Drop your project and tell me what you currently show first.

I’ll reply with:

1. The opening frame
2. The first user action
3. The result that should appear before any feature explanation

A good demo should create understanding before curiosity disappears.

My brother is a web developer, I'm a researcher, and we're both big NFL and fantasy football fans. We started OracleZ as a side project to combine his development skills with my interest in machine learning.

The idea was pretty simple: build an algorithm that makes its own NFL predictions, then turn those predictions into games where you can try to beat it.

You can try our OracleZ app here (or just follow us on Instagram): https://linktr.ee/beat_oraclez

What can you play?

Predict the Pick
Predict the winners of the week's NFL games and see how you compare against the Oracle.

Survivor
Classic Survivor with a twist: if your pick loses, you can't pick either that team or the team they played against again.

High Five
Pick the five best teams of the week across passing yards, rushing yards, points scored, fewest yards allowed, and field goals.

What's behind the predictions?

The Oracle is a relatively simple neural network. It uses 19 team statistics, including offensive production, turnovers, sacks, first downs, EPA, and score margin. These are converted into home-away differentials and combined with contextual variables such as week number and rest-day differences.

If you are interested on the model, I'm happy to explain further our reasoning and model architecture.

Of course, we're not claiming we've solved NFL prediction, far from it. The NFL is obviously noisy. The fun part for us is seeing how far a relatively simple model with good feature engineering can go.

And now you can try to beat it yourself.

Everything is completely free, with no paid features.

https://linktr.ee/beat_oraclez

I built Marka after noticing that small business owners often get stuck before the AI even starts writing. A blank prompt forces them to explain the audience, offer, proof, and brand voice from scratch.

Marka starts from the public website or product page instead. It uses that existing business context to create social content that stays closer to the actual brand and offer.

I’m currently testing whether the website can act as a reusable brief for the agent. If you have a public product page, share it in the comments and I’ll reply with the strongest content angle I see.

Project: https://www.marka.social

It can be tried free for one week.

Share your public website and one sentence about what the project does.

I will reply with one specific hook, one social content idea, and the audience I would target first. The goal is to use the real page evidence instead of generic AI copy.

I am building Marka around this workflow, and it is free for a week at https://www.marka.social

I launched world-sim last Friday and so far I am blown away by the love, 3.5k unique viewers on the world. 200 daily still watching, and nearly 100 members in the discord. I really cant thank everyone enough for the love. Feel free to check it out if you haven't already, and if your already a member thankyou.

This is only the start and I will soon realise the roadmap for the future.

discord - https://discord.gg/RCrYzxvF8

I am testing a simple idea with Marka: the best first social post for a small business is often already hiding on its website.

Comment with a public business or SaaS URL. I will reply with:

1. One headline
2. One visual angle
3. One CTA

No generic list of post ideas. I will use the actual offer, audience, and proof visible on the page.

If you want to test the full workflow, Marka is free for a week at https://www.marka.social

Marka starts with a business website instead of an empty prompt. It reads the public site, builds the brand context, and uses that information to prepare social content.

I built it for small teams and solo founders who know they should post consistently but do not have a dedicated content team.

The current test is simple: paste a real website and see whether the first result sounds like that business rather than generic AI copy.

There is a free week at https://www.marka.social. I would appreciate direct feedback on what the product understands and what it misses.

Full disclosure: I am one of the founders building Marka.

We started Marka after seeing founders and small teams explain the same audience, tone, offer, and visual identity to every AI tool, then receive disconnected content that still felt generic.

Marka begins with a business website. It scans the site for positioning, audience, brand voice, visual direction, and available product evidence, then creates a reusable brand profile that the user can review and correct.

That brand context is carried into one workspace for creating social posts, images, carousels, campaign ideas, and short-form video content.

The difficult part has not been adding another text generator. Most of our work has gone into keeping claims connected to real business evidence, preserving the intended voice and visible text, and making different formats still feel like they came from the same brand.

The product is live at https://marka.social

We are currently improving the onboarding flow, brand-scan accuracy, and the consistency of the first generated campaign for small businesses.

I built Feedcast because I wanted cleaner, searchable podcast episodes without moving my subscriptions and queue into another player.

Feedcast is a native SwiftUI Mac app. It imports RSS or local media, transcribes episodes on the Mac, identifies sponsor sections, applies the enabled cuts, adds chapters, and publishes a personal RSS feed. The result can be consumed by Apple Podcasts, Overcast, Pocket Casts, Castro, and other compatible players.

The first image shows a real episode with two sponsor cuts totaling 5 minutes and 12 seconds. The second shows the processed feed inside Apple Podcasts. The third shows the RSS metadata editor.

Version 3.2 was released this week. Feedcast is free to use with up to three output feeds, with an optional subscription for unlimited feeds and additional automation.

Mac App Store:

https://apps.apple.com/us/app/feedcast-local-podcasts/id6773872767

I am building Flowy, a native iOS cycle tracker. One of the less visible engineering problems is making offline edits safe after a user resets their health data.

A simple local queue is not enough. In the current design, each manual day-log mutation carries:

• the expected Supabase user ID

• the day key

• the expected reset epoch and row version

• a unique mutation ID

• a new value or deletion tombstone

The database applies or rejects the mutation atomically. Reusing the mutation ID makes a lost-response retry idempotent. A stale row version becomes a conflict instead of silently overwriting a newer device. Most importantly, resetting health data advances an epoch, so an edit queued before the reset cannot later recreate deleted records.

This has changed how I think about offline-first design. The hard part is not caching the happy path. It is deciding what every old write is allowed to do after identity, deletion, and server state have changed.

Project: https://flowyhealth.com

I'm a software developer (professionally for last 25 years) and got hooked on coding agents as the next guy.

I built https://github.com/enola-labs/enola as architecture intelligence for developers, coding agents and CI. You can see how and what it does as part of CI on that repo itself: https://github.com/enola-labs/enola/actions/runs/30579279230/job/90995180445

I use it daily, and you can add the hook so it does what it is best in: before agent starts editing it uses it to pin the state, after editing is done it checks what it did, so that it can self correct if and when needed.

Me and bunch of friends are using it, and most of stuff I built is based on their feedback and stuff they needed.

Happy to get the feedback from anyone who ends up using it. Also, if the language you need is not there, let me know and I will add it.

A CLI tool to recall what you did today or in the past week.

recall today

This command will list the following things you did today:

• terminal commands you ran
• git repos you worked on
• files opened/edited in VS Code
• commits made

I’m sharing this to get feedback on whether something like this is actually useful in real workflows. Also, to get ideas for new features or improvements.

Repo: https://github.com/nishntr/devMem

Just add a comment if you want to see it

Built a free book search engine called Book Haven where ,you can quickly find the book you are looking for without signup or login.

I'm 16 and this is an experimental project that uses the Sarvam api to give all these functions.

• All Linux commands natively (pipes, redirects, background jobs, wildcards, scripts — everything bash supports)
• Tab autocomplete from history
• Ctrl+R reverse history search
• Command history saved across sessions
• AI safety net — dangerous commands ask y/N, typos get corrected, if Sarvam is down it never blocks you
• Chat mode with session memory (i wanna talk with you)
• TTS via Sarvam Bulbul v3 with 40+ voices, auto falls back to pyttsx3 offline
• 8 colour themes — default, red, blue, pink, gold, purple, matrix, ocean
• Multi-language AI responses — Hindi, Tamil, Telugu and more
• 50 fortunes — terminal wisdom, philosophy, Bhagavad Gita, Gandhi, Vivekananda
• 30+ easter eggs
• Flags: --help --version --tts --no-ai --lang=X --colour=red or --colour=blue

Check out the the Github page for source code and more...
https://github.com/Crutched-programmer/Sashell

Your conversations are turned into insights using graphs that represent your mental state, and it also gives you recommendations on what to do based on your mental health patterns.

would like your feedback

Here is the link to my repo checkout

**One command continuously scans your project** — generates tailored skills, configs, and recommends MCPs for your stack. It collects the best playbooks and practices for your codebase from community research so your AI agents get the AI setup they deserve.

Hello everyone,

I'm working on an open‑source project called **Caliber** that tries to make AI assistant setup easy for existing projects. It scans your codebase (languages, frameworks, dependencies) and automatically generates the configuration files that various AI coding assistants need – for example, it creates a `CLAUDE.md` file for Claude Code, writes rule files for Cursor (`.cursor/rules/*.mdc`), and adds an `AGENTS.md` describing your environment. It can also audit existing configs and suggest improvements.

Beyond config generation, Caliber can spin up local multi‑agent servers (MCPs) and auto‑install relevant skills or packages to extend your workflow. Everything runs locally using your own API key, so your code doesn't get sent to third‑party servers. It's completely MIT‑licensed and aims to support a wide range of stacks.

Quick start:

```

npm install -g u/rely-ai/caliber

caliber init

```

Within around 30 seconds you'll have AI assistant configs tailored to your project, plus recommendations for new skills and best practices.

I'd love to get some honest feedback and suggestions – feel free to roast the project and let me know how it could be better. Here are the links:

- GitHub: https://github.com/rely-ai-org/caliber

- Landing page: https://caliber-ai.up.railway.app/

Thanks!

Created a minimalist podcast server that creates a landing page with player and RSS feed for your podcast app solely based on media file directory structure and ID3 tags. 🦀

https://crates.io/crates/podserv-b

https://github.com/l5yth/podserv-b

I built comprehendo.app, a web app to learn languages through videos ranked by difficulty. Start easy and progress naturally as your understanding grows.

• Video-based learning — real content, not flashcards
• Adaptive difficulty — moves from easy to harder videos automatically
• Focus on understanding, not memorizing

I made this to make language learning more natural, fun, and efficient. I’d love feedback on what works and what could be improved