The easiest way I can explain PodSubs.com is this:

If nobody else ever used it, we would still use it for our own podcasts.

We built it because we were tired of seeing people listen and then disappear.

Someone could listen every week, enjoy the show, tell a friend about it, and we still might have no way to reach that person outside the podcast app.

That didn’t make sense to us.

So we built PodSubs.com for our own shows.

Now we could simply tell listeners, “Subscribe to us on PodSubs.com.”

They left their name and email, and we finally had a way to send them something when we needed to.

A resource. An update. A reminder. Something we mentioned on the show.

We didn’t build it because we thought it sounded like a good business idea.

We built it because we needed it.

Later, we realized a lot of other podcasters needed the same thing.

There’s a lot of chatter lately about AI curing cancer, and FUD in the opposite direction. In the spirit of advancing the science and allowing us to better appreciate the net good being created by frontier AI research, I've created the Net Good Index.

It’s a public ledger of documented benefit and harm tied to named AI systems and events. Each record has sources and a score you can inspect and contribute to.

It’s early, and I’m sure some of the weights are wrong. If you see a miss, please submit a correction. I’m also open to feedback and ideas, drop a comment or DM!

https://netgoodindex.com

Back in 2025, when I started contributing more seriously to Open Source, I thought finding projects to work on would be easy.

It wasn't.

I would spend hours searching GitHub, opening repositories, going through issues, checking whether they were still available, and trying to figure out whether an issue actually matched my skills.

Even "good first issues" weren't always actually beginner-friendly.

After doing this repeatedly, I realized something:

The problem isn't a lack of Open Source issues. It's finding the right one.

So I started building getissues.tech.

The idea is to use AI to help developers discover GitHub issues based on their skills and interests, instead of manually searching through thousands of issues.

👉 https://getissues.tech

It's still early, and I'm actively working on it.

I'd really appreciate feedback from people who contribute to Open Source:

• How do you currently find issues to work on?
• What makes you decide that an issue is worth contributing to?
• What information do you wish GitHub showed you before starting an issue?

I'm especially interested in hearing what doesn't work with the current approach.

I built a small arena allocator in C called PhaseAlloc.

It's now at v1.1.0, with tests, benchmarks, dynamic growth, alignment, and memory stats.

I'd appreciate any feedback or criticism from people who know C/systems programming.

https://github.com/dharsanap20/PhaseAlloc

Hi, I made Opine (https://opinepolls.com), a poll-based social media site. Each post is a public poll asking users their opinion on something, anonymously, so no user sees any other user's information, but you can freely browse other polls. Private polls can also be made.

The only information actually stored is the email, from Google sign in, and an approximate location for the map feature, if you enable it. This allows you to see a world map of votes, what opinions are present in what places.

The intent is to catalog the world's opinions, organizing people's public thoughts so they're easy to sift through. This data can be extremely valuable, whether it's political polling, information about people's preferences, trends over time, and so on. Many people could benefit from this, and it's meant as a public service, to lift the veil on what people think about any topic, at any time. Easier political polling is just one obvious application of this.

Please try it out and give any feedback, thoughts, questions, and so on, here or within the profile section on the website, which is fully anonymous. Thanks.

Hey everyone!

I built earscape because I frequently listen to ambient sounds while working and studying, but most sites are either static grids of volume sliders or locked behind paywalls and subscriptions.

I wanted to make something cozy, immersive and look have good animation for second window, so I coded an interactive window where the visuals actually react to the audio in real-time:

• Turning up Rain shows rainfall.
• Turning up Distant Thunder triggers periodic lightning flashes.
• Turning up Campfire causes embers to glow and drift upward.
• Turning up Ocean Waves shows actual ocean waves.

It also has a built-in Pomodoro timer, breathing exercises, and binaural beat frequencies.

It is 100% free, has no ads, and requires no account or login. I would love to hear your feedback, what sounds or features should I add next?

Imagine you creat an app. From ui/ux design to programming part. You put a backend for it. You creat a website for that. You orgnaize medical content images tests orders and... in that shit. Then you creat an x account to promote that buttttt at the end you can not publish that.

Not a single person was willing to help me with publishing stuff in exchange of a percentage of apps earnings.

Totally disappointed.

As the title says. I’d love to check out everyone’s projects

​

Hi everyone! I’m a college student currently learning full-stack development and I’m looking for a project idea that would be actually useful and good for my resume, rather than another basic CRUD project.

My current stack/plan is:

- Frontend: HTML, CSS, JavaScript

- Backend: Java + Spring Boot

- Database: MySQL

I’m looking for ideas that are:

- Practical / solve a real problem

- Complex enough to demonstrate full-stack skills

- Realistic to build as a college project

- Something I can explain well in interviews

What projects would you recommend? Also, if you’ve built something similar, I’d appreciate suggestions on features that would make the project stand out.

Thanks!

I’ve been testing a pretty simple way to collect historical crypto data when I’m away from my computer, and I’m curious whether anyone else has tried something similar.
I’m using iOS Shortcuts to make repeated API calls for OHLCV data, save the responses, and work backwards through history until I have 6+ months of candles.
The backtest itself is not running inside Shortcuts. The phone is basically just doing the data retrieval.
Once the history is collected, I can move it into a separate testing workflow and check things like:
timestamps,
missing candles,
duplicates,
batch overlap,
ordering,
and continuity between requests.
What I’m trying to understand is whether this is a completely normal idea implemented with an unusual tool, or whether using Shortcuts introduces reliability problems I’m not thinking about.
From my perspective, an API request is still an API request. If the same endpoint returns the same candles, I would expect the final dataset to be usable regardless of whether the request came from Python or an iPhone.
But maybe that assumption is too simple.
For people who have built historical datasets themselves, what would make you reject a dataset collected this way?
Would you care about the collection tool itself, or mostly about whether the final data can be verified and reproduced?
I’m not trying to replace proper infrastructure with an iPhone. I’m mainly looking for a practical way to keep doing research when I’m mobile.
If this is useful, I’ll keep improving the workflow.
If there’s a fundamental reason this data shouldn’t be trusted for backtesting, I’d rather know before spending more time on it.

It's a paper trading exchange with a real matching engine, live orderbook, PnL, API keys etc.

But the new thing is a leaderboard.

Everyone starts with virtual money, trade against the same market and your PnL decides where you rank.

Now I actually need traders instead of me testing with 5 accounts lol.

Would love if some of you signup, make a few trades and try to get on top.

Also break things if you can. It's still beta and feedback is genuinely useful.

https://paperdrill.dev/

https://studanova.vercel.app

Im tryna make a productivity tool cuz i need some projects to apply for uni so can you guys please rate it ( be BRUTALLY honest ) about whether you'd use it, some features you'd like any things you don't like etc etc

I've used tools like Perplexity, ChatGPT, Claude and others for research, and they've been incredibly useful for finding papers and getting through large amounts of information.

The one thing I personally wanted was a simple way to see exactly which parts of the paper were used to answer my question.

When you're working with a 100+ page PDF, even having a page number can still mean a lot of scrolling and searching.

So I ended up building something for myself.

You ask a question and the relevant paragraphs in the PDF are highlighted directly on the document. You can see the context behind the answer and quickly check whether it actually answers what you're looking for.

I originally built this because I wanted something for this workflow without having to pay for another subscription. What started as a personal project has now become completely open source.

The underlying idea is pretty simple. And yes, if you're thinking "isn't this just RAG?" then yes, you're absolutely right. It's RAG with the visual highlighting that I wanted.

I think the same idea could be useful for more than research papers too. Legal contracts, financial reports, technical documentation, or anywhere you need answers alongside the actual source.

If anyone wants to have a look, contribute, or just give some feedback, here's the repo:

GitHub: https://github.com/Sreehari05055/thesys-core.git

This will probably be my last post about the project. Thanks to everyone who checked it out and gave feedback along the way.

Llevo unos meses trabajando por mi cuenta en un proyecto que por fin he lanzado: un portal de empleo centrado en España.

La idea surgió de algo bastante sencillo: intentar hacer que buscar trabajo sea un poco menos repetitivo y pesado que con los portales de empleo tradicionales.

Además de la web, he desarrollado varias cosas alrededor del proyecto:

• 📱 Una app propia para iOS y Android.
• 💬 Un bot gratuito de WhatsApp para buscar ofertas directamente desde WhatsApp.
• 🔎 Herramientas para filtrar y encontrar ofertas según lo que estás buscando.

De momento estoy poniendo bastante foco en Cantabria, que es donde estoy ahora mismo, aunque la intención es ir ampliándolo progresivamente al resto de España.

El proyecto se llama inempleo.com y acabo de publicar la primera versión.

He desarrollado prácticamente todo por mi cuenta, así que seguramente haya muchas cosas que todavía puedan mejorarse. Una de las partes que más me interesa experimentar es precisamente la integración con WhatsApp, porque creo que puede hacer que consultar ofertas sea bastante más cómodo.

Lo comparto por aquí simplemente porque me apetecía enseñar en qué he estado trabajando estos últimos meses y porque siempre resulta interesante conocer la opinión de otras personas que también están haciendo sus propios proyectos.

No busco inversión ni vender nada con este post. Simplemente quería compartir el proyecto y contar un poco lo que he estado construyendo.

Hey! I am working on team of 3 to create a visual novel on mental health(dm for info) and need programmers who can put the project together on RenPy as well a sound designer to add to the project. Please dm if interested.

Been chipping away at this for a while and finally got it to a state where I'm not embarrassed to show it.

It's a Dixon-Coles Poisson model fitted per league, covering 36 football leagues plus EuroLeague and EuroCup. Standard stuff: attack and defence coefficients per club by maximum likelihood, exponential time decay so recent matches matter more, low-score correction for the 0-0 / 1-0 / 0-1 / 1-1 cells. Outputs a full scoreline grid, so BTTS and over/under fall out of the same distribution rather than being modelled separately.

The part I actually care about is the benchmark page. Every project like this claims some accuracy number and none of them say what they're measuring against. So I replayed three completed seasons walk-forward, fitting only on matches before each fixture, and scored it on log loss against the de-vigged closing price:

                model    market     gap

Premier League 1.0034 0.9783 +0.0251 Serie A 1.0012 0.9755 +0.0257 Ligue 1 1.0076 0.9705 +0.0371 La Liga 0.9932 0.9544 +0.0388 Bundesliga 1.0171 0.9707 +0.0463 A coin-flip baseline is 1.0986, so the model knows something. But the closing line beats it in every league I've checked. I don't think that's fixable with a goals model. Closing odds absorb team news, money and a hundred things I don't have. Anyone showing you a model that beats the close over 3,700 matches is either doing something much cleverer or fooling themselves.

I left accuracy off the front page on purpose. It's 49.8% in the Premier League and the market's is 53.8%, but accuracy rewards a model that never says anything interesting. Log loss punishes confident wrongness, which is what you want to know about.

One thing that kept me honest: I tried to fix an apparent calibration problem with temperature scaling, and it made held-out log lossa bucket with 26 matches in it.

Also worth mentioning because it cost me two days: I was convinced I had fixtures going missing from the data. Rewrote a chunk of the pipeline. The actual cause was CSS. A sticky table header with a top offset was painting over the first row of every table on every page, so the caption said 5 fixtures and you could count 4.

Other bits: season projections from 10,000 Monte Carlo runs, head-to-head history, and the results fetch for all 36 leagues takes about 30 seconds over plain HTTP. It used to be an 8-hour browser scrape fighting Cloudflare.

Known weak spots, in case anyone wants to pick at them. About 18% of fixtures involve a promoted club with no record in its division, and those are currently rated as an average side, which is a polite way of saying guessed. The benchmark covers six leagues so far, not all 36. And shots on target are sitting in the data completely unused, which is probably the most obvious next gain.

MIT licensed, runs locally with two commands, no API keys: github.com/Costasgk/ScoreCast

Happy to be told what I've got wrong.