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AI-based coding agents are changing developer workflows. Proof – the arrival of Gemini 3 Pro in the Gemini CLI. It shows a significant advancement. For instance, it provides advanced reasoning, enhanced tool usage, and natural-language coding right in the terminal. Developers will be able to generate, fix, and refactor code without needing to break their flow by […]
Liquid Foundation Models (LFM 2) define a new class of small language models designed to deliver strong reasoning and instruction-following capabilities directly on edge devices. Unlike large cloud-centric LLMs, LFM 2 focuses on efficiency, low latency, and memory awareness while still maintaining competitive performance. This design makes it a compelling choice for applications on mobile
Large Language Models (LLMs) are the heart of Agentic systems and RAG systems. And building with LLMs is exciting until the scale makes them expensive. There is always a tradeoff for cost vs quality, but in this article we will explore the 10 best ways according to me that can slash costs for the LLM
10 Ways to Slash Inference Costs with OpenAI LLMs Read More »
Imagine a chatbot makes a joke, and the user gives it a negative feedback. The same agent is far funnier on the said topic a week later. This is what we mean by agent’s live adaptation. Most chat agents as of now are static programs in nature. They don’t really learn from user feedback during
Create an Agent that can Learn from Interactions using AutoGen Read More »
