标签：#Product

Learn how GPT-5.5 Instant improves ChatGPT’s health and wellness responses with stronger reasoning, better context, clearer communication, and physician-informed evaluations.

𝗦𝗮𝘆 𝗵𝗲𝗹𝗹𝗼 𝘁𝗼 𝗟𝘂𝗺𝗼𝗿𝗮 — 𝗧𝗵𝗲 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽 𝟱 𝗔𝗱𝗺𝗶𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗨𝗜 𝗞𝗶𝘁. 💎 🔗 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼: https://github.com/Chetankumar-Akarte/lumora 🔗 Demo: https://renukatechnologies.in/demo/lumora/ Don't forgot to 🤩 Star and 👉 Fork the Repo 𝗟𝘂𝗺𝗼𝗿𝗮 is a modern, responsive 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽 𝟱 𝗔𝗱𝗺𝗶𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗨𝗜 𝗞𝗶𝘁 designed for teams that need a polished, enterprise-ready control center without the bloat. Whether you are building for SaaS, CRM, E-commerce, or internal analytics, Lumora provides a scalable, token-driven foundation to speed up your workflow. 𝗟𝘂𝗺𝗼𝗿𝗮 is the result: a complete admin ecosystem featuring everything from KPI blocks and ApexCharts to full E-commerce management flows and authentication screens. 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲? • Full UI Kit with basic and advanced components. • Enterprise pages (Users, Roles, Permissions, Invoices). • Interactive apps like Calendar and Contacts. • Clean, token-driven styling for consistent design. 𝗧𝗲𝗰𝗵 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: • Bootstrap 5.3 • ApexCharts & Chart.js • Vanilla JavaScript • Mobile-first design 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: • 𝗠𝗼𝗱𝗲𝗿𝗻 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: Built with Bootstrap 5.3, Vanilla JS, and CSS3 using a module-first architecture. • 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀: Includes layouts for Analytics, CRM, Project Management, HRM, and more. • 𝗙𝗲𝗮𝘁𝘂𝗿𝗲-𝗣𝗮𝗰𝗸𝗲𝗱 𝗔𝗽𝗽𝘀: Ready-to-use interfaces for Advanced Chat, Kanban boards, Email, and File Management. • 𝗗𝗮𝗿𝗸 & 𝗟𝗶𝗴𝗵𝘁 𝗠𝗼𝗱𝗲𝘀: Clean, professional visuals with seamless theme switching. • 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆: Modular CSS, reusable partials, and organized project structure. I built this to bridge the gap between "pretty" templates and "functional" enterprise tools. Check it out, star the repo, and let me know what you think! I'd love for you to take a look at the code and perhaps even use it for your next project. Feedback and contributions are always welcome! WebDevelopment, Bootstrap5, AdminDashboard, OpenSource, UIUX, JavaScript, GitHub, Bootstrap, CodingCommunity, OpenSourceProject, FrontendDev, LumoraUI

The problem I kept running into I'm a chronic tab hoarder. At any given time I've got 40–80 tabs open across two windows. Chrome's built-in Memory Saver is aggressive in the wrong ways — it hibernates tabs I'm actively referencing. And the built-in task manager is a two-step detour that still doesn't tell me which tabs I should actually close. So I built Tab Memory Manager. What it does Per-tab memory estimates — A live MB count next to every open tab. Sorted by memory usage by default. There's a live total on the toolbar icon so you always know what Chrome is consuming right now. Smart suggestions — The extension flags your biggest, stalest tabs: ones that are idle the longest and consuming the most. It never suggests your active tab, pinned tabs, tabs playing audio, or domains you've whitelisted. Hibernate, don't close — This was the core design decision. Hibernating frees the memory but keeps the tab alive in your strip — it reloads when you click it. Much safer than closing, especially mid-research. Bulk cleanup — Select multiple tabs or hit Apply on the suggestions panel. See the total memory you'll reclaim before you commit. Undo list — Closed something by mistake? There's a "Recently cleaned" panel. One click to restore. Tab grouping — Groups all your open tabs by domain into color-coded Chrome tab groups, instantly. The interesting technical bit: memory estimates Chrome's stable extension API doesn't expose exact per-tab memory. The chrome.processes API that does exists only on Dev and Canary builds — not the Chrome that 99% of people use. So Tab Memory Manager uses calibrated estimates based on tab state, domain patterns, and known Chrome process overhead. These are clearly labeled "est." in the UI. If you're on Dev or Canary, you can switch on real per-tab memory in settings. The warning Chrome shows about "processes requires dev channel" is a Chrome-generated note about that optional API — the extension works completely normally without it. It's not a bug

AI tab manager that suspends background RAM hogs Discussion | Link

Securely execute AI-generated & untrusted code Discussion | Link

A Mac app that asks why you opened that tab Discussion | Link

Clipboard history for Mac, right under the notch. Discussion | Link

An AI desktop cursor that does the work for you Discussion | Link

A couple of weeks ago I published a post with a tidy rule in it. When you add capability to an AI coding agent, reach for the lightest option first: a procedure file before a CLI, a CLI before a heavier integration, and only build the heavy machinery once you've proven you'll reuse it. My whole case rested on context cost. The heavy options load a lot of definitions up front and carry them every turn, so starting light keeps the window clean. I still think the front half is right. But it isn't the rule I'd write now, because a reader took it apart in the comments and handed it back as something better. This post is about that exchange, because the rewrite was sharper than my original, and pretending I arrived at it alone would be both a lie and the less interesting story. The hole, found in one comment The first comment didn't argue with the rule. It walked straight to the blind spot. The moment a tool touches anything external or stateful, lightest-first reverses on you: a lightweight call that fails silently halfway through is harder to debug than a heavier tool that surfaces the failure cleanly. Pay the complexity up front. My first instinct was to defend, and I did, a little. I said we were measuring different things, that I'd optimized for context cost while they were optimizing for failure observability, both real, different axes. I held the line by pointing out you can wrap a lightweight call to fail loudly, so the cheap path stays open. That was true, and it was beside their point, and they didn't let me hide behind it. The question that moved the rule They asked one question that did more work than my entire post: what's your actual trigger for paying the complexity up front, the type of state, or the class of error? Sitting with that is where my own rule changed under me. The honest answer is state type, and the moment I said it out loud, context cost stopped being what the rule was about. What makes a failure expensive isn't the error. It's whether the op

Clone yourself. Let AI do the work before you ask Discussion | Link

The average knowledge worker switches between apps 1,200 times per day, according to a 2024 Harvard Business Review analysis. Each switch is small. The cumulative cost is not. For freelancers managing their own tool stack, the problem is both a productivity drain and a billing leak. What the Research Actually Says The most cited figure comes from Gloria Mark at the University of California, Irvine: it takes an average of 23 minutes and 15 seconds to fully refocus after an interruption. That number gets quoted a lot, but the context matters. Not every app switch is a full context switch. Checking Slack for two seconds is different from switching from deep coding work to a client call. A more useful framing comes from the American Psychological Association, which distinguishes between task switching (changing what you are working on) and tool switching (changing which app you are using for the same task). Both have costs, but tool switching is uniquely wasteful because it does not change the work -- only the interface. You are still working on the same problem but spending cognitive effort navigating a different app. For freelancers, the most expensive switches are the ones between a task manager and a time tracker, between a calendar and a task list, and between a project view and a communication tool. These happen multiple times per hour during active work, and each one breaks the low-level focus that produces billable output. How to Audit Your Current Tool Stack Before consolidating tools, figure out what you actually use. For one week, keep a simple log: every time you open an app to do work (not social media or entertainment), note it. At the end of the week, tally the list. Most freelancers find they use 6-10 tools daily. The typical list looks something like this: Task manager (Todoist, Asana, Notion) Time tracker (Toggl, Clockify, Harvest) Calendar (Google Calendar, Outlook) Communication (Slack, email) File storage (Google Drive, Dropbox) Invoicing (FreshBook

Last month my OpenClaw agent kept making the same mistake: it would run a health check, the script would fail silently, and the agent would report "all systems operational" with total confidence. It wasn't broken. It was just doing what it was built to do — execute tasks — without any mechanism to learn from the outcome. So I built it a self-improvement loop. Every night at 2 AM, an isolated OpenClaw session wakes up, reads the previous day's execution logs, identifies patterns in what went wrong, and updates the agent's memory files. No human in the loop. No re-deployment. Just... learning. Here's what I built, what broke, and what actually works. Why Self-Improvement Is Hard for Personal Agents Enterprise AI labs solve this with massive infrastructure: reinforcement learning pipelines, full fine-tuning jobs, A/B testing frameworks that run for weeks. For a personal agent running on a cron job, that's not an option. The self-improvement loop for a personal OpenClaw setup has to be lightweight. It has to run in seconds, not hours. It has to write to plain text files that the next session will actually read. And critically, it has to avoid the feedback loop problem — an agent that rewrites its own improvement logic can spiral into nonsense if there's no anchor. The key architectural decision I made: separate the executor from the critic . Your main agent runs tasks. A separate isolated session reviews what happened and recommends changes. The main agent applies them on the next run. No single session is both judge and executioner. The Nightly Cron: What Actually Runs This is the cron I have running at 2 AM ET every morning: { "name" : "nightly-self-improvement" , "schedule" : { "kind" : "cron" , "expr" : "0 2 * * *" , "tz" : "America/New_York" }, "sessionTarget" : "isolated" , "payload" : { "kind" : "agentTurn" , "message" : "Review the last 24 hours of OpenClaw execution. Read memory/$(date +%Y-%m-%d).md and memory/yesterday.md. Identify 3 patterns where the agent u

What I actually found when I stopped reading about AI and started running my own experiments. Everywhere you turn right now, someone is telling you how AI is going to transform your workflow, your team, your organization, your life. The content is relentless, and it is almost universally positive. Glowing. Evangelical, even. I'm not here to tell you that's all a lie. I genuinely don't know. That's kind of the problem. We live in a media environment where the line between advertising and information has been blurring for years, and AI is accelerating that blur in ways I don't think we've fully reckoned with. When I read a breathless LinkedIn post about how some engineering leader 10x'd their team's output with AI coding agents, I find myself asking: is this a real person sharing a real experience? Is it a paid placement? Is it content generated by the very tools being promoted? I have no way to tell. Neither do you. And it's getting worse, not better. The most qualified people to evaluate these tools honestly, the ones with enough experience to have real judgment, are also the busiest. They don't have time to write takes. Which leaves a lot of space for everyone else: the shiny-object adopters who are genuinely excited, the vendors with obvious incentives, and an increasingly murky middle ground of content that looks like an opinion but might be something else entirely. The financial relationship between a writer and the tools they're praising is almost never disclosed. And now the tools themselves can generate content praising the tools. Think about that for a second. I'm not making accusations. I'm describing a problem that I think we have a collective responsibility to sit with rather than just nodding along. The appropriate response to an information environment you can't fully trust isn't paralysis. It's going and finding out for yourself. So that's what I did. Why I finally got off the fence I've been watching this space with skepticism for a while. Being a cyn

Your backend returned 200. The mobile app showed an error. The user tapped "Pay" three times. Three pending charges hit their account. One order was placed. Their balance was short. And your incident log showed zero failures. Every engineer on the team did their job. Nobody solved the problem. This is the most common way engineering teams fail, not through incompetence, but through excellent execution of the wrong unit of work. And until you recognise the difference between completing a task and solving a business problem , you will keep shipping systems that work perfectly and experiences that don't. The Ticket-Thinker vs. The System-Owner Most engineers early in their careers think in tickets. Ticket assigned → code written → tests pass → PR merged → ticket closed. Done. This is fine when you're learning. It's a liability when you're trying to grow. The engineer who closes tickets is useful. The engineer who asks "what problem does this ticket actually solve, and am I solving it in the right place?" that engineer is dangerous in the best way. Here's the distinction in practice. The backend engineer builds a payment endpoint. It processes charges correctly, returns the right status codes, has proper error handling. 100% test coverage. Ticket closed. The mobile engineer builds the payment screen. It calls the endpoint, handles the response, shows confirmation or error. Smooth UI. Ticket closed. The problem nobody owned: what happens when the network drops after the backend processes the charge but before the mobile app receives the confirmation? The backend: charge processed. No error. The mobile: timeout. Shows "Payment failed." User retries. The user: charged twice. Both engineers solved their assigned problem correctly. The business problem — charge the user once and confirm it reliably — went unsolved. Because that problem lived in the space between their tickets, and nobody was watching that space. Real Scenario 1: The Payment That Worked and Failed at the Same

Automatic customer context for product teams and agents. Discussion | Link

Arriving six years after Google’s last smart speaker, the new HomePod-style device was redesigned to play host to Gemini’s chatbot.

If you build with AI, three stories this week rhyme into one theme: the hype is colliding with the bill. Here's the builder's read on each — and what I'd actually do about it. 1. Most of a new TikTok feed is now AI slop A Kapwing study reported by Tubefilter hand-checked 10,742 videos across 20 categories and found that 59% of what a brand-new TikTok account sees is AI-generated . Kids content was the worst — 57% slop, with the #CartoonKids tag hitting 97% — and TikTok serves roughly 3x more slop than YouTube. Why builders should care: generation is now free and infinite, so volume is worthless as a moat. The scarce thing is taste and verification. If your product or content can be faked by a feed of bots, it will be. Polish, point of view, and "a human clearly did this" are the new differentiators. 2. Databricks grew 80% — but agents are eating its margins Per CNBC , Databricks' annualized revenue jumped about 80% to ~$6.9B, and its AI products now bring in $1.7B (up from $1.4B). The catch: the CEO says gross margin "will go lower" as customers run more agents. Why builders should care: this is the quiet tax of agentic software. An agent that loops, retries, and calls tools burns far more tokens than a single API call. If you're shipping agents, budget for inference at scale , not the sticker price on the pricing page. Profitability now lives in prompt efficiency, caching, and knowing when not to call the model. 3. 60% of US consumers are turned off by "AI" branding A WordPress VIP survey of 2,000 people, covered by TechCrunch , found that 60% reject "AI" in brand messaging , while 86% still want to check the original sources behind a claim. Why builders should care: "Now with AI!" is starting to read like a warning label. Sell the outcome, not the technology — "2x faster," "fewer errors," "your data stays private" — and cite where your results come from. Trust is becoming a feature you ship, not a slogan you bolt on. The takeaway Feeds are flooded, agents are cost

Most developers run git pull dozens of times a week without thinking about it. And most of the time, it works. Then one day you open a PR and the reviewer says "can you clean up the merge commits?" You look at your branch and see three "Merge branch 'main' into feature/login" commits scattered through history. The feature itself is 5 commits. The log is a mess. That mess comes from one decision: using git pull instead of git pull --rebase . Here's what's actually happening, and why the rebase variant produces cleaner history for teams. The setup: diverged history You're working on feature/login . You commit two changes locally ( X , Y ). Meanwhile, your teammate pushes two commits to main ( C , D ). Your branch and main have now diverged . Neither is a strict superset of the other. Git needs to reconcile them when you pull. Shared history: A → B Your local: A → B → X → Y (you added X, Y) Remote main: A → B → C → D (teammate added C, D) Git has two strategies for this reconciliation. Strategy 1: git pull (merge) A plain git pull creates a merge commit that joins your local history with the remote. Your commits and the remote's commits both appear in the log, connected by a merge node. The git log reads: M Merge branch 'main' into feature/login D fix: timeout on slow connections Y feat: client-side validation C chore: upgrade eslint X feat: login form B (shared) A (shared) This is honest history — it records exactly what happened: parallel development that was joined at a specific point. But it's also noisy history — the merge commit has no meaningful changes, and the log interleaves commits that weren't conceptually related. Strategy 2: git pull --rebase With --rebase , Git takes a different approach. It: Temporarily sets aside your local commits ( X , Y ) Fast-forwards your branch to the tip of the remote ( D ) Replays your commits on top, one by one, creating new commits ( X' , Y' ) The git log reads: Y' feat: client-side validation X' feat: login form D fix: timeo

Emojis and GIFs, Anywhere on Your Mac Discussion | Link

Smart mowers are an expensive alternative to old-fashioned yard work, but they’re finally good enough to consider if you’d rather sip an iced tea and watch a robot tame your lawn.