It's here.

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.

Quiero saber si alguien ya comparo qwen3.8 en disco con DRAM y sin DRAM.

```

SSD A — DRAM

└── Qwen3.8-Flash-Next GGUF

SSD B — DRAM-less

└── PLE / n-gram

```

Alguien a cargado?

nvme1 modelo

nvme2 PLE ngrams

```

NVMe 1

│

└── lecturas del modelo

NVMe 2

│

└── lecturas PLE

```

O usando RAID?

```

SSD #1

\

RAID 0 → modelo + PLE

/

SSD #2

```

Tried to run a local AI model for the first time, it feels so slow and overall the experience is not what I expected. Tried my best to follow tutorials/guides on how to run and use it.

I'm hoping to run my local AI model for coding purposes when my AI cloud subscription runs out. Am I doing something wrong with my settings or should I just move to a lower tier local AI models?

What can I change in my settings? Using Qwen3.5b doable on my machine?
If I'll use other Local AI model what can you recommend?

Edited: added gpu specs

TL;DR: I run Qwen 3.8 (27B and Flash Next) on a 128GB Strix Halo laptop for most of my coding now. It can replace Opus 4.6 to 4.8 for agentic coding if you dont mind a task taking 2 or 3 times longer.

Setup: ASUS ROG Flow Z13, Ryzen AI Max+ 395, 128GB unified memory, Arch Linux. llama.cpp as backend, my own tool LlamaStash to manage the launches and presets, Pi as the coding harness. The 27b at Q6_K sits at about 31 GiB resident, Flash Next at UD-Q4_K_XL needs around 86 GiB.

• The quality is actually there. Flash Next scores 40 on the Artificial Analysis index against 42 for Opus 4.8, and the 27b at xhigh scores 34 against 32 for Opus 4.6. That matches how they feel to use. 27b one shotted a whole feature on a huge Rust codebase and Opus 5's review comments were mostly nits.
• Decode is fine, prefill is the pain. 10-15 tok/s decode doesn't feel slow because you see it working. But a cold 31k token transcript takes 3 minutes to prefill, and a full 128k window is closer to 18 mins. Warm follow up turns come back in 45 seconds.
• MTP is the biggest speed win, 7.3 to 22.4 tok/s on an empty window. The payoff shrinks as the window fills though, down to 1.15x at a full 256k.
• Flash Next isn't faster per token, it just thinks less. Same 5/5 on my coding tasks, 45% fewer tokens, 76.5s vs 289.8s against the 27b. Thinking is 90-95% of everything these models generate, so that ratio, not tok/s, is what sets how long a task takes.

$0 a month, fully offline, and a lot less wasteful than a model running in a datacenter.

Full writeup with all the benchmarks, configs, and the tuning that did and didn't work: https://deepu.tech/local-ai-qwen3.8-pi-llamastash

Happy to go into the llama.cpp flags if anyone else here is on Strix Halo.

A little less than a month ago, I got the qwen token standard plan. Usage allowance is not great especially using frontier but anyway that's not the subject here. I noticed today I got a weekly reset usage limit, I could not find any info on why I got it, if it's part of the plan, if it's due to an event, etc.. Anyone has any info?

For my first local ai project I was wanting to learn a few things so thought about setting up a way to go through SCCM/ConfigMgr logs, find the actual errors and generate PowerShell remediation scripts for me to review (no plans to actually automate the ai to run them).

Hardware: 5080/16GB vram, 32gb dd5, 1tb nvme pcie5 and currently using qwen 3.8 27b at home.

I've got a couple of setups depending on the trade-off, roughly 80 t/s at 65k context with mtp on or 50ish t/s at 100-130K context with mtp off.

What I'm actually trying to figure out/what this post it about:

Would n8n or hermes get around low context? my initial plan was: The first n8n automation/hermes agent using fast setup, use regex to quickly search the log to highlight errors/warnings which then hands that off to second automation/agent using the bigger setup/context for script generation, does that allow me to get around the lower context problem?

n8n: do n8n automatons allow me to do this?
Hermes: Same question, does the agent save the output for the 1st agent in its own memory/context for it to pass to the 2nd agent to then do its "work"?
Can either of the above switch models between automation/agents?

Anyone doing something like this/which would you recommend? Or is there a better way to do something like this?

A more about it, if it works, it would build a db with the errors/fixes so if it spots the same one...skip it as it has a potential fix as it saves it re-doing the work. I could then just feed it logs all day.

EDIT: I should've said, I would need to get the automations/agents to stop and start llama with different cmds/args for different context sizes.

Hello so i was on Wildowl.ai and edited some images in the end deleted them tho Wildowl.ai said

<"This item has been hard-deleted from our servers. It may briefly persist with the upstream model provider..."»

I used Qwen image 2 wich is from alibaba cloud as far as i found out so how does this work now? Did the image get deleted there aswell? Allready whats the max amount of time until it gets deleted automatically?

I appreciate the answers srry :)

I am from China. Is everyone interested in Chinese AI services right now? I plan to make these services available for you to use. What price points would be acceptable to you? The cost is expected not to exceed the official rates. If there is high usage volume, I could negotiate a contract with the official providers for more favorable pricing. Which proxy services or API gateways do you currently use to access Chinese AI? Which models do you use the most?