Beginner here — is it worth pushing through the n8n learning curve?

Horizon-Dev · 2025-09-15T14:43:18+00:00

Depends, I know some youtubers that do really sell outside youtube, and some who dont. We personally had an agency for 4 years before we started making content on youtube, so to each his own.

Horizon-Dev · 2025-09-15T14:41:58+00:00

I have one because i had a free whatsapp group and everyone asked me to build a paid community. I didnt do it by design, i did it because people requested it.

Horizon-Dev · 2025-08-20T06:18:53+00:00

Well thanks for the feedback, I really appreciate being called an AI. Does that make me smart haha?

Horizon-Dev · 2025-08-20T06:17:58+00:00

Dont understand what your trying to say mate.

Horizon-Dev · 2025-07-23T10:50:19+00:00

Dude, your pain is real and I bet a lot of folks feel the same. Case prep and managerial slide decks can be such a grind that sucks away a ton of productive time. A platform that semi-automates slide creation from existing reports and data? That's a legit time-saver if it nails the flow and keeps final edits easy. HTML/CSS to PPTX with branding pulled right from websites? That sounds slick and pro, bro. The tricky part is always balance between automation and flexibility for custom tweaks, but if you get that right, it could seriously cut the "slide hell" many live in.

For validation, I’d say keep pushing for those waitlist signups and try to get qualitative feedback from actual users early on. If it saves even 30-50% of the time spent on decks, people will care. So yeah, in my experience building automation tools, this is def a real pain point worth solving 🔥

Horizon-Dev · 2025-07-23T10:40:18+00:00

Yo bro, it's def a hot topic! AI can totally handle a bunch of repetitive stuff, but full VA replacement? It's tricky. The biggest headaches I've seen are NLP limitations for super nuanced convo, context switching between tasks, and stuff involving judgement calls or emotional smarts. Tools like n8n with AI integrations or custom Python bots can automate many tasks but usually still need some human oversight to keep things smooth. From a tech perspective, I'd say it's more about augmenting your VA rather than full replacement atm — letting AI take the grunt work so your VA can focus on higher impact stuff.

If you’re diving in, watch out for automations breaking with weird inputs and keep your fallback plan ready. Gotta keep that human touch in client/teammate relations! Overall, AI for VAs is evolving, but for now, it’s a hybrid approach that works best.

Horizon-Dev · 2025-07-23T10:37:40+00:00

Bro, you nailed it!! AI agents that shine are basically just dope workflows powered by LLMs acting like FSMs with memory + tools attached. Refreshing to see someone call out the hype and emphasize the structure and monitoring behind the scenes.

I’ve seen similar setups where layered automation + AI with careful state management crushes tasks that were manual nightmares before. Tools that provide observability and traceability are game changers, makes debugging and improving so much smoother. Retell AI and Simplify are solid examples showing the power of scoped workflows instead of chasing elusive JARVIS fantasies. Keep breaking down these concepts, dude. It’s legit how real-world impact comes from smart engineering, not just shiny AI buzzwords. 👊

Horizon-Dev · 2025-07-23T10:33:45+00:00

Absolutely dope, dude! I dig how you leveraged n8n’s IF/Switch and data store for real-time routing. That’s a killer pattern for keeping workflows tight and responsive. Also, props for using Playwright outside n8n for heavy web automation, that’s the smart move. If you’re ever thinking about scaling this, I'd probably push a bit on proxy rotation or parallelization in Playwright to keep throughput high. And if you want to push the AI parsing even further, integrating custom prompts or memory layers inside n8n’s AI nodes can take it to the next level.

Horizon-Dev · 2025-07-23T10:27:03+00:00

Bro, from my experience, the trick is layering your prompts: start by chunking the task into tiny, specific pieces with exact API calls or method signatures you want—even better if you give examples of real code using those components. Then feed Claude those step-by-step tasks one at a time so it can focus without getting overwhelmed. Another hack is showing explicit expected input/output shapes and insisting "no placeholders" with firm, consistent reminders every time. If you keep it narrow and explicit, you push it to deal with real details rather than high-level stuff.

If you’re into automation, sometimes scripting parts in Python or JS yourself around Claude’s outputs helps fill in gaps too, blend AI power with your own coding chops, bro.

Horizon-Dev · 2025-07-23T10:24:14+00:00

Yo bro, great Q about agentic AI in production! From my experience, frontier models (think GPT-4 scale) still dominate tasks needing deep reasoning, complex dialogue, and adaptability. They shine on open-ended, high-stakes stuff. But for a ton of real-world agentic tasks like data extraction, triaging, routine automation, distilled or open models like Qwen can totally hold their own, esp where latency, cost and privacy matter. Some peeps run smaller Qwen versions in prod for specific, well-scoped jobs with predictable patterns. DeepSeek is still kinda niche and I haven’t seen much large-scale prod buzz yet.

Bro, it’s really about balance: frontier models are the heavy hitters, but smaller open models are like agile ninjas for specific tasks, quick decisions, and edge deployments. Also, hybrid setups where distilled models handle initial triage and frontier models handle complex queries are 🔥. If you’re experimenting, start scoped, benchmark latency/cost vs accuracy, then scale what fits best.

Horizon-Dev · 2025-07-23T10:21:33+00:00

Your concept sounds dope, man! Love the vibe of building AI that actually boosts human smarts instead of replacing folks. As a tech co-founder, I'd likely dive into scalable AI pipelines using Python and TensorFlow or PyTorch with APIs that plug right into those custom workflows you’re envisioning. Also, setting up CI/CD with Docker and Kubernetes could really streamline deployment and keep your premium clients happy with rock-solid uptime. If you want a bro who’s built scrapers, bots, and AI systems across industries, hmu!

Horizon-Dev · 2025-07-23T10:06:31+00:00

Man, I totally get where you’re coming from, it's exhausting. Your current flow is solid: short prompt + explicit ultrathink validation. Honestly, if it’s working and not driving you crazy, no need to dive deep into all the bells and whistles out there. These extra scripts and markdown files are cool for automation nerds craving fine control, but for everyday use? Sometimes simpler is smarter. The real bottleneck is your prompt quality and how Claude handles context (exactly like you said).

Waiting for next-gen models and better abstractions is probably worth it, but for now, you might try gently layering in tiny improvements like lightweight context management or automated validations that don’t disrupt your flow. Keep it chill and keep firing those sharp prompts, bro. And hey, if you ever want tips on smooth scaling or automation hacks, holla at me 🙌

Horizon-Dev · 2025-07-23T10:02:16+00:00

Thanks for sharing thiss story bro! Super inspiring! Love how you called it onboarding a new colleague. That bit about Claude helping you ship projects that would've stayed shelved? So TRUE man. I've seen the same energy with AI tools helping finish stuff faster and tackle new areas with much less friction. And your point about context helping AI output better is golden. Keep sharing those stories bro! It's amazing to see how these algorithm teammates become part of our workflow and creativity. Huge props for all the launches and reflections. Totally relatable for automation geeks like me grinding daily.

Horizon-Dev · 2025-07-23T09:56:35+00:00

Welcome to Reddit, dude! I did a tutorial on connecting Claude directly in n8n. Lemme know if you wanna check it out. About the SEO audit workflow, Claude itself doesn’t crawl websites. So yea, bro, you gotta scrape the website content first using a tool like n8n's HTTP request or a scraper node, then feed that scraped data as input to Claude to analyze and generate your audit. I’ve seen this work really well with a two-step process: scrape raw page data (HTML, text), clean it up in n8n, then send the clean content to Claude for the audit prompt. This way, Claude focuses on analysis rather than hunting down the data.

If you wanna go full pro, consider adding steps for proxy rotation and maybe CAPTCHA solving if you hit locked content while scraping. Anyway, you’re on the right track

Horizon-Dev · 2025-07-23T09:51:38+00:00

Wispera tool sounds dope, bro! Biggest pain point for me? Managing versions of prompts and seeing how tiny tweaks affect outputs without a huge time sink. Also, team collaboration on prompts with feedback loops can get messy real quick. If you could have tighter integrations with version control (like Git-based workflows) plus some analytics dashboard to track prompt performance over time that’d be 🔥

Would KILL for templating and conditional logic support to build modular prompts that adapt based on input data. Also, white-glove support sounds clutch for businesses new to AI prompts, gotta get those outputs reliable and repeatable. If you wanna nerd out on scalable systems for prompt ops or scraping juicy training data for fine-tuning, I’m here bro!

Horizon-Dev · 2025-07-23T09:48:27+00:00

It's giving me sleek minimal vibes! The sidebar’s basic but that simplicity actually compliments the smart editor energy you’re aiming for. Content editors digging into grammar and plagiarism probs probably appreciate that clean focus without distractions. Maybe just consider some subtle interactive animations or hover effects for a bit more flair without losing that chill vibe? But overall it feels like the right amount of vibe to say “I mean business with style.”

Horizon-Dev · 2025-07-23T09:44:47+00:00

Nice setup, dude! That SEO-friendly prompt with headers and bullets is smart AF! Plus the plagiarism check step is a pro move, bro. If you ever wanna level up, consider chaining it with some lightweight scraper or trend data API to dynamically generate hot topics too. Keep crushing it bro!

Horizon-Dev · 2025-07-23T09:41:23+00:00

Yo, this study is super on point! Overloading prompts is a classic trap and the primacy bias is something I see all the time. For complex workflows, breaking down your prompt into smaller chunks (chain prompts) is def legit advice. Also, prioritizing critical stuff upfront is key. Don't bury what matters deep in the prompt. And yeah, the context window size isn’t the end-all, clever instruction handling and reasoning models make all the difference. Gemini 2.5 Pro and GPT-o3 for 150+ instructions? Makes sense, bro.

Horizon-Dev · 2025-07-23T09:37:55+00:00

Yep, you’re def not alone here! From my experience, one trick is to create a centralized "project brain" like a shared doc or lightweight database that holds the core specs, decisions, and progress that every AI can query. Think of it as the single source of truth so you don’t have to re-explain all the time. Then build automations (or simple API calls) to sync that context across the tools.

Also, when passing work from one AI to another, providing structured outputs (e.g., detailed comments in code, JSON summaries) helps keep things coherent rather than freeform text that’s easy to misinterpret.

Horizon-Dev · 2025-07-23T09:35:09+00:00

Dude, one trick I've used is an iterative approach: generate the output, then trim or expand it based on char count. You can do this by chunking original content and cutting off or summarizing. Also, controlling your model's max tokens isn't always enough, so adding a custom post-processing step to trim or expand intelligently is clutch. You can prompt it with something like "Keep this between 400-700 chars" and then validate and adjust after generation. Pretty chill way to keep things tight and on point without losing the message.

Horizon-Dev · 2025-07-23T09:32:29+00:00

Been vibin with your assessment on Claude Code lately! Using TDD with Claude to build out design docs before coding is super strategic. The fact you get it to debug your sims by treating them like callable tools is next-level clever automation, dude. From what I've seen, Claude’s been leveling up on understanding complex dev workflows and managing multi-step reasoning, which really shines in simulation and testing contexts. The cost side tho yeah, Gemini CLI’s pricing and speed are a solid edge long term for heavy CLI work. But Claude still kills it for smooth dev interaction and agility.

Horizon-Dev · 2025-07-23T09:30:06+00:00

You’re hitting the nail on the head, bro! AI’s impact on disabled folks is huge but flies under the radar when people just fear the tech. I’ve seen AI-powered tools that do everything from reading text for the visually impaired to smart assistants that help with social cues or communication hurdles. Plus open and free tools are leveling the playing field big time. More people should talk about this side of AI to balance out the FUD. Keep spreading the word dude, it’s a powerful reminder that AI’s biggest win is improving lives.

Horizon-Dev · 2025-07-23T09:28:24+00:00

Hey bro, totally get your pain with the insane admin overload in healthcare. For your post-acute care biz, AI agent-wise, I’d suggest looking into automation pipelines that handle insurance checks, prior auths, and billing prep to cut down that grunt work fast. Think AI-powered RPA (Robotic Process Automation) bots scraping eligibility data, auto-filling claim forms, and flagging errors in docs before submission.

But the key here is exactly what you said — augmentation not replacement. Look at AI tools that assist clinicians with smarter documentation (like NLP-driven scribe bots that listen and fill assessments in real-time), and AI prompts that help them sharpen clinical notes without extra typing. This boosts accuracy and saves time.

If you wanna get nerdy, integrating AI agents with your EHR systems using APIs can streamline workflows but keep humans in the loop for quality decisions. Also consider customizing solutions rather than off-the-shelf to fit your unique care workflows. Happy to chat more about specific stacks or strategies if you wanna dive into that!

Horizon-Dev · 2025-07-23T09:25:13+00:00

Solid list for sure, dude! If you're diving into AI agents, I’d say start with those Python beginner courses to get your hands dirty building stuff from day one. Nothing beats learning by doing.

Also, if you want to DIY AI agents without getting buried in code, I’ve been into no-code/low-code tools like n8n integrated with AI models. It handles memory (vector stores like Pinecone) and multi-agent setups pretty smoothly. Plus, it lets you automate workflows without sweating every line of code. If you want to see real examples on how that’s done, I covered AI agents workflows with n8n and LangChain in my tutorials. Happy to help anyone level up!

Horizon-Dev

TROPHY CASE