1️⃣ Small‑model disappointment for agentic coding
“These LiquidAI models have never worked well for me in practice.” – Gecko4072
“They have serious issues with coherence.” – BoredomIsFun
Many contributors point out that the promised “small, rational, conversational agents” struggle with real‑world coding tasks, citing lack of coherence and poor on‑ground performance.
2️⃣ Viable on‑device assistant / analyst workflows
“We recommend using it for agentic workloads, tool use, data extraction, RAG, and long‑context workflows. It is not recommended for agentic coding and knowledge‑heavy tasks.” – trvz
“I am getting to know Hermes agent … manipulate with excel and word documents, gather data from APIs, … keep the computer awake and when detecting the process is finished, put computer to sleep.” – l3x4ur1n
There is strong interest in using tiny models for local, tool‑oriented tasks such as web search, file manipulation, tax‑return analysis, or personal assistants that run on modest hardware (e.g., i3/i5 laptops).
3️⃣ Skepticism about benchmark claims & model‑size hype
“Note how they're much smaller than all other models in the comparison yet match or exceed them.” – 0xbadcafebee
“Always put ‘Lower is better’ or ‘higher is better’ in benchmarks. Not everyone knows what your numbers mean.” – vezycash
The community questions self‑reported performance, calls out cherry‑picking, and wonders why certain models (e.g., Qwen‑3.5 2B) aren’t in the tables, emphasizing the need for transparent benchmark reporting.