Contents# Introduction# How Claude Code Connects to Any Local Model# Backend 1: Ollama# Backend 2: LM Studio# Backend 3: llama.cpp# The Complete settings.json# Best Local Models for Claude Code in 2026# Troubleshooting Common Issues# Conclusion # Introduction Agentic coding sessions are expensive. A single Claude Code session — reading files, writing code, running tests, iterating — can burn 10–50x more tokens than a …
ContentsWhy diffusion for text?The trade-off with traditional models Why diffusion for text? While the AI research community has explored diffusion-based text generation for years, applying it to large models has remained a challenge. DiffusionGemma changes this by shifting how models use hardware. The trade-off with traditional models Most language models act like a typewriter, generating …
The true bottleneck was never the analysis. The post BI Is Dead, Long Live BI appeared first on Towards Data Science.
Contents# Introduction# Why Gemma 4?# The Modelfile# Wiring Claude Code to the Local Model# Agentic Task Walkthrough# Wrapping Up # Introduction Visualize this: a multi-agent workflow that reads files, writes patches, runs tests, and iterates across four services, making 400 API calls in a single afternoon. The notification arrives. You have crossed the soft limit again. Every token costs money, every …
Local Agentic Programming on the Cheap: Claude Code + Ollama + Gemma4 Read More »
Twenty years ago, translation at Google began as one of our pioneering machine learning experiments to turn the science of language into the magic of human connection. That experiment has come a long way with over a trillion words being translated for billions of users across our products every month. Today, we’re taking our next …
ContentsWith FIFA set to kick off on Thursday, June 11, 2026, the opening match at the Mexico City Stadium, I think it would be fun to build the best ML model we can to predict match outcomes. To do this, I have brought together several databases—49,000 matches—with data on Elo ratings, match results, and cup …
Contents# Introduction# Measuring Calibration# Why LLMs Complicate the Standard Setup# Applying Temperature Scaling# Applying Platt Scaling# Applying Isotonic Regression# What the Literature Leaves Open# Conclusion # Introduction A model that says it is 90% confident should be right 90% of the time. When that relationship breaks down, you get a miscalibration problem. The model’s scores stop telling you anything useful about reliability. For …
3.5 Flash: agentic tasks at scale This balance of speed and performance makes 3.5 Flash ideal for tackling long-horizon agentic tasks. What used to take a developer days or an auditor weeks, 3.5 Flash can now help complete in a fraction of the time, often at less than half the cost of other frontier models. …
. The functions had grown too long and the variable names made no sense anymore. Every time I wanted feedback on a file, I stopped, opened the chat, copied the whole thing in, and waited. Then went back to the editor, applied the change, opened the next file, and did it again. At some point …
My AI Couldn’t See My Files — I Built a Zero-Dependency MCP Server Read More »
Contents# Introduction# Redefining the Baseline# The Orchestration Ecosystem# Shifting the Workflow: From Procedural to Evaluative# The 2026 Skill Stack# The Evolution of Roles# Keeping Pace # Introduction Something has shifted at the intersection of AI and data science, and it’s changed how practitioners work. The systems deployed today don’t just generate a response and stop. They plan. They execute multi-step tasks. They …
