The iPhone’s LiDAR sensor measures the real volume of the food on your plate, so calories start from a measurement instead of a guess.
My own algorithm models your body with a Kalman filter and retunes your calorie target every week.
Scan a barcode or read a nutrition label, and the food is logged in seconds.
Jacob Soendergaard
I build AI agents, automation and native iPhone apps.
- Computer Science, Aalborg University
- Copenhagen
- Danish and English
Two apps, designed and built by me
It reads your records and can change them through tool calls, and every change can be undone.
Log a dose in a tap and see every logged, missed and planned dose on one calendar.
Each dose comes out of the right vial, so you always know how long your supply lasts.
What I do
- An AI agent inside PepNote that edits real records through tool calls, with undo on every action and a spend cap per account
- Custom MCP servers and agent skills for Claude Code, Cursor and Codex, including one that lets agents reuse shared web components
- Hermes Agent and OpenClaw set up for marketing and support, with subagents and model routing
- SEO automation that took a client’s target local Google ranking from page 2 to #2
- CaloMatic and PepNote, two complete apps in Swift and SwiftUI, from onboarding to subscriptions, one submitted to the App Store
- A LiDAR meal scanner that finds the plate’s plane, builds a height map and turns depth into food volume
- iOS App Foundry, my own Swift packages for sign-in, subscriptions and account deletion
- Supabase and PostgreSQL behind both apps, with row-level security and sync across devices
- Edge Functions for streaming AI chat, meal scans, RevenueCat webhooks and account deletion
- Food lookups cut from 28 ms to 2.9 ms
- A neural calorie coach: a Kalman filter tracks each body, and a GRU network trained on 2 million simulated people refines the target
- Trained in PyTorch with domain randomisation and a DAgger round, then ported to native Swift to run on the iPhone
- Multimodal meal scans with structured outputs, on a model chain that falls back automatically
- LoRA fine-tuning of FLUX for images and Qwen for more consistent RAG answers
- Every screen of both apps, from Figma to SwiftUI, including onboarding, paywalls and App Store screenshots
- A 550-icon food library for CaloMatic, built to one style guide
- This site: my name lit on my real GitHub contribution graph in custom 5×7 lettering, and every scene drawn live on a 32×32 cell display
753 contributions
On GitHub in the last 12 months, mostly in private repositories.
Experience
CaloMatic
In developmentAI calorie tracking. Photo recognition on multimodal LLMs, and my own adaptive calorie algorithm, tested in simulation on noisy and missing logs.
PepNote
2026Peptide and GLP-1 tracking. Designed and built in SwiftUI, then taken through App Store submission.
Agent systems
Personal projectsAn MCP server and agent skill that let coding agents reuse shared web components. Hermes Agent and OpenClaw for marketing and support.
Model fine-tuning
IndependentFLUX for image generation, and Qwen with LoRA for more consistent RAG answers.
Freelance AI and web development
2024–2026Client websites built with AI-assisted development and generative visuals. SEO automation took a client’s target local Google ranking from page 2 to #2.
Aalborg University Copenhagen
2025–nowComputer Science, 3rd semester. Algorithms, data structures, web programming and an RSA encryption project in C.
NEXT Sukkertoppen
2025HTX, technical upper-secondary. School project: an Asteroids-style game in JavaScript and p5.js.
