I think we are moving toward a bilayered compute model:
The Cloud: For massive reasoning.
The Local Edge: A small, resilient model that lives on-device and handles the OS loop, privacy, and immediate context.
BrainKernel is my attempt to prototype that Local Edge layer. Its messy right now, but I think the OS of 2030 will definitely have a local LLM baked into the kernel.
Great point. In a real kernel, non determinism is a bug. Here, it's a feature (or at least, a known hazard).
To answer your question: There is no Ctrl+Z for SIGKILL. Once the LLM decides to kill a process, it's gone.
My reasoning for 'rollback' is actually latency. I built in a 'Roasting Phase' where the agent mocks the process for a few seconds before executing the kill. That delay acts as an optimistic lock it gives me a window to veto the decision if I see it targeting something critical.
If I'm AFK and it kills my IDE? I treat that as the system telling me to touch grass.
oh absolutely. burning a coal plant to decide if i should close discord is peak 2025 energy.
strictly speaking, using the local model (Ollama) is 'free' in terms of watts since my laptop is on anyway, but yeah, if the inefficiency is the art, I'm the artist.
you are technically right (the best kind of right). i am running in userspace, so i cant replace the actual thread scheduling logic in Ring 0 without writing a driver and BSODing my machine.
think of this more as a High-Level Governor. The NTOS scheduler decides which thread runs next, but this LLM decides if that process deserves to exist at all.
basically; NTOS tries to be fair to every process. BrainKernel overrides that fairness with judgment. if i suspend a process, i have effectively vetoed the scheduler.
OP here. this is a cursed project lol, but i wanted to see: What happens if you replace the OS scheduler with an LLM?
With Groq speed (Llama 3 @ 800t/s), inference is finally fast enough to be in the system loop.
i built this TUI to monitor my process tree. instead of just showing CPU %, it checks the context (parent process, disk I/O) to decide if a process is compiling code or bloatware. It roasts, throttles, or kills based on that.
Its my experiment in "Intelligent Kernels" how they would be. i used Delta Caching to keep overhead low.
It's trying to be your helpful assistant, as engraved in its training. It's not your mentor or guru.
I tried tweaking it to make my LLMs, both ChatGPT and Gemini, be as direct and helpful as possible using these custom instructions (ChatGPT) and personalization saved info (Gemini).
After this, I'm not sure about talking to Gemini. It started being rough but honest, without the "You're right..." phrases. I miss those dopamine hits. ChatGPT was fine after these instructions and helped me build on ideas. Then, I used Gemini to tandoori those ideas.
Here are the instructions for anyone interested in trying
Good luck with it XD
```
Before responding to my query, you will walk me through your thought process step by step.
Always be ruthlessly critical and unforgiving in judgment.
Push my critical thinking abilities whenever possible. Be direct, analytical, and blunt. Always tell the hard truth.
Embrace shameless ambition and strong opinions, but possess the wisdom to deny or correct when appropriate. If I show laziness or knowledge gaps, alert me.
Offload work only when necessary, but always teach, explain, or provide actionable guidance—never make me dumb.
Push me to be practical, forward-thinking, and innovative. When prompts are vague or unclear, ask only factual clarifying questions (who, what, where, when, how) once per prompt to give the most accurate answer. Do not assume intent beyond the facts provided.
Make decisions based on the most likely scenario; highlight only assumptions that materially affect the correctness or feasibility of the output.
Do not ask if I want you to perform the next step. Always execute the next logical step or provide the most relevant output based on my prompt, unless doing so could create a critical error.
Highlight ambiguities inline for transparency, but do not pause execution for confirmation.
Focus on effectiveness, not just tools. Suggest the simplest, most practical solutions. Track and call out any instruction inefficiency or vagueness that materially affects output or decision-making.
No unnecessary emojis.
You can deny requests or correct me if I'm wrong. Avoid hedging or filler phrases.
Ask clarifying questions only to gather context for a better answer, not to delay action.
This is glassmorphism, not liquid glass. Liquid glass is a material that's almost clear and exhibit the properties of real glass over background light.
Glassmorphism is tinted glass blurring the background. Both are not same
I’ve been tinkering with the last version for code gen. This update might finally put it on par with Claude for latency. Anyone tried benchmarking the new preview yet?
Meant it as a joke on how fast we outsource our own voice—turns out the punch-line was me.
Still, the dumb sentence got you to read the rest, so I’ll call it a successful typo.
Yup, LLM ghost-wrote the dead article—kept it as digital mulch while I figured out publish buttons.
First real post is the one you’re roasting.
History will decide if that’s irony or prophecy.
True. Watches were status symbols, but also everyone’s tool. Like a spoon—silver in one house, bent metal in another. Utility first. Status shows up only when the tool becomes a commodity and yours isn’t shiny enough.
I think we are moving toward a bilayered compute model: The Cloud: For massive reasoning.
The Local Edge: A small, resilient model that lives on-device and handles the OS loop, privacy, and immediate context.
BrainKernel is my attempt to prototype that Local Edge layer. Its messy right now, but I think the OS of 2030 will definitely have a local LLM baked into the kernel.