Maksims Volkovs

Excellent piece by HuggingFace CEO Clément Delangue on the necessity of a robust open source / open weight / open research approach to AI to keep the pace of progress. Closed/proprietary strategies slow down overall progress. I have made that point many times over the last dozen years, and became more vocal about it over the last 3 years, as prominent American AI companies started clamming up. https://lnkd.in/euTH8qwk

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What is Function calling & MCP for LLMs? (explained with visuals and code) Before MCPs became popular, AI workflows relied on traditional Function Calling for tool access. Now, MCP is standardizing it for Agents/LLMs. The visual below explains how Function Calling and MCP work under the hood. Today, let's learn: - Function calling by building custom tools for Agents. - How MCPs help by building a local MCP client with mcp-use and using tools from Browserbase MCP server. In Function Calling: - The LLM receives a prompt. - The LLM decides the tool. - The programmer implements a procedure to accept a tool call request from the LLM and prepare a function call. The tool call request is found in the LLM's response when you prompt it. - A backend service executes the tool. This Function Calling takes place within our stack: - We host the tool. - We implement a logic to determine the tool to invoke and its parameters. - We execute it. So Function Calling requires us to wire everything manually. MCP simplifies this! Instead of hard-wiring tools, MCP: - Standardizes defining, hosting, and exposing tools. - Makes it easy to discover tools, understand schemas, and use them. - Demands approval before invoking them. - Detaches implementation from consumption. For instance, whenever you integrate an MCP server, you never write a line of Python code to integrate the tools. Instead, you just integrate the MCP server and everything beyond this follows a standard protocol handled by the MCP client and the LLM: - They identify the MCP tool. - They prepare the input argument. - They invoke the tool. - They use the tool’s output to generate a response. Everything happens through a standard (but abstracted) protocol. So here’s the key point: MCP and Function Calling are not in conflict. They’re two sides of the same workflow. - Function Calling helps an LLM decide what it wants to do. - MCP ensures that tools are reliably available, discoverable, and executable, without you needing to custom-integrate everything. For example, an agent might say, “I need to search the web,” using function calling. That request can be routed through MCP to select from available web search tools, invoke the correct one, and return the result. Check the workflow in the diagram below. In this setup, to build a local MCP client, I used mcp-use because it lets us connect any LLM to MCP servers & build private MCP clients, unlike Claude/Cursor. - Compatible with Ollama & LangChain - Stream Agent output async - Built-in debugging mode, etc Find the mcp-use GitHub repo in the comments! ____ Find me → Avi Chawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

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As someone deeply invested in agentic systems and reinforcement learning, I find the Chain-of-Agents (CoA) approach both refreshing and practical, it takes the chaos of multi-agent orchestration and distills it into something elegant: a single model with native agent-like behavior. Here’s what I see: Traditional multi-agent setups have been great for modularity and reasoning, but they come with a high tax, too much inter-agent communication, redundant memory syncing, and unnecessary tool calls. What CoA proposes is radical in its simplicity: distill the behaviors of successful multi-agent runs into one model, then train that model to act as a team, internally. They start with multi-agent distillation: successful traces from existing orchestrators like OAgents are transformed into CoA-style traces, complete with planning steps, tool usage, reflections, and execution paths. But they don’t stop at replay, they filter hard cases and focus on high-quality trajectories that demonstrate useful agent behaviors like tool efficiency and coherent planning. This becomes the foundation of their SFT phase. Once that base is established, Agentic RL kicks in. The model is tasked with solving complex reasoning problems and importantly, ones where tools make or break the outcome (web QA, coding, math). Rewarding is done via exact-match or LLM-as-Judge strategies, making it possible to scale reward signals across noisy tasks. This setup tunes the model to reason reflectively, use tools judiciously, and respond with stability under diverse challenges. The kicker: by collapsing the roles of multiple agents into a single coherent model, CoA delivers an 84.6% reduction in inference cost. It’s a major unlock for production-grade agent systems where latency, memory, and tool limits matter. What excites me most is how CoA generalizes the spirit of ReAct/TIR, but upgrades it, dynamically invoking roles, using tools only when needed, and preserving a unified memory state that avoids the “who’s speaking now?” confusion. To me, Chain-of-Agents is a promising new frontier: a single-model, multi-mind architecture that captures the power of coordination while optimizing for real-world deployment constraints. And that’s where agentic AI needs to go. https://lnkd.in/exTxvsDD

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More big news coming out of AiHello! We’re collaborating with Masters and PhD researchers at the University of Toronto to tackle one of the toughest challenges in AI today: reducing hallucinations in large language models. At AiHello, we’ve already built and scaled automation systems that handle over $1B in yearly ad revenue for e-commerce & Amazon sellers worldwide. Running AI at this scale has taught us what works, what doesn't. Now, we’re bringing that same expertise into the next frontier: building a hallucination-free LLM. The goal is simple but ambitious.  Creating an AI that can provide factual numbers because in ecommerce business, hallucinating about your business stats can be devastating. This is more than just research. It’s about applying real-world, scaled experience to solve a problem that will define the next generation of trustworthy AI. Our ML department still has a long way to go but we are on the right track. My goal is to get our LLM on the global leader board before 2025 Excited to start work on it!

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🚀 New blog post: How we're using multiple LLMs (Gemini, Claude, ChatGPT & more) to revolutionize software performance optimization at Fastcode ✨ Feeding academic papers + source code into AI models to uncover optimization opportunities that would take months to find manually 📈 Results: ~67% of AI recommendations lead to measurable performance gains 🔗 https://bit.ly/45WGw6u #AI #PerformanceEngineering #LLMs #SoftwareOptimization #Fastcode

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📝 Announcing our paper that proposes a unified cognitive and computational framework for Artificial General Intelligence (AGI) -- going beyond token-level predictions -- one that emphasizes modular reasoning, memory, agentic behavior, and ethical alignment 🔹 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐁𝐞𝐲𝐨𝐧𝐝 𝐓𝐨𝐤𝐞𝐧𝐬: 𝐅𝐫𝐨𝐦 𝐁𝐫𝐚𝐢𝐧‑𝐈𝐧𝐬𝐩𝐢𝐫𝐞𝐝 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐭𝐨 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐚𝐧𝐝 𝐢𝐭𝐬 𝐒𝐨𝐜𝐢𝐞𝐭𝐚𝐥 𝐈𝐦𝐩𝐚𝐜𝐭 🔹 In collaboration with University of Central Florida, Cornell University, UT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Toronto Metropolitan University, University of Oxford, Torrens University Australia, Obuda University, Amazon others. 🔹 Paper: https://lnkd.in/gqKUV4Mr ✍🏼 Authors: Rizwan Qureshi, Ranjan Sapkota, Abbas Shah, Amgad Muneer, Anas Zafar, Ashmal Vayani, Maged Shoman, PhD, Abdelrahman Eldaly, Kai Zhang, Ferhat Sadak, Shaina Raza, PhD, Xinqi Fan, Ravid Shwartz Ziv, Hong Yang, Vinija Jain, Aman Chadha, Manoj Karkee, @Jia Wu, Philip Torr, FREng, FRS, Seyedali Mirjalili ➡️ 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐨𝐟 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐁𝐞𝐲𝐨𝐧𝐝 𝐓𝐨𝐤𝐞𝐧𝐬' 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞‑𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐆𝐈 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: 🧠 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: Integrates cognitive neuroscience, psychology, and AI to define AGI via modular reasoning, persistent memory, agentic behavior, vision-language grounding, and embodied interaction. 🔗 𝐁𝐞𝐲𝐨𝐧𝐝 𝐓𝐨𝐤𝐞𝐧‑𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧: Critiques token-level models like GPT-4.5 and Claude 3.5, advocating for test-time adaptation, dynamic planning, and training-free grounding through retrieval-augmented agentic systems. 🚀 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐚𝐧𝐝 𝐂𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬: Proposes a roadmap for AGI through neuro-symbolic learning, value alignment, multimodal cognition, and cognitive scaffolding for transparent, socially integrated systems.

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