bohdanstefaniuk

So the HealthKit basically covers both Fitness and Health app. As I understand there is not difference from the api client perspective. For example I ask for the steps count permission and I don't know who actually wrote it. It can be apple watch or garmin watch or something else.

Yeah! It's a great project, but at least for me it lacks numbers/time tracking. And I prefer vertical view, when the row represents a date. But I have both views.

First of all mobile application has offline support. This feature implemented by producing operations log. Basically each action made in the app is a separate operation. Same happens on the backend. Every API call mutates state in the database and creates operations.

When internet connection restored for the mobile app it sends all operations to the server. Server applies and re conciliates properly values and returns the current correct state.

Apple Health integration works buy reading values from HealthKit and pushing those values into one operations log.

MCP integration is very simple. Basically I have an API which users can use to integrate into their own apps and MCP utilize same API with OAuth authentication

Thank you! To be honest it depends on person, I just like to track everything even if sometimes in useless. So I wanted to have a place where I can do this and something a little more friendlier than excel spreadsheet

For now it's not an open-source but I actually have plans to make it open and allow self-hosting. To have two models: paid cloud or self hosting

Hey everyone!

I just want to share an app - Habit Pocket. I built to track my habits and other metrics I like. For me the biggest problem of most trackers is limitation by only habit done and not done state.

But I prefer to track my actual effort and be consistent with using actual numbers. For example I have a goal to walk 10k steps everyday. What if I walked 9k, it's not a 10k but definitely good!

This is why I like to track actual numbers and time. In my case it help me stick to the habits better. Because showing up every day even if it's half of the goal is very important and for myself I like to see any effort I put.

Same with skip, I added support of skips because sometime I can go camping for 3-5 days and I'm not able to do some things, so I need to decide to loose my streak or mark it done and ruin stats. So this is why skips can be handy.

Also it has MCP integration so you can connect your AI of choice. Pull your data using Apple Health integration!

Thank you!

Thank you!

I would like to see more and more local LLMs. Also it would be great to have some very simple tooling to teach LLMs based on my data. For example I have huge documentation or lots of data and I don't want to manually build RAG. I want to have an option to upload all my files, wait few hours-days and then we able to work with my data across all conversation or at least all project scoped conversations

Sure, you will learn some basic syntax and maybe how to solve algorithmic tasks in a vacuum. But actual development is much wider. If you going to build web app you need to know how does web server work, databases, network, HTML/CSS

I think the best way is to find something interesting or even copy someone else idea and start building it. Page by page, button by button

- Thing 3 - 180db player - 1Password

I have kind of the same feeling that right now when subscription is quite cheap and companies spend VC money we get used to using those tools and basically replace manual coding. And I'm worying what will happen when OpenAI and Cluade realize that they need to each money and make profit.

I think the way to solve this are open weights models, but they are still pretty new and less powerful, from my experience, that SOTA models

I would like to see not only token-saving in regular chats but also in codding sessions. Also it would be nice to have some kind of revenue attribution. For example we have a project which earns X amount of money and I would like to see if our token usage actually help us moving our revenue. Or at least what part of our revenue tokens cost

What I like to do, but it sometime requires a lot of effort and/or time:

- I like to dig deeper into some controversial problem and try to build my own understanding and picture of the world. For example trying to understand why people hate new data centers which pops up in US like a flowers. What can be dome to sutisfy both sides. Same with some other hot topics.

- Try to build something without using AI to write a code. Still you can use AI but no more as a consultant or powerful search engine. Try to build something stupid and fun or maybe technical challenging. For example I always want to understand how Uber and delivery apps work with real-time GPS data and how to show and find nearest driver so I build my own thing, locally

- Try to find something what interesting to you outside your typical envrionment - try woodworking, understand how to stain and finish the wood. Try backpacking using only compass and physical map (definitely bring your phone with GPS just in case).

When I work with AI to build something I like to summarize every conversation into separate folder and basically keep a journal for AI. This is how it looks like:

"journal" folder:

- 2026-05-12 Add comments support for the cells.md

- 2026-06-15 Rework onboarding screen.md

Each document contains show description of the problem and explanation how did we solve it. Then If I need to go back in time and improve one of the features I can use one or multiple of those files as a reference so we don't need to "rescan" entire code base and re-analyze it. And the most important - I'm not loosing some decisions we made which from the code alone can be considered bugs but they are features actually

Not too much, speaking of public one which you can try:

- https://habitpocket.io/ - habit tracker which allow to track not only habits (done, not done) but also numbers and time of the day on one grid. Also I built an iOS app but it's in Apple Review right now so it should be ready in the week.

- https://taskpocket.io/ - todo list, my own interpretation of proper todo list. Right now it's completely free. I would like to add one-time payment especially when I add attachments

Both of the apps above was built using AI especially for the frontend. Both backend at some point of time I almost completely refactored/rewrote myself

Speaking of private apps:

- I made a small app which allows me to track business expenses and categorize them Built with AI and using AI.

I use interesting trick - I dress to work. Basically when I start working I put on one of my favorite tshirt and shorts. So I have this feeling like I'm on the work.

Also I use intervals, I have basic analog timer, but I don't use pomorodo technique. If I'm in the flow state I can work much longer then originally intended without interruptions

My approach to work on new project

- Setup proper CLAUDE.md or AGENTS.md

- Use cmux or conductor to manage multiple instances of Claude Code

When working of some business feature I always ask AI to summarize every conversation in following format: problem, brief solution, where change were made. And I store it in the repo under "journal" folder where each file has "2026-06-08-add-offile-firts-support.md"

Also for me personally having separate repository with some boilerplate code become a huge booster of productivity.

Usually inside this boilerplate repo I have:

- Descriptions of desired project structure, so every project follows the same structure I personally like

- Some CI/CD templates, in my case: build docker container, push into github registry, deploy to my bare bone VM

- Authentication + third parties integration

- Custom exceptions and middleware to handle those exceptions

- And more

Looks awesome! I like it! It would be nice to have windows version too, because sometimes I need to do some AI and not only work on Windows machine.

Also quick question - what tool did you use to create such nice video/gif on your "hero" slide?

> To me, engineers are people who build things and take full responsibility for them

But you can apply the same logic for the software engineer, you take responsibility for the project you develop and lead. You take responsibility to make it work properly in different environments and load. What about software engineers who build software for the planes or rockets?

As I know Cursor finally has some gross margin profitability after they introduces their own Composer model.

I think the biggest reason why most products and LLM provides still in negative because they are in the inception phase and trying to extend their market share.

Kind of similar to Uber when they spent huge amount of money to conquer new markets and increase their market share.

I think in the near future we will see significant increase of prices for tokens

I think in the era of AI knowing and understanding concepts of the computer science, know how to properly build an application, understand all supporting harness like logging, devops and other stuff become much more important that knowing some implementation details of each language.

Don't get me wrong, it still important, but I think understanding concepts and principles behind each application become more important

You still need to understand how database works and how to analyze performance, how to avoid N+1 problem. You still need to understand how to run your application in the cloud or some bare-bone vm. You still need to understand security concepts

Genuine question, I'm trying to adopt specs and AI DLC in my team so we can use it as an enhancement and improve our development and the biggest pain right now for us is managing all those md artifacts.

I'm curious how do you manage them? Do you preserve them for the future or delete as soon as task was accomplished? If you're deleting those artifacts after job being done - do you summarize those specs into the Jira ticket or whatever system you use.

I use this approach with worlds news. I've noticed if I don't read any political news, I will eventually know the most important once which affects me.

In most cases documentation is enough for me when I need some help with my current day to day tasks.

If I want do dig deeper - textbooks, white papers are still a good source.

I've found a middle ground where I sketch architecture by myself or using AI as an advisor. Document all my decisions and key points and then use AI coding to extend foundation and add features.

Basically what I do:

- I have an idea of some architecture solution for the current task

- I try to sketch it myself and use AI to find some flaws I missing or try to find alternative approaches

- Review comments from AI and summarize into final decision

- Implement foundational layer myself or using AI to help me

- Document all crucial points like module boundaries, dependencies, key strutural elements

- Use AI to add features on top of newly built architecture

From my team experience repo level documentation, skills and agents are enough to share similar approach of using AI among the team. We also trying to adopt journals for AI. Basically generate a single md file after each conversation to have a context of what was done.

For me the biggest pain right now is to have some kind of central place where other team can post skills they use, prompts and other things. Right now we are thinking about creating a separate repo to store custom skills and other stuff so each team can contribute and use others team work.