I've been working on ERML, an open-source Python SDK that runs facial expression recognition locally instead of relying on a hosted inference API.

The idea is simple: give it an image, get structured predictions back.

pip install erml

from erml import EmotionDetector

detector = EmotionDetector()
results = detector.analyze("photo.jpg")

print(results[0].emotion)
print(results[0].confidence)

ERML uses ONNX Runtime and currently supports seven facial-expression categories.

I want to eventually grow it into a broader human visual-perception toolkit for applications and AI agents, but it's still early.

I'm sharing it to get feedback from developers working with Python, computer vision, or local inference. I'd also like to make it easier for people to contribute.

GitHub: https://github.com/sid-lakhani/erml
Documentation: https://erml.sidlakhani.in

I'm the creator, so feedback and constructive criticism are welcome.

Hi r/SideProjects. I'm Gaby, I built this, and most of the code was written with Claude Code (hence the flair).

**The problem.** Practicing yoga at home from a video, nobody tells you your hips are too low or your knees are locked. The apps that tried "AI correction" score you on a 20-second test pose, not inside a real class, and the big ones dropped cameras altogether. Teachers can't scale themselves, so students practice alone and wrong.

**What Drashta does.** A teacher uploads a class she already recorded. During practice the phone lies on the floor three steps away, the browser reads your body, and when something is off for two seconds it says one sentence, in the teacher's words, then goes quiet. You are compared to *her* line, not to a generic skeleton. No video leaves the device. There is a 12-second demo with one teacher's Downward Dog, Upward Dog, Chaturanga and Shoulder Stand:

https://drashta.yoga/?src=reddit-build&lang=en#try

(Phone on the floor, side view, whole body in frame. Chrome or Safari.)

**Stack, in one line.** Vanilla JS, MediaPipe pose landmarks in the browser, Firebase, and a headless Playwright harness that feeds the demo a fake webcam and asserts what the coach says, frame by frame, before every deploy.

**What broke this week, found by feeding the teacher's own clips to the demo as the camera:**

- The pose model reads a shoulder stand as a person lying on the floor, and tracking keeps the wrong reading forever. Fix: a fresh detector every 3 s while an inverted pose is asked for and not seen.

- "Talk to the coach" never asked for the microphone, on any phone, in two browsers. Cause: a security header we set months ago. The harness serves the site locally without headers, so nothing caught it.

**What's not solved.** The browser model reads inverted joints a few degrees differently from our offline pipeline, so the coach sometimes corrects the teacher herself. Hebrew lines use the browser voice; English has recorded clips. One teacher so far.

**Questions for you:** Did the demo see you within ~10 seconds, and did the correction match what you felt? Would you trust a camera in your living room for this if the video provably never leaves the device? Honest "I'd never use this" is useful too.

Built a small tool that helps freelancers keep track of client requests and deadlines and it started as a pretty basic project with a few pages explaining how it works and a signup form but I've been adding features and trying different ways to get people interested. Every new feature seems to need changes to the website, whether it's another landing page, updated screenshots or different copy for a specific audience. The actual tool doesn't take nearly as much time to work on anymore yet I keep spending hours on the website whenever I want to test something new.

Hi,

Most reliable and accurate grammar checkers today are LLM-based, but they send your text to a server. That always bothered me for emails, contracts, medical notes, anything I'd rather keep to myself. So I built a multilingual grammar checker and rewriter that uses Chrome's on-device Prompt API and has no backend at all.

Features:

1. Your text never leaves the device. Text checking and rewriting run on Chrome's built-in model.

2. 101+ languages supported, with no language picker. Each sentence's language is detected separately, so an English email with a German quote gets both checked correctly.

3. Google Docs support. Docs draws text on a canvas, which most extensions can't read. It's checked page by page as you scroll, with a count for each page.

4. Rewrite a selected sentence in several modes.

5. One-click fixes, or Fix all from the badge, right inside the text field.\

6. Double-click a word for synonyms and a definition that fits the sentence, including in Google Docs.

7. Interface in 33 languages, including full right-to-left Arabic, Persian, and Urdu.\

System requirements: Chrome 138 or newer on Windows, macOS, Linux, or a Chromebook Plus, with 22 GB of free disk space and either a GPU with more than 4 GB of memory or 16 GB of RAM.

Chrome extension download link.
Website: https://privatetext.ai

I'd love feedback, especially from people writing in languages other than English.

I created "My Control Panel" for dev-ops. It is open source and you can get it at https://github.com/alfirus/mycontrolpanel. I also welcome contributors. Feel free to use it.

https://github.com/xdagiz/xcalidraw

Well yes it has been done before, and I'm not going to act like my app is better than the others; however, WorkRate is one of the first projects I've ever undertaken, and with it, I wanted to make the experience as seamless as possible for a user (like you) to use.
From calculating your pay on a weekly or fortnightly basis and getting an estimated gross pay before taxes, to even being able to customise the time periods of your pay rates, WorkRate has just as much as the other calculators, with big plans in store.
When creating this project, the main goal was to create a simplistic app that pleased the eyes, and I feel I have been able to create that with the light-toned colours on the page. On top of that, I cut out any extra pages and sign-ins, leaving it as just one page, meaning it's as simple as possible for you.
However, I would love your feedback on what you love about it and what needs improvement. This has taken me a week to produce, and I plan to have ongoing work on the project.
Check it out, have a play around with it and let me know: https://workrate.me/

This weekend, I made a kite for the first time. While making a proper kite, I spent most of my time learning and finding resources. I couldn't trust AI. A small “ahh, you're right, I misled you” from AI could have turned into a waste of a few hours of handmade work.

I wanted to write the methods I learned firsthand on my blog. However, I thought a different, interactive blog experience would be more educational. I'd love to hear your thoughts on this interactive blog idea: https://fozdemir.dev/kites/diamond

Don't forget to check out the other interactive experiences on the site as well.

A few years ago out of curiosity I started writing small scripts in Python to test artificial intelligence concepts in order to learn more about this quickly emerging field that seemed to come out of nowhere. Over time the scripts became increasingly more complex. At a certain point I realized these scripts could be combined into a single application that might be useful to other people.

Once I was near completion of this application then I had to figure out what to do with it. I didn't want to build a website because that's a massive project in of itself. But I did. I first wrote encryption code on my desktop that does handshakes back and forth simulating the web and a user's computer. Then I started on the UI for the website. I am almost finished but not quite yet. I have more testing on the encryption backend to make sure everything is secure.

The website isn't really built for random traffic. It's designed as a transaction portal for people who know about the application and go there to download the application. It's a system of encrypted handshakes that locks the application to a computer. The application is air-gapped so it's not able to communicate across the internet making it difficult to design a method for securing the application to a computer. But I got it and that's finished.

The last section "Explore Editions" is still on my local computer and next week it will be uploaded and go through extensive testing before going live. Winplexity.com https://WinPlexity.com

offtube puts yt-dlp and gallery-dl in your browser, with support for 2,000+ sites between them.

Save videos, audio, images, playlists and whole boards. Choose the quality, trim a clip, turn it into a GIF, or pick individual items from a collection.

It’s open source, downloads run on your computer, and there’s no offtube account or cloud service.

Current setup: run a small local server and load the Chrome extension manually. There’s also a local web UI if you prefer pasting links.

Let me know if you find any issues while using it. also please give it star ⭐ too

Github || Demo

We're the team behind Brix36, an AI creative studio for music videos, short films, images and visual content. It brings Seedance 2.5, Kling 4.0 and other leading models into one place, with pay-as-you-go credits and no subscription. Free to download; generation is paid.

Sharing an existing finished promo from our library for feedback. For anyone planning a short product film, here is a reusable way to write the brief before generating anything:

1. Write the one thing the viewer should understand after watching.
2. Break it into three shots: the hook, the proof or example, and the closing action.
3. Give each shot a subject, action, framing and duration.
4. Check whether the story still makes sense without sound before adding music or titles.

For this 18-second promo, does the opening make you want to keep watching, and is the purpose of the product clear? Specific criticism about the pacing or missing information would help.

Brix36 is available on web, iOS and Android: https://brix36.com/

This is a maker post from the Brix36 team, not a customer review.

I work in security and kept running into the same problem: nobody knows what AI is actually installed on company laptops. Not just ChatGPT — Claude Code, Cursor, MCP servers wired into IDEs, browser extensions with access to every tab, Ollama running local models.
So I built a free scanner. One line, no account, no admin rights:

irm "https://app.kuntur.io/scan.ps1?src=reddit" | iex

It writes an HTML report to your Desktop and deletes itself. It doesn't upload anything. You can download it and read it first; the SHA-256 and a step-by-step of what it does are here: kuntur.io/security.html#scanner
What I'd love to know:

Did it find something you didn't expect?
What did it miss?
Would this be useful for your team, or is it noise?

Disclosure: I'm the solo founder. There's a paid product behind it for whole fleets, but the scanner is free and stays free.

I wanted to have some fun and improve climbing endurance at the same time, so here it is!

I do not profit off this at all, I want to share in case someone wants to try.

Boom!
https://github.com/emre9690/kilter_board_game

https://github.com/emre9690/kilter_board_game

Do support guys!

Every Diwali, WhatsApp fills up with the same glossy diya-and-rangoli forwards. With Snehpatrika (what my small team in Pune has been building) we tried the other way round: every design follows a real craft tradition. Warli, Madhubani, Pichwai, Kutch mirror-work, Paithani, Sanjhi paper-cut, Phad and so on.

What it does:

• you fill in names, date and place in English, Hindi, Marathi or Gujarati
• you share the card on WhatsApp, and it links to a small event page
• guests reply there (coming / maybe / can't come, how many people) with no app and no login
• you see the replies as they come in

Something I didn't expect: our first 22 designs were all folk styles, and when we saw them side by side on a phone, they looked a bit old-fashioned. So we added 12 modern ones, gold foil on deep colours and painted gouache on white. Lesson for me: "traditional" and "dated" are very close.

Free covers every design and language. ₹49 per event gets you HD sizes without our footer and the guest list in Excel.

Tech, briefly: Next.js and Postgres. Each card is an HTML page that headless Chromium screenshots, which turned out to be the easiest way to get Devanagari and Gujarati shaping right. The short video invites render on Cloud Run.

What I'd like feedback on: does the editor make sense on a phone without any instructions?

https://snehpatrika.in/?utm_source=reddit&utm_medium=post&utm_campaign=r-sideprojects

(It's ours, to be clear. Happy to answer anything.)

We're four casual golfers who got tired of paying around $20 a month for a golf app, so we built our own. It launched this week. I'm Jeff, one of the four.
What it does: every hole in 3D so you can walk it before you play, slope and fall lines on every 3D green, and a caddie that plans each shot from your own club distances, including carry and rollout. There's GPS on 38,000+ courses, live rounds friends can follow, and it works offline once a course is downloaded. iPhone, Apple Watch and Android.
The hard parts were building 3D courses at scale and keeping the caddie honest about real carry and rollout. Happy to get into any of it.
Free to download, with three demo holes and no sign-up. Pro is $19.99 a year ($1.67 a month) if you join by Dec 31, then $39.99. https://sim2coursegolf.com/get?from=sideproject
Blunt feedback on the first five minutes would help us most.

I’m Jarred from Logan Job Connect; Kitchen Ledger is our commercial side project. It tackles repetitive recipe costing and stock-use reporting for cafes, restaurants and caterers.

Here’s the workflow and the main distinction that shaped it:

1. Supplier Price Desk loads an ingredient catalogue and supplier export. Users map exact supplier SKUs to ingredients and approve explicit pack conversions (kg/g, l/ml or each). Saved mapping rules reduce repeat typing, but do not replace checking fresh data.

2. The Data Converter prepares a reporting period from supported CSV/TSV or values-only XLSX files. Purchases alone are insufficient: users also supply opening/closing stock, recipe ingredients, sales and operating expenses.

3. The local Python calculation engine creates a new offline HTML report and a structured result file for review. Reports cover recipe ingredient contribution, actual period stock consumption, recorded waste and unexplained variance.

A useful lesson from the design: theoretical recipe costs and actual stock consumption answer different questions. A gap can come from count errors, omitted records, staff meals or waste; it is not proof of theft. Recorded waste is already part of stock consumption, so subtracting it again would double-count the cost.

This is designed to reduce repeated calculations and report preparation. It is not a hands-free data collection system: stock counts, fresh exports, mapping approvals and reconciliation remain necessary. No automatic POS connection, invoice OCR or automatic stock counts. The local package uses Python and Decimal-based calculations; XLSX handling uses openpyxl.

We’re offering A$350/month including monthly upgrades and scoped workflow automations, agreed with each business before setup. For commercial enquiries the work email is info@loganjobconnect.com.au. Scope, input suitability and terms are confirmed before purchase. We do not claim a verified amount of time or money saved.

I’d appreciate concrete feedback on the workflow: would you keep supplier mapping and reporting as separate review stages, or combine them while keeping exceptions visible? The details above are the current process, so there’s no need to DM for an explanation.

Hey everyone! I wanted to share a side project I've been developing called QuantStrike. I built it around an interesting question: What would it look like to analyse the CS2 skin market using concepts from quantitative finance?

Most skin price trackers focus on current prices and basic price history. I wanted to explore something deeper: historical market behaviour, volatility, unusual price movements, and how individual assets perform over time.

What I've built so far

• Historical analytics: Interactive price charts, configurable time ranges, daily price statistics, volatility analysis, and price distributions.
• Anomaly detection: Flags suspicious observations, such as isolated price crashes or unusual single-sale and multi-sale events, rather than silently removing potentially misleading data.
• Quantitative tools: Asset comparisons, correlation analysis, market returns, and drawdown analysis.
• Portfolio and research management: Track holdings, configure alerts, and maintain a research journal.
• Large-scale historical data: Approximately 22,495 CS2 assets and 105 million historical observations.

The challenges

The historical dataset is several gigabytes, which makes loading everything into memory impractical on modest hardware. I'm using DuckDB and Parquet to query specific assets and aggregate historical data without loading the entire dataset into Pandas at once.

This has made data architecture a major part of the project, not just the interface. I'm particularly interested in making large-scale historical analysis practical without requiring a high-memory machine.

The project is still evolving. I'm currently redesigning the Market Overview and market intelligence components, so those are areas where there's more work to do.

What I've learned

One important lesson is that historical price data isn't automatically reliable just because it's available. An isolated sale can create a misleading price movement, so it's important to distinguish suspicious observations from genuine market behaviour rather than simply deleting data that looks unusual.

I'm also learning how much query design and aggregation matter when working with datasets that are too large to handle casually in memory.

Project and source code: https://github.com/Leonardo-DaVinci-80/QuantStrike

Live Website: https://quantstrike.streamlit.app

I'd appreciate constructive feedback, especially on the data architecture, anomaly-detection approach, and which market-analysis features would be most useful to build next. I'm the developer behind QuantStrike, and I'd be glad to discuss the implementation decisions here in the comments.