标签：#ev

In a dense survival game, danger is not communicated only by the projectile itself. The warning that appears before impact is part of the interface. Its direction, duration, width, and overlap with other warnings determine whether a player can make a meaningful decision. No Humanity provides a useful compact example. The reviewed classic build places a tiny ship inside a vertically framed arena and measures survival time while lasers, projectiles, sweeping shapes, doodled faces, and radial bursts occupy the screen. The ship does not visibly attack in the reviewed footage; survival depends on reading hazards early and preserving room to move. Treat every warning as an event A guide or analysis tool can represent a warning with a small event record: type HazardEvent = { source : ' laser ' | ' radial ' | ' sweep ' | ' projectile ' telegraphRegion : Rect impactRegion : Rect leadTimeMs : number escapeSides : Array < ' left ' | ' right ' | ' up ' | ' down ' > } This is more useful than describing a screenshot as “chaotic.” It separates what the player can know before impact from what becomes visible afterward. A fair hazard may be difficult, but it gives the player a readable interval and at least one plausible escape route. Open space has option value Beginners often move toward the largest empty area. That is not always safe. A large pocket can be a trap if a sweep closes its only exit. Smaller central space can be more valuable because it preserves several escape directions. The strategy is therefore not “find empty pixels.” It is “preserve optional movement.” A rough evaluator might score a position by reachable space after the next known impact, not by current distance from a projectile. position score = future reachable area + escape directions - overlapping impact risk This framing explains why early movement matters. Waiting until the projectile is fully drawn converts a route-planning problem into a reaction-time test. Overlap changes the meaning of each signal T

A weak puzzle walkthrough records every input. A stronger one explains why the board refuses to move. That distinction matters in traffic-sorting puzzles, where a correct tap can still be useless if a garage exit, crossing lane, or temporary holding space remains blocked. I used Car Sort level 13 as a small case study for a better hint model. The useful unit is not “tap car number seven.” It is a dependency: this vehicle cannot leave until that lane opens; that lane cannot open until a matching garage accepts its front car. Model the board as dependencies The visible board can be represented as a directed graph. Cars and blockers are nodes. An edge from A to B means A must move before B becomes actionable. The graph does not need to reproduce the game engine. It only needs to describe the decisions a player can verify on screen. type MoveNode = { id : string color : string blockedBy : string [] releases : string [] checkpoint : string } This structure makes a hint resilient. If a player has already cleared one harmless car, the guide can still say, “restore the center exit, then release the stack behind it.” A memorized tap list often becomes useless as soon as the board differs by one move. Separate release moves from cleanup moves Puzzle solvers tend to treat every successful departure as equal. They are not equal. A release move changes the dependency graph by opening a lane or exposing a buried color. A cleanup move removes a car that was already free. Good guidance labels those roles explicitly. The player should know whether the current move creates new options or merely reduces clutter. That is especially useful on compact boards, where an attractive matching car may tempt the player even though it does not improve the central bottleneck. The reserved route for this analysis is documented as Car Sort puzzle help . The value of that page is its focus on visible blockers and release points rather than an unexplained command stream. Add visual checkpoints After

Last month a model I'd been watching dropped its token price by half, and three people sent me the announcement within an hour. The implied question was always the same: when are you switching? My answer, these days, is: after it survives quarantine. Because the last time I swapped a model based on announcement-day excitement, everything looked fine for nine days. Then a scheduled job started emitting subtly malformed JSON — valid enough to parse, wrong enough to corrupt downstream state — and I spent a weekend reconstructing which records had been poisoned. The money I saved on tokens wouldn't cover one hour of that cleanup. The economics of model swaps are lopsided. The upside is small and predictable (cheaper tokens). The downside is unbounded and sneaky (behavioral regressions in edge cases your happy-path tests never exercised). So I built a pipeline that treats every new cheap model like an untrusted dependency with an attractive changelog: it gets isolated, probed, and graduated in stages. Here's the whole thing. What the pipeline needs (and what it doesn't) Three ingredients: candidate model access, somewhere disposable to run the evaluation, and checks that don't require a second LLM to grade the first one. For model access and the throwaway compute, I'm currently using MonkeyCode's free model access together with its free server option — bursty evaluation workloads are exactly the kind of thing I'd rather not attach to a production billing account. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Nothing in the pipeline below is tied to that provider, though. Every endpoint is an environment variable, and I'd encourage you to wire it to whatever you're actually evaluating. I want to be explicit about two things I'm not assuming: that any particular model is on the free tier when you read this, and that any free offering stays available forever. Treat free infrastructure the way you treat a library's latest tag — convenient, n

Background Registration confirmation emails were not reliably reaching users on Gmail and Outlook outside Japan — sometimes landing in spam, sometimes not arriving at all. Investigation pointed to a single root cause: the wpmm.jp domain had SPF and DKIM configured, but no DMARC record . What each of the three does SPF (Sender Policy Framework) declares in DNS which IP addresses are authorized to send mail for a domain. Receiving servers check the sending IP against the SPF record to confirm the source is legitimate. DKIM (DomainKeys Identified Mail) adds a cryptographic signature to the message headers and body. The receiving server looks up the public key in DNS and verifies that the message has not been tampered with and was signed by a party controlling that domain. DMARC (Domain-based Message Authentication, Reporting and Conformance) sits above both. It tells receiving servers what to do when SPF and DKIM alignment fails, and it collects aggregate reports about how mail from the domain is being treated. The key point is that SPF and DKIM are independent checks. Without DMARC, there is no single authoritative statement about how the alignment result should influence delivery decisions. Major providers including Gmail weigh the absence of DMARC when scoring incoming mail. Adding the DMARC record The following TXT record was added to the wpmm.jp DNS: _dmarc.wpmm.jp TXT "v=DMARC1; p=none; rua=mailto:info@wpmm.jp" p=none means "collect data, but do not reject or quarantine mail that fails alignment." Starting with p=reject or p=quarantine risks blocking legitimate mail if DKIM alignment turns out to be misconfigured somewhere. The safe approach is to start with p=none , monitor the reports, and tighten the policy gradually. rua=mailto:info@wpmm.jp sets the destination for aggregate reports. Google and other receivers periodically send XML summaries showing which mail passed or failed SPF/DKIM alignment. This moves visibility from passive (you notice when users compl

1. Retomando: de imagens a containers em execução Na primeira parte desta série vimos o que é o Docker, o problema que ele resolve e os três conceitos fundamentais — imagens, containers e registries. Agora que a base teórica está posta, o foco deste artigo é prático: os comandos que efetivamente viram hábito no uso diário — run , exec , logs , ps , build — aplicados a containers reais, não só ao hello-world . 2. docker run além do básico O artigo anterior já usou docker run para subir um Nginx. Vale conhecer as flags que aparecem o tempo todo: # Modo interativo, útil para explorar uma imagem manualmente docker run -it ubuntu bash # Variáveis de ambiente docker run -e POSTGRES_PASSWORD = segredo -d postgres # Montar um diretório do host dentro do container (volume bind mount) docker run -v $( pwd ) /dados:/dados -d minha-imagem # Remover o container automaticamente quando ele parar docker run --rm -it python:3.12 python3 # Limitar recursos docker run --memory = 512m --cpus = 1 minha-imagem -it combina -i (interativo, mantém STDIN aberto) com -t (aloca um pseudo-terminal) — é o par de flags para "entrar" em um container e usar um shell como se fosse uma máquina normal. --rm evita acumular containers parados no disco depois de testes rápidos e descartáveis — sem ela, cada docker run deixa um container parado para trás até ser removido manualmente. -e define variáveis de ambiente; imagens oficiais como a do Postgres costumam documentar quais variáveis elas esperam (usuário, senha, nome do banco inicial). 3. Inspecionando o que está rodando O comando mais usado para ter uma visão geral do que o Docker está gerenciando na máquina: docker ps # containers em execução docker ps -a # todos, incluindo parados docker ps -q # só os ids (útil em scripts) Para investigar um container específico mais a fundo: docker inspect meu-container # todos os metadados em JSON: rede, volumes, config docker top meu-container # processos rodando dentro do container docker stats # uso de CPU/mem

Building rich, collaborative web interfaces today requires much more than simply rendering components to the DOM. It demands rigorous planning around rendering performance, deterministic state management, and network resilience. In this article, I will break down the architectural decisions and trade-offs behind a real-time, enterprise-grade Kanban Board. Built with React 19, Vite, TypeScript, and Tailwind CSS, this application is designed to handle high data volumes via virtualization while maintaining a bulletproof, offline-first architecture. 🚀 Live Demo System Architecture: The Smart/Dumb Paradigm To guarantee scalability and testability, the application strictly adheres to the Container/Presentational (Smart/Dumb) design pattern. This ensures absolute separation of concerns. Containers (Smart): Orchestrate state access via Zustand, handle asynchronous actions, and manage event listeners. Presentational Components (Dumb): Pure, stateless functions exclusively concerned with UI rendering and accessibility. They receive data strictly via props. Unidirectional Data Flow: State mutations propagate downward from the global store, ensuring predictable render cycles. Here is the architectural topography of the system: Quality Attributes (NFRs) and Technical Decisions To ensure this MVP could scale into a production-ready product, the system was designed around strict Non-Functional Requirements (NFRs). Performance: DOM Virtualization & React 19 Paradigms Rendering 1,000+ DOM nodes concurrently destroys the framerate of standard React applications. We implemented client-side virtualization via @tanstack/react-virtual. By recycling DOM nodes and dynamically measuring element heights, the browser only renders the exact cards visible within the viewport, maintaining a steady 60fps during complex Drag-and-Drop operations. Furthermore, this codebase natively embraces React 19. It intentionally omits manual memoization (useMemo, useCallback, React.memo), relying entirely on t

The mental model that fixes everything JWT is just a token format . It is not authentication, not a session, and not a database. Once you separate those ideas, most of the pain disappears. A JWT is a JSON object that is signed. That's it. The payload holds claims like sub (subject) and exp (expiration). The signature proves the token wasn't tampered with. What JWT is not Not a session store : You can't revoke a JWT before it expires. If you need revocation, you need a blocklist or short expiry. Not a database : Don't stuff heavy data in the payload. It gets sent on every request. Not a magic bullet : It's a way to pass claims between parties without a shared server-side state. The three flows that matter 1. Access token only Simplest flow: login returns a JWT, client sends it in the Authorization header, server verifies it on every request. // server middleware (Express example) const jwt = require ( ' jsonwebtoken ' ); function auth ( req , res , next ) { const header = req . headers . authorization ; if ( ! header ) return res . status ( 401 ). json ({ error : ' No token ' }); const token = header . split ( ' ' )[ 1 ]; // Bearer <token> try { req . user = jwt . verify ( token , process . env . JWT_SECRET ); next (); } catch ( err ) { res . status ( 401 ). json ({ error : ' Invalid token ' }); } } Works fine for small apps, but every request hits your auth logic and the token can't be invalidated early. 2. Access + refresh token Common pattern for SPAs. Access token lives 15 minutes, refresh token lives 7 days. The refresh token is stored securely (httpOnly cookie) and used only to get a new access token. // issue tokens on login const accessToken = jwt . sign ({ userId }, process . env . JWT_SECRET , { expiresIn : ' 15m ' }); const refreshToken = jwt . sign ({ userId }, process . env . REFRESH_SECRET , { expiresIn : ' 7d ' }); res . json ({ accessToken }); res . cookie ( ' refreshToken ' , refreshToken , { httpOnly : true , secure : true , sameSite : ' strict ' })

If you've ever stared at a 15,000-file monorepo wondering "what actually breaks if I touch this...

When a user closes a tab, submits a form, or clicks an external link, you often need to send one last...

When I first started testing APIs, Postman was one of the first tools I reached for. It was...

Small business websites rarely fail because of one catastrophic bug. They fail from an accumulation of small, fixable problems — a slow hero image here, an unlabeled form field there, a broken tab order that quietly locks out keyboard users. None of it looks dramatic in a screenshot. All of it adds up to lost conversions. Working across client rebuilds and audits at Alynox, the same handful of issues show up repeatedly, regardless of industry. Here are ten of the most common, with the practical, mostly low-effort fixes that address them. Unoptimized Images Dragging Down Load Time The single most common performance killer on small business sites is still oversized images — a 4MB PNG hero banner exported straight from a design tool, served at full resolution to a phone screen 400px wide. Fix: html src="hero-800.webp" srcset="hero-400.webp 400w, hero-800.webp 800w, hero-1600.webp 1600w" sizes="(max-width: 600px) 400px, (max-width: 1200px) 800px, 1600px" alt="Interior of the workshop showing custom furniture in progress" loading="lazy" width="1600" height="900" /> Convert to WebP or AVIF, generate a handful of responsive sizes, lazy-load anything below the fold, and always set explicit width/height to reserve space and avoid layout shift. No Real Mobile-First Design A lot of "responsive" small business sites are really desktop layouts that get squeezed with media queries until they technically fit a phone screen. Buttons end up too small to tap accurately, text wraps awkwardly, and nav menus overlap content. Fix: Design and build mobile-first — base styles for small screens, then progressively enhance with min-width media queries for larger viewports: css .card { padding: 1rem; } @media (min-width: 768px) { .card { padding: 2rem; } } Tap targets should be at least 44×44px (per WCAG and Apple/Google HIG guidance), with enough spacing between interactive elements to prevent mis-taps on smaller screens. Accessibility Treated as an Afterthought Missing alt text, low-contras

One pass of my board eval bills $9.14 on the Anthropic API. Through Claude Code it bills $0. Same model, claude-opus-4-8. That is 27 calls, and it is not an estimate. The CLI prints a total_cost_usd in its envelope: what the run would have cost on the API. It bills the subscription instead, so the number is a receipt for money nobody spent. The switch fixed something better than the bill Running with --output-format json and --json-schema rides the same structured-output machinery the API does, an internal forced tool call. Format reliability on my suite went from 7 out of 15 to 15 out of 15. The schema needs relaxing first: strip pattern , minLength , maxLength , minItems , maxItems , format and the $schema meta-ref, because the CLI validator rejects draft-2020-12. The strict version stays in Zod on the caller side, so nothing is actually loosened, the validation just moves to where it can run. One trap is worth the whole post If ANTHROPIC_API_KEY sits in the child process environment, the CLI quietly bills the API account rather than the subscription. Nothing errors. Nothing warns. The invoice arrives. It gets stripped explicitly at spawn. This is the failure mode I would look for first in anyone else's runner: the money leak is silent, and the only symptom is a bill at the end of the month for a run you believed was free. And the limit, which matters more than the savings This is a dev-loop tool. Anthropic's consumer terms prohibit automated access "except when you are accessing our Services via an Anthropic API Key or where we otherwise explicitly permit it", and the commercial terms governing API use do not cover consumer subscriptions. An eval runner on my own machine is the CLI used as designed. A shipped service is not. Iterate on the CLI, ship on the API. What does one pass of your eval suite cost, and does anyone know it? Originally published at dylan.merigaud.com .

Variable-size nodes break dagre's centering, first paint flickers, and straight chains render with kinked edges. Here is the why and the fix for each. Every React Flow and dagre tutorial shows the same thing: uniform gray boxes, laid out in a neat tree, everything centered. You copy the pattern, wire it up, and it works. Then you replace the gray boxes with real cards. A title that wraps to two lines. A card with a description and one without. And the layout starts to look subtly wrong: a parent sits off-center from its children, edges bend where they should be straight, and everything flashes in the top-left corner for a frame before jumping into place. I hit all three building an approval-workflow graph for a fintech app. The nodes were cards with variable content, so none of the fixed-size assumptions held. It took a while to understand that these are three separate bugs with three separate causes. So here is each one, why it happens, and the fix. At the end: the small package where I put all of it, so you do not have to rebuild this. Why dagre centers nodes off-balance (and the bounding-box fix) dagre centers a parent on the barycenter of its children, meaning the average of their center positions. That is correct when every child is the same size. It is visibly wrong when they are not. Concrete numbers from a graph I probed. A parent with two children, one 40px tall and one 200px tall. dagre puts the children at centers y=20 and y=180, so the parent lands at their average, y=100. But the visual middle of that group, the midpoint of the bounding box from the top of the small child to the bottom of the tall one, is y=140. The parent is 40px off from where your eye says it should be, and the taller the imbalance, the worse it gets. The fix is a post-pass on dagre's output: for every parent with two or more children, recompute its cross-axis position as the midpoint of the children's bounding box, walking deepest rank first so children settle before their parents.

You've seen the headlines by now. An agent in Cursor wiped a company's production database, backups and all, in about nine seconds. Replit's agent nuked another company's prod. Same shape every time: the agent was sure of itself, the SQL was valid, and nobody was in the loop to say wait. Those are the loud failures. The fix for them is boring and you already know it. Don't hand an agent write access to prod. Read-only by default, propose instead of apply, keep a human on the button. But there's a quieter version that a permissions policy won't catch, and that's the one I want to talk about. Your agent is probably doing it right now. It looks completely fine in the diff. The migration that passes review and still takes the site down Ask an agent to make an email column unique. It writes: ALTER TABLE users ADD CONSTRAINT users_email_unique UNIQUE ( email ); Correct SQL. Does exactly what you asked. It sails through review because there's nothing to see. Then on a users table with any real size, it grabs an ACCESS EXCLUSIVE lock and scans every row to build the unique index, and for the whole length of that scan nothing else can read or write the table. The API starts timing out. The connection pool fills. Now you're in an incident over a one-line migration that everybody approved. The agent didn't do anything a decent junior engineer wouldn't have done. That's the trap. The danger isn't the SQL, it's the lock the SQL takes, and you can't see a lock by reading a statement. You'd have to know Postgres locking cold: which DDL grabs which lock, and for how long, and what it shuts out while it holds. And you'll still miss one at 2am. I got tired of missing them. So I measured one. What the lock actually costs I ran the same schema change two ways against a real Postgres 18. Fifty million rows, twenty connections doing ordinary traffic. The unsafe version was a plain SET NOT NULL , which also scans under ACCESS EXCLUSIVE . The safe version was the NOT VALID then VALIDATE da

Someone asked me to share meeting notes in Slack yesterday. I pasted the markdown. Slack ate the table. The code block lost its indentation. The task list rendered as literal [ ] characters. I spent five minutes reformatting something that already looked perfect in my editor. So I sent a link instead. Not to Notion — they would have to sign up. Not to Google Docs — same problem, plus I did not want this living in someone's Drive forever. Not to a pastebin — those are single blobs of text with no structure, no pages, no way to come back and fix a typo tomorrow. I wanted: one link, multiple pages, markdown that actually renders, no account required, and a way for me to edit it later without the URL changing. I have built sharing tools before. FreeShare for files. NotePage for single-page text. SharePad is what I wished existed when I needed something in between a pastebin and a wiki — but without the signup wall. Live here: https://sharepad.in Source: https://github.com/Varshithvhegde/sharepad SharePad — Share notes with one link, no signup Write a notebook of markdown pages and share it with a single link. Password lock, expiry dates, comments and PDF export. Free, and no account needed. sharepad.in The Idea Most "share your notes" products follow the same playbook. Create an account. Verify your email. Create a workspace. Invite people. Configure permissions. By the time you are done, the meeting is over and nobody cares about the notes anymore. SharePad skips all of that. You write markdown. You get two links: View link — /n/kitchen-reno — hand this out freely Edit link — /e/{secret-token} — this is your ownership credential. Do not share it. No account. No password to create (unless you want to lock the view link). No "upgrade to share with more people." The edit token is your identity for that notebook. That one decision — token-based ownership instead of user accounts — simplified everything else. No auth flows. No session management. No "forgot password" for th

Hello I'm Marlene and I invite you to follow my journey developing ProForma.net. But since this is my first post about ProForma, I will give you an overview of what I'm trying to achieve. What ProForma.net is planned to be The main Goal is to develop an application shell for schema based Applications. You will have mainly to different types of UI schemes, first for the Window Layout, there you will tell which elements are contained in the different application sections, like what Buttons or Menus you will have in the window title bar, or what sidebar tabs you will provide for the Ribbons, what the content area is filled with (spoiler I'm going to use flexlayout-react https://github.com/caplin/FlexLayout ). As host application I will write a C# application using the WebView2 abstraction library Photino ( https://www.tryphotino.io/ ). What you can expect In this dev diary series I'll show what I was working on, I'll show you some code and will explain why did to choose the way I did it, or will share some thoughts about the project or the architecture. I also will show you how to write plugins for ProForma, because I plan to handle everything as a plugin so you can change the most aspects of the app. The journey begins: overcome the guard Ok, most of you will know it... parameter checking on top of a method... nearly endless 'if throw' constructs... they are ugly... if (! Directory . Exists ( physicalPath )) throw new DirectoryNotFoundException ( $"Could not find the given path ' { physicalPath } '." ); if ( _directories . ContainsKey ( urlPrefix )) throw new Exception ( $"Key ' { urlPrefix } ' already exists." ); if ( _directories . ContainsValue ( physicalPath )) throw new Exception ( $"Physical Path ' { physicalPath } ' already exists." ); I mean who wants to read that? I don't. So I wanted guards, and I've could used some 3rd Party library, but instead I came up with my own solution for the Guards, since I don't always want to throw the exception on a failed asser

Netflix migrated most of its batch workloads onto Kueue, an open-source cloud-native batch job execution system that has outgrown its homegrown solution over the years. The company mapped the capabilities previously created in-house to Kueue’s functionality and also benefited from new features that would have been costly to incorporate into its homegrown solution. By Rafał Gancarz

The company is also adding blood pressure and insulin resistance trends as proactive monitoring tools in the Google Health app.

The Pixel 11 smartphone lineup is here, alongside the Pixel Watch 5 and the all-new Pixel Tag.

From Magic Capture and Instant Night Sight to a built-in teleprompter, here’s a look at a few camera features on Google’s new Pixel 11 series.