Electrical_Option652

Hey everyone,

I recently open-sourced a small macOS project called FMLD Panel.

It’s a SwiftUI fraud-monitoring dashboard template that shows how you can structure a local transaction analysis tool with rule-based risk scoring and optional local AI analysis via Ollama.

Main ideas in the project:

• SwiftUI monitoring dashboard

• configurable JSON rule engine for fraud detection

• local transaction storage (SQLite)

• basic BIN lookup integration

• optional LLM analysis through Ollama

• modular service architecture

It’s meant more as a developer template / architecture reference rather than a full production fraud system, so it should be easy to explore, modify, or extend for your own

Hey Guys!

I’m 19 years old and currently running 250 mg Sustanon + 300 mg EQ per week (week 6 right now). I know it’s not the smartest stack, but I am trying to manage the bloodwork as best as I can for an upcoming doctor visit.

Current stack for bloods control:

Rosuvastatin 20 mg daily (started 2 weeks ago)

Telmisartan 40 mg daily

High dose Omega-3

  NAC

Cardio wise boxing 3x per week (1h spar seshs)

The panel I will need to keep clean is:

Lipids - Total cholesterol <200, HDL >40, LDL <116, ApoA1 >1.25 g/l, ApoB <1.0 g/l, triglycerides <150, atherogenic index 2.0-3.0

CRP <5.0 mg/L

Uric acid (blood + urine)

Right now the biggest problems for me obviously are

HDL and ApoA1 are st

Hey Guys!

I’m 19 years old and currently running 250 mg Sustanon + 300 mg EQ per week (week 6 right now). I know it’s not the smartest stack, but I am trying to manage the bloodwork as best as I can for an upcoming doctor visit.

Current stack for bloods control:

Rosuvastatin 20 mg daily (started 2 weeks ago)

Telmisartan 40 mg daily

High dose Omega-3

  NAC

Cardio wise boxing 3x per week (1h spar seshs)

The panel I will need to keep clean is:

Lipids - Total cholesterol <200, HDL >40, LDL <116, ApoA1 >1.25 g/l, ApoB <1.0 g/l, triglycerides <150, atherogenic index 2.0-3.0

CRP <5.0 mg/L

Uric acid (blood + urine)

Right now the biggest problems for me obviously are

HDL and A

Hey everyone,

I recently open-sourced a small macOS project called FMLD Panel.

It’s a SwiftUI fraud-monitoring dashboard template that shows how you can structure a local transaction analysis tool with rule-based risk scoring and optional local AI analysis via Ollama.

Main ideas in the project:

• SwiftUI monitoring dashboard

• configurable JSON rule engine for fraud detection

• local transaction storage (SQLite)

• basic BIN lookup integration

• optional LLM analysis through Ollama

• modular service architecture

It’s meant more as a developer template / architecture reference rather than a full production fraud system, so it should be easy to explore, modify, or ex

Hey everyone,

I recently open-sourced a small macOS project called FMLD Panel.

It’s a SwiftUI fraud-monitoring dashboard template that shows how you can structure a local transaction analysis tool with rule-based risk scoring and optional local AI analysis via Ollama.

Main ideas in the project:

• SwiftUI monitoring dashboard

• configurable JSON rule engine for fraud detection

• local transaction storage (SQLite)

• basic BIN lookup integration

• optional LLM analysis through Ollama

• modular service architecture

It’s meant more as a developer template / architecture reference rather than a full production fraud system, so it should be easy to explore, modify, or ex

Hey everyone,

I recently open-sourced a small macOS project called FMLD Panel.

It’s a SwiftUI fraud-monitoring dashboard template that shows how you can structure a local transaction analysis tool with rule-based risk scoring and optional local AI analysis via Ollama.

Main ideas in the project:

• SwiftUI monitoring dashboard

• configurable JSON rule engine for fraud detection

• local transaction storage (SQLite)

• basic BIN lookup integration

• optional LLM analysis through Ollama

• modular service architecture

It’s meant more as a developer template / architecture reference rather than a full production fraud system, so it should be easy to explore, modify, or ex

Hey everyone,

I recently open-sourced a small macOS project called FMLD Panel.

It’s a SwiftUI fraud-monitoring dashboard template that shows how you can structure a local transaction analysis tool with rule-based risk scoring and optional local AI analysis via Ollama.

Main ideas in the project:

• SwiftUI monitoring dashboard

• configurable JSON rule engine for fraud detection

• local transaction storage (SQLite)

• basic BIN lookup integration

• optional LLM analysis through Ollama

• modular service architecture

It’s meant more as a developer template / architecture reference rather than a full production fraud system, so it should be easy to explore, modify, or ex

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