On-device LLM inference means running the full model inference pipeline on the hardware where the application runs — no cloud API, no server, no network dependency. For developers building desktop applications, mobile apps, embedded systems, and edge deployments, understanding the full stack of on-device inference — model formats, runtime engines, hardware acceleration, and integration patterns … Read more
Choosing the best small language model for edge deployment in 2026 means matching model capabilities to the specific constraints of your target hardware — RAM, compute, power budget, connectivity, and whether the deployment is a microcontroller, a smartphone, a Raspberry Pi, or an edge server. There is no single best model; there is a best … Read more
SmolLM2 is Hugging Face’s family of compact language models designed for efficient on-device and edge deployment. Released in late 2024, the family spans 135M, 360M, and 1.7B parameters — sizes that run on smartphones, Raspberry Pis, microcontrollers, and CPU-only environments where larger models are impractical. Despite their small size, SmolLM2 models punch above their weight … Read more
Phi-4’s benchmark results surprised many observers when Microsoft released them in late 2024: a 14B parameter model outperforming models of 30B, 70B, and in some cases even larger on reasoning-heavy tasks. Understanding these results — what the benchmarks actually measure, why Phi-4 does well on them, where it falls short, and what the numbers mean … Read more
Microsoft released Phi-4 and Phi-4-mini as part of its Phi series of high-efficiency small language models, with both positioned as the state of the art in their respective size classes. Phi-4 (14B parameters) competes with models 2–3x its size; Phi-4-mini (3.8B) competes with models in the 7–8B range. Choosing between them is not simply a … Read more
An AI agent that writes code autonomously — reading requirements, generating implementations, running tests, and iterating on failures — is one of the most practically valuable agent applications for software teams. Unlike a simple code generation prompt, a coding agent closes the feedback loop: it sees whether the code it wrote actually works, reads the … Read more
The ReAct pattern — short for Reasoning and Acting — is one of the most influential agent architectures in practical AI development. Published in a 2022 paper by Yao et al., it describes a simple but powerful idea: interleave the model’s reasoning steps with its action steps, making both visible and traceable. Rather than having … Read more
AutoGen v0.4 represents a significant rearchitecture of Microsoft’s multi-agent framework, shifting from the monolithic agent classes of earlier versions to a more modular, event-driven design built around the Actor model. If you have used AutoGen v0.2 or v0.3, v0.4 will feel substantially different — the concepts carry over but the API and the mental model … Read more
The sub-3B parameter range has become one of the most competitive and practically useful tiers in the small language model landscape. Models in this range run comfortably on CPU, fit on smartphones and edge devices, and are cheap to fine-tune on consumer hardware — while delivering quality that would have required a 13B+ model just … Read more
Building an AI agent from scratch in Python means writing the orchestration loop yourself rather than relying on a framework like LangChain or AutoGen to handle it. This gives you full control over how the agent decides what to do, how it calls tools, how it handles errors, and when it stops. For production agents … Read more
