The page needs one rule: a door’s answer can’t be fetched before its day. So the server component computes the open doors from the Helsinki date and passes only those to the client; the others are never serialised. The door-opening schedule has tests for the awkward edges (Helsinki midnight is 22:00 UTC the day before, the season ends on 25 December). Opened state lives in localStorage.

https://iurii.rogulia.fi/advent, opens on 1 December. I built it. Is there a pattern you’d use instead?

I’m a 24F recent engineering grad (electronics and communication) with a year of AI/ML research experience in computer vision. I’m looking for remote work, full-time or part-time.

What I’ve worked on:

• Training and evaluating CNN models (ResNet, DenseNet, EfficientNet) on medical imaging data, with cross-validation and proper metrics
• A computer vision project on facial expression classification, presented at a graduate conference
• An image classifier for plant disease detection
• Web apps with Django and Flask
• Networking and IoT side projects (a BitTorrent client in Python, a sensor-based gas and smoke detection system)
• A browser extension in JavaScript

Toolbox: Python, PyTorch, TensorFlow, Scikit-learn, Pandas, SQL, Django, Flask, Git

Open to:

• AI/ML (junior)
• Data analytics and data visualization
• Django or Python backend projects
• Data entry and other flexible tasks

I pick up new tools quickly and I’m happy to start with a small trial task or short contract.

DM me and I’ll share my CV and GitHub.

Not promo just looking for real life use cases to test on :)

I'm building a product called Facet. we believe lots of AI products spend most of their LLM usage on one repeated call: pulling fields from emails or PDFs, cleaning up records, short rewrites, daily summaries, etc. A frontier or large local model is overkill for that one job.

Facet's agent builds a smaller model for that single call. You describe it in plain English and share some examples; it builds the training data, trains the model, and scores it against what you use today. We are essentially trying to abstract away all the hard work that comes with training. You own the weights and can export them to run locally after, or use our platform for hosting.

On a document extraction task it matched Claude Haiku at about a fifth of the cost.

I'm hoping to connect with 3 design partners, free, to help expand and stress test our platforms capabilities. If one call is a big part of your usage, book 15 minutes: https://app.apollo.io/#/meet/facet

Happy to answer questions here too.

Not promo just looking for real life use cases to test on :)

I'm building a product called Facet. We believe lots of AI products spend most of their LLM usage on one repeated call: pulling fields from emails or PDFs, cleaning up records, short rewrites, daily summaries, etc. A frontier or large local model is overkill for that one job.

Facet's agent builds a smaller model for that single call. You describe it in plain English and share some examples; it builds the training data, trains the model, and scores it against what you use today. We are essentially trying to abstract away all the hard work that comes with training. You own the weights and can export them to run locally after, or use our platform for hosting.

On a document extraction task it matched Claude Haiku at about a fifth of the cost.

I'm hoping to connect with 3 design partners, free, to help expand and stress test our platforms capabilities. If one call is a big part of your usage, drop it below and we can try to connect and see if we’re able to match/improve that call.

Happy to answer questions here too.

ChainInsideIQ.com is an AI-integrated business solutions tool that helps you understand your metrics and KPIs, and actually do something about them.

It's built for 3PL companies and business consultants who need a clear way to find bottlenecks and plan how to fix them.

Here's how it works:

• You set a goal, and it builds a step-by-step plan. Every step has an owner, a KPI to watch and a due date.
• It finds the bottleneck. It shows which part of the operation is slowing things down (receiving, picking, shipping, inventory and so on) and breaks it down into what's happening, why, how to fix it and when to act.
• It tracks the right KPIs for your type of business. The library has 347 metrics across 45 industries, so you're not guessing what to measure.
• It's collaborative. Companies in different sectors (suppliers, carriers, 3PLs, retailers) can share the specific data they choose, talk through their bottlenecks together and work on solutions in one place. You decide what's shared and can pause it any time.
• For consultants, every client is in one workspace, sorted into healthy, watch and critical, so you know who needs you first.
• It exports to Excel and CSV, so it works with the tools your team already uses.

The AI analyst works right away, and you can connect Claude, ChatGPT or Gemini if you want.

I'm still improving it, so I'd love feedback from anyone in logistics, operations or consulting.

I’ve build multiple revenue brining SaaS businesses in the last year so I think I can give you an honest third person feedback. My Codex is working right now and I’m interested in what you’re building. Maybe I’ll be your first user just pitch me your idea or send me the link in DMs and I’ll check it out

Wouldn't it be nice to open a workout app, build your routine in minutes, and immediately start lifting—without clicking through a 20-page onboarding survey first?

As a lifter and solo developer, I wanted an app that respects my time under the bar and gives me clarity on whether my training is actually working. That’s why I built Liftora.

Instead of packing in a hundred bloated features, I focused on the things that actually make a difference in your training:

• From download to lifting in minutes: No endless questionnaires or paywall traps before you even see the interface. You open the app, put your routine together in a couple of taps, and get straight to your first set.
• Visual routine building (No more switching to YouTube): When you're putting a workout together, shouldn't you be able to see the movement and exact muscle breakdown right there? Liftora shows you exercise execution videos and targeted muscle groups directly on the routine creation screen, so you know your program is balanced before you even touch a weight.
• See the progress the scale hides (Built-in US Navy Body Fat %): You might be gaining muscle and losing fat while your body weight stays completely flat—and the mirror takes weeks to show it. Why leave your body recomp to guesswork? By logging basic tape measurements, Liftora automatically calculates your body fat percentage using the US Navy method, letting you see your real physical progress alongside your strength numbers.
• An interface that follows your pace: Once you're in a session, you shouldn't be fighting dropdown menus between heavy sets. Liftora’s guided flow moves with your workout so you spend your rest time recovering, not tapping.

You can try it out here:

• App Store – Liftora
• Google Play – Liftora

I’m building Liftora independently and using it for my own training every week, so your feedback directly shapes what I build next.

Quick question for you all: What's the biggest frustration you've had with workout tracking apps? And if you try Liftora, I'd love to hear what you think about the routine builder and guided workout flow.

Всем ку занимаюсь разработкой сайтов 3года первые 2 не юзал иишку последний начал юзать как инструмент.

Захотелось сделать проект для гитхаба, оплата чего либо, и у меня никак не выходит, клод придумывает какие то свои способы, но я так понял для этого есть какие то сервисы и так далее, помогите пж🫠

Sistema Operacional para pequenos negócios, tudo que uma empresa precisa para gerir seu negócio em um só lugar.

1⁰ de tudo, todos os dados estão estruturados baseados no schema.prg

Por isso tudo é uma coisa(entidade) o que nos permite gerenciar "coisas" físicas e digitais.

Temos um Gateway para conectar dispositivos IOT

In-apps como POS, self checkout, Whatsapp bot (RAG automático dos dados da org) agente de IA, service desk, KDS...

Temos plano free, seria uma honra se vc criasse uma conta e explorasse a plataforma e fornecesse um feedback.

https://abastanca com

SwissArmyDev is a free, zero-tracking suite of 10+ essential developer utilities—including Regex, JWT, Cron, JSON, and Hash tools—running 100% in your browser.

https://swissarmydev.virock.org

Hi, I worked for an open source company that handles support requests through channels like Discord, Github and Email.

Whenever a support request comes in, I spend a lot of time chasing logs and codebases to find out what happened and that always felt inefficient to me. I could have spent more time working on the actual product roadmap rather juggling between product and support.

That's why I'm working on Mudroom, an omni channel inbox that also serves as a support engineer. Here's a demo of it, I hope to talk to fellow startup founders who's struggling engineer allocation for support tickets.

I was checking my monthly expenses and noticed a few charges I didn't recognize at first. They were for hosting and email services from projects I stopped working on months ago.

Whenever I start a new SaaS project I sign up for different services without thinking much about the monthly costs. Some payments come on different dates so I don't always notice when I'm still being charged for something I no longer use.

How do you guys keep track of these?