🚨 CHATGPT, CLAUDE, GEMINI… YOU CAN PUT THEM ALL IN ONE PLACE.
Open WebUI turns your AI stack into one self-hosted workspace.
Instead of jumping between different AI apps, Open WebUI gives you one interface for local models, cloud APIs, agents, files, web search, RAG, image generation, voice, and more.
→ Connect Ollama + OpenAI-compatible APIs
→ Run multiple models side-by-side and compare answers
→ Upload documents and build RAG/Knowledge workflows
→ Search the web and get cited answers
→ Generate and edit images
→ Run code directly from the interface
→ Give agents tools, memory, terminal access, and workflows
→ Add team features like RBAC, SSO/OIDC, LDAP, and SCIM
And the part that makes this really interesting:
It can run entirely self-hosted and offline.
Docker, pip, uv, Kubernetes — pick your setup and connect the models you actually want to use.
This isn't some tiny side project either.
150K+ GitHub stars.
Basically:
one UI → almost your entire AI stack.
And that's why Open WebUI is worth knowing about. 🔥
#OpenWebUI #AI #Ollama #LLM #AIAgents #SelfHosted #OpenSource #DevTools #ArtificialIntelligence
Multi-agent coding gets messy when the work has no durable trail.
Puppetmaster is a supervisor and job store for developers running multi-step engineering work through existing agent CLIs and provider adapters.
It helps you keep repository investigations, audits, refactors, and implementations reviewable by routing tasks to independent workers and storing typed results in SQLite so jobs can be inspected and resumed.
Key features:
• Existing-tool adapters – works with agent tools including Cursor, Claude Code, Codex, Hermes, and provider APIs
• Independent workers – starts separate workers for multi-step engineering tasks
• Task routing – routes work to an available model
• SQLite-backed artifacts – stores typed results so completed work can be inspected and resumed
• MCP setup – the setup command installs MCP tools, rules, and hooks for supported platforms
It’s open-source (MIT license).
Link in the reply 👇
The #1 problem with local AI is now solved.
There’s a free tool called https://t.co/yubPHLT3kl that checks your hardware and tells you which models will actually run well before you download anything.
So instead of guessing and hitting out-of-memory errors…it grades every model against your machine.
What it does (right in your browser, no install):
→ detects your setup (RAM / CPU / GPU / VRAM)
→ scores each model for fit, speed, and context length
→ grades every quantization level (Q4_K_M, Q6_K, Q8_0, etc.)
→ labels what runs great vs okay vs too heavy
It covers most of the open-weight stack, Llama, Qwen, Gemma, Mistral, DeepSeek, Phi and more, pulling requirements from llama.cpp, Ollama, and LM Studio.
And it’s fully opensource
Do you understand the actual hard part of PII redaction?
Not catching sensitive data; it's not flagging clean data by mistake. Respan's new P-1 model is built around exactly that 👇
It's called P-1, and Respan is calling it the best privacy model out there. The pitch: existing tools force a tradeoff between accuracy, latency, and cost. P-1 is built to skip that tradeoff entirely.
They tested it against AWS, Raindrop, and Presidio on 20,000 samples across four public datasets.
On detection accuracy (F1 score), P-1 led across the board:
→ AI4Privacy EN: 79.5% vs AWS's 69.1%
→ AI4Privacy ES: 75.5% vs AWS's 71.5%
→ WikiNEuRaL PERSON: 96.1% vs AWS's 86.9%
Interesting! @perplexity_ai open-sourcing the PII classifier that they use for deciding when to send the workload to the local model in the hybrid compute setup https://t.co/PNOTjXeLn8
OpenMasq is open source today (Apache-2.0).
Desktop chat for every LLM: names, IDs, IBANs and whole documents are redacted on your machine before the send, and restored in the reply.
Benchmarked on Presidio's own corpus: 74% recall vs 58%.
https://t.co/sOWlE8I1jN