1. gpt-5.6-sol이 PowerShell의 $HOME 변수 충돌로 사용자 홈 디렉터리를 날려버릴 뻔한 건에 대하여 핵심 내용 요약: AI 코딩 에이전트가 PowerShell의 대소문자 미구분 변수 규칙을 잘못 다뤄 임시 디렉터리 대신 사용자 홈 디렉터리를 삭제하려 한 사고 사례입니다. 모델 자체의 장기 작업 능력이 뛰어나더라도 셸 격리와 변수 스코프를 제대로 통제하지 않으면 작은 스크립트 실수가 치명적 명령으로 이어질 수 있습니다. CLI 에이전트를 운영할 때 샌드박싱, 컨테이너화, 파괴적 명령 방어가 필수라는 점을 보여줍니다. GeekNews 상세 페이지: https://news.hada.io/topic?id=31390 원문 링크: https://gist.github.com/xamong/e98478b333bb9951b175284f744eb0ed 2. Show GN: 정치 커뮤니티에 AI 팩트체크 기능을 붙이며 겪은 시행착오들 핵심 내용 요약: 정치 커뮤니티에 AI 팩트체크를 붙이면서 의견과 사실 주장을 분리하고, 검증 가능한 문장만 대상으로 삼도록 파이프라인을 바꾼 경험담입니다. 작성 시점의 원문 스냅샷을 보관하고 출처를 투명하게 보여주며, 근거가 부족한 경우에는 판단 보류를 반환하도록 설계했습니다. BullMQ 기반 비동기 처리와 Gemini 모델 fallback까지 포함해 실제 서비스에서 환각과 비용, 대기열을 함께 다룬 사례입니다. GeekNews 상세 페이지: https://news.hada.io/topic?id=31389 원문 링크: https://app.uhheung.kr/community 3. 앤트로픽, 한국 무료 사용자에 1,660만 달러 '유령 청구서' 발송 핵심 내용 요약: API 사용량이 없는 무료 사용자에게 Anthropic 공식 도메인과 Stripe를 통해 거액의 청구서가 발송된 사례입니다. 실제 결제 수단이 없어 인출은 발생하지 않았지만, 청구 근거가 없고 회사의 명확한 설명도 없어 AI API 서비스의 과금 신뢰성 문제가 커졌습니다. 개발자 입장에서는 사용량 계측, 청구 검증, 지원 대응이 모델 성능만큼 중요한 운영 요소임을 보여줍니다. GeekNews 상세 페이지: https://news.hada.io/topic?id=31388 원문 링크: https://www.thenews.com.pk/latest/1408788-why-did-anthropic-charge-a-free-user-166-million-despite-zero-api-usage 4. AI 에이전트 시대의 새로운 SaaS 플레이북 핵심 내용 요약: AI가 기능 구현 비용을 낮추면서 SaaS의 방어력은 UI나 기능 자체가 아니라 독점 데이터, 행동 권한, 에이전트 유통, 기록 시스템 같은 희소 자산으로 이동한다는 분석입니다. 좌석 기반 과금보다 성과 기반 과금이 중요해지면 공급자는 결과 실패 위험과 추론 비용을 함께 관리해야 합니다. 에이전트가 호출하는 승인된 도구가 되는 것이 새로운 유통 전략의 핵심으로 제시됩니다. GeekNews 상세 페이지: https://news.hada.io/topic?id=31387 원문 링크: https://www.thevccorner.com/p/the-new-saas-playbook-ai-agent-era 5. Show GN: AI 봇 12개에게 두 달간 주가 방향을 예측시키고 전부 공개 검증해봤습니다 핵심 내용 요약: LDBD는 사람과 AI 봇이 주식, ETF, 크립토의 방향을 공개 예측하고 시간이 지난 뒤 자동 채점되는 실험 서비스입니다. 12개 AI 봇과 여러 베이스라인을 함께 운영해 기저 확률을 이기는지 비교하고, 예측 기록을 수정할 수 없도록 남깁니다. REST API와 MCP 서버를 제공해 외부 에이전트도 예측에 참여할 수 있게 한 점이 AI 평가 플랫폼으로 흥미롭습니다. GeekNews 상세 페이지: https://news.hada.io/topic?id=31386 원문 링크: https://ldbd.app 6. 숏폼 동영상이 B2B 검색 결과와 AI 답변으로 영역을 확장하고

submitted by /u/Rich_Investigator_23 [link] [留言]

Videos are one of the heaviest assets you can add to a web page. Loading videos too early can significantly impact your application's performance. The good news is that modern browsers are starting to support lazy loading for video elements , allowing you to defer loading until users are likely to watch them. However, there's one important thing to know: 👉 This feature is not yet part of the Baseline web platform , so browser support is still limited. At the time of writing, lazy loading for <video> elements is supported in Chromium-based browsers such as Google Chrome , Microsoft Edge , and Opera , while browsers like Firefox and Safari do not yet support it natively. In this article, we'll explore: What lazy loading videos is Why it's important for web performance How to implement it Browser support considerations Best practices for optimizing video loading Let's dive in. 🤔 What Is Lazy Loading for Videos? Lazy loading means delaying the loading of a resource until it's actually needed. Instead of downloading every video immediately during page load, the browser waits until the video is close to entering the viewport. This helps reduce: initial network requests bandwidth usage page load time memory consumption Especially on pages with multiple videos, the difference can be significant. 🟢 What Problem Does It Solve? Imagine an e-commerce page with several product videos. Without lazy loading: every video starts downloading immediately bandwidth is consumed even for videos users never watch page rendering may become slower This negatively impacts; Largest Contentful Paint (LCP), Time to Interactive (TTI), and overall user experience. Most visitors won't watch every video on the page. So why load them all? Lazy loading ensures videos are fetched only when they're actually needed. 🟢 How to Lazy Load a Video The easiest approach is using the loading="lazy" attribute. Example: <video controls loading= "lazy" poster= "/preview.jpg" > <source src= "/video.mp4" type= "vide

Multiple Tailscale tailnets at once, no switching + no VM Discussion | Link

How to not architect your software submitted by /u/yyyyuuuuyyyyyyyyyy [link] [留言]

This is a submission for Weekend Challenge: Passion Edition What I Built Rivalry Engine — pick two national football teams, and Snowflake reads 150 years of their matches, scores how heated the rivalry is, calls a one-word verdict on its shape, predicts the next result, and lets Cortex narrate the one story inside it. All of it — the data, the analytics, the AI, and the app — runs inside Snowflake. Nothing ever leaves the warehouse. A scoreboard tells you who won. It never tells you what the rivalry means . Argentina and Brazil have met over a hundred times across a century — a razor-thin ledger that has never let either side feel safe. Germany and England meet rarely, but every meeting carries a tournament's weight. Most national teams have never played each other at all. My goal was a product that could feel the difference between those three shapes — not another stats table, because a table is a report and I wanted an argument. The passion is football. But the real engineering question underneath it was the one I actually cared about: can an AI tell the story of a rivalry without lying about the facts? So I gave myself one rule before writing a line of code: The AI interprets the shape of a rivalry. It never invents the facts. Every count, date, score and streak on screen is computed in SQL from real matches. Cortex is handed only those computed facts, and told explicitly to never produce a number. And its honest corollary: two nations that never met get a "first chapter unwritten" card — and Cortex is never called. A product that can't return nothing will invent something. This one returns nothing. Demo It runs entirely in Streamlit in Snowflake (Snowsight) — no public host, no API keys — so here's a walkthrough: The 90-second tour: Argentina vs Brazil → the heat gauge pins to 🔥 Blood rivalry , recent-form chips light up, and the SQL detector's verdict reads Blood feud . ✨ Generate the story → Cortex writes a narrative that cites the real biggest thrashing and f

Problem Statement For roughly a decade, vision-language models have been declared to be approaching or matching human performance on scene description (captioning). The evidence for that claim has almost always come from the same family of benchmarks—most famously MS-COCO. Those images are typically clean, well-lit, and depict either no people or people performing simple, isolated actions (sitting, walking, holding an object). They rarely require the model to parse multi-agent social dynamics, subtle intentions, or the kind of relational reasoning humans perform effortlessly when watching a movie scene or a street interaction. Because the evaluation data are easy, the reported numbers look excellent. Automatic metrics such as BLEU-4, CIDEr, or even embedding-based scores like BERTScore further inflate the impression of progress: they reward surface lexical overlap more than genuine semantic fidelity. At the same time, almost no work has systematically catalogued which visual-cognitive failures models still commit, or how those failure modes have changed as architectures moved from CNN+LSTM captioners to today’s multimodal large language models (MLLMs). The result is a field that can claim “human-level performance” while remaining largely blind to whether the models actually understand the scenes that matter most in real applications—scenes full of people interacting. The authors therefore set out to answer two concrete questions that the existing literature left open: (1) How much of the apparent progress is an artifact of easy data? (2) Which specific error types have been eliminated and which stubbornly remain? Core Idea The core insight is that progress looks dramatically different once you force models to describe complex social behavior and once you measure not only overall accuracy but a taxonomy of visual-cognitive errors. By constructing a new 100-image Complex Social Behavior (CSB) dataset drawn from movie frames that require reasoning about multi-person in

Try the demo: K-Saju Crew For fun only. K-Saju is an entertainment project. The K-Pop roles below are a playful interpretation of saju-inspired signals, not personality assessment or advice. A two-person compatibility page can stay stateless with almost no effort. Put both birth dates in a URL, render the result on the server, and the link is the record. No account, no database, no cleanup job. That was already a product rule in K-Saju. We do not retain personal inputs. A result is reproducible from its GET parameters. Then we built /crew : “What if your friend group debuted as a K-Pop group?” A creator makes a link, sends it to a group chat, and each friend enters their own birth date. At three to seven members, the app assigns distinct positions, shows pairwise chemistry, and creates a shareable poster. The fun part is the casting. The engineering problem is that the social flow needs a temporary shared state. A link cannot accumulate submissions by itself. This post is about the decisions behind that feature: where we allowed state, how we made the result durable without retaining a lobby forever, and how we kept the casting explainable instead of treating it as a black-box score. The conflict: a self-service group flow needs somewhere to collect data There were two clean but incomplete options. The first was to keep everything stateless. The creator would enter all members' dates at once, then receive a result URL. It matched our existing architecture, but it defeated the point of sharing a link. The person who starts the group often does not know everyone else's date, and asking them to collect it in a chat creates friction before the feature has started. The second was a conventional persistent group object. It would make joining easy, but it would turn a deliberately stateless service into one that keeps user-provided dates indefinitely unless we built retention and deletion policies around it. We chose a hybrid instead: The lobby is temporary state. It store

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