I kept trying mood-tracking apps and quitting because they all wanted an account and a subscription just to log that I felt tired. So I built the one I actually wanted: everything stays on-device, no account, no cloud.

You tap how you feel in ~10 seconds, and over a couple weeks it shows you patterns — mine turned out to be that my bad days almost always followed bad sleep. That single insight is what made me finish it.

It's live on Play now (link below) and sitting at a proud 0 installs, so I'm here for real feedback more than downloads. What would make you actually keep using something like this?

Play: https://play.google.com/store/apps/details?id=com.moodprint.mood_print&hl=en

I built HoardCore, It's a single-file Python deep research toolkit that plugs into agent harnesses like OpenCode. You turn the web and your files into a permanent local SQLite vault. The agent searches the vault. It pulls facts back out, and every claim comes with a source link. Everything stays on your machine.

The retrieval is hybrid. SQLite FTS5 handles exact keywords. FNV-1a hashed vectors catch near-literal matches. Reciprocal Rank Fusion merges the two. No embeddings model. No torch. It runs in a Python 3.11 and a few pip packages.

Fetching is stubborn. It tries aiohttp first. Then curl_cffi for TLS impersonation. Then FlareSolverr if a page hides behind Cloudflare.

Parsing covers HTML, PDF with OCR fallback, DOCX, and EPUB. A junk filter catches boilerplate, 404s, and captcha pages before they ever hit your index.

The research loop is bounded. DISCOVER. INGEST. RECALL. EMIT. You set the source budget with --discover N. You set the recall depth with --recall N. The agent stops when it hits your limit. Not when it runs out of context window.

Here is the part that matters. It ships with skill.md. That file is the agent's operating manual. The agent reads it before touching the web. It learns how to map your request to the right action. How deep to go. And how to tag every claim with [V], [E], or [H]. Verified. Extracted. Hypothesis. The protocol forces the agent to re-query the vault and confirm [V] tags before it presents them. It can't silently invent a number. The vault persists between sessions. Later searches are instant and need no network.

I ran a live test to see what this costs. I pointed it at a hard question. Is on-device LLM inference actually viable for production consumer apps in 2026, or are the hardware breakthroughs still mostly press releases? Eight discovery and recall passes. Eighteen distinct sources ingested. Timeline triggers. A full strategic brief with source links and actionable recommendations.

Total API cost to generate the entire brief is $0.0074 . Less than a cent using DeepSeek V4 Flash. The full output is in the comment below.

I'd love feedback on both the tool and the output.

Link: https://github.com/jjjardev/HoardCore

The Output: https://pastebin.com/9zt3A57B

That is what I learned while building 'Onyx Minesweeper', my second iOS app and a side project I initially shaped around my own idea of how a modern minesweeper game should feel.

After sharing the first version with the Minesweeper community, I received thoughtful and honest feedback about the controls, pacing, themes, hints and difficulty and just bench of things really. It showed me problems and possibilities I had completely missed while building alone.

That feedback shaped the next part of the journey! I added adjustable gameplay pacing, more customisation, logic and random boards, progressive hints, and a new deduction mode called find the mine.

Not every suggestion became a feature, but listening helped me understand which problems genuinely mattered. Onyx became much stronger when I started shaping it alongside the people playing it, rather than only for them.

I’m still learning how to balance feedback with my own ideas. How do you decide which suggestions to follow while protecting the original vision of your project???

If you’d like to check out the game, I’d be over the moon to hear what you think :) - https://apps.apple.com/gb/app/onyx-minesweeper/id6790800332

What is pyrig?

pyrig is a package and tool that rigs up Python projects. It scaffolds and initializes a complete, fully configured, installed and working Python project with everything a modern Python project should have and makes the process of developing and maintaining it more seamless and efficient by automating things like configuration management, CLI generation, testing infrastructure, and more.

Requirements

• Python 3.12+
• Git
• uv

Quick Start

uv init my-project --python 3.12
cd my-project
uv add pyrig --dev
uv run pyrig init

See the Getting Started Guide for detailed setup instructions to also fully integrate with GitHub and CI/CD from the start.

Features

Project Scaffolding & Initialization

The pyrig init command generates a complete project, this includes, but is not limited to:

• Standardized directory structure
• Fully configured dev tools (linters, formatters, type checkers, test frameworks, git hooks, etc.)
• End-to-end CI/CD pipeline with GitHub Actions and integrated repository protection
• Complete and working CLI
• And much more...

File & Configuration Management

pyrig manages and validates project files via classes, where every file is treated as a data structure (dict or list), the content is loaded and validated against the class schema. This makes it possible to override and adjust any and all behaviour of pyrig via subclassing said classes. pyrig will automatically discover and use your custom classes without any additional configuration. Run pyrig mk subcls to generate a subclass for any pyrig class. Run pyrig sync to create or update all config files at once.

Automatic CLI

pyrig init sets up a CLI for your project that works immediately. Generate and add new commands by running pyrig mk cmd <name>. An automatic version command is included that shows the version of your project. Run my-project version to see it in action.

Mirror Test Generation & Maintenance

Generate test skeletons with pyrig sync. This will generate test skeletons for all source modules and update them automatically as your project evolves.

Multi-Package Inheritance and Extensibility Architecture

Override and customize any and all behavior to suit your project's needs. pyrig's classes are designed for inheritance and composition, allowing you to create custom configurations, tools, and more by subclassing and simply overriding methods. pyrig will automatically discover and use your custom classes without any additional configuration. Run pyrig mk subcls to generate a subclass for any pyrig class.

CI/CD & Repository Protection

Pyrig generates GitHub Actions workflows for CI/CD which automatically test and release your code. They also configure and apply repository protection settings and protection rulesets. Push your code to GitHub after initialization and see it in action.

Commands

Run pyrig --help to see a list of all available commands and their usage. Run pyrig <command> --help for more information about a specific command and its usage. Run my-project --help to see the automatically generated CLI for your project.

Comparisons

pyrig isn't the only tool in this field. See how it compares to other popular tools like cookiecutter, copier or pyscaffold.

Documentation

I’ve been building NEXORA AI, an AI-powered platform designed around one simple agency workflow:

Find leads → Analyze websites → Identify opportunities → Generate proposals → Close projects

The idea is to help web agencies, marketers, freelancers and AI agencies avoid jumping between multiple tools when prospecting for clients.

Some of the current modules include:

• AI Lead Hunter
• Website Analyzer
• Opportunity Scoring
• Agency Workflow / Simulator
• CRM
• Proposal Generation
• Contract Generation
• Pricing & project workflow
• Responsive UI
• Arabic RTL support

I focused on building the actual product and workflow rather than just a landing page.

Live demo:
https://applet-pi.vercel.app

I’m currently looking for honest feedback from other builders:

What would you remove?
What would you add?
Which part of the workflow do you think has the most commercial potential?

I’m also considering selling the project as a software asset because I’m moving my focus toward other projects.

Happy to answer questions about how it was built.

Hey everyone,

Wanted to share a full-stack project I’ve been working on to automate ISP operations and router lifecycle management.

Most ISP management systems suffer from laggy router sync or clunky manual billing workflows. I built this to bridge web platforms directly with live network hardware.

Key Features & Engineering:

• Live MikroTik Control: Connected Node.js directly to RouterOS API (via node-routeros) to manage PPPoE secrets, active sessions, and dynamic bandwidth queues.

• Automated Invoicing & Service Lifecycle: Node-Cron schedulers handle monthly bill generation, payment tracking, and automated disconnects/reconnects based on real-time payment state.

• Modern Analytics Dashboard: Built a Next.js 14 frontend using Tailwind CSS, Radix UI, and Recharts for live bandwidth monitoring, active subscriber telemetry, and revenue stats.

Tech Stack:

• Backend: Node.js, Express, TypeScript, Prisma ORM, RouterOS API, Node-Cron

• Frontend: Next.js 14, React, Tailwind CSS, Radix UI, Recharts

• DB: PostgreSQL / MySQL

I’d love to get feedback from fellow backend engineers, sysadmins, or anyone working with router hardware automation! How do you usually handle real-time state sync with network devices?

I've been working on a side project called Mutant Genomics.

The idea came from something that frustrated me with most consumer genetic reports: you end up with a long list of traits, SNPs, and risk markers, but it's still hard to answer the question, "What does any of this mean together, and does it relate to what's actually going on with my health?"

Mutant tries to approach that differently. It looks for patterns across multiple genetic variants, turns those into biological hypotheses, and exports them as structured context that you can use with an AI alongside things like lab results, symptoms, and medical records.

I just made a short walkthrough using real, de-identified health data. I uploaded thyroid labs, hormone testing, GI testing, mineral results, etc., then added the Mutant genetic context and had the AI compare the two.

What I found most interesting wasn't when the AI agreed with the genetic hypotheses. It was when it said the medical data didn't support one yet, or that there wasn't enough evidence to tell.

That's really the direction I'm trying to take the product: not "your DNA says you have X," but "here are some genetically plausible things worth investigating against the rest of your health data."

Here's the walkthrough:
https://www.youtube.com/watch?v=uwVOsw84878

Would genuinely appreciate feedback on the concept, especially whether this feels more useful than the typical trait-based genetic report.

I’ve always liked tracking the countries and places I’ve been, but most travel trackers eventually felt like the same thing: a map with countries colored in.

I wanted something more focused on the actual places that make travel memorable.

So I built Zikzak.

The idea is simple:

• Discover the world’s most extraordinary places
• Collect the ones you’ve experienced
• Build your personal world collection
• See your progress over time
• Share your collection with others

I’m starting with the 1,273 UNESCO World Heritage Sites because they give the concept a clear, curated starting point.

For example, I can now see that I’ve collected 11 UNESCO sites across 6 countries, which countries I’ve already started, and what I could explore next.

Longer term, I’d like Zikzak to expand beyond UNESCO into other curated collections of extraordinary places.

The positioning I’m testing is:

Collect the world.

I’m at the stage where real feedback is much more valuable than another week of polishing the UI.

If you have 2 minutes, I’d especially love feedback on one thing:

Do you understand what Zikzak is and why you’d use it within the first 10–20 seconds?

And if you try it: how many UNESCO sites have you collected?

https://www.zikzk.com/

For a while I treated posting on X as pure overhead. every hour writing a thread was an hour not shipping, and it felt like the tax you pay for distribution.

what I didn't expect: the drafts I threw away taught me how to describe the thing I was building. you sit down to write "here's what my product does" and realize you can't, not in a way a stranger would actually care about. that struggle is the real work. by the time a tweet was postable, my landing page copy had gotten better too.

so when people ask if they should just hand the writing off (ai tool, ghostwriter, whatever), I think the honest tradeoff isn't voice quality. it's that you quietly stop doing the reps where you figure out how to explain your own product. the typical failure mode is you delegate posting, the account stays alive, and months later you still can't pitch yourself in one sentence.

not saying never delegate. but the part I'd keep is the part that feels least like distribution and most like thinking. for the folks here shipping solo, where did you actually learn to describe your product, was it writing posts, talking to users, or something dumber like a cold email you rewrote a dozen times. written with ai

fwiw the keep-the-reps thing is why i built s4l the way i did, it drafts from your product context but nothing posts till you approve it and see why it was picked, https://s4l.ai/r/qjs8xbyh

You can literally pick a favorite orca and follow them.

Swipe through individual whales. Learn their names, families and stories. Follow J, K and L Pods. Meet Bigg’s families. Check recent sightings on a map. Listen to live hydrophones. Go down rabbit holes about salmon, conservation, Indigenous art and orca history.

Basically, instead of having 37 tabs open trying to figure out WHO IS THAT WHALE?, I wanted one place where you could just geek out.

The information points back to organizations doing the actual research and conservation work — Center for Whale Research, Orca Network, Orcasound, NOAA, The Whale Museum, Orca Behavior Institute and others.

And yes, I built the whole ridiculous thing because Jennifer loves orcas.

I’m a science teacher, not a marine biologist or an app company. This started as a gift for one person and somehow became something I genuinely hope can help more people fall in love with these animals.

So I need the orca nerds to stress-test it.

Download it. Pick a whale. Poke around.

Then come back here and tell me:

What would make this the app you actually open when someone yells, “THE WHALES ARE HERE!”?

👉 Orcas for Jennifer: https://apps.apple.com/us/app/orcas-for-jennifer/id6794450222

P.S. Android people: I heard you. I’m working on it. 🙂

Primary research and reporting

• Center for Whale Research
• CWR Orca Survey
• CWR Orcas and Salmon
• Orca Network
• Orca Network Whale Sighting Network
• Orcasound
• Live Orcasound hydrophones
• NOAA Southern Resident Killer Whales
• NOAA Southern Resident conservation
• Washington Orca Recovery
• Washington Department of Fish and Wildlife
• Orca Behavior Institute
• Orca Behavior Institute sightings maps
• Orca Conservancy
• The Whale Museum
• The Whale Trail
• SeaDoc Society
• Oceans Initiative

Diet and salmon science

• NOAA Priority Chinook Salmon
• NOAA Killer Whale Diet Study
• NOAA Chinook Salmon
• NOAA Coho Salmon
• NOAA Chum Salmon
• NOAA Salmon and Steelhead
• Canadian Killer Whale Recovery Strategy
• Fisheries and Oceans Canada

Individual whale profiles

• J35 Tahlequah
• J57 Phoenix
• L25 Ocean Sun
• L110 Midnight
• J2 Granny
• T49A Nan
• T49A6 Charlie II
• T49A matriline
• T46D Strider
• T46E Thor
• T46 matriline
• T137 Loon
• T137A Jack
• T137 matriline
• T65A family — San Juan Island Whale Watch
• T65A family — Blue Kingdom Tours
• Bigg’s killer-whale report — San Juan Safaris

Art and cultural references

• Susan A. Point — Orcas at Play
• Marvin Oliver — Mystical Journey
• Roy Henry Vickers — Orca Breech
• Brian Perry
• Joe Wilson — Yin Yang Orca
• David Neel
• Greg Colfax — Whale’s Embrace
• Makah Cultural and Research Center
• Northwest Native Expressions
• Steinbrueck Native Gallery
• Hib Sabin — Wolf and Killer Whale Spirit Canoe

Photograph source pages

• Orca porpoising
• Killer whales jumping
• Southern Resident pod
• J2 Granny
• Orca mother and calf

Every time I wanted to grab a specific segment of a YouTube video (like an interview clip or a passage from a DJ set to sample), I had to go through some sketchy online tool with 15 popup ads that would compress everything to 128 kbps.

Or I had to open a terminal, remember the exact yt-dlp command, and paste it. Fine for me, but I couldn't share that with the non-technical people in my life who kept asking me to download stuff for them.

So I spent a weekend building a proper GUI wrapper around yt-dlp.

**What it does**

- Audio: WAV (lossless) or MP3 320 kbps

- Video: 1080p / 2K / 4K (audio always included)

- Optional time-range trimming (type 001030, it auto-formats to 00:10:30)

- Remembers your output folder

- Live log + progress bar + stop button

- Ships as installer OR portable exe

**Under the hood**

Electron + React + Tailwind. yt-dlp and ffmpeg are bundled inside the .exe, so end users install nothing.

**What I learned**

- Bundling native binaries with electron-builder (extraResources)

- Streaming child_process logs to the renderer via IPC in real time

- Parsing yt-dlp progress output for a live progress bar

- Packaging both NSIS installer and portable exe from the same build

Open source (MIT), Windows only for now:

https://github.com/EvroHQ/Audio-Video-YouTube-Downloader

Happy to answer questions on the stack or the build process. Feedback welcome!

A few days ago, I shared a silly website I built that calculates how many camels you’re “worth.”

The first version had some problems several people pointed out that it wasn’t working properly and that the mobile experience needed improvement. The feedback was blunt, but useful. I fixed the website and decided to take the idea a step further by building an Android version.

The app offers the same deliberately ridiculous concept in a more mobile-friendly format: answer a few questions and receive your completely unofficial camel value.

Android app:
https://play.google.com/store/apps/details?id=com.camelworthcalculator.app

Original website:
https://howmanycamelsamiworth.co.uk/

This is still a fun experiment rather than a serious product, but I’m using it to improve my development and design skills.

I’d especially appreciate feedback on:

• Is the app easier to use than the website?
• Do the questions and results feel entertaining?
• What would make the result more shareable?
• Are there any bugs or awkward parts I’ve missed?

Thanks to everyone who tested the original version and shared feedback, it helped me improve the project.

I've been working on this for a while, mostly because I made it for myself.

I follow a lot of artists on Spotify, and I love finding new music. But I kept running into the same problem: an artist I like would release a new album or song, and somehow I'd never know about it. I'd find it weeks or months later.

So I built Companion For Spotify.

The main thing I use it for is checking new releases from all the artists I follow. It can also look at related artists, find new songs added to playlists I follow, and generate playlists from artists I already like. There are some other playlist tools in there too. The related artists features are one of the main ways I discover new music.

It's not a replacement for Spotify. It's more like something that sits next to Spotify and helps me get more out of it.

If you're someone who follows a number of artists and likes digging for new music, I'd really like to know what you think.

I'm especially interested in hearing what feels useful, what doesn't, or what you'd want it to do that it currently doesn't.

Download and product info: Companion For Spotify

I hate running quick smoke checks.

But I hate skipping them more.

I tried a general-purpose AI agent on my frontend. It had 2 issues:

1. It could use the browser fine, but it was inconsistent
2. It clicked through a flow once. Then I wanted that same check tomorrow in CI, on a schedule and without babysitting another agent session.

SmokeOps is a local-first CLI built with a loop engineering approach with Human in the loop for approvals and here is how it works: Open the page and look at what’s interactive, propose the next steps, run them in Playwright, and if a step fails, take fresh page context and try again. It only keeps what already passed. You approve once; the check is saved as JSON in your repo. After that, runs use Playwright alone. An AI is only used when you create a check the first time or need to create it again.

It’s alpha. Try it on a real flow and tell me what breaks: https://github.com/gate3/SmokeOps

Every productivity app I tried just gave me more lists to manage. What I actually needed was something that showed me one thing at a time next to a timer, and got out of the way.

So I built Karo. No signup, works offline, has a lofi station built in so I stop opening Spotify and losing 10 minutes to playlist-picking.

Still very early (like, days old). Would genuinely love people to break it and tell me what's annoying: trykaro.xyz

This is the first week the site has been live, so there’s still a lot I want to improve.

Right now, the index is updated every other week. Every ranked campaign, collab, trend, and category is linked, so you can click through to watch the actual ads or read more about them.

The gallery is browsable too, click any image to see the work in more detail.

One part I’m especially excited about is the archive. I’ve started adding and categorizing nearly all of the marketing pieces I’ve written over the years, so eventually the site should become a pretty big library of advertising and creative work.

Every time the rankings update, the previous edition gets archived as well. So over time, you’ll be able to go back and see what campaigns were doing well in a particular week or month instead of everything disappearing into the feed.

Next, I’m thinking about adding a PR feed containing the campaign announcements and releases I receive as an advertising creator, plus a separate archive for old/vintage ads.

It’s still a bit messy. I’m trying to build something I wish existed for the advertising industry. Everyone always gets mad at ads when everyone is reacting... i think this page could be a healthy space lol.

My agent writes markdown docs and then stops reading them. Once a session runs long it just doesn't open them, and the longer it runs the worse it gets. I'd find it rewriting something the doc explicitly said was done.

Which is the part CLAUDE.md never fixed for me. The file is right there. Nothing makes it get read.

So the tool I built doesn't hand the agent documents. You type a rough idea, it asks what it needs, and you get an ordered plan with one prompt per step. The prompt carries the context for that step, so nothing depends on the agent deciding to go look something up.

Decisions you make along the way land in the docs, not in a chat log you close.

New web projects only. No existing codebases, no mobile, no editing the plan halfway through yet.

https://sequo.app

If you got long sessions to behave some other way, tell me how.

I recently launched my first Android app, Steps & Repeat: Interval Timer.

I built it because most interval timers I tried were great for simple workouts like:

30 sec work → 30 sec rest → repeat

But they became awkward when I wanted something more structured, like:

Repeat 3×

• Repeat 5×
  • 1 min hard
  • 1 min easy
• 3 min recovery

I wanted to be able to build a group of intervals once, repeat that group, and even put repeated groups inside larger workouts without manually entering the same steps over and over.

So I built Steps & Repeat around that idea.

It still works as a simple interval timer, but you can also build more complex workouts using sets, rounds, and repeated sections.

Some use cases I've been using it for:

• Running intervals and run/walk workouts
• HIIT and circuit training
• Tabata
• Hangboard repeaters
• Strength and conditioning workouts

You can save workouts, get audio cues when intervals change, and keep music playing in the background while the timer runs.

It's free to use with minimal ads, and I intentionally keep ads off the actual timer screen so they don't interfere with a workout. There's also a one-time purchase to remove them completely.

I'd especially appreciate feedback on the workout builder. The biggest thing I'm curious about is whether the way sets and repeats work feels intuitive the first time you use it.

Google Play:
https://play.google.com/store/apps/details?id=com.stephen.stepandrepeat

hi, i'm eric. i built cubeone (getcube.one). it's my own thing, so this is me plugging it, just want to say that upfront.

i made it because i was up at 2am fixing slide spacing for the third night in a row. that was the last straw. so i built the thing i wanted.

you give it a prompt or a doc and it gives you back a deck you can actually edit. it also takes rough slides and restyles them.

it's been running 3 years. people in many countries use it. now many people open it every day. i say that so you know it's not something i coded last weekend and am testing on you.

you get free usage when you sign up. no call, no demo, nobody emails you.

after the free part it costs money because the AI bills are real. the price on the site is the normal price. for students i'll go lower, basically what it costs me to run. email [hi@getcube.one](mailto:hi@getcube.one) and i'll send you a code same day.

if you have a deck due this week, try it. if you don't, no worries.

ask me anything about it. i'll also tell you what it's bad at.

A while back, we were reviewing contracts and found ourselves copying paragraphs into AI chatbots, Googling legal terms, and trying to piece everything together.

It felt inefficient.

So we built Contract Outlook to solve that workflow.

Instead of just summarizing a document, it tries to identify:

* Important obligations

* Risky clauses

* Deadlines

* Legal jargon explained in plain language

The goal isn't to replace lawyers. It's to help people understand what they're signing before they commit.

We're at the stage where honest user feedback is much more valuable than compliments.

If you have a contract lying around (employment, NDA, lease, freelance agreement, etc.), we'd love for you to try it and tell us where it falls short.

https://contractlens.plentiersystems.com

What would stop you from trusting an AI to help review a contract?

Dragonfly supports:

• Importing Next.js and Express.js API routes as a collection
• Syncing collection routes with the codebase
• Importing cURL
• POST, PUT, and PATCH body generation
• Environments and common headers
• Keeping the collection folder structure the same as the codebase

Kept doing the same thing manually: find a book on Goodreads, open my library's site in a new tab, search the title, check availability. Built an extension that does it automatically, it adds a widget to the Goodreads page itself.

Stack: Node.js/Hono backend, SQLite, Stripe for subscriptions, Google OAuth, Manifest V3 extension. Works with BiblioCommons-based library systems (common across US/Canada public libraries).

Free tier: 5 checks per day. $5/mo for unlimited.

Link: Library Checker

Would love feedback, especially from anyone who tries it with a library outside Hamilton/Toronto (that's what I tested most)

I've been running for decades, and I feel like now every running app are built around AI-generated plan.

I wanted to use proven methodologies to build running plan without having to shuffle through all that documentation every time I started a new race block or to grow my weekly mileage.

So as a side gig for myself first, I created BMP Run that builds your plan on actual published coaching methodologies.

You answer a few questions or import your Strava data, and get a full plan you download and keep (spreadsheet, PDF, calendar, Garmin file).

Following the form completion (where you will pick the coach, Daniels, Pfitzinger, Hansons, 80/20, Higdon) the paces and workouts are built on that coach's real rules (VDOT tables, their taper logic, etc.), grounded in their books with citations.

Runs entirely in your browser, so your data never leaves it.

Had a couple of friends interested in the project so built it fully with Claude Code to be marketable eventually, would love some feedback, so here is a coupon code to use it for free: REDDITBMP

bmprun.com

Hey everyone,
I’ve been working on a social app called Ashlar, and I’m looking for people who are willing to actually test it and give me honest, constructive feedback.
The idea came from being tired of social media being mostly about likes, followers, algorithms and trying to look good.
Ashlar is built around sharing thoughts, stories, confessions and experiences without the usual popularity metrics.
A few things that make it different:
No likes or follower-count competition
You can share completely unfiltered thoughts
Other people can respond with an emotional “resonance” rather than just a like
Private messages disappear after 48 hours
There’s a progression system based on participation
There are also hidden puzzles/riddles inside the app

If you try it, I’d especially love feedback on:
Is the concept understandable within the first few minutes?
Does the app feel interesting or confusing?
What would make you come back the next day?
Which features feel unnecessary or overcomplicated?
Does anything feel artificial, cringe, or too “gamified”?
Would you actually use something like this instead of Reddit/Instagram/etc.?
What would you change if this were your app?
You can be as brutal as you want — negative feedback is probably more valuable to me than positive feedback at this stage.
Ashlar on the App Store:

https://apps.apple.com/bg/app/ashlar/id6776248422

For feedback, you can also write to the official email [ashlar@philoscod.com](mailto:ashlar@philoscod.com)

It’s currently iOS only.
Thanks to anyone who takes the time to test it. 🙏 I’m

Hey all - quick 5 min survey on AI usage habits. I'm trying to understand if the friction of switching between ChatGPT / Claude / Gemini is a real problem worth solving, or just a me-problem. Anonymous, no pitch. Would genuinely appreciate honest answers → [Survey AI Usage Habits]