Hi, Product Hunter

I m developing a tool for Shopify store owners that automates the search for potential customers on Threads.

Hey everyone

I m building itembase.dev a tool for game designers to structure and manage item data, progression, and economy systems more cleanly.

The idea came from a simple problem: a lot of game design workflows around items, balancing, and progression still end up spread across messy spreadsheets, docs, and ad hoc systems. That makes iteration slower and harder than it should be.

With itembase.dev I want to make it easier to:

You know that moment when you think "I need to remember this" but opening a reminder app, tapping through date pickers, and saving it takes longer than the thought itself?

Yeah. I built something for that mostly because I keep forgetting everything myself. I even forgot to use the reminder apps I already had.

Just open Telegram and type or send a voice message:

- "remind me tomorrow at 9 go to shop"

Day 16 of building Verso (projectoye.com) a workspace OS replacing your fragmented tool stack with one AI-native environment.

Real-time collaboration week is complete.

Overview

We scanned 1000 random Product Hunt project URLs using the DLX7 free security scanner. The results were highly consistent. Most applications landed between C and B. At a glance, that appears acceptable. It is not.

What the Scores Actually Reflect

Hey PH

We just published a deep breakdown of what "proactive CRM" actually means in practice vs the standard HubSpot reactive database model.

The short version: Kobin's CRM runs 4 automated AI intelligence routines you don't configure or trigger manually:

Risk Detection (multiple times/day): flags deals stuck 21+ days AND blocked tasks in the same client relationship simultaneously

Every API testing eval we found either required source code access, relied on rich documentation, or measured output format rather than whether a test would catch a real failure.

So we built APIEval-20. Twenty scenarios across e-commerce, payments, auth, scheduling, and user management. Each scenario gives a model exactly two things: a JSON schema and a sample payload. No implementation details, no docs, no further context. The model has to generate a test suite from that alone.

The bugs are planted in live reference implementations. A bug is only caught if a generated test produces a response that deviates from correct behavior when run against the implementation.
Submit through the hosted eval harness and get a score back.

Scoring weights bug detection at 70%, API surface coverage at 20%, and test efficiency at 10%.