RT Avi Chawla (@_avichawla)Which one is better?Opus 4.6, Sonnet 4.6, or GPT-5.2-Codex?The good news: this might not matter soon!Because the models are commoditizing, and the real differentiator is moving elsewhere.On general benchmarks like MMLU, frontier models have saturated to the point where there's barely any room to differentiate.And on the agentic benchmarks that actually reflect production work, like SWE-bench and TerminalBench, what's being measured isn't the model alone.It's model plus the infrast ...

Which one is better?Opus 4.6, Sonnet 4.6, or GPT-5.2-Codex?The good news: this might not matter soon!Because the models are commoditizing, and the real differentiator is moving elsewhere.On general benchmarks like MMLU, frontier models have saturated to the point where there's barely any room to differentiate.And on the agentic benchmarks that actually reflect production work, like SWE-bench and TerminalBench, what's being measured isn't the model alone.It's model plus the infrastructure around it. On SWE-b ...

In the last videos, we looked into how you can build agents with Flain. We covered the basics of giving them tools, modifying their behavior through middleware, and have them stream their results to the front end. Those approaches work great for more simpler workflows, but not so much when your agent needs to handle complex multi-step tasks that require some sort of planning, managing large amounts of context, or delegating work to specialized sub agents.That's where deep agents comes in. Deep agents is a s ...

Hi there, this is Christian from Lchain. If you build with coding agents like cursor, you probably recognize this. The first few turns with the agents are great.But then as you keep continuing talking to the agent in the same thread, the quality slides, the decision get more fuzzy and the overall code quality drops and then cursor drops this system line context summarized. That's the moment you know you've crossed the context boundary line. So why is summarization such a big deal for context engineering.Eve ...

Core Business & Services - Kalin builds custom solutions for clients, ranging from Fortune 500 companies to startups, focusing on app development and database migrations [1][2] - The company leverages generative AI to automate business functions, such as intelligent document processing for logistics management, achieving faster and better results than human annotators [20][21] - Kalin offers services ranging from chatbot and co-pilot development to AI agent creation, tailoring solutions to specific client needs [16] Technology & Architecture - The company utilizes multimodal search and semantic understanding of videos, employing models like Nova Pro and Titan v2 for indexing and searching video content [6][7] - Kalin uses various databases including Postgress, PG vector, and OpenSearch for vector search implementations [13] - The company builds AI systems on AWS, utilizing services like Bedrock and SageMaker, and custom silicon like Tranium and Inferentia for price performance improvements of approximately 60% over Nvidia GPUs [27] AI Development & Strategy - Prompt engineering has proven highly effective, sometimes negating the need for fine-tuning models [40] - Context management is crucial for differentiating applications, leveraging user data and history to make strategic inferences [33][34] - UX design is important for mitigating the slowness of inference, with techniques like caching and UI spinners improving user experience [36][37]