标签：#r

Não é novidade para ninguém que estamos passando por uma transformação na área de desenvolvimento de software, em que a IA está assumindo diversas atividades. E isso me faz pensar: o que vamos medir, ou o que teremos como parâmetro para qualidade de software daqui pra frente? É sobre isso que vou falar neste texto. Antes das métricas: entenda o momento do seu time Antes de entrarmos nas métricas em si, precisamos entender o momento em que o nosso time está. É muito fácil eu simplesmente jogar métricas aqui e você aplicá-las ao seu time de maneira automática — mas será que elas fazem sentido para o seu contexto? Uma coisa que eu falo bastante aos meus alunos da mentoria que dou na He4rt Developers é: pra que eu quero isso? Softwares representam necessidades do mundo real, logo, medir o sucesso e a qualidade deles vai depender muito das necessidades que eles buscam suprir. Partindo agora para as métricas, eu gosto de dividi-las em dois grupos: Métricas para stakeholders Métricas para o time Qualidade de software não se resume a número de bugs — ela se aplica tanto em como o software é recebido pelo cliente final, quanto em como ele é desenvolvido. Métricas para stakeholders Uma coisa que eu aprendi neste tempo na empresa em que tenho atuado, principalmente com a transformação digital, é que mostrar número de bugs abertos ou resolvidos não mostra para o público o que realmente importa: como está a qualidade do produto. E, para me ajudar nisso, eu sempre tento me colocar no lugar de um cliente que não tem conhecimento profundo sobre o ciclo de desenvolvimento de software. A primeira coisa que eu gostaria de ver quando um QA, ou o time, vier me mostrar os resultados de uma sprint ou de um quarter é: quantos problemas eu tenho em produção — mas não só isso, quanto tempo tenho levado para resolvê-los. Mean Time to Resolve/Repair (MTTR) Essa é a famosa métrica que vai mostrar o tempo que leva desde que o problema é identificado até ele ser resolvido em produção. Dependendo

The companion to the seven things to check before a WordPress major upgrade is the question that comes right after: when do you actually apply it? A new WordPress major drops today. Do you ship it to production tonight? Tomorrow? In a week? Hold for the next scheduled monthly maintenance? This call tends to live in tribal knowledge, but a few clear axes combined together give you a calibration framework you can apply every time without re-deciding from scratch. Here are five axes worth using. Premise — majors are not security patches The first thing to anchor: a major upgrade is not a security patch . WordPress ships security fixes via minor releases ( 6.4.1 → 6.4.2 , 6.5.0 → 6.5.1 — the second-digit bumps ). Those are same-day apply by default . Major releases ( 6.4 → 6.5 , eventually some 6.x → 7.0 ) carry new features and API changes; they aren't released to be applied immediately for security reasons. Without this distinction, the felt urgency of " we have to apply this for security " pushes you to rush majors that should wait. Minors immediately, majors by judgment — that separation is the first rule worth writing down. Axis 1 — wait for x.0.1 For essentially every major release, x.0.1 (the first patch release) lands within 1–3 weeks and absorbs the critical bugs that surfaced after launch. Past examples have included things like "the admin goes white under specific settings," "a particular theme breaks the block editor," "DB migration stalls in specific environments." Nobody hit these on launch day; they emerged as the world started using it for days or weeks. Just waiting for x.0.1 instead of x.0 sidesteps most of those launch-window bugs. For the first few weeks after a major lands, the world's WordPress installations are effectively running the beta test . Being downstream of the people who hit the mines is the rational position for a maintenance practice. Axis 2 — wait until major plugin vendors update "Tested up to" The thing that breaks most after a majo

​I. The Terminal Velocity of Disembodied Intelligence ​The current paradigm of artificial intelligence is approaching a fundamental cognitive ceiling. We have built vast, sprawling minds, yet we have trapped them in sensory deprivation chambers. Today’s Large Language Models and industrial robotics operate under a fatal flaw: the reliance on linear, arbitrary tokenization. Traditional AI chops raw text into disconnected, non-semantic fragments, wasting immense computational memory tracking the mere phonetic order of spelling and grammar. ​Simultaneously, industrial machines navigate the physical world through siloed sensor pipelines. They process light, space, and kinetics as heavy, raw digital arrays, relying on brute-force algorithmic equations to stitch reality together. This is not intelligence; it is computational exhaustion. To achieve true artificial general intelligence, the machine must stop reading about the world and begin to inhabit it. ​II. The Biological Imperative ​Nature solved the hardware-software integration problem millions of years ago. Human biology remains the ultimate benchmark for flawless, metabolic efficiency. Our somatosensory system does not wait for a central brain to read a text string before pulling a hand away from a fire; it processes physical feedback in milliseconds through decentralized neural highways. ​Human cognition leverages cross-modal integration within the angular gyrus, allowing visual data to be instantly cross-referenced with kinesthetic feedback to trigger anticipatory motor reflexes. Furthermore, biological intelligence is inherently tied to physical survival. We manage thermodynamics through a centralized hypothalamic thermostat, protecting the body from cellular destruction, while routing ambient light data to deep-brain structures to drive a natural circadian rhythm. True AGI requires this exact synthesis of abstract thought and biological mechanics. ​III. The Logographic Paradigm Shift ​To bridge the gap between

This continues from Part ③ . We have the system set to refresh data daily, but unexplained errors can sometimes occur. Symptoms The segments were not properly configured as expected. Checking System → Schedule showed that the automatic refresh schedule itself was fine. The data source refresh also appeared to have no issues. Opening the Unify screen, everything looks like it succeeded at first glance — but checking the Performance screen reveals that something is failing during unification. Drilling in further, the failure is happening at the Customer Profiles step. Resolution Steps Attempt to manually re-run just the "Customer Profiles" task → Error persists Redo everything manually from the data source refresh step onwards This resolved the issue successfully. Takeaway Since this is not a misconfiguration issue, patient repetition of the refresh process is enough to recover. Hopefully this saves someone some troubleshooting time.

If you've been working with cognitive architectures that rely on structured memory injection, you likely know the pain of corrupted or incomplete embedding spaces. The latest update to hermes-memory-installer directly addresses a brittle failure mode: missing embeddings in the gbrain module. This fix introduces an automatic, targeted repair mechanism that detects and rebuilds only the affected subset of embeddings, rather than triggering a full reinstall. Here’s what changed, why it matters, and how to benefit from it. The Problem: Silent Degradation in gbrain In a typical setup, hermes-memory-installer populates the gbrain—a specialized long-term memory store—with precomputed embeddings for core concepts, episodic traces, and procedural patterns. These embeddings are the numeric backbone that allows the agent to query, retrieve, and associate memories efficiently. However, under certain conditions—partial upgrades, concurrent memory imports, or incomplete network transfers—the gbrain’s embedding table ended up with holes. Specific embeddings for targeted contexts were simply missing. The agent would still boot, but retrieval quality degraded silently: queries returned null vectors or fell back to generic responses, breaking fine-grained recall. Users reported that their agents "forgot" recent conversations or failed to recognize learned skills, yet no obvious error was raised. Previously, the only remedy was a full reinstall of the memory installer, which wiped and rebuilt the entire gbrain. That was slow, wasteful, and could erase customized embeddings that were working correctly. The Fix: Targeted Auto-Repair The new update ( v2.1.0 onwards) adds a dedicated repair pass during the installation and upgrade routine. Instead of scanning the entire gbrain, the installer now maintains a lightweight manifest of expected embedding keys for each memory context. During setup, it checks the actual embedding store against this manifest. If any keys are missing, it triggers

Skullcandy’s audio products aren’t exactly known for their stellar audio quality or noise cancellation, but its latest headphones are getting an assist from Bose to turn things around.

Microsoft is looking to sell its in-house AI models as more efficient and cost-effective than its competitors' models.

Your iPhone walks up, the door unlocks. No app, no button. An open Aliro lock with secure UWB ranging + NFC tap, running end-to-end on an nRF5340 https://github.com/asxeem/openaliro

Roughly 100 fires are raging out of control in Ontario, sending smoke streaming 1,000 miles to the south and east.

🔒 ATLOCK v4 — I stopped trusting my own app's encryption, so I rebuilt it TL;DR — ATLOCK is a...