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n8n 2.44.0 refuses to save a workflow that adds or changes a Function or Function Item node, and the Code node is not affected. The two look alike on the canvas and both hold JavaScript, so check which one you actually have before you rebuild anything. The Function node ran your code once and handed you every item in an array called items. Function Item ran once per item and handed you that item's data as item. The Code node replaced both back in n8n 0.198.0, and n8n hides the old pair from the node picker, so anything you added from the picker lately is already a Code node. The old ones live on in older workflows and in exported JSON, still running in vm2, a sandbox n8n calls insecure. Nothing stops running today. The save is what fails: add one, edit one, or import a JSON file that has one. Open the node, and if its panel says Function, replace it with a Code node set to Run Once for All Items, where items becomes $input.all(). If it says Function Item, use Run Once for Each Item, where item becomes $json. Paste the old code in, change those names, and run it once against the same input. It is a beta release, so there is time. Self hosters can set N8N_DEPRECATED_NODES_BLOCK=false while they move. 🔗 https://github.com/n8n-io/n8n/pull/31360 Does any node in your oldest workflow still say Function in its panel?

An Anthropic hackathon winner open sourced his entire Claude Code setup, ECC, and it has passed 270k stars. Installing all of it in one go is what maxes out your credits, and the subagents are not the reason. The drain is every connected tool switched on at once. 🔗 The repo: ➡️ https://github.com/affaan-m/ECC I tested it, and three pieces are worth having. The guide attached to this post installs only those three by hand and leaves everything else in the repo, so nothing else from it touches your context. 💡 The three tools ✅ Memory: learns from your sessions and reuses your last bug fixes, so Claude Code stops falling for the same bug ✅ Planner: writes a detailed plan and waits for your yes before a single line of code ✅ Budget: shows how full your context is and which installed tools are draining it 📎 The free file is attached to this post ➡️ ecc-three-tools.zip The exact copy commands, the one hook the memory skill needs, and how to use each tool once it is in. Which part of your setup do you suspect is eating your tokens? Tell me in the comments. Book a free Consultation: 🔗 https://go.genovaflow.com/skool-ecc-without-the-bloat-3-claude-code-tools-worth-call Sponsorship: 📧 [email protected]

Google opened Gemini 4 Argon to everyone on 13 October, and on Google's own benchmark table it leads Claude Opus 5.5 on automation work while Opus still wins in the terminal. These are Google's numbers, Argon first and Opus 5.5 second: - AutomationBench: 51.3% against 42.5% - DeepSWE v1.1: 77.9% against 74.2% - Terminal Bench 4.0: 57.4% against 66.4% - FrontierSWE v2: 55.0% against 62.3% It is in the Gemini API through AI Studio now, at an introductory $2 per million input tokens and $10 per million output, going to $4 and $20 after 30 November. The Gemini app has it for Pro and Ultra subscribers. The first hands on reports are more mixed than the table. ariwilson on Hacker News ran his own eleven bug fixes through it and it solved ten, but a median run cost him $0.52 against $0.18 for Sonnet 5.5 and took about four minutes against 32 seconds, because it wrote around 24k output tokens where Sonnet wrote about 3k. eyalitki gave it a payroll question, watched it get every edge case right, and then saw it work out 430 minus 60 as 410. Google's own engineers left it running overnight on ADK, their 200,000 line Python agent framework, and the changes it made are now live, with in memory session prep in long sessions down 40 percent. If you build with n8n or Claude Code, I would not move anything yet. An automation step that keeps failing on a long, many step task is the cheap thing to try it on at the introductory price, and for work in the terminal Opus 5.5 still has the better number. 🔗 https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/ The tester's numbers: https://news.ycombinator.com/item?id=50077034 Which step in your own workflows would you hand to a new model first?

Most auto posting faceless channels end up with 40 uploads of recycled content, because the AI is fed yesterday's viral videos and writes what everyone has already seen. This skill does the research first. 📎 The free file is attached to this post ➡️ faceless-research-pipeline.zip It is a Claude Code skill with two agents. The research agent searches the last 48 hours of X, Reddit and the blogs you choose for ideas nobody has turned into a video yet, and checks YouTube for each one. The writer agent then scores every idea on freshness, demand, gap and fit, picks the clear winner and writes it: a script, a voiceover file for any text to speech tool, and a visual plan for every beat. 💡 What you get ✅ Ideas from posts and threads, never from other people's videos ✅ One scored winner a day, and no video at all on a day with nothing strong ✅ A script where every fact links back to its source ✅ Voiceover and visuals files your own voice and video tools can take as they are ⚙️ Setting it up Unzip it into your Claude Code skills folder (~/.claude/skills/), open Claude Code in your channel's folder and say "run the faceless research pipeline". It asks three questions about your niche once, then runs. When the first run looks right, the file has the one line that schedules it every morning. What niche would you point it at? Tell me in the comments. Book a free Consultation: 🔗 https://go.genovaflow.com/skool-faceless-channel-research-and-writer-agents-free-call Sponsorship: 📧 [email protected]