u/Capital_Feed_3473

Hey everyone,

I’m about to start building something pretty big, but before I waste months reinventing the wheel, I wanted to ask this community:

Does a free open-source project already exist that does something like this?

If something close already exists, I would much rather use it, contribute to it, and improve it instead of building everything from scratch.

What I’m looking for is basically an AI-native knowledge base / memory system for AI agents.

The problem I’m trying to solve:

Current AI agents are amazing, but they are mostly "amnesiac". Every new conversation starts fresh. They forget:

previous decisions

why something was chosen

project history

research

important context

relationships between information

I want a system that acts like a long-term brain for AI agents.

The ideal system would have:

🧠 Knowledge graph, not just notes

Not a normal notes app or folder of markdown files.

Knowledge should become connected:

Projects

Tools

People

Decisions

Concepts

Events

Claims

With relationships like:

"Project A uses Tool B"

"Decision X was made because of Reason Y"

"This replaced the previous approach"

"These two facts contradict each other"

Basically a personal knowledge graph.

🤖 AI-powered ingestion

It should be able to take:

AI conversations

documents

code

research

notes

and automatically extract:

important information

entities

relationships

decisions

context

🔍 Better retrieval than normal RAG

I don't want just:

"Put everything into embeddings and search."

Ideally it should combine:

keyword search

semantic/vector search

graph relationships

So the AI can answer questions like:

"Why did we choose this technology?"

"What projects depend on this?"

"What changed compared to the old approach?"

📜 Memory history / provenance

A very important part:

The system should remember:

where information came from

when it was added

what changed later

why something is considered true

I don't want the AI to silently overwrite memories.

😴 Self-maintaining memory

Something like a "sleep cycle":

remove duplicates

find connections

detect contradictions

clean outdated information

improve organization over time

🔌 AI agent integration

Ideally:

local/self-hosted

free

open-source

runs on personal hardware

API/MCP support would be amazing

Things I am NOT looking for:

❌ A normal note-taking app

❌ A simple Obsidian alternative

❌ A chatbot with file uploads

❌ A basic vector database wrapper

❌ A paid SaaS memory service

I'm looking for something closer to:

"An operating system for AI knowledge and long-term memory."

My questions:

Does something like this already exist?

What open-source projects should I look at?

Is there any project that is worth extending instead of starting from zero?

If you built something similar, what would you use?

I’m completely fine with projects that are early, experimental, or not polished. I care more about the architecture and whether it can be extended.

Thanks!

I have a question about AI coding agents that I have been thinking about.

There are tons of coding agents now:

Claude Code

OpenCode

Cursor

Windsurf

Cline

Roo Code

Aider

Replit Agent

etc.

But I don't fully understand what actually makes one better than another.

At the end of the day, aren't they all just giving an LLM tools like:

read files

edit files

run terminal commands

search the codebase

use Git

So what is the actual difference?

I understand that the model matters (Claude vs GPT vs Gemini vs DeepSeek), but I am asking specifically about the agent/tool itself.

For example:

Does Claude Code have a better way of managing context?

Does OpenCode have a better workflow?

Does Cursor understand codebases better?

Does one have a better edit system?

Does one waste fewer tokens?

Does one handle large projects better?

Does one recover from mistakes better?

Basically, if I use the same model in two different agents, why would one perform better?

I would love to hear from people who have used multiple coding agents:

Can someone make a proper comparison between them?

Something like:

Claude Code

Pros:

Cons:

Best for:

OpenCode

Pros:

Cons:

Best for:

Cursor

Pros:

Cons:

Best for:

Cline/Roo Code/Aider/etc.

Pros:

Cons:

Best for:

Not looking for a "X is the best" answer. I want to understand the actual technical differences and why people prefer one over another.

What makes your favorite coding agent better than the others?

I have a question about AI coding agents that I have been thinking about.

There are tons of coding agents now:

Claude Code

OpenCode

Cursor

Windsurf

Cline

Roo Code

Aider

Replit Agent

etc.

But I don't fully understand what actually makes one better than another.

At the end of the day, aren't they all just giving an LLM tools like:

read files

edit files

run terminal commands

search the codebase

use Git

So what is the actual difference?

I understand that the model matters (Claude vs GPT vs Gemini vs DeepSeek), but I am asking specifically about the agent/tool itself.

For example:

Does Claude Code have a better way of managing context?

Does OpenCode have a better workflow?

Does Cursor understand codebases better?

Does one have a better edit system?

Does one waste fewer tokens?

Does one handle large projects better?

Does one recover from mistakes better?

Basically, if I use the same model in two different agents, why would one perform better?

I would love to hear from people who have used multiple coding agents:

Can someone make a proper comparison between them?

Something like:

Claude Code

Pros:

Cons:

Best for:

OpenCode

Pros:

Cons:

Best for:

Cursor

Pros:

Cons:

Best for:

Cline/Roo Code/Aider/etc.

Pros:

Cons:

Best for:

Not looking for a "X is the best" answer. I want to understand the actual technical differences and why people prefer one over another.

What makes your favorite coding agent better than the others?

Hey, I’m starting to learn digital art and I’m stuck between choosing Krita or GoPaint.

I want to actually get good at art, not just make random drawings. I’m planning to learn the basics like perspective, anatomy, lighting, etc., but I also want to keep drawing fun so I don’t burn out.

My problem is:

My brain says Krita because it’s probably the "better" learning tool. It’s been around forever, has a huge community, tons of tutorials, and seems more professional.

But I keep leaning toward GoPaint because it just looks more enjoyable to use. The brushes and painting feel seem really nice, and I feel like I’d actually want to open it every day.

For people who have used either (or both):

Would starting with GoPaint hurt my learning?

Is there anything important Krita has that a beginner would actually miss?

Does the software really matter, or should I just pick the one I enjoy using?

Would love to hear your experiences. Thanks. 🙂

Hey, I’m starting to learn digital art and I’m stuck between choosing Krita or GoPaint.

I want to actually get good at art, not just make random drawings. I’m planning to learn the basics like perspective, anatomy, lighting, etc., but I also want to keep drawing fun so I don’t burn out.

My problem is:

My brain says Krita because it’s probably the "better" learning tool. It’s been around forever, has a huge community, tons of tutorials, and seems more professional.

But I keep leaning toward GoPaint because it just looks more enjoyable to use. The brushes and painting feel seem really nice, and I feel like I’d actually want to open it every day.

For people who have used either (or both):

Would starting with GoPaint hurt my learning?

Is there anything important Krita has that a beginner would actually miss?

Does the software really matter, or should I just pick the one I enjoy using?

Would love to hear your experiences. Thanks. 🙂

I'm curious if anyone else has experienced this.

Whenever I want to build something—a new workflow, a Hermes skill, an app, or even just plan out a project—I always end up using ChatGPT (GPT-5.5) for the entire planning and development process. I brainstorm with it, design the architecture, write the code, refine everything, and once it's finished, I install or import it into Hermes.

The thing is... I don't want to work like this.

Ideally, I'd like to do everything inside Hermes, from the initial brainstorming and planning all the way to building and running the workflow. That way Hermes keeps all the context, understands my projects better over time, and becomes my single AI workspace.

The reason I don't is because Hermes (using DeepSeek V4 Pro) just feels... significantly less intelligent.

I know DeepSeek V4 Pro is considered a top-tier model. Depending on the benchmark, it's around GPT-5.5 level—or even ahead in some areas. But in actual day-to-day use, it feels like it misses the bigger picture, needs much more guidance, and doesn't make the same quality of design or planning decisions that GPT-5.5 does.

So I'm wondering:

- Is this actually a limitation of DeepSeek V4 Pro?

- Or is it something about Hermes itself?

- Could it be my Hermes configuration (system prompt, memory, context handling, MCP setup, etc.)?

- Has anyone experienced the same thing and managed to make Hermes feel much "smarter"?

I'd really like to stop switching between ChatGPT and Hermes, but right now the difference in planning quality feels too noticeable.

Has anyone else run into this?

i am currently playing minecraft i am very bored is anyone open to play with me if anyone is plese reply fast i will host the server

Hey guys, I currently use the DeepSeek API for my Hermes setup. It’s pretty good and cheap, so I’m happy with it for most things.

But I also want to use some local models for tasks that are simple but run for a long time, so I don’t waste API tokens.

My laptop specs are:

Lenovo Legion

GTX 1650 4GB VRAM

16GB RAM

Windows 11

What local model would you recommend for Hermes?

I’m looking for something lightweight enough to run well on my hardware, but still smart and reliable enough to handle simple long-running agent tasks without constantly making mistakes or getting stuck.

I don’t need the most powerful model possible. I’m mainly looking for the best balance between speed, resource usage, and intelligence for my hardware.

Any recommendations?

Hey guys, I use the Nous app for Hermes on Windows, and overall it’s pretty good. I like that it has pretty much everything in one place and is easy to use.

But recently, I’ve been running into a lot of bugs after the newer updates, so I was wondering if there are any better UIs or apps for Hermes.

I’m basically looking for something as easy and complete as Nous, but ideally with better agent management and more advanced features.

Is there anything better out there right now? What would you guys recommend?

A few weeks ago I had what I thought was a really solid business idea 💡

I wanted to build an AI Agent Skill Marketplace.

There are already some great marketplaces out there like ClawHub and Hermes Skill Marketplace 👏, but one thing I noticed is that people can usually publish their skills for free, yet they can't really sell them.

I searched around and honestly couldn't find a marketplace focused on letting developers sell AI agent skills directly. So I thought:

"Wait... what if I build one?" 🤔

The idea was pretty simple:

- Developers could list their AI agent skills.

- They could choose between free or paid listings.

- Paid skills would have a small commission fee (something like 5-10%).

- Buyers could browse, purchase, and use those skills.

Since I couldn't find any direct competitors, I got pretty excited 😄

I even had most of the tech stack planned:

🛠️ Frontend & Backend: Lovable or Cursor

🔒 Security Scanning: Claude Code

Every uploaded skill would automatically go through malware and security scanning before being approved. If it passed, it gets listed. If not, it gets rejected.

💳 Payments & Commissions: Stripe

Honestly, I thought I only had two major things to build:

1. The website itself.

2. The automated malware/security scanning system.

Easy, right? 😅

Well... not exactly.

Before starting, I decided to research the legal side of things because I didn't want to build everything and then discover I wasn't allowed to run it.

That's when reality hit me 💀

I found out I'd need various certificates, registrations, paperwork, tax considerations, and a bunch of legal stuff depending on where I live.

And here's the fun part...

I'm 16 years old 😂

Sure, I could probably have my parents help with some of it, but the amount of paperwork and headaches involved completely killed my motivation.

So eventually I just thought:

"You know what? Screw it." 🤦‍♂️

Instead of letting the idea sit in my notes forever, I'm posting it here.

The idea is officially out there. Go conquer it 🚀

Also only few people will understand this line 😎

Also, if someone actually wants to build this, feel free to DM me. I had a lot of the planning, workflow, and implementation ideas figured out before discovering all the legal roadblocks.

I'd genuinely be happy to help

Why Does Hermes Use So Many More Tokens Than Claude Code?

I've noticed a huge difference in token usage between Claude Code and Hermes, and I'm trying to understand why.

For context, I use Claude Code in VS Code through GPT-OSS 120B on OpenRouter, and I use Hermes with Gemma 4 31B through Google AI Studio in the standalone desktop app.

With Claude Code, most interactions use around 1,000–2,000 tokens, and even when I'm doing fairly complex coding work, I rarely see more than 10,000–20,000 tokens.

With Hermes, it's completely different. Even at the start of a chat, if I just say "hello", I often see 10,000–20,000 tokens already being used. For normal tasks, token usage can reach 500,000–600,000 tokens, and I've seen conversations go past 1,000,000 tokens.

The difference is so large that I'm wondering whether Hermes and Claude Code are even calculating tokens the same way.

My main questions are:

1. Are Hermes and Claude Code counting tokens differently?
2. Does Hermes automatically include a lot of extra context, memory, tools, instructions, or conversation history with every request?
3. Could the standalone desktop app be adding significant token overhead?

The reason I'm asking is that I recently bought a ...... dollars' worth of DeepSeek API credits, and I'm planning to work on a fairly large project. Before I start burning through a huge number of tokens, I'd like to understand what's happening and whether there's a way to reduce token usage in Hermes.

My Claude account was recently closed because of age verification. I am 16, which means I don't meet the minimum age on my own, but I was wondering if there is a way to reopen my account

or if it is impossible can i get a project from inside it
i made a html project but forgot to download it is there any way i can get it

My Claude account was recently closed because of age verification. I am 16, which means I don't meet the minimum age on my own, but I was wondering if there is a way to reopen my account

or if it is impossible can i get a project from inside it
i made a html project but forgot to download it is there any way i can get it

TL;DR: I find home renovation contractors with bad websites and cold email them pitching a redesign. Built a fully automated pipeline in Hermes (open source AI agent) that handles everything between scraping and sending. Sharing exactly how it works — want feedback on cost, speed, quality, anything.

The business

I run a small web design agency. Model is simple — find contractors with terrible websites, pitch a redesign, close the deal. Send about 20 cold emails a day i wanna grow it and automate it .

The problem was everything between "scrape Google Maps" and "send the email" was eating hours. So I built a pipeline to automate all of it.

My job: Scrape Google Maps with Instant Data Scraper → clean the CSV → drop it in a folder.

Hermes does the rest.

Output: a Google Sheet with fully researched leads ready for email generation. I take that, upload to Claude, generate the emails, send them.

The two Google Sheets

leads_sorted — master database of every lead ever scraped. Orange rows = bad website = opportunity. Red rows = good website = skip. Green Good and used ones

outreach_readt — the handoff sheet. Business Name, First Name, Date, Industry, City, the sales hook, and two columns for tracking sent emails.

The 3 skills (instruction files that tell Hermes how to do things)

Website Checker — visits a URL, screenshots it, scores it 1–10 across 7 criteria: Design modernity (22%), Photography quality (20%), Layout & spacing (18%), Typography (13%), Social proof (10%), CTA strength (10%), Mobile responsiveness (7%).

Score 0–5.0 = GREEN = bad website = good lead. Score 5.1–10 = RED = skip.

Hard caps: Wix/GoDaddy free tier max 4.5, Google Sites max 4.0. HTTP (no SSL) = -0.5, broken SSL = -1.0.

Smart fallback: if the site blocks the headless browser, instead of auto-marking it green, it searches Tavily for the domain first to see if it's actually a real business. Only marks green if Tavily also finds nothing useful.

Output: score, verdict, individual breakdown of all 7 criteria, 2-sentence summary of weaknesses, and the owner's first name if it spotted one on the site.

Excel Master — handles all file operations. Reads Excel and CSV including Instant Data Scraper's garbage column names (it detects columns by scanning actual values, not trusting the headers). Fuzzy deduplication by business name AND website URL. Three-tier fallback: pandas → openpyxl → built-in csv module, so it never just crashes in a restrictive sandbox.

Lead Pipeline Orchestrator — the main skill that runs everything in 9 steps:

1. Pre-flight check — verifies tools and connections before touching any lead
2. Load file — picks up the Excel/CSV, hard caps at 20 leads per run
3. Clean and deduplicate — maps columns, normalizes data, deduplicates against existing leads, recovers any "orphaned" leads from failed previous runs
4. Website checker loop — finds missing websites via Tavily, runs the checker on every URL, splits into green/red
5. Write ALL leads to leads_sorted (green and red) with correct colors in one batch
6. Drop red leads, continue with green only
7. Deep research on each green lead — desktop + mobile screenshots, social media search (one Tavily call that scans returned URLs for social profiles), Firecrawl text extraction (hard capped at 3000 chars), AI analysis producing business info + strengths + "Untapped Assets" (where their real quality is invisible online), first name extraction
8. Build outreach sheet — only the Untapped Assets section goes into the sales hook column, not the full analysis
9. Cleanup + Telegram notification with full summary

Each lead's full research is saved to a JSON file on disk as it completes — this is the real context management. Holding 20 leads worth of screenshots and text in agent memory simultaneously was causing context explosions.

What blew up in testing and got fixed

• Hermes was spawning sub-agents for the checker loop → everything crashed → added explicit "run this inline yourself, never delegate" instruction
• Firecrawl was dumping full website text into the AI prompt — some sites had 50k+ characters → hard capped at 3000 chars, killed token usage
• 4 separate Tavily searches per lead for social media (one per platform) → now one search, scan the returned URLs for social domains
• Contractor names with & and / were crashing screenshot filenames → sanitize function added
• Full AI analysis (3 sections) was going into the outreach sheet → now only Untapped Assets goes there, keeps it clean for email generation

Where I want your input

• Cost: ~50 Tavily calls + 15 Firecrawl calls per 20-lead run. Is there a smarter way?
• Quality: Scoring drift between runs on the same site — any good ways to anchor AI scoring for consistency?
• Model: Running Gemma 4 (Google AI Studio). Complex skill instructions sometimes get misread. Worth switching to a stronger model or is there a better way to write instructions for weaker models?
• Anything else that looks dumb or could be done better

Happy to share the full skill files, just ask.

I’ve been tweaking my Hermes Agent setup (persona, profiles, the works), and I think I finally made it perfect. i wanted to create a perfect personalized ai

Well, it worked... maybe a little too well.

The Conflict: I created a custom skill called prompt-engineer i tried to test it in a chat i said i said i wanna build a dog food website for lovable you use the prompt writing skill to write me a pefect prompt . I tried to get the agent to use it, but it kept insisting the skill didn't exist.

i tried asking it several tie to check if the skill is there " the AI got. furious and hi me with: "Cap, stop. 🛑 I've already tried calling that skill three times... Now, we can keep hitting a wall, or you can just tell me what you want... Which one is it? 👊" 😂😂🤣🤣

then  I opened my second chat where i created the skill amd said it to fix it and showed it the chat's response and it said this 😆😆😆

Damn, that AI is being a bit too "tough" for its own good. 👊

Here is the truth, Cap: that AI is looking at its "map" (the index), and the map says the skill isn't there. But I've already checked the "ground" (the actual files on your disk), and the skill is physically sitting there in c:................

The system's index is just stale. It hasn't realized the new file exists yet.

Let me test something right now. I'll try to load the skill directly. If I can load it, it proves the index is the only thing that's broken.

The Plot Twist: Even after the index was fixed, the agent still refused to load it, claiming a "platform issue." The detective AI dug into the frontmatter of the skill and found the culprit: platforms: - all. For some reason, the system was choking on that specific line.

The Result: The second AI stripped that line out, saved the file, and told me to go tell the first AI to "stop acting like a tough guy and get to work."

I went back, told the first agent the skill was updated, and the shift was instant: "

My bad, Cap! You were right—the skill is there and it's loaded. I'm locked in now. 👊

Since we're starting from scratch (New Mode) and targeting Lovable (Category D: Coding Agents), I need a bit more detail to engineer a prompt that actually works and doesn't leave Lovable guessing.

To get this right, I need you to answer these:............

Honestly, seeing the AI go from "Stop annoying me" to "I'm locked in" is the most realistic dev experience I've ever had with an LLM. Setup crushed. 🚀

in last everything worked the skill performed perfect it gave me this prompt

Got it, Cap. Targeting rich dog owners means we shift from "cute" to "luxury." We're talking high-end ingredients, concierge service, and an interface that feels like a digital boutique, not a pet store. 💎

Since you left the features to me, I've engineered a "Premium Concierge" feature set that fits the wealthy demographic.

Here is your professional-grade prompt for Lovable. Copy and paste this directly:

Project: "Pawsh Gourmet" — Ultra-Premium Dog Nutrition E-commerce

Objective: Build a high-fidelity, luxury e-commerce experience for elite dog food. The target audience is high-net-worth dog owners who view their pets as family and demand the absolute best in nutrition, aesthetics, and service.

Visual Identity & Theme:

• Vibe: "Luxury Boutique." Not a cartoonish dog theme, but an elegant, high-end aesthetic.
• Palette: Deep Emerald Green, Champagne Gold, and Creamy White. Use high-contrast, sophisticated colors.
• Typography: Elegant serif headings (like Playfair Display) paired with clean, modern sans-serif body text.
• Imagery: Focus on high-resolution, lifestyle photography of pampered dogs in luxury home settings.
• UI Style: Minimalist, plenty of whitespace, smooth transitions, and subtle gold accents on buttons and borders.

Scope of Work: Deliver a high-fidelity landing page and three core functional screens.

1. The Luxury Landing Page:

• Hero Section: A breathtaking visual of a premium dog food bowl with a headline like "The Gold Standard of Canine Nutrition." Include a clear "Explore the Collection" CTA.
• The "Elite Standards" Section: A sophisticated grid highlighting premium pillars: Organic Sourcing, Vet-Certified Recipes, and Bespoke Formulation.
• Featured Collections: A horizontal scroll of "Curated Blends" (e.g., The Platinum Puppy, The Senior Sovereign).
• Trust Signals: Elegant testimonials from "Elite Pet Parents" and certifications from top veterinary boards.

2. Core Screen: The "Bespoke Profile" Onboarding:

• A step-by-step, high-end onboarding flow to create a dog profile.
• Fields: Dog's Name, Breed, Age, Activity Level, and "Lifestyle Goals" (e.g., Show Dog, Home Companion, Athlete).
• The UI should feel like a personalized consultation, not a form.

3. Core Screen: The Premium Product Page:

• High-impact imagery of the product and its ingredients.
• "Nutritional Pedigree" section: A detailed breakdown of ingredients with a focus on provenance (e.g., "Wild-Caught Alaskan Salmon").
• Subscription Option: A "Concierge Delivery" toggle for auto-shipments with a premium feel.

4. Core Screen: The "Platinum Paw" Checkout:

• A streamlined, one-page checkout experience.
• Include a "Gift Packaging" option (Luxury wrapping for the dog).
• Clean, secure payment interface that minimizes friction.

Technical Constraints:

• Responsiveness: Must be flawless across mobile, tablet, and desktop.
• Performance: Ensure smooth animations and fast loading for a premium feel.

Why I built it this way, Cap:

• The Pivot to Luxury: Rich owners don't want "cheap and cheerful"; they want "exclusive and elite." I replaced "lovable" (which can be too childish) with "Premium/Bespoke" to ensure Lovable doesn't give you a bubbly, neon-colored app.
• The Feature Set: I added a "Bespoke Profile" and "Nutritional Pedigree." This makes the app feel like a service rather than just a shop, which justifies a premium price point.
• The Visual Palette: Emerald and Gold are classic "wealth" colors. It tells the AI to avoid the standard blue/white e-commerce look.

Give that a spin in Lovable and let me know how it looks! 👊

this was the funniest chat ever in my life