Connect with the Product Hunt community

TL;DR: every fact in our graph is its own node, with properties on it. The vocabulary behind those nodes is RDF/OWL. Neither camp would call this clean, and Wikidata is not strictly one or the other either.

Here is why we ended up there.

What forced it

Our facts need to carry things that are not part of the fact: where it came from, how confident we are, whether a human has reviewed it, and when it was true. Not this document mentions X , but this specific assertion came from page 4 of this version of this document, confidence 0.8, reviewed by nobody yet, valid from March .

Every session of Claude Code or Codex re-reads CLAUDE.md, AGENTS.md, what they import, the rules and the memory index before your first prompt. And pays for it again, every time.

Lancetta's Maintenance pane lists those files, shows what a start costs against the size where Claude Code warns, and runs two dozen checks:

Why Dots isn't just another notch music player

Most notch apps focus on media controls, battery indicators, or simple bounce animations. We wanted to build something that actually drives daily productivity no matter what field you're in.

Dots transforms your Mac notch into a customizable workspace HUD that adapts to diverse workflows:

• Developers & Engineers: Monitor live AI agent workflows, background scripts, and Apple Shortcuts without context-switching.

• Creators & Editors: Drop files into the top-edge shelf for instant conversions, or trigger picture-in-picture videos while keeping your main timeline clear.

• Managers & Presenters: Use built-in screen highlighting tools to spotlight key data during live calls and screen-shares.

• Students & Professionals: Stay locked into deep work with discrete Pomodoro focus timers right in your line of sight.

Hey all, let's use this thread for feedback. Thank you!

Quick build-in-public update from Sponkly (real-time voice translation for chat, calls and meetings in 32 languages).

This week:

• NVIDIA Inception: accepted

• AssemblyAI Startup Program: accepted

• AWS Activate: $10,000 in credits issued today

What it changed right away: we moved Video Studio's speech recognition to AssemblyAI and raised the video limit from 5 minutes to 1 hour. A 30-minute video is now transcribed in ~10 seconds and is ready with subtitles in under 2 minutes. The AWS credits give us room to scale the backend as usage grows.

Sponkly for Chrome is live on the Chrome Web Store.

You join Google Meet, Microsoft Teams or Zoom in the browser as usual, click the Sponkly icon, pick the language they speak and yours, and every sentence appears as a live subtitle in your language over the meeting.

• Live subtitles in your language, with the original line underneath

• A translated voice if you would rather listen (the original sound is lowered)

• "Translate my voice too": the others hear you in their language a moment after you speak

• Works on any tab: YouTube, webinars, online classes

• Nobody else has to install anything

• Nothing is recorded or stored

It uses your Sponkly plan's translation minutes, and every new account starts with a 7-day free trial.

Imagine two buyers asking ChatGPT the same question about the best CRM software.

One manages a startup with five employees and needs affordable software with a simple setup. The other works at a large enterprise that needs advanced reporting, security controls, and integrations with existing systems.

I have a bunch of updates to products I need to do (and new products ready for release), but I lost access to the Apple ID that I set up about 15 years ago and haven't been using, except it is the owner of my developer account.

I have been going through their process to get my account back multiple times, and each time they have a ten day waiting period after which, for me, nothing happens. This is getting extremely frustrating and there seems to be no way of getting through to anyone that can help me there.

Our WordPress plugin was just approved on WordPress.org.

SpeakBot by Sponkly adds a chat bubble to your WordPress site. Visitors write, call or video call in their own language; your team answers in its own, translated live.

• Translated live chat, plus voice and video calls from the page

• An AI receptionist that answers in the visitor's language when nobody is available, only from the information you give it

• Works with any theme, WooCommerce, the block editor and Elementor

• A loader of about 4 KB, no cookies, no tracking, calls are never recorded

• 32 languages

Setup takes two minutes: install "SpeakBot by Sponkly" from Plugins Add New, create a site in your Sponkly dashboard, and paste the site key into Settings SpeakBot.

Something we've been watching while building N8N Chat UI: US shoppers are starting to buy by asking questions.

Adobe tracked US retail sites over the 2025 holidays. Visits that came from AI assistants like ChatGPT were up more than 7x on the year before, and those visitors converted 31% better than everyone else. They also left less often (33% fewer bounces), and revenue per visit was up 254%.

But it cuts both ways. In a September survey of over 2,000 US adults by Semrush, around 58% of people who use AI said they'd bought something a chatbot recommended, and about the same share said they'd dropped a purchase because of what an AI told them.

Our takeaway: people now arrive at your site used to asking questions and getting a direct answer. If your site can't answer, that question goes to someone else's AI, and you don't control what it says.

We always hear about Silicon Valley, San Francisco and others.

But lately it seems like there are more emerging...

A Mac mini or Mac Studio has no built-in screen to fall back on, so when the monitors sleep and drop their link, every window piles up on whichever display comes back first. The write-up covers why it often starts after changing a monitor, a Terminal command to see which display macOS treats as main, and the settings that reduce it, including putting the main monitor on HDMI.
https://manydisplays.com/mac-min...
If you run a Mac mini or Mac Studio with several monitors, I'd like to hear how yours behaves.

We've been building a library of ready-made n8n recipes for chatbots, and we're giving them away free. There are 16 so far, and we'd like to know whether we're building the right ones.
What's in there at the moment:

• Lead capture: into HubSpot, Mailchimp, ActiveCampaign, Pipedrive, Airtable or Google Sheets, with consent handled where it matters

• Bookings: Calendly and Cal.com

• Support and orders: Zendesk tickets, plus Shopify and WooCommerce order lookup

• AI: an FAQ bot with email handoff, and a bot that answers from a Supabase knowledge base

• No AI needed: button-menu bots for when a decision tree does the job better and cheaper

They're all at n8nchatui.com/recipes.
What I'd really like to know:

1. What do you end up building over and over for clients?

2. Which tools are we missing? (Notion, Slack, Intercom, Stripe?)

3. Do you prefer AI-driven bots, or simple button flows?

I recently launched Pinky Promise, a little app for making promises with friends.

The feedback has been lovely. People call it cute, say it's a great idea, and seem to like the concept.

But here's the thing: I'm not sure people would actually use it.

A few months ago I posted here that AI access is easy and governance is the next problem. That thread turned into 70+ comments, and honestly, it shaped our roadmap.

The same things kept coming up:

"Governance is the part everyone skips until the bill surprises them"

People using AI outside official tools (shadow AI)

We tested a question asking for research from a particular university, on trapped electrons, within Atomic Physics.

Text search put the correct paper seventh. A basic graph walk put it fifth. Ranking candidates by which relationships actually satisfied the question put it first.

One convenient example proves very little, so we tested 500 questions from the public STaRK-MAG benchmark.

Across two splits, our graph-constraint ranking put a correct paper first on 62.4% and 66.0% of questions. Lexical text search scored 36.4% and 40.4%. A basic graph approach scored 51.2% and 52.4%.

Your website tells one story about your brand. Reviews, comparison articles, industry publications, and community discussions might tell another.

When buyers ask AI for recommendations, these external sources can influence which brands appear and how they're described.

I maintain Termexo, a local-first workbench for coding-agent terminals. v0.11.0 is now available for Windows x64 and both Apple Silicon and Intel Macs.

AI-assisted capability diagram, not an application screenshot. This post was written with AI assistance and checked against the release notes.

we keep getting surprised by what people do with their calendars once an agent can read them. so far we've seen home automation arming the alarm when nobody should be home, families running their shared iCloud calendar through ChatGPT, and people letting Claude set up their syncs.

mine: "find me 2h of focus time this week and block it everywhere"

what's yours? something you already do, or something you wish worked. we read every reply and it shapes what we build next

what kind of checks and balances to you implement to make sure the numbers are accurate, at least the critical ones. Anomalies are easier to spot, but minor but important inaccuracies are tough to identify.

I'm trying to get better at separating a useful request from a loud one. A feature can sound urgent in a conversation, but that doesn't tell me how many people need it or whether they'll use it after it ships.

My current approach is to look for the problem behind the request and wait until the same problem appears more than once before it reaches the roadmap. I still worry that this can make a small team too slow to notice a good idea.

What made you decide not to build a feature people kept asking for? Did the data show weak demand, or would the feature have pulled your product away from its main job?

Mine has two phases

My first project was a prototype in lovable, worked great. Then I started adding features such as login and storage/database. Then things started breaking. I started researching and realized I should have

• Planned for authentication from day one

• Connected a database from day one

Phase 2, when I eventually moved it to Claude Code, I should have researched the model capabilities instead of choosing the cheapest model (Haiku ) to build main features. Learned the hard way

Many makers have shared the same experience: posts about what broke are 1000x more interesting than posts about polished things that are working perfectly =D

And sometimes, nothing is actually broken.
You just discover something that makes you change direction.

For us, we built a CMS where users could create their own pages:

userpage/ourdomain.com

68% of US Google searches in early 2026 ended without a click. For every 1,000 searches, only 276 clicks reached the open web.

But fewer clicks don't necessarily mean fewer opportunities for brands to get discovered.