45 items
Evaluated Qwen 3.6 27B across BF16, Q4_K_M, and Q8_0 GGUF quant variants with llama-cpp-python using Neo AI Engineer. Benchmarks used: HumanEval: code generation HellaSwag: commonsense reasoning BFCL: function calling Total samples: HumanE…
↯ Llama↯ Function Calling↯ Qwen 3.6humanevalfunction-callingllama+1
Ran a head-to-head on two open-weight models for tool-calling on a 4-core CPU, no GPU, no cherry-picking. Wanted to see if the small specialist (Needle, 26M, distilled from Gemini 3.1 for function calls) actually holds up against a small g…
↯ Function Calling↯ Gemini 3.1function-callingtool-callinggemini
Not benchmarks — actual tasks, actual results. Claude Sonnet 4.6 for: - Long documents that need nuanced analysis - Writing where voice and precision matter - Reasoning through edge cases in code - Anything where "think carefully" is the r…
Hey Everyone, Over the last few months, I noticed a massive gap in how we learn about Agentic AI. There are a million theoretical blog posts and dense whitepapers on RAG, tool calling, and swarms, but almost nowhere to just sit down, run a…
↯ Fine Tuning↯ Function Calling↯ Opus 4.7function-callingfine-tuningrag+4
Has anyone been able to solve or mitigate context checkpoints being erased during single user inference, specifically when function calling is part of the chat history? I've been using Qwen 3.5 35B A3B for some time (now using 3.6), tested…
↯ Llama↯ Function Calling↯ Qwen 3.6function-callingllamaqwen
Finally got Llama 3 70B running locally and wired up function calling so it can search the web. First tried self-hosting SearXNG, but the results are pretty messy.
More and more developer tools are adopting the llms.txt standard to build AI-friendly versions of their docs. The problem is that it's very hard to search across them.
↯ Mistral↯ Function Callingfunction-callingvector-databasemistral+1
Gemini 3.8 Live is the default option for most low-latency voice agent experiences and real-time dialogue without reasoning-induced delays. It supports interleaved reasoning, asynchronous function calling, full session client content updat…
↯ Gemini 3.8↯ Function Calling↯ Gemini 3.8↯ Gemini 3.8↯ Gemini 3.8↯ Gemini 3.8↯ Gemini 3.8↯ Gemini 3.8↯ Gemini 3.8↯ Gemini 3.8function-callinggemini
Indirect prompt injection defense and protection for AI agents using tool calls (via MCP, CLI or direct function calling). Detects and gates prompt injection attacks hidden in tool results (emails, documents, PRs, etc.) before they reach y…
↯ Security↯ Function Callingfunction-callingprompt-injectionsecurity+1
Hey HN, most agent systems default to server-side Python inside containers and chain frameworks. I wanted to see how far we could push agent in the browser with vanilla JavaScript https://buttercup.sh The reason this is interesting is beca…
▎ What problem does this solve? Right now most agents call tools based on a name and a JSON Schema.
Hi, would appreciate your help. I have a summary of a given topic plus past conversation history.
i am working on a router slm that helps in multiple agent orchestration , excels in tool calling but every option comes with a tradeoff of its own , you are invited to give your approaches to refine the architecture 1 - if we use multiple…
Switched from prototype to production last month and our AI bill was 3x what we estimated. Not because we picked the wrong model - we just didn't know what we didn't know.
AI TOOL CALLING · WASM · NO_STD Below is a 26M-parameter tool-calling transformer running entirely in this tab — no server, no API key, no data leaving your device. The model is Needle by Cactus Compute; needle-rs is the pure-Rust runtime…
Hi r/LocalLLaMA, I built an open-source MIT-licensed desktop app - cursor-aware AI overlay, hold a key, ask AI about whatever's around your cursor, vision LLM answers with a screenshot of the cursor region as context. Currently it routes t…
↯ Tool Use↯ Function Callingfunction-callingtool-usegemini+3
You can compare models on function calling, multi turn tool use, schema adherence. Basically, there's a good amount of public data at the model layer.
I’ve been working on adding function calling to an LLM-based support system over the past few weeks. Thought I’d share a few things that didn’t behave the way the demos suggest.
TL;DR - AutoBe — AI backend auto-generation agent - Production-grade backend from natural language conversation - 4 AST types + 4-tier compiler validation + self-healing loops - Schema specs are the new prompts - Typia — The infrastructure…
Detailed Article: https://autobe.dev/articles/local-llm-benchmark-about-backend-generation.html Five months ago I posted the "Hardcore function calling benchmark in backend coding agent" thread here. As I wrote in that post, it was an unco…
↯ Glm↯ Function Calling↯ Sonnet 4.6function-callingglmgpt-5+3
Talk at Qwen Meetup Korea end of May. Looking for review on this draft before I build PPT slides off it.
Trying to get an honest read on this from people actually shipping. Every other AI announcement lately is "agentic" or "multi-agent," and I can't always tell if it's a real architectural shift or rebranded function calling with extra steps.
Hey Everyone, Over the last few months, I noticed a massive gap in how we learn about Agentic AI. There are a million theoretical blog posts and dense whitepapers on RAG, tool calling, and swarms, but almost nowhere to just sit down, run a…
↯ Fine Tuning↯ Function Callingfunction-callingfine-tuningrag+3
Hey Everyone, Over the last few months, I noticed a massive gap in how we learn about Agentic AI. There are a million theoretical blog posts and dense whitepapers on RAG, tool calling, and swarms, but almost nowhere to just sit down, run a…
↯ Fine Tuning↯ Function Callingfunction-callingfine-tuningrag+3
An agentic harness is the orchestration and control layer wrapped around a base language model that transforms it from a stateless text predictor into an agent capable of taking actions, calling tools, maintaining state across steps, and e…
Prompt injection defense framework for AI tool-calling Indirect prompt injection defense and protection for AI agents using tool calls (via MCP, CLI or direct function calling). Detects and neutralizes prompt injection attacks hidden in t…
↯ Security↯ Function Callingfunction-callingtool-callingprompt-injection+2
Large Language Models (LLMs) with function-calling capabilities are becoming critical for modern agentic AI systems. Nevertheless, current deployments typically route inferences to powerful cloud-based models, incurring significant energy…
Large language model (LLM)-based agents have shown strong capabilities in using external tools to solve complex tasks. However, existing evaluations often overlook the temporal dimension of tool use, especially the impact of tool response…
The growth of agentic AI has drawn significant attention to function calling Large Language Models (LLMs), which are designed to extend the capabilities of AI-powered system by invoking external functions. Injection and jailbreaking attack…
Large language models increasingly operate over large collections of tools, functions, APIs, and specialized agents. As the candidate action space grows, a function-calling model must process more schemas, consume more prompt tokens, and d…
Function calling represents the core capability of agentic large language models (LLMs). Existing research has focused on enhancing LLMs function-calling accuracy through fine-tuning, reinforcement learning (RL), and multi-agent frameworks…
↯ Fine Tuning↯ Function Callingfunction-callingfine-tuningagentic
Recent agent benchmarks increasingly ground evaluation in executable environments, from code repair to web navigation, app APIs, and function calling. Yet completing consequential work beyond code requires more than producing a plausible r…
Reliable evaluation of tool routing is critical as Large Language Models increasingly operate as autonomous agents. Current benchmarks face three structural limitations: data distributions that follow a power law leave rare scenarios under…
Spoken Language Understanding (SLU) is the core component of task-oriented dialogue systems and a pivotal link in achieving seamless human-agent interaction. While traditional SLU can effectively extract user semantics for closed-set tasks…
In the rapidly evolving domain of artificial intelligence, Large Language Models (LLMs) play a crucial role due to their advanced text processing and generation abilities. This study introduces a new strategy aimed at harnessing on-device…
The language models that underpin agents have seen a rapid rise in performance on function calling benchmarks. However, the metrics used in the training and evaluation of these models often encourage models to make positive claims even whe…
We have SenseNova Office Skills(repo: https://github.com/OpenSenseNova/SenseNova-Skills ) for example: Skill Directory Structure: skills/ ├── sn-image-base/ # Tier 0 - Low-level tools (T2I, recognition, text optimize) │ └── SKILL.md ├── sn…
Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments. Although recent tool-use benchmarks increasingly cover complex task settings, they still largely assume clean, stabl…
Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents. Yet MCP-style tool use requires more than isolated function calling: an agent must discover tools from live catalogs, satisfy schemas, preserve depend…
Agent-related developments are some of the hardest signals to track right now - they're spread across arXiv papers, GitHub repos, model release notes, incident reports, and policy documents simultaneously. I've been running a pipeline that…
Model Overview Description: DiffusionGemma 26B A4B IT is an open-weights multimodal generative model developed by Google DeepMind that processes text, image, and video inputs to produce text output via discrete diffusion. Built on the Gemm…
↯ Deepmind↯ Gemma↯ Function Calling↯ Gemma 4function-callingdeepmindgemma+1
Hi everyone, I'm on a mission to become a serious expert in Claude and AI, and I'm building a structured learning path. I want to create content that's actually valuable - with real practical applications, not surface-level tutorials.
Hi guys, Idk what you think, but for me, one of the biggest discussions in the AI engineering field is this issue: ReAct vs. CodeAct.
Ran a full eval against four local models last weekend and the spread between them is wider than I expected. All running through Ollama on CPU, no cloud, same prompts, same hardware.
↯ Ollama↯ Function Calling↯ Qwen 3.6humanevalfunction-callingollama+1