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repliedto fdaudens's post 8 days ago
Ever wanted 45 min with one of AI’s most fascinating minds? Was with @thomwolf at HumanX Vegas. Sharing my notes of his Q&A with the press—completely changed how I think about AI’s future: 1️⃣ The next wave of successful AI companies won’t be defined by who has the best model but by who builds the most useful real-world solutions. "We all have engines in our cars, but that’s rarely the only reason we buy one. We expect it to work well, and that’s enough. LLMs will be the same." 2️⃣ Big players are pivoting: "Closed-source companies—OpenAI being the first—have largely shifted from LLM announcements to product announcements." 3️⃣ Open source is changing everything: "DeepSeek was open source AI’s ChatGPT moment. Basically, everyone outside the bubble realized you can get a model for free—and it’s just as good as the paid ones." 4️⃣ Product innovation is being democratized: Take Manus, for example—they built a product on top of Anthropic’s models that’s "actually better than Anthropic’s own product for now, in terms of agents." This proves that anyone can build great products with existing models. We’re entering a "multi-LLM world," where models are becoming commoditized, and all the tools to build are readily available—just look at the flurry of daily new releases on Hugging Face. Thom's comparison to the internet era is spot-on: "In the beginning you made a lot of money by making websites... but nowadays the huge internet companies are not the companies that built websites. Like Airbnb, Uber, Facebook, they just use the internet as a medium to make something for real life use cases." Love to hear your thoughts on this shift!
repliedto Kseniase's post 2 months ago
6 Comprehensive Resources on AI Coding AI coding is moving fast, and it’s getting harder to tell what actually works. Agents, workflows, context management and many other aspects are reshaping how software gets built. We’ve collected a set of resources to help you understand how AI coding is evolving today and what building strategies work best: 1. https://huggingface.co/papers/2508.11126 Provides a clear taxonomy, compares agent architectures, and exposes practical gaps in tools, benchmarks, and reliability that AI coding agents now struggle with 2. https://huggingface.co/papers/2511.04427 This survey from Carnegie Mellon University shows causal evidence that LLM agent assistants deliver short-term productivity gains but have lasting quality costs that can slow development over time 3. https://huggingface.co/papers/2510.12399 Turns Vibe Coding from hype into a structured field, categorizing real development workflows. It shows which models, infrastructure, tool requirements, context, and collaboration setups affect real software development outcomes 4. https://huggingface.co/papers/2511.18538 (from Chinese institutes and companies like ByteDance and Alibaba) Compares real code LLMs, shows how training and alignment choices affect code quality and security, and connects academic benchmarks to everyday software development 5. Build Your Own Coding Agent via a Step-by-Step Workshop⟶ https://github.com/ghuntley/how-to-build-a-coding-agent A great guide that covers the basics of building an AI-powered coding assistant – from a chatbot to a file reader/explorer/editor and code search 6. State of AI Coding: Context, Trust, and Subagents⟶ https://www.turingpost.com/p/aisoftwarestack Here is our in-depth analysis of where AI coding is heading and the new directions we see today – like agent swarms and context management importance – offering an emerging playbook beyond the IDE If you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe
repliedto Kseniase's post 5 months ago
13 Outstanding MCP Servers MCP is redefining how AI assistants connect to the world of data and tools, so no wonder MCP servers are in high demand now. That’s why we’ve curated 13 cool MCP servers to upgrade your workflow: 1. Hugging Face Official MCP Server -> https://github.com/evalstate/hf-mcp-server Provides an access and interaction with Hugging Face models, datasets, and Gradio Spaces for dynamic tool integration and configuration across environments. 2. Browser MCP -> https://browsermcp.io/ An MCP server +Chrome extension. It allows to automate your browser with AI apps like VS Code, Claude, Cursor, and Windsurf. 3. Bright Data MCP -> https://github.com/brightdata/brightdata-mcp This one is for working with data in real-time: searching the web, navigating websites, taking action and retrieving data. 4. JSON MCP -> https://github.com/VadimNastoyashchy/json-mcp Interact with JSON files: split, merge, find specific data, and validate content within them. 5. Octagon Deep Research MCP -> https://github.com/OctagonAI/octagon-deep-research-mcp Allows for deep research via AI agents, integrating seamlessly with MCP clients like Claude Desktop and Cursor for powerful, unlimited research capabilities. 6. VLM Run MCP Server -> https://docs.vlm.run/mcp/introduction Provides an agent the ability to see, understand and process visual content. Read further in the comments 👇 P.S.: Our most read explanation of MCP on Hugging Face https://huggingface.co/blog/Kseniase/mcp Our first list of 13 awesome MCP servers: https://huggingface.co/posts/Kseniase/204958200717570 If you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe
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