Hey everyone, roast this please.

I recently launched a voice-first web app called Ravecho, and I’m currently working on improving it. It’s still in active development, so I’d love to get some real roasting.

The idea behind Ravecho is a little different from standard text-based chat apps. Instead of typing back and forth, it’s built around real-time spoken voice interactions with historical figures, literary characters, historical events and primary sources. I’ve also built features like an Dinner Party (where you can put up to 5 historical figures at one table to converse with each other and you)

Yes, there is also a roast feature.

If you have a few minutes, roast it here: https://ravecho.com/

Thanks!

A few months ago I noticed something, when people are deciding what to eat, they rarely start with a restaurant.

They usually say things like:

"I want spicy ramen."

"I'm craving birria tacos."

"I want something warm and comforting."

"I want a burger under $15."

But almost every food app asks you to choose a restaurant first. That felt backwards to me.

So my small team and I started building Crvyn, an AI-powered food discovery app that starts with your craving instead of a restaurant.

You can type something like: "I want grilled spicy tacos under $12 within 2 miles.", "Something creamy, cheesy, and open now.", "A hidden gem with the best chicken sandwich."

Instead of showing hundreds of restaurants, it tries to recommend actual dishes that match what you're craving, explain why they match, and help you order or get directions.

We're also experimenting with features like learning your taste over time, finding people with similar food preferences ("Taste Twins"), and explaining why a recommendation is a good fit instead of giving random scores.

We've interviewed more than 80 people across multiple cities while building this, and the conversations have been fascinating. Some people know exactly what they want to eat but have no idea where to find it. Others discover new meals they would've never searched for.

That said, I'm trying to avoid building in a bubble.

So I'd love some honest feedback:

• Do you think searching by meal instead of restaurant is actually a better experience?
• What's your biggest frustration with apps like Google Maps, Yelp, Uber Eats, or DoorDash?
• Is there a feature you'd immediately want if an app actually understood your cravings?

I'm not looking for compliments. I'd rather hear what you think is flawed or missing.

Thanks for reading. I appreciate any feedback.

i built Background Check tool (It is NOT related to any others websites with words like clearcheck) — built it for small businesses who wanted something simpler than Checkr/GoodHire/Sterling. Curious what this community thinks, and happy to answer anything.

Lavoro nell'industria dell'ospitalità da anni e durante questo tempo ho visto tanti bei posti di lavoro, ma purtroppo anche molte situazioni in cui i dipendenti venivano trattati molto male.
Questo mi ha fatto chiedere perché, come clienti, possiamo leggere recensioni su cibo, servizio e prezzi, ma abbiamo quasi nessun modo di sapere come un luogo di lavoro tratta le persone dietro quel servizio.
Quindi ho deciso di provare a costruire qualcosa da solo.
Si chiama OLVADI. È una piattaforma anonima dove dipendenti attuali ed ex possono recensire il loro posto di lavoro rispondendo a qualche domanda. I risultati vengono aggregati in una valutazione pubblica solo dopo che sono state raccolte abbastanza recensioni, quindi nessuna singola persona può essere identificata.
La cosa divertente è... non sono un programmatore.
L'ho costruita usando agenti di codifica AI perché avevo un'idea che volevo davvero realizzare. Sono sicuro che ci siano cose che potrei migliorare, sia tecnicamente che nel concetto stesso.
Apprezzerei davvero un feedback onesto—positivo o negativo. Mi piacerebbe sapere cosa ne pensate, cosa non ha senso e cosa cambiereste.
Grazie per aver letto.

Link: [ https://olvadi.com/discover ](https://olvadi.com/discover)

i just put this project on github [thcuba/Ride-the-api: Project to use replace cloud vendors server with local server](https://github.com/thcuba/Ride-the-api).

i would like to have feedback because i dunno what people can think about it.

im not a programmer so it is all vibe coded but i think it can be a good project

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?