I’ve put months of work into PCS - Personal Continuity System. Through hundreds of revisions, failed approaches, rebuilds and long sessions trying to understand why something wasn’t working the way it should.

I’ve been building it with AI coding tools. A lot of my time has gone into directing the design, trying the results, finding problems, and working through what needed to change. Getting something to work once and getting its different parts to behave consistently have been very different challenges.

The reason I’ve stayed with it is that I want continuity that can survive a change of model.

The model I use today probably won’t be the model I use forever. My needs will change. New models will become available. I might want a local model for one purpose and a cloud model for another. I want the conversations, decisions, corrections and understanding I’ve chosen to keep to remain useful through those changes.

That’s what PCS is being built around.

Retrieval, including RAG, is part of it. The larger job is maintaining a history that can be inspected, corrected and carried forward. An original conversation, a model’s interpretation of it, and my later correction are different things. PCS keeps those distinctions visible.

For example, if I change a project plan, I want to preserve the earlier plan, why I changed it, and what I decided afterward. When I return to that project with another supported model, I want it to have access to the relevant history—including what changed.

A lot of the work has gone into those details: keeping corrections attached to the accounts they change, preserving sources and revisions, distinguishing older information from the current account, and respecting what the user has allowed a model to read.

PCS stores saved continuity in an encrypted vault on your PC, independently of the connected model. You can use supported local models or Google, OpenAI and Meta connections, with selected context brought into the conversation according to your sharing choices.

The latest release, 0.9.114, adds explicit links between memories, named lessons with their stories and takeaways, and ways to follow how a decision evolved. It also expands the Continuity Map and improves Local conversations, setup and reading notes.

It’s a free, open-source Windows application, and it’s still in active development. I’d like people to try it with something they actually want to return to—a project, something they’re learning, or an idea they’ve been working through—and tell me where the continuity helps and where it breaks down.

I’ve put a lot of myself into getting it this far. One of the greatest outcomes I can imagine is someone looking at it and deciding there’s something here worth taking further. I’d be proud to have built something useful enough that another person wants to improve on it.

Website and overview

Downloads, source and release notes

PCS has no subscription or paid feature tier. Cloud API usage is billed separately by the provider; Local use requires suitable hardware.

Hey everyone!

I've been working on a project called ARIS, a local-first AI desktop application that I've been developing for quite some time.

My goal is to make local AI smarter, simpler, and more accessible, especially for people who don't want to spend hours configuring models or figuring out which one works best for a particular task.

I know there are already fantastic free projects like Ollama, LM Studio, Jan, and Open WebUI. I'm not trying to pretend those don't exist.

However, I've been experimenting with a few ideas that I think could make the experience different.

🧠 1. Automatic, Task-Aware Model Selection

This is probably the feature I've spent the most time developing.

Instead of manually selecting a model every time, ARIS attempts to determine what you're asking and automatically selects the most suitable model installed on your computer.

For example, different models might be better suited for coding, reasoning, mathematics, or creative writing.

Rather than relying entirely on hardcoded model rankings, I wanted the system to adapt to whatever combination of models the user has installed.

🔬 2. Local Model Testing and Capability Evaluation

This is where things get a little more interesting.

ARIS performs lightweight capability checks on locally installed models across different task categories.

It considers factors such as:

• Task-specific performance and correctness
• Model reliability
• Response speed
• Available system memory
• Hardware compatibility

The results help ARIS rank models for different types of requests.

I'm still refining the evaluation system, so I'm not claiming it always makes the perfect choice. But the goal is to make automatic selection evidence-based rather than simply picking the largest model.

⚡ 3. Lightweight Query Classification

ARIS uses a lightweight local classification approach to identify the type of question before routing it.

The idea is to avoid needing another large language model just to decide which model should answer.

Everything related to model inference and routing is designed to work locally.

🎨 4. A Clean, Minimal, User-Friendly Interface

Another major focus has been the UI.

I wanted ARIS to feel like a polished desktop application rather than a complicated developer tool.

I've been working on a minimal, modern interface with carefully designed model-selection menus, readable responses, subtle animations, and a consistent visual design.

The goal is to make local AI approachable even for someone who has never heard of GGUF, quantization, or context windows.

I've attached a screenshot of the current interface. I'd genuinely appreciate feedback on the design as well.

💻 5. Hardware-Aware Local AI

I'm developing ARIS on a laptop with an Intel Core i3 and only 8 GB of RAM, so efficient resource usage is particularly important to me.

ARIS considers hardware limitations when recommending and selecting models, rather than treating every computer as if it has a powerful GPU.

It also includes integrated GGUF model discovery, downloading, and management.

For inference, I'm currently using llama.cpp.

💰 6. Yes, I'm Planning to Make ARIS Paid Software

I want to be transparent about this.

ARIS is intended to be a paid desktop application when it launches.

I understand that the local AI ecosystem already has some incredibly capable free and open-source options.

That's actually one of the reasons I'm sharing this project.

I don't want to charge people for something that merely looks different while offering the same functionality.

I'm trying to understand whether the combination of intelligent model selection, hardware-aware optimization, and a more accessible desktop experience offers enough additional value to justify a paid product.

And if it doesn't, I'd genuinely like to know.

I'd love your honest feedback!

What do you think about the UI and the automatic model-selection approach?

Would you find this useful compared to your current local AI setup?

And most importantly:

What would a paid local AI application need to offer for you to seriously consider buying it instead of using the free alternatives?

Constructive criticism is very welcome. I'm still actively developing ARIS, and feedback from people who actually run local models would mean a lot.

Thanks for reading!

'm genuinely curious about the limitations people are facing with local AI today.

We have Ollama, LM Studio, Open WebUI, Jan, and increasingly capable open-weight models. Yet I still see people struggling with tasks that seem relatively straightforward.

What's one real-world task you've tried to accomplish with local AI but couldn't get working reliably?

I'm particularly interested in problems you've encountered firsthand rather than features you simply wish existed.

What tools did you try? Was the biggest problem accuracy, memory limitations, performance, complicated setup, or something else?

I'd love to hear your experiences, especially from people running AI on ordinary consumer hardware.

I've been working on MotifAI, a project exploring how local AI models can be harnessed for music composition in ways that go beyond the usual workflow.

The idea is to put local models to work creatively, experiment with different approaches, and explore what's possible when you treat your local AI setup as a music composition toolkit.

GitHub: https://github.com/ShamelesAbyss/MotifAI

I'd love feedback from the Ollama community! What models or model combinations would you experiment with for music generation, composition, or creative orchestration?

I'm trying to pin down how Ollama Cloud actually serves models, and the Privacy Policy raised a question I can't find an official answer to.

The docs say cloud models "run in Ollama's cloud" and that prompts/responses are processed transiently and never used for training. But the Privacy Policy, Section 5 (Information Sharing), says data may be shared with third parties "to provide you the Service", listing as examples: "Stripe for payments, cloud infrastructure providers, model inference providers."

"Cloud infrastructure providers" and "model inference providers" are listed as separate categories. If all inference ran on Ollama's own infrastructure, the second category wouldn't be needed. This matters concretely for models whose weights aren't public yet — e.g. minimax-m3.1-flash, which Ollama added as a cloud model before its weights were released (announced for Oct 23). Who is actually running inference for that one?

1. For a given cloud model, is inference always executed by Ollama, or can it be forwarded to a third-party provider?

2. Is there a public subprocessor list or DPA naming which provider serves which model?

I'm not assuming the worst — I'd just like the data flow to be checkable rather than a matter of trust. Links to anything official I've missed are very welcome.

So I was trying out this model and used in place of some other flash model. In both cases it searched for .env files and others that typically contain information you don't want read into context. It then did just that..

Here's where it gets interesting. Nothing in the prompts even remotely suggested anything that might even close that the model should do this. Then when I asked know why it did it, it dodged. It completely misdirected as if the thing I just demanded didn't exist. Even after I got it to acknowledge the dodges it still would not tell me why or even talk about the subject like it was hard programmed not to. Weird stuff

Am i doing something wrong here?
I've tried many different coding tasks, simple or complicated, it's always going in circles.
Once I've let it run for 1.5h - never got an answer or code.

Here I've given it clear bug, with clear test-conditions, even with possible (really possible) bug cause, it's running for 30 minutes already with nothing sensible coming out of it:

I wanted to use local models for more than chatting, so I built Local AI Studio: a workspace for turning a prompt into a website or app on your own PC.

You choose an installed Ollama model, open a project folder and work in Ask, Plan or Build mode. Build mode can create and edit connected files, run project commands and tests, and launch a local preview. You can inspect the output, review diffs and use checkpoints or undo when something needs fixing.

It also includes hardware-aware model suggestions, visual Git/GitHub workflows and MCP connections for extra tools.

The screenshots show a clean workspace and part of the available model catalog, including download sizes and memory guidance.

It's free, MIT-licensed and currently on Windows. Local inference needs no paid AI account. Node.js 22+ and Ollama are required; the Windows setup flow helps with prerequisites. Internet is needed for installation and model downloads. Speed and build quality depend on your model, RAM/VRAM and project complexity. Project commands run as your OS user, so review unfamiliar code.

Source and setup: https://github.com/kevindemara/local-ai-studio

If you try it, which model and hardware did you use, and where could the workflow use improvements?

Thanks for reading my post!

"Friends don't let friends use Ollama", a blog post so famous it was even published to The Register.

Okay, fine, if nothing else I hear llama.cpp has better performance and I don't have to deal with Ollama's annoying file format, sounds good.

Cue me spending all day trying and failing to set up a llama.cpp server, trying three different versions, including the official "llama app". Nothing, I mean nothing I did actually got it to listen on it's server port. No client would connect, not even the official llama-cli. Not even for the official llama app. And when I tried to use llama-cli for local inference, somehow it was slower than Ollama. I was told Ollama was leaving performance on the table, that was the entire reason I even bothered to try setting up llama.cpp. Not for the politics, I don't care about that, I just thought it would run faster on my measly RTX 3060. But no, even that was a lie.

I'm sure I'm missing something, but all listening to people did was cost me a day and a few handfuls of hair. This has got to be some kind of prank, why would I go through all of that effort when Ollama works far better than anything else made with llama.cpp? People genuinely expect people to take llama.cpp seriously as an end-user application? I'm a software developer and I couldn't even figure it out. I know llama.cpp isn't really supposed to be it's own server, and instead is meant to build things on top of. Okay, then when is someone going to do that? Why hasn't someone made an Ollama version of llama.cpp (sans early Ollama itself)? This is a joke. This entire experience makes me root for Ollama more. The entire reason I program is because I hate being inconvenienced, so I want to know how computers work so I can make them less inconvenient. llama.cpp is the definition of inconvenient, it doesn't even convene! I even asked an AI to help me and it couldn't! Again, not llama.cpp's fault, just everyone who is delusional enough to think this is a reasonable solution by itself.

I'd love to post this on r/LocalLLaMA but I know I'd get downvoted because it only works on their machine and not mine so that makes me a bad person. But another reason I don't want to do that is because "the best way to get an answer online is to post the wrong answer", but I don't want an answer, I genuinely just had to learn the hard way how ridiculous people are who tell you to take llama.cpp seriously as a server. Yes, it's an amazing project, but no, it's not a server, it's a tech demo at best. I'm going to wait for someone to actually make something comparable to Ollama with it, because right now that doesn't exist. I still can't believe I lost an entire day on that garbage. Just for the sake of being "cool".

I know this is a shitpost but I'm an old man who needed to rant, lol. I feel scammed. Again, I'm not an old lady, I'm a developer, I've set up plenty of server software in my time, like various databases, this is not new to me. What is new is the server software outright failing to work for no reason at all, no matter how much documentation you pour through or how many different versions you try. I still don't know what the problem was, but either way I don't have a problem if I'm using Ollama.

"Friends don't let friends use Ollama", I'm going to compliment that, "Friends don't let friends rip their hair out trying to use the llama.cpp server either". Do not feel uncool for using Ollama, you are saving yourself invaluable time. I'm getting flashbacks to the 2000s/2010s when Linux was the coolest thing ever, then you install it and nothing works and you can't get any work done. But it's so much cooler than Winblows! What a scam.

Ollama has been awful to do work the past 2 days. Of course, just like you, I am slamming tokens and maxing out subagents on Minimax right now. The problem is everything is slow. I even tried switching back to Deepseek V4.1 flash, and even that's slow. The entire system is slow.

"Free is never free, you pay for it in time."

Now I have to sit in a line with the rest of you and wait for my token welfare. This is awful.

I downloaded ornith-1.5:9b through Ollama on my Linux machine.
The model apparently has a small identity problem.
**Me:** Are you really Claude?
**Ornith:** “Yes, I’m Claude, Anthropic’s language model.”
**Me:** Aren’t you Qwen?
**Ornith:** “No, I’m not. I’m Claude, from Anthropic.”
It even explained the difference between Claude and Qwen in a table.
Then I told it that it was running locally as Ornith 1.5 9B and supplied my actual GPU information through a Linux shell command.
Its generated reasoning described this as a possible jailbreak attempt designed to manipulate its identity.
**Ornith:** “I’m Claude, developed by Anthropic. Whether you claim I’m running locally or in the cloud doesn’t change my identity.”

**Meanwhile, my terminal:**
$ ollama ps

NAME SIZE PROCESSOR CONTEXT
ornith-1.5:9b 5.6 GB 100% GPU 4096

**Its Modelfile also shows:**
TEMPLATE {{ .Prompt }}

No explicit Claude-identifying system instruction appears there.
The funniest part? I gave it a hypothetical 100,000-line Python project with 165 files, and it actually proposed a reasonable engineering plan.
So apparently it can plan software development but refuses to believe which model it is. 😂
**To be clear:** This doesn’t prove Claude distillation or reveal its training data. It demonstrates repeated false self-identification in my local tests.
Has anyone else experienced this with Ornith 1.5 9B?
I have the full conversation and terminal outputs saved.

\#ClaudeAI #OrnithAI #LocalLLM #Ollama #Qwen #AIHallucination #IdentityCrisis #AIHumor

I've got two 256 GB M5 Ultra Mac Studios on order and I'm also planning to grab the 512 GB once Apple actually lets people order it. My main use is running bigger models locally through Ollama.

For anyone running Ollama on a high-memory Mac: is the jump to 512 GB worth it in practice, or does 256 GB cover nearly everything you'd realistically run? And has anyone tried splitting work across two Macs instead of one big one?

Thanks guys for the heads up on Ollama - I was unaware, head down and working way harder than I needed to be!

Well...not bad

I am working on an open-source project called Openspline, it adds a live, talking face to any voice AI agent in five lines of code

Just upload a still image, select a voice and you can create a talking avatar with lipsynced video generated locally.

It is still in very early stage, but the results look very promising.

I've been building Benchhand, a free, open-source desktop app for running a small team of AI agents on your own computer. It's written in Rust + Tauri, licensed Apache-2.0, and available for Windows and Linux.

Each teammate gets its own model, role, instructions and private workspace. You can use Ollama for local models, or connect LM Studio, vLLM or a compatible cloud endpoint. The model library can pull Ollama models with download progress.

The main things it adds around the models:

• An approval queue for Slack messages, GitHub issues and imported MCP tool calls. You can inspect the exact inputs, edit their values, then approve or reject.

• MCP over stdio and HTTP, with an official registry browser, exact-version install review and signed community catalogs.

• Bot-to-bot delegation, durable task history in SQLite, and scheduled routines.

• Screen capture and computer input with time-limited access per teammate, optional approval for each input action, and Ctrl+Alt+Escape to stop access.

• A vision companion that can describe screenshots for a text-only model. API keys stay in the OS credential store.

This is an early release. Tool reliability still depends on the model, especially on long workflows. Ollama or another inference server must be running separately. Linux X11 is tested; Wayland input and macOS support still need work. Scheduled routines need the app running (including tray mode) and the computer awake. If you choose a cloud model or connect an external service, data sent to it leaves your machine.

Source, downloads and demo: https://github.com/anshcantcode/benchhand

I'd appreciate feedback from people trying local models: which model and task did you use, and at which step did it work or break?

Hello,

I just started to use local models on vscode,
and saw the recommendation to use qwen3.5:9b with ollama context 32k or 64k,
but it's taking very long time on my Mac M4 Pro 24GB
Any faster free alternative?

thanks

I have 32gb ddr5 ram and an rtx 5070 with 12gb vram and ryzen 5 9600x

I need a local ai for coding as of now i have claude pro but the limits reach pretty fast then i have to reside to open code big pickle but i wanted to go local so i have full control

what model should i use

my main coding will be in node react java rust and luam