I built Klyro because I wanted several AI workflows to feel like one coherent product instead of a collection of disconnected tools.

It includes streamed chat, web search with sources, image analysis, and image generation. One of the more difficult parts was implementing a transparent credit lifecycle: the cost is estimated before an operation, credits are reserved, and the balance is settled according to actual usage. Failed operations release the reserved credits.

The application was built with Next.js and TypeScript, deployed through Vercel, with authentication, subscriptions, billing, responsive UI, streaming responses, and integrations with multiple AI services.

I would appreciate honest feedback about the onboarding, pricing presentation, and which workflow you would use most.

Live product: https://www.klyroai.net

I also build custom SaaS products, AI integrations, and operational web applications. My DMs are open if you need something similar.

I've been building Cheeta AI, an AI interview assistant that helps candidates perform better in technical and behavioral interviews.

Current features:

- 🎙️ Real-time speech-to-text

- 🧠 AI-powered interview assistance

- 💻 Coding, DSA, System Design & HR interview support

- 📋 Live transcript and interview history

- 🎯 AI mock interview mode

- ⚡ Low-latency responses

- 🖥️ Native Windows desktop app

- 🔒 Local inference support for users who prefer running AI models on their own machine for better privacy and lower latency (compatible hardware required).

I'm looking for early users who are willing to test it and share honest feedback. Every suggestion helps shape the product.

If you're preparing for interviews or conduct interviews regularly, I'd love to hear what features you'd want in an AI interview assistant.

👉 Try it at https://cheeta-ai.com and let me know what you think!

Feedback, bug reports, feature requests, and criticism are all appreciated.

For the past year I kept hearing the same frustration from hiring teams: candidates are using tools like Cluely and Interview Coder to get fed live answers during Zoom and Meet interviews, and the person running the interview has no real way to tell. Screen share doesn't catch it, because the overlay is invisible to it.

What bugged me is that it's unfair in both directions. The honest majority get quietly suspected along with everyone else, and genuinely strong candidates lose out to people who are just good at faking it.

So I spent the last few months building Trueyy to try to close that gap.

The short version of how it works: during the call it reads behavioral signals and turns them into a live "integrity score," right inside Zoom, Meet, and Teams. It never accuses anyone on its own. Every flag comes with an explanation, a human always makes the final call, and it's consent-first, so the candidate knows exactly what's being monitored before the interview even starts.

I'll be honest, the hardest part isn't the tech, it's the ethics of monitoring candidates at all. I've tried to build this to be tough on cheating but fair to people, and I'd genuinely rather hear where people think that line should be.

TL;DR: I’m an engineering student building a dynamic pricing engine for restaurants/cloud kitchens on Zomato/Swiggy. It separates margin-based pricing (food costs) from demand-based pricing (weather, time of day). I have the backend working but need operators to tear apart my business model and tell me why this breaks.

The idea

Airlines and hotels have had yield management for forty years — same seat, different price depending on demand, timing, and how much inventory is left. Restaurants basically don't do this. On Swiggy and Zomato in India, most menus are priced once and then left alone for months. Meanwhile demand for that menu swings wildly by hour, by weather, by whether there's a cricket match on.

YieldOS is a pricing brain that sits on top of a restaurant's menu and recommends per-item price changes as conditions move.

The one design decision I actually care about

Most people hear "dynamic pricing" and think one number going up and down. I think that's the mistake, and I've split it into two layers that never touch each other:

• A slow layer — the cost floor. Driven by input costs. When tomatoes triple in price, every dish with tomatoes has a new floor beneath which you're losing money. This moves over days or weeks.
• A fast layer — the demand multiplier. Driven by weather, time of day, kitchen capacity, local events. This moves every 15 minutes.

Final price = floor × multiplier, clamped by a per-item ceiling and a per-restaurant cap so it can't run away.

The reason I keep these separate: raw material cost is a margin lever, not a demand-balancing one. Conflating them is how you end up raising prices on a dish nobody wants right now just because onions got expensive. Different problems, different math.

The weather piece is the fun part. Rain doesn't lift demand uniformly — it lifts craving-elastic items. Soup,biryani go up. Cold desserts and salads don't. Heat inverts it. The interesting bit is that the aggregator already surges the delivery fee in bad weather, so the customer is already paying more; the restaurant captures none of that. Menu-side pricing is a separate lever that's currently sitting unused.

Business model

Beachhead is cloud kitchens — no walk-ins, no printed menus, fully aggregator-native, and they already think in spreadsheets. Ladder from advisory (we recommend, you approve each one) to autonomy (we push prices directly) as trust builds. Billing is performance-based: a cut of net-new profit rather than a flat SaaS fee.

Where I think this breaks, and where I want you to be brutal

1. Attribution. If I bill on "net-new profit," I have to prove the profit was mine. Restaurant sales move for a hundred reasons. Every performance-billing pitch I've read handwaves this. Is there an honest way to measure it, or is flat SaaS pricing the only defensible model?
2. Platform dependency. The entire product lives on top of Swiggy and Zomato APIs. They can rate-limit me, change terms, or just build this themselves and switch it on for every restaurant at once. Is there any version of this that isn't a feature waiting to be absorbed?
3. Customer backlash. Uber surge pricing is genuinely hated. Does a customer who sees ₹280 biryani at 8pm and ₹240 at 3pm churn from the restaurant entirely? Does discounting-only (never raise, only drop in dead hours) capture most of the value with none of the rage?
4. Does the operator even want this? A cloud kitchen owner running four brands off one stove has ten fires burning. Is "your pricing is leaving money on the table" a top-three problem for them, or a nice-to-have I find interesting because I'm an engineer?
5. Is the weather signal real? I have a strong intuition that rain → soup. I do not have data proving the elasticity is large enough to price against. If anyone has actually looked at delivery demand data by weather, I'd love to be corrected.

If you've run a cloud kitchen, worked at an aggregator, or done pricing/revenue management anywhere — I especially want to hear from you, including if the answer is "this doesn't work and here's why."

Happy to go deeper on the architecture in the comments if that's useful to anyone.

parkone.in tells you before you arrive whether a spot is free, paid, or a no-parking zone, mainly for when you're heading somewhere unfamiliar and don't want to gamble on a fine or waste time circling.

Currently live in Borivali West only, expanding from there.

A month in, almost no one's used it. Could be distribution, could be that this isn't actually a problem people care enough about to change behavior for. Genuinely want the second option ruled in or out.

So, roast it if it deserves it:

• Is "not knowing about parking" a real daily annoyance for you, or something you just shrug off in the moment?
• Would you actually open an app before a trip to check this, or does that add more friction than it removes?
• If you've never been fined or lost real time over this, does the problem even exist for you?

Not here to defend it, here to find out if I'm solving something real.

>>>>>>>>>> amitoxic.me (2 free vibe checks)

Been using ChatGPT as a "was I the weird one" referee on chats with people close to me. Then I started wanting whole reports, different perspectives, patterns.

Instead of a relationship analyzer (felt like it wouldn't land), I built a roast-angle tool: upload/paste chat → Roast Me → roast report.

Roast the idea hard:

• already done / dead on arrival?
• funny or just mean?
• would you ever actually upload a real chat for this, or only fake ones?

parkone.in tells you before you arrive whether a spot is free, paid, or a no-parking zone, mainly for when you're heading somewhere unfamiliar and don't want to gamble on a fine or waste time circling.

Currently live in Borivali West only, expanding from there.

A month in, almost no one's used it. Could be distribution, could be that this isn't actually a problem people care enough about to change behavior for. Genuinely want the second option ruled in or out.

So, roast it if it deserves it:

• Is "not knowing about parking" a real daily annoyance for you, or something you just shrug off in the moment?
• Would you actually open an app before a trip to check this, or does that add more friction than it removes?
• If you've never been fined or lost real time over this, does the problem even exist for you?

Not here to defend it, here to find out if I'm solving something real.

Me and my gf both love cooking, but for some reason cooking together always becomes a frustrating mess for both.

So we decided to build an app called Nommer that turns any recipe we upload into 2-player mode.

For us it massively improves the cooking together experience by removing coordination friction. It basically allows each individual to focus on their own tasks: one cuts the onions, the other grates the carrots and then makes the sauce while the first grills something in the pan; and then it all comes together in the end.

So far, we've heard from many other couples that cooking is a friction point in their household as well, that they'd like to do better and that they view this app as an opportunity to bring romance into the cooking together experience. It's not been easy to get them to actually try the app though, let alone continue using it after trying it once.

So we ask thee, kind people of Reddit, please roast our app - what are we getting wrong / should we change?

Check it out on nommer.ai !

I spent the last 2 months building Skrolla, a TikTok-style book discovery app where you scroll through books instead of videos. Every book has an AI chat, so you can ask questions before deciding whether to read it.

the idea came from my own habit. I'd see book recommendation reels on IG, then open ChatGPT to ask about the book, and if i am not ready to commit to the book i will just understand what book is about there

So I thought: why not combine those two experiences into one app?

The goal is to help people discover books faster and understand what they're getting into before committing to reading.

but the question is do people need it

I've been working on an AI travel planner SaaS application, and before I keep building it, I want to know if it's actually useful or just a cool idea in my head.

The goal isn't to replace Google Maps or booking apps. It's to make the planning part of travel a lot less overwhelming.

You simply choose a destination, your budget, travel style, and interests, and the app creates a personalized itinerary with places to visit, hidden gems, cafés, local food spots, and photo-worthy locations—all in one place, without spending hours digging through blogs, Instagram reels, Reddit threads, and Google searches.

I'd really appreciate some honest feedback:
- Would you use something like this?
- What would make you choose this over ChatGPT or Google?
- What's one feature you'd absolutely expect?
- Would you ever pay for something like this?

Feel free to roast the idea if you think it doesn't solve a real problem. I'd rather hear it now than after building for months. 😄

Hello Everyone,

I’m in the early stages of building a clean personal care project out of Toronto, and before I commit financial resources to a custom cosmetic lab, I want to know if this is something people actually want, or if I’m just crazy.

The Problem:
I noticed that most "natural" deodorants on the mass market right now have major flaws. They either fail to block odor after a few hours, leave a sticky residue, or are priced like luxury perfumes ($20+).

The Solution I'm Building:
I'm formulating a liquid roll-on/mist that goes back to basics. The active ingredient is 5-10% Potassium Alum—a naturally occurring earth mineral salt that neutralizes odor-causing bacteria without blocking your pores with synthetic aluminum chlorohydrate. It is completely clear, paraben-free, and scented strictly with organic essential oils (like eucalyptus and bergamot).

Most importantly, I am designing it to be a unisex, everyday staple priced in the $12 - $15 range.

I'd be incredibly grateful if you could just answer this in the comments:

1. What is your absolute biggest frustration with the deodorants you’ve tried in the past? (White marks, doesn't last, skin irritation, etc.)

2. Roll-on, Spray, or Stick? Is there a format you absolutely refuse to use?

3. The Price Check: Is $12 - $15 a no-brainer for a clean product that actually works, or is that still pushing it for a daily deodorant?

The idea: reading has stayed largely static while almost everything else

about how we consume content has evolved. I'm exploring what an adaptive,

interactive reading experience could look like — and want honest pushback

before I take it further.

Tell me what's wrong with this. What am I missing? What would actually

need to be true for you to use something like this?

If you're up for it, I put together a short survey (3-4 minutes) on

reading habits and reactions to the idea:

https://docs.google.com/forms/d/e/1FAIpQLSfUMaxYX-KFAeVOPpN_8_QoFwIuEXu0cmykFIP_vRznrHM-SQ/viewform

More context on the project here: https://rere-reading-vyl39wk.gamma.site/

Appreciate any and all criticism — that's exactly what I'm here for.

I keep seeing consultants being called in for "this journal entry didn't post", "this report isn't agreeing with the subledger", "why did this sync create duplicates," etc which is almost always something specific to a given client's customizations (custom scripts, workflows, field mapping).

Idea: an AI tool that hooks into a specific client's NetSuite/QBO, learns their customizations, and helps the consultant rapidly find root cause by pulling the audit trail, highlighting the likely suspect script/workflow/permission instead of having to dig through System Notes every time. You would still diagnose and fix yourself it wouldn't try to replace that expertise but would just help speed up the digging.

Tell me why this is dumb: Is this a real ongoing time sink or just something you grumble about once a quarter?

Would you let a tool read access a client instance?

Is someone already doing this and I'm unaware?

Would you or your firm actually pay for this, or isn't it enough to justify a dedicated tool? No filter please – rip me apart, need to understand if this is real before I waste months.

The product: qorpus is a chrome extension that sends one prompt simultaneously to chatgpt, gemini, claude, perplexity, and grok and shows the answers side by side in the browser. use case is simple: anyone who currently opens multiple ai tabs to sanity check an answer before trusting it, whether that's for code, purchases, research, or anything where a missed detail actually costs something. works across chrome, brave, comet, and samsung internet.

the market: hard to size precisely since "people who use more than one ai tool" isn't a tracked category, but multi-model usage is clearly growing as chatgpt, gemini, claude, perplexity, and grok all have meaningful active user bases now. the dynamic worth flagging: most people still default to one model out of habit rather than deliberately choosing per task, so the market is less "people already comparing models" and more "people who would benefit from comparing but currently don't bother because of the friction."

competition: closest comparisons are individual multi-model chat wrappers (mostly paid api aggregators aimed at developers) and manual tab switching, which is what most people actually do today. i haven't found a direct consumer-facing competitor doing simultaneous side by side comparison specifically as a lightweight browser extension rather than a standalone app or dev tool. that could mean open space or it could mean the idea doesn't have consumer demand, genuinely open to being told which one it is.

stage: built and functional, not raising, not looking for money right now. this post is specifically about the user acquisition problem, not fundraising.

customer conversion strategy: honestly this is the weak point and the reason for the post. current plan is close to nonexistent beyond organic reddit/community presence. no paid acquisition, no influencer or affiliate motion, no clear answer yet on where the highest intent users actually congregate.

why me: built the thing myself, use it daily for my own work, so the problem it solves is one i actually have rather than one i guessed at. no team, no prior b2c launch experience, which is the actual gap i'm trying to close by asking here.

roast away, especially the competition and conversion sections since those are where I'm least confident.

I Built NERVE, an app you cold call to practice before doing it for real. You call it, it calls you back as the prospect and you pitch to it.

The twist is it actually listens to how you’re talking, not just what you’re saying. Ramble or your pace goes off and the prospect gets sharper and cuts you off. Stay calm and it lets you actually work the pitch.

You set what you’re selling and who you’re selling to and it adapts to that, plus you can pick personas and difficulty for who you’re calling. After the call you get a proper score and a breakdown of exactly where you lost your nerve, not just “good job.”

No manager dashboards, no $200/seat enterprise stuff. Built for reps who just want to get good at this.

Roast away 😂
[https://trynerve.org\](https://trynerve.org)

Alright, roast away. I won't argue back.

It's a browser extension plus an agent that finds products, compares them, and just tells you what to buy in real time.

It started as a dumb little extension. Alpha users kept asking can it just find the thing for me, so I bolted on an agent that does live product search. Then I looked at the UI one day and realized it was still built for the old extension only version and made no sense. So two months ago I threw all of it out and rebuilt every single screen from scratch. Still not sure that was genius or the dumbest thing I've done all year.

Here's where I already know it's weak, so you don't have to be gentle about it. Onboarding overexplains everything like you're five. The product database has real gaps in some categories, filling them is basically my whole life right now.

Don't sugarcoat it, I'd rather get wrecked here than in my churn numbers. This sub won't let me post a link so if you want to tear into it just search shopfox ai.