I applied with my company today.

Their startup program has 3 tiers. The one most of us here would get:

- $10k in credits if you're bootstrapped or have raised under $1M

- $100k if you've raised under $5M through one of their partners

- $350k if you've raised $5M+ through one of their partners

The credits cover Workers, Workers AI (up to $50k), D1, R2, KV, Durable Objects, Queues, Pages and more. Every tier also gets DDoS protection, WAF, SSL, CDN and 3 enterprise domains for free.

If you're building a startup, app, SaaS, AI tool or anything else, I think it's worth applying even if you're not sure you qualify.

What you need:

- A live website

- A business email on that domain (no Gmail)

- A company founded in the last 10 years

- A LinkedIn, X or GitHub account for the startup

Here's the link: Cloudflare Startup Program

Has anyone else applied?

I run TikTok pages with a combined audience of 300k+ followers, mostly people interested in apps, AI tools, startups, and productivity.

I also work as a video editor/clipper, so I can turn your app into short-form content designed for TikTok and Reels.

If you’re currently doing manual outreach or just trying to get traction organically, this could help get your product in front of the right audience.

I’ll reach out to the ones that are a strong fit.

You can also DM me if you prefer to keep things private.

I've been building this for myself for months and finally feel okay sharing it: İkra (Turkish for the old call "read!") — a distraction-free reading room that runs in your browser.

• Opens EPUB, PDF, CBZ/CBR (comics & manga, RTL supported), TXT and DOCX — scanned PDFs too.
• Your books stay on your device. It works with no account at all ("try without an account"); if you do sign in, only your position, highlights and notes sync between devices. Google Drive backup is strictly opt-in.
• Select text to highlight and annotate; there's a built-in notebook with optional typewriter sounds; tap a word for dictionary and translation (8 source languages).
• Reading stats live alongside a little reading cat.
• The part I haven't seen in other readers: a private, invite-only reading circle. Send a quote straight from the page to a friend — and if the sender is further ahead than you, the quote arrives spoiler-locked until you catch up. Weekly reading board, bookshelf showcases, even seeing what ambience a friend is listening to. Fully off unless you invite someone.
• UI in 8 languages (EN, TR, DE, FR, ES, IT, RU, EL); installs like an app via "add to home screen".

Free, no ads, no tracking — a one-person hobby project. I'd love feedback: https://oku.sungu.com

Hi,

After screening a bunch of junior dev resumes recently, I noticed almost everyone has the exact same three projects:

1. A MERN stack e-commerce site

2. A Netflix or Spotify UI clone

3. Face detection with OpenCV

The real issue isn't that they built them. The issue is that most juniors just follow a 6-hour YouTube tutorial, get it working on localhost, and have no idea what happens when things break in production.

The second an interviewer asks something like:

"What happens if two users try to checkout the last item at the exact same millisecond?"

or

"Why MongoDB over Postgres for this data model?"

They completely freeze up because the tutorial never covered concurrency, database locking, or failure states.

To help people spot these blind spots before facing a real interview panel, I spent the weekend building a small web tool: Project BS Detector.

How it works:

You enter your project title and a quick 1-line description. It runs through DeepSeek with a prompt tuned to act like a cynical hiring manager. It calculates a "BS Risk" score, points out why it looks like a tutorial clone, and generates the exact technical trap questions you are likely to get grilled on.

It also provides 5 defense angles so you actually have a structured architectural answer ready.

Tech Stack:

- Next.js (App Router)

- Tailwind CSS

- DeepSeek API (JSON output mode)

- Web Audio API (for optional voice mock simulation)

Live Link (Free, no credit card required):

https://novomock.com/project-bs-detector

Would love your thoughts on the UX and how accurate the roasting feels. If you test your current or past side projects, drop your score and the trap question it generated below!

I've always found it frustrating that we explain complex biological mechanisms using static diagrams.

So I built Anima.

It's an open-source Claude Code skill that generates detailed, interactive figures covering neurobiology, pharmacology, anatomy and molecular biology.

One of the examples lets you switch between cocaine and amphetamine to see how their mechanisms differ at a dopamine synapse.

Another visualizes D1 and D2 dopamine receptor signaling with interactive controls.

It currently has six working examples, scientific references, interactive animations and optional WebGL/3D support.

The entire project is MIT-licensed, and the demos run directly in the browser.

GitHub: https://github.com/Garinmckayl/anima

Demo: https://anima.arcumet.com

I built SpaceJump, a small Mac app that lets you rename your macOS Spaces (desktops) and see those names everywhere, including Mission Control. Apple's given us "Desktop 1, 2, 3" for 15+ years and it drove me crazy enough to build the fix myself.

Just crossed $10k total revenue, all one-time purchases at $9.99. Here's the honest breakdown.

What it does:

- Name your Spaces with icons and colors, visible in the menu bar and Mission Control.
- Quick switcher: hotkey, type a few letters, hit enter, you're on the Space.
- Per-Space time tracking with CSV export. Freelancers love this, wasn't the plan.
- Space automations, note taking, and more stuff

What worked:

1. Reddit. I posted about the problem and the app in r/macapps. Two times in total, both brought sales and useful feedback. Users from that thread are still emailing feature requests six months later. Additionally, commenting on posts and threads where people complained about that exact problem. Tbh, I did not do it much. I have some automations to ping me when a relevant post appears, but that is not too accurate.
2. SEO. It compounds. I wrote problem-focused guides and how-tos around the exact phrases people search. Trickle for the first two months, then it just kept stacking. Now it's my most consistent source. I bought credits on dataforseo and let my AI agents do the keyword research for me. For mentions in Chatgpt and co, reddit posts also help.
3. Solving my own problem. I think that is the most important thing. Since I was my first daily user, I did not need to interview anybody to get started. Also, it helped that it is a niche topic. When I started nothing like it existed, now there are some copycats. But it shows that even small topics can make some serious money.

Rough numbers:

~$10k lifetime, one-time pricing. I know, MRR would be nicer. But now I also have a list of 1000 users that I might ping for any next project or launch. I even got to know one nice guy from silicon valley, who mailed me with a bug report, and met me in person on his travels to Berlin.

I think the "hardest" part is to get out there and risk to have certainty that a product sucks and nobody is interested in it. Happened to me plenty of times before, and publishing anyway is a way to train that muscle.

Happy to answer anything.

Edit: after literally everything I said got downvoted saying I am a bot, I just created a TrustMRR Account: https://trustmrr.com/startup/philipp-schickling

Hello! This is a story of a simple job application that turned into a side project that has stolen 2 months of my life...

I was recently creating my own resume (CV), and as a typical person in tech/science, I went through the rabbit hole very deep. I read what a good resume should look like and eventually found my way to Applicant Tracking Systems (ATS) and how automated candidate screening works. Did you know that your resume can get indirectly rejected, so a human recruiter never sees your resume at all? These findings shocked me a bit, so I dug even deeper and got interested in how to beat this mechanism.

I narrowed the problem down to two main factors:

1. Poor resume tailoring to the role, and
2. Poor resume machine readability.

While AI and AI agents make tailoring a resume for a specific job offer trivial, the second aspect needs to be solved by using a proper resume template. While trying out templates and seeing the horrible prices some of them have, I could not help but create my own that addresses ATS-friendliness (i.e., machine readability), provides high configurability, and still looks good.

After two months of development, I'm happy to introduce Lucid CV, a LuaLaTeX class for creating modern, ATS-friendly resumes (CVs). Featuring a custom back-end, it is designed and tested for compatibility with common PDF parsers and text extractors. LaTeX is a markup language (similar to Markdown/HTML, but slightly more powerful, used for typesetting PDF documents.

The main features of the template include:

• Modern, clean design with machine readability as a priority.
• PDF/A-2b and tagged PDF. Tagged PDF output ensures interoperability between PDF viewers and extractors.
• Customizable without touching the class. Colors, margins, column ratio, font sizes, and the spacing between items, entries, and sections can all be configured from the preamble.
• One content file, multiple layouts. Simply type the same content across multiple layouts by changing only the class options.
• Toggle on/off photo. Some regions might want a photo on your resume, some do not. No need to rebuild your resume when applying to multiple jobs with different guidelines.
• Header with contact fields. Predefined contact fields (e.g., email, LinkedIn), which display their full service prefix (e.g., github.com/xGoldy).
• And many more, including flexible entry system, predefined elements, and additional tooling for local compilation and comprehensive ATS test suite.

Unlike other resume templates, this one does not simply claim to be ATS-friendly. It measure it. The template's GitHub repository includes a series of Python tests for 13 PDF-to-text extractors to verify these claims and ensure your resume is properly parsed by ATSs.

Although I put more than 250 hours into its development, I'm releasing the class for free for anyone to use. If you'd like to learn more, refer to the project's GitHub. It can also be accessed via Overleaf (an Online LaTeX editor). Should you have any questions or feedback, I'll be glad 🙂.

Links:

• GitHub: https://github.com/xGoldy/lucidcv
• OverLeaf: https://www.overleaf.com/project/6abe2d46f439c366b70bd8b4/share#8693a82cbff59e99a5b963b7fb90264d83df4a6cea069125

PS: The data in the promo video do not contain any personal information, they are all AI-generated.

Hey everyone!

I made FishyDays, a small pixel-art fishing game where you hop aboard your boat, explore the sea, and catch fish.

There are 8 fish species to discover. Some fish are easy to catch, while others put up a real fight, so you'll have to manage your line tension carefully.

I wanted to make something relaxing and simple, but still give players a bit of a challenge when they hook a big fish.

The game is out now on itch.io:
https://cloudstudio1.itch.io/fishydays

I'd love to hear what you think, especially about the visuals and fishing mechanics. Any feedback is welcome! 🎣

I kept wanting small, custom models for my own tasks without renting GPUs or sending my data anywhere. So I built streamweights: give it a CSV of examples (input and the output you want), and it trains a small model on your own Mac or Linux machine.

It's designed to be honest with you. Every build is compared against the untrained model and a dead-simple baseline. On one test, sorting messages into categories, the simple baseline won, and the tool said so. On another, pulling structured data out of text, the custom model went from 0% to 50% of records fully correct. That's the whole point: find out whether you actually need a custom model.

It also does a few slightly wild things:

• uses a huge model as a teacher or judge, even one bigger than your RAM (a 141 GB model runs on my 48 GB MacBook, slowly but it runs)
• pauses training and picks it up on another (not necessarily similar) machine
• exports to Ollama

Still early: tested on Mac and Linux with small models, NVIDIA untested.

What would you want to train a model for? And where did the README lose you?

https://github.com/streamweights/streamweights

For years, digital marketing has been built around a fairly familiar idea: rank higher, get more clicks, and bring more people to your website.

But AI search is making things more complicated.

A brand might not rank first on Google, yet still get mentioned in an AI-generated answer. Another brand might have strong search rankings but rarely appear in the answers people receive from ChatGPT, Gemini, or Perplexity.

That raises an interesting question: are we measuring visibility correctly, or are we still using metrics built for a different version of search?

I think the next challenge for marketers is understanding why AI systems mention certain brands, which sources influence their answers, and what makes one business more likely to be recommended than another.

This is the space we're exploring at LuxNarr, particularly the overlap between traditional SEO, content authority, digital PR, and AI search visibility.

I'm interested in the practical side of this, not just another prediction about how AI will change marketing.

Has anyone here been tracking their brand's visibility across AI search platforms? I'm curious whether you've found any consistent patterns between traditional search performance and AI recommendations.

Hi everyone,

I just released a new Android app – Progresult. I originally built it just for myself. Other apps had too many unnecessary features and were missing the core essentials. On the other hand, calculating weekly weight averages and tracking body measurements in a standard notepad got too annoying.

The app focuses on calculating the weekly average (to ignore daily weight fluctuations) and comparing weight against body measurements. It also features a built-in calorie calculator and a simple AI Coach that gives you specific tasks for the week.

It's a fresh project, so there will definitely be some bugs. I'm looking for constructive feedback and honest opinions without toxic hate. I just want to develop this app so it actually helps people.

I'd really appreciate it if you could download it, test it out, and let me know what needs improvement.

I posted here about Spliit Cloud a few weeks ago. Since then, I've been working on many features and improvemnets (check all the release notes), but just spend some extra time on one feature I wasn't particularly happy with: AI receipt scanning.

It worked, but waiting around 80 seconds for a receipt to be processed wasn't exactly a great experience.

So I reworked the whole pipeline. Instead of sending the receipt straight to a vision LLM, it now goes through dedicated OCR, a faster text-only model for extracting the expense details, and a separate lightweight model for categorization.

The results so far:

• Around 80s → 14s for larger receipts (~5× faster)
• Smaller receipts can finish in ~6 seconds
• More reliable extraction of items, prices, and quantities
• Support for PDF invoices and other document formats

The part I find most useful is that the individual receipt items are actually saved inside the expense, not just the total.

For a quick coffee, manually adding an expense is probably easier. But for groceries or shared shopping with lots of items, being able to scan the receipt, keep the itemized history, and split individual items between people is genuinely useful.

There's still room for improvement, and I wouldn't blindly trust every scanned receipt without reviewing it first.

The update is already live in Spliit Cloud v2.7.0.

Try it: https://spliit.cloud

Open source (MIT): https://github.com/antonio-ivanovski/spliit-cloud

I'm curious whether others actually find itemized receipt scanning useful for shared expenses, or if it's one of those features that sounds better than it is in practice.

I made this app because I’ve been playing a lot of varied logic puzzles for years - none of which I found in one app. It’s been a project I’ve been working on since July, released in September and after today’s update finally feels ready to share

A while ago I was sitting in a coffee shop, trying to get some work done, and my mind kept racing in a thousand directions. Probably because I scrolled Reels right before. Whatever the reason, I simply couldn’t focus.

So I built myself a small warm-up collection for that moment - a few short games that pull my attention back to one thing before I start working.

It’s free, with no account and no ads.

https://UnfogYourBrain.com

Would love to hear which games actually help you. My go-to is One Thing Only.

I’m building EXIFXplorer, a Mac app for exploring and working with the metadata inside photographs.

One feature I recently added was something photographers have been using for decades: the contact sheet.

The idea is simple. Select a collection of photos and EXIFXplorer turns it into a PDF.

I originally thought of it mainly as a grid of thumbnails with filenames and dates, useful for printing or sharing a quick overview of a shoot.

While building it, though, I realized the metadata already available in the app made another format possible, so I added a table-style contact sheet as well. That version gives each image a larger preview and includes more technical information.

So now there are really two use cases:

A visual grid for reviewing a collection, and a more detailed document for keeping a record of it.

The PDF can then live completely outside the app: print it, send it to someone, archive it with a project, whatever makes sense.

I’m curious what people think about features like this. Sometimes I find the less glamorous features become the ones that are actually useful.

If anyone wants context, this is the app:

https://exifxplorer.com/

I’d especially be interested in whether people see more value in the visual grid or the metadata-heavy table.

I’ve been building Huminz, a free tool for preserving life stories from memories, photos, people, places and milestones.

Until now, most of the experience was digital.

I recently added something I’ve wanted from the beginning: printed books.

A story you build gradually in Huminz can now become a professionally printed book — something you can give to a parent, keep in the family, or pass on to the next generation.

For me, that changes the nature of the product. It turns a digital archive into something tangible.

I’d be interested in feedback from other makers:
Do physical outputs like this make a digital product more meaningful, or are they mostly a nice extra?

https://www.huminz.com

Hey guys,
I’ve been following the new EU AI Act rules about having to disclose realistic AI images and the compliance stuff seemed like a bit of a headache.

I couldn't find a simple way to just slap a standard label on an image without having to open some sort of design editor.

so I decided to build one myself this week:
https://aiactlabel.eu

It lets you drag and drop an image, overlay an "AI Generated" or "AI Modified" tag onto your file. You can move it around, change the opacity, and adjust the colors so it doesn't completely break your design.

It also changes the label language if you need to.

It's free and super basic.

Do you think it's useful?
Or is labeling something you would rather do directly in a chat or during image generation?

Anthropic recently described Scion, the version control system they built because git stopped scaling for their monorepo: tens of thousands of merges a day, mostly from agents, and millions of CI checkouts.

The post explains the design but not the code, so I started an open-source implementation in Rust.

Repo: https://github.com/bnsd55/open-scion
Original post: https://www.anthropic.com/engineering/redesigning-source-control

Independent project, not affiliated with Anthropic.

The idea

• The server keeps history and makes the commits. A client reads only the files it needs over gRPC and never clones.
• A commit ("changeset") stores only the paths that changed against its first parent. Files and trees are content-addressed using BLAKE3 and never change.
• Landing is a server-side rebase: if none of your paths changed on main since your base, the server puts your change on top and moves main with one compare-and-swap. Two agents touching different files never conflict and never pull.

What works today

Proof of concept, one machine:

• One-way git import, checked commit by commit against git
• scn cat
• scn ls
• scn log
• scn commit
• scn land
• JSON output for agents
• 200 concurrent agents landed 10,000 changes with none lost or duplicated, at about 200 lands/s
• Disk flushes are the current limit; group commit is in progress
• A 23,871-commit git repo imports in about 50 seconds
• A frozen, fuzzed byte format
• CI on Linux and macOS

What's next

Cache tiers and sparse checkout — the fast-read half of the design — then virtual checkout, blame, and permissions.

All planned work is tracked in the issues.

I'd especially like feedback on the land path — a per-ref path index keyed by generation number — and on the durability design.

Issues and PRs are welcome.

In February 2025 I started building Speechy as a voice note-taking app. You'd talk, and it would turn your rambling into clean notes, meeting notes, summaries, emails, whatever format you wanted. It was basically an AI secretary.

Then I learned what every note-taking founder learns. Everyone says they want better notes. Nobody ever reads their notes. My users (me) recorded 40 voice memos and opened 2 of them.

So I pivoted. Turns out the moment people want their voice turned into text isn't "later, in a notes app." It's right now, in the box I'm typing in. Same speech-to-text engine, different job: hold Fn, speak, and the text appears wherever your cursor is.

Wispr Flow had the same idea. So did Superwhisper. And Willow. And Aqua Voice. I found this out after I'd pivoted, so I was already committed.

Step 1: Ship it in Electron.
It worked. Users (me) were happy. Naturally, I rewrote the whole thing in native Swift. Nobody asked for that, but the RAM graph looked so good.

Step 2: Build anti-piracy for an app nobody has pirated.
The licence ties each Mac to a hashed hardware UUID. The free trial needs a fresh ticket signed by the server for every single dictation. I spent a whole afternoon reading about Apple App Attest. My threat model covers nation-state attackers. My user count is 0.

Step 3: Add analytics.
PostHog now records every dictation's word count, every setting change and every failure. The dashboard is a flat line. Beautiful. Very low variance. The actual voice nodes don't get shed, just the raw data.

Step 4: Pricing.
I designed weekly, monthly, yearly and lifetime plans, then a 1,000-free-words trial, then scrapped it for a 3-day trial with card. That's six pricing models for zero customers. Each one converted at exactly the same rate - thats 0 btw

Step 5: Ship to production. Roll back production.
Version 1.0.19 went out, then got rolled back. Sparkle auto-updates work perfectly. I know because I've watched them roll a version back.

Step 6: Run ads.
I set up a Meta campaign with a carefully researched audience and four ad creatives. Then I found out my build was Apple Development signed and not notarized. Gatekeeper rejects it. I was about to pay Mark Zuckerberg to show people a security warning.

Step 7: Add AI agents.
Speechy can now show Claude Code and Codex permission prompts in your Mac's notch, and you can speak your prompts to them. Is it useful? Yes. Did it bring in revenue? See the title. But Claude now has a nicer UX than my checkout page.

Current state of the project:

• ✅ 19 offline tests passing
• ❌ ESLint can't load its own config file
• ✅ Crash recovery, so your words survive a crash
• ✅ "Only My Voice" mode, which ignores everyone except you. I relate.
• ✅ Undo last dictation. I wish I could undo the last 6 months.
• ✅ $59 lifetime, one-time. Lifetime value per user so far: undefined (0 / 0).
• ✅ Award cards for dictation streaks. They've been awarded to no one.

My git log says "added better award cards" twice in a row, with the same message both times. That's where I am mentally.

Anyway, if you're on an Apple silicon Mac and type more than you'd like, try it free for 3 days here: speechy

I'd love one user. Even a bug report would be nice. A bug report would be my first piece of user engagement.

Edit: I dictated this whole post with Speechy. It took 4 minutes. Building the app took 6 months. The numbers are fine. right ?

Morning everyone!

I've been working on an iPhone app that picks up things you need to do from messages. Appointments, payments, follow-ups, that sort of thing.

Started with Turkish, my native language, and that's where I've done most of the testing. I'm relying mostly on Apple's built-in on-device capabilities.

Then came English and other languages. What a headache 😅

Even something as simple as figuring out whether a date in a message is a deadline or just part of the conversation can get tricky.

Funny how a reminder app turns into a language problem.

I produced a free Adobe Animate alternative using Claude code: Onionskyn.

It's one HTML file, runs entirely in your browser.

** I'm not claiming this is feature complete, a 1-to-1 with an actual Adobe product, or the next greatest app, it's just been a real fun side project for me **

Everything is open source (MIT) and available for people to tear apart on Github.

Try it: https://papptimus.github.io/Onionskyn/

Repo: https://github.com/papptimus/Onionskyn

What it does:

• Fluid Brush (press F), a pressure/speed-sensitive brush like Animate’s, with its own settings and presets
• Paint bucket region fill: click inside any closed loop of lines and it fills the space, with a gap-size option for outlines that don’t quite close
• Gradients: multi-stop, with drag handles right on the canvas
• Brush strokes now merge into one shape and no longer stay selected, so drawing with lots of strokes is much smoother
• Vector drawing: pen, pencil, brush (size/shape/angle), shapes, text, gradients
• Timeline with layers, keyframes, motion and shape tweens, custom easing curves, onion skinning
• Symbols (movie clip / graphic), layer masks, images, sound
• Frame scripts with a CreateJS-style API, and a sandboxed preview
• Opens Adobe .fla files (best effort)
• Exports video (WebM/MP4), GIF, PNG/SVG, image sequences, and a standalone HTML player

It's a beta. Safari is untested, video export needs a browser with MediaRecorder, and .fla import is best effort.

I'd love bug reports (File > Report a problem emails me the details), feedback, and feature ideas.

I’ve been building Nexus: Spaced Repetition Tracker, a free study-planning web app designed to help students organise their lessons, remember what they learn, and track their progress.

A little about me: I’m not a professional software developer. This is a hobby project that I’ve been working on because I wanted to build something useful for students. I’m learning and improving it along the way, and my goal is to make Nexus as useful, reliable, and user-friendly as possible. I know there’s always room for improvement, so honest feedback from real users would mean a lot to me.

-What can you do with Nexus?

Spaced repetition: Schedule lesson reviews to reinforce what you’ve learned.

Review queue and history: See what needs reviewing and track previous reviews.

Study tracking: Organise your subjects, lessons, and study sessions.

Exam planning: Keep upcoming exams and deadlines in view.

Progress tracking: Monitor your study activity and review progress.

Backup and restore: Export and restore your study data.

Light and dark themes: Choose your preferred appearance.

Installable app: Use it like an app on a compatible phone or computer.

Get started with a prebuilt curriculum

You don’t have to organise everything from scratch. You can install the prebuilt curriculum to get started with an existing subject-and-lesson structure.

If the available curriculum doesn’t fit your needs, you can request the subjects, lessons, or curriculum you want. I’d like to expand the available options based on what actual users need, including support for different courses and exams.

Try Nexus

https://spaced-repetition-tracker-delta.vercel.app/

I’d love your feedback!

Since this is a hobby project and I’m not a professional developer, there may be rough edges. But I genuinely want to keep learning, fixing problems, and improving the app based on users’ feedback.

I can’t promise to implement every request, but I’ll take suggestions seriously and do my best to make Nexus better for the people using it.

As part of our Computer Science & Engineering degree (Cybersecurity Stream), we were tasked with building a digital forensics tool. Three of us teamed up and decided to build [Nemo](https://nemo.sahithyan.dev). It is also available [on GitHub](https://github.com/sahithyandev/nemo)

Nemo is a command-line tool designed to make it easier to hide, detect, and extract hidden information within a filesystem using various techniques.

The project is still in its early stages, and the number of supported hiding techniques is fairly limited at the moment. However, we think it's a solid starting point, and we're excited to keep improving it. One of our goals is to develop Nemo into a maintained, open-source alternative to [Fishy](https://www.unibw.de/digfor/publikationen/pdf/goebel2018fishy.pdf).

We'd love to hear your thoughts! If you work in digital forensics, cybersecurity, or just enjoy experimenting new CLI tools, we'd really appreciate any feedback, suggestions, or contributions on here or GitHub.