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Ignesso Lesias

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replied to Kseniase's post 3 days 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

replied to Kseniase's post 2 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

replied to Kseniase's post 3 months ago

5 New implementations of Diffusion Models Diffusion models are widely used for image and video generation but remain underexplored in text generation, where autoregressive models (ARMs) dominate. Unlike ARMs, which produce tokens sequentially, diffusion models iteratively refine noise through denoising steps, offering greater flexibility and speed. Recent advancements show a shift toward using diffusion models in place of, or alongside, ARMs. Researchers also combine strengths from both methods and integrate autoregressive concepts into diffusion. Here are 5 new implementations of diffusion models: 1. Mercury family of diffusion LLMs (dLLMs) by Inception Labs -> https://www.inceptionlabs.ai/news It applies diffusion to text and code data, enabling sequence generation 10x faster than today's top LLMs. Now available Mercury Coder can run at over 1,000 tokens/sec on NVIDIA H100s. 2. Diffusion of Thoughts (DoT) -> https://huggingface.co/papers/2402.07754 Integrates diffusion models with Chain-of-Thought. DoT allows reasoning steps to diffuse gradually over time. This flexibility enables balancing between reasoning quality and computational cost. 3. LLaDA -> https://huggingface.co/papers/2502.09992 Shows diffusion models' potential in replacing ARMs. Trained with pre-training and SFT, LLaDA masks tokens, predicts them via a Transformer, and optimizes a likelihood bound. LLaDA matches key LLM skills, and surpasses GPT-4o in reversal poetry. 4. LanDiff -> https://huggingface.co/papers/2503.04606 This hybrid text-to-video model combines autoregressive and diffusion paradigms, introducing a semantic tokenizer, an LM for token generation, and a streaming diffusion model. LanDiff outperforms models like Sora. 5. General Interpolating Discrete Diffusion (GIDD) -> https://huggingface.co/papers/2503.04482 A flexible noising process with a novel diffusion ELBO enables combining masking and uniform noise, allowing diffusion models to correct mistakes, where ARMs struggle.

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replied to Kseniase's post 3 days ago

That's a fascinating look at how far on-device AI has come. It really changes the game for personal experimentation and learning when you don't need cloud infrastructure.

replied to Kseniase's post 2 months ago

Cool list — MCP servers are getting seriously impressive. The Browser MCP alone could save so much time, and the Hugging Face one looks perfect for anyone jumping between models and datasets all day. Earlier I was checking some Peoplesmart reviews https://peoplesmart.pissedconsumer.com/review.html and it reminded me how useful the right tools can be when you're juggling data. Might try the JSON MCP next since I deal with messy files way too often.

replied to Kseniase's post 3 months ago

That’s actually wild. I’ve been reading about Mercury and DoT lately the way they’re making diffusion models compete with ARMs is insane. My buddy’s into AI safety stuff https://malwarebytes.pissedconsumer.com/customer-service.htmland was ranting how this tech could backfire if not monitored properly.