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12 Excellent MCP Servers
The family of MCP (Model Context Protocol) servers keeps expanding to bridge agents, models, tools, web, data and apps. Here are 12 useful MCP servers that will help you create convenient agentic ecosystems:
1. Chrome DevTools MCP → https://github.com/ChromeDevTools/chrome-devtools-mcp
Lets your coding agent (Gemini, Claude, Cursor, Copilot) control a live Chrome browser with full DevTools access for automation, debugging, and performance analysis
2. Windows-MCP → https://github.com/CursorTouch/Windows-MCP
Provides interaction between agents and Windows, handling file navigation, app control, UI actions, QA testing
3. MCPControl → https://github.com/claude-did-this/MCPControl
Windows control server for programmatic control of mouse, keyboard, window management, and screen capture
4. MetaMCP → https://github.com/metatool-ai/metamcp
A proxy that aggregates multiple MCP servers into one, with middleware support. Works as a standard MCP server for any client
5. MindsDB → https://github.com/mindsdb/mindsdb
Humans, models, agents and apps get accurate answers from large-scale data sources
6. Playwright MCP → https://github.com/microsoft/playwright-mcp
Lets LLMs interact with web pages via structured accessibility snapshots, no need for screenshots or visually-tuned models
7. MCP Access Point → https://github.com/sxhxliang/mcp-access-point
Bridges MCP clients with HTTP services, no server-side changes needed
8. Browserbase MCP Server → https://github.com/browserbase/mcp-server-browserbase
Connects LLMs to external data and tools, adding cloud browser automation via Browserbase and Stagehand. It enables LLMs to browse, capture, extract, and act on web pages with precision
9. Yutu → https://github.com/eat-pray-ai/yutu
Automates YouTube workflows, managing videos, playlists, channels, comments, captions, etc.
3 more below ↓
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1 day ago
12 Excellent MCP Servers
The family of MCP (Model Context Protocol) servers keeps expanding to bridge agents, models, tools, web, data and apps. Here are 12 useful MCP servers that will help you create convenient agentic ecosystems:
1. Chrome DevTools MCP → https://github.com/ChromeDevTools/chrome-devtools-mcp
Lets your coding agent (Gemini, Claude, Cursor, Copilot) control a live Chrome browser with full DevTools access for automation, debugging, and performance analysis
2. Windows-MCP → https://github.com/CursorTouch/Windows-MCP
Provides interaction between agents and Windows, handling file navigation, app control, UI actions, QA testing
3. MCPControl → https://github.com/claude-did-this/MCPControl
Windows control server for programmatic control of mouse, keyboard, window management, and screen capture
4. MetaMCP → https://github.com/metatool-ai/metamcp
A proxy that aggregates multiple MCP servers into one, with middleware support. Works as a standard MCP server for any client
5. MindsDB → https://github.com/mindsdb/mindsdb
Humans, models, agents and apps get accurate answers from large-scale data sources
6. Playwright MCP → https://github.com/microsoft/playwright-mcp
Lets LLMs interact with web pages via structured accessibility snapshots, no need for screenshots or visually-tuned models
7. MCP Access Point → https://github.com/sxhxliang/mcp-access-point
Bridges MCP clients with HTTP services, no server-side changes needed
8. Browserbase MCP Server → https://github.com/browserbase/mcp-server-browserbase
Connects LLMs to external data and tools, adding cloud browser automation via Browserbase and Stagehand. It enables LLMs to browse, capture, extract, and act on web pages with precision
9. Yutu → https://github.com/eat-pray-ai/yutu
Automates YouTube workflows, managing videos, playlists, channels, comments, captions, etc.
3 more below ↓
Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
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8 days ago
10 awesome advanced LoRA approaches
Low-Rank Adaptation (LoRA) is the go-to method for efficient model fine-tuning that adds small low-rank matrices instead of retraining full models. The field isn’t standing still – new LoRA variants push the limits of efficiency, generalization, and personalization. So we’re sharing 10 of the latest LoRA approaches you should know about:
1. Mixture-of-LoRA-experts → https://huggingface.co/papers/2509.13878
Adds multiple low-rank adapters (LoRA) into a model’s layers, and a routing mechanism activates the most suitable ones for each input. This lets the model adapt better to new unseen conditions
2. Amortized Bayesian Meta-Learning for LoRA (ABMLL) → https://huggingface.co/papers/2508.14285
Balances global and task-specific parameters within a Bayesian framework to improve uncertainty calibration and generalization to new tasks without high memory or compute costs
3. AutoLoRA → https://huggingface.co/papers/2508.02107
Automatically retrieves and dynamically aggregates public LoRAs for stronger T2I generation
4. aLoRA (Activated LoRA) → https://huggingface.co/papers/2504.12397
Only applies LoRA after invocation, letting the model reuse the base model’s KV cache instead of recomputing the full turn’s KV cache. Efficient in multi-turn conversations
5. LiLoRA (LoRA in LoRA) → https://huggingface.co/papers/2508.06202
Shares the LoRA matrix A across tasks and additionally low-rank-decomposes matrix B to cut parameters in continual vision-text MLLMs
6. Sensitivity-LoRA → https://huggingface.co/papers/2509.09119
Dynamically assigns ranks to weight matrices based on their sensitivity, measured using second-order derivatives
Read further below ↓
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