Reviews get faked at scale now. AI-generated, coordinated, timed in bursts right after launch. 30% of reviews on Amazon are fake, and most shoppers have no way to tell.

Merzify checks the reviews, the seller, and the price, then gives you one of four verdicts. Safe to Buy, Proceed with Caution, Uncertain, or Trust Cannot Be Established. If the data isn't good enough to provide a reliable answer, it says so rather than guessing.

Built solo from Lagos. Pre-launch right now. Happy to run a free analysis on anything you're about to buy if you drop the link.

If you publish sports content regularly - match recaps, weekly digests, tournament coverage - the format you distribute it in may be worth a closer look.

Static PDFs have been the default for a long time. They are familiar and easy to produce. But there are a few practical limitations that tend to surface when you are publishing on a fast turnaround schedule and trying to reach an audience that reads mostly on mobile.

What an Interactive Digital Magazine Could Offer

Hey Product Hunt folks,

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.

Hey PH

I've been building EstateAI it lets real estate agents, photographers, and brokerages upload a photo of an empty room and get back a realistic, furnished version in minutes, while preserving the original walls, windows, doors, and layout.

The idea came from a real problem in our other business (Pixel Perfects Solutions) doing manual virtual staging for real estate clients. It was slow and expensive at scale. So we built EstateAI to make it instant: pick a room type, pick an interior style (Modern, Luxury, Scandinavian, Boho, Minimal, Industrial), and get a staged result, no prompting or design skills needed.

We're still early and actively shipping based on feedback. A few things we're working on right now based on user input:

I built BenchReport for the step after a VNA export: comparing several Touchstone measurements, applying a frequency-bounded acceptance limit and producing something another engineer can review.

The free browser edition overlays up to three S1P/S2P files, lets you select a golden reference, identifies the exact sampled frequency behind a failure and produces a printable report. File contents stay in the browser. Three synthetic filter measurements are built in, so the complete workflow can be tried without an upload or account.

I have also just published v0.4.0 with the guided demo, parser tests and RF-specific guides. I am looking for feedback on difficult-but-valid Touchstone structures and information missing from the report. Please use only synthetic or non-sensitive examples.

Try it: https://edwarddeakin06-maker.git...

I ve spent 10+ years building mobile apps, SDKs, and backend systems, and one thing I ve learned is that writing the code is only part of launching a product.

Deployment, configuration, payments, analytics, distribution, and growth can quickly become a collection of disconnected tasks.

That experience led me to build PlayLaunch a platform focused on making the app launch journey simpler for developers and app founders.

We re still early, and I d love feedback from other makers:

I m building NovaCloud22, a secure file workspace, and I m trying to rethink what a file workspace should actually be.

The basic problem I started with was simple:

Storing a file is easy. Managing what happens after you share it is much harder.

That led me to build things like protected sharing links, expiry controls, download limits, file requests, access tracking, and multiple-drive management.

For years I've seen teams perform the same activity: A feature is implemented. Before anyone creates a maintainable automation suite, developers and testers spend time exploring the system. Sometimes manually. Sometimes using fuzzers, mutation tools, schema generators, protocol analyzers, API scanners, AI-assisted tools, or other forms of generated testing. The goal is not regression protection. The goal is discovery.

Hundreds of tests may be generated and executed. Many are never maintained. Some reveal defects. A few become future regression tests. Yet we usually classify this activity as either exploratory testing or test automation, even though it doesn't fully fit either category.

Hey Product Hunt

I built Storecheckr after watching a pattern repeat itself too many times: store owners spending $500/month on Facebook ads for a store that converts at 0.8%.

The ads weren't the problem. The store was.