I built Renewal Radar a subscription tracker with a twist.

Most people have no idea how much they're spending on subscriptions each month until the charge hits their bank account. I was one of them. So I built something to fix it.

After building it for my own content workflow, I decided to open the source and give it to the community.                                                     

 What Flixty does:                                                                 

  Write one post, publish to X, LinkedIn, Facebook, Instagram, TikTok, and YouTube simultaneously                          

  AI-assisted content generates platform-optimised copy for each network automatically                               

I lost more deals to my own spreadsheet than to prospects saying no.

Three tabs, a CSV of 400 companies, sticky notes for who I'd already called, and a shared list where a teammate and I dialed the same guy twice in one afternoon. He was not thrilled.

The calling was fine. The 45 seconds between calls killed me. Find the next number, check who owns it, log the outcome somewhere I'd never look again.

So I built Tepio (https://trytepio.com) to do the boring part. It hands you one company to call at a time, and the moment you talk to someone, that company becomes yours. No more double-dials. Import is a CSV or Excel drop and it maps the columns for you.

Work hands you the problem. Side builds train choosing, cutting scope, and shipping when nothing is spelled out. Your own deploy surprises make work incidents feel familiar.

AI didn t delete the work. It moved the bottleneck up the stack. The scarce part isn t faster typing. It s naming the problem, picking limits, and knowing what good enough means for a real user.

Hi everyone! As a long-time web novel addict, I ve always struggled with two things: intrusive ads and forgetting complex character relationships in 2000+ chapter epics.

So, I built AIReader. It s a clean, ad-free mobile reader, but with some "magic" built in:

• AI Plot Summaries: It automatically summarizes the context so you can pick up exactly where you left off.

• Dynamic Character Wiki: It extracts characters and their latest status as you read, creating a living database of the cast.

• AI Illustrations: Long-press any description to generate high-quality AI art of your favorite characters or scenes.

I m looking for a handful of avid readers to join our early internal beta. You'll get free, unlimited AI power to help us shape the future of reading.

Hey Product Hunt community!

I'm the maker of Aivlo, and I ll be honest, we launched just a few hours ago... and it feels like the thrusters are struggling to leave the launchpad!

Hi everyone,

I m helping with ProofWrite AI, a web-based writing tool that helps users turn AI-assisted drafts into clearer, more natural-sounding content while preserving the original meaning.

Few months ago I was trying to run local LLM on my m1 macbook and it was a bit painfull to find exact model that can run on it and how to do it properly.
Informations were across reddit, forums etc so I created https://llmconfigurator.com/ to make it all in one. You just putting your hardware or one click analyser and it showing you models that can run. I am keeping model list curated.

Since then it grew up a bit, I am constantly adding guides, tutorials, blog posts. I hope someone find it useful and this is my small addition to LocalLLM community

Here is the issue we ran into while building CareerButler V2:
We built this powerful AI to tailor resumes to job descriptions. But we noticed that if the user uploaded a weak resume to start with, the AI couldn't save it.
If your resume says "I led a team to success" without any numbers, the AI doesn't know what 'success' means. It can't optimize what isn't there.
So it either:
Polishes the fluff (still useless).
Starts hallucinating and making up numbers (dangerous).
We realized we couldn't just build a 'tailor'. We had to build a 'fixer' first.
It's called Resume Critique.
Before we begin optimizing your resume for a specific job, we force a deep scan. It parses your resume section-by-section to fix the foundation.
We actually highlight the specific bullet points that are weak, right on the screen, and give you tips on exactly how to improve them.
And I don't mean generic advice like 'Add more metrics.' That is lazy.
I mean specific, actionable pushes.
Here is the difference between Generic Advice vs. CareerButler V2:
Generic Tool: "You should add more metrics."
CareerButler: "In your role as Project Manager, you listed 'Led a team to success.' This is vague. Consider adding the team size and a specific outcome. For example: 'Led a team of 10 engineers to deliver the product 2 weeks ahead of schedule.'"
Generic Tool: "Your summary is too long."
CareerButler: "Your professional summary is currently 6 sentences long. Recruiter heatmap research shows they stop reading after sentence 2. We recommend cutting this down to a highlight reel of your top 3 hard skills."
It s like having a career coach proofread your resume before you start applying. It stops you from optimizing a bad resume.
Fix the foundation first. Then tailor it.
We are launching V2 on Product Hunt this Sunday. Come see if your resume passes the check.