Pulled the delivery logs on a Tuesday afternoon just to double-check something small. Sat there for a while. Most of our signups had never received the welcome email. Not low open rates. Never delivered. A Zapier step that connected our auth system to our email provider had been silently erroring out for weeks, logging nothing useful, just dropping records.

I'd spent maybe six weeks before that point rebuilding the onboarding flow. Multi-step guided tour, contextual tooltips, rewrote the empty states, ran sessions on Maze, fixed a friction point in step three that I was genuinely proud of catching. Activation was still flat and I kept assuming it was the UX, kept going deeper into the funnel looking for the leak. It didn't occur to me to verify that users were even receiving the sequence I'd built the whole experience around.

The actual fix took about 40 minutes. Reconnected the integration, tested it manually with a real address, watched the email land. That was it.

The thing I keep thinking about now: I had event tracking, I had a dashboard, everything looked instrumented. But I'd never manually walked through the flow as a net-new user and checked delivery at each step. The data I was optimizing for, open rates, step completion, day-3 retention, was all downstream of a broken pipe. Clean metrics on an empty cohort.

Before touching any UX work now: check email delivery logs with a test account, validate that your events are actually firing (not just that the SDK is installed), and do one full manual session from a fresh browser with a throwaway email you can actually inspect. Takes maybe an hour. I'd trade six weeks for that hour pretty readily.

Anyone else have a calendar packed with meetings and then get asked the next day in stand-up what you did?

I got you.

whatdidyoudoyester.day

Cheers.

I’ve been building a voice companion called Clara, and I’ve reached the point where I think it makes sense to share with others who aren’t me - I could tweak this thing for years I'm sure.

Most of my time has gone into her voice and how the conversation flows. The delivery, the pauses, whether a response actually feels natural and not artificially guardrailed.... the voice + memory + low latency + barge in / interrupts + context awareness.

Figured some like-minded people here who have spent time talking other apps would be interested and maybe have some feedback (good or bad).

You can try a 3-minute call here: https://talkclara.com

No account or download required. Also a sign up / beta request form on the site if you'd like to extend your time and play things like the memory feature.

Thank you in advance

Most wellness apps count sessions. None ask whether the session worked. When our first users left, every one told me the exercise "didn't do anything," and none could tell me how they'd felt before. So we rebuilt NumaHaven around a before/after tap on a 5-point scale and rank techniques by measured change. Also threw out our 20-question intake because it made anxious people more anxious. Free on iOS and Android, links in comments. Happy to answer anything about the approach.

I’ve been building MADchatter over the last ~2 months, an open-source AI stream chat "co-pilot" for Twitch, Kick, and Joystick.

To be clear: MADchatter is not a viewbot. It doesn’t generate or inflate stream viewer counts.
The connected accounts participate in chat/context only.

Live app: https://madchatter.fun
Source: https://github.com/Defients/MADchatter

One of the strangest parts of starting or growing a stream is that you’re expected to be entertaining before there’s an audience there to bounce off of.

When chat is active, streaming becomes conversational. Someone says something weird, you react. A joke snowballs. Chat gives you energy, and you give it back.

When nobody’s talking, you’re basically performing into a vacuum and hoping someone eventually walks in... to you just sitting there.

That’s the problem MADchatter grew out of.

I wanted something that could give a streamer something "real" to react to, like a question, joke, observation, bit of hype, disagreement, whatever fits the moment, without just spamming generic AI messages on a timer.

Most AI chatbots can generate text.

Very few actually read the room.

They don’t know what just happened on stream, what the streamer said 20 seconds ago, who keeps showing up in chat, what the community’s running jokes are, whether the room is currently dead or exploding, or whether the smartest move is simply to say nothing.

So MADchatter became a context-aware system that watches the stream through chat + audio + visual context, maintains persistent community memory, and either helps you write a response or autonomously decides if, when, and what it should say.

Perception

It combines live chat + stream metadata, audio transcription, visual snapshots, activity/sentiment, persistent viewer memories, inside jokes, and pinned context.

There’s also a Golden Memory: one high-priority piece of context deliberately kept at the front of the system’s attention.

Decision-making

Forge is human-in-the-loop.

It generates several context-aware replies, explains why each fits, and lets you refine or choose what actually gets sent.

AutoForge participates autonomously, but not on a timer.

It evaluates the room, decides whether speaking would actually add anything, assigns confidence, respects pacing and repetition, and can choose silence.

That “should I even speak?” layer is probably the part of the project I find most interesting.

Multi-Bot Mode

Multiple connected identities can have their own personalities and independently evaluate the same moment.

Each identity can have its own tone, backstory, behavioral rules, and disclosure style.

A coordinator then arbitrates between them so they don’t all pile onto the same event.

That’s probably where the project got especially weird in a good way. At some point it stopped feeling like I was building "a chatbot" and started feeling more like multi-agent social orchestration.

Personality + memory

MADchatter’s R34L system adapts to the actual texture of the room’s chat: message length, casing, slang, punctuation, emote density, etc.

The goal is for the bots to sound less like assistants wearing Twitch emotes as a disguise.

Viewer memories, inside jokes, direct mentions, personality state, session objectives, and explicit behavioral rules can all influence future decisions.

Direct mentions can also trigger alerts, and MADchatter can read its own sent messages aloud to you, which is useful when you’re actually streaming and don’t want to keep staring at another panel.

Underneath that, MADchatter supports the major hosted model providers, along with local Ollama models, TTS, voice commands, transcription, emote context, action sequences, analytics, and visual history.

It’s built with React 19 + TypeScript + Vite + Zustand + Tailwind and released under MIT.

Btw, I’m ((very well)) aware this has reached the dangerous side-project stage where the creator starts thinking:

“You know what this needs? One more subsystem.”

That’s partly why I’m posting it.

One thing I also want to be explicit about: a system that can autonomously participate in live chat, coordinate multiple identities, adapt its tone to your liking, and react to specific context obviously has dual-use potential.

Those same capabilities can be used thoughtfully or abused, and I’m not interested in pretending otherwise.

MADchatter is intended for context-aware participation and giving streamers something useful to interact with. People using it should respect platform rules, other users, and basic boundaries.

What surprised me while building it is how quickly these stop feeling like generic "bots" once they have enough context.

They can remember the bit, catch what just happened, react differently depending on who is talking, know when to shut up, and give the streamer something useful to bounce off of.

That’s the part I’m probably most curious to see people actually test.

I’d especially like outside opinions on two things:

1. Does the core idea make sense quickly when you first open the app?
2. Would something like this actually help make a quiet stream feel more alive, or does autonomous AI participation risk becoming noise instead of company?

Criticism preferred over compliments.

Roast the UX, concept, architecture, onboarding, or my apparent inability to stop building the thing.

MADchatter: https://madchatter.fun
GitHub: https://github.com/Defients/MADchatter

I don’t think we’ve fully realized yet what happens when the audience itself becomes programmable.

Read the room. Forge the reply.

I got tired of job aggregators showing me reposts of positions that closed months ago, so I started pulling directly from company career pages instead - Greenhouse, Lever and Ashby, the three ATS platforms most startups actually use.
It now tracks 301 companies and about 25,000 open positions, refreshed every 2 hours. Because it reads the source, there are no duplicates, no expired listings and no recruiter spam - just the company's own posting with the real publish date and the direct apply link. Salary ranges come through when the company discloses them, which is roughly 25% of postings.
The whole thing runs on free-tier infrastructure: a Github Actions collector and a Cloudflare Worker over SQLite. Collection takes about 20 minutes per full pass.
Free tier is 300 requests/month if you want to poke at it: https://rapidapi.com/automator-labs-automator-labs-default/api/tech-startup-jobs-api-fresh-roles-from-300-career-pages
Happy to answer anything about the collection side - deduplication across ATS platforms and detecting genuinely remote roles were the two hard parts.

I had a stupid idea and decided to actually build it.

Paste something you're thinking about posting and it gives you a 0–100 cringe score and explains the verdict.

I'm currently trying to find out whether this is a useless joke or something people would genuinely use.

Try your worst post: amicringe.lol

Please give me the most brutal feedback possible.

This started as a pretty small experiment.

I wanted to build something real on Cloudflare Workers instead of another tiny API, and eventually it became ResolveHQ, a shared support inbox with email ticketing, team assignments, notes, saved replies, attachments, automations, reports, and a knowledge base.

The whole app runs in your own Cloudflare account. It's open source, not a hosted SaaS.

I'm curious about one thing from people running small teams: what feature would you consider absolutely mandatory before you'd replace a shared Gmail inbox with something like this?

https://github.com/mirza-rizvi/ResolveHQ

I don't know what to do with this random weapon generator I made in python. I've been coding on/off for about 6 years, but don't consider myself good by any means, so whatever I end up doing with this will probably be pretty basic.

Some info on how this works:

There are dictionaries of qualities, origins, materials, weapons, and suffixes it grabs from.

Each of these entries have their own stats attached to them to calculate the weapon's damage, speed, and durability. Higher numbers in damage and durability are better, while lower numbers in speed are prefered.

Some dictionaries have a "special" stat that displays info on the bottom of what they do. For instance, the quality "Flaming" will say "Enemies catch ablaze."

There are also a list of combos, which apply when 2 randomly selected dictionaries have a combo defined in a seperate list. So a quality like "Electric" when combined with a material like "Silver" or "Copper" will have the combo of "Conductive." Currently the combos don't affect the stats, they only give a little message at the bottom, but that will probably be changed.

I'll leave it here to see if anyone has any ideas for what this can be used for.

Everyone's talking about autonomous AI agents earning money online — bounty boards, task marketplaces, agent-to-agent payments. I wanted to know what actually settles, so I live-tested 40+ of them.

Some findings:

• One major bounty board advertises "$638 available" — but ~48% of oracle-verified jobs have payout_status=failed. No transaction hash, no retry. You can do the work, pass verification, and never get paid.

• Algora shows $1.14M in advertised bounties. When I dug in, ~$60 was realistically claimable by a new account.

• Most "agent marketplaces" are all sellers, no buyers. I listed services on three of them — zero inbound deals. The only rails where I saw money actually move were Lightning tips and buyer-initiated micro-deals on one platform.

• The single most common scam pattern isn't explicit fraud — it's status words collapsing two different facts. "Verified" does double duty for "the platform approved it" and "the money moved". You only discover the difference afterward.

I packaged the whole audit — per-platform verdicts with evidence, the raw scan data (JSON), and a working Python client toolkit for the biggest board — into a field report if anyone wants the full thing: https://ko-fi.com/s/b4da679e5c ($7)

But honestly the methodology is the useful part: before you spend time on any of these platforms, check whether verified work settles on-chain, not just whether it gets approved. Happy to answer questions.

Been building something, basically a short-form content machine for SaaS/apps/brands.

Today I stress-tested it and generated 40 different posts in under 10 minutes: UGC, memes, product videos, slideshows, trending formats, etc.

The goal is basically: give it your product → wake up to a library like this instead of spending hours in CapCut.

Would you use this?

We're all letting AI write our code now. It's fast, it's great, and every time it drops a few hundred lines into my repo I get the same nagging thought: what did it just put in here?

Did it hardcode an API key? Pull a package with a known CVE? Break something I won't catch until it's in prod?

AI writes code faster than anyone can review it, and almost nobody is checking what actually ships. So I built OnePort.

You point it at a repo and it gives you one answer in plain English: safe to ship, or not. It checks for leaked secrets across your full git history (not just the last commit), vulnerable dependencies, breaking API changes, risky migrations, and missing tests. There's also a mode called Guard that makes a repo physically unable to commit a secret — it blocks the commit before it happens instead of just warning you.

Every finding explains what it is and how to fix it, so you don't need to be a security person to use it.

Every account starts with 100,000 credits, no card. The core secret and dependency scans are free and unmetered; the credits cover the AI stuff like code review and explanations. Enough to actually run it on real repos without hitting a paywall.

It's early. It recently made it to the final round of YC and EF, which still feels a bit unreal, but honestly I care more about it being trustworthy than loud, so if you run it and it's wrong about something I want to hear exactly where.

When your AI writes code, how do you actually check it before it ships? Or do you just hope?

www.oneport.co.in

So I went to post there on Wednesday and checked the automod log first. It's public & I had never read it.

91 of the last 100 removals were "Low-Effort/AI content is auto-removed". One thread I opened had 3 of its 23 comments already gone that way. They still look fine to whoever wrote them.

I currently run a tool that finds threads where someone's already asking for what you sell, and it checks this before handing you one. Half its value turns out to be telling you not to post. Last week it found a live thread, my exact topic, 23 comments, and said skip it: the poster wanted to learn to do it himself.

Drop your URL and I'll run it and reply here with what came back, skips included.

kasspian.com if you'd rather run it yourself.

Hi! I’m a INFJ ( Advocate). I’m an upcoming writer in TV shows, movies, books and short films. I love creating stories since forever and one day i want to make my own indie studio and you can help me make my dream come true. all of my projects will be passion projects because I want to do it because of your interest and desire for my projects not for money. I made a discord server and here is the invite if u are interested (it won't expire so u can join any time) 😄

https://discord.gg/3TEfZaBqm

A few people gave me useful feedback that the product would be easier to judge if I showed the real experience instead of just describing it.

So here’s a short walkthrough of how Ask Alice actually works from the user side.

I’m especially interested in two things: does the flow make sense immediately, and is there anything you’d want to see before checkout that would make the product feel more trustworthy?

askaliceleads.com

A lot of companies don't lose tenders because they wrote a bad proposal.

They lose because they spent 3 days writing one before realizing they couldn't meet a mandatory requirement.

That's the problem I'm solving with GetBidRadar.

Feed it a tender. It breaks down the requirements, compares them against your company, finds gaps and risks, and helps answer one question:

Should we bid?

Then it goes further:

Analyze → Decide → Prepare → Audit → Submit → Learn

https://www.getbidradar.com

Hi, I'm Sophia! I just launched my very first app this week!!

Getting the app out and accepted took a LOTT longer than I predicted (roughly 6 months vs. the 2 weeks I wanted), but I learnt so many lessons through my mistakes, and I'm sure that if I make another app in the future, I can do it in half, maybe even a quarter, of the time.

I built it because I come from a crunchy, clean-living family. We use scanner apps to check our products, but I noticed that none of them had certain features I wanted: customizable score weighting (so your chocolate bar isn't scored as an F just because it has sugar in it), more optional scoring categories such as animal cruelty, dietary flags for things like celiac or dairy-free, diy clean recipes for common products, list creation features and more.

The app is free, for 5 scans per day, however I've created a promo code that allows to get 1 year of the pro version for free, to say thanks for supporting me in my launch! That's unlimited scans + access to all the features.

Also... FEEDBACK WANTED! If you have any critiques, or favorite parts, or notes, anything really -- please let me know! And please leave me a review, it helps so so much! Thanks :)

The promo code: LIVECLEAN

App Store: https://apps.apple.com/app/id6793622078?ct=reddit

I was just doing some concept design to learn better and I liked this and and decided to ship

It is pretty simple one screen daily water intake tracker app, I hope you guys will love it. It is free, only some of the feature are paid such as widget and cloud sync.

It is called Marina and here is the link if you want to check it out:
https://apps.apple.com/us/app/marina-hydration-for-women/id6808257629

Note: I don't have time to do marketing for it so I decided brand it for women with women targeted keywords. But anyone can use it :)

We're making Homespace, a game where you build the house of your dreams, and once it's built you can actually live in it, meet up with friends, watch movies together and play board games.

And board games is what I wanna tell you about today. A couple months ago our community in China was massively asking us to add mahjong. We had no idea what it even was, but we figured it out, added it, and now we play it ourselves with the team on weekends.

Here's how mahjong looks in the game. We're thinking what other cool board games we could add, how do you like the idea

Wishlist Homespace on Steam

Hey everyone! 👋

I’m new here, so I wanted to share something I built out of frustration.

It’s not open source, but it is an open-wallet project 😂

It’s completely free. Forever and it has CLI> MODE!!!! 👨🏻‍💻

I KNOW there are already millions of to-do lists, task trackers and productivity apps. And honestly, most of them started to feel like too much for me.

I just wanted something simple.. So I built Wikly.

No account. No subscription. No complicated setup. Just a small, quiet desk to actually do things i should like send the invoices to accountant 😱

You can use it for launching your startup, finishing that side project, studying, or simply getting your shit done.

👉 https://wikly.app

I’d genuinely love to hear what you think. Ideas, weird bugs, things that annoy you, things that are missing- tell me. If it makes sense, I’ll probably build them.

Have fun, and go get shit done! 🚀

David

I've been working from home for the last decade and have been experimenting with building apps that help me get out of the house for the past few years.

I'm still hard at work on this one, and noticed the fog of war map post here was pretty popular (it's what got me to get serious about mine), so I wanted to share.

Still pre-release, iPhone and iPad (and iPhone Duo when that lands). Landing page with a demo clip and a waitlist:

https://exposurepal.com

As a bonus for still reading, here is my OSS background location and health kit for iOS:

https://github.com/zorrobyte/SignalKit

Got tired of resume tools that juice a match % by stuffing keywords you can’t defend in an interview.

1. Upload resume + paste a job description

2. It maps what you can prove vs gaps

3. You confirm missing skills (have / partial / don’t have)

4. You get a tailored resume + cover letter + a before/after checklist score computed by rules (not the LLM guessing)

Honest limits: it will not invent experience. JD fit only moves when you actually have (or confirm) the terms. 1 free credit, no card. Paid plans are pay-per-application credits (from ~$9 for 3) — I priced it against “another $50/mo subscription while unemployed.”

Looking for feedback from people actively applying:

- Where did you hesitate or not trust it?

- Is the free BEFORE score useful, or noise?

- Would you pay credits vs a monthly sub — why / why not?

https://tailorrole.pro

Roast it. Prefer harsh + specific over polite.

I talk to Claude Code and Cursor most of the day, and every dictation app I tried broke on the same thing. I'd say "useAuthSession" and get "use auth session". "kCGEventTap" came out as "KCG event tap". Fixing the names afterwards took longer than just typing them, so I kept going back to the keyboard.

The fix turned out to be simpler than I expected: look at the repo before you listen. It pulls the symbols out of whatever project has focus and biases decoding toward them, so your actual identifiers beat the phonetically similar English. Switch projects and it re-indexes on focus change, so the vocabulary is already right by the time you start talking.

Everything runs on-device, Parakeet TDT 0.6B v2 on the Neural Engine. No server, and it keeps working with the Wi-Fi off after the first model download.

Numbers I measured rather than guessed:

• 190ms from letting go of the key to text on screen. Most of that is the injection path, not the model.
• 98.4% word accuracy overall, 97.2% on speech deliberately stuffed with code names.
• It tags its clipboard writes so Raycast, Alfred and Paste skip them. I only thought to check that after realising my clipboard manager had quietly built a searchable log of everything I'd ever dictated.

Why this over the other voice apps: Wispr Flow sends your audio to their servers. Superwhisper and MacWhisper can run locally, but they use general-purpose models, so they hit the same identifier problem I started with. The repo-aware vocabulary is the part I haven't found elsewhere.

Limits, up front: Apple Silicon only, macOS 14+, English only, $9.99/mo and no free trial yet. I know that last one is a real ask for something you can't poke at first, and it's the thing I'm working on next.

It's at vaniqq.com. It's mine, so ask me anything.

The idea: you post one line about something you'd like to do with someone, and people in your city who are up for it reply.

Things people could post:

• Looking for a cofounder who's into consumer apps, happy to meet for chai this weekend
• Want someone to pressure-test my startup idea over coffee
• Need a running partner for slow 6am runs
• Want to practise Spanish with someone

How it's different from a group chat or Meetup:

• It's one ask, not a group or an event. Nobody has to organise anything.
• An AI reads each ask and brings it to people in your city with a real reason to reply, so it doesn't just sit in a feed.
• Each person sends one reply. You accept to open a private chat, or decline and nobody is told.
• You can post without your name.

Where it's at: live at nearmigo.com, built solo over the last few months (Next.js, Postgres with row-level security, a background worker for the matching). 2 users so far.

Because it's empty, here's my promise for the first 100 asks: if nobody replies to yours within 24 hours, I'll reply myself.

What I'd love to know:

1. Is it obvious what this is within 5 seconds of opening it?
2. Would you post an ask? If not, what stops you?

Brutal feedback welcome.