alapaca papaer trading automated on a raspberry pi - algo trading is there for a while and this would help me test my strategies

A library of components for Godot that could be used in different kinds of 2D games: https://github.com/InvadingOctopus/comedot

Dreaming about a new programming language made for coding gameplay logic: https://news.ycombinator.com/item?id=45865379

And an iOS expense tracker focused for frequent travelers, and macOS photos viewer based on the filesystem instead of a monolithic opaque "library", 2 needs that I had since forever but could never get through Apple's atrocious developer documentation far enough to finish making them :')

I am currently working in a big Italian company that makes 90% of the glasses, and recently it’s collaborating with Meta to make smart glasses!

easyanalytica.com - Build dashboards from spreadsheets

use it to view all dashboards in one place.

A JVM for Java 21 written in go[0]

[0] jacobin.org

I am building a coding agent for small businesses. The agent runs on Linux box on own cloud. Desktop and mobile apps to chat with AI models and generate software as needed.

SSH based access with HTTP port forward. Team collaboration, multiple models, git based workflow, test deployment automation, etc.

Very early stage but it now work on its own source code (Bash tool is missing): https://github.com/brainless/nocodo

Great game love it

my life: https://www.techrxiv.org/users/717330/articles/702287-on-fer...

a 3D Graphics framework and a c++ library for parsing valve .map files

Here’s mine:

I’m building a small live NFL game-prediction tracker and writing up what I learn as I go:

https://michellepellon.com/portfolio/nfl-game-predictions

# What’s under the hood today

ELO translated to the NFL with margin-of-victory adjustments, a modest home-field term, and week-to-week recency weighting.

Post-hoc calibration with isotonic regression so 70% predictions land near 0.70 empirically.

Monte Carlo to roll games forward for distributions on weekly win odds and season outcomes, plus basic reliability/Brier/log-loss tracking.

# Where I’m taking it (ensemble ideas)

Blend a few complementary signals: (1) pure ELO strength; (2) schedule-adjusted EPA/Success Rate features; (3) injury/QB continuity and rest/travel effects; (4) a small “market prior” from closing lines; (5) weather/play style pace features.

Combine via a simple stacked model (regularized logistic, isotonic on top), or a Bayesian hierarchical model that lets team effects evolve with partial pooling.

Separate models for win prob vs. expected margin, then reconcile with a consistent link so the two don’t disagree.

Emphasis on calibration over leaderboard-chasing: reliability diagrams, ECE, PIT histograms, and backtests that penalize regime drift.

# Why I’m doing it

It’s a sandbox to teach myself Monte Carlo and ELO end-to-end—data ingest → feature plumbing → simulation → calibration → eval—on a domain with immediate feedback every week.

# How this connects to my day job (healthcare ops)

I work at BlueSprig, running ~150 ABA therapy clinics. I’m exploring whether ELO-like ideas can augment ops decisions:

“Strength” ratings for clinics, care teams, or scheduling templates based on outcome deltas and throughput (margin-of-victory ≈ effect size/efficiency).

Opponent/schedule ≈ case-mix, payer mix, staffing constraints, geography.

Monte Carlo for expansion planning (new-site ramp curves), capacity/OT forecasting, and risk-adjusted outcome monitoring with calibration so probabilities mean something.

Guardrails for fairness and interpretability so ratings don’t become blunt scorecards.

# Help

If you’ve shipped calibrated ensembles in sports or have pointers on applying rating systems to multi-site healthcare operations, I’d love to trade notes or if you need someone to this and other kind of work for their dayjob email me at mgracepellon@gmail.com -- I would love to do this fulltime.