标签：#Productivity

I’ve been building a small set of calculator pages for freelancers and small-business owners. The first version was pretty simple: put the formula on a page, add a few inputs, show the result. That works for a demo. It does not always work for a real user. The more I worked on it, the more I realized the hard part is not the math. The hard part is making the page feel trustworthy when someone is using it for a business decision. Here are the changes that mattered most. 1. Explain the assumption close to the input At first I wrote explanations below the calculator. Most people never read them. So now I try to put small notes near the input itself. For example, if a calculator asks for payment processing fees, the user should not have to scroll down to understand whether that means percentage fee, fixed fee, or both. This is boring UI work, but it reduces bad inputs. 2. Show intermediate numbers A single final result can feel like a black box. For business calculators, I’ve found it helps to show a few intermediate numbers: subtotal estimated fees estimated tax net amount break-even number Even if the final number is the same, users trust it more when they can see how the result was built. 3. Avoid pretending the answer is exact Small-business math has a lot of messy edges. Taxes vary. Fees vary. Local rules vary. Some people include owner salary in cost. Some do not. A calculator should not act like it knows every detail. I now prefer wording like: estimated monthly amount or rough break-even point That feels less flashy, but it is more honest. 4. Make the empty state useful A blank calculator page is not very helpful. I started adding realistic default numbers or examples where it makes sense. Not because the default is “correct”, but because it helps the user understand what kind of number belongs in each field. This is especially useful for people who are not spreadsheet-heavy. 5. Keep the result easy to copy A lot of users are not trying to stay on the page. They

Hey everyone! 👋 First of all, thank you so much for all the support, feedback, and feature suggestions since the Product Hunt launch. Every comment has been incredibly valuable, and I've already started implementing some of your ideas. Today I'm excited to share a new update to Rizzzler ! 🌙 ✨ What's New? 🎨 2 Brand-New Themes I've added two new themes , giving you even more ways to personalize your profile and match your own style. Whether you prefer something clean, vibrant, or expressive, there's now even more variety to choose from. 👀 Live Preview Panel One of the most requested improvements is finally here! You can now preview your showcase page while editing it . No more guessing how your profile will look after saving—see your changes in real time before publishing. 🎵 Audio Preview Choosing background music is now much easier. Instead of selecting tracks blindly, you can now preview audio directly before applying it to your profile. ✨ Profile Picture Decorations Want your profile to stand out even more? You can now add Profile Picture Decorations to give your avatar a unique look and make your showcase even more personal. More decoration styles will be added in future updates! ❤️ Thank You The feedback from the GitHub community and Product Hunt has been incredibly helpful. Many of these improvements came directly from community suggestions, and I'll continue building Rizzzler based on your ideas. If you have feature requests, bug reports, or suggestions, I'd love to hear them! 🔗 Try Rizzzler Website https://rizzzler.onrender.com Product Hunt https://www.producthunt.com/p/rizzzler-show-yourself-off GitHub https://github.com/DeveloperPuneet/Rizzzler-Stable Thank you for supporting Rizzzler! 🚀

If you have worked with APIs for a while, you have probably realized that designing an API is only...

Vi um post legal esses dias sobre rodar duas contas do Claude Code na mesma máquina compartilhando tudo entre elas: mesmas skills, mesmos servidores MCP, mesmos hooks. O artigo era: "Um cérebro, duas carteiras" Eu rodo, exatamente o oposto, e acho que pra muita gente o oposto é a escolha certa. Minhas duas contas não são uma pessoal e uma reserva pra quando os créditos acabam. Uma é pessoal, outra é de trabalho. A última coisa que eu quero é meus MCP servers de trabalho, meus hooks de trabalho e meu histórico de projeto de trabalho vazando pras sessões pessoais. Então em vez de ligar as duas, eu mantenho elas separadas de propósito . O setup inteiro O Claude Code guarda o estado num diretório de config ( ~/.claude por padrão) e lê a variável CLAUDE_CONFIG_DIR pra apontar pra outro lugar. Esse é o único mecanismo que você precisa. Sem shim, sem symlink, sem jq . bash # ~/.zshrc # Claude Code: contas isoladas (pessoal vs trabalho) # Cada uma usa um CLAUDE_CONFIG_DIR proprio -> credenciais/sessao separadas. # ~/.claude = pessoal (default) # ~/.claude-work = trabalho claude-work () { CLAUDE_CONFIG_DIR = " $HOME /.claude-work" claude " $@ " ; } claude-personal () { CLAUDE_CONFIG_DIR = " $HOME /.claude" claude " $@ " ; } # 'claude' sozinho continua sendo a conta pessoal. source ~/.zshrc claude-work # pede login OAuth da conta de trabalho, uma vez só Usei funções de shell em vez de alias por um motivo: a função repassa "$@" limpo, então claude-work --resume abc e claude-work chat funcionam sem a variável de ambiente vazar pra nada mais no shell. Um alias faria quase o mesmo aqui, mas a função deixa a passagem de argumentos explícita. É isso. claude puro é pessoal. claude-work é trabalho. Nada é compartilhado, e é aí que mora a graça. Por que eu não compartilho o cérebro A versão de cérebro compartilhado faz symlink de skills , plugins , settings.json e dá merge no bloco mcpServers entre as duas contas pra elas se comportarem igual. Se as suas duas contas são de fato a mesm

Every AI writing tool does the same thing: you type a prompt, it spits out text, and you copy-paste it into Word. Then you spend the next 30 minutes manually applying headings, fixing list styles, rebuilding tables, and cleaning up formatting that broke on paste. Co-pilot lives inside Word, sure, but it's a writing assistant, not a formatting one. It drafts text, rewrites paragraphs. What it doesn't do is look at your messy 40-page report and restructure it with your actual Word styles, proper heading hierarchy, and correctly formatted tables. That's the gap we set out to close with Stylifyword. What Stylifyword Actually Does: Stylifyword is a Microsoft Word add-in paired with a companion desktop app. You give it a document, a messy draft, a copy pasted ChatGPT output, a stitched together report from five different authors and voila, it writes & edits your text when you ask it to (drafting, rewriting, summarizing the usual AI stuff). Also formats the entire document with your headings, lists, tables, and all. Outputs every change as a tracked redline in Word's Review tab. Nothing touches your document until you accept it. That third point is the key differentiator. It's non-destructive. The AI proposes, you dispose. Why Local-AI support Matters: Here's the thing that got me building this: most professionals who need AI the most can't use cloud AI tools. Corporate attorneys can't paste M&A deal terms into ChatGPT. Healthcare teams can't upload patient data to a cloud endpoint. Defense contractors, compliance officers, financial analysts, they're all locked out of the AI productivity wave because of legitimate data sovereignty concerns. Stylifyword's default mode runs entirely on your machine: 100% on-device inference via a companion desktop app. No Ollama, no command line, no model downloads to configure. Works offline, airplane mode, air-gapped networks, whatever. Zero data leaves your device, ever. Install the app, open Word, and it works. Three Ways to Run It: Not

Microsoft PowerToys includes a Hosts File Editor. It is free, signed, and already on many Windows machines. It is a good editor. It is not always a good hosts workflow . What PowerToys Hosts does well Opens the real Windows hosts file with the right elevation story Simpler than hunting C:\Windows\System32\drivers\etc\hosts in Notepad Free if you already use PowerToys Fine for a handful of static lines When people search for an alternative You switch environments all day Local shop in the morning, client staging after lunch, cutover IP at night. An editor with one big file turns into commented chaos. You want named profiles you can toggle, not archaeology in comments. You also use a Mac or Linux box PowerToys is Windows-only. Your hosts process should not fork by OS if the team shares domain names. You forget ipconfig /flushdns Same bug as every other hosts tool without auto flush: file correct, browser wrong. Alternatives on Windows SwitchHosts Free, open source, profiles, also runs on Mac/Linux. Best PowerToys alternative when you need environment switching and maybe multi-OS later. Locahl Paid one-time. Windows, macOS, Linux. Automatic DNS flush and backups. Best when PowerToys feels too manual and you want the apply step to include flush + safety. Notepad as Administrator Still works. Still easy to save the wrong copy or skip flush. Only fine for rare edits. Hostly / CLI hosts switchers Interesting if you want hosts open Dev from scripts. Check that the project is maintained before you depend on it in CI. Feature snapshot (Windows view) Tool Profiles Auto flush Multi-OS Cost PowerToys Hosts Limited No No Free SwitchHosts Yes No Yes Free Locahl Yes Yes Yes One-time Notepad Admin No No Manual Free Practical upgrade path Keep PowerToys for now if you only have 5 stable lines When you start commenting / staging blocks every week, move to SwitchHosts or Locahl Always backup before the first import After every apply: ipconfig /flushdns ping myapp .test If PowerToys is

I had a website blocker, a Pomodoro timer, a tab suspender, and a time tracker installed at the same time — four separate extensions, four separate settings pages, none of them talking to each other. Starting a focus session meant manually turning on the blocker, then starting the timer, and neither knew the other existed. So I built TabInsights , which does all four and actually connects them. What it does Website blocker — block by domain, category, or schedule, with an optional typed "unblock challenge" for the days willpower isn't enough. Pomodoro focus timer — one click starts a 15/25/45-minute sprint, which also auto-blocks distracting categories for the duration and unblocks them automatically when it ends. This is the part that actually solves my original problem — the timer and the blocker are the same feature, not two extensions coincidentally running at once. Memory saver — auto-suspends tabs you haven't touched in a configurable window (15–60 min), freeing roughly 50MB of RAM each via chrome.tabs.discard() . Suspended tabs stay in your tab bar and reload exactly where you left off with one click. Automatic time tracking — logs time per domain with no manual start/stop, and shows a daily focus score. A few implementation notes Manifest V3 removed persistent background pages, which meant every "ongoing" feature — sprint timers, the daily summary, auto-suspend checks, license re-validation — had to be rebuilt on chrome.alarms instead of a long-lived timer. The gotcha: Chrome clamps alarm intervals to a minimum of 1 minute in packaged (published) extensions, so anything needing finer granularity has to accept that floor rather than fight it. The blocker uses declarativeNetRequest — you hand Chrome a set of match rules and it enforces them at the browser level. The extension never actually reads the blocked request; it can't, by design, which is also the honest answer any time someone asks whether a blocker "sees" their browsing. The bigger architectural deci

Everything Claude Code: How One AI Assistant Becomes a Full Engineering Team Most developers use AI coding assistants in the same way. They open a repository and type: Build this feature. The assistant reads a few files, generates code, and reports that the task is finished. Sometimes the result is impressive. Sometimes it solves the wrong problem with perfectly formatted code. The issue is not always the intelligence of the model. The issue is the workflow around it. A single prompt often expects one AI assistant to behave like: a product planner a software architect a frontend developer a backend developer a security engineer a test engineer a debugger a code reviewer a release manager Real engineering teams do not work that way. They divide responsibility. One person plans. Another implements. Someone reviews security. Someone tests edge cases. Someone challenges the architecture. The final result improves because different people examine the same work from different angles. That is the idea behind Everything Claude Code , often shortened to ECC . ECC is an open-source collection of specialized AI agents, reusable skills, commands, security tooling, hooks, memory systems, and development workflows designed to turn Claude Code into something closer to an AI software engineering team. Instead of asking one general-purpose assistant to do everything in one pass, ECC separates software development into focused roles. That architectural idea is more important than any individual command. This article explains how ECC works, why developers are excited about it, how to install only what you need, and what security boundaries you should establish before giving any AI agent access to a real codebase. What Is Everything Claude Code? Everything Claude Code is not a new language model. It does not replace Claude. It is a workflow and configuration layer built around Claude Code. Think of Claude Code as one highly capable engineer. ECC attempts to turn that engineer into a co

The original concept behind this tool won the runner-up award for the Github CLI Challenge earlier this year. As a reminder, ReflectCLI is a tool to promote thoughtful coding through structured reflection. What does it do? Before each commit, answer reflective questions to build your personal developer knowledge dataset. The tool is local-first by design. It stores everything locally inside the current Git repo: .git/git-reflect/log.json No account, no cloud sync, no authentication, and no external AI API call. Why build it? AI-assisted development is powerful, but it can encourage cognitive offloading, letting tools do the thinking instead of developing deeper understanding. git-reflect interrupts this pattern by making reflection part of your workflow. Each answer becomes part of your personal knowledge base, documenting not just what you built, but why you built it that way and what you learned. Over time, this dataset reveals patterns in your thinking and helps you grow as a developer. If you want to read about the original tool, check out my previous post here . Since building the original version, I've spent months researching how AI changes the way developers learn, reason, and make technical decisions. Many of the ideas from those discussions have now been incorporated into ReflectionCLI 2.0. What started as a simple Git pre-commit reflection hook has evolved into a local-first thinking tool for developers working alongside AI assistants. What's New Comprehension debt tracking You can now record artifacts you shipped but don't fully understand yet. reflection debt add "Understand why the cache invalidates on user updates" \ --project api \ --tags cache,ai \ --context "AI generated most of the invalidation logic" These artifacts can easily be retrieved and resolved later. reflection debt list reflection debt resolve debt-001 Explain-back mode This workflow focuses on active recall and asks a series of questions to assess your ability to explain the piece of c

Moving from Windows to macOS often creates a small workflow problem for developers: where did my familiar SSH tools go? PuTTY has been a classic SSH and Telnet client for Windows for many years. Many developers know its interface and workflow, so after switching platforms, the first thought is usually finding a macOS version. The reality is that the macOS ecosystem works differently. There are several practical options depending on how you manage remote systems. Option 1: Use the built-in SSH client on macOS For many developers, the SSH client that ships with macOS is already enough. A typical workflow looks like this: Use the terminal for server access Manage hosts through ~/.ssh/config Store aliases, usernames, ports, and key settings in one place Automate common connection tasks with scripts For example: ssh production-server can replace a long connection command when your SSH configuration is organized properly. This approach works especially well for developers who prefer command-line workflows and manage Linux servers regularly. Option 2: Use a GUI SSH client Some developers prefer a graphical interface because they manage many machines, protocols, or connection profiles. A good GUI client can help with: Organizing dozens or hundreds of servers Saving authentication settings Switching between SSH, RDP, VNC, and other protocols Reducing repetitive configuration work The macOS App Store has many SSH clients available. The right choice depends on whether you only need SSH or you need a complete remote management workflow. Tools like DartShell are designed around this multi-protocol scenario, where developers may need SSH for Linux servers, RDP for Windows machines, and other remote access methods in one place. Choosing the right workflow The decision usually comes down to how you work: A few Linux servers: macOS Terminal + SSH config is usually enough. Many servers and different protocols: a GUI management tool can save time. Team environments: centralized connec

A few weeks ago a Business Manager handed me a 50-page AI-generated technical spec. The document was impressive. The perspective behind it was the problem. I had talked to him a few days earlier about a new internal tool the company needed. We discussed the use case and the requirements in depth. Later on, I discussed those same requirements with my IT team and assigned them the job of making the technical specification. But before my team finished, the Business Manager handed me his own spec. A spec ready to be executed. The document was generated with AI assistance and it was impressive — fifty pages long, detailed feature breakdown, implementation timeline, cost projections. Everything looked professional. The AI had done exactly what it was asked to do. I read the whole document and noticed a problem. Not with the quality of the document but with the perspective that shaped it. The spec called for a manual Excel-based workflow with several manual steps and validations in-between. All seemed clean and manageable, matching the Business Manager's mental model of how that business workflow should work. When I checked the spec my team was working on using the same AI assistance tools, I noticed they had produced something completely different: automated data ingestion, real-time dashboards, API integrations with existing systems. Both specs addressed the same business need. Both were technically sound. Both could be built in roughly the same timeframe. But they were fundamentally different architectures, shaped by fundamentally different perspectives on how work should happen. The Business Manager's version was optimized for control and visibility — he could see every step, every piece of information and approve everything manually at any stage. The technical lead's version optimized for efficiency and scale — minimal manual intervention, automated error handling, designed to handle 10x the current volume without breaking. Neither person was wrong. But the AI amplifi

When we first decided to bring AI into analysts’ work, the task seemed fairly down-to-earth. The company has several development teams and roughly fifteen analysts. They collect requirements, prepare tasks, describe changes in Confluence, think through testing, and help align future implementation with both business and development. A lot of this work is repetitive, so giving analysts a tool that could prepare first drafts felt like an obvious idea. The problem is that a document in this kind of process almost never stands on its own. The same change exists in Jira, in Confluence, in mockups, in test scenarios, and in technical notes. Sometimes there are also separate instructions for making changes in the codebase. Each artifact has its own purpose, but all of them describe the same future system behavior. If the wording drifts apart, that can go unnoticed for several days, until different people begin working from different versions. A typical case looked roughly like this (the details and names are changed, but the mechanism is real). Jira said that a status field could have three values: draft , active , and archived . Confluence still contained an older table with only two values, while the test scenario also checked for disabled , which had been discussed early on and later rejected. The developer implemented what was written in Jira. The tester opened the scenario and filed a defect because disabled was missing. Only after that did the analyst compare the documents and realize that each of them preserved a different version of the decision. Nothing catastrophic happened. We simply had to go through the documentation again, update the tests, clarify the task, explain to the developer that the code did not need to change, and send the package through review one more time. It’s exactly these “nothing serious” moments that add up to delays, when the same task travels two or three times between an analyst, a developer, and a tester. At some point, it becomes diffi

Hey everyone. Just an update on the Salesforce Certification. I am planning on releasing articles...

We’ve all been there: you are busy coding, and suddenly you get a scary error saying your code conflicts with a teammate's work. Git won't let you pull their changes because it's afraid of overwriting your unsaved files. Instead of panicking, I used git stash. This command acts like a temporary clipboard—it safely hides your current work away so your workspace becomes completely clean. With a clean screen, I was able to safely update the project, fix the conflicting lines of code by hand, and make sure everything compiled perfectly. Once the team's updates were safely pushed, I ran git stash pop to bring my hidden work right back. Git wasn't trying to break my project; it was just protecting my code. Next time you get stuck, remember the golden rule: Stash your work, fix the conflict, and pop it back!

Artificial intelligence is changing the way websites are planned, built, managed, and improved. WordPress developers now have access to tools that can help with coding, content creation, customer support, automation, analytics, and search engine optimization. However, using an AI plugin does not automatically make someone an AI-ready developer. A genuinely AI-ready WordPress developer understands how to combine technical experience, business thinking, automation, and human judgment. The goal is not to add AI everywhere. The goal is to use it where it solves a real problem. AI Should Solve a Clear Business Problem Many businesses make the mistake of choosing an AI tool before deciding what they actually need. A better approach starts with a practical challenge. For example, a company may want to: Respond to customer questions more quickly Organize website enquiries Improve WooCommerce product recommendations Automate repetitive administrative work Connect website forms with a CRM Generate content drafts Analyze customer behaviour Improve internal support processes An experienced developer will first study the workflow, the expected result, the available data, and the possible risks. Only after that should a suitable plugin, API, automation platform, or custom solution be selected. This approach prevents businesses from spending money on features that look impressive but provide little value. AI-Generated Code Still Needs Human Review AI coding tools can produce functions, snippets, plugin ideas, and debugging suggestions within seconds. That speed is useful, especially when a developer is working with repetitive tasks or unfamiliar code. The danger is that AI-generated code may look correct while containing hidden problems. It can include: Outdated WordPress functions Weak security practices Plugin compatibility issues Poor database queries Unnecessary scripts Incorrect assumptions Performance problems That is why generated code should never be added directly to a li

Suppose leadership rewards teams for increasing the percentage of “AI-assisted pull requests.” The dashboard rises. Did productivity improve, or did people learn which box to tick? Before launching that metric, I would run a consequence-mapping session: Intended behavior Plausible adaptation Counter-metric try useful assistance label trivial PRs as assisted retained task outcome ship faster split work into tiny PRs lead time per task share adoption avoid difficult non-AI work task-mix distribution accept suggestions reduce review scrutiny rollback and defect rate The metric card should make disagreement possible: name : ai_assisted_pr_share purpose : detect workflow adoption, not productivity owner : developer-experience known_game : self-label inflation counter_metrics : [ task_mix , review_minutes , rollback_rate ] review_date : 2026-08-19 retire_when : classification cannot be audited Then interview both high and low scorers without treating the score as performance. Ask what work disappeared, what new verification appeared, and what behavior the dashboard encouraged. Include an anonymous channel: a metric cannot reveal pressure if challenging it carries career risk. The SPACE framework argues that developer productivity cannot be captured by one dimension. That is especially relevant when AI telemetry is easy to count but verification and rework are harder to observe. My launch gate is not “the metric is accurate.” It is: teams can inspect its definition, challenge its interpretation, and show where it changes behavior. If the counter-metrics diverge, pause incentives before refining the chart. What behavior would your current AI dashboard accidentally reward?

An accepted AI suggestion is an event, not a durable outcome. If the code is rewritten tomorrow, acceptance rate still calls it a success. For an adoption review, I would connect three timestamps: suggestion : accepted_at : 2026-07-19T09:00:00Z repository : api task_type : test change : retained_lines_24h : 31 rewritten_lines_24h : 9 reverted_at : null review : human_minutes : 12 incident_link : null Then report a funnel rather than one flattering percentage: shown → accepted → merged → retained_24h → retained_14d A useful unit is cost per retained task : (tool cost + review labor + rework labor) / retained tasks “Retained” needs a written contract. For example: the change remains merged after 14 days, passes required checks, and has not caused a linked rollback. Line survival alone is weak because formatting and refactoring can change lines without rejecting the solution. Segment the result by task type and repository. Boilerplate tests and unfamiliar security changes should not be blended into one portfolio average. Also publish counter-metrics: review time, escaped defects, rollback rate, and developer-reported interruption. GitHub documents available fields and limitations in its Copilot metrics API . Those product metrics can be inputs, but the retained-task join belongs to the adopting organization and should be versioned like any other analytics contract. My pilot gate would be simple: expand only if retained-task cost beats the existing workflow without worsening rollback rate. Otherwise, change the workflow before buying more seats. Record the baseline before enabling the tool, and keep one comparable task cohort outside the rollout; without that counterfactual, a rising retention rate may only reflect easier work entering the sample. What retention window would make an accepted change meaningful for your team?

버그를 발견한 순간, 텍스트로 상황을 설명하려다 시간을 낭비한 경험이 있을 것이다. PromptShot의 그리기 기능과 단축키(Alt+Shift+S)를 쓰면, 브라우저에서 발견한 버그의 상태와 재현 경로를 캡처·주석·프롬프트 복사까지 한 흐름으로 처리할 수 있다. 텍스트만으로 버그 상황을 기술할 때 생기는 문제 버그를 텍스트로 설명하는 일은 생각보다 까다롭다. "오른쪽 상단 버튼을 누르면 모달이 이상하게 뜬다"는 문장을 받은 개발자는 어떤 버튼인지, 어떻게 이상한지 확인하기 위해 다시 질문해야 한다. 그 왕복이 쌓이면 간단한 버그 하나에도 맥락 공유만 수십 분이 걸린다. 특히 Cursor나 Claude 같은 AI 코딩 도구에 버그를 설명할 때, 텍스트만 붙여 넣으면 도구가 UI 상태를 추측해야 한다. 추측이 틀리면 엉뚱한 수정 코드가 나온다. 결국 개발자가 다시 맥락을 보완하는 루프가 반복된다. 문제는 두 가지다. 위치 : 텍스트로는 "어디에 있는 요소인지"를 정확히 전달하기 어렵다. 상태 : 에러 메시지, 깨진 레이아웃, 빈 영역 등 시각적 상태는 캡처 없이 설명하면 정보가 줄어든다. 그리기 도구로 화면에 직접 표시하고, 그 이미지와 함께 프롬프트를 LLM에 붙여 넣는 방식이 이 문제를 해결하는 가장 빠른 경로다. PromptShot 그리기 도구로 버그 위치를 어떻게 표시할까? PromptShot의 그리기 도구는 캡처 직후 바로 활성화된다. 별도 이미지 편집기를 열 필요가 없다. 영역을 드래그해 캡처하면 편집 패널이 열리고, 여기서 선택할 수 있는 도구는 다음과 같다. 도구 용도 버그 리포트에서 쓰는 방식 펜(자유 드로잉) 특정 영역에 자유롭게 표시 깨진 텍스트나 이미지 경계에 동그라미 형광펜 반투명 강조 에러 메시지 텍스트 강조 화살표 방향 지시 "이 버튼을 누른 뒤 저 팝업이 열림" 순서 표시 텍스트 짧은 주석 "여기서 클릭 → 404" 같은 재현 경로 메모 예를 들어, 가상의 시나리오를 생각해 보자. 결제 플로에서 "다음" 버튼이 특정 해상도에서 잘리는 버그를 발견했다고 가정하면, 화살표로 버튼 위치를 가리키고 텍스트 도구로 "1280px에서 clip 발생"이라고 짧게 메모한다. 이 이미지 하나가 세 문단짜리 텍스트 설명보다 정확하다. 캡처 범위는 전체 화면이 아니라 문제가 발생한 컴포넌트 영역만 잘라도 된다. 오히려 범위를 좁히면 LLM이 어디에 집중해야 하는지 명확해진다. 클립보드 즉시 복사로 버그 리포트 프롬프트를 작성하는 방법 주석 작업이 끝나면 Alt+1 또는 Alt+2로 복사 대상을 선택한다. Alt+1 : PNG 이미지만 클립보드에 복사 Alt+2 : 프롬프트 텍스트 + PNG 이미지를 함께 클립보드에 복사 버그 리포트 프롬프트를 만들 때는 Alt+2를 쓴다. 복사된 내용을 Cursor, Claude, ChatGPT에 그대로 붙여 넣으면 이미지와 텍스트 맥락이 동시에 전달된다. 프롬프트 텍스트 부분에는 캡처한 URL, 브라우저 정보, 캡처 시각이 자동으로 포함된다. 여기에 직접 한 줄을 추가해 재현 조건을 붙이면 완성도 높은 버그 리포트 프롬프트가 된다. 실제로 AI 코딩 도구에 붙여 넣는 프롬프트 구조는 이런 형태다. [캡처 이미지 첨부됨] URL: /checkout 재현 환경: Chrome 124, 1280×800 재현 단계: 1. 장바구니에서 '결제하기' 클릭 2. 배송 정보 입력 후 '다음' 클릭 3. 위 이미지처럼 버튼이 뷰포트 밖으로 잘림 예상 동작: 버튼 전체가 화면 안에 표시됨 실제 동작: 버튼 오른쪽 일부가 잘려 클릭 불가 위 이미지와 재현 단계를 기반으로 원인을 분석하고 수정 방향을 제안해 주세요. 이 구조를 매번 타이핑하지 않아도 된다. PromptShot에서 프롬프트 템플릿을 저장해 두면, 다음 버그부터는 캡처 → 주석 → 붙여넣기 세 단계로 끝난다. Alt+Shift+S로 이슈 트래킹을 어떻게 빠르게 처리할까? Alt+Shift+S는 PromptShot의 전역 캡처 단축키다. 브라우저 외 어떤 창에서도 작동하며, 누르는 순간 드래그 선택 모드로 바로 진입한다. 이슈 트래킹

I run several AI coding agents at once and never really knew what they were doing — what it cost, which one was stuck, what they just changed. So I built agentglass : real-time mission control and a workspace for AI coding agents, across every provider and every project on your machine. ▶ Live demo (no install) · ⭐ GitHub What it does Point any agent at it — Claude Code hooks or any OpenTelemetry exporter (Codex, Gemini, Bedrock…) — and watch everything move live: 💰 Cost per event / session / model ⏱️ Tool latency (real p50 / p95) 🔴 Error timelines + a "what needs you" alert center 💾 Persists (SQLite) — history's there the moment you open it More than a viewer — a workspace All one keystroke away, same cockpit: 🔬 Diff & review — every Edit/Write, syntax-highlighted 🌿 Source control — lazygit in the browser 🐳 Docker — lazydocker in the browser ▶ Real terminal — an actual PTY shell, not an emulation 💬 Chat — drive local Claude sessions Open source (MIT), local-first Built solo. Small stack: Bun + SQLite, React/Vite, Tauri desktop, a stdlib-only Python hook forwarder. If you run more than one agent at a time, I'd love your feedback 👇 ⭐ https://github.com/SirAllap/agentglass

Online file converters are one of those categories where the market leaders have gotten steadily worse. Signup walls, daily conversion caps, "premium" file size limits, upsell banners between you and your download. So when I found digiutil.com, I ran it against the usual suspects for the conversions I actually do. Here's how it stacks up. Where the incumbents lose you If you've used the big converter sites recently, you know the pattern: Account required before you can download your file, or after your first couple of conversions Daily limits on the free tier — often 1–2 files per day File size ceilings low enough that any real video file pushes you to a paid plan Ad-heavy pages where the actual convert button is the fifth-most-prominent thing on screen None of this is about the cost of conversion — it's funnel design. The product is deliberately inconvenient so you'll pay to make it convenient. What digiutil does differently No signup, period. Not "free account required," not "sign up to download." You drag a file in, it auto-detects the format, routes you to the right converter, and you get your file. That alone puts it ahead of most of the category for one-off conversions. No logs, per their policy. Most converter sites are vague about retention. digiutil's stated policy is no logs. Standard caveat still applies — anything you upload to any third-party service is a trust decision — but an explicit no-logs stance is more than most competitors offer. Broad format coverage in one place. Video, audio, images, PDFs, documents, data files, archives, fonts, and subtitles. With most alternatives you end up on a different site for each category (one for HEIC → JPG, another for subtitles, another for fonts), each with its own limits. Here it's one tool: MP4 → MP3, MOV → MP4, HEIC → JPG, DOCX → PDF, and the long tail. Compression too. Video, image, audio, and PDF compression alongside conversion — a task the dedicated compressor sites usually gate hardest behind paid tiers.