Izzet Ali Arslan@arslannizzet
2
Events attended
1
Submissions made
Turkey
1 year of experience
About me
> Final-year Software Development student at Istanbul Aydın University, focused on AI, big data and backend development. I'm currently interning at İSBAK, Istanbul's smart-city technology company, and at Konsalt, a DevOps firm. Before that, I worked as a big data intern at Istanbul Metropolitan Municipality's Smart Cities team, working with large-scale urban data. > > I compete in hackathons across Europe and placed 3rd at Turkcell CodeNight. I enjoy building products where sports and technology meet, like CurryShot, an LLM-powered NBA analytics dashboard, and a tournament management system for football and volleyball events. > > Before software, I played chess professionally. Years of competitive chess taught me to think several moves ahead, stay calm under pressure and make decisions with limited time, which is pretty much what a good hackathon looks like. > > Always up for meeting builders who like shipping fast and thinking big. Yaklaşık 950 karakter. Satranç kısmını öne almak istersen ya da derece/unvan eklemek istersen söyle.
🤓 Latest Submissions
CareCenter: AI Shift Handover on AMD
In disability and elderly care homes, the most dangerous moment of the day is the shift change. A night-shift carer may have recorded a fall at 5 a.m., a low blood sugar reading, or a resident who refused medication, but the incoming team has minutes to read dozens of scattered notes per resident. Important details get lost, and residents pay the price. CareCenter is an in-house care management system for disability and elderly care facilities. Staff record medications, vital signs, care tasks, observations and incidents for each resident, manage documents with expiry tracking, and administrators see every action in a plain-language audit log. The system is already in daily use at a real disability care facility. For this hackathon we added an AI Shift Handover Summary. With one click, a carer picks the last 12, 24 or 48 hours and receives a short, bullet-point handover: urgent items first, then medications given, vital signs, care performed and what the next shift must do. The summary is generated by an open model (DeepSeek-V4-Flash) running on AMD GPUs through AMD Token Factory, connected via an OpenAI-compatible provider layer. Because this is health data, privacy is built in. Before anything leaves the system, names, national ID numbers, phone numbers and dates are masked, and a final check blocks the request if any personal data remains. The model is instructed never to diagnose or invent information, staff notes are treated as data rather than instructions to resist prompt injection, every summary is logged without storing its content, and the UI clearly labels the output as AI-generated so carers verify it against the original records.
18 Oct 2026
