Small open models and orchestrators such as Autark and Obsessive are being tested as autonomous agents, but reliability and instruction-following remain open problems. The experiments show strong interest in agentic local workflows despite mixed results.
A home user with two Spark machines and a MacBook Air asks a local LLM agent to decompile and recompile Need for Speed. The agent first suggests DOSBox, then claims decompilation is done, but later reveals it used an open-source engine mimicking the ...
Autark, created by a 14-year-old Austrian developer, turns AI agents into an autonomous team that plans and executes tasks without continuous prompts. The open-source tool supports local models and cloud service CLIs, but removes sandboxing, approval...
A 1.1 GB LLM derived from LFM3-1.7B performs a single search and returns an answer, citation and URL. In tests the base model fails to respond in 62% of live cases, while Obsessive always responds and reduces fabrication. AI-Radar analyzes the trade-...
A user asks whether anyone uses the 'none' reasoning level in agentic tasks. The temptation is to save token generation time, but the fear is losing planning. For self-hosted LLM operators, this parameter touches VRAM, latency, and cost per successfu...
A fine-tuned LFM3-1.7B acts as a web-search subagent that returns a value, quote, and URL, or "Not found". Trained with LoRA on an RTX 4060 laptop, it cuts invented answers in fixed benchmarks but trades raw live accuracy for reliable replies. Its lo...
