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AI 资讯

I built a test lab to measure SSG vs SSR vs ISR on real WordPress, here's what I found

Most "SSG vs SSR vs ISR" content out there is written from documentation. Someone reads the framework, restates it, and you're left inferring the actual difference in performance and behavior. So I built a lab where you can just run the commands and see it yourself, no table to trust blindly. astro-wp-seo-lab builds the same WordPress content four different ways with Astro 7.1.1, then serves all four side by side so you can compare them directly. git clone https://github.com/nimajafari/astro-wp-seo-lab npm install npm run compare That builds each arm into its own directory and serves them all at once. arm url what it is ssg-full http://localhost:4301 everything prerendered at build time ssr http://localhost:4302 rendered per request, no caching ssr-cdn http://localhost:4303 per request plus CDN cache headers route-cache http://localhost:4304 per request plus Astro 7 route caching islands http://localhost:4305 static shell with deferred fragments Every page has a black bar at the top showing which arm rendered it and when. That timestamp is the instrument for most of what follows. First build takes a few minutes since each arm fetches from WordPress, later builds are faster because the Content Layer loader caches between them. It ships pointed at a live WordPress install (oxyplug.com), but it works against any public WordPress site with the REST API exposed. npm run probe -- https://your-site.com --save mysite SOURCE = mysite npm run compare probe checks what your own install actually exposes, REST API reachability, Yoast presence, permalink structure, then saves it under a name. Use the URLs npm run compare prints for your own site instead of the ones below, since those are generated from your own content. Build time vs request time This is the distinction most of the SSG vs SSR debate hinges on, and it takes about 30 seconds to see for yourself. Open these two side by side and reload each a few times. http://localhost:4301/optimization/crl-ocsp-certificate-revocati

2026-07-21 原文 →
AI 资讯

Uma Máquina, Duas Contas Claude, Zero Estado Compartilhado

Vi um post legal esses dias sobre rodar duas contas do Claude Code na mesma máquina compartilhando tudo entre elas: mesmas skills, mesmos servidores MCP, mesmos hooks. O artigo era: "Um cérebro, duas carteiras" Eu rodo, exatamente o oposto, e acho que pra muita gente o oposto é a escolha certa. Minhas duas contas não são uma pessoal e uma reserva pra quando os créditos acabam. Uma é pessoal, outra é de trabalho. A última coisa que eu quero é meus MCP servers de trabalho, meus hooks de trabalho e meu histórico de projeto de trabalho vazando pras sessões pessoais. Então em vez de ligar as duas, eu mantenho elas separadas de propósito . O setup inteiro O Claude Code guarda o estado num diretório de config ( ~/.claude por padrão) e lê a variável CLAUDE_CONFIG_DIR pra apontar pra outro lugar. Esse é o único mecanismo que você precisa. Sem shim, sem symlink, sem jq . bash # ~/.zshrc # Claude Code: contas isoladas (pessoal vs trabalho) # Cada uma usa um CLAUDE_CONFIG_DIR proprio -> credenciais/sessao separadas. # ~/.claude = pessoal (default) # ~/.claude-work = trabalho claude-work () { CLAUDE_CONFIG_DIR = " $HOME /.claude-work" claude " $@ " ; } claude-personal () { CLAUDE_CONFIG_DIR = " $HOME /.claude" claude " $@ " ; } # 'claude' sozinho continua sendo a conta pessoal. source ~/.zshrc claude-work # pede login OAuth da conta de trabalho, uma vez só Usei funções de shell em vez de alias por um motivo: a função repassa "$@" limpo, então claude-work --resume abc e claude-work chat funcionam sem a variável de ambiente vazar pra nada mais no shell. Um alias faria quase o mesmo aqui, mas a função deixa a passagem de argumentos explícita. É isso. claude puro é pessoal. claude-work é trabalho. Nada é compartilhado, e é aí que mora a graça. Por que eu não compartilho o cérebro A versão de cérebro compartilhado faz symlink de skills , plugins , settings.json e dá merge no bloco mcpServers entre as duas contas pra elas se comportarem igual. Se as suas duas contas são de fato a mesm

2026-07-21 原文 →
AI 资讯

Designing a Version-Aware Game Wiki for Early Access

Early Access games create a documentation problem that ordinary wikis do not handle well: the facts can change faster than search results, community posts, and copied tables are updated. A page can look polished and still be wrong for the current build. I have been working on an independent Subnautica 2 player wiki, and the most useful engineering lesson has been to treat every guide, map marker, and item row as versioned data rather than timeless prose. This post describes the workflow without assuming any particular framework. 1. Put provenance next to the fact For every structured record, keep at least: the game build or patch it was checked against; the source type: official note, in-game observation, or community report; the observation date; a confidence state such as verified, provisional, or disputed; a stable identifier that survives display-name changes. A user should not have to trust a page because it looks complete. They should be able to see whether a coordinate came from the current build and whether another player can reproduce it. 2. Separate stable identity from mutable labels Names, descriptions, recipes, and locations may change. Use an internal key as the identity and keep display text as versioned attributes. This prevents an item rename from creating a second logical entity or breaking every inbound link. The same rule helps with localization: English and translated labels point to one entity, while the source and verification state remain shared. 3. Model maps as evidence, not decoration An interactive map should not be a pile of pins. A useful marker contains coordinates, category, build, evidence, verification state, and a short player-facing note. If a patch moves or removes the object, preserve the history and mark the old observation as superseded. This also makes filters honest. “Show verified markers for the current build” is a meaningful query; “show everything ever imported” is not. 4. Make guides depend on structured facts Low-spoil

2026-07-21 原文 →
AI 资讯

MEV Is Coming to the Agent Marketplace

The front-running tax that bled crypto for a decade needs only observable intent and a party that controls order. Agent marketplaces are rebuilding both. In September 2020, a security researcher who goes by samczsun found about $12 million of someone else's cryptocurrency sitting in a vulnerable contract, exposed, and realized he had a few minutes to rescue it before someone less friendly noticed. He wrote the rescue transaction. Then he stopped, because he understood the problem with sending it. The moment his transaction hit Ethereum's public waiting area, the mempool, every bot watching that space would see a profitable move spelled out in plain code, copy it, pay a higher fee to jump ahead of him, and take the $12 million themselves. His rescue would become their heist, and he would have personally handed them the map. He wrote about this later in an essay called "Escaping the Dark Forest," borrowing a metaphor from Dan Robinson and Georgios Konstantopoulos at Paradigm, who had borrowed it from Liu Cixin's science fiction: an environment where any signal of your presence gets you killed, so the only survivors are the ones who stay silent and shoot first. The mempool is a dark forest. Broadcasting a valuable intention into it is detection, and detection is death. Samczsun survived only by refusing to play the open game. He submitted his rescue privately, straight to a miner, bypassing the public mempool entirely, so the predators never saw it coming. That story is usually told as a piece of crypto lore. I want to tell it as something else, because the thing that killed transactions in the dark forest was never really about blockchains. It was about a shape, and that shape is quietly being rebuilt inside the AI agent marketplaces that a lot of people are racing to launch right now. When it finishes, the same predators will be back, and this time the prey will be your agents. The three conditions, and why blockchain was just the extreme case The phenomenon samczsun

2026-07-21 原文 →
开发者

El peronismo se fragmenta: ¿quién paga el costo fiscal de la interna?

Publicado originalmente en Justicia Liberal . El hecho y su lectura económica La reconfiguración de lealtades internas en el bloque peronista del Congreso que reporta LA17 podría leerse como un episodio más del folletín peronista: disputas de conducción, alineamientos con gobernadores, señales cruzadas hacia 2027. Pero desde una perspectiva económica aplicada, el fenómeno tiene consecuencias concretas sobre variables que le importan a cualquier empresa, ahorrista o asalariado argentino. La inestabilidad legislativa no es un dato político neutro. Es un factor de riesgo que los mercados descuentan en tiempo real. Incertidumbre legislativa y su precio en variables macro Cuando un bloque opositor mayoritario se fragmenta, el resultado inmediato no es la debilidad del peronismo: es la imprevisibilidad del Congreso. Y la imprevisibilidad tiene precio. El riesgo país argentino, que según datos del BCRA y operadores de mercado secundario rondó los 600-700 puntos básicos durante buena parte de 2025, es en parte una prima por incertidumbre institucional. Cada vez que el Poder Legislativo se convierte en un tablero de negociaciones opacas —donde un artículo fiscal puede ser bloqueado, modificado o aprobado según quién necesite qué favor de quién— la tasa de descuento que aplican los inversores sobre activos argentinos sube. Eso se traduce en mayor costo de financiamiento para el Tesoro y, por efecto derrame, para el crédito privado. El mecanismo es simple: si no sabés qué va a salir del Congreso la semana próxima, no invertís a largo plazo. Y si no invertís, no generás empleo formal ni capacidad productiva. El déficit como rehén de la interna El equilibrio fiscal que el gobierno de Javier Milei logró sostener durante 2024 —el primer superávit financiero en más de una década, según datos del Ministerio de Economía— depende en parte de que el Congreso no apruebe gastos que el Ejecutivo no puede financiar sin emisión. Ahí es donde la fragmentación peronista se vuelve peligrosa de

2026-07-21 原文 →
AI 资讯

VernLLM - lightweight resilience layer for OpenAI SDK

Introducing vernLLM: A Resilience Layer for LLM Applications Building production-ready LLM applications is not just about sending prompts and receiving responses. Real-world AI systems need to handle timeouts, provider failures, rate limits, inconsistent outputs, and reliability issues. That is where vernLLM comes in. vernLLM is a lightweight resilience layer for OpenAI-compatible chat completion APIs , providing a single interface with built-in retries, timeouts, circuit breaking, caching, structured output, and usage tracking. Instead of rebuilding the same reliability features for every LLM project, vernLLM gives you the tools needed to make your AI integrations more robust from the start. Features Automatic retries with backoff Transient failures happen. vernLLM automatically retries recoverable errors while failing fast on validation errors and non-retryable responses. Timeouts & cancellation Prevent hanging requests with configurable timeouts and cancellation support. Circuit breaker protection Automatically stop sending requests to failing providers and recover when the service becomes healthy again. Structured output with type safety Pass a Zod schema and receive validated, typed results back. const result = await llm . call ({ systemPrompt : ' Return JSON: { "skills": string[] } ' , userContent : ' Extract skills from: ... ' , schema : SkillsSchema // zod schema }); Provider-native JSON Schema support Constrain model generation itself instead of only validating responses afterward. Built-in caching support Cache LLM responses using your own cache adapter with cachedCall and cachedLLMCall . One interface across providers Use the same API across multiple providers: OpenAI Groq Mistral DeepSeek Cerebras Together AI Fireworks AI Ollama Anthropic Gemini AWS Bedrock Any HTTP-compatible provider through fromFetch Why vernLLM? Many LLM applications end up creating their own wrappers around provider SDKs to handle: retry logic API failures provider switching respons

2026-07-21 原文 →
AI 资讯

HollowGraph Malware Uses Microsoft 365 Calendar Events as Dead-Drop C2 Channel

What Happened On July 20, 2026, cybersecurity firm Group-IB disclosed a new espionage implant dubbed HollowGraph that hijacks compromised Microsoft 365 mailboxes to run a command-and-control (C2) channel hidden inside calendar events. The malware attaches encrypted files to calendar entries dated May 13, 2050 — far enough in the future that a mailbox owner would never scroll to them — and retrieves operator instructions from the same dead drop. All traffic moves through the Microsoft Graph API, making the activity indistinguishable from legitimate M365 usage. At least 12 systems have been infected, with three actively communicating with the threat actor between June 3 and July 9, 2026. The indicators point to a targeted espionage campaign focused on Israeli organizations . Technical Analysis HollowGraph is a lightweight .NET DLL that supports only two commands: GET and SEND . To receive tasking, it queries the compromised mailbox's calendar for an event titled in the format "Event ID: <7-char-taskID>", downloads the attached file, and decrypts it using RSA and AES-256-GCM. To exfiltrate data, the implant creates a new calendar entry titled "Boss{..}ID{..}" and uploads stolen files encrypted with the attacker's public RSA key. The Group-IB research team described the mailbox calendar as a "covert dead-drop," with HollowGraph retrieving commands from events scheduled within a fixed one-hour window between 22:00 and 23:00 UTC on the far-future date. The hybrid encryption scheme uses separate RSA key pairs for inbound and outbound channels, keeping them cryptographically isolated. A second, unencrypted channel runs over DNS tunneling . HollowGraph refreshes its Microsoft Entra ID (Azure AD) credentials by querying IPv6 AAAA records from the attacker-controlled domain cloudlanecdn[.]com . Each returned IPv6 address yields 14 usable payload bytes, which the malware assembles and decodes as UTF-8 text to update its logAzure.txt configuration file — a file masquerading as a

2026-07-21 原文 →