DEPT. OF MOLECULAR BIONICS · FIELD JOURNAL
Vol. I — experiments, specimens & transmissions
If found, return to:
Robin Kun
Molecular Bionics Engineering · est. 2026
Fig. 02 · in vivo dissection
designation: unknown
— dissection table 02 · do not remove specimens —
Cerebral Processing — Logic
First-principles reasoning, a stubborn debugging instinct, and far too much coffee. Specialises in the messy seam between code and cells.
Power Core — Ambition
Stitching together synthetic circuits and living tissue. Bridging the gap between machine and biology, one highly questionable experiment at a time.
Motor Functions — Hobbies
Off the bench: sketching imaginary creatures, lifting heavy things, and dismantling gadgets that worked perfectly fine.
Fig. 03 · the corkboard
5 specimens currently on file
Automated slicing and exploded-view rendering of high-res CT/MRI scans. Uses a 3D U-Net to highlight micro-lesions and tumours, dissecting the data so the surgeon doesn't have to.
[ Python ] [ PyTorch ] [ Three.js ] [ Medical Imaging ]
Fine-tuning protein language models (ESM) to hallucinate novel antibody sequences. In silico screening for antigen binding affinity. Predicting immunity before it naturally evolves.
[ Transformers ] [ PyTorch ] [ MLOps ] [ Immunology ]
A scalable MLOps pipeline evaluating sgRNA safety for in vivo editing. Predicts unintended DNA cleavage sites with terrifying accuracy. Containerised and ready for the wet lab.
[ Docker ] [ FastAPI ] [ Genomics ] [ TensorFlow ]
Extracting individual finger intent from messy, high-noise forearm surface electromyography. A tiny neural net deployed straight to a microcontroller. It reads the twitch.
[ C++ ] [ TinyML ] [ Signal Processing ] [ Bionics ]
A machine learning model fed on bulk RNA-seq data to predict true biological age. Identifying the exact transcriptomic signatures of cellular decay.
[ Python ] [ R ] [ Bioinformatics ] [ Genetics ]
Fig. 04 · the specimen shelf
do not feed the specimens
Digital Synapses
do not make eye contact
Algorithmic Pathways
the grit of every algorithm
Wetware & Hardware
some assembly required
Fig. 05 · classified
subject file — access restricted
File No. MBE-074 · Dept. of Molecular Bionics
SUBJECT: Viktor Robin Kun
CLASSIFICATION: TOP SECRET // CLEARANCE LVL 05
Education
B.Sc. Molecular Bionics Engineering — Pázmány ITK (Expected 2027)
Foundation: Chemical Technician & Laboratory Assistant — Petrik Lajos Institute.
Field & Clinical Experience
API Manufacturer — Sanofi Pharmaceuticals
· Synthesized high-grade active compounds. Mastered industrial-scale wet lab operations.
Freelance & Volunteer — MLOps / Software Engineering
· Self-taught algorithmic architect. Deployed autonomous data pipelines to compensate for human sleep cycles.
Laboratory Diagnostics
Extensive in-vivo capabilities: precise titrations, complex solution engineering, and genetics laboratory protocols. The subject thrives in the wet lab environment.
Threat Assessment
Formal awards: None (yet).
Psychological profile notes highly dangerous levels of motivation and a relentless, unyielding generation of experimental ideas. Do not underestimate.
// remainder of file sealed — clearance required
Fig. 06 · transmission
Out of lab time. Catch me on one of these frequencies before the next experiment —
