Daily Dev Tips
Hello! While implementing multi-turn image editing with Gemini 3 Pro Image (Nano Banana Pro), we ran into a troublesome problem: it worked sometimes and failed other times. In this article, we share the symptoms, the investigation process, and the solution. The Symptoms Environment Google GenAI SDK library (pip): google-genai 1.
LLM
Hello! Why can OpenAI and Anthropic build world-class LLMs while no LLM from Japan stands shoulder to shoulder with them? Is it a gap in technical capability? A talent problem? The answer is simpler than that: the number of GPUs, and money. Today we focus on "LLM training"
Generative AI Frontlines
Hello! Today we take up Claude Code, the AI coding agent that has been generating a lot of buzz among engineers lately. AI coding assistants have multiplied rapidly over the past year or two, and many of you probably feel you can no longer tell which one to choose. In
LLM
⚠️ A newer version of this article is available The latest LLM ranking (March 2026 edition) is here Read the latest LLM ranking (March 2026 edition) → Introduction This report is a comprehensive analysis of the performance of Japanese-capable LLMs, based on benchmark data from Nejumi Leaderboard 4 (the 2025/12/18
AI Agents
Hello! This time, we present a grand tour of the more than 20 flourishing AI coding tools out there—introduced and explained all in one go! AI-assisted coding is by now simply how things are done, and new tools appear at a dizzying pace. We use several tools in our
LLM
Hello! Today's topic is the Anthropic Python SDK, a convenient way to work with the Anthropic Claude API. A fairly significant change landed about two weeks ago, so let's walk through it. Introduction "Wait, client.count_tokens() stopped working..." You updated the Anthropic Python
LLM
Hello! MCP is spreading through the LLM industry at breakneck speed, and today we want to focus specifically on the implementation side. We say "MCP" as if it were a single thing, but the specification people call the "standard" has actually changed quite a bit in
LLM
Introduction This report analyzes the performance of Japanese-capable LLMs based on Nejumi Leaderboard 4 benchmark data (October 11, 2025 edition), offering a comprehensive picture of the field. Nejumi Leaderboard 4 is known as a highly reliable benchmark that evaluates LLM performance on Japanese-language tasks from multiple angles. In this analysis,
LLM
Hello! In this article, we take a detailed look at how image token counts are calculated when using Claude 4.5 Sonnet/Haiku and Claude 4.1 Opus via the API. How Image Token Counts Are Calculated Images sent to the Claude 4.5 API are counted as tokens, just
LLM
Hello! OpenAI's vision-capable models (that is, LLMs that accept image input) use two different methods for converting images into tokens. The latest GPT-5 and GPT-4.1 families introduced a patch-based method that differs from the traditional tile-based approach. This change significantly improves the efficiency of image processing and
LLM
Hello! In the previous installments of our LLM Inference Infrastructure Provisioning Course, we covered defining inference speed, estimating request volume, calculating memory consumption, and selecting an inference engine. This time, we work through the remaining steps — GPU node sizing, load testing, and trade-off analysis — in one go, and close with
LLM
Hello! In the previous installments of this course, we covered in detail how to estimate the request volume an LLM service needs to handle and how to calculate the memory a model consumes during inference. This time we dig into the fourth of the seven steps: selecting an inference engine.
LLM
Hello! Last time we covered how to estimate the request volume for an LLM service. This time we dig into the third of the seven steps: estimating the memory your model consumes during inference. LLM Inference Infrastructure Provisioning Course: Series Index * Part 1: Basic Concepts and Inference Speed * Part 2:
LLM
Hello! Welcome to Part 2 of the LLM Inference Infrastructure Provisioning Course. LLM Inference Infrastructure Provisioning Course: Series Index * Part 1: Basic Concepts and Inference Speed * Part 2: Estimating Request Volume for an LLM Service * Part 3: Estimating Inference-Time Memory Consumption for Your Model * Part 4: Selecting an Inference Engine
LLM
Hello! Today we begin a multi-part series on inference infrastructure provisioning — GPU provisioning in particular — for building your own LLM service in-house. Introduction As LLMs have evolved, the use of public LLMs such as ChatGPT and Claude has spread rapidly through the enterprise. Forward-looking companies, however, are moving beyond these
LLM
Hello! When you use Gemini 2.5 Pro and Gemini 2.5 Flash, accurately tracking token counts is critical for cost calculation and context window management. In this article, we take a detailed look at how tokens are calculated for multimodal content: images, video, and audio. Basic Concept: What Is