标签：#TC

Shinkei makes a refrigerator-sized robot called Poseidon to kill fish quickly and humanely.

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.

DNC removal requests shouldn't take more than a few seconds to process. If your ops team is manually logging into each system, finding the number, and removing it one platform at a time, every request is an open compliance window. Here's how to close it automatically. The Problem With Manual DNC Processing A number comes in flagged for removal. Someone on the floor submits it. If you're running Convoso alongside Zoom Contact Center, Zoom Phone, and Telesero, that means logging into each system separately — find the number, remove it, move to the next platform, repeat. At multiple removal requests per week across several systems, you're looking at significant manual work each week. More importantly, every minute between the request and the removal is a minute of active compliance exposure. A TCPA violation starts at $500 per call. When the pattern is systematic — a number that should have been removed staying active across multiple campaigns — class action exposure enters the picture. The gap between when a removal is requested and when it actually completes isn't just inefficiency. It's risk that compounds with every dial attempt on a number that should be off the list. How Automated DNC Removal Works The automated version uses a Slack slash command as the intake point. An ops manager types the number into a command and hits send. The request routes immediately to a cloud service — deployed on Google Cloud Run — that fans out across every active system in parallel. Not sequentially. Simultaneously. In a contact center running multiple Convoso campaigns alongside Zoom Contact Center, Zoom Phone, and Telesero, a single command hits every platform in parallel. Each system processes the removal independently. Results log to cloud storage with a timestamp and each system's individual response recorded separately. A confirmation returns to the Slack channel before the manager has switched back to their next task. Wall-clock time from submission to confirmed removal across

The Breville Barista Express and Ninja Luxe Cafe Pro are two of the best early Prime Day deals I’ve seen in 2026.

About 47% of singles look negatively at the use of AI in dating -- but, many dating app users are open to AI helping with profile punch-ups and conversation starters.

AWS is in talks to sell its chips to other data centers. CEO Andy Jassy has said this represents a $50 billion opportunity for the company.

We are thrilled to announce the winners of the Hermes Agent Challenge! Over the past few weeks, the...

Chi-Hua Chien has spent more than two decades as a venture capitalist, but he thinks like a cultural anthropologist.

A new FTC lawsuit reveals how sophisticated subscription app operators can allegedly use shell companies and payment infrastructure to stay active on app stores despite mounting consumer complaints.

World models are the next big thing in AI beyond LLMs and, with this round, Odyssey has cemented itself as one of the startups to watch.

Pinterest has launched 'Ask Pinterest,' an experimental AI-powered shopping app that lets users seek recommendations and inspiration through a conversational interface.

Anthropic's popularity with business users is growing so well that the latest beef with the government might actually boost it, data from Ramp suggests.

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

What makes this combustible: at the very moment that tens of thousands of workers are being shown the door, a small cohort of AI insiders is becoming wealthy on a scale that's hard to comprehend.

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

The old-fashioned drip coffee maker has come a long way. These impressive machines can turn your barista into a stranger.

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

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

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