A developer testing local LLMs for code refactoring found that heavily quantized models struggled with complex multi-step tasks, while DeepSeek V4 Flash completed the task in about 2 minutes. The expe…
A developer reports that Ling 3.0 Tiny, a model by Ant Group related to Alibaba, now works with llama-cpp via a GGUF file, and it performs fast and accurate tool calling on their MCP server. The devel…
A developer has switched from OpenCode to Pi Agent Harness, citing Pi's faster speed, minimalism, and pure open-source nature, and expressing concern that OpenCode is focused on selling AI subscriptio…
A developer optimized a local MCP server for llama-cpp, achieving 146 tokens per second with the LFM2.5-2.6B-Q4_K_M.gguf model and reducing task times from 1 millisecond to 15 microseconds through Rus…
Prompt caching can reduce the cost per task for LLM API calls by reusing the expensive prefill computation for repeated input prefixes, with cache hits skipping the heavy computation and lowering time…
A developer using the LFM 2.5 2.6B model with llama-cpp found that adding detailed descriptions to JSON schema parameters improved tool call correctness from 0% to nearly 100%, enabling the use of a f…
A developer is building a customer service MCP server that uses an LLM (such as LFM2.5-2.6B-Q4_K_M.gguf in llama-cpp) to parse natural language requests and provide hints to the view_file tool, improv…
A developer exploring local AI tasks found that writing custom code to handle natural language queries would be faster than LLM inference but would fail on complex prompts due to the inherent complexi…
A developer reports greater success and enjoyment using agentic AI with small local models, such as Gemma 4 E4B via llama-cpp and an MCP server, for disliked tasks like website management, compared to…
A developer created a local MCP server in Rust to run commonly-needed commands via an AI agent, arguing that the approach offers error correction, command chaining, and an improved browser-based UI ov…
Liquid AI released LFM 2.5, an 8-billion-parameter agentic model with 1 billion active parameters focused exclusively on tool calling, achieving nearly 200 tokens per second on a GPU. The model excels…
A developer testing Nanbeige 4.2, a 3-billion-parameter looped-transformer model from a Chinese company, found it well-suited for tool calling with a Rust MCP server, outperforming Gemma 4 12B in that…
The developer rewrote their MCP server to use Stdio input and output instead of HTTP, reducing code from 300 lines to about 60 lines using the Rust crate RMcp. The Stdio MCP server functions as a cons…
A developer created a simple MCP server for local LLMs to call Rust-coded tools, but encountered CORS errors. The llama-cpp feature ui-mcp-proxy, passed as an argument to llama-server, sets up a proxy…
A developer created a local MCP server to extend llama-cpp's tool capabilities, finding that models like Gemma 4 12B reliably call MCP-provided tools. The server, written first in Python then Rust, of…
Poolside AI's Laguna XS model, a small local LLM, performed well in refactoring Rust code via OpenCode, making relatively few errors and handling tool calls correctly except for escaped quotes. The 20…
A developer proposes replacing traditional Bash scripts with Markdown files processed by local AI agents, arguing that code is a maintenance burden. Using llama-cpp's tool-use feature, an AI agent lik…
A developer used AI agents Gemma 4 12B and Qwen 3.6 35B to rewrite Rust code without doc-comments, but found direct rewriting unreliable. Instead, the developer had Qwen 3.6 generate a Python script t…
A developer is using llama-cpp's tool-calling feature to let large language models execute system commands such as reading files and editing code, comparing the approach to bash scripting but easier t…
A developer reports that speculative decoding techniques MTP, EAGLE-3, and DFlash can significantly speed up local inference of large language models in llama-cpp. Testing on an NVidia 3060 RTX 12 GB …