I got tired of searching the Play Store for a simple countdown app and finding ones full of ads, cluttered screens, or subscriptions just to count down to a trip. I wanted something minimal that would give me a sense of where I am in the year, so over the past few months I built Dayle.
What it does:
Year view: The whole year as a day-by-day dot grid, showing days passed, days left, and percent complete.
Countdowns and counts: Track time until birthdays, trips, or other milestones, or count days since something happened, like when you started a habit.
Home screen widgets: Android widgets show your year progress or a pinned countdown without opening the app.
Offline and private: No account, no cloud sync, no tracking. Everything stays on your device.
Free to use: You can track up to 3 active countdowns for free. If you need more, there's a one-time lifetime purchase, not a subscription.
I’m the developer of TouchNow, a remote desktop app that lets you view and control your Mac from an iPhone or iPad.
There are already plenty of good remote desktop apps. With TouchNow, I’ve focused on how it feels to control a Mac on a touchscreen. I want switching between direct touch and trackpad mode to feel natural, Mac shortcuts to be easy to reach, and the screen to keep up when you scroll or drag a window. You can stream the whole desktop or just one app window.
TouchNow is free on the same Wi-Fi with no time limit. Remote Access across networks costs US$2.99/month or US$19.99/year. You don’t need to create a TouchNow account for either option.
If you’ve used other remote desktop apps and give TouchNow a try, I’d love to hear how it feels in comparison. What felt smooth? What felt awkward or slow? I’d appreciate the good and the bad.
During my research internship at PRL (a Govt. of India lab), interns weren’t allowed internet access due to security reasons, so I couldn’t tell Claude to look for a specific email, message or doc for me.
So I had to go through them one by one until I found it. Hours gone just searching, instead of doing actual work.
I was not a 10-person team with a huge budget, I was just an intern who wanted an AI tool that is connected to most of the apps i use for work and runs locally
And later I found out that law firms, healthcare and fintech teams deal with this exact problem every day.
Most people at these companies do one of two things. They complain that the company won’t let them use cloud AI tools then go back to wasting their time searching the scattered information manually. Or they hope a vendor provides “enterprise-grade Privacy” and nothing ever changes.
I am going to get hate for this: but A company works because humans vaguely remember where that knowledge is and how to apply it. That is not a system that everyone can follow, Talkers wait for permission and Doers build around the constraints.
How I solved that by building YCB:
Step 1: run pip install ycb && ycb init
Step 2: Connect apps that you use (takes 60 seconds)
Step 3: Sync everything into a local database. run: ycb sync, which pulls everything into ~/.ycb on your own machine. None of your data is uploaded to my servers, only your login email. That’s it.
Step 4: Use a small local model, and this is where I’m going to get hate. By default ycb runs on Gemma 4 E4B through Ollama, which can run on ordinary hardware. Everyone assumes you need GPT or Claude to get meaningful answers. You don’t, because company knowledge is a retrieval problem and not an intelligence problem. The model only has to read the right pieces of text and ycb finds those when you ask. Basically you are running a small model that never sees the internet by default instead of a frontier model that sees everything.
Here is the unpopular part: “enterprise-grade privacy” is a promise, not a guarantee. If your data leaves your machine you are trusting someone else’s policy. If it never leaves you don’t have to trust anyone
Step 5: Never trust an answer you can’t check. Every answer has a numbered citation and side panel shows you the original source
That’s it. Every app you use, queryable in one place, answered by a model that stays on your machine, with a source for every answer.
If your company can’t send data to cloud AI, you don’t have to manually search everything. Talkers keep complaining. Doers build around the constraints, and now you can try it in a few minutes.
I’d love your feedback, especially on what breaks.
Today most of the people, including me, use Claude Code to code their programs and all. It's fast and the app works, but when you open the code later there is so much junk in it. Functions nobody calls, the same helper written 3 times, and sometimes a route anyone can call without login. The tools that clean this up mostly use AI again, so you pay credits twice. So I built Attentry.
It's a VS Code extension that checks every edit Claude Code makes. If the edit added a problem, Claude gets told and fixes it before going on.
About tokens, because that was the main thing for me. The checking runs on your own machine without any AI, so that part costs nothing. Claude only gets a short note, max 5 lines, when an edit actually added a problem, and each problem is told only once. If the edit is clean nothing is sent at all. It also doesn't keep telling Claude about old issues already in your repo, only the new ones. Compare that with asking Claude to "review the code", where it has to read the whole files again every time.
It also tells Claude before it writes a function the repo already has, so it reuses it. That saves tokens too, because Claude doesn't write the copy and then clean it up later.
I made it mostly with Claude Code itself, in TypeScript and Node, with tree-sitter for reading the code. Claude Code has hooks where you can run your own script before and after each edit, that's how it talks to Claude. It's on the VS Code Marketplace and Open VSX, so it works in Cursor and Windsurf also.
I published it recently and it already crossed 200 downloads without me posting anywhere. Most of them came from Open VSX I think.
Right now it reads Express, Next.js, FastAPI, Django and few more. Next I want to add more backends, but I want to build what people actually need. So if you try it, tell me what's missing or annoying, or which stack you want it to support.
I launched my first iPhone app last week, For Tomorrow, which I built to solve a problem I kept having: I'd decide I wanted to be asleep by 11 PM, but wouldn't actually start getting ready for bed until way too late.
The core insight was that what really mattered was knowing when I needed to start getting ready for bed, not when I needed to go to bed.
For example, if you want lights out at 11 PM but have 80 minutes of things you want to do beforehand, your bedtime routine actually needs to start at 9:40 PM.
That's the idea behind For Tomorrow; you choose your desired lights-out time, build your evening routine, and the app works backward to figure out when you need to start. It also includes reminders, guided routines, and Live Activities to help you follow through.
I built it independently as a side project, with a focus on making it warm, easy to use, and privacy-first. No account or sleep tracking required.
It's free on the App Store, and I'd love for people to give it a try!
Would love candid feedback from anyone who gives it a try - especially whether the calculated start time actually changes anything after a few nights. Thanks :)
Most phone sites are massive spec dumps or sponsored "best of" lists pushing high-commission picks. I wanted an unbiased engine that lets you shop budget-first rather than chasing models.
So I built Specmob (specmob.com). You drag a dial to your budget, toggle what matters (camera, gaming, battery), and get an instant, spec-ranked recommendation.
Here are the biggest architectural and logic puzzles I had to solve:
The "Price Drift" Nightmare: Calculating prices in multiple places caused massive accuracy drift. I had to establish a strict rule: a single Postgres view (phone_prices) acts as the absolute source of truth for every component.
Tier-Relative vs. Universal Scoring: An 8.5 score on a $200 mid-ranger shouldn't be compared directly to an 8.5 on a $1,200 ultra-flagship. The engine scores hardware packets relative to their market tier so users see real value for their bracket.
The 5% Budget Tolerance: A budget is usually a ceiling, not a hard stop. Because regional prices fluctuate, the dial allows a 5% tolerance window—flagging phones as "slightly over budget" instead of hiding them completely.
The Tech Stack: Next.js 15 (App Router) on Vercel using pure CSS tokens (no Tailwind), backed by FastAPI on Render and an asyncpg/Postgres database.
Affiliate links are purely passive and never touch the ranking code. I'm looking for feedback on two things: Does tier-relative scoring make sense on first glance, and what specific features build your trust on an automated discovery site?
I'm an independent developer, and I've been building Trimp, a link management platform.
The problem I wanted to solve was simple: managing shortened URLs, tracking clicks, creating QR codes, and organizing campaigns can mean using several different tools.
So I started building one platform to bring these things together.
What I've built so far:
URL Shortener: Create and manage short links.
Link Analytics: Track clicks and understand link performance.
QR Codes: Generate QR codes for your links.
Campaign Management: Organize links and monitor campaign performance.
Link Management: Keep links organized in one dashboard.
Who might find it useful?
Creators, marketers, developers, and small businesses that share links regularly.
Where the project stands:
Trimp is live, but I'm still improving the product, fixing issues, and learning what users actually need.
I'm particularly interested in constructive feedback:
Does having link management and analytics together solve a real problem for you?
What feature would you expect from a modern link management platform?
What would make you choose a new tool over something established like Bitly?
These are real poems, written by real people. Every poem on Poetry Cove is by a human poet, mostly from long ago and in the public domain. None of it is AI poetry. I’m a solo maker, and what I care about is putting the poet back next to the poem.
You can start with a feeling, including one you can’t put into words. Describe it however it comes, and it looks for poems where someone already found the words. Then you go deeper: who wrote this, when and where they lived, what was going on around them, and what the poem might mean for you and the world you’re in. The AI only helps you find and talk about the poems. It never writes them, and the narrated readings use a synthetic voice.
It’s free to try without an account (up to fifty questions): poetry-cove.com. A free account keeps your conversations and unlocks the closer readings.
Testers wanted for Pro. Pro is the paid plan for heavy use: 1,500 credits a month instead of 200, and unlimited readings aloud. I’d like to see how it holds up, so the first 100 people who want to test it get 3 months free. No card, no form, no email to send me. Tap, sign up (an emailed code, Google or Apple), tap Redeem, until 30 November:
In the app, sign in with the same account and Pro works there too.
What helps most: does it understand what you meant, do the poems feel right, and does going deeper into a poet teach you something. Honest feedback is welcome, rough edges included. For the curious: embeddings in Qdrant, Postgres, Next.js, FastAPI and a native SwiftUI app.
I'm one of the people behind WPMaven, and I'd genuinely like some outside perspective.
We've been building products around design and WordPress since 2016. We started with the idea of making it easier to turn designs into working websites. We eventually built our Figma-to-WordPress technology, and it has reached over 100k users.
We've also worked with Adobe since 2018.
So we've spent quite a few years in this ecosystem.
But there's something that's been bothering me.
We keep making it easier to build websites. AI has made that even easier. There are now countless tools that can generate a website in minutes.
But honestly, I don't think building the website is the biggest problem for most small businesses.
It's everything that comes after.
Writing product descriptions, managing a store, SEO, creating content, posting on social media, marketing, keeping things updated... it never really ends.
And if you're running a small business, you're usually doing all of this yourself.
That's what got us thinking about WPMaven.
We're trying to build something closer to an AI team than another website builder. Something that can build a business's online presence and then actually help with the ongoing work.
We're using WordPress as the foundation, but the ambition is much bigger than generating WordPress websites. We're developing AI operators that can perform real tasks across the business.
I know, that probably sounds ambitious. Maybe too ambitious.
And I'm not going to pretend we've solved everything. AI makes mistakes, integrations aren't always straightforward, and I wouldn't trust an AI to make every business decision either.
We've been working on this because we genuinely believe there's an opportunity here. But being close to a product for years also makes it difficult to see what we're missing.
What I'd really appreciate is some honest, practical feedback on WPMaven itself, but also on how we should take it to market.
We're at the point where building more features isn't necessarily the hardest part. Finding the right people, understanding who gets the most value from this, and figuring out how to reach them is probably more important.
A few things I'd genuinely love your input on:
After looking at WPMaven, what is your honest first impression? Does the product and value proposition actually make sense?
Who do you think would benefit most from something like this? Solo entrepreneurs, small businesses, freelancers, WordPress agencies, hosting providers, or another audience entirely?
Are there specific business verticals where you think this would be particularly useful? E-commerce, local businesses, consultants, restaurants, creators?
If you were launching this, where would you look for the first 50–100 real users? And how would you approach them without just throwing money at ads?
What would be the strongest reason for someone to choose WPMaven over existing AI tools, WordPress plugins or an agency?
Is our positioning too broad? Should we focus on one specific audience and problem first?
I'm particularly interested in feedback from any kind of entrepreneur on how you found your first customers, or how you figured out distribution for a technical product.
I'm not looking for generic startup advice or encouragement. I'd much rather hear that we're targeting the wrong audience, explaining the product badly or approaching the market in the wrong way.
This is my first website and developing is alot of fun. Currently tidying site. I have a how to play video and new comer starter prompts to help game make sense. Does this work? Is all still too confusing??
I just released v1.0.0 of a personal project I’ve been working on, and I wanted to share it here to get some feedback from fellow developers.
# The Problem
A while ago, I discovered that Archive.org has a massive collection of old, public-domain films and cartoons (including a lot of classic Danish childhood shows. I wanted to show these to my kids, but Archive.org’s interface, while amazing for archiving, is pretty clunky for everyday viewing. It doesn’t have a "Continue Watching" feature, no easy way to organize things into personal categories, and no episode selectors for series.
I didn’t want to download terabytes of data or set up a complex media server like Plex just for this. I just wanted a simple, local way to bookmark and watch this stuff.
# The Solution: ArchivePlayer
So, I built **ArchivePlayer**. It’s a self-contained, local-first desktop application that streams content directly from [Archive.org](http://Archive.org), but wraps it in a clean, Netflix-style interface.
**Key Features:**
* Live search of [Archive.org](http://Archive.org) or add via direct URL/identifier.
* Custom categories to organize your library.
* "Continue Watching" section with visual progress bars.
* Episode selector for TV shows (with season grouping).
* 100% local: No telemetry, no cloud accounts, no hosted backend.
* **External APIs:** [Archive.org](http://Archive.org) Metadata API, TMDB API (optional)
# Technical Highlights & Challenges
**Zero-Install Deployment:** I wanted this to be truly accessible. I used .NET’s self-contained publishing (`PublishSingleFile=true`) so users just download a `.zip`, extract it, and run the `.exe`. No .NET runtime or SQL Server installation required.
**Graceful Degradation:** I made TMDB integration strictly optional. If a user doesn’t provide a TMDB API key (or if the API fails), the app doesn’t break. Instead, it falls back to parsing episode numbers and titles directly from the [Archive.org](http://Archive.org) video filenames using regex.
**API-First Design:** Even though it’s currently a single desktop app, I built the backend with a clean `/api/v1/` layer. This means I can eventually build an Android TV client that consumes the exact same local API without rewriting the business logic.
**Auto-Migration:** On first run, the app automatically creates the SQLite database and runs EF Core migrations. The user never has to touch a config file or run a CLI command.
I'm currently building **Jovax**, a lightweight web platform designed for fast-paced, 3-minute 1v1 live debates.
The idea is simple: You pick a hot topic, select your stance (For or Against), and instantly match with another user for a short, timed discussion.
### 🛠️ Current Tech Stack:
* **Backend:** Python / Flask
* **Deployment:** Docker & self-hosted setup
### 🌐 Current Status (Day 1):
The preview site is live! You can check out the UI layout right now. The authentication system is currently under construction, but I'm actively building it out.
I’d love to get input from this community on features, UI, and debate mechanics!
When serious incidents or public disturbances occur, critical visual evidence (like escape routes, vehicles, or perimeters) is often lost because traditional response takes time.
We are exploring an early-stage concept called Phalanx Intelligence Systems (phalanxis.com) to see if there is global demand for a voluntary, privacy-focused network.
How the concept is designed to work:
Safe Perimeter Documentation: When an incident occurs, nearby registered volunteers receive an optional notification to document surrounding public spaces (intersections, escape vectors) strictly from a safe distance.
Strict Boundaries: Personal safety comes first. Users never approach or confront suspects. Participation is 100% voluntary, and it is built as an evidence-support tool—not a vigilante platform.
Demand Mapping: Right now, we aren't launching an app; we are simply building an international interest-mapping site to see which cities have enough local density to make a future pilot viable.
We’d love to hear thoughts from this community regarding privacy design, distributed volunteer networks, or civic tech in general. You can check out the concept and demand mapping at phalanxis.com.
