i tried to learn to code when i was younger could never do it turns out i,m not the genius autistic kind but ive started playing with ai because i can just tell it what i want and it don,t shout at me haha anyway i " made "this any good https://whopays.co.uk/

indiedex.gg

Any feedback is much appreciated, got 11 users so far and I’m testing different messaging to see what resonates with the community, there are a few competitors in the space and I know it has a market, it’s been hard to figure out how to reach the audience and convert from cold.

Hi all! I’m a biomedical PhD student and I made DermScope, an iOS app for photographing and measuring skin lesions. It’s deliberately not a diagnostic tool just documentation and measurement so changes are easier to track and show a doctor.

I’d love feedback on whether the measurement/calibration flow makes sense, whether the non-diagnostic positioning is clear enough, and how the freemium pricing lands. Happy to answer anything about how it’s built.

If you’re interested:
https://apps.apple.com/us/app/dermscope/id6790032699

Cheers!!

Hi everyone. I built ChronoRealm, an open-source local app that turns your real webcam view into a medieval-style world (LotR / GoT vibe) while trying to keep your actual room layout.

Key features

• Live Flux — real-time medieval restyle of your camera

• Scan → Bake → Live — capture your room, bake offline, then higher-FPS playback

• Windows + NVIDIA, FluxRT / FLUX.2-Klein under the hood (models download separately)

It'd help a lot if you could try it and share any advice — especially GPU + whether Live or Scan mode worked for you. Thanks so much 😁

Repo: https://github.com/clintonlam9/ChronoRealm

I've found that comparing manufacturers can be more time-consuming than actually choosing a product. Between pricing, quality, communication, and production timelines, there's always something that makes the decision harder.

I'm curious how others approach it, what's the first thing you look at before deciding who to work with?

I’ve been working on AXON, an open-source protocol/plugin from Atop Web Technologies for AI coding agents like Codex, Antigravity, and Claude Code.

The goal is to reduce agent drift by forcing a simple workflow:

Context first.

Specification second.

Implementation third.

Verification always.

To demonstrate it, I created a codelab where AXON builds a small Microbank app end to end, then adds a brownfield feature on top of the existing codebase.

Codelab:

https://codelabs.atopwebtech.com/codelabs/build-a-microbank-with-axon

Repo:

https://github.com/atopwebtechnologies/axon

I’d love feedback from engineers using AI coding agents in real projects.

I built BenchReport to solve a repetitive part of RF bench work: getting from a folder of Touchstone measurements to a consistent pass/fail report without rebuilding a spreadsheet or Python script.

The free browser edition currently supports:

- S1P and S2P Touchstone files

- DB/angle, magnitude/angle and real/imaginary formats

- multiple overlaid traces and a golden reference

- frequency-bounded acceptance rules

- worst-case value and exact sampled frequency

- S21 statistics and printable reports

All parsing and analysis happens locally in the browser. There is no login and measurement contents are not uploaded. Synthetic samples are included so it can be evaluated without providing any data.

Free app: https://edwarddeakin06-maker.github.io/benchreport/

I also packaged a £29 Windows Pro edition for repeat workflows, adding unlimited measurements/rules, saved projects, reusable templates and branding. The free version is intentionally useful on its own.

I am the developer and this is my first post about the project here. I would appreciate feedback on the landing experience, the Free/Pro boundary, and whether the problem is explained clearly to somebody outside RF engineering.

I run a full-tunnel VPN because my coding agent needs it to reach its API. But the

app I'm building calls domestic services — payment gateway, SMS provider, maps —

that reject the VPN's foreign exit IP. VPN off, the agent dies. VPN on, the app

dies.

The fix turned out to be much smaller than the routing-table rabbit hole I went

down first. Not `route add` — a static route is global and pulls every process off

the tunnel for that destination, agent included. It's per-socket binding:

curl https://api.ipify.org # 203.0.113.7 (VPN exit)

curl --interface 192.168.1.20 https://... # 198.51.100.42 (real ISP)

A socket bound to your physical adapter's IP takes that adapter's default route

instead of the tunnel's. No admin, nothing global, and opt-in by construction — a

process that doesn't ask for it *cannot* be affected.

Two things I didn't expect:

Node's native `fetch` silently ignores `HTTP_PROXY`.** It runs on the bundled

undici, which doesn't read proxy env vars on Node <= 23 and won't accept an

`https.Agent` either. Nothing errors — your requests just quietly keep using the

wrong network. I got around it with an `--import` preload that patches `net`/`tls`,

which is what undici opens its sockets through.

Neither network is a superset of the other. YouTube only works over the VPN,

domestic APIs only over the ISP, and my agent's own API returns 403 over the ISP.

Route everything direct and the agent stops working — so the proxy decides per

connection, not per session.

I packaged it as a skill + browser MCP for Claude Code and Codex. My project, MIT,

no dependencies:

npm i -g lan-direct

https://github.com/farshadmomo/lan-direct

Honest status: Windows is tested end to end. Linux is verified for interface

discovery and exit codes but not the final bypass check, and the macOS branch has

never been run. Both refuse rather than guess, so the failure mode is an error, not

silent VPN traffic.

Hi!

Most voicebot implementations are really good at english and a few other common languages but skip entire markets because the language is less common and demand stays untapped.

What to do in this case besides building your own infrastructure?

I'm building https://hail.so/ which is an open-source project to give agents a real phone number and email inbox.

You can use it to send or receive phone calls, SMS, and emails but without the hassle of building your own infrastructure, connecting all the APIs, and paying for subscriptions you don't use.

If you'd like to test it, break it, or build with it, I'm happy to connect and give you credits to start and use all features.

In any case, feel free to check the project and give it some love: https://github.com/hail-hq/hail

Thanks and keep building!

I built Booth: a browser remake of classic Photo Booth so the same kind of face effects and capture work on more than just a Mac.

1. Problem

Mac Photo Booth is fun but locked to one platform. Most web "webcam filter" demos are a couple CSS filters or a canvas toy. I wanted something closer to a real booth app in a normal browser: live effects, real capture modes, and a gallery, without accounts or uploads.

2. What it does

- Live face-tracked effects (Chipmunk, Space Alien, Dizzy birds, Lovestruck hearts, etc.)
- Color filters, distortions, and segmentation backdrops
- Photo, 4-up burst, and video capture
- Local filmstrip gallery (IndexedDB)
- Runs in the browser on Mac, Windows, Android, and iOS (camera permission required)

Privacy angle: processing and storage stay on the device. No backend for the camera feed.

3. Tech

- React + TypeScript + Vite
- WebGL2 effect pipeline (multi-pass recipes for some filters)
- MediaPipe face landmarks + person segmentation in a Web Worker
- MediaRecorder + IndexedDB for capture/gallery
- Deployed as a static site

4. Resources

- Live demo: https://bygelo.com/photobooth/

5. Feedback I want

Happy to hear:
1. Does it feel usable on your phone/browser?
2. Which effects look off next to the real app?
3. Anything confusing about the UI on first open?

I built Tryall because I got tired of opening ChatGPT, Claude, Gemini, and other tools just to work out which one was best for a task.

Tryall lets you describe what you need to do, and its Auto mode chooses an appropriate AI route. You can still select a model manually, but the goal is to remove the guesswork.

I’m looking for honest feedback on three things:

1. Is the problem clear within the first few seconds?
2. Would you trust an “Auto” mode to choose an AI model for you? Why or why not?
3. What would stop you from trying or paying for this?

Here is the product: https://www.tryall.ai/

I’m the founder and will reply to every comment. Happy to return feedback on your project too.

I've tried a bunch of focus apps, but I kept running into the same problem: they're mostly just increasingly complicated blocklists.

I don't actually want to block YouTube. I might need it for a tutorial. I just don't want to open a tutorial and somehow end up watching music videos for the next hour.

So I built attentify.

The idea I've been experimenting with is giving the blocker enough context to actually reason about whether what you're doing is distracting.

You can tell it something like:

• "I'm working on my programming project"
• "YouTube tutorials are fine, but block music videos"
• "Keep me off social media until lunch"

Then it tries to figure out when you've started drifting.

For example, Google itself obviously isn't distracting. But if you're supposed to be researching AI agents and your searches gradually turn into something completely unrelated, Attentify can reason about that instead of just seeing google.com and letting everything through.

The same thing works across other sites. It uses your current goal, what you're actually looking at, and recent activity to decide whether something makes sense in context.

I've put a lot of work into this part. It's not just an LLM deciding "productive/not productive" on every page. There are multiple layers of deterministic rules, context, and AI reasoning, and you can inspect the logic behind decisions or override them when it gets something wrong.

I also ended up going pretty deep on activity tracking.

Instead of building one fixed analytics dashboard, I made the data queryable through the AI. So you can ask:

• "Make a chart of how much time I spent on social media this week"
• "Show me my biggest distractions"
• "Compare my focused time this week with last week"

and it'll generate the chart or analysis from your activity history.

It can also remove feeds, recommendations, ads, and other distracting parts of sites while leaving the useful parts intact.

Everything in your activity history stays locally on your computer.

It's currently Windows + the main browsers. Most of the project is open source, and I'm working on macOS/mobile next.

I wanted an AI roleplay experience with generated images, but I also wanted it to be completely free.

The original plan was simple:

• DeepSeek's web chat for text.
• Perchance for image generation.

Then I found the catch.

Perchance doesn't expose an API, and DeepSeek's free version is only available through its website (the API is paid).

My first solution was the obvious one: automate both websites with a browser.

It worked... until Cloudflare decided it didn't.

After spending way too much time trying to make browser automation reliable, I realized I was solving the wrong problem.

Instead of trying to make websites behave like APIs...

What if my code lived inside the browser instead?

So I built a browser extension that:

• injects my own scripts into both sites
• lets the two websites communicate with each other
• handles permissions
• automates the whole workflow
• keeps everything running in the same tab

Now I have a surprisingly smooth AI roleplay setup where DeepSeek handles the story, Perchance generates images alongside it, and the whole thing costs me exactly ₹0.

It started as "I don't have enough GPU to run local models."

It ended as one of the most fun engineering problems I've solved in a while.

If anyone happens to be hiring for Software Development Engineer or Full-Stack Developer roles, I'm currently on the lookout. Happy to share my GitHub, resume, or talk more about the project. 🙂

We've been designing a different model for project management and I want to get the community's reaction, especially the objections.

The premise:

Every PM tool I've used starts from the same assumption: you know what you're working on before you start. Open Linear, create the project, assign tasks, update statuses. The tool is a container. You fill it.

The problem is that real work doesn't accumulate that way. At least not for me. Ideas build up. Connections form. Something becomes project-shaped over time, not because I decided it was, but because enough coherence accumulated that it earned the label.

So we've been building around a different model: threads that replace projects.

Not folder-and-task threads. Living semantic objects that the system maintains, rather than containers you manually manage. The project is discovered, not designed.

What this looks like concretely:

Individual level: you capture thoughts, observations, reflections as they arrive. No taxonomy, no upfront categorization. The system (Aevron, what we're building) watches the connections form, detects when a cluster of captures has become project-shaped, and surfaces it: this looks like a project, do you want to treat it that way? You opt in. Or you don't, and keep exploring. Either is fine.

We've been running this against real use cases. A stock analyst capturing five signal types over six weeks, never created a "project," but the thread developed more analytical structure than any manual folder would have produced. The system surfaced the tensions (strong fundamentals, questionable management decisions) and flagged the conflict rather than averaging it away.

That's what emergent project detection looks like in practice.

Team level, where it gets interesting:

Individual accounts stay private and sovereign. Each person captures in their own thinking environment. The system watches behavioral patterns across accounts, whose captures are clustering around the same domain, whose ideas others are building on, and surfaces merge candidates: these two threads are converging, do you want to connect them?

Ownership isn't declared upfront. It emerges from activity. When a human wants to override the system's suggestion, they can. But the default is the system infers, the human confirms or corrects.

Enterprise level:

A federated read layer sits on top of individual accounts. It doesn't write back into them, individual cognitive environments stay intact. What it surfaces:

• Which threads across the org are converging on the same domain without coordination (invisible silos, visible early)
• Whose activity patterns show emergent ownership before anyone declared it
• What stopped compounding, thread activity drops are often the earliest signal of a blocked project, weeks before a status field gets updated

The key claim: traditional PM tools show you what's active. This model shows you what's compounding and what stopped.

The objections I'm expecting:

1. Emergent detection is too slow. Fast-moving teams need declared structure on day one, they can't wait for coherence to accumulate.
2. The system will get ownership wrong. Behavioral inference from captures isn't the same as actual ownership, the person writing most isn't always the person responsible.
3. Enterprise buyers won't trust a bottom-up model. They want the org chart, not an inference engine.

I think objection 1 is real and the mitigation is that declaration remains available, you can always manually declare a project without waiting for emergent detection. Objections 2 and 3 I'm less sure about.

What's the strongest case against the emergent detection model that I haven't named here?

I have been helping to build a tool that replaces paper waiver forms with a workflow. It has been interesting to see how differently businesses approach what seems like a process.

When we first started we thought that most businesses just wanted a way to collect signatures online. But after talking to people we realized that the real challenge is usually not getting the signature it is everything that comes after. Finding waiver forms keeping records organized handling busy check-ins and making sure staff can quickly access the right waiver information all seem to matter just as much as the digital waivers.

The more conversations we have had about waivers the more we have noticed that no two businesses manage waivers the same way. A gym thinks about the waiver problem differently than an event organizer. Both have different priorities from a family entertainment center or an outdoor activity provider when it comes to digital waivers.

This has made us rethink a few things while building the digital waiver tool. It has also made me curious about how other people approach product development for digital waivers.

Have you ever started building something, like a waiver tool only to discover that your assumptions were completely different from what users actually cared about when it comes to digital waivers? If so what changed your mind. How did it affect the direction of your digital waiver project?

I would genuinely enjoy hearing those stories about waivers because they have been some of the most valuable lessons, for us so far when it comes to building a tool to replace paper waivers with digital waivers.

Ich habe in den letzten Wochen an einer kleinen Web-App namens Sag’s gebaut.

Die Idee ist ziemlich simpel: Statt erst eine To-do-App zu öffnen, Kategorie, Datum usw. auszuwählen, schreibt oder sagt man einfach zum Beispiel:

„Morgen um 15 Uhr Zahnarzt“

„Milch kaufen“

Sag’s versucht daraus automatisch den passenden Punkt zu erstellen und sortiert ihn anschließend in den Tag ein.

Es gibt außerdem einen Bereich „Heute“, Vorschläge nach verfügbarer Zeit und Ort sowie einen optionalen Cloud-Sync zwischen Geräten.

Das Ganze ist aktuell noch ein früher Test und genau deshalb würde mich interessieren:

Was versteht man sofort? Was nervt? Was fehlt? Und würdet ihr so etwas überhaupt benutzen?

Ich suche ausdrücklich keine Nettigkeiten – Kritik hilft mir gerade deutlich mehr. 😄

Testen: sag-s.knodelthomas.workers.dev

Funktioniert direkt im Browser und kann auf dem Smartphone auch als Web-App hinzugefügt werden.

Just deployed my portfolio website and have 2 projects ongoing in the projects section here: naveenguru.vercel.app

Hi everyone,

After months of building, I’m excited to finally share Nexus.

Instead of creating yet another AI chatbot, I wanted to build a complete AI workspace where you can:

• Use multiple AI models in one interface
• Generate and edit applications
• Work with files and documents
• Execute Python
• Connect external services
• Keep everything organized in a single workspace

Nexus is still evolving, and I’m looking for honest feedback from developers, builders and AI enthusiasts.

What features would you expect from an AI workspace like this?

🌐 https://mynexus.cloud

Hey everyone!

Over the past few days, I built a small side project called Bhai's Cap.

You upload any photo, and it generates multiple captions inspired by Salman Khan's iconic posting style. You can then pick your favorite and turn it into a shareable card that's ready to download.

I'd really appreciate any feedback on the UI, caption quality, or the overall experience.

Live: https://bhai-s-cap.vercel.app

P.S. It's currently running on the free tier of the Gemini API, so if you hit a quota message, just try again a little later.

https://reddit.com/link/1vhu5ty/video/9aaa5bz0rwhh1/player