标签：#Product

The all-in-one workspace for serious exam aspirants. Discussion | Link

Voice dictation that adapts to what’s on your screen Discussion | Link

A supercharged terminal for Mac with built-in local AI Discussion | Link

Give agents reliable access to 2,000+ APIs w/ durable state Discussion | Link

Landing page builder for Ghost CMS Discussion | Link

A fully private & offline location based music journal app Discussion | Link

The rest of Dyson’s promised 2026 vacuum lineup is here, from the new Dyson V16 Piston Animal to an updated version of the favored Dyson V8 Cyclone.

Whether you want to buy cheaper electricity, store solar energy, or guard against outages, EcoFlow’s home battery might be just what you need.

The default for AI-assisted development is one of two failure modes. Either you're babysitting the agent line by line — approving each diff, re-explaining context it dropped three messages ago — or you've handed it the wheel and you're hoping the PR that lands at the end resembles what you asked for. Son of Anton is neither. It's a delivery orchestrator built on a single claim: there are exactly three moments where a developer's judgment is irreplaceable. The orchestrator owns everything in between. The three gates Every project moves through three human decision points. Nothing important happens without you signing off. Gate 01 — Approve the WHAT ( /soa plan ) A grill-me session forces the AI to surface its assumptions, constraints, and scope decisions back to you before a single ticket exists. You say yes or you refine. It does not proceed until you have. Gate 02 — Approve the HOW ( /soa decompose ) The approved plan becomes a ticket stack — ordered, dependency-aware, sized for review. Architectural judgment stays with you. Ticket authorship goes to the agent. Gate 03 — Approve DONE ( /soa closeout ) An adversarial subagent reviews every ticket before its PR opens. When the phase is complete, you decide whether to accept. Closeout squash-merges the stack onto main. Nothing merges without you. Between the gates, you are not needed That's the whole point. Once you've approved the plan and the tickets, the orchestrator runs the loop:

Most of the "Cursor vs Claude Code" takes I read are framed wrong. It's not a cage match. They're not competing for the same job — they're good at different jobs, and once that clicked for me, both got more useful. After months of leaning on both for actual day-to-day work (not demos, not toy repos), I've settled into a pretty stable split: Cursor handles about 90% of my coding, and Claude Code handles the 10% that actually moves the needle. Here's where I draw the line, and the rule of thumb that decides it. The 90%: why Cursor owns my day Most coding isn't dramatic. It's small, local, iterative work: tweak this function, rename that, fix the bug in the file I'm already staring at, ask "what does this block do" without breaking focus. That's exactly Cursor's home turf. It lives inside the editor, so I never leave my flow. Inline edits, fast completions, quick questions about the code in front of me — all without context-switching. When the work is local and I want to stay in the loop keystroke by keystroke, an in-editor copilot is the right tool. It keeps me fast and in context, which is most of what a normal coding day actually is. The 10%: where I close the editor and open Claude Code Then there's the other kind of task — the one where I don't want to babysit every edit. Claude Code is terminal-native and agentic. Instead of sitting beside me suggesting the next line, it works more like something I hand a well-described task to and let run across the whole project. That changes what it's good for: Codebase-wide refactors that touch a dozen files at once "Understand this whole repo and do X" type tasks, where the work depends on grasping how everything connects Jobs I want to delegate and step away from , rather than steer line by line The mental model that finally made it stick for me: Cursor is a copilot sitting next to you. Claude Code is more like handing a ticket to a capable teammate and checking the result. Different relationship, different jobs. How I actu

Three changelogs from every release -for indie SaaS founders Discussion | Link

Few lines of code look more innocent than this: retry ( 3 ) It feels responsible. Professional. Resilient. After all, networks fail. Servers become unavailable. Databases occasionally time out. Retrying seems like the obvious solution. And sometimes it is. But after enough years building production systems, I've become convinced of something: Retry is one of the most dangerous keywords in software. Not because retries are bad. Because retries amplify everything. Good systems become more reliable. Bad systems become disasters. The problem is that many developers treat retries as a reliability feature when they're actually a distributed systems feature. And distributed systems are where simple ideas go to become complicated. Why Retries Exist Imagine: await fetch ( " /api/users " ); The request fails. Maybe: Network hiccup Temporary database issue Load balancer restart Service deployment The operation might succeed if attempted again. So we write: retry ( 3 ) Seems reasonable. And in many cases: It Works Which is why retries become popular. The Dangerous Assumption Most developers unconsciously assume: Failure = Operation Did Not Execute Unfortunately that's not always true. A request can: Execute Successfully ↓ Response Never Arrives From the client's perspective: Failure From the server's perspective: Success Now a retry becomes dangerous. The Double Payment Problem Imagine a payment service. await chargeCard ( order ); The card processor successfully charges: $100 The response is lost due to a network issue. Client sees: Request Failed and retries. await chargeCard ( order ); again. Now: Charge #1 = Success Charge #2 = Success The customer paid twice. Nobody wrote bad logic. The retry created the bug. The Email Storm Problem Consider: await sendWelcomeEmail ( user ); Email provider accepts the message. Response times out. Application retries. await sendWelcomeEmail ( user ); again. Customer receives: Welcome! Welcome! Welcome! Welcome! Support ticket created. Marke

Get clean structured docs instantly. Discussion | Link

Open-source AI coding agent for your terminal Discussion | Link

It is one of the most frustrating experiences in online learning: you sit down to focus on a Udemy course, but the video player constantly pauses, freezes, or refuses to load. Meanwhile, YouTube, Netflix, and every other app on your Mac run perfectly fine. Because other platforms work without a hitch, it is easy to assume the issue lies with Udemy's servers. However, the root cause is usually a silent conflict between your browser settings, macOS security features, and Udemy’s strict digital rights management (DRM) protections. If you are stuck on a looping loading wheel, here is exactly why it happens and how to fix it in less than two minutes. Quick-Fix Troubleshooting Checklist Save or screenshot this step-by-step breakdown to instantly diagnose and fix your playback issues: Step 1: Open Chrome in Incognito Mode Open a new Incognito window ( Cmd + Shift + N on Mac). Try playing the video again. If it works: Disable ad blockers. Disable VPN privacy shields or browser extensions one at a time. If it still fails: Continue to Step 2. Step 2: Disable Hardware Acceleration Open Chrome. Go to Settings → System . Turn off Use graphics acceleration when available . Relaunch Chrome. Test the video again. Step 3: Check Mac Security and Display Connections Disconnect any external monitors or docking stations. Close applications that may interfere with video playback: Zoom Discord OBS Studio Screen recording tools Test video playback again. Step 4: Clear Temporary Browser Data Open Chrome. Go to Settings → Privacy and Security → Clear Browsing Data . Select: Cookies and other site data Cached images and files Clear the data. Restart Chrome and try again. Still Not Working? If the issue persists after completing all four steps: Update Chrome to the latest version. Update macOS. The Main Culprit: Hardware Acceleration Conflict The most common reason Udemy videos stutter or freeze on a Mac is a feature called Hardware Acceleration inside Google Chrome. What is Hardware Accelerat

Everyone's debating whether coding is dead. I actually do this job.. with AI writing code beside me for most of my working hours. Here's what the headlines get wrong. Open your feed right now and you'll find the same headline in a dozen costumes: "Why AI will replace 80% of software engineers by 2026." "Is coding dead?" "Should you still learn to code?" It's the most-clicked anxiety in tech, and it's everywhere for a reason, it taps a real fear about real careers. But here's the thing about almost every one of those posts: they're written from the sidelines. Predictions about a job by people who don't do it. I'm writing this from the other side. I'm an engineer, and I drive AI coding agents every single day. They read code, write changes, run tests, and open reviews for most of my working hours. So when someone asks "should you still learn to code in 2026?" , I'm not guessing. Here's my honest answer: Yes. Absolutely. But the job you're learning for has quietly become a different job and almost nobody is telling you which one. The hype isn't entirely wrong Let me start by giving the doomers their due, because pretending the shift isn't real would make me exactly the kind of person I'm criticizing. The productivity jump is genuine, and it's not subtle. Industry surveys in 2026 put the share of new code that's AI-assisted somewhere north of 40%, and developers using these tools self-report double-digit speedups on routine work. That matches my experience. The agent now handles: Boilerplate and glue code —-> the stuff I used to type on autopilot, gone in seconds. First drafts —-> "scaffold something that does X" gets me 80% of the way instantly. Syntax recall —-> I stopped breaking focus to look up things I half-remember. Tedious refactors —-> rename-this-everywhere, migrate-this-pattern, done fast. and all the kludgy things that I dread to do. If your mental image of "coding" is typing syntax into an editor , then yes.. a big chunk of that is being automated. The vira

有時候我剛跑完一個指令，馬上發現其實只需要改其中一小部分。 例如我剛用 ffmpeg 轉了一個影片： ffmpeg -i calligraphy01.mp4 -c :v libx264 -c :a aac calligraphy_good_01.mp4 然後我想用同樣的指令處理下一個檔案： ffmpeg -i calligraphy02.mp4 -c :v libx264 -c :a aac calligraphy_good_02.mp4 如果用傳統方法，我可能會按上箭頭，然後手動把兩個地方的 01 改成 02 。 但在 zsh 裡，可以直接輸入： !! :gs/01/02/ 意思是： 拿上一個指令，把所有的 01 都換成 02 ，然後執行。 所以這個： !! :gs/01/02/ 會展開成： ffmpeg -i calligraphy02.mp4 -c :v libx264 -c :a aac calligraphy_good_02.mp4 語法是什麼意思？ !! 代表「上一個指令」。 :gs/01/02/ 代表「把所有 01 全部替換成 02 」。 所以完整的： !! :gs/01/02/ 意思就是： 使用上一個指令，把所有 01 改成 02 ，然後執行。 先預覽，不要馬上執行 有時候我不想讓它立刻執行，尤其是指令比較長、比較重要，或者執行成本比較高的時候。 這時可以加上 :p ： !! :gs/01/02/:p 它會只印出修改後的指令，不會執行： ffmpeg -i calligraphy02.mp4 -c :v libx264 -c :a aac calligraphy_good_02.mp4 如果看起來沒問題，就按一下 上箭頭 ，把剛剛印出的指令帶回命令列，再檢查一次，然後按 Enter 執行。 比較安全的流程就是： !! :gs/01/02/:p 確認輸出結果後： 上箭頭 → 再看一次 → Enter 這對比較長、比較容易打錯、或不想立刻執行的指令很有用。 只替換第一個地方 如果你只想替換第一個出現的地方，也可以用： ^01^02 不過像上面的 ffmpeg 例子，輸入檔名和輸出檔名裡都有 01 ，所以通常會比較適合用： !! :gs/01/02/ 這種小技巧每次可能只省幾秒鐘，但如果你常常在 command line 裡重複處理檔案、日期、編號、影片、圖片或 migration，累積起來真的會讓工作順很多。

I have been frustrated with YouTube for a while. Not the content, but the everything around it. The homepage full of bait, the auto-play into things I didn't ask for, the Shorts that hijack your scroll, the recommendations that somehow know exactly what will keep you there longest. So I built NoSuggest. What it is A YouTube feed reader that shows you only the channels you follow, nothing else. No algorithm, no recommendations, no Shorts, no homepage, no auto-play, no endless side cards of videos. You add a channel, it fetches their latest videos, done. It lives at nosuggest.com and installs as a PWA on any device — iPhone, Android, desktop — straight from the browser. No app store. The interesting technical constraint: one HTML file The entire app is a single index.html. No account setup, no sign-in, no data collection. Everything that needs to persist — your channel list, saved videos, settings — lives in localStorage. No search history. No watch history. No "you might also like." No trending section. No notification badges designed to create anxiety. No dark patterns anywhere. Every time I was tempted to add something convenient, I asked: does this serve the user's intention, or does it serve engagement? If it was the latter, it didn't make the cut. Try it nosuggest.com — Source Available here , free forever. Curious what others think about this as useful. Thank you.

AI Fluency for Software Engineers: A Practical Playbook Beyond Prompting A few years ago, being productive with AI mostly meant knowing which tool to open and what question to ask. Today, that is not enough. For software engineers, AI is no longer just a chatbot sitting outside the workflow. It is becoming a thinking partner for architecture decisions, code reviews, production incidents, documentation, test planning, onboarding, and product discovery. But there is a problem: many teams are using powerful AI tools with weak operating habits. They ask vague questions. They paste too much context. They trust the first answer. They forget privacy boundaries. They use AI for speed, but not always for better engineering judgment. That is where AI fluency matters. AI fluency is not just prompt engineering. It is the ability to work with AI clearly, safely, and practically while staying in control of quality, reasoning, and responsibility. Here is a practical playbook I would recommend for software engineers and engineering teams. 1. Start with clarity, not clever prompts A weak prompt sounds like this: “Review this design and tell me if it is good.” The AI can answer, but the answer will likely be generic. A stronger prompt gives the AI a clear role, context, constraints, and output format: You are a senior backend architect. Review this proposed API design for a high-traffic order processing system. Evaluate: - correctness - scalability - failure handling - observability - backward compatibility - operational complexity Do not rewrite the whole design unless required. Separate critical risks from optional improvements. Output format: - Executive summary - Key risks - Recommended changes - Open questions - Final decision recommendation The difference is not word count. The difference is control. A fluent AI user does not hope the AI understands the task. They make the task hard to misunderstand. 2. Give enough context, but not everything AI output quality depends heavily o

Strip the noise, Disrupt the bug. Discussion | Link