Codex

Frugal Tokens is a free, local, read-only web dashboard that shows exactly how many tokens each of your AI coding agent sessions consumed and what those tokens cost, across OpenCode, Claude Code, PI, Codex, and Cursor. It runs entirely on your machine, never mutates your agent databases, and surfaces hidden costs like prompt-cache misses that most developers never see. This guide walks you through installing it, pointing it at your agents, and reading the per-session explorer to cut your coding agent spend. ...

agent-shell is a native Emacs shell that lets you talk to any AI coding agent from a single buffer, powered by ACP (Agent Client Protocol) rather than any single vendor. It supports 18+ agents including Claude Agent, Codex, Gemini CLI, Goose, and Grok Build, and by September 2026 it has grown to roughly 1,824 GitHub stars. You install it from MELPA, configure authentication with environment variables, and switch agents without leaving Emacs. ...

Frugal Tokens is a free, local-first, read-only tool that lets you see exactly how many tokens each of your AI coding agents consumed and what that cost you, across OpenCode, Claude Code, PI, Codex, and Cursor. It runs locally in Deno, never touches your agent databases, and surfaces per-session token usage plus the hidden cost of broken prompt-cache reuse. It is the sharpest per-session cost diagnostic we tested for teams that want to measure ROI on AI coding spend. ...

Heimdall is an open-source, CPU-only knowledge layer that gives AI coding agents a verified, self-healing memory across every repository you work in. Instead of returning plausible-but-unverified matches, every search result carries a trust verdict — STRONG, WEAK, REBUILT, or STALE — re-checked against your live filesystem at query time. It indexes with tree-sitter and local embeddings, spends zero tokens on memory maintenance, and re-anchors moved files automatically. What is Heimdall and why does it exist? Heimdall is a trust-verified knowledge layer built specifically for AI coding agents. It was released on Hacker News on August 22, 2026, and is published on npm as @ariantdeva/heimdall at version 0.2.1. The project’s core claim is simple: most agent memory tools return results that look right but have not been checked against the actual state of your codebase. Heimdall exists to close that gap. ...

The agent handoff contract standard you are looking for is Relay Task Kernel (RTK): a zero-dependency CLI that consolidates the four per-agent instruction files — AGENTS.md (Codex), CLAUDE.md (Claude Code), GEMINI.md (Gemini CLI), and .github/copilot-instructions.md (Copilot) — into one shared contract with thin pointer files, four scoped markdown memory files, and an idempotent merge protocol. Instead of maintaining the same rules four times and watching them drift apart, RTK gives every agent one source of truth. This guide explains the problem, how RTK works, the merge and memory contracts, presets, and how it compares to alternative handoff tools. ...

What is Kontext CLI? Kontext CLI is an open-source credential broker and runtime security tool built specifically for AI coding agents. It intercepts every tool call made by agents like Claude Code, Codex, and Claude Cowork, brokers short-lived credentials at request time, and enforces security policies before any action reaches your infrastructure. Unlike traditional secrets managers that just store credentials, Kontext actively controls how and when agents use them. What is a Credential Broker for AI Agents? A credential broker is a security layer that sits between an AI coding agent and the services it needs to access. Instead of giving the agent a long-lived API key or database password, the broker issues short-lived, scoped credentials on demand for each individual tool call. The agent never holds the actual secret — it receives a temporary token valid only for the specific operation it needs to perform. ...

Introduction — The Rise of Kotlin-Specific AI Coding Benchmarks For years, AI coding benchmarks have been dominated by Python and TypeScript. SWE-bench, HumanEval, and MBPP all lean heavily on these languages, leaving Kotlin developers wondering how well AI agents actually handle JVM-based code, Android development, and Kotlin-specific idioms. In July 2026, JetBrains changed that by releasing the official Kotlin Benchmark for AI Coding Agents — a rigorous, open-source evaluation framework built on Multi-SWE-bench infrastructure that measures how well AI coding agents resolve real-world Kotlin software engineering tasks. ...

Scientific computing is undergoing a fundamental transformation as agentic AI systems take on increasingly complex roles in research software development. OpenAI’s July 2026 field report on eight agent-assisted scientific computing projects demonstrates that coding agents like Codex and Claude Code can modernize legacy research libraries, automate simulation workflows, and free scientists to focus on verification and discovery rather than implementation. This shift promises to accelerate research across life sciences, materials science, and beyond. ...

One Context MCP shared memory is a unified storage layer that lets AI coding tools like Claude Code, Cline, and Codex access the same project knowledge — coding conventions, architectural decisions, and past conversations — through the Model Context Protocol (MCP). Instead of each tool maintaining its own fragmented context, a single MCP memory server acts as the shared brain, eliminating the need to re-explain project details every time you switch tools or start a new session. ...

If you’ve been using AI coding agents for more than a few months, you’ve hit the same wall I have: every new project needs the same setup — linting rules, test conventions, deployment scripts, API patterns — and you end up repeating yourself in prompts or pasting the same instructions into every new session. Agent skills are the fix, and in 2026 every major platform has shipped their own version. But they’re not all the same, and picking the wrong one for your team costs real time. ...