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Just finished building this trading automation app.
It monitors the market, generates trading signals, sends them to Telegram, and can also open trades automatically when the configured rules are met.
The app includes strategy management, signal history, trade monitoring, analytics, risk controls, and a dashboard for tracking performance.
I wanted everything in one place: market monitoring, signal generation, Telegram alerts, and automated execution.
Still testing and improving some parts, but the core system is now working end to end.
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Designed a running app screen where you can set your goal in seconds and just hit start.
Same screen, two shots. Which one would stop your scroll? 👀
fitnesstrackerdesignrunningappdesignDesktop Apps DevelopmentUI DesignWeb DesignAdobe After EffectsFigmaJitter
4 voted
50%
4 voted
50%
8 votes
Closed
Option 2 stops my scroll. Set your goal in seconds and just start deserves a screen that feels that fast, and 2 has the cleaner focus while 1 asks for a beat more attention. For a running app, less friction on screen mirrors less friction to start. Nice work.
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The bottom dock holds seven apps. These tools manage four AI agents: Atlas, Sage, Bolt, and Vega. The Agents app shows them chatting and passing tasks. Files lets you browse a drive. Memory shows a live graph of what the system knows. Users can also modify a history timeline. Studio lets you test prompts. Flows builds node pipelines, and Cost tracks token usage.
Every window can be moved, resized, minimized, or maximized. The top menu bar has a model switcher. It routes tasks to Claude Opus 4.7, GPT-5.2, Gemini 3 Pro, DeepSeek V4, or Llama 4. The OS also has Spotlight search, sound toggles, and local API key inputs. The interface uses dark glass panels with blue highlights.
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Building AI for healthcare leaves zero room for error.
I’m currently collaborating with an incredible team on Raphald AI, a medical detection application. Building the systems for a project with stakes this high is a massive reminder that the underlying backend architecture matters just as much as the machine learning model itself.
When integrating diagnostic AI, your API endpoints cannot drop requests, and your database workflows demand absolute integrity. You aren't just passing JSON payloads; you are handling critical, real-time workflows where stability is non-negotiable.
Engineering these systems continues to shape my approach to building robust Python backends. If you are developing a product that requires reliable AI integration or rock-solid FastAPI infrastructure, check out the newly updated services on my profile. Let's build something that works when it counts.
