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Ubiquitous Web / Front-end / React / HTML / CSS

허리도 가늘군 만지면 부서지리
Joined May 2009
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⭐ AiSOC ⭐ github.com/beenuar/AiSOC 1. AI가 보안 경보를 대신 살펴보는 보안 관제 센터(SOC)를 오픈소스로 만든 프로젝트가 나옴. 내 서버에 직접 설치해서 쓸 수 있음 2. AI 에이전트가 경보를 조사하는데, 프롬프트와 도구 호출까지 한 단계씩 전부 기록해서 나중에 다시 확인 가능. 대응 조치는 사람이 승인해야 나감 3. 보안 장비 로그를 받아 탐지 규칙 2530개를 돌리고, 관련 경보를 묶어 사건으로 정리함. 새 위협 정보가 나오면 이미 모아 둔 기록도 다시 훑어봄 4. Splunk, CrowdStrike, Okta, AWS 등 연결 도구 87개 제공. 클로드·Cursor·Continue에 붙일 수 있는 MCP 서버도 들어 있음 5. MIT 무료, 스타 5,254개. API 키 없이 실행 가능. Docker 메모리 8GB·디스크 20GB 필요, 첫 실행 때 약 2GB 모델을 내려받음. Docker 없이도 pip 데모로 30초 안에 경보 1건 체험 가능 설치: git clone github.com/beenuar/AiSOC && cd AiSOC && make up ━━━━━━━━━━ 🇺🇸 1. An open-source AI Security Operations Center (SOC) that looks into security alerts for you. You can self-host it on your own server 2. AI agents investigate each alert, and every prompt and tool call is recorded step by step so you can replay it later. Response actions only go out after a human approves 3. It takes in security tool logs, runs 2,530 detection rules, and groups related alerts into incidents. When new threat intel arrives, it re-scans the data you already collected 4. 87 connectors including Splunk, CrowdStrike, Okta, and AWS. Also ships an MCP server you can plug into Claude, Cursor, and Continue 5. Free under MIT, 5,254 stars. Runs with no API keys. Needs 8 GB memory and 20 GB disk in Docker, and the first run downloads a ~2 GB model. Without Docker, a pip demo walks one alert through in under 30 seconds Install: git clone github.com/beenuar/AiSOC && cd AiSOC && make up #AI #CyberSecurity #AIAgents #OpenSource #MCP
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THIS IS ACTUAL F*CKING GOLD Anthropic just shared one of the best tricks for Claude Code. The idea: run Sonnet, Haiku and Opus 5.5 together, each one doing only the job it's best at. Opus-level judgment on every big call, without paying Opus rates for every routine turn. You just launch Claude Code like this: claude --model sonnet --advisor opus here's the split: → Sonnet 5.5 runs the session and writes the code → Haiku 5.5 explores files and docs in parallel → Opus 5.5 stays on call as advisor for the big calls → you stop spending Opus on work that doesn't need it Opus only steps in at three moments: → before a plan locks → when the same error shows up twice → before calling it done Sonnet builds. Haiku explores. Opus reviews. JEV engineering is the same move one layer down: the forks that need no thinker (which file, which tool, retry or stop) go to JEV in milliseconds, and the big models only see the ones that split the full tree > Sonnet 5.5 runs the main session and edits code > search maps the repo on Haiku 5.5 > grep finds call sites on Haiku 5.5 > docs pulls the API reference on Haiku 5.5 > Opus 5.5 on call as advisor, reads the whole transcript when consulted > JEV routes micro-forks in milliseconds paste the tree and this prompt into Claude Code ↓ "Rebuild my Claude Code setup around this tree: 1. Run the main session on Sonnet 5.5 with Opus 5.5 as advisor. 2. Create three read-only Haiku 5.5 subagents: search (maps the repo), grep (finds call sites), docs (pulls the API reference). 3. Add one rule to CLAUDE.md: consult the advisor before locking a plan, after the same error twice, and before calling a task done. 4. Route fast deterministic forks (tool choice, path selection, retries) through JEV. Show me every change as a diff first. No edits until I say go." ↳ code.claude.com/docs/en/advi… save it, you'll use this every single day.
REA is the most dangerous repo on GitHub right now It gives your AI agent a real disassembler, so it reads any app down to the binary Every claim comes back with the address behind it I broke down the exact 8 steps:
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it's ok, css is already one step ahead @​media (device-posture: folded) { h1 span { opacity: 0.25; } }
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palmier-pro: AI 협업용 오픈소스 macOS 영상 편집기 타임라인에서 영상/이미지를 생성하고 내장 에이전트와 같은 프로젝트를 편집 MCP로 Claude/Codex/Cursor 연동 편집기/MCP/에이전트 챗은 오픈소스, 생성형 AI만 비공개이며 로그인/구독 필요 macOS 26/Apple Silicon 전용 news.hada.io/topic?id=32484
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Anthropic Opus 5 출시 Anthropic이 2026년 7월 24일, Claude Opus 5를 공개했습니다. Opus 티어의 "세대 교체급" 개선 모델로, Fable 5(프론티어 모델)에 근접한 지능을 절반 가격에 제공하는 것이 핵심 포지셔닝입니다. 핵심 요약 - 포지셔닝: Fable 5의 … news.hada.io/topic?id=31783
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Claude 5 모델을 위한 새로운 컨텍스트 엔지니어링 규칙 - Claude Opus 5와 Claude Fable 5에서는 Claude Code의 시스템 프롬프트를 80% 이상 줄이고도 코딩 평가에서 측정 가능한 성능 저하가 없었음 - 과거 모델의 최악 상황을 막던 세부 규칙은 시스템 프롬프트·Skills… news.hada.io/topic?id=31782
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graphify - 코드베이스를 질의 가능한 지식 그래프로 바꾸는 도구 - AI 코딩 어시스턴트에서 `/graphify .` 하면 코드/문서/PDF/이미지/영상 전체가 지식 그래프로 매핑되어, 파일을 grep하는 대신 질문으로 탐색하는 방식으로 전환 - 질문하면 관련 서브그래프만 반… news.hada.io/topic?id=31743
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introducing Gemini 3.5 Flash-Lite: our fastest, most cost-effective 3.5 model for high-throughput execution available now in AI Studio and the Gemini API
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introducing Gemini 3.6 Flash: our latest model that balances speed with intelligence to deliver strong performance in agentic and multimodal tasks built directly on developer and customer feedback from 3.5 Flash available now in AI Studio and the Gemini API
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안녕들 하십니까.. 저는 회사를 그만두고 쉬다가 집에서 이런거 만들고 있습니다. github.com/Julian-adv/OpenMM… 브라우저에서 돌아가는 오픈소스 일인 개발(+AI) mmo입니다.
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𝘀𝘁𝗼𝗽 telling Claude Code/Codex "no em dashes". 𝘀𝘁𝗼𝗽 telling Claude Code/Codex "stop saying delve". 𝘀𝘁𝗼𝗽 telling Claude Code/Codex "don't sound like AI". you never gave it a 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺. every README, PR description and landing page ships in the same AI voice, and you ban words one at a time. orwell wrote the fix 𝟴𝟬 𝘆𝗲𝗮𝗿𝘀 𝗮𝗴𝗼. six rules, 1946. paste them into your global CLAUDE.md / AGENTS.md and every session picks them up: → never use a long word where a short one will do → if it is possible to cut a word out, cut it out → never use the passive where you can use the active 6 blocks you can copy-paste directly 👇
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MICROSOFT JUST OPEN-SOURCED SELF-EVOLVING AGENT SKILLS. it's called skillopt. skills that improve themselves the same way you train an ai model. no more guessing whether your prompt tweaks helped. a base model runs the task, an optimizer evaluates the output and rewrites the instructions itself. → isolates successful paths from failures to find precise improvements → auto-rejects any edit that doesn't beat your benchmark score → beats hand-crafted prompts and optimizers like textgrad → zero model lock-in, the skill transfers to any model you switch to 100% free. open source.
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루프 안의 인간은 지쳤다 - LLM 프로그래밍은 생산성을 높이는 동시에 개발자가 의도와 품질을 계속 통제해야 하는 감독 피로를 키워, 만족감과 지속 가능성을 흔듦 - 모델은 그럴듯한 코드를 빠르게 만들지만 복잡한 변경의 일관된 의도를 놓칠 수 있어, 인간이 늘어난 결… news.hada.io/topic?id=31546
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人間には読めるのにAIには別の文字に見えてしまうフォント「Decoy Font」が登場
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不需要再开多个 terminal 跨 Agent 沟通了。 tmux-bridge 让 Claude Code、Codex、Gemini CLI、Kimi Code 彼此能读取对方的终端并协同运作,全程通过 MCP。 npx tmux-bridge-mcp setup — 搭完。
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Apple的设计风格 终于被蒸馏成Skill了 赶紧上链接: github.com/emilkowalski/skil… 目前已有有 14.5K 人点赞!!! 作者把这些经验整理成17条设计和动效原则, 再转换成 Web 里能落地的写法 效果绝对能打!!!!
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HAN LIBERADO GRATIS EL FRAMEWORK COMPLETO DEL “LOOP ENGINEERING” El propio jefe de Claude Code en Anthropic lo dice: “Ya no prompteo a Claude. Tengo loops corriendo que promptean a Claude. Mi trabajo es escribir loops.” Deja de promptear a tu agente. Construye el sistema que lo promptea por ti. descubrir → planificar → ejecutar → verificar → repetir Antes tú estabas dentro de ese loop Loop engineering es salirte de él por completo El repo incluye: → 7 patrones listos para producción (triaje diario, CI sweeper, PR babysitter) → starters clonables para Claude Code, Codex y Grok → un CLI que puntúa si tu codebase está lista para loops → plantillas SKILL.md, sistema STATE.md, docs de seguridad completos Empieza con un solo comando (copia y pega): npx @cobusgreyling/loop-init . –pattern daily-triage –tool claude Guárdate esto antes de que explote🔖 Enlace abajo👇
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Anthropic 공식, AI 교육을 위한 무료 강의 코스 놀라웠던건 기능적인 것 뿐만 아니라 체계적인 학습을 위해 무료로 공개함. 📙 AI Fluency for pK-12 Educators anthropic.skilljar.com/path/… 연결된 두 코스 모두 Anthropic이 교육 단체와 협업해 만든 무료 강의. 1. AI Fluency for pK–12 Educators 2. AI Fluency for pK-12 Train the Trainer 이게 교육을 위한 당사자 뿐만 아니라, 동료들에게도 전파할 수 있는 그런 교육들로 구성되어 있어요. 커리큘럼 흐름엔.. AI Fluency 4D Framework, AI 작동 원리, 발표 자료, 진행자 가이드 등.. 워크북까지 가득하다옹!
We're introducing Claude for Teachers: free access to premium Claude capabilities for verified K-12 educators in the US, with a library of teaching skills and a direct connection to evidence-based curricula, mapped to academic standards in all 50 states. claude.com/solutions/teacher…
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Google 这次把本地 RAG 的门槛直接打下来了。 原本要占 31GB 内存的向量索引,现在压到只要 4GB。 1000 万份文档,一台普通电脑就能跑。 更狠的是: → 单个向量压缩 16 倍 → 检索速度比 FAISS 快 10%–19% → 不用训练 → 不用重建索引 → 不需要 GPU 集群 → 全程本地运行 这意味着以后做知识库、文档搜索、个人 AI 助手,可能根本不需要昂贵的云服务。 Google 开源的不是一个小优化,而是在重新定义本地向量搜索的成本。
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