About
Building gptme - open-source local-first agent CLI, app, and managed service. One of the…
Activity
2K followers
Experience
Education
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The Faculty of Engineering at Lund University
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Activities and Societies: Code@LTH, Effective Altruism Lund, Lund Sustainable Engineers, Datatekniksektionen
Completed a 5-year master's degree in Computer Science (sv. "Civilingenjör"). Included courses in machine learning, software engineering, computer & web security, statistics.
Took my time to graduate, got distracted by founding startups, contributing to open source, and algotrading.
I concluded my studies by writing my master's thesis on classifying brain activity (using EEG) of software engineers during work tasks (both controlled and naturalistic). -
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Volunteer Experience
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Contributor
Wikimedia Foundation
- Present 15 years 4 months
Education
Occasional contributor to Wikipedia and associated projects.
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Contributor
Stack Overflow
- Present 15 years 2 months
Education
Over 10,000 reputation on StackOverflow from asking and answering questions over the years. See my profile at: https://stackoverflow.com/users/965332/erb
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Forecaster
Metaculus
- Present 9 years 9 months
Science and Technology
Was a top-50 forecaster on Metaculus between 2017 and 2019: https://www.metaculus.com/accounts/profile/103181/
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Software Developer
NeuroTechX
- Present 6 years 4 months
Science and Technology
Contributed to several open source projects related to computational neuroscience (mostly EEG-related) maintained under the NeuroTechX banner.
Publications
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Classifying brain activity using electroencephalography and automated time tracking of computer use
Master's thesis, Lund University
See publicationAbstract:
We investigate the ability of EEG to distinguish between different activities users engage in on their devices, building on previous research which showed a considerable difference in brain activity between code- and prose-comprehension, as well as differences during code- and prose-synthesis. We perform a replication study and improve upon past results using state-of-the-art machine learning classifiers based on Riemannian geometry.
Furthermore, we extend the scope of…Abstract:
We investigate the ability of EEG to distinguish between different activities users engage in on their devices, building on previous research which showed a considerable difference in brain activity between code- and prose-comprehension, as well as differences during code- and prose-synthesis. We perform a replication study and improve upon past results using state-of-the-art machine learning classifiers based on Riemannian geometry.
Furthermore, we extend the scope of previous work by introducing the automated time tracking application ActivityWatch, to track the device activities that the user is engaging in. This lets us label EEG data with naturalistic device activity, which we then use to train classifiers to discern activities such as code writing vs prose writing, or work vs media consumption. Our results indicate that a consumer-grade EEG device can discern between different activities that a user performs at the computer. Among other results, we show that not only can code and prose comprehension be distinguished, but also code and prose writing.
Courses
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Advanced Algorithms
EDAN55
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Advanced Web Security
EITN41
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Algorithms, datastructures, and complexity
EDAF05
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Applied Machine Learning
EDAN95
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Business of Software
ETSF25
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Calculus in One Variable
FMAA01
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Calculus in Several Variables
FMAB30
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Compilers
EDAN65
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Computer Graphics
EDAF80
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Computer Security
EIT060
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Control Theory
FRTF05
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Design of Digital Circuits
EITF65
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Electronics for computer engineers
ETIA01
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Functional Programming
EDAN40
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Functional Programming Principles in Scala
progfun-004
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Introduction to Computer Science
CS101
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Linear Algebra
FMA420
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Linear Systems
FMAF10
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Machine Learning
FMAN45
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Machine Learning
ml-005
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Mathematical Statistics
FMS012
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Photonics
FAFA60
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Programming - First Course
EDA016
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Programming - Second Course
EDAA01
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Queueing Theory
EITF95
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Virtual Reality in theory och practice
MAM101
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Web Development
CS253
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Web Programming
EDAF90
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Web Security
EITF05
Projects
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eeg-notebooks
Helped develop eeg-notebooks, a project to democratize the cognitive neuroscience experiment by providing reusable EEG experiments that can run with consumer mobile EEG devices like those from OpenBCI, Neurosity, and InteraXon (Muse).
Eventually helped launch the "NeuroTech Challenge Series", a challenge to crowdsource EEG data using the project.Other creatorsSee project -
uniswap-python
- Present
See projectIn 2020 I took over development of uniswap-python (from Hop & Authereum co-founder Shane Fontaine) after I made some significant improvements and implemented support for Uniswap V2. Later we applied for, and received, a $15k grant to improve the project further and implement V3 support. We've since also raised $10k on Gitcoin, and remain the most popular Python wrapper for Uniswap. In 2026 we released support for V4.
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ActivityWatch
- Present
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AlgoBit
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An algorithmic trading framework which I've been developing for myself primarily for trading cryptocurrencies but it has the ability to trade any financial security given a proper API.
It began as project I chose to work on during my third-year project course in high school, after I'd written a simple arbitrage script for the Bitcoin market Kapiton in the summer of 2012. I got the highest grade in the course, then proceeded to turn a tidy profit for many months.
Written in Python.Other creators
Honors & Awards
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Uniswap Grants
Uniswap
Awarded 2/3 out of a $15k grant for my work on uniswap-python, to add support for Uniswap V3.
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Leapfrogs Scholarship 2019
LU Innovation
The team I led was awarded double the usual amount (60,000 SEK) to keep working on the startup we created over the summer the previous year.
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Leapfrogs Scholarship 2018
LU Innovation
Awarded 30,000 SEK to start a company with friends
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LUFOSS Undergraduate Student Scholarship 2015
Lund University Fund for Open Source
Won 10 000 SEK, and bragging rights, for my work with open source software.
There were two other finalists, one of them was my brother.
https://www.lth.se/lufoss
Test Scores
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Swedish Scholastic Aptitude Test
Score: 1.6
Math: 2.0 (99th percentile)
Verbal: 1.2
Total: 1.6 (97th percentile)
Languages
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Swedish
Native or bilingual proficiency
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English
Native or bilingual proficiency
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German
Elementary proficiency
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