I'm the founder of Litos. A couple hundred people already use the platform, and we're expanding. Today is our Product Hunt launch.
We focus on the repetitive work between finding a job and sending an application: tailoring the resume to the role using your real experience, filling the same work-history fields again, and keeping track of applications.
Litos supports Workday, Greenhouse, Lever and Ashby. You can review before sending; automatic sending is optional. Autofill is free, and resume tailoring is included in Litos+ with a 7-day trial.
Here's the launch: https://www.producthunt.com/prod...
Hey everyone, I'm Andrej from the FastRouter team. We launched on Product Hunt today, and I'd love to get the community's take on a problem we've been working on.
Running AI in production tends to mean too much plumbing and too much guesswork: multiple SDKs, scattered logs, fragile failover, and no clear answer to "how do we make this cheaper without making it worse?"
FastRouter is our answer: one OpenAI-compatible API across 200+ models, automatic failover, zero markup on tokens, and weekly Insights that analyze your real traffic and suggest where to cut spend, with model-switch recommendations checked by evals first.
A few questions we'd genuinely like your input on:
I'm finishing FormRoute, a form backend for static sites, React, Next.js, Astro, or anything that can send a POST request.
The problem I kept hitting: you build a landing page or static site, you need a contact form, and suddenly you're setting up a backend just to receive an email. Or you're paying $30/month for a tool that still makes you use Zapier for everything.
I'm a solo founder. I run a convenience store and build small software on the side. I made GenID Protocol to document that you generated an AI image. When you right-click an image and choose "Certify with GenID", it downloads a PDF certificate, embeds a C2PA content-credentials manifest in the image, and anchors a hash on Polygon so the record can be verified later.
I've tested it on Gemini and Runway images. Install steps: https://genid.onrender.com/exten...
I m currently building UGCFlow AI, and the beta version is launching soon.
The idea came from a simple problem: creating good UGC ads can get expensive very quickly. Brands can spend thousands on actors, creators, agencies, video production, editing, and multiple ad variations just to test what works.
I want UGCFlow AI to make that process much more accessible.
Everyone chases signup numbers early on. i think that's backwards.
10 people who actually use your product and complain about it teach you more than 100 who signed up and forgot you exist.
I've got 8 paying users right now for Banalo.Business. haven't even scaled outreach yet, just been obsessing over what those 8 actually do inside the product.
Curious what others think, early on do you optimize for more users, or deeper usage from fewer?
I'm building KubeAgent, a CLI for Kubernetes teams who are tired of finding out about cluster issues only after something is already on fire.
The idea is simple: run `kubeagent onboard` once to scan your cluster and build a local knowledge base, then run `kubeagent watch`. When something looks wrong, KubeAgent checks logs/events/describes, tries to explain the likely root cause, and suggests a fix. Safe actions like pod restarts can be applied automatically; risky actions wait for human approval through Slack, PagerDuty, Discord, Teams, Telegram, or a webhook.
I'm building it because I wanted something between "another alert in a dashboard" and "give an AI agent full access to prod." The hard part has been drawing the safety line clearly enough that it can be useful without being reckless.
