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LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
llama.cpp11 min read
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Agents on live hardware: reason where you must, engineer everywhere else
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Case Study: Refactoring at Scale with Agents
A legacy uplift that would traditionally have been a 12–18 month, high-risk engineering project was completed in three weeks of part-time work for roughly $4,000 in AI costs.
CodeHealth MCP Server by CodeScene Blog7 min readWhy AI Coding Agents Need a Deterministic Code Health Gate
CodeHealth is an aggregated metric based on 25+ factors scanned from the source code. The code health factors correlate with increased maintenance costs and an increased risk for defects, and predicts whether AI agents will introduce defects or not.
CodeHealth MCP Server by CodeScene Blog15 min readCodeHealth as a Prerequisite and Compass for Coding Agents
Healthy code works better for both agents and humans 1. Healthy code is more AI-friendly: it is easier for coding agents to analyze, modify, and extend without introducing unintended side effects. High-quality code has never been more important than in the AI era.2.
CodeHealth MCP Server by CodeScene Blog5 min readAnnouncement: Deterministic PR Refactoring Agents
CodeScene’s new PR Refactoring Agent analyzes the branch using deterministic Code Health data, performs targeted refactorings, and commits improvements back to the PR. The result is code that’s easier to review, AI-friendly, and more token efficient.
CodeHealth MCP Server by CodeScene Blog4 min readUnhealthy Code is Burning Your Token Usage - Here's the Data
Our research across C++, Java, and Python shows that agents working on unhealthy codebases consume 35-45% more tokens to complete the same tasks. That’s in addition to the increased defect risk covered in earlier research. You're not just getting worse output.
CodeHealth MCP Server by CodeScene Blog5 min readMaking Legacy Code AI-Ready: Benchmarks on Agentic Refactoring
Most enterprises are now on a trajectory towards agentic AI. The general assumption is that faster coding will translate into increased productivity across the board.However, there’s a potential clash between expectations and reality: The Code Health benchmarks show that the average Hotspot Code…
CodeHealth MCP Server by CodeScene Blog6 min readAgentic AI Coding: Best Practice Patterns for Speed with Quality
This year has marked an interesting and fundamental shift. I’ve been coding for almost 40 years, 30 of those professionally, and yet I’m now at a point where 100% of my production code is written and maintained by AI agents.
CodeHealth MCP Server by CodeScene Blog9 min readStrengthening the Inner Developer Loop: Turn AI Into a Reliable Engineering Partner
What if AI could accelerate development without leaving a trail of complexity, defects, and hard-to-reason-about code that even your own AI agents fail to understand? That’s the promise of a well-designed inner developer loop: a feedback system that keeps AI productive and predictable.
CodeHealth MCP Server by CodeScene Blog6 min readHuman, What Must I Tell You?
Explore explainable AI for software engineering: from code generation to program repair, discover why explainability is key to trust and adoption.
CodeHealth MCP Server by CodeScene Blog13 min readRequirements on Technical Debt: Dare to Specify Them!
Technical debt slows teams down. Learn how explicit requirements, ISO 5055 standards, and CodeScene’s Code Health can guide sustainable code quality.
CodeHealth MCP Server by CodeScene Blog11 min read
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