Playwright and Selenium are great until you hit bot detection. Google OAuth, Cloudflare, and Vercel checkpoints all flag headless browsers. Here's how to control a real Chromium instance via CDP using Python and websockets. Why Not Playwright? Playwright launches a headless browser with automation flags. Even in headed mode with Xvfb, Google detects it. The CDP Approach Launch Chromium with remote debugging: chromium-browser --user-data-dir = /path/to/profile --remote-debugging-port = 9222 --no-first-run Connect via WebSocket in Python: import asyncio , json , websockets , urllib . request async def get_page_ws (): resp = urllib . request . urlopen ( ' http://localhost:9222/json ' ) targets = json . loads ( resp . read ()) for t in targets : if t [ ' type ' ] == ' page ' : return t [ ' webSocketDebuggerUrl ' ] async def cdp_call ( ws , method , params = None ): msg_id = cdp_call . id = getattr ( cdp_call , ' id ' , 0 ) + 1 msg = { ' id ' : msg_id , ' method ' : method } if params : msg [ ' params ' ] = params await ws . send ( json . dumps ( msg )) while True : resp = json . loads ( await ws . recv ()) if resp . get ( ' id ' ) == msg_id : return resp Key Advantages Real browser fingerprint, no automation flags Persistent sessions, cookies survive across runs Google OAuth works, existing sessions carry over No bot detection, it IS a real browser Follow for more tutorials on browser automation and AI agent architecture.

5 Ferramentas de IA Gratuitas que Todo Desenvolvedor Deveria Usar em 2026 A inteligência artificial não é mais o futuro — é o presente. E o melhor: muitas ferramentas poderosas são gratuitas . Neste artigo, vou compartilhar 5 ferramentas de IA que transformaram minha produtividade como desenvolvedor. 1. 🤖 GitHub Copilot (Gratuito para opensource) O Copilot se tornou indispensável para qualquer desenvolvedor. A versão gratuita oferece: Autocomplete de código em tempo real Sugestões contextuais inteligentes Suporte a +30 linguagens Como usar: Instale a extensão no VS Code e comece a digitar. O Copilot sugere código automaticamente. # Exemplo: Digite um comentário e o Copilot gera a função # Função para ler JSON de um arquivo def read_json_file ( filepath ): import json with open ( filepath , ' r ' ) as f : return json . load ( f ) 2. 🔍 Perplexity AI (100% Gratuito) Pesquisa com IA que cite fontes. Perfeito para: Pesquisar documentação Entender conceitos complexos Encontrar soluções para bugs Dica: Use o modo "Pro Search" para respostas mais detalhadas. 3. 🎨 v0.dev (Vercel) — Frontend com IA Gere componentes React/Next.js com descrições em linguagem natural. # Exemplo de prompt: "Um card de produto responsivo com imagem, preço e botão de compra" O v0 gera o código completo, estilizado com Tailwind CSS. 4. 📝 Notion AI (IA gratuita integrada) O Notion permite usar IA para: Resumir documentos longos Gerar templates de código Traduzir conteúdo automaticamente Criar documentação técnica Atalho: Pressione Ctrl/Cmd + J para ativar a IA em qualquer bloco. 5. 🔧 Cursor (Editor com IA) O Cursor é um fork do VS Code com IA integrada nativamente: Chat com IA sobre seu código Edição por comando ("adicione tratamento de erros") Compreensão automática do contexto do projeto Diferencial: Ele lê todo seu projeto e entende o contexto, não apenas o arquivo atual. 💡 Dica Extra: Combinando as Ferramentas O segredo não é usar uma ferramenta isolada, mas combiná-las : Perplexity para pesquisa

Headline: The wins come from context, not cleverness. An AI with your codebase, your design system, and your deploy logs in scope writes code that ships. Without that scope, it writes plausible code that doesn't. Two years ago, AI coding tools were autocomplete with attitude. In 2026 they are a credible second engineer — provided you build the workflow around them. This is the workflow I run today at Devya Solutions and on personal projects like eng-ahmed.com . The Stack Claude Code in the terminal — long-horizon, multi-file edits with skills and subagents. MCP (Model Context Protocol) servers for live access to docs, deployments, browser, and design tools. Cursor or VS Code for inline edits when I want to stay in the IDE. Why Context Is Everything The single highest-leverage move in AI-assisted dev is feeding the model the right context. MCP servers do this without prompt stuffing. Docs MCP — pulls current library docs at call time, so the model doesn't hallucinate the Tailwind v3 API in a v4 codebase. Browser MCP (Claude-in-Chrome) — lets the agent open the running dev server, screenshot the page, and verify the change actually rendered. Vercel MCP — fetches deploy logs and runtime errors directly. No more pasting logs. Context-mode MCP — keeps file scans, search results, and command output in a sandbox, only surfacing what's relevant to your conversation. A Real Workflow The blog page redesign I just shipped was built in a single 45-minute session. Rough flow: State the goal — two sentences, not a spec doc. Let the agent scout — Claude Code greps, reads a few files, proposes a plan. Iterate visually — screenshot the result, feed it back. The agent fixes the sticky-filter scroll bug in one turn. Commit and push — a single cm shortcut runs build, commits, and pushes. Vercel deploys on push. What the Agent Is Still Bad At Holistic taste — it copies the closest example in your codebase. If that's mediocre, the new feature is mediocre. Domain knowledge — it doesn't kn

I thought it'd be interesting to use Linux PSI (Pressure Stall Information) for an LLM runtime to trim the KV cache. This is mainly useful imo for edge devices like the Jetson Orin super nano kit which have unified memory. I haven't benched much, but plan to do so more over time and see if I can make a real use of it as I run local LLMs. Let me know if it makes sense :P (I of course vibed this idea)

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What This Guide Covers When you start building LLM-powered applications, one pattern becomes unavoidable: RAG (Retrieval-Augmented Generation) . LLMs only know what they were trained on. Your company's internal documents, the latest spec sheets, project-specific information — none of that exists in the model. To handle data the model doesn't know, you need a system that retrieves relevant knowledge in real time and injects it into the context. That's RAG. In this guide, we'll implement a RAG system from scratch using pgvector and Gemini, then extend it step by step through Tool Use, AI Agents, MCP, and cloud deployment. Step 1: Embedding · Vector DB · RAG — core implementation Step 2: AI Architect perspective — design decisions explained Step 3: Tool Use — LLM autonomously searches the DB Step 4: AI Agents — combining multiple tools Step 5: MCP — exposing tools as a server Step 6: Cloud deployment — Render × Supabase Three Concepts to Understand First Embedding Computers can't measure "semantic similarity" from raw text. Embedding converts text into a list of numbers (a vector), and semantically similar words produce numerically similar patterns. "dog" → [ 0.82 , 0.75 , 0.10 , ... ] 768 numbers "cat" → [ 0.78 , 0.72 , 0.12 , ... ] ← similar pattern to "dog" "bank" → [ 0.08 , 0.10 , 0.85 , ... ] ← completely different Gemini's embedding model handles this conversion. Vector DB A regular DB searches by keyword matching. A vector DB searches by numeric distance — meaning it finds semantically related documents even when the exact words don't match. -- Regular search (misses if keywords don't match) SELECT * FROM docs WHERE body LIKE '%F1 score%' ; -- Vector search (finds semantically related docs) SELECT * FROM docs ORDER BY embedding <=> query_vector LIMIT 3 ; Search for "how to measure model performance" and it finds "F1 score calculation" — even without matching words. We use pgvector , a PostgreSQL extension, for this. RAG LLMs are limited to their training data. R

為什麼那個會「注意你」的展品，反而讓你更想靠近 博物館互動設計的隱形槓桿 東京。 teamLab 展覽入口。 地面是一整片黑色的水面，倒映著數位花朵。 你踏進去。 花朵在你腳步周圍散開，隨著你的移動一圈一圈地綻放和飄落。 你停下來，花也停下來。 你開始走，花就跟著你。 你以為是感應。但仔細看——延遲了大概 0.3 秒。 不是「立刻反應」，是「好像在觀察你，然後才決定」。 你站在那裡又多看了三秒。 你第一個「對」 讓我問你一個問題。 你去過那種「互動博物館」嗎？牆上寫著「請觸摸」，但你碰了之後什麼都沒發生——或者是那種「語音導覽機」，你對著它說話，它說「請靠近一點」。 然後你就失去興趣了。 現在讓我想另一個場景。 一個會動的恐龍骨骼。你站在它面前的時候，它頭轉過來看了你一眼。 你知道這是感應器。你知道工程師設計了「檢測到人」的時候讓它轉頭。 但你還是覺得—— 「它在看我。」 兩種互動，哪一個讓你停留更久？ 你第一個「咦」 這裡有一個秘密。 讓人停留更久的，通常不是「立刻反應」的互動。 是那種「 好像在決定要不要理你 」的互動。 為什麼？ 因為「立刻反應」讓你確認了——「這是機器」。 但「好像在決定要不要理你」讓你的大腦進入了一個不確定的狀態—— 「它真的知道我來了嗎？」 「它在決定什麼？」 「我想看看它決定什麼。」 這個「我想看看」就是互動設計裡最重要的瞬間—— 參與者的好奇心，被啟動了。 玉樹真一郎在《任天堂的體驗設計》裡，分析了一個現象： 《超級馬里奧》裡，當玩家靠近一個問號磚塊，頂了它，沒有任何東西掉下來。 玩家不會覺得「這個遊戲壞了」。 玩家會想：「 為什麼這次沒有？ 」 然後再頂一次。 為什麼「沒有東西掉下來」沒有讓玩家放棄？ 因為設計師在玩家心裡創造了一個「 還沒發生的確定事件 」。 玩家知道「遲早會有東西掉下來」。所以他們願意等待、願意再試一次。 博物館的互動設計也應該這樣。 不是立刻給答案。是讓你相信「答案快來了」，然後讓你一直站在那裡等。 你最後「我要改變做法」 讓我說一個失敗的設計。 一個科技博物館有一面「觸控牆」。牆上有很多按鈕，碰了就會播放影片、發出聲音、變色。 一開始很多小孩去碰。 但大概十五分鐘之後，那面牆就沒人碰了。 為什麼？ 因為碰了 100 次，沒有任何一次比另一次更「值得等待」。 每一次都是立刻發生，每一次都是同樣的結果。 沒有任何一件事需要「決定」。 現在讓我說一個成功的設計。 同一個博物館的另一區，有一面「情緒牆」。 你站在牆前，系統會掃描你的臉——不是真的分析情緒，而是給你一個顏色。 每個人的顏色都不太一樣。 但顏色不是立刻出現的。 大概等了兩秒——然後它慢慢浮現出來。 在這兩秒裡，每個站在牆前的人都沒有動。 他們在等。 他們相信顏色一定會出現。但他們不確定會是什麼顏色。 三個馬上可以用的方向 第一：不要立刻給回饋。 加入一個 0.3 到 2 秒的「思考時間」。 讓互動看起來像「系統在決定」，而不只是「系統在檢測」。 壞掉的燈 vs 正在決定的燈——後者讓人更想站在那裡等。 自己試試看：30 行做出「延遲反應」的燈 // p5.js — 試試延遲回饋的感覺 let lights = []; const DELAY = 12 ; // 幀數延遲（約 0.2 秒） function setup () { createCanvas ( 400 , 400 ); for ( let i = 0 ; i < 5 ; i ++ ) { lights . push ({ history : [], lit : false }); for ( let j = 0 ; j < Math . max ( DELAY , 1 ); j ++ ) lights [ i ]. history . push ( false ); } } function draw () { background ( 30 ); let hovered = floor ( mouseX / 80 ); for ( let i = 0 ; i < lights . length ; i ++ ) { lights [ i ]. history . push ( hovered === i ); if ( DELAY > 0 ) lights [ i ]. history . shift (); lights [ i ]. lit = DELAY > 0 ? lights [ i ]. history [ 0 ] : ( hovered === i ); } // 畫燈泡 noStroke (); for ( let i = 0 ; i < lights . length ; i ++ ) { fill ( lights [ i ].