Hi everyone!

I'm a 90's bass head and wanted something that supports my needs. I wear earbuds at the gym, IEM's at home. I got tired of adjusting the phones EQ along with other EQ's to get the sound I wanted, which was meh for the most part. Podcasts and e-books don't need to be bass heavy, only my music. I want bass and clarity when watching movies. So, I built TruEQ out of frustration. With existing Android equalizers being either too stripped down or bloated with subscriptions and tracking. I wanted a clean, system-wide audio engine that works globally across Spotify, YouTube, YouTube Music, games, and streaming apps without needing a specialized music player.

I wanted a simpler handoff between AI coding sessions:

- Point a local tool at a repository

- Keep architectural decisions, evidence, and unfinished work connected

- Know when remembered context has become stale

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?

https://www.producthunt.com/prod...

I built NogginLogger because every calorie tracker I tried died the same death scanning barcodes, scrolling through 40 versions of "grilled chicken breast," and giving up by Tuesday. The friction wasn't the math, it was the input. So I made one where you just say what happened: a five-second voice note gets transcribed by Whisper, parsed by Claude into a structured entry (category, calories, macros, tags), and the dashboard shows calories consumed vs. burned in real time. Exercise calories are calculated server-side from your body weight and MET formulas, not AI guesswork. Once I had the voice-to-data pipeline working I started logging everything spending, mood, sleep, weight, even golf scores all in one place. What surprised me wasn't the tool, it was the accountability: when you can't hide from the data, you make slightly better choices, and slightly better choices compound. I lost 20 pounds in the timeframe I'd planned, with no dramatic diet just say it now, see it later. Set your goal and know where you are at any given moment of the day. Would love feedback from anyone who's bounced off traditional trackers for the same reasons.

Hi everyone!

I'm currently building BloxDonate, a platform that helps Roblox creators get discovered, receive donations, and support other creators.

Hey everyone, we launched Noa on Product Hunt today.

We built it because keeping life organised still feels way more fragmented than it should be, especially when you re planning with a partner or family.

What do you cook when you only have random ingredients left in your fridge?

I often find myself opening the fridge, seeing a few ingredients, and thinking: Okay... what am I supposed to make with this?

Hey Product Hunt,

I built RedMarker because worksheet marking doesn t scale especially at home or in small groups. The goal is simple: less time marking, more time helping kids understand.

RedMarker is for drill-style maths worksheets, initially for children aged 4 10: scan worksheet pages, see what s correct, spot what needs a second look, and keep work organised by student and class.

How do you choose a movie when nobody can agree?

Choosing a movie with friends can somehow take longer than watching the movie itself.

Hey PH folks,
I am building an AI product for fashion ecommerce brands. Still in a very early state, raw and we've a lot to work on.
I am seeking conversations with operators in this space and trying to live a day in their shoes.

This will help me in driving my PLG charter and build something really meaningful and useful by speaking to people on the ground.
By which I mean, I want to speak to the designers, people who click the pictures, people who are in production, people who do merchandising/cataloging, person who does content, someone who uploads things on the website & people who take it to the market... etc
I want to understand the deep operational dynamics of a fashion businesses.
How do they operate

What are the processes they follow
What are the challenges they face in their daily tasks
If they had a "genie in a lamp" what would they wish to get solved magically
Be it optimization of speed, cost or efficiency.
All of this will help me in building something that truly matters to them.
Any help from the community is much appreciated.
If you're somebody who understands these dynamics, please let me know.
If you run a DTC ecommerce business, even great.
If you know someone who operates in this domain, if you can connect me to them (I'll be very grateful)
In return, I will pick one problem, that's most critical to them & solve it using what I'm building.
They'll walk out with a solution and I'll walkout with golden insights.
P.S. For Mods, not sure if this is the right forum to seek this kinda help. If not, please guide me to a right forum. I genuinely need some help here.