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Volvo is updating three of its electric vehicles with new hazard alerts to warn drivers when there are animals or vulnerable road users ahead. The new connected safety features are based on Volvo's cars talking to one another, as opposed to crowdsourced alert systems used by popular navigation tools like Google Maps and Waze. The […]

The federal cyber agency's warning comes amid a wave of suspected Iran-backed cyberattacks targeting critical water systems across the United States.

A solid observability setup usually comes down to three pieces working together: something that collects metrics, something that exposes system-level metrics, and something that visualizes it all. Here's what each one does and how to install them. The Theory: How This All Fits Together Before installing anything, it helps to understand the model, because it's a bit different from how logging or alerting tools usually work. Pull, not push. Most people's first instinct is "the app should send its metrics somewhere." Prometheus flips that around — it pulls metrics on a timer instead. Every target (a machine, a service, an app) exposes a simple HTTP endpoint, usually /metrics , that just returns plain text numbers. Prometheus visits that endpoint every N seconds (the "scrape interval") and saves whatever it finds, with a timestamp attached. Nothing gets pushed to Prometheus — Prometheus goes and asks. This means for anything to show up in Prometheus, it has to satisfy one requirement: something has to expose a /metrics endpoint Prometheus can reach. That's the whole game. Everything else in this stack exists to satisfy that one requirement or to make the data useful afterward. Why Node Exporter exists. Your operating system doesn't naturally speak Prometheus's language — it doesn't expose CPU/memory/disk stats as a /metrics endpoint by default. Node Exporter's only job is to read stats the OS already tracks (via /proc and /sys on Linux) and republish them in the text format Prometheus expects, on port 9100. It's a translator, not a monitoring tool by itself — it collects nothing, decides nothing, alerts on nothing. It just answers "what does this machine look like right now?" whenever asked. Why Prometheus itself is separate. Prometheus doesn't know anything about CPUs or memory — it has no idea what it's scraping. It just knows: "go hit this list of URLs on a schedule, and remember what comes back." The intelligence is in the config (which targets to scrape, how often)

I published a post called "I Audited My Own Portfolio and Found 20 Problems" . It was an inventory: I went through my own site — a React 19 + Vite SPA with Sanity as the CMS — wrote down everything that was wrong with it, fixed what mattered, and put the before and after numbers next to each item. If you haven't read it, the only part that matters here is the methodology, and one line of it in particular: I went through the build output chunk by chunk in build/assets/ . I called that the step that hurts and the one most people skip. I still think that is true. It is also the step that guaranteed I would miss the largest thing wrong with the site. The step that worked Weighing the build output worked exactly as advertised. Finding 1 of that audit was an unoptimized PNG of a developer illustration on /gabriel-abreu , my contact page, 993 KB, sent to every visitor who landed there. It went to 23 KB. A second image, the cutout of me that sits in three different greetings, went from 358 KB to 45 KB. Those two are bundled assets. A component imports one: import p from " ../assets/developer-illustration.webp " ; Vite follows that import, hashes the file, and emits it into build/assets/ . After the build it is a file on disk with a size. Listing the directory finds it. Sorting the listing by size finds it first. There is no way to ship it and not have it show up in that step. So the method was sound within its domain: both of those images are bundled assets, and the step found both. On August 23 I opened the blog index in a browser and watched what it actually requested. Sixteen post covers, 9.88 MB. None of that could have appeared in the audit. Not because I was sloppy that day — because of where those bytes come from. Two lifecycles A bundled asset exists at build time. An import makes it a build input, the bundler makes it a build output, and anything that reads the build output sees it. A CMS image is never a build input. Nothing imports it. It arrives as a string in a

The focus is on agentic capability and predictable enterprise deployment.

Bill Gates has been reflecting a lot on AI lately, and the process has triggered a stark awakening. Once a staunch AI optimist, the Microsoft cofounder is now deeply pessimistic about what AI means for our collective future. Having been conspicuously quiet on AI issues recently, Gates is back with a nearly 6,000-word essay seeking […]

A steel frame cargo bike with a unique twist: You can let it choose your gear.

Runable says 60%–70% of its 1 trillion-plus token usage in the last 90 days came from paying customers.

Anthropic reliability engineer Alex Palcuie shares practical lessons on using LLMs for real-world incident response. He explains where AI acts as a superhuman for observing logs and traces, why it still struggles with causation versus correlation during root-cause analysis, and how engineering leaders can integrate AI into on-call workflows without eroding human expertise. By Alex Palcuie

Xbox and Ikea collab alert! This unique nine-piece collection for gamers arrives this fall and has a slew of Easter eggs.

要点 呼び方の揺れにはシソーラス、置き場所の迷いにはタクソノミーが役立ちます 複数の情報がどう関係するかまで扱うなら、オントロジーを検討できます すべてを整えず、目の前の困りごとに合う方法から小さく始められます Slackで共有された仕様を探し、Notionの議事録とFigmaの画面を見比べ、GitHubのIssueで変更の経緯をたどる。情報は揃っているのに、呼び方や置き場所が違うだけで確認に時間がかかることがあります。そんな状況を整理する手がかりとして、シソーラス・タクソノミー・オントロジーという3つの考え方を眺めてみます。 情報の量だけでなく、整理の基準にも目を向ける 情報過多というと、保存する量を減らすことに目が向きがちです。ただ、同じものが違う名前で呼ばれ、保存場所が人によって変わり、情報同士の関係も見えにくいことが負担になっている場合があります。 同じ「ユーザー」という言葉でも、仕様書ではサービスの利用者、データモデルではログイン済みのアカウントを指しているかもしれません。反対に、「モーダル」「ダイアログ」「ポップアップ」のように、違う言葉で同じ画面を指していることもあります。認識を揃えないまま設計と実装が進み、後から齟齬が分かって仕様変更になった経験もあるかもしれません。 言葉と分類と関係を整理することは、情報を探しやすくするだけでなく、こうした手戻りを減らす助けにもなりそうです。3つの考え方は、それぞれ次の役割を担います。 シソーラス：言葉を揃える タクソノミー：置き場所を決める オントロジー：意味のつながりを記述する シソーラス：呼び方を揃える シソーラスは、言葉同士の関係を整理した語彙集です。同じ意味の言葉を代表語にまとめるほか、上位語・下位語や関連語も記録します。 たとえば、チーム内で同じUIを「ダイアログ」「モーダル」と呼んでいるなら、代表語を一つ決め、もう一方を同義語として結び付けられます。「オーバーレイ」を上位語、「ドロワー」を関連語として扱うこともできます。仕様書とデザインシステムで表記が違っていても、同じ情報へたどり着きやすくなるでしょう。 タグの表記揺れを抑えたいときや、社内検索で資料の取りこぼしを減らしたいときに使いやすい方法です。よく使うUI用語を一覧にして、代表語・同義語・関連語を記録するだけでも、小さなシソーラスになります。 教育文献データベースのERICでは、シソーラスの統制語を各文献に付与しています。「Indexing」には同じ検索先へ導く語や上位語、関連語がまとめられ、表現が異なる文献も共通の語彙から探せます（ ERIC Thesaurus ）。 タクソノミー：置き場所を決める タクソノミーは、情報を一定の基準で分類し、主に階層として整理する仕組みです。たとえばデザインシステムなら、「基礎」の下に「色」「余白」「文字」、「コンポーネント」の下に「入力」「ナビゲーション」「フィードバック」を置く、といった構造が考えられます。 上位の分類から下位へたどれるため、情報の全体像を見渡しやすくなります。ドキュメント、社内Wiki、デザインシステムなど、共通の置き場所を用意したい場面に向いています。 一方で、複数の軸を一つの階層へ押し込むと、迷いが生まれることもあります。「エラー表示付きの入力フォーム」は、「入力」と「フィードバック」のどちらにも置けそうです。この場合は主となる分類軸を一つ選び、ほかの軸をタグで補う方法もあります。 Google Merchant Centerでは、商品を「Apparel & Accessories > Clothing > Outerwear」のように、大きな分類から具体的な分類へ配置します。独自の商品名でも共通の階層に対応させることで、商品群の整理や広告運用の軸として使えます（ Google Merchant Centerの商品データ仕様 ）。 オントロジー：意味のつながりを記述する オントロジーは、ある領域に存在するものの種類、性質、関係、必要に応じて制約を明示したモデルです。単に「近い言葉」「同じカテゴリ」として結ぶだけでなく、何と何が、どのような意味で関係しているかを表します。 たとえば、「機能」「画面」「コンポーネント」「API」「Issue」という種類を定義し、「画面は機能を提供する」「画面はコンポーネントを使う」「機能はAPIに依存する」「Issueは機能を変更する」といった関係を記述できます。すると、「このAPIの変更で影響を受ける画面と関連Issue」のような、複数の関係をたどる問いにも答えやすくなります。 Schema.orgには「Person」「Event」「Product」などの型と属性が定義されています。Webページの情報を意味のある

Puzzles and games have been central to AI development since the very beginning. Just as we humans like to test our smarts with crosswords or logic puzzles, developers can test how far models have advanced with a gaming gauntlet. The term “machine learning” was popularized in a 1959 article by the IBM computer scientist Arthur…

디지털 자산 시장은 지금 변곡점에 서 있다. 블록체인 기술이 본래 파괴적이고 반체제적인 힘에서 벗어나 전 세계 금융 시스템의 점점 더 통합된 구성 요소로 진화하면서, 심오한 변화를 목격하고 있기 때문이다. 이러한 패러다임 전환은 흥미로운 이중성을 보여준다. 한편으로는 전통 금융 기관(TradFi)이 기존 시스템을 강화하기 위해 블록체인을 적극적으로 수용하고 있고, 다른 한편으로는 암호화폐 기반 기업들이 주류 금융과의 간극을 메우기 위해 규제적 정당성을 끊임없이 추구하고 있다. 최근의 이러한 움직임들은 분산원장기술(DLT)이 새로운 하이브리드 금융 아키텍처의 토대가 되는 미래를 예고하며, 이 복잡한 춤사위를 더욱 부각한다. 이러한 흐름의 중요한 한 걸음은 미국 주() 은행 협회들이 2027년 출범을 목표로 전국적인 블록체인 네트워크인 "뱅크체인 얼라이언스(BankChain Alliance)"를 발표한 일이다. 39개 주 협회의 지원을 받는 이 이니셔티브는 스테이블코인, 결제, 토큰화된 예금을 은행 시스템의 규제 범위 내에서 육성하는 것을 목표로 한다. 동시에, 암호화폐 인프라 기업인 제로해시(Zerohash)가 초반의 난관에도 불구하고 미국 통화감독청(OCC)의 신탁은행 인가를 확보하려는 끊임없는 노력은 암호화폐 산업이 주류의 수용과 규제 통합을 향해 나아가려는 의지를 잘 보여준다. 이러한 사건들은 고립된 현상이 아니다. 블록체인의 혁신적인 잠재력이 기존 금융 구조에 의해 형성되고 흡수되는 한편, 암호화폐 벤처들은 확립된 법률 및 규제 준수 프레임워크 내에서 운영하려 하는 중요한 단계를 나타낸다. 광범위한 기술적 야망의 맥락에서, 일론 머스크의 스페이스엑스(SpaceX)가 루이지애나에 1,000억 달러 규모의 우주공항을 건설할 계획이라는 소식은 블록체인과 직접적인 관련은 없지만, 미래 인프라를 재정의할 최첨단 기술에 막대한 자본과 전략적 투입이 이루어지고 있음을 보여준다. 이는 디지털 자산 인프라에 대한 금융 부문의 대규모 구축과도 유사하다. 이 글은 이러한 금융 블록체인 발전의 함의를 깊이 탐구하고, 기술적 기반, 실제 선례, 그리고 내재된 한계를 분석할 것이다. 블록체인 기술의 탄생은 특히 2009년 비트코인(Bitcoin)과 함께, 2008년 금융 위기 동안 전통 은행 시스템의 실패와 중앙집중화에 대한 인식에 대한 직접적인 대응이었다. 탈중앙화, 투명성, 중개자 제거라는 핵심 원칙은 가치 이전과 기록 보관에 대한 대안적인 비전을 제시했고, 이는 처음에는 전통 금융의 회의적인 시선을 받았다. 그러나 기반이 되는 DLT가 성숙해지면서, 금융 기관들은 운영 효율성을 높이고, 결제 시간을 단축하며, 비용을 절감하고, 데이터 무결성을 개선할 수 있는 심오한 잠재력을 인식하기 시작했다. 이처럼 전면적인 거부에서 전략적 채택으로의 점진적인 변화는 지난 10년간의 특징적인 흐름이었다. 전통 금융이 블록체인에 매력을 느끼는 이유는 현재 번거롭고 비용이 많이 드는 프로세스를 간소화할 수 있는 능력 때문이다. 블록체인에서 실제 자산을 나타내는 토큰화된 자산은 즉각적인 결제, 분할 소유권, 그리고 유동성 증가를 약속한다. 특히 규제 대상 기관이 보유한 법정화폐 준비금으로 뒷받침되는 스테이블코인은 암호화폐의 프로그래밍 가능성과 효율성을 전통 화폐와 관련된 안정성 및 신뢰와 결합한 디지털 교환 매체를 제공한다. 이러한 융합은 미국 내에서 복잡하고 진화하는 규제 환경 속에서 진행되고 있다. OCC, SEC, 그리고 주 은행 부서와 같은 다양한 연방 및 주 기관들은 디지털 자산과 DLT 응용 프로그램을 어떻게 분류하고 감독할지에 대해 고심하고 있다. 기술적 야망의 엄청난 규모는 금융 분야에만 국한되지 않는다. 스페이스엑스가 2027년 건설을 시작하고 2029년 첫 비행을 목표로 루이지애나에 1,000억 달러 규모의 우주공항을 건설할 계획을 발표한 것은 다양한 분야에서 최첨단 인프라에 막대한 투자가 이루어지고 있음을 증명한다. 이 프로젝트는 블록체인과는 별개이지만, 궤도 데이터 센터든 차세대 금융 레일이든 미래 기술 패러다임을 지원하기 위한

Honestly, when I saw the headline "Someone in Korea used AI to cut the prep time for a dose of Korean herbal medicine from 300 minutes to 5," the first thing that caught my eye wasn't "whoa, robots can make herbal medicine now." It was how they did it—because they happened to get right the one thing most people get wrong when they think about applying AI. What Onerve Did Let's start with the facts. There's a Korean startup called Onerve (오너브), backed by the Korea Institute of Oriental Medicine, working on automating the manufacturing of Korean herbal medicine (한약). Their system is called HAP. It connects AI with electronic medical records (EMR) to automate the entire flow—from prescription input, to manufacturing, cleaning, packaging, and inventory management. The key is the raw material: they use standardized, freeze-dried herbs in a "cartridge" format—turning herbs that used to require on-site boiling and heavy manual labor into uniform, standardized modules. The result: prep time for a single dose of Korean herbal medicine dropped from around 300 minutes to around 5. They won a CES Innovation Award and closed a Series A round of roughly 6.2 billion won. And they're not alone—another Korean company, Camelotech (with its Cameleon system), is doing almost the same thing and also showed up at CES. So "Korean herbal medicine automation" is turning from a one-off experiment into an actual category. What I'm Actually Paying Attention To Isn't the Speed—It's Which Layer They Automated If all you take away from this is "300 minutes became 5," you're missing the most important part. When people see AI moving into an industry with a thousand-plus years of tradition behind it, the gut reaction is usually panic: "Are even Korean medicine doctors about to get replaced by AI?" But if you look closely at what Onerve actually automated—it's the manufacturing , not the diagnosis and prescribing . Deciding which medicine a person should take, how to adjust the dosage, how to read t

The False Dichotomy The agent ecosystem is split into two camps: Camp Loops (Boris Cherny, OpenAI Agents SDK, LangGraph): > "Agents are loops. Plan → act → observe → repeat. The loop is the atomic unit." Camp Graphs (Steve Yegge, Gas Town, LangGraph DAGs, CrewAI): > "Agents are graphs. Nodes are agents/tools. Edges are handoffs. The graph is the architecture." Both are right. Both are incomplete. What Loops Get Right Loops capture temporal behavior — the iterative, self-correcting nature of agent work: - Replanning on failure (AdaPlanner, ReAct) - Budget enforcement (token caps, step limits, cost ceilings) - Verification gates (process reward models, extraction floors) - Learning loops (feedback → lessons → advisory → suppress) A loop is a control structure. It says: keep going until condition X. What Graphs Get Right Graphs capture structural composition — how capabilities connect: - Handoffs (peer-to-peer control transfer) - Parallel execution (swarms, polecats, fan-out/fan-in) - Supervision trees (Erlang/OTP-style restart strategies) - Provenance (who called whom, with what context) A graph is a dependency structure. It says: A feeds B, B feeds C, C can restart A. The Missing Layer: A Compiler Between Repos and Runtime Here's what neither camp addresses: Where do the nodes come from? Today: - You find a repo on GitHub - You hope it implements what it claims - You wire it into your graph/loop - You pray it works There's no verification layer. No SBOM. No attestation. No provenance. HURCULES: The Compiler Between Repos and Runtime HURCULES sits between the repository and the agent runtime: GitHub Repository → HURCULES → Verified Capability Package → Agent Runtime (Loop or Graph) It doesn't care if your runtime is a loop or a graph. It produces verified capabilities that work in either. What HURCULES Compiles | Input | Output | |-------------------------------|---------------------------------------------------| | Raw repo (any language) | Deterministic map (file tr

SpaceX aims to launch Starship from Florida by the end of the year. 2027 seems more likely.

Anthony Ralphs was frustrated by the San Diego City Council's support for Flock. He decided it would take something more than reasoned argument to get their attention.

In the span of a few hours on Monday, the Department of Homeland Security announced that it would be implementing a fee of over $103,000 on H-1B visas, and news broke that the State Department plans on revoking the visas of up to 200,000 asylum seekers - which, according to The Associated Press, would be […]

These are the lessons we learned evaluating LLMs for real-world secret scanning. The post How to evaluate LLMs before production appeared first on The GitHub Blog .

Record-breaking robot races are less substantial than household chore challenges.