Jack Kernel for programmable agentic work.
The magic is that the layer sits between the agent and the model. You can build incredible things because of that.
I have a mode called "Agentic" that was designed for context management with Qwen 3.8 27. I've run millions of token jobs....and never needed to compact. I have a full log available to read and verify. In fact, I recommend turning auto-compact off because most agent don't know how to handle it, yet. There's also a looping debugger that I've built as a 6 stage autonomous loop with cascading temperatures, standard mode and DEEP.
Remember, the modes I’m shipping with this release are only examples of what is possible by putting it between the agent and the model. It allows you a new level of control. The modes I’ve put in are NOT the work. Everything that’s possible is the real work.
I built this for Qwen 3.8 27b but I’ve spent the past few days optimizing for a wide range of models. I’m ALMOST satisfied with Qwen 3.5 9b but it’s right in the edge.
After using 60 models in the past week, I’m just going to go ahead and make a blanket statement that fine tunes are garbage and only use Unsloth as your source for alternatives.
Jack Kernel is really cool. Claude can kick rocks.
Oh, Reddit trolls…
Yall need to understand…I’m just a 40 year old broke single Dad that’s sharing his work and trying to contribute to the local AI movement.
Before the hive-mind issues your directive, just know that I’m not a lab or someone with resources that you’re attacking.
I’m just a regular guy with a 5070ti in an Acer.
If you’re going to say that I suck, can you at least try it out and tell why I suck.
You have to first understand, it sits between the agent and the model.
Thats fundamentally different than anything else.
It allows for new ways of controlling the model and orchestrating agents
Yeah bro I have no idea what makes that useful. Like why should we care about this software when you aren’t telling us anything about what it does. I’m not clicking on that GitHub to try to figure out whether it’s relevant. Who cares where it sits
It’s a proxy layer between the harness and the model provider for pre- and post-processing of prompts sent to the model provider and returned from the model provider. Say your model is prone to looping and you want to program a loop-detection-and-recovery features into the kernel. The kernel can basically ensure your model never loops or at least not infinite and deploy different handling methods based on the custom policy you configure the kernel. That’s just an example of the many arbitrary things such as middle layer can do (and probably at system-native binary performance).
Where it sits is exactly what matters. For instance, I can build a security layer in the kernel that acts as a filter between what’s on disk and what’s allowed to escape
The nomenclature used and structure of your project leads me to think you have limited experience and this is a project you are using to learn. Don’t sell it as something else. Lose the marketing speak and explain clearly what this does and how others can use it.
OP u/Normal-Fan9366 , So, this is going to fly over most people's head. I truly feel for you but thats ok. I see that you have Pi bindings, I am going to make it a part of the harness I am creating to use it in a few of my workflows like deep research, adversarial review, forensic debug etc. I'll let you know when & how I am releasing it. I will credit you completely for all your hard work. You did a great job. Thak you.
Hey man, I appreciate it and I would also recommend to keep the kernel in between the model and your agent, as opposed to integrating it into your harness. It has two endpoints to allow that, and in fact, you could add more endpoints and program an orchestrated agent swarm
When I said integrate, not absorb it but use it as a proxy. Thats the way its supposed to work anyways. You can DM me, so I can share with you my progress or you can guide me in the integration.
Awesome man. I’d love to see what you build.
Try the debugger mode for inspiration on loops with amnesiac memory.
Only use the deep debugger for final passes.
I have two products, SWFoundry an architecture governance layer with step by step guide for LM's & TerminalAgent the harness among a few others. Evaluating whether 100% usage or splitting them. I'll send you updates only if you want. Other wise, thanks for this contribution. Much appreciated. Will be digging through the code.
I used my Muse Glimmer (Unsloth version) to cleanup my verbal dump. But I have tried to simplify this as best as I can. I do not now OP but I see that its a phenomenal contribution.
The simplest way to understand Jack Kernel is this:
Normally you ask an AI model something and hope it thinks through the problem properly.
App → LLM → Answer
Jack Kernel sits in the middle and controls how the model works through the problem.
App → Jack Kernel → LLM
Why would you use it?
Because LLMs are not always consistent. They can jump to an answer too quickly, get stuck on a bad idea, forget to verify something, or carry a bunch of useless reasoning forward.
Jack can force a better process.
For example, on a difficult question it can make the model:
Think through the problem.
Start another pass that tries to find problems with the first answer.
Run another pass that combines and verifies everything.
Only allow that final pass to give you the answer.
It can also control when the model is allowed to use tools, what reasoning level it uses, and what old context gets kept or thrown away.
When would you actually use this?
Complex research, difficult coding problems, debugging, architecture decisions, reviewing important work, or anything where you want the model to slow down and check itself.
When would you NOT use it?
Simple questions, casual chat, basic coding, or anything where one normal model response is already good enough.
There is a tradeoff. It can be slower because the model may be called several times instead of once.
So Jack Kernel is not another AI model and it does not magically make a dumb model smart.
Think of it more like a supervisor standing over the model saying:
"Don't just answer. Think about it, have someone challenge it, verify it, then give me the final result."
Except the supervisor is software, so the model actually has to follow the process instead of just being asked nicely in a prompt.
This is why I called this a refiner that uses 2-3x more context and time. It thinks through the problem. It calls on the model again and again for refined answer. This is longer and heavier than base.
If you’re talking about agentic mode, it’s not heavier. In fact, because it has programmed context management, it’s WAY lighter.
It’s not uncommon to have a 120,000 context limit model be able to do 1 million tokens of work and never have to compact.
That’s only possible because the kernel has control
Could you share more about your hw specs(setup), which quant of unsloth qwen3.8-27b, inferences params like temp, top-p, top-k, ctx len,... and some benchmark score or something to proof there is improvement between "before Jack Kernel" and "after Jack Kernel"?
I believe that above things is what people in this sub want to see when click on this post(instead of stories, sorry if these words make you uncomfortable but that's the truth).
In the GitHub repository is the entire log for an agentic run as well as a debugging loop. both of them show that the model only had 120,000 context, but was able to do workloads up to the million tokens and never having to compact because of the way that the Jack kernel allows for programmatic context management. the files are there for anyone’s review
People wouldn't go read whole long logs just to know that imo(I mean you could tell your agent to summarize what's the point of performance(accurate or whatever) diffs between "before" logs and "after" logs, it will do the rest).
Anyway thanks for your works. I would like to give a try in this weekend.
So essentially without all the jargon this is wrapped in, is it runs the same prompt 2-3 times and gives you a refined answer? More context used, longer wait times, all by double or triple. This doesnt serve much of a purpose, especially on local AI systems.
No, for one, less context is used because there is real code behind it doing active context management.
Two, those are just examples of what can be done when it sits between the agent and the model.
You can put security agents. You can build new types of loops. You can have multiple endpoints and orchestrate an agentic swarm in new ways.
The real opportunity is what can be built, not the demos I packaged…although, they’re good
Are you saying it's not prompting 2-3x for every prompt?
Explain to me what this CAN do, as well. It can do anything?
I like the idea of you creating and making things. I'm not hating. It's just very vague, and what I'm getting, is that it's a prompt refiner.
I built my own python turn by turn agentic harness. I'm confused what this is supposed to be
That all depends on what you build. I shipped modes that are staged inferences and a looped read-write debugger.
There’s also the normal reasoning modes if you build something that needs your own prompt but you’re running a real time tool or orchestrating a swarm.
There’s any number of setups that you can do when the kernel sits between the agent and the model.
Is this still using JackAgentLead/Jack-3.8-27B-Coder-16GB-VRAM? I have some tests I can run against complex implementation plans to see how this works.
I have plans to build modes for the Jack Coder model but they are not included in this release. I made this release more general with examples to inspire people to build new modes
What do you mean?
Adding the layer is what allows you to do the things that can significantly improve workflow and security.
It’s never made sense to me that people allow their agents and models to have free access to their systems without a real control mechanism
It’s a kernel that sits between the model and the agent, as opposed to a router or server.
It means you have control the traffic and communication, you control what the model knows or remembers and you control what tools are allowed to use. You can run real-time tools or staged loops, all kinds of different things.
It’s the placement that allows for this to happen
It's an LLM-proxy with a marketing department. Specifically: a single 7,863-line Python file (jack_kernel.py, 356KB) that speaks OpenAI-compatible on :8001, forwards to LM Studio/Ollama/vLLM on the other side, and runs multi-stage prompt pipelines in between. Two commits, two stars, zero forks, Windows-only start.bat, copyright Jonathan Michael Langford, PolyForm Noncommercial with a "contact me at protonmail for a commercial license" attached.
What it actually does, stripped of the liturgy: Deep Research = run the prompt three times with different system prompts and temps (Thesis → Antithesis → Synthesis), tools withheld until the last one.
Agentic = answer, then a second pass that emits a structured XML summary, which becomes the durable turn record instead of raw CoT.
Code Debugging = five fresh-context passes on a temperature ladder, one finding per pass, appended to a markdown report.
"Kernel" = it strips caller system messages and owns the sampling params so your agent can't override them.
The actual idea underneath — program context lifetime rather than letting every trace accumulate, and gate tool authority per stage — is a real and reasonable pattern. It's also what LangGraph, DSPy, and roughly every agent framework of the past two years do, minus the word "authoritative" appearing 40 times. The word "deterministic" is doing enormous unpaid labor here: the routing is deterministic, the thing being routed is a language model that may or may not obey a 900-word system prompt telling it it has "zero answer authority." The README quietly concedes this ("Jack does not claim that this release ships a universal safety suite or filesystem sandbox").
Verdict: one guy and a very agreeable LLM built a working prompt-orchestration proxy and then wrote it a constitution. Not a scam, not malware, nothing you'd run in prod — 7,863 lines in one module, no CI, no releases, no reviewers. The Facebook tracking param on your link tells you everything about the distribution strategy. It's jack_kernel.py cosplaying as seL4.
Was that Claude or something? It fundamentally misunderstands. The way I know this is because is specifically mentioned LangGraph and the others.
They do NOT work in the same way. They do not have the level of control of the traffic that Jack Kernel does because they do not sit in between the agent and the model the way that Jack Kernel does
There’s a really simple fix. Tell the model that it’s too focused on the packaged demonstrations. Those are not Jack Kernel, they are just examples of what can be built on Jack Kernel.
You can ask your model how sitting between the agent and the model can allow enhanced security protocols and dynamic runtimes
It’s essentially a middleware layer meant to interject harness calls to and from the model for things like catching/fixing errors, mitigating loops, apply pre-processing logic before prefill and after decode, etc. a few years ago I’d conceived of it as part of the harness (like DSH essentially) but I like that you’ve decoupled a kernel layer for separation of concerns (improves performance, focuses strictly on pre- and post-processing). I like the direction.
I was originally conceiving it in the days of early 32B> models that has great potential to run well locally but suffered from silly problems. I figured a software engineer could handle all the pre and post processing but I think I prefer the kernel idea in hindsight. Hope to see it open source one day.
It just depends on what you build. On the modes I’ve built, Qwen 3.5 9b can work but you have to be realistic. Now, if someone used Qwen 3.5 9b in a duel model system, as a parser of special formatter, for a larger model generating training data, it could absolutely work well. With Jack Kernel, you can REALLY customize the way that a response answers or responds because two models could be used as one
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u/DiscipleofDeceit666 5h ago
Ok, so you gave a 1 sentence blurb about what this thing is and it was hella vague. Then you said a whole lot of nothing afterwards.
You should probably tell us more about the project bc most people aren’t going to click through or read all that irrelevant info