitting a bill, or paying a freelancer in real time. But there's a solution — and it lives on top of Bitcoin. Enter the Lightning Network The Lightning Network is a Layer 2 (L2) payment protocol built on top of Bitcoin. Instead of recording every single payment on the blockchain, it lets two parties open a private payment channel, transact off-chain as many times as they want, and only settle the final balance on-chain when they're done. Think of it like running a tab at a bar: Opening the tab = one blockchain transaction Each round of drinks = instant off-chain payment Closing the tab = one final blockchain transaction The result? Near-instant payments, near-zero fees, and massive throughput — without sacrificing Bitcoin's security. What is LND ? LND stands for Lightning Network Daemon. It's the most widely used implementation of the Lightning Network protocol, built and maintained by Lightning Labs. Key facts for developers: Written in Go 🐹 Exposes a gRPC API (port 10009) and a REST API (port 8080) Controlled via a CLI called lncli Uses macaroons for authentication (think JWT, but for Lightning) Connects to a Bitcoin node (bitcoind or btcd) as its source of truth Other Lightning implementations exist — like Core Lightning (CLN) and Eclair — but LND has the largest developer ecosystem and is the best entry point. How LND Fits Into the Stack Here's the architec

After failing 3 coding interviews, I realized the problem wasn't practice it was how I was practicing. I spent 6 months grinding LeetCode before my first FAANG interview. 400+ problems solved. Every "Blind 75" problem is memorized. I felt ready. Then the interviewer asked a sliding window variation I hadn't seen before. I froze. Drew a blank. Bombed the interview. The problem wasn't that I hadn't practiced enough. The problem was that I had practiced incorrectly. I memorized solutions instead of understanding patterns. I can recite code, but I struggle to adapt when problems change slightly. So I built something different. Introducing AlgoPatterns A pattern-first DSA learning platform with visualizations that actually show you how algorithms work. algopatterns.in What Makes It Different 1. Pattern-First, Not Problem-First Most platforms throw 2000+ problems at you and say, "Good Luck." AlgoPatterns organizes everything around 17 core patterns: Two Pointers Sliding Window Binary Search BFS/DFS Dynamic Programming Backtracking And 11 more... Master the patterns, and you can solve any variation. 2. Visualizations That Actually Help We have 50+ interactive visualizers that show algorithms step-by-step: Watch two pointers converge in real-time See the DP table fill cell by cell Trace BFS spreading level by level Visualize the call stack during recursion Reading code is one thing. Seeing it executed is completely different. 3. Curated, Not Overwhelming 315 hand-picked problems organized by pattern. Each problem includes: Company tags (Google, Amazon, Meta, etc.) Frequency indicators Pattern classification Difficulty rating No more random grinding. Practice the right problems in the right order. 4. Real Code Templates Every pattern comes with: Java templates (copy-paste ready) "When to use" indicators Common mistakes to avoid Key insights from each pattern Who It's For Interview preppers who want to learn patterns, not memorize solutions CS students who find textbook expla

Token economics is not only about token price. It is about designing the rules, incentives, and long-term logic of a Web3 ecosystem. When people start building a Web3 project, they usually focus on the visible parts first. They think about the smart contract, the frontend, the wallet connection, the token launch, the whitepaper, and maybe the community. All of those are important. But there is one part that can decide whether the project survives or fails: Token economics. A project can have clean smart contracts, a nice UI, and strong marketing, but if the token economy is weak, the project can slowly collapse. Users may come only for rewards, early investors may dump, inflation may destroy value, and the token may lose its reason to exist. That is why token economics should not be treated as just a “crypto finance” topic. For developers and Web3 builders, token economics is closer to system design . It defines how value moves inside the ecosystem, how users are rewarded, how supply is controlled, how governance works, and how the project can grow without depending only on hype. What Is Token Economics? Token economics, often called tokenomics , means the design of how a token works inside a project. It answers questions like: Why does this token exist? Who receives the token? How is the token used? How many tokens will exist? How are rewards distributed? When can team and investor tokens unlock? How does the project treasury work? What creates real demand for the token? In simple words, token economics is the rule system behind a token. A token is not only something people buy and sell. In a real Web3 product, a token can be used for payments, staking, governance, access, rewards, collateral, or network fees. If the token has no clear role, it becomes only a speculative asset. That is dangerous because speculation can bring attention, but it cannot support a project forever. Why Developers Should Care Some developers think token economics is only for founders, eco

Dulu, nyari query yang bikin database spike itu bisa makan berhari-hari. Yang nyari capek, yang nge-fix juga capek. Sekarang? Hitungan jam — dan bonusnya, sambil belajar hal baru juga. Ceritanya begini. Kalau kamu pernah kerja di aplikasi yang pakai ORM (Object-Relational Mapping — semacam "penerjemah otomatis" antara code dan database), pasti familiar sama situasi ini: database tiba-tiba lambat, kamu dapet raw query yang jadi biang kerok, tapi di codebase kamu nulis pakai syntax ORM yang bentuknya beda jauh dari SQL mentah itu. Buat yang belum pernah deal sama ORM, bayangin gini: kamu nulis pesan dalam bahasa Indonesia, lalu ada "penerjemah otomatis" yang convert jadi bahasa Jepang sebelum dikirim ke penerima. Suatu hari ada masalah di pesan yang terkirim — tapi kamu cuma bisa lihat versi bahasa Jepang-nya. Nyari bagian mana dari tulisan Indonesia kamu yang bikin terjemahan-nya bermasalah? Itu effort-nya yang bikin pengen balik tidur aja. Sekarang dengan bantuan Kiro, cukup kasih raw query + akses ke codebase, dia otomatis nyari bagian mana di code yang nge-generate query bermasalah itu. Yang dulu butuh berhari-hari, sekarang bisa selesai dalam hitungan jam — dan itu baru tahap investigasi, belum termasuk fixing-nya. Ceritanya Kenapa Bisa Pakai Kiro Akhir-akhir ini lagi aktif pakai Kiro di tempat kerja. Awal tahun lalu kantor dapat credits melalui program Kiro for Startup , jadi ya sekalian dimaksimalkan. Selain buat debug dan explore query di MS SQL Server, kadang pakai Kiro juga buat analisa log AWS CloudWatch — sambil kasih context aplikasi yang running biar analisa-nya lebih akurat dan gak generic. Di tulisan kali ini, saya mau sharing gimana pakai Kiro sebagai partner beberapa minggu terakhir buat improve query performance di aplikasi .NET Core. Kenapa "partner"? Karena Kiro-nya gak boleh langsung akses ke database — jadi wajib melalui perantara saya. Kita discuss, kolaborasi, dan nge-solve bareng. Bukan AI yang dikasih tombol terus disuruh jalan sendiri. Wakt