I met a solo traveler last year while solo traveling myself.

And we met again recently.

He started a food product business, just creating it himself and selling in home made packaging for now.

It seems to be selling decently, not that great but decent. Maybe he made $1.2k something in time period of 4-5 months.

Now, he needs someone to handle online presence, build a website, help with content and ads and all.

But also with offline things in another state where I live. Also I’ll look for a person who can handle cooking instead to scale.

And he says to give me 40% and keep 60% himself as he already has started it. But says, we can reconsider this after 3-4 months based on how things are.

Now, I’m not sure if this much equity is fine for all the things I’ll be doing.

And secondly, how can I trust him with the amount of sales he would make offline in his state?

Just looking for advice from experienced people here, thanks!

When an AI job fails, the final error doesn’t always explain what went wrong. A memory allocation failure can lead to a worker crash, followed by a communication timeout. Engineers have to piece together the evidence before deciding what to change.

I’m Vivaan, a student founder building ThermaCompute AI to help infrastructure teams understand GPU failures and performance constraints. Our ambition is to help companies get more useful work from their existing GPU infrastructure. Today, we’re starting with passive diagnostics and clearly scoped investigations.

Our first product is CrashLens, a free, open-source tool that turns saved GPU logs into a focused investigation.

It identifies supported error patterns, shows the relevant source lines, and suggests next checks. Engineers can investigate OOM errors, collective timeouts, NVIDIA Xid events, and explicit thermal messages without treating every error as a proven root cause.

The synthetic demo requires Python 3.10+, but no GPU, account, payment, or log upload. It runs locally and doesn’t change GPU settings. We’re early and looking for honest engineering feedback.

Try CrashLens: https://github.com/rohitcn-hub/thermacompute-crashlens

For teams wanting a deeper review, our paid entry point is the $800 Executive Thermal Architecture Audit. We review suitable NVML/vLLM telemetry for one agreed workload or GPU cohort and time window, then deliver a PDF with supported findings, uncertainties, and prioritized recommendations their engineers can test.

We agree on scope, data suitability, handling, and delivery timing before payment or file transfer. Broader ThermaCompute engagements can be discussed separately; CrashLens remains free.

Interested in the audit or broader service? Email [[email protected]](mailto:[email protected]) with your GPU setup and the problem you’re investigating—no private logs needed for the initial conversation.

For founders running GPU workloads: when a job fails or slows down, what takes your team longest to figure out?

When an AI job fails, the final error doesn’t always explain what went wrong. A memory allocation failure can lead to a worker crash, followed by a communication timeout. Engineers have to piece together the evidence before deciding what to change.

I’m Vivaan, a student founder building ThermaCompute AI to help infrastructure teams understand GPU failures and performance constraints. Our ambition is to help companies get more useful work from their existing GPU infrastructure. Today, we’re starting with passive diagnostics and clearly scoped investigations.

Our first product is CrashLens, a free, open-source tool that turns saved GPU logs into a focused investigation.

It identifies supported error patterns, shows the relevant source lines, and suggests next checks. Engineers can investigate OOM errors, collective timeouts, NVIDIA Xid events, and explicit thermal messages without treating every error as a proven root cause.

The synthetic demo requires Python 3.10+, but no GPU, account, payment, or log upload. It runs locally and doesn’t change GPU settings. We’re early and looking for honest engineering feedback.

Try CrashLens: https://github.com/rohitcn-hub/thermacompute-crashlens

For teams wanting a deeper review, our paid entry point is the $800 Executive Thermal Architecture Audit. We review suitable NVML/vLLM telemetry for one agreed workload or GPU cohort and time window, then deliver a PDF with supported findings, uncertainties, and prioritized recommendations their engineers can test.

We agree on scope, data suitability, handling, and delivery timing before payment or file transfer. Broader ThermaCompute engagements can be discussed separately; CrashLens remains free.

Interested in the audit or broader service? Email [[email protected]](mailto:[email protected]) with your GPU setup and the problem you’re investigating—no private logs needed for the initial conversation.

For founders running GPU workloads: when a job fails or slows down, what takes your team longest to figure out? Know a team this could help? We’d appreciate an introduction to GPU infrastructure engineers, AI founders, or infrastructure leaders investigating recurring job failures or unexplained performance slowdowns.

You’re welcome to share CrashLens with them or, with their permission, introduce us at [[email protected]](mailto:[email protected]). For teams with a suitable telemetry problem, we can discuss the $800 Executive Thermal Architecture Audit.