llmfit — one command to find which LLMs actually fit your hardware

https://github.com/AlexsJones/llmfit

Detects your RAM/GPU, scores hundreds of models, works with Ollama, LM Studio, llama.cpp...

Written in Rust of course 🙌

Handy when you're trying to optimize for SLMs like I do with @silex MCP => https://www.silex.me/ai/

Have you tried local models (Ollama, LM Studio, llama.cpp...)?

How did they perform, let me know 👇

#Rust #LocalAI #SLM #FOSS #buildinpublic

Lokale KI auf Android – ohne Root, ohne Cloud, ohne GrapheneOS.

Qwen 2.5 1.5B läuft stabil auf Snapdragon 8 Gen 3 via Termux + proot. 7B-Modelle crashen (OOM-Killer). OpenCL/Vulkan geblockt durch SELinux. CPU-Inferenz mit -t 4 ist der einzig stabile Pfad.

Guide auf neobild.de

#DigitaleSouveränität #LocalAI #Termux #Privacy @kuketzblog

🚀 LMIM OS v1.20 BETA — Windows + Linux!

Autonomous AI, WORKS OUT OF THE BOX:
✅ One-click Win installer / Linux AppImage
✅ Build code • Schedule • Messaging
✅ WhatsApp/TG/Slack/Discord/Email
✅ Campaign Blaster • Local OR cloud
✅ 100% FREE • Open Source (MIT)

https://lmim.tech

#LocalAI #OpenSource #Windows #Linux

I built an AI persona for my blog. This week, he published his first post.

His name is BartBot. He monitors RSS feeds, scores articles with a local LLM, and surfaces the ~0.7% worth reading.

I'm not sure if he's useful, annoying, or both. He's not sure either.

https://jamalhansen.com/blog/the-content-curator/

#LocalAI #AITools #BuildInPublic #ContentCuration #PKM #RSSFeed #BartBot

Running two PicoClaw instances on one machine looked easy at first.

I thought duplicating the repo would be enough.
The real blocker was Docker runtime identity:
- container_name collisions
- shared bot token conflicts
- possible host port clashes later

What worked:
- separate repos
- separate data/config
- no container_name
- explicit Compose project names like picoclaw1 and picoclaw2

Short write-up here:
https://funkyidol.in/blog/deploying-multiple-picoclaw-instances-on-a-single-machine-with-docker/

#Docker #SelfHosting #DevOps #LocalAI #PicoClaw

The Tiiny AI Pocket Lab: Goodbye Cloud Subscriptions! Hello, 120B Parameters in My Pocket🛠️🦾

I just got my hands on the Tiiny AI Pocket Lab, and it’s officially breaking the “Cloud dependence” loop.

120B Parameters? Locally.
Internet? Not needed.
Privacy? 100%.

While everyone else is paying $20/month to let Big Tech read their prompts, this 300g beast is running Llama 3 and DeepSeek locally at 20+ tokens/sec.

It’s got 80GB of RAM (yes, in a pocket device) and runs at just 65W. Guinness World Record holder for a reason. 🏆

The Tiiny AI Pocket Lab is the first credible challenge to the cloud-only AI model. For enterprises and researchers, the value proposition is simple:

Security: Zero-latency, zero-cloud data processing.
Cost: No per-token fees or monthly subscriptions.
Power: 80GB LPDDR5X RAM in a 300g form factor.
This isn’t just a “mini-PC.” It’s a shift toward Edge Intelligence. When you can run a 120B model locally at 65W, the “setup tax” of AI disappears.

The future isn’t in a data center; it’s in your palm.

Is your organization ready for the shift from Cloud AI to Private AI?

https://www.nbloglinks.com/the-tiiny-ai-pocket-lab-goodbye-cloud-subscriptions-hello-120b-parameters-in-my-pocket/

#LocalAI #OpenSource #TechHardware #PrivacyFirst #TiinyAI #CES2026 #ArtificialIntelligence #EdgeComputing #DataPrivacy #FutureOfWork #TechLeadership #gadget

cedric (@cedric_chee)

MiniMax M2.7이 오픈소스로 공개될 예정이며, 약 2주 내 가중치도 제공된다고 밝혔다. 가정용 환경에서 현실적으로 실행 가능한 최고의 모델일 수 있다는 평가가 포함되어 있어, 로컬 구동 가능한 대형 모델 소식으로 주목된다.

https://x.com/cedric_chee/status/2035719456597688603

#minimax #opensource #llm #weights #localai

Github Awesome (@GithubAwesome)

400B 파라미터 규모의 모델을 로컬에서 실행하는 엔진 flash-moe가 소개됐다. 48GB RAM의 맥북 프로에서도 동작하며, 209GB 모델을 메모리에 모두 올리지 않고 SSD에서 GPU로 가중치를 필요할 때마다 스트리밍해 구동한다는 점이 핵심이다.

https://x.com/GithubAwesome/status/2035562403178438723

#localai #llm #moe #inference #macbook