I've been a fan of UptimeRobot for years. It's reliable, simple, and does exactly what it says.

But there's one thing it doesn't do: explain why your site broke.

That's where PulseBoard comes in.

PulseBoard monitors your websites and APIs but when something fails, Vigil AI investigates:

I run LogSheet, a time-tracking tool for small companies a company admin sets it up, employees log their hours, the month exports to Excel and then locks so it stays auditable. It has been a web app from the start.
Ten days ago I shipped the iPhone app. 6 Downloads...
What I genuinely can't tell yet is this: did the app bring new companies in, or did it just make the existing ones happier?
My honest expectation was the second, because most people arrive already invited by their employer. You can start from the app the login screen has both "register a company" and "I'm self-employed", so someone who finds LogSheet in the App Store first isn't stuck but both hand off to the website in the browser, and then you come back to sign in. It works. I just have no idea what that round trip costs. I built the app anyway, for three reasons:
People kept asking. "Can I clock in from my phone" was the single most common question I got.
Being in the App Store is a trust signal. When a small company is deciding whether a one-person vendor is real, an app on the store is evidence.
The web app worked on a phone browser, but it was never good.
So, three things I'd like to hear from anyone who has done this:
1. You added a mobile app to a web-first B2B product. Did new-user acquisition actually move or did retention move and you told yourself it was acquisition?
2. Did App Store search ever send you anyone meaningful, or was it all traffic you already had?
3. My app sends people to the website to create an account and asks them to come back to sign in. If you've made the same handoff, do you know what it costs you? And has anyone here built real in-app sign-up instead and found it was worth the duplicated flow?
If it helps to see what I'm describing, it's at logsheet.ch and the app is LogSheet on the App Store. Happy to put whatever numbers I have in the comments including the unflattering ones.
If you want to see the app and leave a score!
apps.apple.com/app/id6795027347
Thanks
Ben

Built PostFormatter (dctify) for people who post on LinkedIn daily.

LinkedIn still has almost no real formatting controls and no mobile preview. This tool adds:

Unicode bold / italic / underline / strikethrough that pastes into LinkedIn

One-click spacing cleanup

I launched my product on Product Hunt, but honestly, the response was much quieter than I expected.

I m trying to understand what I got wrong: positioning, timing, launch page, or simply not doing enough community participation beforehand?

For founders who have launched successfully: what made the biggest difference for you?

I d genuinely appreciate feedback on the launch page: https://www.producthunt.com/prod...

Don't miss out on leads.

The average sales team takes hours to follow up on new leads. Furthermore, most leads are passed on to the representative who contacted them first. LeadArrow solves this problem.

The moment a lead is added to your CRM, LeadArrow immediately calls and connects them to the appropriate representative. The representative can accept the call with just one tap. There is no need to check the dashboard, and no leads are missed. If the current representative is busy, the call is automatically forwarded to the next one, reducing the processing time for every lead from hours to seconds.

Integrate with your existing tools (Close, HubSpot, GoHighLevel, Slack) to accurately track your team's response speed using built-in analytics.

Hey PH

I built Purrnal a health diary for cat and senior dog owners. Log symptoms, vomit, litter box, weight, meds, and food in two taps, and it generates a clean PDF report you can hand your vet instead of scrolling through a notes app.

It's in closed testing on Google Play right now, and I need a few testers to clear Google's 14-day requirement before going live.

If you've got an Android phone and want to help:

I've been building AEGIS, an open-source defense layer for LLM apps/agents (prompt injection detection, policy enforcement, tool-permissioning for agents). Before I launch it here Tuesday, I want to share something I almost didn't publish.

Most guardrail projects report a round-1 catch rate against a fixed test set which tells you very little about a real attacker who adapts. I ran our detectors through adaptive red-team campaigns that breed new attacks from whatever got past the last round. The honest result: hardened, real-model detectors converge to roughly the same ~48% overall bypass rate as unhardened stub detectors by round 3. Full methodology and numbers are in the repo if anyone wants to pick it apart.

Genuinely asking this community: is round-1-only red-teaming basically theater at this point? Has anyone here seen a detection approach that actually holds up past round 2-3 against an adaptive attacker, rather than just looking good on a static benchmark?

Hello community, what is your preferred AI platform for learnings based on your exact situation/background. We are working on a platform (CapxuleHub) on similar concept and would love to gather your feedback and suggestions. Thank you!

We ve been building CapxuleHub around a question I keep coming back to:

A year ago, I started building Crayon because I wanted a creative editor that combined the simplicity of Canva with the power of professional design software.

As I kept building, I realized creators often had to jump between multiple apps just to finish one project. Design in one app, animate in another, edit vectors somewhere else, then export and repeat.

I wanted to solve that.

Here are a few features I'm most proud of:

Hey Product Hunt

I'm Iyoopp, an independent Web3 dev from Indonesia. I built TokenScan SD to solve a problem I kept running into myself: figuring out if a new token is safe before I ape in.

What it does:

Scans tokens on BSC & Solana (DEX) using GoPlus Security API for on-chain risk data (honeypot checks, ownership, liquidity locks, etc.)