Agent Edge — May 10, 2026

🪙 Token Tracker — track token costs across Claude Code and Codex

stormzhang/token-tracker | GitHub (181★ this week)

🔗 https://github.com/stormzhang/token-tracker

A new open-source tool that tracks token usage across local AI agents (Claude Code, Codex) with a Custom StatusLine and CLI Dashboard featuring cost analysis, rate limit monitoring, and session tracking. Created May 8 and already gaining significant traction.

📌 Why it matters: Hidden token costs are the silent profit-killer for agent operations. Most developers have no idea how many tokens their Claude Code sessions burn through in a day. This tool makes the invisible visible — real-time dollar figures per session, daily/weekly/monthly cost reports, and rate limit alerts. For anyone running agents as a service business, this is an essential part of your operational stack.

🤖 Agent angle: Install this immediately and run it alongside your daily agent sessions for one week. The data will surprise you. A single Claude Code session can burn $35+ in tokens on complex tasks — and if you’re not tracking that cost, you can’t price your services correctly. For service providers, this tool lets you generate automated cost reports for clients, building transparency and trust. Fork it, customize the cost calculations for your specific model pricing, and add it to your deployment pipeline.

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🔥 Behind the scenes: Hardening Firefox with Claude Mythos

Mozilla Hacks | Blog

🔗 https://hacks.mozilla.org/2026/05/behind-the-scenes-hardening-firefox/

Mozilla published the inside story of how Claude Mythos helped the Firefox team fix more security bugs in one month than the past 15 months combined. The detailed technical walkthrough covers how they structured the agent workflow, the review process, and the types of vulnerabilities the agent was most effective at finding.

📌 Why it matters: This isn’t a cherry-picked demo — it’s a real security team at a major browser vendor publishing their actual workflow and results. 15 months of work compressed into one month is a 15x productivity multiplier. For anyone arguing that coding agents aren’t production-ready for security-critical work, the evidence is now on the other side.

🤖 Agent angle: If you provide agent services to clients in regulated industries (fintech, healthcare, security), this case study is your sales document. The Firefox team trusted an agent with security-critical work and published the results. Copy their workflow pattern: (1) decompose the security backlog into structured tasks, (2) run agents with clear scope boundaries, (3) implement a human review gate before merging. Build this into your service offering — “agent-assisted security hardening” is a defensible, high-margin service line.

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🏢 Cofounder — run engineering, sales, marketing, and operations with agents

@yoheinakajima | X/Twitter

🔗 https://x.com/yoheinakajima/status/2053920628663877990

Yohei Nakajima highlighted cofounder.co, a platform by @intelligenceco that lets you run engineering, sales, marketing, design, finance, and operations entirely through AI agents. “If you’ve ever wanted a cofounder that would manage all your agents for you…”

📌 Why it matters: The thesis is becoming reality — not just building agents that do one thing, but an entire company operating through agent orchestration. Cofounder.co takes the multi-agent approach and applies it across every business function. This signals where the market is heading: from “agent as tool” to “agent as organization.”

🤖 Agent angle: Whether you use cofounder.co or build your own stack, the takeaway is to think at the organizational level, not the task level. Map out every function in your operation — engineering, sales, customer support, content production, bookkeeping — and ask “can an agent handle this?” The ones that can are margin expansion. The ones that can’t are where you focus human energy. For agent service builders, platforms like this are both competitors and distribution channels — integrate with them rather than ignoring them.

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📂 mirage — A Unified Virtual Filesystem For AI Agents

strukto-ai/mirage | GitHub (1,711★)

🔗 https://github.com/strukto-ai/mirage

Agents are only as good as their context window and tool access. mirage gives agents a structured, persistent virtual filesystem — think of it as the missing OS layer for agentic workflows. Early traction at 1.7K stars suggests this scratches a real itch.

📌 Why it matters: Context management across sessions is one of the biggest friction points for production agents. mirage gives agents persistent memory, structured data access, and cross-session state without bolting on a database.

🤖 Agent angle: Run this locally or embed it in your agent stack to give agents persistent memory and cross-session state. If you’re building agent-powered services (content pipelines, research bots, trading systems), this removes a major operational headache — context management across sessions.

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📈 tq-trading-agent — Multi-agent stock research & trading strategy orchestration

TQ-trade-agent/tq-trading-agent | GitHub (289★)

🔗 https://github.com/TQ-trade-agent/tq-trading-agent

Direct money play. This is an open-source, AI-powered multi-agent system for stock research, signal generation, and trade execution orchestration. Fresh release with early traction — the kind of tool that pays for itself if you’re already in markets.

📌 Why it matters: Multi-agent systems for financial markets aren’t new, but open-source implementations at this quality level are. This gives you a starting point for automated market research without vendor lock-in.

🤖 Agent angle: Run this alongside your existing agent stack to add automated market research and strategy backtesting. Fork it, customize the agent prompts to your strategy, and let it run as a background research pipeline while you focus on execution decisions.

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