惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

量子位
F
Fortinet All Blogs
J
Java Code Geeks
Y
Y Combinator Blog
Stack Overflow Blog
Stack Overflow Blog
V
Visual Studio Blog
M
MIT News - Artificial intelligence
腾讯CDC
Last Week in AI
Last Week in AI
The Cloudflare Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
Jina AI
Jina AI
Microsoft Security Blog
Microsoft Security Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
P
Proofpoint News Feed
博客园 - 叶小钗
Recent Announcements
Recent Announcements
T
Tailwind CSS Blog
Engineering at Meta
Engineering at Meta
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
人人都是产品经理
人人都是产品经理
L
LangChain Blog
博客园 - 司徒正美
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
Optimizing your Claude Code usage (and spending less $$)
Anil Murty · 2026-05-16 · via DEV Community
Cover image for Optimizing your Claude Code usage (and spending less $$)

Anil Murty

This post from Anthropic is causing a lot of angst among its most ardent users. While it is disguised as a credit, it really is them tightening API token limits (aka charging more money). The reality is that this is going to become the norm across all model providers soon. Why? because all the labs have been deeply subsidizing tokens in a frenxy to attract the most number of users their platforms. They are doing this because more users leads to better models (more data to train on), drives mindshare and usage which drives up valuations.

Most Claude Code users I've talked to don't know how their tokens are being spent. Which project, which model, which sessions, what the monthly run rate is, when their credit pool runs out. Heck, I'll admit this was me until I started to work on tokenjam.dev

Here's the funny part: Claude Code already writes a JSONL log for every session to a folder on your laptop. Every API call, every tool result, every token count. The data you'd want to look at is sitting right there.

So we shipped a feature in TokenJam that reads and analyzes it.
it's called "tj optimize" see https://tokenjam.dev/claude-code

tj optimize does three things:

  1. Backfills up to 30 days of your existing Claude Code session logs into a local DuckDB.

  2. Flags sessions that match the structural shape of a smaller model's workload — short inputs, short outputs, few tool calls — and shows projected savings if you switched.

  3. Projects your monthly spend against a budget you set, per provider, and tells you when you'll run out.

Three commands:
pip install "tokenjam[mcp]"
tj onboard --claude-code
tj optimize

Would love your feedback! If you find it useful, please check out the repo and give us a star: https://github.com/metabuilder-labs/tokenjam