标签：#product

A terminal built for Claude Code w/ one-click session resume Discussion | Link

A few months back I was running Sales Navigator searches for a client project — filtering down to "VP Sales, fintech, based in Italy or Spain" type lists — and the results were genuinely good. 60, 80 leads that actually matched. Then I hit the part nobody warns you about: there's no button on that page that says "save this." So I did what everyone does. Opened a spreadsheet, alt-tabbed back and forth, typed names and job titles by hand. Around profile 40 I gave up and went looking for a better way. This is what I found, roughly in the order I found it, including the tool I ended up building because none of the existing options quite fit what I needed. First: the export LinkedIn actually gives you LinkedIn has a real, built-in data export, and most people don't realize how narrow it is. It's under your profile photo → Settings & Privacy → Data Privacy → Get a copy of your data . From there you either tick specific categories (that email usually lands within minutes) or request the full archive, which takes closer to a day and sometimes arrives in two batches. Either way you get a download link that expires after 72 hours — and it's desktop only, the mobile app won't let you request one. What you get back is genuinely thorough: connections, messages, your own profile history, activity, even the ad-targeting data LinkedIn holds on you. A couple of quirks worth knowing before you rely on it: some connections' email addresses will just be missing, because sharing an email on download is something each person opts into individually, and you won't get a list of who viewed your profile or any "People You May Know" data. If you're in the EU, EEA, or Switzerland, LinkedIn also runs a separate API for pulling your data on a schedule rather than as a one-off request. Here's what this export is not built for, though: it has no idea what you searched for yesterday. It's an archive of your own account, not a way to capture a live search. Run a Sales Navigator query and pull 80 lea

Search your highlights and notes inside Claude and ChatGPT Discussion | Link

A code-first language for durable, governed AI workflows Discussion | Link

Listmargin works out your eBay final value fees, ad fees, and what you actually keep on a sale. Most fee calculators copy a four-line summary of eBay's rates. This one reads the full published schedule: all 46 category rates, the store subscription tables, and the four categories where crossing a price threshold re-rates the entire sale. It's free, with no signup and no paid tier. There's an embeddable version if you run a blog or a tool site. A weekly monitor re-reads eBay's own fee pages, so when a rate moves the calculator gets corrected instead of drifting out of date.

Get your clips without AI slop or hiring hassle Discussion | Link

Conference database for B2B teams Discussion | Link

Two numbers from my own systems, side by side. When I deploy, a check confirms the pages are actually live. It retries three times, twenty seconds apart, so it tolerates up to a minute of "not there yet" before calling anything wrong. When my monitor decides whether production is down , it waits five seconds and retries once. The check that guards the more consequential claim is the more impatient one. I did not decide that. I never compared them. Until last week I had never seen those two numbers in the same place, and neither had anything else. Where the numbers came from The deploy one has an origin story I'd have told you proudly a week ago. I shipped nine pages, then checked the URLs immediately instead of trusting the CLI's success message. Four returned 404. Nothing was broken — CDN propagation — and twenty seconds later all nine were 200. A single check at the wrong moment would have told me, with total confidence, that a perfectly good deploy was broken. So I wrapped it in a retry loop. Three attempts, twenty seconds apart. Problem solved, and it even sounds like engineering. Here is the part that isn't. I picked twenty because it was the first interval where the false alarms stopped. My sample was about three deploys. I have never recorded how long propagation actually takes. I widened the tolerance until the red went away, and then I wrote about it as if I'd learned something. Someone in a thread named this before I saw it: a tolerance chosen that way is the same muting I'd been criticizing, relocated inside the assertion where it reads as rigor instead of avoidance. The test I was given, and the answer I didn't want In that same thread I speculated that my deploy tolerance was probably leaking into my outage detector through a shared helper. It sounded plausible and I said it like a finding. The reply was sharper than the guess: that's a falsifier, not evidence. Here's the concrete test — do the two checks consume the same retry policy or threshold confi

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You know exactly what you want. You can see it. You just don't know what it's called. So you open your AI tool and type "pixelated fade effect". Then "retro dot gradient". Then "that grainy old-computer image style". Six rounds later you have something almost right, and almost right is the most expensive kind of wrong. The word was dithering . With it, one prompt gets you the real thing. This gap has a shape. AI collapsed the cost of building, so the bottleneck moved: it's no longer "can the AI do it", it's "can you name it". Every field you touch as a builder has a precise vocabulary, and the words you're missing are costing you rounds of generation, wrong libraries, and vague briefs. Vocabulary is the highest-leverage thing you can pick up right now, and nobody teaches it. Jargon Buster is the cure. It's a free reverse-lookup glossary built for exactly this moment: you describe the thing in your head, it gives you the word. Reverse lookup: describe it, get the word Press Cmd+K on any page and type what you'd say to a colleague, not the term: What you type What you get "the glowy circles behind them" Bokeh "the grid of differently sized cards" Bento grid "the scroll that takes over the page" Scrolljacking "grainy speckles when I turn the number up" ISO "why is my payout smaller than my sales" Settlement "the inside of the letters fills in when I bold it" Counter Misspellings work too. "Ditter" lands on Dithering. That's deliberate: the fuzzy phrasings and typos people actually reach for are stored on every entry as first-class search data, not errors to correct. A normal glossary is indexed by the words you don't know. This one is indexed by the words you do. Every entry ends prompt-ready Knowing the term is half the loop. Each of the 2,142 entries closes the other half: A plain-language one-liner for the "that's the word!" moment A short explainer : what it is, when to reach for it, the gotcha A prompt-ready snippet : the concept translated into an instruction an

Hardware for AI coding Discussion | Link

Open-source local voice assistant for your desktop Discussion | Link

The free screen recorder that cannot upload your video Discussion | Link

Handify ai Like many side projects, this one started because I had a simple problem to solve. I wanted a way to convert digital text into realistic handwritten notes without spending hours writing everything manually. Most existing tools I tried either looked too robotic or offered very little customization. So I decided to build my own. The Goal Instead of just changing a font, I wanted the output to actually feel handwritten. Some of the features I focused on were: 📝 Convert typed text into realistic handwriting 📄 Upload your own notebook or paper template ✍️ Multiple handwriting styles 🔀 Mix two handwriting fonts for a more natural appearance 🎲 Character variation so repeated letters don't always look identical 📥 Export high-quality PDFs ready for printing Challenges Making handwriting look "real" is much harder than simply rendering a handwriting font. Some of the biggest challenges were: Preventing repeated letters from looking identical. Keeping line spacing and word wrapping natural. Supporting different paper templates. Generating high-resolution PDFs without losing quality. Making the experience fast enough to generate pages within seconds. Small details make a surprisingly big difference when people compare AI-generated handwriting with actual handwriting. * Tech Stack * The project is built using: React TypeScript Firebase Vite Capacitor (Android App) Google Analytics What I Learned Building the product was only half the work. The harder challenge has been: SEO Google Search indexing Play Store optimization Improving conversion rates Understanding user behavior through analytics A great product doesn't automatically get users—you also need to make it discoverable. Current Progress The project is still growing, but it's already receiving organic traffic from Google and users have started using it for: Study notes College assignments Personal journals Printable handwritten documents Seeing people use something you built is incredibly motivating. I'd Love Yo

A few weeks ago Boris Cherny, who leads development on Claude Code, mentioned during a talk at Acquired Unplugged that he doesn't really write prompts for Claude anymore. Instead he writes loops that keep prompting Claude until the work is actually done. The clip went viral on X, racked up nearly 700k views in under 24 hours, and Loop Engineering became the latest term making the rounds in AI development circles. The core idea is straightforward enough. Rather than obsessively tuning a single prompt to get a perfect output on the first try, you build an iterative system around the model: give it a clear goal, feed it the right context, give it tools to work with, evaluate what it produces, and define conditions for when it can stop. Wire those pieces together and the agent stops being a one-shot call and becomes something that iterates, self-corrects, and keeps working until the output actually meets your bar. The efficiency gains over prompt-tuning are real, and that is why the concept resonated so quickly. What struck us as we built and shipped the loop system for our own platform Octo is that almost all of the current conversation around Loop Engineering stays at the single-agent level. You have one model, one cleverly designed loop, one sandbox, and the agent grinds away iteratively until its output passes whatever checks you have set up. That solves a real problem: how one person works faster with AI. But real work, especially inside an organization, rarely fits cleanly inside a single agent loop. A product feature going from idea to shipped code needs someone defining requirements, someone designing the approach, someone writing the implementation, someone verifying quality, someone feeding back results. Those are not different iterations of the same loop. They are interconnected loops that need to pass context and outputs between each other. When loops need to share state, trigger each other, and respect organizational boundaries, single-agent loop design sto

Animated SVG mascot studios for apps that need a personality Discussion | Link

Turn your Mac into a spinning vinyl player. Discussion | Link