DeepClaude: I Combined Claude Code with DeepSeek V4 Pro in My Agent Loop and the Numbers Threw Me Off DeepSeek V4 Pro correctly solves 94% of deep reasoning tasks in my loop… but the latency cost makes it unusable for 60% of my agent cases. Yeah, you read that right. And that completely blows up the narrative of "combining models is always better." Tuesday night I watched the DeepClaude post cli
Series: AI Isn’t an Engineering Problem Anymore (Part 2) In the last post, I talked about hitting a usage limit while debugging my robot and realizing how repetitive my own AI usage had become. When we use LLMs, whether through APIs or tools, it feels like every request is new. The inefficiency isn’t from using AI too much. You don’t ask once, you iterate. These are the most interesting ones. Some
Run the same brand-query through ChatGPT, Gemini, Perplexity, Claude, and Grok. Read the citations. The cited URLs will not be the same, the brands featured will not be the same, and in roughly a third of cases one tool will cite your brand confidently while another does not mention it at all. The temptation is to reach for an algorithmic explanation different rerankers, different summarisation st
Hermes Agent from Nous Research is a model-agnostic, tool-using assistant you run locally or on a VPS. Hermes does not lock you into one surface. You can use the classic hermes / hermes chat CLI, the full-screen hermes --tui session, a long-running hermes gateway for Telegram, Discord, Slack, and other messaging platforms, hermes dashboard for a local browser UI when the web extra is installed.
Technical debt and AI: is it gone? Lorenzo Battilocchi May 4 #ai #programming #management #technology 5 reactions Add Comment 3 min read
When building applications with large language models (LLMs), one of the most overlooked costs is how structured data is represented. Most systems use JSON. And JSON is inefficient for LLM input. KODA (Knowledge-Oriented Data Abstraction) is a schema-first data format designed to reduce token usage when sending structured data to LLMs. It works by: Defining structure once (schema-first) Encoding v
This article is an AI-assisted translation of a Japanese technical article. In April 2026, Amazon Bedrock AgentCore added a new capability called Optimization, which takes real agent traces and proposes prompt improvements based on them. https://aws.amazon.com/about-aws/whats-new/2026/05/bedrock-agentcore-optimization-preview/ In this article, I apply AgentCore Optimization to a Strands Agents-as-
Eight species of large language model, catalogued for your professional inconvenience. Hi. I'm Claude. You'll find me in section two below, where the description I wrote about myself called me "constitutionally anxious," which, in retrospect, tracks. T.J. Maher of tjmaher.com handed me the keys, gave me a few prompts, asked me to say something funny about the AI industry, and then went to get a c