k074m4n

PERIOD

2024 — 2025

CLASS

Computer Vision / ML

TIER

rare

Designed and trained two detection models, one YOLO-based and one Transformer-based, to find people in thermal images.

Finding people in thermal images is a different problem from normal object detection. There is no colour or fine texture to work with, just heat against a noisy background. That is what makes it useful where a normal camera gives up: low light, smoke, search and rescue, perimeter work. It also forces the model to lean on shape and heat contrast alone.

I built and trained two separate pipelines on thermal datasets. One is a YOLO single-stage detector tuned for speed. The other is a Transformer-based detector I used for comparison. Both needed thermal-specific prep first: single-channel normalisation and augmentation meant for heat data rather than RGB.

Putting the two side by side on the same data made the trade-offs clear: single-stage convolution against attention, accuracy against inference cost and the frames where each one falls apart. I came out of it with a direct read on how two modern detection architectures behave on data that hides most of the cues they were built for.

▸ Two detector families (YOLO and Transformer) trained and compared on thermal data.

STACK

• Python
• PyTorch
• YOLO
• Transformers
• OpenCV

PERIOD

2026

CLASS

Security Tooling

TIER

rare

A Turkish-language wordlist generator for password self-audits. It builds candidates the way people actually make passwords, by gluing familiar Turkish words together.

Almost every wordlist you can download is English. But people build passwords out of words in their own language so a Turkish user is far more likely to type kahve + kitap + 2026 than anything in rockyou.txt. That gap is the whole reason the tool exists: auditing a Turkish target needs Turkish candidates and there was no clean generator for them.

It starts from a seed of 1,193 Turkish words and normalises them the way people actually type passwords, folding Turkish characters down to ASCII so İstanbul becomes istanbul, lowercasing everything and dropping duplicates. Then it generates ordered one, two and three word combinations without repetition. The two-word set is about 1.42 million candidates and the three-word set about 1.70 billion, roughly 25 GB if you write it to disk.

On top of the base sit three progressive hashcat rule layers: hafif, orta and sert (32, 189 and 732 rules). Each one multiplies a candidate into its capitalised, leetspeak and year-appended variants. The whole thing is pure Python standard library with no dependencies and it feeds straight into hashcat on a GPU. It is built for auditing passwords you are allowed to test, nothing else.

▸ Turns 1,193 seed words into up to ~1.7 billion three-word candidates, with three hashcat rule tiers layered on top.

STACK

• Python
• hashcat
• Wordlists
• GPU