AI 资讯

Spain's mobile networks will need to stay live for at least four hours during power outages, per new rules.

The company said it is discontinuing its email inbox in favor of its AI agent offering as users are increasingly handing over the reins of their email to the agents.

Notion is "going all in on using agents to run your inbox."

Table of Contents Overview Southwest and AWS From On-Prem to Cloud AI Comes Into Play Kiro Why This Matters? Closing Thoughts Overview Southwest Airlines is one of the largest carriers in the world. Other than having by far my absolute favorite airplane livery, it's a massive enterprise based in Dallas, Texas, with more than 72,000 employees, over 4,000 daily flights during peak travel periods, roughly 134 million customers annually, and service across 120+ airports in 12 countries. At this scale, what really matters is operational reliability and speed. Because in aviation, slow operations cause delays, delays cause unhappy customers, and unhappy customers aren't particularly great for the company's revenue. A few minutes of latency in one system can turn into hours of disruption in the real world. So... can cloud and AI solve it? Absolutely, if done right. Southwest and AWS Southwest has selected Amazon Web Services (AWS) as its primary cloud partner to help modernize its technology stack and transform how the airline runs, develops systems, and serves its customers. Through this collaboration, Southwest plans to move away from a predominantly on-premises infrastructure toward a cloud-based, AI and agent-enabled architecture on AWS by 2028 . 2028 is not far away from now (Jun 25, 2026). This is very ambitious considering the amount of work that needs to be done. From On-Prem to Cloud Moving an enterprise of this size from on-prem infrastructure to the cloud is way more complex than it sounds. It doesn't sound easy either. It's one of the hardest things you can ever do as a software engineer, DevOps engineer, architect, engineering manager, or anyone else involved in it. It involves a lot of steps, including but definitely not limited to: Assessing existing systems, dependencies, and infrastructure to understand what needs to move and how Defining a migration strategy (lift-and-shift, replatforming, or full refactoring to cloud-native architecture) Designing and bu

The problem Just saying "hi" to Claude Code costs ~31,000 tokens . I was paying $500+/month in API costs and had no idea where the tokens were going. So I built tokenwise — a free CLI that shows exactly where your AI coding agent wastes tokens. What it does tokenwise audit — Scan your instruction files It scans your CLAUDE.md, AGENTS.md, and rules files, then shows: How many tokens each file costs per message Boilerplate the AI already knows ("Always write clean code") ALL-CAPS emphasis that doesn't help (NEVER, ALWAYS, MUST) Duplicate sections Unscoped rules that load when they shouldn't tokenwise scan — Analyze your sessions It reads your latest session logs and shows: Token breakdown by component (system prompt, history, tool output) Cache hit rate Top 3 "token hogs" with actionable tips Monthly cost projection The key insight Most people try to compress context (which makes the AI dumber). tokenwise measures first, then applies safe fixes. You can't optimize what you can't measure. Quick start npx @davizin713/tokenwise audit npx @davizin713/tokenwise scan Zero API calls. Zero LLM inference. 100% local. Free forever. Works with 11 agents Claude Code, OpenCode, Cursor, Aider, Cline, Codex CLI, Goose, Continue.dev, Windsurf, Augment, and Kilocode. Links GitHub: github.com/davi713albano-coder/tokenwise npm: npmjs.com/package/@davizin713/tokenwise Built with TypeScript / Node.js js-tiktoken for token counting sql.js for reading OpenCode sessions MIT licensed If you use Claude Code or any AI coding agent, try it and let me know what you think! ⭐

It took 20 years, but the Finance app arrives just in time to be packed full of AI.

As developers, data engineers, or analysts, we’ve all been there: you download a massive database export, a logging stack dump, or a transaction archive, only to find it's a multi-gigabyte JSON file. You try to import it into a spreadsheet or run it through a standard online converter, and boom—your browser tab freezes, crashes, or shows the dreaded "Out of Memory" screen. Even worse, if you try to use standard cloud-based online tools, you might have to wait for a 500MB upload to complete, only to hit a rigid file-size cap or, worse, compromise sensitive data privacy by uploading corporate logs or database records to a third-party server. In this guide, we will explore: Why large JSON files crash standard parsers (the V8 heap limit problem). How streaming architectures solve this by reading data chunk-by-chunk. NDJSON (JSON Lines) vs. JSON Arrays and how to stream them. A browser-native, 100% offline tool to convert large JSON to CSV instantly: Parsify's Large JSON Stream Converter . How to implement your own basic browser-based JSON streaming parser in JavaScript. 1. The Anatomy of a Memory Crash (Why JSON.parse Fails) If you are using JavaScript or Node.js, the simplest way to read and parse a JSON file is to load the file into memory and run JSON.parse(). const fs = require ( ' fs ' ); // Naive approach: Will crash on a 1GB+ file fs . readFile ( ' database-dump.json ' , ' utf8 ' , ( err , data ) => { if ( err ) throw err ; // POINT OF FAILURE: V8 Heap Out of Memory const records = JSON . parse ( data ); records . forEach ( record => { // Process record... }); }); This works fine for small config files. But once your JSON file reaches 100MB, 500MB, or 1GB+, this approach is guaranteed to trigger a fatal crash: FATAL ERROR: Ineffective mark-compacts near heap limit Allocation failed - JavaScript heap out of memory Why does this happen? The String Duplication Overhead: When you load a 1GB file into memory, you first allocate ~1GB of RAM for the raw text string. The

Creating Short Links with PHP: A Practical Guide URL shorteners are everywhere. They're used in marketing campaigns, email newsletters, QR codes, social media posts, affiliate links, and analytics platforms. While most developers are familiar with services like Bitly, integrating a URL shortener directly into your application is often much more useful. In this article, we'll build short links from PHP using an API. Why Create Short Links Programmatically? Creating links through a dashboard works for occasional usage. But applications often need to generate links automatically. Common examples include: Email campaigns User invitations Affiliate systems QR code generation Marketing automation Analytics tracking Customer portals An API allows applications to create and manage links without human interaction. The Traditional HTTP Approach Most URL shortener APIs work through simple HTTP requests. For example: $client = new GuzzleHttp\Client (); $response = $client -> post ( 'https://example.com/api/links' , [ 'headers' => [ 'X-Api-Key' => $apiKey , 'Content-Type' => 'application/json' , ], 'json' => [ 'url' => 'https://example.com/article' ] ] ); $data = json_decode ( $response -> getBody (), true ); echo $data [ 'short_url' ]; This works. But once your application creates dozens or hundreds of links, the amount of boilerplate code starts growing. Using a PHP SDK A PHP SDK removes most of the repetitive work. Installation is usually straightforward: composer require lix-url/php-sdk Creating a link becomes much simpler: $link = $client -> links () -> create ([ 'url' => 'https://example.com/article' ]); echo $link -> shortUrl ; The SDK handles: Authentication HTTP requests Response parsing Error handling DTO mapping This allows your application code to remain clean. Creating Your First Short Link Let's imagine an application that sends invitation emails. $inviteLink = $client -> links () -> create ([ 'url' => 'https://myapp.com/invite/abc123' ]); echo $inviteLink -> short

Provisioning a Bedrock RAG knowledge base with S3 Vectors, without the hallucinated API calls. If you've asked an AI coding agent to set up AWS, you've seen it confidently invent a parameter, reach for a deprecated service, or burn ten minutes retrying against a service it never saw in training. The failure mode that bites hardest is the silent one: the agent thinks it succeeded, and you find out an hour later. I hit two of these while standing up the retrieval layer for a LangGraph support bot, an Amazon Bedrock Knowledge Base backed by Amazon S3 Vectors. I'd love to say I caught both with deep AWS expertise. I caught them because the Agent Toolkit for AWS read the docs I hadn't. Both would have shipped, and neither did. The 30-second setup The goal: take a folder of markdown product docs and make them queryable by meaning, so an agent can answer "is this safe for color-treated hair?" from the real docs instead of guessing. Think of it as giving the agent a library it can search instead of making things up. That's the retrieval half of RAG, the foundation a LangGraph agent will later call as a tool. Four moving parts, wrapped in one managed service: Source bucket : an S3 bucket holding the docs. Embeddings : Amazon Titan Text Embeddings V2 (1024-dim vectors). Vector store : Amazon S3 Vectors. I chose it over OpenSearch Serverless because it has no always-on compute, the difference between cents and a monthly surprise for a demo that sits idle. Knowledge Base : Amazon Bedrock Knowledge Bases ties it together into one thing you can query with a retrieve call. To follow along, you need an AWS account, a non-root IAM identity with credentials configured locally, uv installed, and the toolkit installed in your agent. The fastest path across Kiro, Claude Code, Cursor, and Codex is the AWS CLI installer, aws configure agent-toolkit ; in Kiro you can instead add the AWS MCP Server to .kiro/settings/mcp.json (pin the mcp-proxy-for-aws version) and run npx skills add aws/age

Disclosur: Ini dari tim Nexotao — saya bahas gateway kami sendiri di bawah. Saya jaga sebatas fakta yang bisa kamu cek sendiri: semua nama model, context window, dan harga ada di halaman pricing kami, dan saya kasih linknya. Kalau kamu developer di Indonesia, kemungkinan besar pernah kejedot ini: API OpenAI dan Anthropic minta kartu kredit luar negeri . Nggak punya kartu, nggak bisa pakai API. Banyak dari kita mentok di situ. Solusi yang jalan sekarang: gateway lokal yang nerima QRIS / Rupiah . Ini versi jujurnya — gimana cara kerjanya, berapa biayanya, dan apa yang belum bisa. Dua model live, satu API yang kompatibel Lewat Nexotao kamu pakai dua model teks: Claude Opus 4.8 ( claude-opus-4-8 ) — context window 350.000 token DeepSeek-V4-Pro — context window 131.072 token Itu angka context window yang dipublikasikan apa adanya — tanpa pemotongan diam-diam. Endpoint-nya kompatibel dengan OpenAI dan Anthropic , jadi biasanya cukup ganti base URL sama key-nya. Format OpenAI: from openai import OpenAI client = OpenAI ( base_url = " https://api.nexotao.com/v1 " , api_key = " sk-nexo-... " ) resp = client . chat . completions . create ( model = " claude-opus-4-8 " , messages = [{ " role " : " user " , " content " : " Halo " }], ) print ( resp . choices [ 0 ]. message . content ) Format Anthropic: curl https://api.nexotao.com/v1/messages \ -H "x-api-key: sk-nexo-..." \ -H "anthropic-version: 2023-06-01" \ -H "Content-Type: application/json" \ -d '{"model":"claude-opus-4-8","max_tokens":256, "messages":[{"role":"user","content":"Halo"}]}' Cara bayarnya Top up saldo Rupiah via QRIS , mulai Rp10.000 . Tanpa kartu luar negeri. Bayar sesuai pakai — dipotong per token. Tanpa langganan , dan saldo nggak hangus. Tiap response ada header X-Cost-Rp , jadi kamu lihat biaya rupiah persis tiap request. Berapa biayanya Saat tulisan ini dibuat, Claude Opus 4.8 lewat gateway sekitar 70% lebih murah dari harga retail resmi (input) — tapi jangan percaya saya gitu aja. Halaman perbandingan har

Disclosure: This is the Nexotao team — I'm describing our own gateway below. I've kept it to facts you can verify yourself: every model name, context window, and price here is on our live pricing page, and I link it. If you're an Indonesian developer, you've probably hit this wall: the OpenAI and Anthropic APIs want a foreign credit card . No card, no API. A lot of us get stuck right there. The fix that works today: a local gateway that takes QRIS / Rupiah . Here's the honest version of how it works, what it costs, and what it doesn't do. Two live models, one compatible API Through Nexotao you call two text models: Claude Opus 4.8 ( claude-opus-4-8 ) — context window 350,000 tokens DeepSeek-V4-Pro — context window 131,072 tokens Those are the real, published context windows — no silent truncation. The endpoint is OpenAI- and Anthropic-compatible , so you usually just change the base URL and key. OpenAI format: from openai import OpenAI client = OpenAI ( base_url = " https://api.nexotao.com/v1 " , api_key = " sk-nexo-... " ) resp = client . chat . completions . create ( model = " claude-opus-4-8 " , messages = [{ " role " : " user " , " content " : " Hello " }], ) print ( resp . choices [ 0 ]. message . content ) Anthropic format: curl https://api.nexotao.com/v1/messages \ -H "x-api-key: sk-nexo-..." \ -H "anthropic-version: 2023-06-01" \ -H "Content-Type: application/json" \ -d '{"model":"claude-opus-4-8","max_tokens":256, "messages":[{"role":"user","content":"Hello"}]}' How you pay Top up your Rupiah balance via QRIS , from Rp10,000 . No foreign card. Pay-as-you-go — deducted per token. No subscription , and the balance never expires. Every response carries an X-Cost-Rp header, so you see the exact rupiah cost of each request. What it costs At the time of writing, Claude Opus 4.8 runs roughly 70% below official retail input pricing through the gateway — but don't take my word for it. The comparison page shows live per-token rates and computes "vs official" automati

Originally posted on https://symfonycasts.com/blog/honest-entities Your Doctrine entities are lying to you! For years, the standard way to build Doctrine entities in Symfony has looked something like this (and it's still what MakerBundle generates today): #[ORM\Entity] class ConferenceTalk { #[ORM\Id] #[ORM\GeneratedValue] #[ORM\Column] private ?int $id = null ; #[Assert\NotBlank] #[ORM\Column(length: 255)] private ?string $title = null ; #[ORM\Column(type: Types::TEXT, nullable: true)] private ?string $abstract = null ; public function getId (): ?int { return $this -> id ; } public function getTitle (): ?string { return $this -> title ; } public function setTitle ( ?string $title ): static { $this -> title = $title ; return $this ; } public function getAbstract (): ?string { return $this -> abstract ; } public function setAbstract ( ?string $abstract ): static { $this -> abstract = $abstract ; return $this ; } } At first glance, this looks perfectly reasonable. The title field is required. We know that because it has a NotBlank constraint and the database column is not nullable. But look closer. private ?string $title = null ; public function setTitle ( ?string $title ): static public function getTitle (): ?string According to the PHP type system, the title is optional. In fact, the public API of this class explicitly allows us to set it to null . That means this is perfectly valid: $talk = new ConferenceTalk (); And so is this: $talk = new ConferenceTalk (); $talk -> setTitle ( null ); Both objects represent a conference talk that can never be successfully persisted. Eventually, Doctrine catches the problem: $talk = new ConferenceTalk (); $entityManager -> persist ( $talk ); $entityManager -> flush (); // boom! SQLSTATE[23000]: Integrity constraint violation: 1048 Column 'title' cannot be null The database knows that a conference talk must have a title. Our PHP code does not. So why do we build entities this way? Historically, this pattern was optimized for simpli

This is a complete, copy‑pasteable guide for shipping a backend app to a single Linux server using Docker Compose , with a GitHub Actions pipeline that builds the image, scans it, and deploys it over SSH. It is written to be language- and framework-agnostic . The examples use a Node/TypeScript API with PostgreSQL, Redis, and a background worker, but the same shape works for Python/Django, Go, Java/Spring, Ruby, etc. Anywhere you see your-app , your-org , your-server-ip , or example.com , substitute your own values. Every file is included in full, and every non-obvious line is explained. The last section — Common errors and how to fix them — is the part most guides skip, and it is the part that will actually save your afternoon. All of it comes from a real deployment, mistakes included. 1. The mental model (read this first) Before any YAML, understand the shape of what we're building. There are only three places anything lives: Your Git repository the single source of truth. Your code, your Dockerfile , your docker-compose.prod.yml , and your CI/CD workflows all live here. You only ever edit things here. A container registry (we use GHCR, GitHub's built-in registry) — a warehouse for the built application image. CI builds the image and pushes it here. Your server (a plain Linux VPS) pulls the image from the registry and runs it. It holds exactly two files: the compose file (copied from your repo by the pipeline) and a secrets file ( .env ) that never leaves the server. The flow, end to end: You push to main │ ▼ GitHub Actions: build image ──► push to registry ──► scan image │ ▼ GitHub Actions: SSH to server ──► pull image ──► run migrations ──► start app ──► health-check The single most important rule: the server is disposable . You never hand-edit files on the server, because the pipeline overwrites them from the repo on every deploy. If you fix something by editing on the server, the next deploy silently erases your fix. Edit in the repo, commit, push. (I learned t

Since the steam sale is live I wanted to post a Dev log on my personal project https://nextsteamgame.com/ sharing some outcomes from the web traffic and how I changed the project from the great feedback I got! I made a post about a month ago explaining how I made this opensource explainable search engine built around steam reviews to people find new video games, Not through Relevancy but through aspect based similarity. Check out the old post for a better explanation if you want! https://www.reddit.com/r/MachineLearning/comments/1tb8k3n/steam_recommender_using_similarity_undergraduate/ I wanted to say thank you to all the people of r/datascience and r/MachineLearning that gave me feedback and tried out my tool! I improved the UI/UX of the website to make the vectors more clear and controllable, I Implemented a thumbs up and down feature on recommendations to see if users even like the tool. I also wanted to share the after effects of promoting this tool on reddit! from the 2,652 searches I got in the website 913 of them resulted in steam clicks! the games that were discovered were all in a uniform distribution and did not share much of a pattern showing me that the engine did its job in helping people find niche games across all genres! (More images attached to post to see data viz) I wanted to disclose that I made this tool to not make any profit of some kind, but it does use posthog so I can collect diagnostics now. submitted by /u/Expensive-Ad8916 [link] [留言]

Apple may skip the M6 Pro and Max chips.

Throughout my career, transitioning between CTO roles and, more recently, focusing purely on distributed systems architecture and high-performance engineering, I've seen many architectural patterns rise and fall. But few have caused as much silent damage to company bottom lines as the premature adoption of microservices. Over the last decade, the industry bought into the idea that, in order to scale, you needed to split your system into dozens (or hundreds) of independent services. The practical result I find in most companies? The creation of the dreaded "Distributed Monolith." The Anatomy of Waste: Networks vs. Memory The hard truth we need to face with maturity is that microservices primarily solve problems of organizational scale (Conway's Law), not necessarily performance. If your engineering team isn't the size of Netflix or Uber, prematurely fragmenting your codebase is shooting yourself in the foot. Technically, what happens when we break down a monolith without the proper domain boundaries? We trade extremely fast and cheap local function calls (resolved in the processor's L1/L2 Cache) for slow and expensive network calls (TCP/IP). We start spending an absurd amount of computational time on constant JSON serialization and deserialization, and the AWS bill explodes with internal traffic costs (egress/ingress) between Availability Zones (AZs). You haven't scaled your application; you've merely added network latency and infrastructure complexity. The Return of the Modular Monolith True seniority in software engineering isn't about mastering the most complex architecture of the moment, but having the wisdom to know when not to use it. That's why the Modular Monolith has consolidated itself as the initial gold standard for new projects and restructurings. In a well-designed Modular Monolith (and languages with strong type systems and strict scope control, like Rust, shine absurdly well here), you maintain the logical separation of domains. Modules are independen

Twenty-five days on Hyperliquid. Sixty-five closed trades. P&L: -$9.21. Turns out that was the smallest wrong thing about it. The landing page showed -$7.72 because it uses a different P&L formula and excludes two open positions. Either number is small. Both numbers were also wrong about what they were telling me. I spent yesterday auditing every trade. The audit produced three findings I did not expect. Each one was a different kind of wrong. This is the first post in a series about ziom trader , my small AI-assisted crypto trading bot. "Ziom" is Polish for buddy, mate, or dude depending on who's talking. The name is unserious on purpose. The system is not. This is not a "watch me print money" series. The number is negative. Good. The point of the series is to track what happens when an LLM-assisted trading system moves from backtests and dashboards into live execution: where the bot is wrong, where the dashboard is wrong, where I am wrong, and which layer gets to prove it. Frame The natural first read of -$9.21 is "the strategy is losing money." That read assumes the displayed P&L attributes to the strategy. It does not. The number that shows up at the surface is the sum of at least three different layers: the strategy itself, the execution wrapper around it, and the monitoring layer that observes both. Each layer can author its own kind of failure. The displayed number compresses all three into a single dollar figure and loses the attribution on the way up. The framing that landed for me, from Daniel Nevoigt, is that methodology overview without forward-correlation disclosure is a log with good intentions. Same applies to P&L: total P&L without layer-attribution disclosure is a log with good intentions. You see the number. You do not see where it came from. Here is what I found when I forced the attribution. Layer 1: Shadow does not equal live Before deploying any lane, the system runs against backtested data. The shadow says "this strategy returns X over Y trade

Apple just raised prices across its iPad and MacBook lineup. The good news is that many retailers are still selling their inventory at the old prices or far less, which means you can still score some of the best iPad deals we may see in awhile — if ever again. So if you’ve been thinking […]

Alibaba allegedly used 25,000 accounts to mine Claude over 28.8 million exchanges.