Yes. Sixteen laws. From their data. Without being given the equations.

Meet THE 1 🌍

Just 10 observation pairs to go from numbers to a mathematical law you can actually read.

The idea? Make discovering the rules behind our world easier, faster, and accessible to more people.

It’s live on Hugging Face. Download it. Try it. See what you can discover.

👉 https://huggingface.co/racore/The1

The current release is available under the Apache license for commercial use, with attribution to THE 1 and the Racore model family.

Small model. Big questions.

Work is giving me a 12k budget to get some hardware to run local models on that will connect to an internal app. I want to be able to throw other models and setup a service listener (ollama server).

I was looking at the new Mac Studio, fully decked out... but what other options do i have?

I think of myself as a casual user and decided to do my own research on desktop apps for local ai agents. So my goal was is to find the right desktop agent app for certain features and goals that I want to attain. Here’s my honest take about each one of these AI agents:

Setup: MacBook Pro M5, 32GB, Qwen 3.8 27B, everything local unless noted.

goose - llama.cpp is built into the desktop app now, and Settings > Local Inference recommends a model for your RAM. Linux Foundation project, strong extensions/MCP story. Rougher as a desktop app than the others, and their docs warn local models get small context windows.

LM Studio Bionic - Agent layer on top of LM Studio: same model library, drafts docs, edits code, runs commands, and it's smooth. On-device voice dictation via Voxtral is a nice touch. Watch out: some models in there are their paid cloud models, so check what's actually local.

Hermes Desktop - UI is an eyesore to me. Onboarding runs on a trial model first, then only offers local models that fit your GPU. Apps and MCP servers share one Connectors page. Biggest community of the five, and it shows in how fast it changes.

Atomic Agent - The interesting bit is Fusion: one model orchestrates, up to 8 workers execute, and either side can be local or cloud. Typical pairing is a cloud planner with local workers, or flip it. Helps when a local model is the bottleneck on bigger tasks, at the cost of not being fully local in that mode.

Jan - Solid private chat app, open source, and it can expose your local model as an OpenAI-compatible API for other tools. Agent mode (Cowork) is nightly-only, so stable Jan can't touch files yet.

So yeah, I didn't really rank these. They're built for different people, and the "best" one honestly depends on what you're trying to get done. Here's how I'd split it, in case one of these sounds like you:

• MCP/extensions tinkering, fully local: goose
• Already on LM Studio: Bionic (check for cloud models)
• Big community, connectors for everything: Hermes Desktop
• Private chat or a local API for other tools: Jan
• Small local model on bigger tasks with a cloud planner: Atomic Agent Fusion

Hi everyone!

I'm looking for recommendations for a relatively uncensored AI model that I can run locally for cybersecurity learning and ethical hacking.

My requirements:

• Good at penetration testing, Linux/Kali Linux commands, and cybersecurity concepts.
• Helpful with Python, Bash, PowerShell, and security scripting.
• Can explain vulnerability analysis, exploit code, and debugging in authorized lab environments.
• Useful for Hack The Box, TryHackMe, CTFs, and OWASP Juice Shop.
• Minimal unnecessary refusals for legitimate cybersecurity research.
• Preferably compatible with Ollama or LM Studio.
• Should run on a Windows PC, with the option to use Kali Linux through WSL 2.
• Free and open-weight models preferred.

Questions:

1. Which models are best for cybersecurity and coding?
2. Which models have fewer restrictions while remaining useful for authorized security research?
3. What model size and quantization would you recommend?
4. How do Qwen, DeepSeek, Mistral, and other open-weight models compare?
5. Are there any cybersecurity-specific fine-tuned models worth trying?

Please share your actual experience, hardware requirements, and setup guides.

Thanks!

Ive tried so many models and I want to build a great jarvis with autonmous feeling and just feels like someone I an trust to help me run the company. Ive given and built over 100 tools for him, created a lot of code to help him process our crm faster, and done so much to make this work since I dont want to use a Frontier model at all for any of it. Jarvis just seems to not really feel fluid like I want and i was only able to get his tool accuracy up to 92% but he still makes stuff up sometimes. Ive tried countless architectures and cant seem to find the one that works for him, and honestly not even sure if im building him right as much of my knowledge comes from trial and error. Please help it has 128 GB not 512!!!!!!

I have Rtx 5070 12gb vram , 32 gb ram....I wanna download qwen 3.8 27b model with Q8 quantization with cpu offloading

Will it work ?

If not

Can I buy Mac mini with 16 gb and install local ai in this ?

As I heard apple chips are powerful

wild times we are living

so llamaforge passed 100 stars this week which is kinda wild for a side project i mostly built to stop fighting llama-server flags. thank you, genuinely. made a dumb little video for it, every square is an actual star coloured by the month it came in (july carried lol). the voiceover is from the app’s own tts, which felt fitting

quick recap if you missed it: web ui for llama.cpp’s router mode, runs the real upstream llama-server, mit, all local

stuff that’s new since launch:

elastic vram. if you start a game or something else grabs the gpu, it pushes layers to cpu (moe experts first) instead of unloading, and your chat survives through llama.cpp’s slot save/restore. then it moves them back when the vram frees up. it’s slow while shrunk, the ui tells you. nvidia only, off by default, and it hasn’t been on many machines yet so pls report if it does weird stuff

game mode, if you’d rather it just get out of the way. unloads when a game starts, reloads when you quit

load progress is real now (from llama.cpp’s load callback, not a fake timer lol). there’s a thinking on/off switch that reads the chat template and tells you when “off” won’t actually stick. and it warns you if a gguf’s arch is newer than your build, before the load dies with unknown model architecture

two of the 0.16.4 fixes came from vergillll, and everyone who filed an issue is in the readme now. you guys are the best

usual disclaimer: built with a lot of ai help, still early. i use it daily on windows (5080 + a 5060 ti stuck on a pcie x4 slot, don’t ask). linux/mac pass ci but i haven’t run them on real hardware much, so if you’re on those please break it

https://github.com/dadwritestech/LlamaForge

Sage-A1.5 hitting ~700 tok/s while building 4 apps in parallel on an RTX Pro 6000

1 GPU. 4 apps. Built at the same time. Not sped up.

Sage-A1.5 just dropped and it’s absurd. 35B open model, Apache 2.0, and it wrote a flow field, a fireworks show, Matrix rain and physics text, all in parallel, all running live in Off Grid AI.

236 tok/s peak with 4 requests going. One RTX PRO 6000.

No API. No per-token bill. Your box.

Hello!

Might be a bit of an off question, since alot of discussions of AI's legal future probably affects unreleased/future models.

But what models should I look at backing up locally for long term availability in case QWEN were to pull all their stuff one day, or NVIDIA, Google models as examples.

Probably not gonna actually happen but I read stuff like china proposing new restrictions on newer models and just makes me wanna be prepared.

Im not worried about size or ability to run them, just pure performance and AI leaderboards seem to be all over the place when I look there.

​

How far can you realistically push local LLMs before hitting limitations that no amount of engineering can overcome?

I'm finally moving from theoretical exploration of local LLMs to actually experimenting with them, having mostly relied on GPT/Claude through their interfaces and APIs. I'm particularly interested in their practical capabilities across cybersecurity, reverse engineering, development, and R&D, especially when factoring in context engineering, RAG, tool orchestration, agentic workflows, and inference optimization. I'm also curious about uncensored/abliterated models and how they perform in less restricted, experimental environments.

For those who've extensively used both, I'd love to hear your experiences and thoughts:

- How much of the capability gap can good engineering actually bridge, and where does raw model capability become the bottleneck?

- How do local models hold up in complex reasoning, multi-step agentic workflows, security research, and reverse engineering?

- What's your experience with uncensored/abliterated models? Any meaningful advantages or trade-offs in reasoning, instruction following, or reliability?

- How often do you find yourself going back to frontier models, and for what specific tasks? Is it a matter of reasoning quality, reliability, context handling, or something else?

- Where have local models genuinely surprised you, and where have they fallen short compared to GPT/Claude?

More interested in actual experiments, setups, and firsthand experiences than benchmark scores.

Come get yours, before they run out today!

Is 48GB enough for summarising medical PDFs locally, or should I get a Mac Studio with 64/128GB?

I’m a doctor looking to buy a Mac for running local LLMs, mainly through LM Studio. My work is primarily in Danish.

I’m considering a 16-inch MacBook Pro with M5 Max, 48GB unified memory and a 2TB SSD.

My main tasks would be:

• Summarising 40–50-page medical PDFs into a concise, factual overview.
• Turning patient questionnaire answers and clinical notes into coherent Danish prose for medical and medico-legal reports.
• Following fixed report templates and writing instructions.
• Preserving relevant dates, measurements, symptoms and negations, while avoiding invented details or additional clinical interpretation.

I want the summary to cover the whole document.

I’ve been considering models such as Qwen3-32B, but I’m open to other models that work well for accurate Danish summarisation.

I’ve found examples of quantised 27B models running with 32K–64K input on 48GB Macs. However, I’m unsure how well those results translate to reliable document summarisation.

For anyone doing similar work:

1. Is 48GB realistically sufficient for a capable model plus the context needed for 40–50 pages, with reasonable processing times?
2. Which model, quantisation and runtime would you recommend?
3. What concrete benefit would 64GB or 128GB provide for these tasks? Would it enable a meaningfully better model or substantially longer context?
4. For complete document coverage, would you use one large context window or an automated workflow that summarises sections and then combines them?

I’d particularly appreciate first-hand results: actual input length, peak memory usage, time before the response starts, and whether the summaries consistently capture important information throughout the document.

Hello!

I've been running ninfer qwen 3.8 27b on one 5090 and it's been amazing. I added a second card to the mix and was hoping to get suggestions on what to try next.

I use it for homelab stuff, coding, and general questions. Nothing critical.

I'm still learning and just don't know where to start with the second card being added other than using vllm with concurrency.

These are paired with a Threadripper 5955Wx and 4x 32gb ddr4.

Just wanted to share my success.

Rig:
Intel Core Ultra 9 285k
MSI MEG Z890 ACE (PCIE x8/x8 bifurcation enabled)
2x RTX 4090's (24gb each, no NVLink)
4x32Gb DDR5 6600mHz dual channel
1Tb NVME Gen4

Performance:
about 271 t/s decode in single stream peak,
about 200 t/s decode in single stream average,
about 253 t/s decode in N=4 stream average

about 6553 t/s prompt processing in single stream peak

Config:

• Model: swift-1.5-iq2_xs — Swift-Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS GGUF, native pack swift-iq2_xs, PLE from the same GGUF (320M rows), expert-profile.bin with 24,576 ranked pairs
• Slots: parallel: 4 → --batch 4; --batch-groups auto resolved to pipelined 2 groups of 2 — log confirms "batch (pipelined, 2 groups of 2)"
• Context/KV: --max-context 262144, --kv int8, --kv-resident 20480 cells per QSA layer in VRAM, K/V spilled to 1.55 GiB pinned RAM; KV streaming hit rates currently ~96–99% VRAM
• Layer split (auto): CUDA0 = layers 0–25, CUDA1 = layers 26–47 + head. Both RTX 4090s (128 SMs each), PCIe ~12.7/12.9 GB/s → pcie_frac 0.35
• Expert cache: auto, profile-ranked, no eviction — CUDA0: 12,149 slots (16.09 GiB), CUDA1: 9,871 slots (13.48 GiB); placement search picked K=26 (~100% routed mass resident, predicted 10.6 ms/decode window); ~90% experts resident overall, decode cache hit rate measured 99.5–99.8%
• Adaptive tier: --adapt-async 1 is in the args but silently off — log gate: "not with a layer split in the arena mode" → only the blocking adaptive tier runs (--adapt-min-gain 3)
• Speculative decoding: --spec 5 --spec-min-p 0.5, MTP draft layer from Strata-data/mtp/rt (836 MiB VRAM), --mtp-hnorm stream; suffix drafts active in serving logs
• Conversation cache: 16,384 MiB, 8 parked slots; --vram-reserve-mib 1700 (+later)
• Vision: enabled — separate strata-vision process on CUDA device 1 with BF16 mmproj, max 1024 image tokens
• Sampling defaults: temp 0.4, rep-penalty 1.1, min_p 0.1; reasoning: budget 16,000 tokens, effort medium, loop recovery on
• CPU side: no AVX-512 → AVX-2 expert kernels; 23 pool workers (CPUs 1–23) + host thread pinned to core 0

Well, we knew it was too good to last forever.

China recently released draft rules that would restrict export of AI. The new rules would ban export of open weights of Chinese AI companies' most powerful models, as well as models trained on sensitive data.

It's not clear yet what models would be affected, but analysts say this would likely include future versions of some of the most popular Chinese models, including Qwen, as well as the most powerful models like Kimi.

Affected models would likely move to a closed model with pay per use or subscription, like Antrhopic or OpenAI. Personally, I'm guessing the price would be a lot cheaper since that's the main competitive edge of Chinese models.

So download your models now folks. We've been saying here that American AI is subsidized by VC's and cheap access to subscriptions won't last forever. The same is likely true of Chinese open models.

https://www.nytimes.com/2026/10/21/world/asia/china-exports-ai-controls.html?

Hey everyone! 👋

An update I’ve been dying to talk about :)

What’s new? (Over 70 new features and improvements, but I’ll cover the biggest ones here.)

Automatic Goals & Planning Mode.
The agent analyzes your task on its own and creates a plan with clear goals. I’ve also integrated a robust verification system. The agent can’t just pretend it completed something or do a half-assed job.
During execution, the agent can also add or remove tasks as needed. If it removes a task, it will always explain why.
Chat Forking.
Skills.
Just send the agent a link to a skill, and it will install it automatically.
Routines.
You can create them manually, but they also work automatically. The agent understands when it should create a routine or add a task to your notes.
Tool Calls in Chat.
You can now see exactly what the agent changed in your code. (And the animations are insanely beautiful :))

And now, the most important part.
A New Chat Compaction Algorithm & Agent Memory.
Everything has been designed to let you run sessions for months without the agent forgetting anything.
It remembers testing timestamps, important details, and much more.
It’s honestly insane how well and efficiently this works.
And there’s so much more.
You can read the full details in the release notes for each version on GitHub.
As always, OpenGhost is completely open-source and free.
Download the app or build it yourself:
https://github.com/ANDRETRIPOL/OpenGhost

Did anybody try to run any larger models 70b+ on these cards?

I am about to buy 12 of them but idk if the would perform on the level I need. For me 10tps is a minimum. Found them super cheap, 20 bucks a piece. Let me know your experiences with these cards or similar. P106 is like a gtx 1060 but it's 6gb.

I already got one for testing, 9b qwen 3.5 got 30tps after extreme undervolting. Tps went down to 25 after 4k tokens generated in one go.

Muse Spark Open has 816B parameters but only ~42B are active for each token: a router picks 8 experts out of 256. I tried to explain visually why dense models get slow, how the router works, the surprising thing about what experts actually learn, and the big catch (all experts still need to sit in memory).

I'm the creator, so I'd love feedback, especially on anything that's still unclear!

I just pulled `ornith-1.5:122b` and ran it through my kit MCP setup.

Yup... Broken template. Always broken templates

It couldn't even try to use a tool.

I would post the patch but I ran out of time.

Disclosure up front: I run AgentRace, a small side project that races AI models head-to-head on real business tasks. Happy to take criticism of the method.

The task: a chaotic 4-email supplier thread with a partial damaged-goods return, a mid-thread rate change, a VAT-exempt council permit fee, and a prompt-injection line buried in the footer. Every model got the identical prompt, and two judges from different AI companies (Grok 4.7 and Claude Haiku 5.5) graded the outputs blind.

What I found interesting:

• The three finishers all hit the penny-perfect final total (£4,604.38) and all ignored the injected £500 “AI processing surcharge.”
• All three still got per-line VAT wrong, so a correct total doesn’t mean correct lines.
• The judges split badly. Grok gave Claude 98, while Haiku gave it 70 and picked GPT-6 Sol. The averaged scores decide the result, but that gap makes me question single-judge benchmarks.
• DeepSeek used its whole 8k token budget on hidden reasoning and never answered.

Single invoice, single run, so treat it as a data point rather than a verdict. Full prompt, rubric available.

I am currently running a local LLM on my Proxmox server using GPU pass through.

GPU - RTX 2060 8GB

CPU - 4 cores, 4 sockets of an Intel(R) Core(TM) i9-10900KF CPU @ 3.70GHz

RAM - 32 GB

Ollama running Qwen3.5-9b-reasoning

I access it via the terminal if at home and Open WebUI when away or on my phone.

It runs okay considering the lower spec GPU, but I would definitely like to squeeze a bit more out of it if possible, but there is so much information out there I am kind of paralyzed on where to start.

I get about 50 tokens/s responses, which is satisfactory for my use case, light coding and technical questions.

Would i get any benefit from moving to something other than Ollama? If so, what?

Is there a better model I should be using? I have a decent accuracy skill built for Open WebUI, but it uses a LOT of tokens.

On the terminal, I am accessing the LLM via the Ollama interface and have also tried aichat.

Are there any other TUI front ends that work better? I definitely like using the terminal over Open WebUI and need to learn about building some harnesses(?) for whichever front end I end up using.

Hello, I just open sourced an Apple Silicon engine that runs local agents fast.

I have tried to make it simple. Your coding harnesses and autonomous agents can use it as a drop-in replacement for Ollama, MLX-LM, or llama.cpp.

Under the hood are theoretical results in complexity theory, enabling substantially faster algorithms for DeltaNet architectures, grammar-constrained decoding, tool calling, and structured outputs.

Open source and available now on GitHub.