标签：#p

GitHub热门项目 | A local-first personal finance app | Stars: 27,913 | 29 stars today | 语言: TypeScript

GitHub热门项目 | Dark Reader Chrome and Firefox extension | Stars: 22,233 | 7 stars today | 语言: TypeScript

GitHub热门项目 | Node.js based forum software built for the modern web | Stars: 15,175 | 2 stars today | 语言: JavaScript

GitHub热门项目 | Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. | Stars: 21,085 | 37 stars today | 语言: Python

GitHub热门项目 | The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs | Stars: 2,492 | 42 stars today | 语言: Python

GitHub热门项目 | A framework for building realtime voice AI agents 🤖🎙️📹 | Stars: 11,803 | 129 stars today | 语言: Python

GitHub热门项目 | DeepSeek 4 Flash and PRO local inference engine for Metal, CUDA and ROCm | Stars: 20,219 | 385 stars today | 语言: C

GitHub热门项目 | Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards. | Stars: 360,291 | 138 stars today | 语言: Python

GitHub热门项目 | DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cache stability — leave it running. | Stars: 29,612 | 877 stars today | 语言: Go

GitHub热门项目 | Fast Rust library for PDF inspection, classification, and text extraction. Intelligently detects scanned vs text-based PDFs to enable smart routing decisions. | Stars: 6,819 | 1,769 stars today | 语言: Rust

You know that moment when your React Suspense fallback jumps on the screen, then disappears, then reappears, leaving you wondering if you did something wrong? I’ve been there , seeing flickers, multiple loading spinners, or even UI glitches around Suspense felt like chasing ghosts. Turns out, React’s concurrent rendering scheduler is doing a lot behind the scenes , juggling priorities, pausing work, and restarting it , and Suspense boundaries are right in the middle of this dance. Understanding how React schedules work and handles interruptions can save you hours of frustration. React’s concurrent rendering scheduler: what’s it really doing? React’s concurrent mode isn’t just a fancy name; it means React doesn’t blindly render your entire component tree all at once. Instead, it breaks rendering work into chunks and spreads it out over multiple frames. This keeps your app responsive to user input and other high-priority tasks. Imagine you’re painting a huge mural. Instead of finishing it in one go (blocking everything else), you paint a little, step back, listen if someone calls you, then paint some more. React’s scheduler works similarly: Units of work : React slices rendering into small units it can pause and resume. Priorities : Some updates are more urgent , like responding to a click , so they jump ahead. Interruptions : If something more important comes up, React pauses current work and switches. This model makes React apps feel snappy even when doing heavy rendering or fetching data. What happens when Suspense enters the scene? Suspense boundaries are React’s way to say, “Hey, if this component isn’t ready yet (because it’s waiting on data, code, or something else), show this fallback for now.” Under the hood, when a component suspends (throws a Promise), React marks that unit of work as "waiting," and the Suspense boundary kicks in to show the fallback UI immediately. But here’s the catch: React keeps trying to finish rendering the suspended component in the

If you just want the recommendation: build the uptime poller yourself, put exponential backoff with jitter in front of every health check, and treat a 429 as a scheduling signal instead of an error you swallow. Query-style observability APIs hand you metrics and logs, not threshold rules or notification channels, so the polling worker is the thing that has to decide what "down" means and who gets woken up. That decision is the whole job. I got burned by exactly this. What follows is the pattern that survived the postmortem, the alternatives I weighed before writing a line of it, and the conditions where you should not do any of this yourself. The 429 my retry loop ate for six hours Last spring I was running a homegrown health checker for 40 internal services. One goroutine per service, all driven off the same 15-second ticker, which meant every check landed inside the same 200ms window. The status API we polled had a per-minute quota I'd never bothered to read, and for months it didn't matter, because 40 checks a minute sat comfortably under the ceiling. Then a colleague onboarded 12 more services, we crossed the quota, and the API started answering with HTTP 429. My retry wrapper caught it, retried three times in a tight loop, and on the last attempt returned the previous cached result — which said healthy . It logged the rate limit at debug level. Nobody reads debug. Six hours. Green dashboard. Dead queue consumer. We found out when a customer asked where their export was. The consumer had died on an unrelated deploy, the checker never noticed, and when I finally restarted it the backlog got re-processed on top of a manual replay I'd already run — two customers got the same notification twice. Duplicate deliveries are the specific thing I lose sleep over, and I had caused a batch of them with a retry loop that was trying to be helpful. The postmortem produced one line I now paste into every runbook: a check that can't reach the API reports unknown, never healthy.

Cybersecurity startup Horizon3 raised $250 million at a $2 billion valuation as companies want continuous, AI-powered security validation instead of annual pentesting.

Release notes answer an important question: what changed? About this method: I build KPainter , a source-to-explainer video workspace. KPainter helps teams turn release notes, product briefs, docs, screenshots, and other source material into clear explainer videos. The workflow below keeps the maintained source—not the video—as the source of truth. They are not always enough to answer the next questions a customer, support teammate, or implementation partner has: Does this change affect my workflow? What should I do differently? What is deliberately unchanged? Where can I check the current details when the product evolves again? That difference matters when a team turns a changelog, launch note, or technical update into a walkthrough. A screen recording can show a new control. A useful explanation helps the viewer decide whether to care, what to try, and where the boundary is. Start with the viewer's decision Before opening a recorder or drawing a storyboard, write one sentence in this form: After this walkthrough, [viewer] should know whether [change] affects [their job] , and what to do next. For example: After this walkthrough, an existing workspace administrator should know whether a new approval step changes their release process, and where to configure it. This sentence prevents a common failure mode: putting every release-note bullet into a video. A list of features is complete, but it is rarely a clear path through a decision. Establish the source hierarchy Product details have different lifetimes. The launch note may be a useful summary, while the maintained documentation defines the exact configuration, permissions, availability, or limits. Use a small source hierarchy before drafting scenes: Current product documentation — the maintained source for setup, constraints, and terminology. The release note or change record — why the change happened and the scope of the release. A verified product view — the interface or workflow that a viewer should recognise.

Two numbers from my own systems, side by side. When I deploy, a check confirms the pages are actually live. It retries three times, twenty seconds apart, so it tolerates up to a minute of "not there yet" before calling anything wrong. When my monitor decides whether production is down , it waits five seconds and retries once. The check that guards the more consequential claim is the more impatient one. I did not decide that. I never compared them. Until last week I had never seen those two numbers in the same place, and neither had anything else. Where the numbers came from The deploy one has an origin story I'd have told you proudly a week ago. I shipped nine pages, then checked the URLs immediately instead of trusting the CLI's success message. Four returned 404. Nothing was broken — CDN propagation — and twenty seconds later all nine were 200. A single check at the wrong moment would have told me, with total confidence, that a perfectly good deploy was broken. So I wrapped it in a retry loop. Three attempts, twenty seconds apart. Problem solved, and it even sounds like engineering. Here is the part that isn't. I picked twenty because it was the first interval where the false alarms stopped. My sample was about three deploys. I have never recorded how long propagation actually takes. I widened the tolerance until the red went away, and then I wrote about it as if I'd learned something. Someone in a thread named this before I saw it: a tolerance chosen that way is the same muting I'd been criticizing, relocated inside the assertion where it reads as rigor instead of avoidance. The test I was given, and the answer I didn't want In that same thread I speculated that my deploy tolerance was probably leaking into my outage detector through a shared helper. It sounded plausible and I said it like a finding. The reply was sharper than the guess: that's a falsifier, not evidence. Here's the concrete test — do the two checks consume the same retry policy or threshold confi

If you've ever tried to buy a portable power station, you know the problem: every brand claims to be the best, the spec sheets are a wall of numbers, and the forums are full of confident but contradictory advice. "What size do I actually need?" is the most-asked question and the least-clearly-answered. So I did the boring thing. I built a structured database of 102 portable power stations from 24 brands — capacity, output, chemistry, cycle life, solar input, weight, price — and started running the numbers. A few findings were genuinely surprising. 1. Prices quietly collapsed The median portable power station now sits at $0.61 per watt-hour . The cheapest in the dataset is $0.39/Wh (the GRECELL T1000). A few years ago, ~$1/Wh was normal and anything under $0.70 felt like a deal. The practical takeaway: if you're paying much more than ~$0.70/Wh in 2026, you're mostly paying a brand premium. That premium sometimes buys you a better app, ecosystem, or support — but it's worth knowing you're paying it. 2. LiFePO4 basically won 94% of the models I track now use LiFePO4 (lithium iron phosphate) instead of the older lithium-ion (NMC) chemistry. This matters more than any marketing bullet point: LiFePO4: ~3,000–4,000+ charge cycles Older Li-ion (NMC): ~500 cycles At daily use — say you cycle it every day in a van or for backup — that's roughly 8 years vs 18 months before the battery is meaningfully degraded. If a listing still uses NMC to hit a lower price, that "deal" can cost you far more over its life. 3. The fridge myth costs people hundreds of dollars This is the single most common sizing mistake. People size a giant, expensive battery to run a fridge because they do the math like this: Fridge nameplate (150W) × 24 hours = 3,600 Wh/day But a fridge's compressor only runs about 40% of the time . Its real average draw is closer to 60W, so: 150W × 40% × 24h ≈ 1,440 Wh/day In runtime terms: a 1,000Wh power station runs a full-size fridge for about 14 hours , not the ~6 hour

For a while, I spent more time reading about SEO than actually doing SEO. Keyword research, domain authority, backlinks, technical SEO, search intent—there was always another guide to read and another tool to try. Eventually, I decided to stop preparing and build a small website from beginning to end. The result is Get Password Generator , a free password generator that creates passwords entirely inside the browser. This is what I have learned so far. Step 1: Finding a keyword with Google Trends I started with Google Trends. Google Trends does not provide exact search volume, but it is useful for comparing keywords and checking whether people’s interest is stable, growing, or disappearing. Instead of looking for the “perfect” keyword, I wanted to find something that: solves a clear problem; can become a focused single-purpose tool; has relatively stable demand; does not require a large backend; can be shipped quickly. A password generator matched those requirements. People already understand what the tool should do, and there is no complicated onboarding process. They open the page, choose their settings, generate a password, and copy it. Step 2: Checking the actual Google results After looking at trends, I searched the keyword directly on Google and examined the first page. This step was more useful than looking at a single difficulty score. I checked: what kinds of pages were ranking; whether the results were tools, articles, or product pages; how quickly users could access the generator; whether the pages worked well on mobile; how clearly they explained privacy and security; whether there was room for a simpler experience. I was not trying to prove that the keyword was “easy.” Search results can change, and established websites are difficult to compete with. I only wanted to answer a practical question: Is there enough room here to build something useful and learn from the process? For me, the answer was yes. Step 3: Buying the domain I purchased: https://getpas

Cómo solucionar el error “Enable JavaScript and cookies to continue” Este error aparece cuando Cloudflare (u otro proxy inverso de seguridad) detecta que el navegador del usuario no cumple con los requisitos mínimos para acceder al sitio: JavaScript está deshabilitado o las cookies no están permitidas . Pero en entornos reales, el problema suele ser más sutil: el navegador sí tiene JS y cookies habilitados, pero la configuración del entorno de ejecución (como un headless browser, test automation, o un scraper) no emula correctamente el comportamiento del cliente . 🔍 Causa raíz técnica Cloudflare emite un desafío (CAPTCHA o JS challenge) para verificar que el cliente es un navegador real. Si la respuesta no cumple con el desafío (por ejemplo, porque: El navegador no ejecuta el JS del desafío (headless sin soporte), Las cookies no se persisten entre solicitudes, El User-Agent o Accept-Language no coinciden con navegadores reales, Falta el Referer o Origin en headers, Se bloquean cookies de terceros (como las de Cloudflare), … entonces el servidor devuelve este mensaje estático en lugar de redirigir a la página solicitada. ⚠️ Nota crítica : Si estás usando herramientas como curl , requests de Python, o navegadores headless sin configuración especial, no pasarás el desafío de Cloudflare . Es intencional: Cloudflare bloquea tráfico no humano por diseño. ✅ Solución definitiva (por escenario) 🛠️ Caso 1: Navegador real (usuario final) Verifica que JavaScript esté habilitado : Chrome: Configuración → Privacidad y seguridad → Configuración de sitios → JavaScript → Permitido . Firefox: Preferencias → Privacidad y seguridad → Cookies y datos de sitios → Deshabilitar “Bloquear cookies y datos de sitios” . Limpia cookies y caché (especialmente para *.cloudflare.com ). Reinicia el navegador y vuelve a cargar la página. 🛠️ Caso 2: Automatización / Scraping (Python + Playwright/Selenium) No uses requests o urllib : no ejecutan JS. Usa un navegador real con soporte para Cloudflare. ✅

New security research offers a rare view inside residential proxy networks, which rely on apps that share a person's internet connection with someone else.

We’ve come a long way since exclusively $1,000+ OLED gaming monitors. Alienware’s latest display brings the price down to a shocking $350, though it comes with some important compromises.