Simplify Claude Code with an MCP Gateway for Better Control
Discover how an MCP gateway consolidates multiple tools into a single interface for Claude Code, cutting configuration overhead and enhancing security without disrupting workflows.
#token optimizasyonu
AI uncovers smarter compute strategies that slash LLM token costs by 70%
A new framework from Meta and Google researchers automates the design of reasoning strategies for large language models, cutting token usage by nearly 70% while maintaining accuracy in production deployments.
Boost AI Code Analysis with 70x Fewer Tokens Using Graphify
Developers are cutting AI token usage by 70x with an open-source tool that maps code relationships into a queryable graph. Learn how Graphify transforms AI-assisted development without rewriting a single line.
How to Structure AI Agent Workflows for Faster, Smarter Coding
AI coding agents can waste tokens and slow down projects by re-reading unnecessary files and rediscovering old decisions. A lightweight, spec-driven workflow helps them stay focused, efficient, and aligned with your intent from the start.
Jira CLI vs MCP for AI agents: Which tooling approach wins today?
An engineer built a JSON-first Jira CLI for AI agents, sparking a debate over whether traditional command-line tools or MCP servers better serve modern workflows. Here’s what the team learned—and which approach they’re keeping.
Optimize Claude Code Costs by Trimming Token Waste in API Calls
High API bills aren’t caused by excessive chat turns—inefficient file reads, bloated outputs, and missing caches drive up costs. Learn where tokens go and how to cut waste in every session.
How prompt compression cuts LLM costs by 65% without losing answers
A lightweight tool scores and removes irrelevant tokens from LLM prompts before processing, slashing costs while preserving accuracy. Discover how it works and why it matters for developers.
AI agents need dynamic memory—here's how MRAgent cuts costs by 97%
Long-horizon AI tasks often fail because context windows fill up with irrelevant data, but a new memory framework rebuilds reasoning from scratch in real time. MRAgent reduces token usage by 97% compared to static retrieval systems while improving accuracy.
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