标签：#product

Netflix has walked back plans to put AAA games on its streaming platform. Instead, it's pivoting to marketing highly anticipated titles like Grand Theft Auto VI.

I'm part of an awesome community where senior devs, product managers, founders, and experienced folks from different domains discuss how they use AI and automation tools to boost productivity — without sacrificing real learning. The group is a goldmine . Tool recommendations. Automation workflows. Latest trends. Migration war stories. I've learned more from this group than from most tutorials. Last week, I needed to find something specific about running local LLMs. I searched with keywords. I scrolled. I found a whole lot of messages — but none of them answered my question directly. I still had to manually read through dozens of messages , open the links they shared, and try to piece together the context myself. It took me over an hour, and I wasn't even sure I'd found everything. Someone might say: "Just Google it." Or "Ask an LLM." But that defeats the purpose. The value here isn't just information — it's the context . The "this library is a game changer" comment only makes sense if you know what the person was working on before, and who else agreed or disagreed. That context is lost in scrollback. And I'm tired of trying to keep it all in my head . I've seen people send important links to themselves on WhatsApp. But that becomes a messy pile with no structure. No connections. No way to see how one message relates to another. So I'm curious: How do you deal with this? Have you lost important knowledge in group chats? Do you have a system for recovering it? Or are you also just scrolling endlessly? I have an idea I'm working on. But I'd love to hear your approaches first. Drop your thoughts in the comments — I'll document what I learn .

Alienware’s latest gaming monitor explores a new size and resolution for ultrawide monitors, and I have a feeling PC gamers are going to love it.

You’re hunched over your desk and phone for hours. I rounded up gadgets, a DIY trick, and even some yoga advice to help you straighten up.

Let us help you choose the right Pixel phone. Plus, check out our Pixel accessory recommendations and smart software tricks to try.

Trajectory-aware LLM routing that cuts agent cost Discussion | Link

One thing I learned from working with experienced engineers is that solving a problem and approaching a problem are two different skills. During one of my projects, I had the opportunity to work closely with Microsoft engineers. Since I was working independently, whenever I faced an issue, I would first spend time exploring it myself. I would check the data, logs, code, test different possibilities, and eventually figure out a solution. But sometimes, when I discussed the same issue with them, I was surprised by how differently they approached it. Instead of immediately looking for a fix, they would pause and ask a few simple but thoughtful questions. Those questions often narrowed the scope of the problem quickly and helped uncover the root cause much faster than trial and error. Over time, I started adopting that mindset. I learned that spending more time understanding why something is happening often leads to a better outcome than rushing into how to fix it. I also picked up many small but valuable engineering habits from everyday discussions, habits that continue to help me in my work today. Courses and certifications definitely help us learn new technologies. But some of the best learning in my career has simply come from working with skilled people, observing how they think, and applying those learnings in my own way. Grateful for the experiences, mentorship, and the people who generously shared their knowledge along the way. Learning #ProblemSolving #CareerGrowth #DataEngineering #GrowthMindset #ProfessionalDevelopment

I have been an office worker for several decades. When I started, the internet was still clearing its throat. Salespeople carried pagers. Windows 95 was teaching us that folders could live inside other folders, which felt almost philosophical. Documents traveled by fax or post. Work was slow and inefficient. Then came mobile phones, email, and the internet in our pockets. We could send more information than a human being could understand to the other side of the planet in a second. Work that once took a day took half a day. Then a third. We thought, “Wonderful. Now we can go home early.” Naturally, we did not. We poured new work into the empty space. Technology saved time, and we immediately gave that time back to work. It was rather like receiving a larger bowl and celebrating by filling it with more soup than we could eat. Now generative AI has arrived. It writes, calculates, summarizes, and never asks for a lunch break. Once again, it gives us time. Once again, we reward ourselves with more tasks, more speed, and less breathing room. People worry that AI will take our jobs. Perhaps the stranger problem is that it has not taken enough of them . Technology was supposed to make life easier. Instead, we have used it to become more efficiently exhausted. Why? Maybe human beings possess a peculiar and rather useless seriousness. We do not merely work hard. We work hard at looking as though we are working hard. So perhaps the next great innovation is not another machine. Perhaps it is this: Let us stop treating the appearance of seriousness as a virtue. A little idleness may not be laziness. It may simply be the time technology was trying to return to us.

Local-first and privacy-focused web utilities are having a massive comeback. With browser engines becoming faster and WebAssembly/Web Workers maturing, there is rarely a reason to push sensitive user documents to an external backend for simple conversions. While building MD-Convert (a zero-upload document to Markdown converter), I explored how to parse real-world documents into clean Markdown entirely on the client side. Here is a breakdown of the core architecture and libraries that make purely in-browser document processing possible. 1. Converting Web Articles with Readability + Turndown Converting messy web markup into clean Markdown involves two distinct steps: Content Extraction: Stripping ads, navbars, sidebars, and trackers. HTML-to-Markdown Transformation: Translating semantic DOM nodes into markdown tokens. Mozilla’s @mozilla/readability paired with turndown is an incredible combination for this: import { Readability } from ' @mozilla/readability ' ; import TurndownService from ' turndown ' ; function htmlToCleanMarkdown ( rawHtmlDocument , sourceUrl ) { // 1. Extract pure article content const reader = new Readability ( rawHtmlDocument ); const article = reader . parse (); if ( ! article || ! article . content ) { throw new Error ( ' Unable to extract main content ' ); } // 2. Initialize Turndown const turndownService = new TurndownService ({ headingStyle : ' atx ' , codeBlockStyle : ' fenced ' }); // Ensure image URLs remain absolute turndownService . addRule ( ' absoluteImages ' , { filter : ' img ' , replacement : ( content , node ) => { const src = node . getAttribute ( ' src ' ); const alt = node . getAttribute ( ' alt ' ) || '' ; if ( ! src ) return '' ; try { const absoluteUrl = new URL ( src , sourceUrl ). href ; return `\n\n` ; } catch { return `\n\n` ; } } }); return turndownService . turndown ( article . content ); } Offloading Heavy PDF Parsing to Web Workers Parsing large PDFs using pdf

Every extraction pipeline I have ever pointed at a language model shares the same dirty secret: the JSON comes back almost valid. Almost is where the bugs live, because almost passes your eyes and then fails your schema at midnight. So I built a loop where the model grades its own homework, then let it run for 48 hours on a free server to see what breaks. The experiment The idea was simple: take plain-text payloads that look like webhook bodies, extract five fields against a small schema, and give the model exactly one chance to fix its own mistakes. I wrote the rules down before writing any code, because rules written after a failure are just excuses. Pass one asks the model to return the fields as JSON. A validator checks the result against the schema. If validation fails, pass two sends the original payload, the bad JSON, and the exact validation errors back to the model. Every attempt, raw text included, lands in a JSONL log. I ran that loop for 48 hours on MonkeyCode's free server option, using its free model access for both passes. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Here is the loop, trimmed to the parts that mattered. import hashlib import json import time from datetime import datetime , timezone import jsonschema import requests SCHEMA = { " type " : " object " , " required " : [ " event " , " customer_id " , " amount " , " currency " ], " properties " : { " event " : { " type " : " string " , " enum " : [ " charge.succeeded " , " charge.failed " ]}, " customer_id " : { " type " : " string " , " pattern " : " ^cus_ " }, " amount " : { " type " : " integer " , " minimum " : 0 }, " currency " : { " type " : " string " , " minLength " : 3 , " maxLength " : 3 }, }, } SEEN : set [ str ] = set () def now_iso () -> str : return datetime . now ( timezone . utc ). isoformat () def call_model ( prompt : str ) -> str : # Point this at the free model endpoint you are testing. resp = requests . post ( " https://your-endpoint.e

I thought publishing more useful content would automatically give my website more opportunities to rank. Then I noticed something uncomfortable. Two articles on my site were covering almost the same subject. Both were useful. Both were properly indexed. Both answered similar questions. And both were competing for overlapping search intent. I had accidentally created a small SEO architecture problem inside my own blog. I'm building "SabrTime.in" ( https://sabrtime.in/ ), a small Islamic companion app focused on practical digital tools for everyday worship. As a solo developer, I also manage the website, content, SEO, and product development myself. While working on the site's content, I published two articles around Tasbeeh: How to Do Tasbeeh — A Complete Guide Digital Tasbeeh Counter: How It Works & Why Muslims Are Switching The first article is about the practice itself. The second is supposed to be about the technology and use case of digital Tasbeeh counters. Sounds different enough, right? At first, I thought so too. But when I looked at the actual content and search intent, the overlap became obvious. The Problem Wasn't Duplicate Content This is where SEO gets misunderstood. Keyword cannibalization doesn't necessarily mean you have two pages containing identical paragraphs. The more interesting problem is intent overlap. If two URLs are trying to satisfy essentially the same searcher's question, a search engine has less information about which page should be the primary result. For example, imagine these two pages: /page-a "How to Do Tasbeeh" /page-b "Digital Tasbeeh Counter" Their titles are different. But if both pages explain: what Tasbeeh means how many times to recite it common Tasbeeh counts the same hadith how to count Tasbeeh why Muslims use a counter FAQs about Tasbeeh ...then the distinction between the pages starts becoming blurry. That was happening on my site. The two articles were not technically duplicates. But parts of their search intent were d

The Three-Eyed Raven Problem: What Bran Stark could see the past, understand the present, and glimpse what might come next. Modern logistics AI is being asked to do something surprisingly similar. There is a moment in Game of Thrones when Bran Stark stops being merely a person who remembers events and becomes something much more powerful. As the Three-Eyed Raven, Bran has access to an enormous history of people, places, decisions, betrayals, and consequences. He does not simply possess information. He can retrieve the right information from the past and use it to understand what is happening now . That distinction matters. Because the logistics industry is beginning to face its own Three-Eyed Raven problem. We already have enormous amounts of data. Shipment events. GPS signals. Carrier performance. Customs documentation. warehouse scans. Purchase orders. invoices. weather feeds. port congestion. customer commitments. emails. SOPs. tariffs. exception histories. The problem is no longer simply: Can AI access all of this information? The more important question is: Can an AI system remember the right things, at the right time, for the right shipment—and forget what it should not retain? That question may become one of the defining problems of enterprise AI. AI Is Moving From Intelligence to Memory Much of the first wave of Generative AI focused on what models know . The next wave is increasingly about what AI systems can remember, retrieve, reason about, and act upon over time . This distinction becomes particularly important with AI agents. A chatbot might answer: “What documents are normally required for this shipment?” An AI logistics agent needs to understand something much harder: Which shipment are we discussing? What happened to it yesterday? Which carrier is moving it? Has this lane experienced similar delays before? What did the customer request? What customs rules apply? Was an exception already escalated? What action worked the last time this happened? Has a

HelloFresh’s organic meal kit Green Chef offers transparent sourcing, layered cooking, and trustworthy gluten-free dishes.

OpenAI’s new report explores how students and educators use ChatGPT to make learning more continuous, with support that extends beyond the classroom.

ChatGPT for Teachers is expanding to 55 U.S. school systems, bringing secure AI tools, training, and support to over 100,000 more educators and staff.

A technically perfect app can still fail. It can have clean code, modern architecture, powerful APIs, and impressive features—and still leave users uninstalling it, abandoning transactions, or switching to a competitor. Because users don't experience your code.They experience the product. That is why developers and businesses need to look beyond functionality and ask a more important question: “Does this software make the user’s life easier?” The Real Cost of a Poor Digital Experience Customer expectations are rising quickly. According to PwC’s 2025 Customer Experience Survey, 70% of executives say customer expectations are evolving faster than their companies can adapt. Even more importantly, 29% of consumers said they stopped using or buying from a brand because of poor customer experience. That means a frustrating digital experience isn't simply a UX problem. It can become a business problem . A confusing checkout flow, slow screen, unnecessary registration step, broken search function, or poorly designed notification can turn a potential customer into a lost customer. And users rarely tell you exactly what went wrong. They simply leave. More Features Don't Always Mean More Value One of the biggest mistakes in software development is assuming that adding more features automatically makes a product better. It doesn't. Imagine an app with: 30+ features AI integration Multiple dashboards Complex personalization Advanced analytics …but users struggle to complete the one task they downloaded the app for. That's not innovation. That's friction. A better development approach starts with identifying the core user problem and then building around it. Before adding a feature, ask: What problem does this solve? If the answer isn't clear, the feature may not belong in the product. Performance Is Part of User Experience Developers often separate performance from UX. Users don't. To them, a slow API, delayed screen, frozen button, or failed transaction is simply a bad experien

Vibecoding: How to Manage an AI Coder and Not Drown in Spaghetti Code Forget fairy tales about AI doing everything for you at the touch of a button. Without strict control, vibecoding quickly turns into a mess of broken, unmaintainable code. Modern vibecoding isn't blind generation — it is strict architectural supervision . To build real products, you must change your approach to context and redefine your role in the process. Forget Persona Prompting: Context is the Only King of Modern Prompting Fables like "Act as a Senior Developer" were left back in 2023. Modern LLMs don't need roleplay — they need the cleanest, deepest context possible . Why "Persona Prompting" is Outdated AI doesn't start coding better just because you called it a senior dev. It needs concrete technical boundaries. Skip the foreplay and provide the AI with technical specifications: Stack and versions: Not just "React", but React 18, Next.js 14 (App Router), Tailwind CSS . Architectural constraints: Show folder structure, naming conventions, and API response formats. Rule files ( .cursorrules / .clauderules ): Load strict rules into the project that the AI must follow at all times (e.g., "Never use any in TypeScript, write functional components only" ). Humans as Strict Regulators, Not Blind Consumers of Code The biggest danger of vibecoding is shipping AI-generated garbage straight to production without looking. If you blindly consume whatever the AI spits out, your project is doomed. 1. Total Quality Control and Code Review You act as the Technical Regulator and Censor . You never take the AI's word for it. Read every diff : Check exactly what the AI changes. Don't let it rewrite working modules from scratch just to add one button. Don't know how to code? Use basic logic: adding a single button shouldn't make 30 lines of code disappear from main.py or app.js . Force it to justify decisions: If the AI suggests a library, ask: "Why this one over a native solution? Will it impact performance?" 2.

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

1. Retomando: o que é o Fzf Na primeira parte desta série vimos o que é o fzf, como instalá-lo e como usá-lo direto no shell com Ctrl+R , Ctrl+T e Alt+C . Quem também usa tmux no dia a dia ganha um segundo nível de integração: o fzf pode rodar dentro de janelas flutuantes (pop-ups) do próprio tmux, sem interferir no layout de painéis já aberto, e servir de seletor para operações do próprio tmux — trocar de sessão, de janela, de painel, matar processos em outro painel etc. 2. Por que integrar Fzf com Tmux Sem integração, usar o fzf dentro de uma sessão tmux funciona normalmente, mas cada busca ocupa o painel inteiro: se o objetivo é só escolher um arquivo ou trocar de branch rapidamente, o conteúdo do painel (um editor, um servidor rodando) é temporariamente coberto e é preciso "voltar" depois. Além disso, o tmux tem sua própria lista de coisas que fazem sentido filtrar de forma fuzzy — sessões, janelas, painéis — e não há um binding nativo do tmux para isso. O fzf-tmux , incluído na instalação do fzf, resolve o primeiro problema: roda o fzf em uma janela sobreposta (pop-up ou split temporário) que desaparece assim que a seleção é feita, sem afetar o conteúdo do painel original. Combinado com bindings customizados no tmux.conf , também resolve o segundo. 3. fzf-tmux: pop-ups nativos fzf-tmux é um wrapper de shell em torno do fzf que aceita as mesmas opções, mais flags de posicionamento e tamanho da janela sobreposta: # pop-up centralizado, 80% da largura e 60% da altura do terminal fzf-tmux -p 80%,60% # split na parte de baixo do painel atual, ocupando 40% da altura fzf-tmux -d 40% # split lateral à direita, ocupando 50% da largura fzf-tmux -d 50% -r A flag -p (disponível a partir do tmux 3.2, que suporta display-popup ) é a mais usada hoje: cria uma janela verdadeiramente flutuante, sobreposta ao conteúdo do painel, que não reorganiza o layout existente — diferente do -d , que faz um split real e temporariamente redistribui o espaço entre painéis. # substitui o Ctrl

Spot potential misunderstandings across global Englishes Discussion | Link