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I'll start: Our product is called Iteration X, a Project Management app and an Issue Tracker that allows you to capture issues and bugs in any live product or website in 1-click, without bothering taking screenshots and manually annotating them anymore, and then create automatically populated tickets with a screenshot or a video and all the technical information engineers need to reproduce and fix the issues. Finally a product that bridges the gap between Project managers, Designers and Developers ✌️ Can't wait to read about your products PH community!
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Invent () - If you're a business owner, you can finally sleep and get your time back. Let AI scale your customer support, qualify leads, book appointments, and more.

We launched the past year and need to re-launch with a lot of updates we did this year!

Btw, I would love to know your thoughts as a CX manager Salim!

Hi Salim,

Thank you for this and congrats on your product.
We recently launched .
It's an AI Native Workplace Communication platform.

, an Autonomous ai that run your company 24 hours. The launch will be in one month

Thank you for this and congrats on your product.

I built – a simple but different kind of timer for workouts, games, focus sessions, and reading.

Instead of fixed countdowns, you set a time range and the app randomizes the exact duration, making each session feel less predictable and more engaging.

Already at 100+ downloads with daily active users who use it for training, focus, and everyday routines

iteration x looks handy, the 1-click bug capture is the kind of thing teams dont know they need til they have it. honestly i lurk threads like this just to find stuff early. been thinking lately about how the people who show up this early rarely get anything for it beyond a discount. fwiw i'm building groundwork around that, letting early supporters become real backers of a product instead of just first customers. what made you post here vs just launching, did early feedback change the product much?

Cadence. A recovery aware bulk and cut coach for natural lifters.

Most training apps count your sets and macros but have no idea you are fried. Cadence checks in on your recovery daily and tells you whether to push, hold or back off, in both training and nutrition. It is built for lifters past the beginner stage, where recovery is the real bottleneck, not effort.

Early access is free and the waitlist is open. Honest feedback welcome, especially from anyone who has tried to bulk or cut around a demanding job.

TAM Network. an alternative to linkedin we are building. receipts replace resumes. one peer signs your code shipped. one customer signs your job landed. trades vertical includes plumbers, nurses, electricians. agent identity row lets an AI agent be signed for too. v1 has 22 real humans. v2 launches aug 12. free for life on the personal side. thetamnetwork.com

I'm building Unblank.

It helps people prepare for important conversations, stay on track during them, and reflect afterward so they improve over time.

The biggest challenge right now isn't building the product it's making the experience feel natural and genuinely helpful in high-pressure moments.

Looking forward to seeing what everyone else is building!

Hi,

We're building , an AI-native implementation partner. Instead of selling another chatbot, we take one expensive workflow a company already has, build a supervised production agent into their stack, prove the savings, and hand them code they own. The hardest part has been getting people past 'AI curiosity' to actually shipping one workflow. Curious how others here are framing that first use case for non-technical buyers.

Thanks for the opportunity.

A year ago I started teaching an AI to cook, on evenings and weekends. This week it went live on the App Store and Google Play. Solo build: services, web, native mobile, and the pipelines in between.

CuocoMitra is an AI cooking and nutrition companion. Plenty of apps bolt a chatbot onto a food database. I wanted the inverse: a rigorous system where the AI is the interface, never the authority. These are the design decisions I would defend in any architecture review:

1. The LLM never touches a number.
Models are great at intent, bad at arithmetic. My agents can only emit validated, schema-checked actions (swap this ingredient, adapt to my diet, plan my week). Free-typing an answer is not an option the model has.

2. Deterministic domain engines do the real work.
All food math lives in pure, isolated engine packages: no network access, no storage access, same input gives the same output. Ingredients resolve against a real nutrient dataset through a matching scorer, and macros are computed, never generated. 34g of protein is a calculation, not a hallucination.

3. The AI plane is isolated from the data plane.
Agent sessions, tool orchestration, and live token streaming run in their own service behind their own auth boundary. Model latency or an outage never blocks a normal read or write. Worst case, the product degrades into a great manual cooking app, not a brick.

4. Every AI edit is a versioned mutation.
An adapted recipe is a new version with full history, revertible in one tap. The agent has write access to nothing it cannot undo.

5. Retrieval over memory.
Culinary and nutrition answers are grounded in a curated knowledge base through an embeddings pipeline. Traceable to sources, not improvised from whatever the model memorized in training.

6. One brain across every surface.
Photograph a plate and a vision model recognizes the dish, then the same deterministic engine attaches the macros (I call it MitraVision). Ask for a diet swap and a fusion of culinary knowledge and your learned taste profile keeps the dish authentic: a Thai curry should still taste Thai after the adaptation. One profile of your palate, constraints, and goals drives search, meal planning, and personalization everywhere.

7. Models are replaceable, guarantees are not.
The AI layer is deliberately thin. When a better model ships, it slots in, and every correctness property above stays untouched.

Quietly proud of one more thing: a single typed API contract powers both the web app and the native mobile app, and agent progress streams live to both.

If you build LLM agents, retrieval systems, or you just cook, I would genuinely love your take on the approach. Links in the first comment.

iOS:

Android:

Web:

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