AI 资讯

Introduction: The Question Beneath All Questions I think a lot of people are searching for the same thing but using different words. Some people call it: success happiness freedom purpose wealth enlightenment achievement legacy But underneath all of it is a deeper question: "What is worth dedicating my life to?" That question is harder than finding a job. It is harder than making money. It is harder than becoming successful. Because even if I get everything I think I want, I still have to answer: "Why did I want it?" Chapter 1: The Strange Reality of Modern Life We live in one of the wealthiest civilizations in human history. We have: technology beyond anything past generations could imagine medicine that would look like magic centuries ago instant communication across the planet access to unlimited information Yet so many people feel like they are barely surviving. Why? Because humans were built for survival. Our brains evolved to constantly ask: What could go wrong? How do I stay safe? How do I belong? Am I enough? Am I falling behind? The threats changed, but the machinery stayed the same. Ancient humans worried about predators. Modern humans worry about: money careers status loneliness comparison identity the future The battlefield moved from the physical world into the mind. Chapter 2: Money Is Not Evil — Money Is Power There is a tendency to pretend money does not matter. That is false. Money changes what is possible. Money can buy: freedom of time experiences travel education tools comfort opportunities the ability to help others A person with resources has access to a larger range of possible lives. Wanting money does not automatically mean being greedy. Sometimes wanting money is really wanting: freedom adventure security creativity proof that you overcame something The question is not: "Do I want money?" The better question is: "What will I use money for?" Money is a tool. A hammer can build a house or destroy something. The tool is not the meaning. The pu

Every "build an AI app" tutorial stops at the demo. Prompt goes in, response comes out, ship it. Nobody covers the part where that demo has real users and you're staring at a Gemini or OpenAI invoice trying to figure out which feature did that. I've spent the last several months building the AI layer for a consumer app that fires vision and language calls on almost every user action. Not a chatbot getting occasional traffic. A product where the model call basically is the product. Here's what I actually had to build, in the order I had to build it. Four problems, not one Cost first, obviously. Tokens are metered, and past a certain volume, calling the model on every request means paying for answers you already gave someone five minutes ago. Latency next. A cache hit lands in milliseconds. A cold model call takes seconds. Users feel that, especially anything camera-driven where they're staring at a loading spinner over their own kitchen counter. Reliability too. Your provider will have an outage or a degraded day at some point. Not if, when. And blast radius. One bug, one bot, one traffic spike, and a $50/day bill becomes a $5,000/day one while everyone's asleep. You don't see any of this in a demo. It shows up with real traffic, and by then it's a lot more expensive to fix than it would've been to build right the first time. Cache on what the query means, not its exact string Key your cache off the literal request text and you've built something close to useless. "What can I make with chicken and rice" and "chicken and rice, what should I cook" mean the same thing and share almost no characters. So embed the query, run a vector similarity search against everything already answered, and if something clears a high threshold, serve that instead of paying for another call. I use 0.95 cosine similarity as the bar. async function checkSemanticCache(embedding: number[], taskType: string, threshold = 0.95) { const { data } = await db.rpc("find_similar_response", { query_emb

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.

Cars24 uses OpenAI-powered voice and chat agents to handle 1M+ monthly conversation minutes, recover 12% of lost leads, and bring agentic workflows to teams across the company.

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

An AppleCare Plus subscription for a Mac or iPad will cost more soon, with prices going up by $0.50 per month or $5 per year for new sign-ups while remaining the same for existing subscribers, according to Bloomberg's Mark Gurman. Gurman says that under the new AppleCare Plus pricing, "a plan for a new 13-inch […]

Union wants to halt a "perpetual cycle" of layoffs, get back to contract bargaining.

Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on reliable multi-step execution. But the ambition runs well ahead of the reality: most deployed “agents” are still chatbot wrappers, the control plane enterprises expect is deliberately hybrid to avoid lock-in, and real-time fiscal control over token burn remains the exception. This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them. The central finding is a gap between orchestration ambition and orchestration reality. Enterprises are consolidating fast onto the major model platforms: Anthropic’s Claude is the primary platform for 40%, more than double any rival, followed by Microsoft (18%) and OpenAI (13%). The choice is driven by “model gravity” — native alignment with a state-of-the-art base model (21%) — and success is judged by reliable, multi-step execution (task completion reliability 32%, multi-step workflow management 28%). Yet asked to assess their portfolios honestly, 71% say a quarter or fewer of their deployed “agents” are true multi-step orchestrated workflows rather than single-prompt chatbot wrappers, and only 10% have crossed the halfway mark. The orchestration layer is being built well ahead of the orchestrated portfolio it is meant to run. That gap shapes the architecture enterprises are putting in place. By the end of 2026 a clear majority (51%) expect a hybrid control plane — provider-native plus external orchestration — and only 6% expect to hand control to a provider-managed service, because vendor lock-in (35%) is the risk they fear most if control lives inside a mo

Valve has been incredibly friendly to customers who need repairs - which is why it was so surprising to hear that Valve was already discontinuing the battery for the Steam Deck LCD handheld. It would have meant you could no longer just buy a battery from iFixit and install it yourself. Why would Valve stop […]