BenedictNaught Fusion: Workload-Level Fusion of Local and Remote Heterogeneous Compute
An experimental runtime for placing stages of a workload across local and remote compute and measuring the full cost of using it.
03 / THE NOTEBOOK
Notes.
Writing.Now.
Research, field notes, and ideas in progress.
A record of the questions I keep coming back to.
Why AI Applications Should Submit Workloads, Not Select GPUs
AI applications should describe the work they need executed instead of hard-coding GPU providers, instance types, regions, and fallback logic.
Your AI Agent Can Reason. But Where Does Its Work Actually Run?
AI agents can reason and call tools, but reliable execution requires scheduling, GPU routing, logs, retries, recovery, and artifacts.
Jungle Computing for the AI Era: The Vision Behind Jungle Grid
The world may not have a compute shortage. It may have a compute coordination problem. Compute...
AI Doesn’t Just Have a Compute Shortage. It Has an Execution Problem.
The AI industry is spending unprecedented amounts on infrastructure, yet expensive GPUs remain idle, workloads fail unnecessarily, and engineering teams still manage execution by hand.
Building Jungle Grid: Jungle Grid vs Renting GPUs Directly
A clear comparison between direct GPU rental and using Jungle Grid as a managed execution layer for AI workloads and agents.
The AI Workload Execution Layer Is Becoming Its Own Infrastructure Category
AI companies have more ways than ever to access compute. What they still lack is a neutral layer that turns workload intent into reliable execution.
AI Agents Can Think. But Who Actually Runs the Work?
AI agents are becoming capable of making decisions and planning tasks. Jungle Grid is building the execution layer that helps them run real AI workloads.
Building Jungle Grid: What Happens When a GPU Job Fails?
A technical look at GPU job failures, retries, logs, lifecycle states, and why failure visibility matters for AI infrastructure.
Building Jungle Grid: How Logs Work for Remote AI Jobs
Why logs are essential for remote GPU workloads, and how Jungle Grid thinks about workload visibility, lifecycle events, and debugging.
Building Jungle Grid: Real AI Workloads You Can Run Without Manually Picking GPUs
A practical walkthrough of real AI workloads developers can run on Jungle Grid, from inference to batch jobs and agent-triggered execution.
We were spending ~$5K/month on AI compute… so I stopped choosing GPUs
I was leading a project running a bunch of AI jobs. The models weren't huge, but our compute bill...
We were spending ~$5K/month on AI compute… so I stopped choosing GPUs
I was leading a project running a bunch of AI jobs. The models weren't huge, but our compute bill...
How Jungle Grid handles the messy parts of GPU orchestration so you don't have to.
If you've spent any time running AI workloads — inference, training, batch jobs — you've lived the...
Stop Picking GPUs. Ship Models Introducing Jungle Grid
If you’ve worked with AI workloads long enough, you already know this: The hardest part isn’t...
No entries match that search. Try another word or publication.