#Toon Data Type Github Updates

JSON vs TOON in 30 seconds …!

Comment "AGENT" to get this Free Gtihub repo filled with 4000+ n8n AI Agents and Automation templates. Someone just dropped thousands of AI agents and automations into one huge GitHub repo. And these aren’t your usual, simple workflows. They’re real, ready-to-use templates for automating everything from social media and emails to databases and CRMs. Here’s the deal: there are 4,343 production-ready workflows you can use right away. They cover 365 unique integrations — think Slack, Google Workspace, Stripe, Airtable, Notion, and many more. All together, these workflows include 29,445 nodes working behind the scenes. And they’re organized neatly into 15 categories. The best part? Every single one imports cleanly into N8N, so you can start automating in just a few clicks. Why does this matter? Well, automating your daily digital tasks—like posting on social media, sending emails, or updating your CRM—is usually a headache. You have to build everything from scratch or piece together disconnected tools. But this repo gives you a giant toolbox with flexible, battle-tested workflows ready to plug into N8N, a popular no-code/low-code automation platform. If you already use N8N, this is a time-saver. You don’t have to build your own workflows or code integrations. Just download, import, and connect to your accounts. For those new to N8N, it’s a visual tool that lets you create automations without coding, but with the power to go deep when needed. I recently tested some of these workflows, and setting them up took minutes—not hours. It made automating my business tasks way simpler and faster. If you want to manage complex automations without writing code, this repo is a goldmine. Just pick what fits your needs and instantly add AI-driven automation to your toolkit. In short: this massive library turns automation from a chore into a few clicks of convenience. #n8n #n8nautomation #aiagents

TONL is a LLM-friendly serialization format that can cut your data size by up to 50–60% compared to JSON, which also means dramatically lower LLM token costs. #github #opensource

Comment OPEN to get the link. This directory doesn’t just list tools — it shows activity, stars, community health, and docs so you can judge whether an open-source option is stable enough to trust. Use the Alternative To tab to compare feature-by-feature, then open each project page to check updates and license types. Start by swapping just one tool you already use. #aitools #opensource #aicommunity #chatgpt #openai

BILLION-dollar AI secrets just got leaked 💣 10,000+ system prompts from Cursor, Perplexity, Manus, Lovable & more are now public on GitHub. These are the exact high-level prompts top AI startups use-worth studying if you’re into AI, automation, or building your own tool. Comment “prompts” and I’ll send you the link. #PromptEngineering #AILeak #SystemPrompts #AIStartup #OpenSourceAI #CreateWithAI #AutomationTools

Let’s talk about useEffect. One of the most powerful (and most misused) hooks in React 👇 💬 Comment “react” and I’ll DM you: 1️⃣ My GitHub with real-world useEffect examples 2️⃣ My favorite Next.js course for clean, modern apps 3️⃣ My blog post on React 19 & Next.js 15 updates If you’ve ever written a messy useEffect and thought “wait… why is this even here?”, this one’s for you. ✅ When to use useEffect: - Fetch data - Sync external state - Handle subscriptions/events ❌ When not to: - Updating local state after a click - Doing what props or variables can already handle Sometimes, useEffect is a sign you’re forcing React to act imperatively, instead of thinking declaratively. #reactjs #useeffect #nextjs #reacthooks #webdev #javascript #frontend #TheDeveloperShow #reactdeveloper #developer #software

No side projects? Not anymore. Watch me build a full backend API with task filtering, CRUD ops, and a sprinkle of TypeScript chaos #CodingStudent #WomenWhoCode #DevInTraining #JuniorDeveloper #100DaysOfCode #CSStudent #TechStudentLife

2000+ n8n Agenten auf Github 📊🤖 #ai #n8n #künstlicheintelligenz

Comment ‘LINK’ to DM the repo Stop overpaying for your AI projects! 💸🤖 Every developer knows that LLM API token costs can add up fast, especially when scaling. But did you know the way you format your data can literally cut your bill in half? Most of us default to JSON, but there’s a more efficient way: TOON (Token Oriented Object Notation). By switching formats, you can significantly reduce your token count without losing data integrity. I’ve compiled a full guide and a GitHub repo to help you implement this in your next project. 👇 Comment ‘LINK’ below and I’ll send you the repo! #LLM #AIProgramming #DevTips #CostOptimization #JSON TOON SoftwareEngineering TechHacks ArtificialIntelligence CodeEfficiently

Real-time data access 😎 There’s plenty of static data available out there, but real-time data can be perfect for real world applications. Data for weather apps, crypto trackers, current events updates, you can find it all in this repo! Follow for more free coding resources ✅ #code #coding #tech #learntocode

All the AI links I share in ONE place, finally… why didn’t I do this earlier 😂 🔗 links.Sabrina.dev Categorized by type and tags, as well as a short description. #ai #sabrinaramonov

TOON promises to cut your LLM token costs in half while improving accuracy on structured data. When you’re sending customer lists, database queries, or product feeds to ChatGPT, JSON’s verbosity becomes expensive fast. A 100-customer JSON file consumes 6,000 tokens - and that’s just the beginning. The format’s redundancy can also cause AI to drift, miss fields, or miscount entries. TOON takes a different approach. Inspired by CSV but designed specifically for LLM interactions, it uses explicit structure with declared array lengths and field definitions that help AI validate data in real-time. The same 100-customer list? Cut to under 3,000 tokens. This analysis breaks down when TOON excels versus when traditional formats still win: - Token efficiency comparisons on real datasets - How explicit structure reduces AI parsing errors - Ideal use cases: logs, database results, product catalogs - Where JSON and XML still outperform TOON - Implementation considerations from the GitHub documentation The format isn’t replacing JSON everywhere - deeply nested or non-uniform data still favors traditional approaches. But for high-volume structured data? TOON might be the optimization you didn’t know you needed.