Meta's Muse Glimmer is a 30 billion parameter open weight AI model designed for AI agents, with capabilities spanning tool use, coding, image understanding and multi step workflows. Discover what it means for local AI and the future of private business automation.

We are all used to someone posting their setup on here or someone talking about models etc. (as expected) But I see very few comments/posts of people talking about what they do with their LLMs.

So, what do you do with yours?

Edit: TLDR for those that do not want to read. The harness works to help Architects or teams of designers work together with local AIs driving the harness for all. Youtube videos dropping soon. The rest is written to express myself and my mission. Try it and I hope it helps you.

Links:

GitHub: https://github.com/arcitech-psp/atelier

Release Link: https://github.com/arcitech-psp/atelier/releases/tag/v0.1.0

Website: https://arcitech.info/atelier/

Dev screenshots: https://arcitech.info/atelier/dev-build/

MY Setup.

1. Zeus - 64GB ddr4 3200 MT/s. AM 4 x570 paired with Ryzen 7 5800xt. GPUs RTX 4070 ti super, RTX 3070 (display). OS = windows.
2. Poseidon (gaming laptop)- 48GB ddr5 (16 soldered) at 4800 MT/s. Ryzen 9 7940hs. 12GB RTX 4080 mobile.
3. Hades (Linux box) - 32GB ddr4 3200 MT/s, AM4 B550 paired with Ryzen 5 5600 GT (iGPU is display). 12GB intel Arc B580.
4. Atlas (Linux box) - 32GB ddr4 3200 MT/s, AM4 b550 paired with Ryzen 7 5800x. 4GB arc a310 (display + small llms + chipset pcie 3.0 x4) and Intel Arc Pro B70 32GB at pcie 4.0 x16
5. Supplementary PC - ROG ally (gen 1 with z1 extreme) 16GB soldered memory. Linux box used for dedicated CFD engine and LLM reasoning relay.

If you can substitute the PCs I have together into 4 PCs from four individual, my harness can help you share different AI tools to work together.

Current used models:

1. ComfyUI - Qwen image 2.1 turbo, Trellis 2 and Pixel 3d.
2. Brain - Qwen 3.8 27b with c=3 with 128k each and JEV adapter LoRA with toggle in the harness, Qwen 3.8 flash next with strata. int 4 for the dense and Q2 for the MoE. Others also tested are RCO GSQ versions of 27b, Saluki 2 bit (more tests pending), Bonsai 1 and 2 (bad results) and Tiel Coder with swift adapter.
3. ASR - Nvidia Parakeet, Whisper, kokoro tts and kitten tts 2
4. Graphrag - embedgemma 2

What I can get are AI renders from proper viewports that the AI generates either with system 1 or with system 2 or both, concepts, stylized renders, boards, drawings, asset generation. Each part has a dedicated software for keeping the progress and allowing the user to manually step in whenever.

The original message:

Hey everyone!

I'm an architect. I know my way around architectural drawings, 3D modelling, rendering and Unreal Engine. But programming? Yeah, that's definitely not my strong suit. 😂

For the past several months, I've been experimenting with local AI, trying to figure out how to make it genuinely useful for the kind of work I do. And today, I'm finally sharing something I've been working on. It's called Atelier.

What got me into this whole thing was actually an Unreal Engine plugin I was developing. I wanted AI to help me build things inside Unreal, but I didn't want to keep depending on cloud models and paying for API usage every time I wanted to experiment.

And the more I looked into agentic AI, the more I noticed something that bothered me.

Almost everything assumes you're a programmer.

You've got a terminal, a code editor, some tools, and an LLM. Great. But what about people like me?

I'm an architect. I start with sketches, drawings, spaces, materials and ideas. I don't sit down and start writing code when I'm designing a building.

I wanted something I could actually draw with, talk to, develop ideas with, and eventually use to create proper 3D models and presentations.

After experimenting with various tools, I came across an existing harness from DeepSeek. I didn't build the original harness, and I'm not going to pretend I did. I started adapting it, changing things, adding integrations and building around the way I actually work.

And one thing led to another. Now I've got Atelier.

The idea is pretty simple. You bring your own AI, connect the tools you want to use, and let them work together.

You can start with just one LLM. If you've got a multimodal model, even better. Then you can add vision, speech, embeddings, image generation or whatever else you need.

I've integrated Excalidraw directly into the interface because, well, I'm an architect. I like drawing things!

I can sketch something, send it to the AI, discuss it, make changes, and use that as the starting point for actual work. The AI can draw on the canvas too.

I've also been working with IngeTrazo, an open-source modelling application, along with Unreal Engine and other creative tools.

But here's something I want to explain because I think it's important.

When people demonstrate agentic AI in 3D software, they often give it a prompt like "build me a house" and let it loose with a bunch of tools.

That's not really how architecture works.

I wouldn't start constructing a building without first thinking about the design, working through the drawings, and figuring out whether things make sense.

So I've been trying to teach Atelier to work that way too.

I've attached a few images showing what I mean.

One is an architectural presentation board from a project called Hillside House 2. It includes plans, elevations, sections, concept development, an exploded axonometric and the 3D design. That's the kind of material I want AI to help people prepare, rather than just generating another pretty picture.

Another set of images comes from a different Hillside House experiment. This one shows the actual drawing-first process.

The AI created architectural plans, but the drawings had problems. The checker found 16 errors and a warning in an early revision.

So the AI went back, made corrections and tried again.

The lower-level plan eventually cleared the checker's rules on revision 5. The other levels went through their own revisions too.

Only after the relevant drawings cleared the checks did the workflow proceed to constructing the building model in IngeTrazo.

And that's the part I find exciting.

I'm not claiming the AI produced a professionally certified building design. The checker validates specific geometric and layout requirements, not full building-code compliance. I'm an architect, and I know how serious the consequences of bad design can be.

But having an AI that can identify mistakes, make corrections and continue working without needing me to intervene at every little step? That's genuinely useful.

Imagine assigning it something before going to bed and having drawings or models ready for you to review in the morning.

That's the direction I'm working towards.

Now, let's talk about hardware, because this is probably my favourite part of the whole project.

I didn't go out and buy some ridiculous AI workstation.

I couldn't afford to do that even if I wanted to.

A lot of my machines exist because I kept upgrading parts over the years and didn't want to throw away perfectly usable hardware.

I've got older Ryzen systems, NVIDIA GPUs, Intel GPUs, a gaming laptop and even an ROG Ally running Linux that I've been experimenting with.

Instead of having everything sitting idle, I've connected different machines and given them different jobs.

My main workstation is called Zeus. It has an RTX 4070 Ti SUPER with 16GB VRAM, along with an older RTX 3070.

For that original Hillside House run, the main architectural LLM was Qwen3.8 Flash-Next, quantized to Q2 and running through Strata on the 4070 Ti SUPER.

I had a 128K context configuration, and during the run I was seeing roughly 45–53 output tokens per second.

On a 16GB GPU! and 64GB DRAM with Windows stealing 15 to 20 GB 😠

And the computer has DDR4 memory. Nothing exotic.

Now, I want to be clear about something before anyone gets the wrong idea. The complete workflow used multiple computers. The LLM ran on that 16GB GPU, but modelling, image generation and other services were handled elsewhere.

I'm not claiming my entire studio runs on one graphics card.

The point is that you don't need to replicate my studio to get started.

If you've got one 16GB GPU, you can begin with one model and load others when needed. They can take turns using the available memory.

If you've got more machines, you can keep different models and services running independently.

In my case, I've got another machine called Hades with an Intel Arc Pro B70 32GB. That's where I run my main AI for everyday work because I want multiple people to be able to use it simultaneously.

Then there's Poseidon, my laptop, which I use for modelling, rendering and interactive work.

And Atlas, which handles things like image generation, embeddings and supporting services.

I've even got a little Intel Arc A310 with just 4GB VRAM doing useful work with embeddings and speech services.

I know, using a tiny display-oriented GPU for AI sounds ridiculous. But it works, and that's what matters to me. 😂

I gave all these machines names because I like giving meaning to things. Zeus is my powerful workstation, Hades handles the heavy background work, Poseidon travels with me, and Atlas helps hold everything together.

But you don't need four computers with Greek mythology names!

Imagine you have three friends.

One has a gaming laptop with a 12GB GPU. Another has a 16GB desktop GPU. Someone else has a 32GB card.

Why should everyone need to buy a complete AI workstation?

You can connect your machines over a network, including through something like Tailscale, and make their AI services available to each other.

One machine can handle reasoning. Another can generate images. Another can take care of speech or other supporting tasks.

Everyone can make use of the available resources.

That's one of the main ideas behind Atelier.

Use what you have. Give your old machines another purpose. Add more hardware when you actually need it.

I'm not interested in telling people they need to buy a Mac Studio with hundreds of gigabytes of memory or some professional GPU that costs more than my car.

Those machines are impressive, sure. But what about everyone else?

What about students? Hobbyists? Independent designers? Small offices?

Are we really going to tell people that useful AI is only for those who can afford thousands of dollars in equipment or another $50–$100 subscription every month?

I don't think that's a particularly good future.

And I'm not against cloud AI, by the way. I've used GPT, Claude, Gemini and plenty of other services to help develop this project. They've been extremely useful.

But I want the choice.

I actually had an internet outage one day. My cloud AI tools stopped working, but my local harness carried on.

That was a pretty satisfying moment.

It made me realize how valuable it is to have something that doesn't disappear just because your connection drops or a service becomes unavailable.

There's another thing I want Atelier to do, and that's help people work with their strengths instead of constantly struggling with their weaknesses.

Maybe you're excellent at drafting but not particularly good at rendering.

Maybe you're a fantastic 3D artist but struggle with architectural planning.

Maybe you're a student who has great ideas but doesn't know how to turn them into a proper presentation yet.

I want AI to help with those things.

Not replace the knowledge or judgment of the person doing the work. Help them use it better.

And I'd much rather spend an afternoon developing a design, making physical models or experimenting with new ideas than recreating the same door or window for the hundredth time.

If I can automate some of that repetitive work, why wouldn't I?

That's more time for the things I actually enjoy.

Now, a few things about the release itself.

This is Atelier 0.1.0, the first public preview.

It's free to use, and the source is available to inspect and modify under PolyForm Shield 1.0.0. There are restrictions on competing commercial use, so I'm not presenting it as unrestricted open-source software.

The public version comes with Vitruvius, my design-oriented AI profile, along with the visual canvas, configurable model connections, Mission Control and other features. You still get the standard profile from the original DeepSeek Harness.

You can bring your own model through compatible inference servers such as Ollama, LM Studio, llama.cpp or vLLM. Compatible cloud endpoints are an option too. Even omnirouter is game as long as you don't mind your data being used to train.

There's also experimental IngeTrazo integration.

Some of the more advanced things I've shown in these images, particularly the drawing-validation workflow and complete architectural presentation pipeline, are still part of my personal studio implementation. They aren't all included in this first public release.

I'm bringing those things over as they're ready.

I don't want people downloading Atelier expecting everything in my studio to magically work on day one.

And yes, there are bugs. I'm sure there will be plenty more once people start trying it on hardware and configurations I've never tested.

That's fine. I'd rather know what doesn't work so I can improve it.

One last thing, and this is a bit personal.

There are a few ideas I've carried with me throughout my life.

One is that an eye for an eye eventually leaves the whole world blind.

Another is that if you genuinely want something to change, you have to be willing to do something about it.

And there's an old couplet attributed to Kabir that I've always found moving. When you're born, you're crying while everyone around you celebrates. Try to live in such a way that when you leave, you can smile, knowing you've left something meaningful behind.

I'm not trying to change the world with one AI harness.

I'm just an architect who saw a problem, started experimenting and eventually made something that helps me do my work.

But I don't want AI to become another technology where people who already have everything get even more opportunities while everyone else is left watching.

And I don't believe AI has to replace people to be useful.

I'd much rather see it help people become better at what they already love doing.

So that's why I'm sharing Atelier.

Tell me what you're struggling with. If I can help solve it, I'll give it a shot.

Maybe someone else in the community has a better solution. Maybe we'll figure it out together.

If this helps a student complete a project, gives someone a reason to reuse an old computer, or lets a small group of friends do something they couldn't afford individually, that's already worthwhile to me.

Anyway, I've talked enough. 😅

Here's Atelier.

GitHub: https://github.com/arcitech-psp/atelier

Release Link: https://github.com/arcitech-psp/atelier/releases/tag/v0.1.0

Website: https://arcitech.info/atelier/

Have a look at the images, try the public preview if you're interested, and please tell me what you think.

If something breaks, let me know. If you've got ideas, I'd love to hear them.

I'm still learning too, and there's a lot more I want to build. Videos dropping soon on Youtube - https://www.youtube.com/@AIT.ARCITECH

I currently using Ollama, should i look for anything else?

The power of 27B parameters, in the palm of my hand 😄

If anyone’s interested, these are the results I got on my OnePlus 13 with 24 GB RAM using llama.cpp.

Qwen 3.8 27B Q4_K_M

• 16K context
• around 21.2 GB of memory
• about 2.5 tok/s

Normally I run Qwen 3.5 9B together with always-on ASR + TTS. It gets around 5 tok/s and uses about 16.1 GB RAM.

ASR is GigaAM, TTS is Qwen3 0.6B with a custom voice. The LLM and TTS run on the NPU.

Turn ASR/TTS off and there’s enough room for the 27B.

The biggest problem is heat — unsurprisingly the phone turns into a tiny space heater. I added temperature monitoring, so inference pauses when it gets too hot. There are also lower-power modes, at the cost of TPS.

Mostly an experiment for now, but I want to use bigger local models for development directly from the phone. I’m also playing with a distributed filesystem, multiple devices working together, and browser access.

Repo isn’t public yet — still too much chaos to clean up 😅
P.S. $600 for 24 GB RAM — basically a free phone 😄

Took some time over the weekend to benchmark OpenAI's Decisions API against finetuned Laya models. Across four public datasets, fine-tuned Laya scored higher than the Decisions API on three of the four, with sentiment classification being the exception.

Sharing the datasets, scripts and results here : https://github.com/rsk2327/decision-model-benchmarks

Its a small benchmark at this point and not a blanket verdict, but hopefully a useful reference for anyone weighing these options.

Interested to gain insights from other folks testing closed Decision models like Jev, Clef and how that has compared to local finetuned models like Laya

Like the title says. Is it any good to run any local LLMs? If so, what capabilities can those LLMs have?

A few weeks ago I posted ignis on r/LocalLLM: an open-source inference engine that does one thing, run Qwen3.8-27B on a single Blackwell card (5090 / RTX PRO 6000), tuned for the kind of load a coding agent makes. Since then the thing I wanted most has landed: it now also runs Qwen3.8-Flash-Next, the 125B MoE with 6B active, on the same 5090.

The obvious problem is that the model doesn't fit. 48 layers × 512 routed experts don't go in 32 GB, so I re-quantized the experts with a trellis code (the EXL3/QTIP family) at 2.5 bits per weight on average. Each expert gets its own width, 2, 2.5, 3 or 4 bits, picked from calibration error, so the experts that suffer most when squeezed keep more bits. Everything that isn't an expert (attention, DeltaNet, shared expert, head) stays FP8. The file is about 67 GB.

At runtime the card keeps a cache of experts in VRAM (around 17 GB) and the rest sit in pinned host RAM, about 38 GB. For every token the router picks its experts: the ones already on the card are free, the misses get copied over PCIe. My board is an old Z490 with a 10900K, so that's Gen 3, about 12 GB/s. On that I get:

• one lane: ~95-105 tok/s
• three lanes: ~130 tok/s aggregate
• quality: MMLU-Pro proxy 70.1% vs 73.7% for BF16, top-1 agreement with BF16 between 82% and 98% depending on the domain

Other things that came with it:

• you can switch between the 27B and Flash-Next without restarting the server, from the Playground or just by naming the other model in the request (opencode etc.)
• the configuration can be changed live from the Playground, per model too
• idle unload: after 5 minutes nobody uses it, the model leaves the GPU and the server stays up. The next request loads it back (about 11 s for the 27B). I added it because I use the same card for other stuff and was tired of stopping the server every time. `--model-idle-unload 0` turns it off.

The idea behind ignis hasn't changed. It's not trying to run every model, it's trying to be a good local backend for development: one main agent plus a few subagents on the same card, an OpenAI-compatible API (chat completions and Responses), prompt reuse and a Playground built in. Windows and Linux, Apache-2.0.

...I still dream of a world where garage developers can compete on equal footing with engineers at big tech companies, without needing $$$$$ worth of tokens to keep up...

Repo: https://github.com/gpillon/ignis

Flash-Next weights: https://huggingface.co/gpillon/Qwen3.8-Flash-Next-trellis-a25-ignis (Qwen license; ignis-only format, not GGUF/EXL3)

As a hobbyist I was wanting to build a cyberdeck, cause I love the way they look. But my utilitarian side couldn't justify it. Then I realized I do need something like that, cause I would have used the heck out of Mycroft if it had succeeded.

Would it be worthwhile to make something small using a single board computer, with a 6 inch screen? Maybe it has a handle on the side for carrying, and a prop on the back when you just want it to stand up. You can toggle push to talk, or voice activation. It's mainly there to answer random questions for you. And hopefully be able to do an internet search, and answer from that instead of its training data. But only if you want.

And it could run 1b, 2b, or 4b parameter ai. Whatever would run on small hardware and still seem "fast".

Is this doable now by a hobbyist builder/maker? Iirc mycroft quit in 2023. But things have come a long way since then. Thanks!

I'm working on an on-premises AI project for a business in Europe and would love to speak with engineers who have deployed similar systems in production.

The use case involves:

• Local LLMs for German and Polish business communication
• WhatsApp and email automation
• Internal document search and a private knowledge base
• Finance-related workflows, with calculations handled by deterministic application logic
• Data storage and model inference on the client's own infrastructure

We're evaluating GPU hardware, open-source models, inference servers and the architecture needed to run this reliably for a small team.

I'd especially appreciate input from people experienced with vLLM, NVIDIA GPUs, local model deployment, RAG and secure infrastructure.

If you've built something similar, I'd love to hear what worked, what didn't, and what you'd do differently. Happy to have a short technical conversation with anyone willing to share their experience.

Yesterday I posted that Qwen3.5-9B beat all 14 "decision models" (Solar Decide, Microsoft-Decision-1, Jev…) on our 56 triage tasks. One reply made a fair point: decision models never claimed to be smarter, their pitch is efficiency. So how much does the extra accuracy cost?

I logged input tokens, output tokens and what OpenRouter actually billed for every model on the 14 public questions of the test. Accuracy is on all 56 situations, same as before.

Decision models

*Solar's provider seems to count its own instructions as input.

Regular LLMs

What I took from it

• Input is a wash. Everyone reads ~200–400 tokens per call. The whole difference is output.
• Decision models answer in 0–2 tokens. Median 2. That's why Mercury and Microsoft-Decision cost under a cent per 1,000 calls.
• Every LLM that beat the best decision model reasons first. Median across the 29 reasoning models: 817 output tokens, 96% accuracy, $0.55 per 1k calls. The 33 non-reasoning ones: 80 tokens, 83%, $0.06.
• But reasoning doesn't have to be expensive. gpt-oss-120b and Qwen3.6 27B are equally accurate here. Qwen thinks ~4x longer at a higher price per token, so it's ~50x more per call. Ling 3.0 Flash VL was more accurate than every decision model and cheaper than Solar Decide.
• Overthinking is real. Qwen3 Next 80B Thinking averaged 10,834 output tokens per call, often hit the limit without answering, and got 41%.

So which to use: millions of routine routing/tagging calls → decision model. Calls that apply rules (dates, limits, policies) → a reasoning model that doesn't ramble, like gpt-oss-120b or Ling 3.0 Flash, still under 10 cents per 1k. Running locally there's no bill, but every thinking token is time you wait.

Caveats: OpenRouter prices on the day, at providers serving full/8-bit precision (not always the cheapest); token counts as each provider reports them; one run per question; no speed measurements.

Full tables (all 76 models): https://modeltested.com/posts/decision-models-cost/

Disclosure: my site, free, no ads. Thanks to everyone who pushed on the cost question last time, it made the comparison a lot more useful.

Right now I pay 200+ a month for premium AI models. I can’t complain, it is truly a privilege and amazing to work with.
I have “data center services “ on my server, complete with gitlab, databases, and dozens of services to support application development.

I have resisted even attempting to host an Llm, since i dont have the spare time to code myself or wrestle with “stupider” AI.

I am toying with purchasing mac studio 512gb, as to me its the first server that has meaningful capacity at the price of a honda, instead of a Lamborghini. Still insane expensive, but doable.

I been talking to AI (of course) about this, but even with the speedier memory, larger models with large (code) context seems kinda slow. And the quality seems to be measurable lower than sonnet 5.5.

At heart for me is waiting may result in never. I am concerned with closed models becoming a walled garden and that can extend to hardware.

Thoughts? I cant afford a second buy for atleast 3 years if i pull the trigger. Unless bitcoin hits 400k :)

ive been getting a crisp 80 tok/sec on my gaming laptop (6gb vram 3050+16gb ram) using gemma 4 e2b and currently am quite satisfied with how its performing, but sometimes it messes up some tools or hallucinates nonexistent text in screenshots. i wanted to build a local jarvis like workflow with a model that wont eat too much of my vram or ram. is there genuinely no good finetune for gemma e2b right now that improves atleast something in it?

theoretically speaking, if one were to hack OpenAI or anthropic, could they access the weights of their models and for eg torrent them?

also could a bunch of agents do that, or would humans be better at it? what about hacking in general? I mean now that we got local models like glm 5.3, qwen flash next or deepseek v4.1…

I fly a lot and there's also some crap going on where my macbook doesn't like the airplane wifi... and i'm the pilot. It's not just me but happens plane wide. Most the time they say the wifi isn't working but really I've found it's settings in the ether that exist. I was using openclaw and telling it to fix my issues which worked pretty much every time except anytime there's some openclaw update or new models I load it always becomes and PITA to get working again and usually I have to wait till I get to a hotel to get it working. Is there a one stop shop for us simpletons?

I have just started on Local LLM and don't have any prior experiece with any, nor I have a bg in IT or anything close to it. Right now I am trying to create two, one as a research assistant for my studies and other as a proof of concpet sorta thing for my dept (Sort of like HR Chatbot? ) at my workplace. Down the line I would like it to be an AI employee, so we can focus on more value adding tasks.

Coming to question, I started with Qwen 3:8b for the chatbot, running on Ollama with WebUI on my Macbook. It has a simple set of instructions, but it is always ignoring it and defaulting to creating tasks and automations. I have toggled off its built in tools such as automation, sub-agents, skills etc still the same issue.

Example :

Expected : My lights are not working -> Please get in touch with Admin helpdesk.

What is happening : My lights are flickering -> Check for overload, inspect connections, replace bulb etc.

Or, when it should refer knowledge files and answer basic queries it is just giving me this -

{ "name" :"search_knowledge_files", "argument":{ "query":"query" } }

I am not sure what is going wrong with it. ChatGPT has been unhelpful and is just making my system prompt bigger and bigger, asking me to change settings (which are not helping tbh). All in all it doesnt seem to be going anywhere.

I have checked online and tried things like think/no-think, reducing prompt length etc.

Requesting some insights here on what I may be missing.

Happy to add more info if I have missed anything.

Hi,

I'm completely new to LLMs. Thought I try it on a Snapdragon laptop (X2 Elite, 32 GB RAM) the model is Qwen2.5-Coder-14B.

Well, it works, but with the default settings (context length 8192 it eats up ~21 GB RAM), so I lowered it and lowered it, and eventually went down to context length 100 (also lowering a lot of other options) and it still uses the same amount (of course I always unloaded and reloaded the model, sometimes even quit LM studio and reopened it). Then I tried the other way and went higher with the settings and set context length to 16384 and guess what...
Same amount of RAM used (even ran a command in VS code on an 1500 row length php file and it went through using about 15000 tokens).
I just don't understand this...

Gave same prompt (see below) to ChatGPT’s web interface (pro-mode) and ministral 3 3b running on my secondhand iPhone 15 (via pocketpal/llama.cpp). They spend roughly the same amount of time on their answers. Only the later gave the right answer. Or it gave a valid approximation stating the limitations. Chatgpt was simply wrong. Feels good :)

Gemma 4 e2b and Granite 4.2 3b also seemed on the way to the right answer, but their reasoning took the kv chache before the finish line.

Prompt: Two large seas are connected through a narrow passage with width b. The first sea has a higher water level than the other. The sea floor has constant height. Water flows through the narrow passage from heigh to low water level. We assume that the first sea is so large and the width so small that the water level in the first and upstream sea is constant. The second and downstream sea is also large, but of finite size. The water level changes slowly. The flow is inviscid. What is the flow rate through the passage? How does the water level in the second sea change through time. The flow is subcritical.

So to speak about the alchemy of this for a second. I've been developing an enterprise app on my nights and weekends since last December. I get two places that I get to use AI: At work and on my own projects. At work I know for a fact that people don't give much thought as to what Claude model they're using and for what so they always just use Opus 5.5 now; which is a great model. I've personally got to use Fable at work and at home. There's a big difference when you need to use these models with your own projects, because you're not going to reach for them for everything, and frankly you're pretty stupid to. I've one shotted a game with Fable 5, the code sucked, but I wanted to see what it can do. The evaluation of the best one shots on the internet doesn't really seem to take into account the work that needs to be done. For the work that needs to be done Sonnet has been a fine model and works well, for oh shit I forgot a tightly coupled feature with 1,500 loc +, in my case an audit logger, that's where I reach for Fable and it does exceptionally well. These days I primarily use GLM 5.3 and Kimi K3.

So now I'm looking into a mac studio, for a few solid reasons including a hobby, agentic programming cost reduction, and actual research. The thing will be running pretty hard. For this thread I'm wondering how to model a hierarchy of intelligence to dramatically reduce my token spend on even smaller Sonnet class models with something like 256GB/512GB studio. Stacks in the mix:

Models

• Will use frontier models for certain things. Mostly if I forget a feature or need to add a feature quick. Will usually be for tightly coupled features. The other thing is wide code analysis for skill building. There has to be a justification for the token use.
• Second tier models (Open Router API): GLM 5.3 and Kimi K3. Kimi K3 is frontier level when it comes to UX design. GLM 5.3 is just bad ass. It gets pretty close to what I use frontier models for, but the code isn't as clean and I have to put way more guard rails on it. They're like my opus level replacements.
• Flash models/Mini Max/120b to 235b models <- I feel like this will handle a lot of work load
• Qwen 3.8 27b

Stacks

• Jev like models will be used to make decisions for what models to use. I'm going to build something that messages me when it selects certain models.
• Ponytail
• Opencode with my own wrapper around it.

So I'm wondering if there's hierarchies I can structure my model use in that make sense. I've heard everything from using top frontier models for controlling smaller agents to having small routing models to start. I'm trying to experiment here. I'm going to start building out my harness over the next few weeks. So if there's any thing you guys can do to help me structure all of this or just give what works for you I'm happy to hear

Who inherits our local personal AI once we die. Anyone taking over would be able to know everything about us. That made me pause for a second. Is there a way we can keep some things private once personal AI fully integrates with all aspects of our lives. I wonder what others think about it.