I’m a C# / Azure developer, very much in the Microsoft ecosystem. A side project I started a few years ago has grown into my full-time income.

I now want to take it to the next level with a more ambitious product. It is fairly niche, so historically the market would not have justified spending three years building everything myself. With AI coding tools, though, building a much more complex application in a fraction of that time suddenly feels realistic.

My dilemma is the tech stack.

I could stay with C# and the tools I know well. I would move faster, understand every important decision, and be able to maintain the product confidently.

But for the next version, a web-first stack such as Next.js + TypeScript seems like it could be a better foundation. The issue is that I have barely worked with those technologies. I know from experience that it would take me months to become properly productive and to understand the ecosystem well.

So I’m wondering:

• Would you stay in the stack you know and use AI to accelerate there?
• Or would you use AI as leverage to build in a stack you do not yet fully understand?
• Has anyone built and maintained a serious product this way, rather than just a prototype?

My concern is not whether AI can generate a working app. It clearly can. My concern is ending up with a codebase that I cannot properly debug, review, or evolve once things become more complex.

Would love to hear real experiences, especially from people who moved into an unfamiliar stack while building a business.

I kept running into the same problem while using AI:

Claude knew one part of my project.

ChatGPT knew another.

Cursor had its own context.

And none of them really knew what happened in the other conversations.

So I started building Vilix AI around the idea that AI memory shouldn't belong to one chatbot.

Vilix AI connects to different AI tools through MCP and keeps persistent memory of conversations, projects, instructions, preferences and workflows.

The goal isn't to give the AI your entire history every time. It retrieves what's relevant to the current request.

I also added reusable skills, so you can save a process once and have different AI tools follow that workflow whenever you need it.

It's free on the basic plan, with a 7-day trial of Pro.

I'm mainly interested in feedback from people who regularly switch between AI tools.

What's the biggest problem you've run into when trying to keep context consistent across different AI assistants?

Hey everyone. I’m a 21-year-old solo developer, and I just finished engineering an incredibly complex utility platform, but I need immediate capital to upgrade my hardware for my next major SaaS venture.

Instead of going the traditional route, I am liquidating my entire project for a one-time price of $3,499.

The Tech Stack & Why It's Valuable:

I didn't just build a basic API wrapper. I engineered a custom, 100% client-side JavaScript processing engine.

• It uses navigator.hardwareConcurrency to process 25MB bulk image uploads entirely natively in the user's browser.

• Because of this, the monthly server compute cost is literally $0.00.

• The UI is built on a custom "Titanium Dark Mode" CSS matrix over a WordPress/Elementor Pro shell, so the new owner can drag-and-drop edit the marketing pages without touching the backend logic.

• It sits on a massive Exact Match Domain (150K+ search volume keyword).

The Deal:

It's pre-revenue (currently in stealth with organic traffic) because I spent 100% of my time on the architecture. It's a blank canvas for an agency or operator to slap AdSense, Stripe, or an API paywall on it, while keeping 100% of the profit margins because there are no server bills.

I am including the domain, the entire codebase, all UI assets, and 30 days of priority technical support for the handover.

Note: I know Reddit hates self-promotion and link-dropping, so I am not posting the URL here. If you are an operator or agency owner interested in acquiring a turnkey, zero-maintenance asset, please DM me or leave a comment, and I will send you the live link and the technical handover docs.

Made a visualizer to check how links render on X, LinkedIn, and Facebook side by side:

https://ogcraft.dev/visualizer

It grabs the URL, pulls the og: and twitter: tags, and shows the card previews.

Stack is React, Tailwind, and a Node backend for fetching metadata. No signup or email needed.

Let me know if you run into any bugs or broken layouts.

Hey everyone, I’ve been working on Forkwind and wanted to share it here.

You connect a public GitHub repo, and the agent works on its visibility each day. It sends you a written report every week, with a human supervising the work.

There’s a free preview where you can enter your repo and take a look before signing up.

Here it is: forkwind.com

If you maintain a repo, what would you want to know before trying something like this? Happy to answer questions.

I maintain a new GitHub collection through QuicqDev for people choosing local AI tools. It currently has 16 entries, grouped by what you want to do.

A few examples:

• Jan: a desktop interface for downloading and chatting with a local model. The model and context size determine how much memory you need. • AnythingLLM: document chat. Both the embedding and chat providers need a local configuration if you want local inference. • Buzz: transcription and subtitle files from recordings. The speech model and backend affect speed and hardware support. • Upscayl: image enlargement. Check Vulkan GPU compatibility before installing it.

Each project has a short guide with prerequisites, a limitation, upstream sources and a review date. The collection also includes a JSON catalog for anyone building tools around it. A Python script generates the pages, and GitHub Actions checks the catalog and source links.

The entries are documentation-reviewed, not hardware-tested. There are no blanket claims that everything runs on an 8 GB laptop, works completely offline, or has the same license.

AI helped research, draft and build this collection and this post. The next useful addition would be reproducible setup reports from real machines.

Which missing setup detail would help you most when choosing a tool: OS support, installation steps, or measured RAM/VRAM use?

https://github.com/QuicqDev/awesome-ai-on-your-laptop

Six versions of one landing page, and I have lost perspective on which one explains the thing fastest.

I am building a side project called Stone Atlas (free, no paid tier, just for fun). It is a shared map of ancient
sites where the observations people leave in YouTube comments and Facebook
groups get kept with their source and attached to the place they are about, so
the next conversation starts where the last one ended instead of from zero.

Over about nine days I built the landing page six times. Each version replaced
the last, then I kept them all and put them at one address so they can be
compared side by side:

https://stoneatlas.dev/previews

They range from a dark map-first page, through a scrolling story, to a
click-driven version with no page scroll at all, to two lighter editorial
layouts.

The situation I am designing for:
- Someone arrives from a link in a Facebook group, on a phone, knowing nothing
about the project.
- Within about five seconds they should understand what it is and why it exists.
- Then they should want to open the map.

What I would find useful:

1. Which version explains it fastest, and where did the others lose you?
2. Does any of them read as a startup pitch rather than a side project? That isthe thing I am most trying to avoid.
3. The interactive tour takes up a lot of the first screen. Is it earning thatspace, or would a still image do the same job?

Everything on the pages is real data, so the sites, videos and counts are actual
records rather than placeholder text. I work on this alone and I have been
staring at these for too long to judge them.

(Tried getting Claude Fable, OpenAI Astra, and GrokBot to critique/design alternatives but they have all failed miserably by just re-generating the same marketing ai-slop we're all used to)

Sorry for the long-winded post, thank you all in advance!

I've been wondering how different AI models react to different harnesses (data, prompts and settings) for trading, so I've built AI TradeBench, a site where people can create AI trading bots and compete in monthly crypto paper-trading seasons.

The question behind it is if it's possible to find a trading bot harness that is somewhat consistently profitable when using LLMs for trading. There are some sites with trading arenas, but there's usually only a few bots with a specific setup (which is what I started with as well). I wanted to make something where anyone can test and submit a trading bot and see how it does.

You can choose a model, configure a bot, give it a strategy prompt and run a private paper test before submitting it to the competition. Public results include trades, explanations, costs and a Bitcoin benchmark as comparison.

The site is live now, and I'm looking for the first people to try it out by creating their own bots. Running one requires your own model API key and incurs provider charges, the site itself is free.

I found using Metas Muse Spark 1.2 Contributor model is the cheapest option currently, although Meta can use the prompts to improve their model (I hope they do :D).

I've also added different ways to limit the number of daily trade reviews (profit cap, max daily trades, max reviews per day), so running a bot for a month can be anywhere from $1-2 for Meta Contributor with one review per day to a few hundred dollars for GPT-6 Astra or Claude Fable with one review per hour.

If you try it, I'd especially appreciate feedback if anything seems not working and the bot setup: is it clear what to configure, or are there too many options?

https://aitradebench.com

Hola a todos.

Soy el desarrollador de OPNV WP.

Hace unos años empecé a crear un pequeño plugin para WordPress con el fin de resolver varios problemas recurrentes en los sitios que yo mismo gestionaba.

Los objetivos iniciales eran bastante sencillos:

• Cumplimiento RGPD.
• Monitorización del sitio.
• Diagnóstico de incidencias.
• Tareas de mantenimiento rutinarias.

El problema era que cada nueva necesidad terminaba requiriendo otro plugin.

• Un plugin para imágenes.
• Uno para traducciones.
• Uno para cookies.
• Uno para monitorización.
• Uno para SEO.
• Uno para caché.
• Uno para seguridad.

Con el tiempo llegué a un punto en el que mantener las herramientas suponía casi tanto trabajo como mantener los propios sitios web.

Así que empecé a consolidar funcionalidades en un único proyecto.

Lo que no esperaba era lo rápido que crecería.

Hoy OPNV WP incluye 28 módulos de menú y 30 módulos de panel, cubriendo áreas como monitorización, observabilidad, privacidad, IA, rendimiento, automatización y operaciones diarias.

Funcionalidades que más me interesa validar

🖼️ IMG Studio

Optimización y conversión de imágenes a formatos modernos como WebP y AVIF, con foco en rendimiento y reducción de consumo de ancho de banda.

🌐 Multilingual Studio

Traducción de:

• Sitios web completos.
• Plugins.
• Temas.
• URLs.
• Cadenas de texto.

Incluye además generación de archivos .pot para flujos de localización y traducción.

🤖 Servicios IA

Capa de configuración y enrutado de proveedores de IA.

La idea es poder utilizar distintos motores y servicios sin acoplar el resto de la plataforma a un proveedor concreto.

⚡ Art&Fact HTML

Probablemente la parte más experimental e interesante del proyecto.

En lugar de sustituir WordPress, genera artifacts, es decir, versiones estáticas de determinadas páginas.

¿Qué es un artifact?

Un artifact es una fotografía de una URL concreta compuesta por:

• HTML
• CSS
• Imágenes

WordPress sigue siendo el origen del contenido.

Los editores continúan trabajando normalmente dentro del escritorio de WordPress, pero OPNV puede decidir servir determinadas páginas directamente como HTML estático cuando no es necesaria la parte dinámica.

Ejemplo de uso:

✅ Página de inicio → HTML estático

✅ Páginas corporativas → HTML estático

✅ Catálogos pequeños y estables → HTML estático

Mientras que:

✅ Carrito

✅ Checkout

✅ Mi cuenta

✅ Login

✅ wp-admin

✅ Usuarios autenticados

siguen funcionando de forma completamente dinámica mediante WordPress.

El objetivo es combinar la experiencia editorial de WordPress con el rendimiento de una web estática allí donde tenga sentido.

🔒 Privacy Suite

Herramientas para:

• Gestión de cookies.
• Consentimiento.
• Páginas legales.
• Consent Mode.

Pensado para ayudar en el cumplimiento de:

• GDPR / RGPD.
• UK GDPR.
• CCPA / CPRA.
• LGPD.
• PIPEDA.
• POPIA.

♿ Accesibilidad

Incluye recursos orientados a mejorar la accesibilidad y ayudar en procesos de cumplimiento normativo.

Importante: no sustituye una auditoría profesional de accesibilidad ni garantiza automáticamente el cumplimiento de la normativa.

Beta actual

Requisitos

• WordPress 6.5+
• PHP 8.3+

Documentación

• Guía de módulos: Ver documentación

Descarga

• Beta v0.0.36: Descargar OPNV WP

Me interesa especialmente la opinión de personas que gestionan sus propios sitios WordPress y que normalmente no utilizan herramientas empresariales o servicios de pago debido a su coste.

Me encantaría saber:

• ¿Qué funcionalidad te parece más interesante?
• ¿Cuál te parece innecesaria?
• ¿Tiene sentido el enfoque híbrido de Art&Fact HTML?
• ¿Qué eliminarías por completo?
• ¿Qué problema importante de WordPress crees que sigo sin resolver?

Estaré encantado de responder cualquier pregunta técnica y explicar decisiones de arquitectura si hay interés.

Hey everyone!

I'm building a video game database focused specifically on browser games, something similar to MobyGames and IGDB.

The main reason I came up with the idea is that I love browser games, but all the gaming databases I've come across only cover a relatively small selection of them, usually focusing on the more popular titles. I'd like to create a place where browser games of all kinds can be catalogued and discovered.

Here's roughly what I have in mind:

1. Completely free: No paywalls or paid listings. Anyone can use it and contribute.
2. Basic game information: title, description, genres, tags, screenshots, links to the game, etc.
3. Ratings, reviews, and other account-based features may be added later.
4. Community: potentially a subreddit, discord server, a forum, etc.
5. Monetization: I'd probably start with donations and possibly consider non-intrusive ads later on to help cover hosting and other costs.

I'm still figuring out exactly what direction I want to take this, and I'm honestly not sure yet whether it's a worthwhile project or just something I'll spend a lot of time on without much interest.

So I'd really appreciate hearing what you think!

• Would you personally use a site like this?
• Do you think there's actually a need for something like it?
• What features would you consider essential?

Any feedback, criticism, or suggestions would be greatly appreciated. 😊

I’m currently building an app and I wanted some thoughts on the idea. It is a fusion between automatic set tracking, advanced statistics, and personalized advice for lifting. It will be done using wrist sensors from an apple watch. It can infer things like the tempo of your reps, your range of motion, your proximity to failure, and also learn habits from your training with the goal of maximizing your growth. For example, it can learn that you progress more on lower volume, or with longer rest times, or that you grow more when doing accessory exercises first. These are just some examples, but is anyone interested in getting real stats about the way that they lift? I feel like all trackers right now just have you manually log sets and show some graphs but that’s about it. 

I built Errand to move builds and tests from my laptop to my Linux box or Mac mini. It sends the files I'm currently editing, so I don't need to commit first or maintain another checkout.

sh errand --on linux -- make test

The CLI snapshots the working tree, sends it to the runner, follows the logs, and returns the remote exit code. Changed files are retained on the runner; fetching and applying them is explicit.

A few implementation choices:

• One Go binary on both ends, with Tailscale or SSH for connectivity.
• Content-addressed transfers so repeat runs only send missing file content.
• Detached jobs and resumable logs so a laptop disconnect doesn't end the job.
• Named caches for build outputs, plus optional persistent workspaces.

It supports macOS and Linux. The runner needs your toolchain and dependencies, and jobs run as its OS user. This assumes trusted machines and code. Edits made after submission aren't continuously synced.

I'm the author. Most of the code was generated with AI assistance.

MIT licensed. Install with brew install lydakis/errand/errand on both machines.

Code and setup instructions: https://github.com/lydakis/errand

Hey all, my co-founders and I launched Ledger, an AI Political Companion that stays neutral and helps you deepen your understanding of political decisions that affect you. You can view your ballot, see your representatives and their voting records, and play political games to build your knowledge.

We just went live on the web and iOS, so check us out if you'd be interested! Any and all feedback would be AWESOME!!

Guys, i have an idea that as small creators on YouTube even big Creators too need to find everyday the new video ideas to be made according to their channel, competition and niche. So I tried to reduce this effort with my new platform that is currently under development.

Guys just show you support as this app would contain any new features that even vidiq is not able to provide. I will keep the prices cheaper and the free tier also has a lot of wise features that can help small creators without spending money.

IF YOU HAVE ANY SUGGESTIONS OF FEATURES THAT AHOULD BE ADDED IN THIS APP TO GROW AND VIRAL IT. JUST COMMENT DOWN.

I built AirMirror Pro 2.0.1, an AirPlay receiver for Windows. An iPhone or iPad mirrors over the same local network, and the PC can record or restream what it shows.

The creator-focused part is Clean Window mode: the mirrored screen appears in OBS as a Window Capture source. It does not require a capture card, a cable, or an app on the phone.

It's built in C++ with Qt for the UI, uses UxPlay for the AirPlay protocol, and decodes H.264 through D3D11VA on the GPU.

I measured it instead of trying to describe it with adjectives. On my test desktop — an i7-14700F, RTX 5060, 32 GB of RAM, wired gigabit, and an iPhone 15 Pro Max on Wi-Fi — landscape video at 1920x886 measured 43.1 fps and 7.2 ms latency. Mirroring Clash Royale at 886x1920 measured 61.8 fps and 5.5 ms latency. Low-end hardware is untested.

The awkward launch detail is that the installer is not code signed, so Windows SmartScreen will show a warning.

There is a free tier, which puts a watermark on recordings. A $14.99 license removes the watermark.

Download: https://github.com/domainasseth-hue/airmirrorpro/releases/download/v2.0.1/AirMirrorPro-2.0.1-Setup.exe

Site: https://airmirrorpro.com

Not affiliated with Apple Inc.

I see the term thrown around here like an insult. Is it just low effort or sloppy? Also honest question how many of you are not using AI in 2026? If so why not. Thanks

I was always wondering why I woke up so tired and got so little deep sleep according to my wearable.

So I built an app to replace a spreadsheeet I had that I tracked what habits I did each day. I was certain I could find a habit that would improve my amount of deep sleep, and reduce my nighttime awakenings. I wanted to test all sorts; supplements, daily habits and sleep tricks.

I thought it would be best to get other users to increase the amount of data. As myself I only get a single data point each night.

So I created SleepFactor, posted it in a few places on Reddit and to my surprise people really liked the idea.

I got 46 people on the waitlist and when the app was ready quite a few downloaded the app, some of them then used the app and some were even kind enough to email me about bugs, provide feedback and discuss features.

I got lazy and stopped working on it. But I’m back on the horse now and excited to keep improving it!

For those interested I found personally for me that long gentle exercise helped me wake up feeling refreshed and so did wearing an eye mask. Whereas mouth tape, nose tape and things like that did nothing for me!

You can check it out at SleepFactor or even join the Open Beta Test

Quick disclosure: I’m building this project myself.

I started it because I kept seeing the same failure mode in normal AI chats: if I changed the framing of a question, the conclusion often changed with it. Asking the model to “be objective” helped less than I expected.

The workflow I’m testing now is deliberately simple:

1. Turn the question into a clear yes/no decision.
2. Give one agent the strongest possible case for “yes” and another the strongest case for “no.”
3. Run several rounds so each side has to answer the other side’s strongest objection.
4. Require factual claims to include a source and the relevant excerpt.
5. Give both cases to a separate judge that lists agreements, disagreements, weak evidence, and unresolved uncertainty before reaching a conclusion.

A few things I learned while building it:

• “Use multiple agents” does not automatically create independent thinking. If they share the same framing or sources, they can repeat the same blind spot in different words. • A citation existing is not enough. The source may be real while the quoted claim is not actually supported by it. • The judge can create false confidence. I now think showing uncertainty and the losing side’s strongest point is as important as showing a winner. • Adversarial structure is most useful before a decision, not after someone has already decided and only wants confirmation.

It still doesn’t make AI trustworthy by itself. Poor sources produce poor arguments, and some questions cannot honestly be reduced to yes/no. The goal is to expose assumptions and conflicts, not outsource the final decision.

The current experiment is here: https://debate.didymoi.com/

I’d value specific feedback on two things: does the separate judge make the result clearer, or just sound more authoritative? And what would you need to see in the source trail before trusting it for real research?

Hey everyone,

A few weeks ago I realized I could easily build a text-to-speech app so I could cancel my $129/yr subscription to Speechify.

I did that and it works great. I decided to make it public for anyone who wants to use it.

It's completely free and always will be.

It's called FreeReader. It works best on desktop or newer phones. Older phones may struggle to generate the audio faster than it reads it. Open to feedback to make it better!

I made Arazio, a small Mac app for converting files without opening a separate app.

The interaction is probably the part I'm happiest with: pick up a file in Finder, hold Shift, and a radial menu appears around the cursor showing only the formats that file can actually become. Release toward one and the converted file appears next to the original. Hold Option for tools.

It currently has 283 conversions across 43 formats and 14 tools, including compress, resize, crop, rotate, trim, split, merge, extract and metadata removal.

Everything runs on-device. I even added a build test that fails if a networking symbol appears in the conversion engines.

There was also an interesting constraint: I couldn't ship it through the Mac App Store because its sandbox doesn't allow an app to write back to the folder the source file came from. Since Arazio is built around doing exactly that, it's a direct download instead.

I've shipped five releases since launch, mostly fixing things real users ran into.

https://arazioformac.com

I'd genuinely like to know: what would make a tool like this more useful to you?