Hi everyone,

I'm an 18-year-old founder currently researching a startup idea, and before I spend months building it I'd really like to understand whether this solves a real problem or whether I'm completely wrong.

The vision isn't to replace researchers with AI or build another ChatGPT wrapper.

The idea is to build a development platform specifically for computational work (quant finance, scientific computing, optimization, simulation, eventually quantum computing).

Think of it as four pieces working together:

• IDE – where you write your code
• AI Assistant – understands mathematical and computational problems (not just autocomplete)
• Runtime – analyzes the workload, suggests optimizations, and prepares it for execution
• Hardware Layer – executes the workload on the most appropriate hardware (local CPU, GPU, cloud GPU, and eventually quantum hardware)

The goal isn't to hide everything behind AI.

It's the opposite.

I want developers to keep writing normal Python/Qiskit/CUDA-Q/etc., but remove the headache of figuring out:

• Which algorithm should I use?
• Is this workload GPU-friendly?
• Should I run locally or on cloud hardware?
• Is there any advantage to quantum for this specific problem?
• What's the cheapest way to run this?
• Why is my implementation slow?

For example, imagine a quant researcher writing a portfolio optimization algorithm.

Instead of manually benchmarking different hardware and execution strategies, the runtime could say:

>"This is a convex optimization problem. GPU is estimated to be 12× faster than CPU. Quantum offers no advantage for this workload. Estimated cloud cost: $1.87."

Or, for another workload:

>"This problem can be reformulated as a QUBO. A hybrid quantum-classical workflow may reduce execution time."

The developer still has complete control—the platform just provides recommendations and execution options.

My questions

1. Is this solving a problem you actually have?
2. What is the biggest bottleneck in your computational workflow today?
3. Would you trust a runtime to recommend execution strategies if it explained why it made each recommendation?
4. Am I missing something fundamental that makes this a bad idea?
5. If you could wave a magic wand and improve one thing about your current workflow, what would it be?

I'm not looking for validation—I'd honestly prefer someone tells me why this won't work before I spend a year building it.

Any criticism is appreciated.

Hi everyone,

I'm an 18-year-old founder currently researching a startup idea, and before I spend months building it I'd really like to understand whether this solves a real problem or whether I'm completely wrong.

The vision isn't to replace researchers with AI or build another ChatGPT wrapper.

The idea is to build a development platform specifically for computational work (quant finance, scientific computing, optimization, simulation, eventually quantum computing).

Think of it as four pieces working together:

• IDE – where you write your code
• AI Assistant – understands mathematical and computational problems (not just autocomplete)
• Runtime – analyzes the workload, suggests optimizations, and prepares it for execution
• Hardware Layer – executes the workload on the most appropriate hardware (local CPU, GPU, cloud GPU, and eventually quantum hardware)

The goal isn't to hide everything behind AI.

It's the opposite.

I want developers to keep writing normal Python/Qiskit/CUDA-Q/etc., but remove the headache of figuring out:

• Which algorithm should I use?
• Is this workload GPU-friendly?
• Should I run locally or on cloud hardware?
• Is there any advantage to quantum for this specific problem?
• What's the cheapest way to run this?
• Why is my implementation slow?

For example, imagine a quant researcher writing a portfolio optimization algorithm.

Instead of manually benchmarking different hardware and execution strategies, the runtime could say:

>"This is a convex optimization problem. GPU is estimated to be 12× faster than CPU. Quantum offers no advantage for this workload. Estimated cloud cost: $1.87."

Or, for another workload:

>"This problem can be reformulated as a QUBO. A hybrid quantum-classical workflow may reduce execution time."

The developer still has complete control—the platform just provides recommendations and execution options.

My questions

1. Is this solving a problem you actually have?
2. What is the biggest bottleneck in your computational workflow today?
3. Would you trust a runtime to recommend execution strategies if it explained why it made each recommendation?
4. Am I missing something fundamental that makes this a bad idea?
5. If you could wave a magic wand and improve one thing about your current workflow, what would it be?

I'm not looking for validation—I'd honestly prefer someone tells me why this won't work before I spend a year building it.

Any criticism is appreciated.

Hi everyone,

I'm an 18-year-old founder currently researching a startup idea, and before I spend months building it I'd really like to understand whether this solves a real problem or whether I'm completely wrong.

The vision isn't to replace researchers with AI or build another ChatGPT wrapper.

The idea is to build a development platform specifically for computational work (quant finance, scientific computing, optimization, simulation, eventually quantum computing).

Think of it as four pieces working together:

• IDE – where you write your code
• AI Assistant – understands mathematical and computational problems (not just autocomplete)
• Runtime – analyzes the workload, suggests optimizations, and prepares it for execution
• Hardware Layer – executes the workload on the most appropriate hardware (local CPU, GPU, cloud GPU, and eventually quantum hardware)

The goal isn't to hide everything behind AI.

It's the opposite.

I want developers to keep writing normal Python/Qiskit/CUDA-Q/etc., but remove the headache of figuring out:

• Which algorithm should I use?
• Is this workload GPU-friendly?
• Should I run locally or on cloud hardware?
• Is there any advantage to quantum for this specific problem?
• What's the cheapest way to run this?
• Why is my implementation slow?

For example, imagine a quant researcher writing a portfolio optimization algorithm.

Instead of manually benchmarking different hardware and execution strategies, the runtime could say:

>"This is a convex optimization problem. GPU is estimated to be 12× faster than CPU. Quantum offers no advantage for this workload. Estimated cloud cost: $1.87."

Or, for another workload:

>"This problem can be reformulated as a QUBO. A hybrid quantum-classical workflow may reduce execution time."

The developer still has complete control—the platform just provides recommendations and execution options.

My questions

1. Is this solving a problem you actually have?
2. What is the biggest bottleneck in your computational workflow today?
3. Would you trust a runtime to recommend execution strategies if it explained why it made each recommendation?
4. Am I missing something fundamental that makes this a bad idea?
5. If you could wave a magic wand and improve one thing about your current workflow, what would it be?

I'm not looking for validation—I'd honestly prefer someone tells me why this won't work before I spend a year building it.

Any criticism is appreciated.

Hi everyone,

I'm an 18-year-old founder currently researching a startup idea, and before I spend months building it I'd really like to understand whether this solves a real problem or whether I'm completely wrong.

The vision isn't to replace researchers with AI or build another ChatGPT wrapper.

The idea is to build a development platform specifically for computational work (quant finance, scientific computing, optimization, simulation, eventually quantum computing).

Think of it as four pieces working together:

• IDE – where you write your code
• AI Assistant – understands mathematical and computational problems (not just autocomplete)
• Runtime – analyzes the workload, suggests optimizations, and prepares it for execution
• Hardware Layer – executes the workload on the most appropriate hardware (local CPU, GPU, cloud GPU, and eventually quantum hardware)

The goal isn't to hide everything behind AI.

It's the opposite.

I want developers to keep writing normal Python/Qiskit/CUDA-Q/etc., but remove the headache of figuring out:

• Which algorithm should I use?
• Is this workload GPU-friendly?
• Should I run locally or on cloud hardware?
• Is there any advantage to quantum for this specific problem?
• What's the cheapest way to run this?
• Why is my implementation slow?

For example, imagine a quant researcher writing a portfolio optimization algorithm.

Instead of manually benchmarking different hardware and execution strategies, the runtime could say:

>"This is a convex optimization problem. GPU is estimated to be 12× faster than CPU. Quantum offers no advantage for this workload. Estimated cloud cost: $1.87."

Or, for another workload:

>"This problem can be reformulated as a QUBO. A hybrid quantum-classical workflow may reduce execution time."

The developer still has complete control—the platform just provides recommendations and execution options.

My questions

1. Is this solving a problem you actually have?
2. What is the biggest bottleneck in your computational workflow today?
3. Would you trust a runtime to recommend execution strategies if it explained why it made each recommendation?
4. Am I missing something fundamental that makes this a bad idea?
5. If you could wave a magic wand and improve one thing about your current workflow, what would it be?

I'm not looking for validation—I'd honestly prefer someone tells me why this won't work before I spend a year building it.

Any criticism is appreciated.

Hi everyone,

I'm currently building an AI-native engineering workspace called FORGE, and I'd really appreciate feedback from engineers before I continue building it.

The idea is to create a workspace that combines the best parts of Notion + Cursor + Figma + Blender, but specifically for engineering workflows.

Instead of constantly switching between CAD software, simulation tools, documentation, spreadsheets, and AI chatbots, everything would happen in one place.

The vision is for an engineer to be able to:

• Generate or edit 3D designs with AI using natural language
• Build assemblies in an interactive workspace
• Run simulations (CFD, FEA, thermal, etc.)
• Get AI suggestions to improve performance, reduce weight, or optimize materials
• Automatically generate engineering reports and documentation
• Keep project notes, requirements, and simulations linked together in one workspace

I'm not trying to replace SolidWorks or ANSYS overnight. The goal is to make engineering workflows much faster by putting AI at the center of the design process rather than treating it as a separate chatbot.

I'd love some honest feedback:

• Is this something you would actually use?
• Which part of your engineering workflow wastes the most time today?
• Would this solve a real problem, or is it just "nice to have"?
• If it genuinely saved you hours every week, what would you realistically pay as a monthly subscription? ($20, $50, $100, more?)

I'm looking for brutally honest feedback from mechanical, aerospace, civil, electrical, manufacturing, and robotics engineers. Thanks!

Hi everyone,

I'm currently building an AI-native engineering workspace called FORGE, and I'd really appreciate feedback from engineers before I continue building it.

The idea is to create a workspace that combines the best parts of Notion + Cursor + Figma + Blender, but specifically for engineering workflows.

Instead of constantly switching between CAD software, simulation tools, documentation, spreadsheets, and AI chatbots, everything would happen in one place.

The vision is for an engineer to be able to:

• Generate or edit 3D designs with AI using natural language
• Build assemblies in an interactive workspace
• Run simulations (CFD, FEA, thermal, etc.)
• Get AI suggestions to improve performance, reduce weight, or optimize materials
• Automatically generate engineering reports and documentation
• Keep project notes, requirements, and simulations linked together in one workspace

I'm not trying to replace SolidWorks or ANSYS overnight. The goal is to make engineering workflows much faster by putting AI at the center of the design process rather than treating it as a separate chatbot.

I'd love some honest feedback:

• Is this something you would actually use?
• Which part of your engineering workflow wastes the most time today?
• Would this solve a real problem, or is it just "nice to have"?
• If it genuinely saved you hours every week, what would you realistically pay as a monthly subscription? ($20, $50, $100, more?)

I'm looking for brutally honest feedback from mechanical, aerospace, civil, electrical, manufacturing, and robotics engineers. Thanks!

Hi everyone,

I'm currently building an AI-native engineering workspace called FORGE, and I'd really appreciate feedback from engineers before I continue building it.

The idea is to create a workspace that combines the best parts of Notion + Cursor + Figma + Blender, but specifically for engineering workflows.

Instead of constantly switching between CAD software, simulation tools, documentation, spreadsheets, and AI chatbots, everything would happen in one place.

The vision is for an engineer to be able to:

• Generate or edit 3D designs with AI using natural language
• Build assemblies in an interactive workspace
• Run simulations (CFD, FEA, thermal, etc.)
• Get AI suggestions to improve performance, reduce weight, or optimize materials
• Automatically generate engineering reports and documentation
• Keep project notes, requirements, and simulations linked together in one workspace

I'm not trying to replace SolidWorks or ANSYS overnight. The goal is to make engineering workflows much faster by putting AI at the center of the design process rather than treating it as a separate chatbot.

I'd love some honest feedback:

• Is this something you would actually use?
• Which part of your engineering workflow wastes the most time today?
• Would this solve a real problem, or is it just "nice to have"?
• If it genuinely saved you hours every week, what would you realistically pay as a monthly subscription? ($20, $50, $100, more?)

I'm looking for brutally honest feedback from mechanical, aerospace, civil, electrical, manufacturing, and robotics engineers. Thanks!

Im waiting for financial actuarial mathematics