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AI Engineers gonna love this MCP server! KitOps MCP serve eliminates the "it works on my machine" problem by packaging AI/ML projects (models, datasets, code, config) into a shareable, deployable ModelKit. Sharing projects is as easy as sharing zip files! 100% open-source.

Akshay 🚀

264,504 subscribers

87,067 views • 1 year ago •via X (Twitter)

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11 Comments

Akshay 🚀1 year ago

ModelKits can be centrally managed and deployed using a single Docker command here: And here's @Kit_Ops official GitHub repo: (don't forget to star 🌟)

Akshay 🚀1 year ago

ModelKits are Docker and more — here’s what stood out to me: - Selectively unpack kits — skip pulling what you don’t need - Doubles as your private model registry - One-command deployment Overview of our MCP server:

Akshay 🚀1 year ago

If you found it insightful, reshare with your network. Find me → @akshay_pachaar ✔️ For more insights and tutorials on LLMs, AI Agents, and Machine Learning!

Sumanth1 year ago

Amazing tutorial, Akshay. Can't wait to try KitOps!

Akshay 🚀1 year ago

Glad you found it helpful!

Mr. Ånand (Studio1HQ)1 year ago

Kitops is very useful for teams involved in ml deployments! Thanks for sharing MCP, Akshay!

Akshay 🚀1 year ago

Absolutely! 💯 Glad you liked it Anand!

Jozu1 year ago

Thanks for sharing this amazing Tutorial and covering @Kit_Ops and Jozu!

Akshay 🚀1 year ago

@Kit_Ops Incredible work, team! 🙌 Loved working on this one. I'll be using KitOps going forward

Avi Chawla1 year ago

I have used DVC + Git and it is indeed a pain to manage multiple version control systems. This will surely be helpful. Can you share the code for the MCP server?

Akshay 🚀1 year ago

Avi, you highlighted the exact pain point here! Here's the code for MCP server:

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Akshay 🚀

218,487 views • 1 year ago

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Matt Dancho (Business Science)

35,452 views • 1 year ago

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Matt Dancho (Business Science)

242,084 views • 1 year ago

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123,052 views • 1 year ago

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Theoriq

82,463 views • 1 year ago

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35,299 views • 1 year ago

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142,010 views • 1 year ago

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53,516 views • 1 year ago

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Andrew Ng

81,696 views • 10 months ago

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Ben Burtenshaw

16,905 views • 2 months ago

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Avi Chawla

28,027 views • 7 months ago

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J∅kër Kîng 👑

40,502 views • 3 months ago

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Alvaro Cintas

15,692 views • 10 days ago

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Santiago

63,262 views • 1 year ago

Excited to share that I've stepped into a new role as VP of the Agentic AI Foundation (AAIF) 🎉 As AI reshapes our industry, it's critical that this technology evolves in ways that are open and collaborative. Hosted by the Linux Foundation, Agentic AI Foundation is a neutral organization focused on advancing adoption of key open source efforts like MCP, AGENTS.md, and Goose. Our members include Anthropic, OpenAI, Google, AWS, Microsoft, and 100+ others. This work is bigger than any single company or product. I'm ecstatic to help drive AI adoption and interoperability at an industry-wide level.

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Angie Jones

76,169 views • 4 months ago

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Taranjeet

471,972 views • 1 year ago

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!

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Avi Chawla

186,931 views • 7 months ago

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.

Santiago

56,815 views • 1 year ago

🚨 AI coding agents hallucinate because they can't actually read your codebase. This MCP server fixes that. It's called Context+ and it gives AI 99% accuracy on large-scale engineering projects by building a real semantic map of your code before touching a single line. Here's what makes it different from every other MCP tool: → Tree-sitter AST parsing across 43 file extensions. Not grep. Not regex. Actual syntax trees. → Spectral Clustering that groups semantically related files into labeled clusters. Your AI finally understands what belongs together. → Obsidian-style wikilinks that map features to code files. Navigate entire codebases like a knowledge graph. → Blast radius tracing. Before any change, it shows every file and line where a symbol is imported or used. No more orphaned references. → Shadow restore points. Every AI-proposed commit creates a restore snapshot. One command to undo any change without touching git history. → Semantic search by meaning. Ask what something does. Not what it's called. The `propose_commit` tool is the wild part. It validates changes against strict rules, creates a shadow restore point, and only then writes to disk. AI can't just freestyle your production code. Works with Claude Code, Cursor, VS Code, and Windsurf. One line to install with bunx or npx. This is what responsible AI coding infrastructure actually looks like. 100% Opensource. Link in comments.

🚨 AI coding agents hallucinate because they can't actually read your codebase. This MCP server fixes that. It's called Context+ and it gives AI 99% accuracy on large-scale engineering projects by building a real semantic map of your code before touching a single line. Here's what makes it different from every other MCP tool: → Tree-sitter AST parsing across 43 file extensions. Not grep. Not regex. Actual syntax trees. → Spectral Clustering that groups semantically related files into labeled clusters. Your AI finally understands what belongs together. → Obsidian-style wikilinks that map features to code files. Navigate entire codebases like a knowledge graph. → Blast radius tracing. Before any change, it shows every file and line where a symbol is imported or used. No more orphaned references. → Shadow restore points. Every AI-proposed commit creates a restore snapshot. One command to undo any change without touching git history. → Semantic search by meaning. Ask what something does. Not what it's called. The `propose_commit` tool is the wild part. It validates changes against strict rules, creates a shadow restore point, and only then writes to disk. AI can't just freestyle your production code. Works with Claude Code, Cursor, VS Code, and Windsurf. One line to install with bunx or npx. This is what responsible AI coding infrastructure actually looks like. 100% Opensource. Link in comments.

Ihtesham Ali

31,051 views • 4 months ago