标签：#t

After five years of shipping features at Tittri, the team gave me a piece of feedback that stung a little: I was good, but I only ever built what I was asked to build. And we delivered, end to end, every week. From dashboards to multi-step workflows to third party integrations, all of it to make dense workflows understandable. Somewhere along the way I'd become all about business critical web apps, where the frontend is not just visual, it's how people execute operations reliably. But it just wasn't enough for me. So in January 2026 I stopped waiting to be asked, and pitched an AI integration into a UK-based fintech client's product, one that handles real business loans. When I finally presented the idea to the team and the client, after enough brainstorming and planning and identifying the best way for the brokers to benefit from it, expectations rose. No prior AI integration experience, no matching tutorials, real users, real data, real money. Then I had to actually build it. What is the client and why AI The client is one of the UK's pre-eminent business finance brokers and comparison services that combines advanced technology with a team of finance experts to help small and medium-sized enterprises (SMEs) and their advisors find, compare, and apply for the most appropriate and affordable funding options from across the entire market. The client's B2B product is a commercial finance brokerage SaaS, where brokers manage deals, calls, emails, documents, lenders, AIPs (Agreement In Principle), credit/KYC, corporate structures, properties...etc. The brokers use the SaaS to run the whole lifecycle of a deal, and before AI every single step of it was manual. A typical deal goes like this: An enquiry comes in by call or email, something like "my client needs £400k to buy out a GP partner". The broker gets on a discovery call that can run 30–90 minutes, writes up the notes afterwards, creates the deal, and then starts gathering everything a lender will want to see. Finan

Originally published at https://eveningdesk.cloud/blog/woocommerce-customer-support-workflow.html . WooCommerce support often starts in a shared mailbox and ends in several tabs. A customer asks where an order is. An agent searches the inbox, opens the store admin, finds the order, checks fulfilment, then returns to write a reply. That sequence is manageable at low volume. It becomes fragile when several people share the work or when the same question arrives all day. The goal is not to add a complicated service desk. It is to make the support workflow reliable: one owner, the right order context, and a reply a human has reviewed. Start With One Support Inbox Use a dedicated address for customer questions, such as support@yourstore.com , rather than mixing them with supplier, marketing, and personal email. Every incoming request should have a clear status: New, when nobody has handled it. Assigned, when one person owns the reply. Waiting, when the customer or carrier needs to respond. Resolved, when no further action is needed. The important part is ownership. If two people read the same message and neither knows who is replying, the customer waits. If both reply, the customer receives conflicting information. Keep Order Context Beside the Conversation Support agents should not guess about an order. A useful workflow links the conversation to the order reference, then checks the information that matters for the question: Order number and purchase date. Payment and fulfilment status. Items, quantities, and shipping method. Tracking or carrier information, when it exists. Previous support messages about the same order. For a connected WooCommerce store, eveningDesk can sync an order record into the support workspace. The agent still reviews the information and decides what to promise. The software provides context. It does not invent delivery dates or approve refunds on its own. Use a Repeatable Reply Process Most store questions fall into a small set of categories: d

How Normal Software Engineers Actually Use AI in Their Daily Work Let's cut through the hype. You're not building the next AGI. You're a working software engineer with deadlines, legacy code, and a backlog that never shrinks. So how do you actually use AI tools in your day-to-day work? After surveying hundreds of developers and reflecting on real-world usage patterns, here's what normal software engineers are doing with AI—no Silicon Valley theatrics required. The Mundane But Invaluable: Code Completion and Boilerplate The most common use case is the least sexy: letting AI handle repetitive code. GitHub Copilot, Cursor, and similar tools excel at generating boilerplate that you'd otherwise copy-paste from Stack Overflow or previous projects. Real example: Writing CRUD endpoints in Express/TypeScript: typescript // Type this comment and let AI complete: // Create a REST endpoint for user registration with email validation app.post('/api/users/register', async (req: Request, res: Response) => { try { const { email, password, name } = req.body; // Email validation const emailRegex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/; if (!emailRegex.test(email)) { return res.status(400).json({ error: 'Invalid email format' }); } // Check if user exists const existingUser = await User.findOne({ email }); if (existingUser) { return res.status(409).json({ error: 'User already exists' }); } // Hash password and create user const hashedPassword = await bcrypt.hash(password, 10); const user = await User.create({ email, password: hashedPassword, name }); res.status(201).json({ userId: user.id, email: user.email }); } catch (error) { res.status(500).json({ error: 'Internal server error' }); } }); Did AI write perfect code? No. But it gave you scaffolding to refine, saving 10-15 minutes of typing. That's the real win. The Game-Changer: Explaining Legacy Code and Obscure APIs Every developer inherits someone else's mess. AI tools shine when deciphering undocumented code or unfamiliar libraries. Pract

The first Lego X-Files set costs $200 and will be available in August.

I've spent my career helping people build software. This is a series of letters about what happens when the tools for building change faster than the principles behind building. Each one is a reminder that while the technology changes quickly, the questions that matter often stay the same. -- Dear past Jenna, The thing that drew you to tech in the first place, that it's always changing, is the thing that will keep you here. Tools change (sometimes for the better and sometimes not) almost weekly. People who never consider themselves technical, much less a developer, will build apps in an afternoon with this new programming language called English. Products will go from idea to deployed before you finish your first cup of coffee (you have a toddler now, so you rarely get the full cup before it goes cold anyways). "I don't know how to code" or "I'm not technical" is no longer a barrier to building. And that's exciting, given you've focused nearly your entire career helping others build software. But we can't confuse the ability to build with the wisdom to build. One of the most valuable habits you've developed over the last two decades is asking a simple question: "Should we build it?" For most of your career, "Can we build it?" was a hard question. Time, budget, complexity, maybe the tech wasn't there yet. But those constraints were usually temporary. With enough people, time, and money, just about anything is possible. But the real questions were always: Should we build this? Is this the best use of our time? Does this solve a problem our customers actually need solved, or does it create new problems? What are we choosing not to build? Those questions haven't changed, but the environment around them has. Today, almost anyone can build software. Between tools like Lovable, Bolt, Replit, Claude Code, Codex, and whatever's next, the barrier to building software is lower than it's ever been. Now the question "Can we build it?" is too easy to answer. It's almost always ye

You followed an old breeding guide. You put Penking + Bushi into your breeding farm, dropped the cake, waited for the egg... and out hatched Sibelyx , not the Anubis the guide promised. You're not alone. Your guide isn't broken. The recipe changed. When Palworld hit 1.0, the breeding table got quietly rewritten. Almost every pairing that players had memorized from early-access now produces a different Pal. There's no in-game notice, no patch note that lists the hundreds of changed combos — just a lot of confused hatchings. So I dug into the data. Here's what I found, and the small tool I built to make sense of it. What actually changed in 1.0 I compared two snapshots of the game's breeding data — the pre-1.0 set and the current 1.0 release — both sourced from the open-source PalCalc project. The headline number: 97.7% of comparable pre-1.0 parent pairs now produce a different Pal. That's not a typo. If you take the breeding pairs that existed before 1.0 and run them against the current data, nearly all of them hatch something new. A few concrete examples players keep running into: Old recipe (pre-1.0) What it makes now (1.0) Penking + Bushi Anubis → Sibelyx ... (more pairs in the tool) The mismatch matters because most breeding guides and calculators on the internet still show the old results. Players follow them, breed, and get confused. The subtle trap: renumbering vs. real change There's a detail that trips up every breeding tool that tries to track this. When Palworld 1.0 launched, it also renumbered parts of the Paldeck (Pal #139 vs #116, etc.). A naive diff tool would see the number change and wrongly report "the breeding result changed!" — when really only the number changed, not the actual Pal. To avoid that false signal, I match Pals by their internal game name , not their Paldeck number. A renumbering is not mistaken for a changed breeding result. Only genuine recipe changes are counted. The tool: PalShift I wanted a dead-simple way to answer one question:

Treasury Secretary Scott Bessent said the U.S. could sanction Chinese open AI models over alleged IP theft, expanding the Trump administration's campaign to slow China's AI advances.

I'm the developer of DeviceShelf, a local-first network scanner for desktop, mobile and a headless server edition. Until this week the iOS app only existed on TestFlight. Apple has now approved version 1.3.0, so for the first time you can get it straight from the App Store: DeviceShelf on the App Store . What the app does on a phone The iOS app is not a companion viewer. It runs the same scanning engine as the desktop version: it scans the network you're on, identifies devices (vendor, type, OS fingerprint), shows open ports per device, builds a security report, and raises presence alerts when devices appear or drop off. You can export and share results from the phone. The multicast entitlement iOS restricts multicast traffic for ordinary apps, and SSDP/UPnP discovery depends on it. Apple grants the multicast entitlement on request, and DeviceShelf's App Store build has it. In practice, UPnP/SSDP devices show up in scans on the phone the same way they do on desktop. Licensing The download is free and comes with a trial. Full features unlock in one of two ways: activate a DeviceShelf license, which covers desktop, mobile and the server edition with a single purchase, or use the in-app purchase upgrade on iOS. Pricing is on the website if you want the details. Local-first, on mobile too Scans stay on the device. There is no cloud account, and the AI-assisted device identification is bring-your-own-key; no key is bundled or required. The app is still young, and a phone is an unforgiving place for a network scanner. If it mislabels a device on your network or misses one entirely, I'd genuinely like to hear about it. Website: deviceshelf.app

Spotify's DJ-inspired tools are loads of fun, but probably won't land you any bar mitzvah gigs.

Nostalgia for a time before smartphones Discussion | Link

Brain-like knowledge base with AI writing & deep research Discussion | Link

Rspack, developed by ByteDance, has released version 2.0, featuring enhanced performance, reduced dependencies, and a focus on modern ECMAScript modules. Key updates include a pure ESM core, improved static analysis, and support for RSC. Performance benchmarks show big improvements in build times. The project has experienced substantial growth, with npm downloads exceeding 5 million weekly. By Daniel Curtis

Tomorrow's foldable-centric Galaxy Unpacked event looks like it will be Samsung's biggest in years. The company is expected to launch new versions of its Fold and Flip phones while also introducing a brand new Fold form factor that's shorter and wider. It would be the second upheaval to Samsung's foldable lineup in less than 12 […]

"Current economic conditions" have shifted TreeSize's business model.

The new Light Flip is a mashup of the Light Phone III and an old-school flip phone.

A flip phone with 5G, USB-C, a headphone jack, and a removable battery? Say it ain’t so.

Nvidia’s Vera Rubin platform combines CPUs and GPUs into a single system, reflecting the company’s growing ambition to power every layer of AI infrastructure.

Light co-founder Kaiwei Tang helped create the Motorola Razr, and he's as surprised as anyone that over 20 years later, the flip phone is relevant again.

Google is launching Gemini 3.6 Flash alongside a new security model dedicated to quickly finding and patching security vulnerabilities. In a blog post on Tuesday, Google describes Gemini 3.5 Flash Cyber as a "cost-efficient and highly capable alternative" to larger, more expensive AI systems, such as the one offered by Anthropic's Mythos. The cybersecurity model […]

A few years ago, the team at Light shipped the most smartphone-like device they'd ever created. The Light Phone III still kept to its minimalist, no-distractions ethos, but it was a phone-size rectangle with a phone-size touchscreen, a camera, and a number of other features. Some people loved having an almost-smartphone, but Kaiwei Tang, Light's […]