标签：#AR

"Deu erro ao salvar, umas duas da tarde" É a única informação que você tem. O cliente não lembra o que clicou, não tirou print e já fechou a aba. Você abre o laravel.log . Quarenta mil linhas no dia. Faz um grep por "erro". Aparecem 1.200 ocorrências, e a maioria é isso: [2026-08-14 14:03:11] production.INFO: entrou [2026-08-14 14:03:11] production.INFO: erro aqui [2026-08-14 14:03:12] production.INFO: passou [2026-08-14 14:03:12] production.ERROR: Erro ao salvar Erro ao salvar o quê ? De qual usuário? Qual pedido? Qual valor? Aquele entrou da linha de cima é do mesmo request ou de outro cliente que estava usando o sistema no mesmo segundo? Você tem log. Você não tem informação. São coisas diferentes. O problema não é a falta de log. É o excesso de log inútil. public function emitir ( Pedido $pedido ) { Log :: info ( 'entrou no emitir' ); try { $nota = $this -> sefaz -> emitir ( $pedido ); Log :: info ( 'emitiu' ); } catch ( Throwable $e ) { // parabéns, você registrou que algo deu errado em algum lugar 🎉 Log :: error ( 'Erro ao emitir nota' ); return back () -> withErrors ( 'Falha na emissão' ); } } Repara no que esse catch jogou no lixo: a mensagem da exceção, o stack trace, o ID do pedido, o CNPJ, o retorno da SEFAZ. Tudo estava ali, na mão, e foi substituído por uma frase genérica. E os Log::info('entrou') espalhados? Aquilo foi debug que virou permanente. Hoje eles só servem pra empurrar as linhas úteis pra fora da tela. Duas perguntas que todo log precisa responder Um log serve pra duas plateias: você, com sono, às 3h da manhã — e uma máquina , filtrando milhões de linhas. As duas querem a mesma coisa: O que aconteceu , numa mensagem que não muda nunca. Com quem aconteceu , em dados separados da mensagem. Essa separação é o pulo do gato. Repare na diferença: // ❌ mensagem única pra cada pedido. impossível agrupar ou contar. Log :: error ( "Falha ao emitir nota do pedido { $pedido -> id } do cliente { $cliente -> nome } " ); // ✅ mensagem estável + contexto est

Ele começou com um if . Hoje tem 80 linhas. Sabe como é: precisava barrar quem não tem assinatura ativa. Um middleware, três linhas, resolvido. Depois entrou o período de teste. Depois o plano legado que tem regra diferente. Depois "aproveita que já buscou a assinatura e desconta um crédito". Depois o e-mail de aviso quando faltam 3 dias pro vencimento. Hoje esse arquivo tem 80 linhas, faz quatro queries, altera dado no banco e dispara e-mail. Ele não é mais um middleware. É um Service que mora na pasta errada e roda em todo request. E o pior: essa regra não existe pro resto do seu sistema. O middleware que virou gerente class VerificarAssinatura { public function handle ( Request $request , Closure $next ): Response { $assinatura = $request -> user () -> assinatura ; if ( ! $assinatura || $assinatura -> venceu ()) { return redirect () -> route ( 'planos' ); } // "aproveita que já tá aqui" 🙃 if ( $assinatura -> creditos < 1 ) { return redirect () -> route ( 'planos' ) -> withErrors ( 'Sem créditos' ); } $assinatura -> decrement ( 'creditos' ); $assinatura -> update ([ 'ultimo_acesso' => now ()]); if ( $assinatura -> vence_em -> diffInDays ( now ()) <= 3 ) { Mail :: to ( $request -> user ()) -> send ( new AssinaturaVencendo ( $assinatura )); } return $next ( $request ); } } Funciona. Passa nos testes de feature. E tem quatro problemas escondidos que só aparecem meses depois. Problema 1: middleware só existe no HTTP Esse é o grande. Middleware é uma camada de request HTTP . Ela não roda em outro lugar nenhum. Então: O comando php artisan relatorio:gerar não desconta crédito. O job na fila não desconta crédito. Sua rota de API que você esqueceu de agrupar não desconta crédito. O tinker passa por cima de tudo. Você não criou uma regra de negócio. Criou uma regra da porta da frente . Qualquer outra entrada no sistema ignora ela. E, sério, isso não é hipótese: um dia alguém vai criar um endpoint novo, esquecer o middleware, e a assinatura vira um detalhe decorativo. Probl

When every unauthorized request becomes the same dashboard line, diagnosis turns into guessing. ASP.NET Core 10 authentication metrics give me a better split: did the handler have nothing to authenticate, reject supplied credentials, or accept them? That distinction matters because a client deployment that drops credentials needs a different response from a surge of malformed or expired credentials. ASP.NET Core 10 added built-in authentication and authorization instruments to System.Diagnostics.Metrics . I can collect them without rewriting each handler, and I can lock their behavior into an offline test before wiring up a production exporter. Why one 401 hides two different problems A protected endpoint normally challenges an unauthenticated caller. The final status is 401 whether the caller sent nothing or the handler rejected what it received. The authentication duration histogram exposes the missing context through aspnetcore.authentication.result : Result What the handler reported A common interpretation none No authentication result No applicable credentials were available failure Authentication failed Supplied credentials were rejected or processing failed success A principal was created Authentication completed successfully _OTHER Another framework result Preserve it as an explicit catch-all none is a handler result, not a universal synonym for “missing Authorization header.” A policy scheme or custom handler can make a different choice. I verify the behavior of the schemes I actually deploy instead of building an alert from the label alone. Likewise, success means the handler produced an authentication ticket. Authorization can still deny that principal, so it does not promise a 2xx response. The separate aspnetcore.authentication.challenges counter answers another question: how often was a scheme challenged? Both a none result and a failure result can be followed by a challenge, so challenge count cannot replace the result split. A challenge is an authent

SpaceXAI has introduced Grok Bot, a system of persistent AI agents that operate on dedicated cloud computers and can interact with websites, applications, inboxes, and other tools. By Daniel Dominguez

Go beyond Do Not Disturb: control what alerts you get and when instead of being overwhelmed with pings all the time.

There is a moment in every high-stakes environment when something goes completely, objectively wrong, and the only viable response is to keep working. In my case, it was a pantry clerk who walked into the dry storage room carrying a stack of boxes, clipped a fire sprinkler head, and discharged what I can only describe as an impressive quantity of initially greasy water across an active commercial kitchen. We were told to continue service. It took four hours for the sprinkler system technicians to arrive and resolve the situation. We dried our shoes afterward. I have thought about that shift many times since leaving commercial kitchens for the technology industry. Not because it was the strangest thing I witnessed. It wasn't. Not by a significant margin. However, because the response to it was so instinctively correct. Nobody called an all-hands. Nobody convened a retrospective on the water. We just kept swimming. It turns out that lesson travels extremely well. A few weeks ago I wrote about how a non-linear career isn't actually non-linear, that the industries change but the underlying questions stay remarkably consistent. I want to make that argument concrete. Here's what commercial kitchens specifically taught me about performing under pressure, and why none of it required translation when I showed up in technology. The Kitchen Never Lies I spent years in commercial kitchens before I spent years in technology. Western Culinary Institute. Private golf clubs. A Lebanese restaurant. Bulk production facilities turning out ten thousand pounds of macaroni and cheese a day, five days a week. Country clubs. A casino. Catering. Culinary competitions. The environments were different. The underlying dynamics were identical. High pressure. Constrained timelines. Mismatched team experience levels. Leadership of wildly variable quality and sobriety. Outcomes that mattered regardless of what had happened behind the scenes to produce them. Customers who neither knew nor cared abo

African defense tech company Terra Industries announced an additional $18 million in funding, bringing its seed round to $52 million.

The Gasolini AR1 uses 2-cylinder Honda bike engine, and an EV is underway, too.

title: Why I left Warehouse out of our Fabric deployment scope published: true tags: microsoftfabric, datawarehouse, cicd, devops Our Fabric deployment pipeline handles sixteen item types. Warehouse is not one of them, and that was deliberate. DEFAULT_ITEM_TYPES = [ " DataPipeline " , " Lakehouse " , " Notebook " , " SemanticModel " , # "Warehouse" is intentionally excluded. Warehouse schema deployment must # be handled separately to avoid schema reset risk during publish. " Environment " , " Eventhouse " , ... ] The reason Publishing a warehouse through this path can reset its schema. Not "might behave unexpectedly". The failure mode is that a deployment intended to be additive removes structure, and the thing that removes it is the same routine that successfully deploys the other sixteen types. The choice that follows Two options once you know that. Include it and hope nobody deploys a warehouse without reading the docs. The pipeline supports everything, and one day someone promotes a change on a Friday and finds out. Or exclude it, document why, and handle warehouse deployment as its own problem with its own tooling. I took the second. An automation that covers most cases and silently corrupts the rest is worse than one that covers most cases and refuses the rest. The refusal is visible. The corruption is not. Making the exclusion loud An exclusion is only useful if someone notices it. Three things help: The comment sits inside the list , not in a doc nobody opens. Anyone reading the item types sees the gap and the reason in the same glance. It is in the README under known limitations, next to the other things the framework does not do. There is a test. It asserts Warehouse is absent from the default scope: def test_warehouse_stays_excluded ( self ): """ Warehouse publish can reset schema, so it is handled separately. """ self . assertNotIn ( " Warehouse " , deploy . DEFAULT_ITEM_TYPES ) That test looks silly. It is asserting that a string is missing from a list.

Par Jacques Gariépy • Guide technique, retour d'expérience, dépannage Windows pas-à-pas et utilisation Web & CLI. Table des Matières Introduction & Architecture Globale Pourquoi ce Setup ? (RTX 3090 24 Go + UD-Q4_K_XL) Comment Obtenir & Générer vos Clés d'Accès Dépannage & Installation d'Unsloth Studio : Le Bug SSLKEYLOGFILE Installation & Compilation de DeepSeek Harness Démarrage du Serveur Local Haute Performance (llama.cpp CUDA 13) Configuration Automatique & Fichier .env Utilisation : Interface Web & Mode CLI (Style Claude Code) Résolution des Pièges & Erreurs Courantes sous Windows Benchmarks Réels sur RTX 3090 Résumé des Commandes & Scripts Clés 1. Introduction & Architecture Globale Faire tourner un agent autonome d'ingénierie logicielle directement sur sa machine locale (100% privé, sans frais d'API et à latence minimale) est devenu une réalité grâce à la convergence de trois briques technologiques de pointe : DeepSeek Harness ( dsh ) : Le framework open-source d'agents de DeepSeek conçu pour orchestrer des workflows complexes de développement logiciel (gestion de sessions, modes Plan/Exécution, sandbox système, sous-agents, exécution de terminaux et édition de code). Unsloth Engine ( llama.cpp CUDA 13) : Le moteur d'inférence C++/CUDA ultra-optimisé intégrant FlashAttention-2 et la quantisation dynamique du cache KV. Qwen 3.8-27B en Quantisation Dynamique ( UD-Q4_K_XL ) : Les modèles de code open-source les plus performants, optimisés par Unsloth pour offrir une précision équivalente au 5-bit avec l'empreinte mémoire d'un 4-bit. Diagramme d'Architecture ┌──────────────────────────────────────────────────────────────────────────────┐ │ INTERFACES UTILISATEUR │ ├──────────────────────────────────────┬───────────────────────────────────────┤ │ Interface Web (Navigateur) │ Interface Console (CLI) │ │ http://127.0.0.1:3080 │ Style Claude Code │ └──────────────────┬───────────────────┴───────────────────┬───────────────────┘ │ │ │ (WebSocket / HTTP) │ (Console I/

Uber is teaming up with drone company Zipline to start airborne takeout deliveries later this year, with the goal of reaching one million daily drone deliveries by 2029. Uber also said it was making a strategic investment in Zipline, a California-based company that has been orchestrating drone deliveries in Texas since 2025. The news comes […]

Lea's pushing back on light/dark mode implementations that display three state options for visitors: light, dark, and system. Dark mode toggles: two states are enough originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

Grab is using AI agents to automate analytics workflows, cutting mechanical analyst work from 44% in February to 30% in June. Its approach combines agent autonomy, certified data, context management and human oversight, with self service analytics increasingly handling metric, data and SQL requests without analyst intervention. By Leela Kumili

You probably use Bluetooth regularly, but different accessories call for different types of Bluetooth.

By the end of this page you can read a budget vs actual table without being fooled by it, and build one in Excel that does not fool anyone else. You will know the variance formula, why analysts write F and U instead of trusting plus and minus, the two ways percent variance lies, and how to say the whole table in one sentence. It is about twenty minutes. Here is what to actually do today. Open the last variance table you were sent and find its biggest percentage. Then find its biggest dollar amount. If they are different rows, and they usually are, you now know which row deserved the attention, and it is probably not the one that got it. The short version: variance is actual minus budget. On a revenue line, positive is good. On a cost line, positive is bad. So analysts label every line F for favorable or U for unfavorable, rank by dollars, and flag by percent. The sign flip is the trap people fall into first, so it gets the picture. The original carries a diagram here. In words: Two panels, each showing a pair of vertical bars rising from a shared baseline. In the left panel, labeled revenue, a shorter bar marked budget stands next to a taller bar marked actual. The extra height of the actual bar above the budget level is shaded in the accent color and marked with the letter F and a check mark, because collecting more revenue than budgeted is favorable. In the right panel, labeled cost, the bars have the same shapes: a shorter budget bar next to a taller actual bar. But here the extra height above budget is shaded in the warning color and marked with the letter U and a cross, because spending more than budgeted is unfavorable. A dashed horizontal line runs across each panel at the budget height. The two panels are geometrically identical, and only the meaning of the line decides whether the overshoot is good or bad. That is why the sign of a variance cannot be read without knowing the line type. Every number on this page is verified. The worked example is a small dep

By the end of this page you can say, out loud and in your own words, what every core operations number does. What the unit of work is. Throughput, and why a count on its own answers nothing. Cycle time, and the rule that ties it to how much work is sitting open. Backlog. Utilization, and why aiming for 100 percent makes everything slower. Error rate, rework and first pass yield. Service levels, and why the average hides the customers you are failing. That list is most of what an operations analyst job, a technical screen, and a first real dataset will ask of you. Here is what to actually do with it. Go through once end to end without stopping, just for the shape. Then come back to the retrieval sheet near the bottom, cover the right-hand column, and try to say each answer before you read it. That second pass is where the learning happens, and there is measured evidence for it further down. The short version: operations analytics is the study of how work moves through a process. Every number in it is either how much, how fast, how much is stuck, or how much was wrong. One idea decides more of your operations work than any other, so it gets the picture. Work arrives, waits, gets done, and leaves. How much is in progress and how long each item takes are two different spans over that same picture, and they are locked to each other. The original carries a diagram here. In words: A left-to-right process diagram. On the far left an arrow labelled "arriving" points into a row of three small stacked boxes labelled "waiting", representing a queue. An arrow leads from the queue into a single larger rounded box labelled "working", representing the person or machine doing the job. A final arrow leads out of that box to the right and is labelled "done". Above the queue and the working box, a bracket in a strong accent colour spans both and is labelled "in progress", showing that work in progress includes everything waiting as well as everything actively being worked on. Below, a

When this workbook is finished, you can change one number and watch the whole thing follow. Move a cut-off from 65 to 70 and every row re-bands, every fill recolors, every count updates, and the legend still matches the map. Nobody can color a cell by hand, because no cell has a color of its own. That is the whole trick, and it takes about twenty minutes to build. The example here is a security risk index across twenty sites. The same shape works for vendor scoring, lead scoring, incident triage, or any list where a number has to turn into a label and a color. The fault, and where it actually comes from You have met this file. A scored list, colored by hand, that nobody quite trusts any more. Look closely and the same faults turn up every time: Two rows score 61.4. One is amber, one is yellow. The same band is drawn in two shades, because two people picked from the palette on two different days. A row sits below the cut-off and is colored red anyway, because somebody knew that site was a problem. A score lands exactly on 65, which appears in two bands, so the answer depends on who typed it. One row has no band at all. It quietly drops out of every count. These look like five separate mistakes. They are one mistake, five times. The rule lives in the formatting instead of in a column. A color is not a value you can test. You cannot write a formula that asks "is this row the right shade of amber," so nothing checks it, and it drifts. The test: can you sort by band? If the band is only a color, you cannot sort it, count it, or filter it, and neither can anybody else. That is the tell. The chain: score, then band, then color Everything below is one idea applied three times. Each thing is derived from the thing before it, and only the first one is typed. Layer Where it lives Who decides it Sub-scores Four columns, one per category Your source data. Typed once. Composite score A formula, from the sub-scores and the weights The weights row Band A formula, from the score The

Cloudflare has released cloudflare/ci, a CI SDK that defines pipelines in TypeScript on top of Cloudflare Workflows, giving each step durable retries and replay, concurrent steps by default and Sandbox snapshot caching. It targets the Workers runtime and depends on Artifacts, still in private beta, so the transferable lesson is the durable-step model rather than a drop-in CI replacement. By Mark Silvester

Last week my feed filled with screenshots of MiniMax H3 benchmark results, and every post seemed to reach a different conclusion about whether the release mattered. I have been through enough launch-day hype cycles to know that a public leaderboard does not predict how a model will behave on my team's actual error logs. So I treated the H3 discussion as a trigger for a controlled experiment instead of as evidence that we should switch tools. This article walks through a lightweight, reproducible smoke test you can run on a free model tier before you commit to a new model. It focuses on code-generation and debugging tasks because those are the areas where a strong vendor benchmark often hides the biggest day-to-day failures. The goal is not to rank MiniMax H3 against every other option; the goal is to create a baseline you can rerun whenever a new model appears. We can run this workflow on MonkeyCode's free model access and free server option, which removes the cost of a quick initial evaluation. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The idea is to use that free capacity for a time-boxed, reproducible test rather than for unstructured prompt tinkering. Why a public benchmark can mislead you A vendor benchmark is usually a point-in-time measurement with a specific harness, sampling strategy, and temperature setting. When a model scores high on a general coding benchmark, it tells you very little about the three failure modes that actually break your work: internal tool calls, long-context edits, and boundary handling in your language stack. I prefer to start with a fixed set of five tasks that I can run in about 30 minutes on any model endpoint. Each task returns a machine-readable result, so the output can be diffed across runs and across models without relying on my memory of how good a response felt. The smoke test harness The Python script below sends five prompts to a generic HTTP endpoint and records latency, output leng

What kind of data is being collected by our devices, and how is it being used? Should we "Ask app not to track"?