标签：#ui

For the past few months, I've been working on a project that translates online meetings in real time. At first, I thought the problem would mostly be about choosing the right speech recognition model and finding a good translation API. It turned out those were the easy parts. The real challenge was making everything feel instant. People don't wait for subtitles. If the translation appears two or three seconds after someone finishes speaking, the conversation has already moved on. Technically it works, but from a user's perspective, it feels broken. That completely changed how I approached the project. Instead of optimizing only for accuracy, I had to optimize the entire pipeline for latency: Capturing audio continuously Streaming audio to ASR Performing speech recognition incrementally Translating partial sentences Updating subtitles without flickering Handling corrections when the speech recognizer revised previous words Every stage might only take a few hundred milliseconds, but together they determine whether the experience feels "real-time." Another lesson surprised me even more. Translation quality isn't just about picking a better LLM. Spoken language is messy. People interrupt each other, change direction halfway through a sentence, use filler words, and rarely speak in complete grammatical sentences. A model that performs well on benchmarks can still struggle in a live conversation if the input arrives one fragment at a time. That forced me to rethink prompts, buffering strategies, and when to display or revise translated text. Building this project also gave me a new appreciation for streaming systems in general. Real-time applications are fundamentally different from batch processing. Instead of asking, "How accurate is the result?", you're constantly balancing three competing goals: Latency Stability Accuracy Improving one often makes another worse. I'm still learning every day, and there are plenty of problems left to solve. Over the next few weeks, I'd

I'm building a small tool that converts bank CSV files into .qbo files for QuickBooks ( qbofile.com ). When a generated file is wrong, QuickBooks rejects it with vague errors and the OFX spec doesn't tell you what QuickBooks actually checks. So I ran some experiments. Notes below, in case someone else hits the same wall. The file is not XML .qbo is Intuit's version of OFX 1.0.2, which is SGML. Leaf tags have no closing tag: <TRNAMT> -42.50 <FITID> 8f3a2b... Only aggregate tags close. The file also needs a 9-line key:value header, then one blank line, then the body. Line endings are CRLF. My first bug was closing every tag like XML. "Missing bid data" means one tag: INTU.BID QuickBooks checks <INTU.BID> against an internal list of banks that pay Intuit for Web Connect. I tested three variants on QuickBooks Desktop for Mac 2024: Variant Result No <FI> block, no <INTU.BID> Rejected: "Missing bid data" Only <INTU.BID> Accepted <FI> block + <INTU.BID> Accepted So the whole <FI> block (bank name, org id) can be dropped, but INTU.BID cannot. I have only tested the Mac version. If you know whether Windows versions behave the same, I'd like to hear. FITID decides duplicates QuickBooks dedupes on FITID, not on date + amount. If a converter generates random FITIDs, re-importing an overlapping date range creates duplicate transactions. I hash account + date + amount + description, so the same transaction always gets the same FITID. Credit card statement cycles never match calendar months, so overlapping imports happen more often than I expected. QuickBooks cannot export .qbo This one surprised me. No version of QuickBooks can produce a .qbo file. The format only goes one direction, from bank to QuickBooks. Every .qbo file in the world came from a bank's download button or from a converter. That's what I have so far. The tool is free for single files and runs fully in the browser, nothing gets uploaded. I have only tested against QuickBooks Desktop — if you use QuickBooks Online

The plaintiffs say they invested nearly $1.2 million in the company, and are accusing Gomez of failing to build and market the startup.

Robot lawnmowers are finally good enough to take a lot of work out of maintaining a yard, but they’re still not set-it-and-forget-it machines. If you don’t want these autonomous cutting machines to tear up your lawn or go roaming in your neighbors’ yard, you’re still going to need to keep an eye on them. But […]

Comfy MCP is Comfy's first-party local Model Context Protocol server. It lets an MCP-capable coding agent inspect the models and nodes in your ComfyUI installation, validate workflows, run them, and retrieve the outputs. The detail that prevents the most confusion is that two processes are involved : comfy launch starts ComfyUI. Your AI client starts comfy-mcp as a local stdio server. If you run comfy-mcp directly and it appears to do nothing, it is probably waiting for an MCP client. That is normal for a stdio server. Disclosure and verification scope: AI tools assisted with drafting and editing this adaptation. I reviewed the finished article and checked the commands and material claims against Comfy's official documentation, repository, and PyPI pages on 13 August 2026. I have not run a generation on my own hardware for this article, so this is a documentation-verified setup guide, not a hands-on performance test. Comfy's documentation currently labels the MCP offering a public beta, so tools and behaviour may change. What you need Before starting, have: Python 3.10 or newer. The examples below use Python 3.11. comfy-cli 1.14.0 or newer. A ComfyUI workspace, either created with comfy install or selected with comfy set-default . An MCP client that can start a local stdio server, such as Claude Code, Cursor, or Claude Desktop. The models and custom nodes required by the workflow you want to run. The MCP bridge is not what determines the hardware requirement; the selected ComfyUI workflow does. A small image workflow and a large video workflow can have very different memory needs. 1. Install comfy-cli and comfy-mcp I prefer a dedicated virtual environment. It keeps the executables in a predictable place and avoids mixing these packages with unrelated Python projects. Windows PowerShell mkdir comfy-mcp-guide cd comfy-mcp-guide py -3 . 11 -m venv . venv . \.venv\Scripts\Activate.ps1 python -m pip install --upgrade pip python -m pip install "comfy-cli>=1.14.0" comfy-mc

Protect your Samsung phone with these cases and screen protectors.

Whether you need a portable, around-the-house entertainment center or a full-on laptop replacement, these are the best Android tablets.

Nothing ruins a great night of sleep faster than getting too hot. We slept on a myriad of cooling mattresses to find which ones drew the heat away best.

Nothing ruins a great night of sleep faster than getting too hot. We slept on a myriad of cooling mattresses to find which ones drew the heat away best.

How long does it take to publish one article? If you run a one-person company, the honest answer isn't "the few hours of writing." It's everything that comes after — moving it into your newsletter, copying it to one dev community, pasting it into another, translating it for an English audience, cutting a short version for X, dropping a note somewhere else. That relay of copy-paste-reformat costs more energy than the writing itself. This week I killed that chore. Now I finish a draft, drop it into a single spreadsheet, run one command, and it fans out to eight platforms on its own. But I don't want to talk about "I built an automation tool." That's the small part. I want to talk about three judgments that are worth far more: where a solo founder's real bottleneck lives, how this machine should actually be built — and how it nearly lied to me. Writing isn't the bottleneck. Distribution is. Most people assume the hardest thing about a one-person company is content output. It isn't. AI has already crushed the marginal cost of writing to the floor. What actually eats your time is the repetitive labor of pushing the same piece across a dozen platforms: log in, paste, fix formatting, upload the cover, edit the tags, publish, log it. Five minutes per platform, eight platforms, forty minutes — every single day. This labor has one especially miserable property: it creates no new value. It's pure hauling of things that already exist from one place to another. And yet you can't skip it — no distribution means even great writing goes unread. So when you work alone, the first thing to industrialize isn't "write faster." It's "hand the entire distribution chore — the certain, repetitive, judgment-free part — over to the machine." A central dispatcher plus plug-in channels I didn't build one giant "fully automatic publisher" for this. Those look great in a demo, but they're a nightmare to change — adding a single platform means touching the whole flow. Instead I split it into two l

Gearing up to shred the slopes or dive into the seas? These photography tools are made for danger.

Every mobile developer knows the frustration of "Design QA Day." You finish building a screen and send it to your UI/UX team or QA engineer. Then you receive a list of minor padding mismatch tickets. "This card padding should be 16dp, not 12dp." "The title baseline is slightly off on smaller device densities." Usually, fixing these issues means taking screenshots on devices, placing them onto a Figma canvas, lowering the opacity, and measuring pixels. You can streamline this entire process directly on your physical Android test devices using Designer Tools. Overlay Figma Comps Directly Over Live Apps Instead of comparing your app build side-by-side with a Figma preview on a monitor, you can overlay the target design file onto your screen using SYSTEM_ALERT_WINDOW permissions. Export your frame from Figma as a PNG. Load it into Designer Tools under Image Mockup Overlay. Set the opacity to 50%. When you open your app build underneath, any layout misalignment, incorrect text scaling, or constraint issue will become quickly visible as a drop-shadow ghost. Verify Density-Independent (dp) Grids Testing layouts across different device densities (mdpi, hdpi, xxhdpi) often results in unexpected spacing bugs. With the Custom Grid System in Designer Tools, you can create a grid native to dp: Set standard 4dp/8dp vertical and horizontal spacing grids. Set origin points (Top-Left, Center, or Safe Area bounds). You can verify component placement on target hardware instantly without manual measurements. Infinite Alignment Guides For measuring dynamic list items, headers, or bottom sheets, you can place interactive vertical and horizontal guidelines directly onto the active screen. This removes uncertainty about distances between different UI elements. Try It Out Designer Tools is lightweight and privacy-focused, as all image assets stay strictly on-device. It is built for Android engineers, QA teams, and UI designers. Download on Google Play: ( Pro Design: Designer Tools ) How doe

Judges tell Meta that Section 230 provides a defense, not immunity from lawsuits.

There are two ways to work with an AI agent and I had tried both. Write the thing yourself and hand over only the tedious parts. Or hand over the whole task and audit whatever comes back at the end. The first is slow. The second is fast right up until it is wrong, and by then the wrong thing is finished. I expected this series to be about forcing a third option into existence. Nine parts of making an agent follow a workflow it would rather skip. That is not what happened. I never had to enforce it once. The queue that started this had a payload contract nobody had verified, and each phase after that cost me something before it gave anything back. A plan that would not move until the risk register named the provider contract the brief had only guessed at. A build that missed nothing except what my own brief left out. A review that stopped handing back a feeling and started handing back a verdict on every requirement I had already called done. A matrix instead of a trusted green run. A rollback with a name on it before anything got called shipped. And a retrospective that would not let a lesson through until it had checked itself against the trail. Eight parts of that. What I did not expect was which part turned out to be automatic. The Fight I Expected Never Started By the time I finish writing a requirement, I already know roughly what it is going to cost. Most engineers do. You can feel the difference between a one-line fix and something that is going to touch four files and a migration before you have written a single line of it. What I assumed was that the agent could not feel that, and that policing the gap would be my job forever. Reminding it to run the chain. Catching it when it decided a spike was small enough to skip. It has not needed the reminder. Small bugs do not trigger a brief and a plan, and they should not. A standard requirement, a spike, anything long or cross-cutting, runs the full cycle in order. The classification lands where I would have put i

Joby Aviation reached an agreement to acquire Resonant Sciences, which will kick off its new defense business.

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This lawsuit follows one filed last month by Runlayer that accused Rippling of stealing its product ideas. It's a seller- and buyer-beware market warning.

Aptoide has brought its games store back to Google Play after more than a decade, as court-ordered changes open Android to competing app stores.

Adding a “Control de Obra” Module to Ventas → Desarrollos (NestJS + Next.js) TL;DR: I built a brand‑new Construction feature (Control de Obra) inside the Ventas → Desarrollos flow, wiring a NestJS controller, a migration for branding_settings , and a Next.js page. While doing that I also fixed the setToken bug that stopped the BrokerDashboard from refreshing its session. The result is a clean, testable API endpoint and a functional UI component that talks to it. The Problem Our product needed a way for sales teams to track the construction status of each development (obra). The UI already had a “Desarrollos” list, but the backend had no endpoint to create, read, update, or delete construction records. At the same time the BrokerDashboard ( apps/web/src/app/portal-broker/page.tsx ) was failing to refresh the user session after a token rotation. The console showed: Error: setToken is not a function at Object.<anonymous> (src/portal-broker/page.tsx:78:15) Both issues were blockers: No API → the UI could only display static data. Stale token handling → users were logged out unexpectedly after a token refresh. What I Tried First I first tried to reuse the existing VentasPropertiesController ( apps/api/src/ventas/ventas-properties.controller.ts ). The controller was already imported in AppModule , but it was dead code (the class had no routes) and its methods lacked the AuthGuard we use across the API. I added a couple of ad‑hoc routes inside that controller, but: The routes conflicted with the existing /ventas namespace. The controller’s @UseGuards(AuthGuard) was missing, causing 401 errors in the browser. The migration for branding_settings was still out of sync, leading to a “column does not exist” error when the new endpoint tried to read branding data. After a few hours of chasing 404s and 401s, I decided the cleanest path was to create a dedicated module for construction and keep migrations in sync. The Implementation 1. Register the new controller in AppModule // a

Automating Multi‑Platform Content Publishing with a Node.js Scheduler TL;DR: I extended the content-automation repo to generate weekly newsletters, Dev.to articles, and platform‑specific markdown in a single CI run. The key was a tiny Node.js scheduler that reads a JSON manifest, writes files, and flips “generated” flags in metadata.json so downstream pipelines know what to publish. The Problem Our content pipeline had three independent manual steps: Write a weekly newsletter markdown file. Draft a Medium article. Publish a Dev.to post. Each step required copying the same body copy into a different folder ( weekly/ , content-automation/medium_* , content-automation/substack_* ) and then manually toggling flags in metadata.json . During a production run on 2026‑08‑08 the CI job failed with a cryptic log line: Error: Conn The truncated message was coming from the Prisma client that our automation script uses to fetch the latest draft from the CMS. Because the script never updated the metadata.json flags after a successful write, the next run tried to re‑process the same draft, hit a stale DB connection, and blew up. In short: the automation was not idempotent , and the state tracking was brittle. What I Tried First My first attempt was to wrap the whole generation flow in a try / catch and, on any error, abort the job without touching the manifest. I added a quick if (fs.existsSync(filePath)) return; guard to each write operation. // naive guard if ( fs . existsSync ( targetPath )) { console . log ( ` ${ targetPath } already exists – skipping` ); return ; } That prevented duplicate files, but it also silently skipped a legitimate update when we intentionally rewrote a newsletter (e.g., after a typo fix). Moreover, the guard didn’t address the stale Prisma connection, so the same Error: Conn kept surfacing in later runs. The Implementation 1. Central Manifest ( metadata.json ) The manifest now lives at content/2026/08/08/content-automation/metadata.json . I added expli