Automation
AI agents and workflow automation that runs your business.
8 articles
Ling-3.0-tiny: A 7.9B AI Model With Just 1.3B Active Parameters for Fast Local Reasoning
InclusionAI’s Ling-3.0-tiny is a lightweight hybrid reasoning MoE model with 7.9B total parameters and only 1.3B activated per token. Built for coding, reasoning, agentic workflows, and local deployment, it can reach around 86–90 tokens per second on an M4 Pro MacBook while using roughly 8.34 GiB of peak memory at an 8K context length.
Meta Muse Glimmer 30B: The New King of Local AI Agents
Meta's new 30-billion parameter multimodal model brings agentic reasoning, native tool calling, and speculative decoding to consumer hardware. Here is everything you need to know.
NVIDIA Nemotron 3.5 Lightning 30B: A 3B-Active Model Built for Long-Running AI Agents
NVIDIA’s Nemotron 3.5 Lightning 30B-A3B-NVFP4 combines a 30-billion-parameter hybrid Mixture-of-Experts architecture with only 3 billion active parameters, a 1M-token context window, NVFP4 quantization, and speculative decoding designed for fast agentic workloads, coding, reasoning, RAG, and long-running AI systems.
Kimi K3: The Open-Weight AI Agent With 2.8 Trillion Parameters and a Million-Token Context
Moonshot AI’s Kimi K3 is an open-weight multimodal agentic model with 2.8 trillion total parameters, 104 billion activated parameters, native vision, and a 1-million-token context window. Its combination of long-horizon coding, tool use, multimodal reasoning, and open weights makes it one of the most ambitious AI releases of 2026.
Why Qwen 3.8 27B Just Changed the Math on Local AI Agents
With a 262K context window, native vision, and benchmark-beating SWE-bench performance on consumer hardware, Alibaba’s open-weights release shifts the center of gravity from cloud endpoints to local machines.
AI Automation Workflows: A Practical Guide to n8n, Make, and Zapier
We rebuilt the same five AI workflows in n8n, Make, and Zapier — lead routing, content pipelines, support triage — and compared cost, breakage, and debuggability. Here is what we kept.
AI Agents Explained: What They Are, What They Are Not
Tool use, planning loops, memory, and MCP — the honest primer on AI agents in 2026, with concrete examples of what works today and what is still science fiction.
How to Automate Workflows with AI Agents in 2026: A Practical Playbook
From Zapier to Lindy, Relevance AI and n8n — learn how to build AI agents that run your business on autopilot with real templates.