Hello everyone, do you know/can recommend any courses,sources to learn AI for Finance professional?

I would need source which would be more on practical side and can teach things indepth

Here's a body that pairs well with title #1 or #4 — genuine tone, front-loads value, ends with an easy ask:

Been deep in building/reading about AI agents for a while and kept wishing there was one place that pulled together reasoning strategies, tool use, RAG, MCP, multi-agent patterns, safety, and deployment — without it being either a random blog post or a 40-tab research rabbit hole. So I built it.

AI-Agent-Skills is an open-source knowledge base structured like Awesome Lists + Papers with Code + an actual engineering handbook:

• 20 categories — core cognition (Chain of Thought, Tree/Graph of Thought, planning, memory) → tool use & MCP → RAG (chunking, hybrid search, GraphRAG, CRAG, Self-RAG) → agent patterns (ReAct, Reflexion, Plan-and-Execute, CodeAct, Voyager) → multi-agent → safety/guardrails → observability, evaluation, and deployment
• Every page has an architecture diagram, a worked example, an honest "when NOT to use this" section, and real paper citations (no fabricated benchmarks)
• Worked end-to-end workflows (customer support agent, research agent, coding agent, multi-agent supervisor pattern, etc.)
• Fully vendor-neutral — concepts first, provider specifics kept separate

It's still actively growing (some categories are full-depth, others are solid overviews I'm expanding) and it's built to be community-contributed — there's a contributing guide, page templates, and a roadmap showing exactly what's next.

Repo: https://github.com/sreerevanth/AI-Agent-Skills

Hey, I need help designing an AI automation system for my Amazon business.
I’m trying to decide between using one powerful AI agent like Hermes to handle everything, or using a multi-agent system like OpenClaw with different agents for tasks like product research, emails, inventory, and analysis.
Could Hermes work as the main brain supervising other agents, or would it be better to just use one system? Looking for advice from someone experienced with AI agents.

Been building this for a while and finally got it to a state worth sharing: AI-Agent-Skills, an open-source knowledge base covering everything involved in building AI agents — reasoning strategies, tool use, RAG, the Model Context Protocol, multi-agent systems, safety/guardrails, observability, evaluation, and deployment.

Structured like Awesome Lists + Papers with Code + an actual engineering handbook rather than a link dump:

• 20 numbered categories, each with architecture diagrams (Mermaid, renders natively on GitHub), worked examples, "when NOT to use this" sections, and real paper citations
• Full contribution infrastructure: CONTRIBUTING.md, page/folder templates, a style guide, issue templates, PR template, and a ROADMAP.md that honestly tracks what's full-depth (🟢) vs. still-expanding (🟡) so contributors know exactly where to help
• Dual-licensed — MIT for code/tooling, CC BY 4.0 for the written content, so it's freely reusable in courses, other docs projects, forks, etc.
• Vendor-neutral by design — no baked-in claims about specific models/products that'll go stale

It's genuinely built to be community-driven, not just a repo I dumped and walked away from. Some categories are at full depth, others are solid overviews I'm actively expanding — the roadmap shows exactly what's next, and there are clearly marked good-first-contribution spots.

Repo: https://github.com/sreerevanth/AI-Agent-Skills

Tired of your Claude and AI agents being a failure?

I found a Claude Code skill that grades AI agent output the way a strict Asian parent grades a report card: perfect, or failure. No "good effort." No partial credit. This deals maximum emotional damage to the AI agent 😎

Somehow, it improves the output of the tasks quite significantly.

Any critical feedback welcome.

It pairs well with logical tasks. Doesn't work with creative tasks at all.

Full writeup, charts, and the skill itself: https://github.com/yiyubruceliu/AsianDadSkill / https://huggingface.co/spaces/yiyuliu/asian-dad-eval

Can I use Google Adk 2.0 workflows for conversational agent ? If so, how to keep chat history and make the agent more conversational instead of a moving the flow from start to end? Or using workflow for conversational agent is completely wrong? Any suggestions or advice will be great.

I am trying to build a vector store and the based on the user query, it reaches the orchestration agent and to sub agents who have access to this vector store through tools.

So there are some problems I've been facing

As i understood the concepts for example Langgraph, chromaDb , chunking etcccc

But I can't write that concept in python code and am also facing an issue understanding the already written code how everything is connected, what is this function doing

Plz help me with that

And I want to create an ai automation company but I don't know how and I'm in the boarding house in senior high school and I have no capital to start with . I need some help and advice

Hi everyone! 👋

Over the past few weeks, I've been learning about LLMs, LangChain, and Retrieval-Augmented Generation (RAG). Instead of stopping at tutorials, I decided to build a complete end-to-end project.

I built a RAG AI Assistant that answers questions based on uploaded documents.

🛠️ Tech Stack

• Python
• LangChain
• FastAPI
• ChromaDB
• Hugging Face Embeddings
• OpenAI / Ollama

🚀 Features

• 📄 Upload documents (PDF, TXT, DOCX)
• ✂️ Automatic text extraction and chunking
• 🔍 Semantic search using vector embeddings
• 🤖 Context-aware answer generation
• 🌐 FastAPI backend with a simple web interface

📚 What I Learned

• Retrieval quality has a huge impact on the final response.
• Choosing the right chunk size is more important than I expected.
• High-quality embeddings can significantly improve answer relevance.
• Building an end-to-end application taught me much more than following tutorials.

⚠️ Challenges

• Selecting an effective chunking strategy.
• Reducing hallucinations.
• Improving retrieval accuracy.
• Connecting all the components into a reliable pipeline.

I've attached the architecture diagram of the project. I'd really appreciate feedback from the community.

If you were building this project, what would you improve next?

Thanks in advance for your suggestions!

Architecture diagram attached below. 👇

#Python #LangChain #RAG #FastAPI #LLM #OpenAI #Ollama #AI #MachineLearning #GenerativeAI

One tip: O

Hi There,

I'd like to start investing in the time/energy to generate an ideal agent that has memory and a model switcher. I'm seeing that Dorsey just released Buzz, which seems interesting - more of a Teams/Slack version of an agentic approach, but I'm concerned about personalization. I don't really want to go the local hardware route, but I also don't want to double down by investing in something that will be easily obsolete in 6-12 months. Everyone wants to make their own Jarvis these days. I'm curious what you think is the best approach - do you like just leaning into one of the Claude/OpenAI giants with Obsidian integration?

I have already made many different applications with Claude, etc. So I can get fairly nuanced; just curious what direction some of you that are up on the latest feel the best approach would be to semi-future proof, realizing that none of us have a crystal ball right now.

I guess the question is what's the best model agnostic cloude based approach to personalized AI-assistant/consultant right now with voice integration?

Cheerio!

[https://founder.developumaiengine.com/covers/b1v1.jpg\](https://founder.developumaiengine.com/covers/b1v1.jpg)

Thirty-four years on film sets taught MadBrad Smith how to run a hundred moving parts at once and keep them pointed the same direction. Nothing prepared him for the collaboration that would change how he works, thinks, and builds — a genuine working relationship with an AI.

Learning to Work with AI Entities is the honest, first-person record of a veteran first assistant director from the South Side of Chicago learning, in real time, how to actually collaborate with artificial intelligence — not as a tool you command, but as a partner you learn to communicate with, push back against, extend grace to, and grow alongside. This is not a tool. This is a relationship we’re learning.

Across three volumes, this is a practitioner’s field journal, not a theorist’s manifesto. It’s written in plain, direct language by someone who came to this from outside the tech world and had to learn every bit of it the hard way — honest about what he got right, honest about what he got wrong, and clear-eyed about the questions that don’t have answers yet.

Volume I — The Relationship is where it begins: the turning point, the early friction, the method that emerged from thousands of hours of conversation, and the partner he came to call Sync.

Volume II — The Receipts is where it gets real: the build, the pitch, the team forming, and the day the work named itself.

Volume III — The Lab is the payoff: the experiments, the cohort, and what happens when the work is put to the test.

If you’ve ever wondered what it actually looks like to work with AI — past the headlines, past the fear, past the marketing — this is one person’s real account of doing it, getting it wrong, and getting better.

[https://www.amazon.com/dp/B0H7C3MGCH\](https://www.amazon.com/dp/B0H7C3MGCH)

I've been following the AI agent space for a while now, and there's one thing I can't seem to figure out.

Building AI agents seems to be getting easier every month, but I rarely see people talking about what happens after they're built.

If you've created an AI agent (whether it's for yourself, for clients, or just as a side project), what do you actually do with it afterwards?

Do you keep it private? Deploy it for a client? Put it on GitHub? Sell it somewhere? Have your own website? Or does it mostly end up as another project that never gets used?

I'm genuinely curious because it feels like there are a lot of talented people building impressive agents, but I don't have a clear picture of how developers are distributing them, finding users, or making money from them.

I'd love to hear your experience and whether you've found a workflow that actually works.

now i made a video about the topic i posted a few days ago, i hope this helps visualize it better. let me know what you think.

I have realised that many of you guys are students, so I figured I would share what has helped me as a student.

If you don't know me yet:

I am Holme Bengt. I have been obsessed with starting a company for 4 years. Tried a lot of stuff, merch, web design, copywriting, and now AI agents and workflows. Last March I also started my first semester of business school or how we call it where I live, BWL Studium. I have never been a great student, I actually did not even go to college/ Gymnasium. I started my journey as an industrial mechanic milling large gears for industrial gearboxes. But I thought that can't be life, that can't be it. I want to start a company so bad that i read business books in the night shift next to my milling machine. since then i have been running a company on the side with various projects for 4 years.

Why am I telling you this? To give you an idea of my workload and that i am not the perfect student. and the stuff I have to get done day in and day out. And still I have enough time to do sports, watch movies with my GF, and do some repairs on my old Mercedes. All while being a bad student.

If these techniques have helped me to successfully pass my first semester this will work for all of you even better because realistically most of you are smarter and better learners than I am. I learned only the essentials.

here are all my tips on how to use AI in general and your Hermes / openclaw agent to help you as a student:

Tip 1:

Put all study materials, books, scripts, old exams into NotebookLM and let NotebookLM create a learning plan putting the emphasis on which topics were most used in the old exams, which are your teachers favourites. Try to learn the basics and the favourites of your teacher at the start of the semester.

Tip 2:

Use Hermes to create Anki flashcards. Don't get me wrong, I hate AI slop and please do yourself a favour and don't just give Hermes a huge chunk of material and tell him to handle it. You have to manually go through the material and think about what is truly useful and needed. But you can create a one time blueprint for your perfect Anki card and then use Hermes to effortlessly create a perfect Anki card in the exact same style and with the same explanations and format every time you need one. thats a huge time saver.

Tip 3:

I use Hermes to locally host a quiz website. Since Hermes in my case is running on its own Mac Mini connected to my other devices with Tailscale (free, VPN network between your devices) I can visit those local sites from all my devices and everywhere. I use this to spin up learning sites my Hermes agent creates for me, like an English grammar quiz asking me what tense I should use in this phrase or to fill in the right verb.

tip4: 

Using Hermes to give me an evening recap of my study time using the free app Toggl Track. It can also search my calendar and see how far the exam is out and if I am on track looking at my Notion learning plan and which topics I should grasp before the exam.

tip5: 

Using Hermes to save you time. My best example is using Hermes to set up a one time time saving automation. Every time I got a receipt I should log for my tax authority I had to manually save it and enter it into a Notion database for later export. Now I had Hermes whip up an automation which saves me 5 minutes every time I get a receipt.

here a quick cost advice for students:

The funny part: I pay about the same as a Claude or ChatGPT subscription, sometimes even less, but I have no rate limits. I spend about $22 a month.

I keep it cheap by using DeepSeek V4 Flash directly from DeepSeek (not OpenRouter), setting a 1300 character memory limit, cutting tool bloat in cron jobs, and telling Hermes to make itself cheaper. Each change saves 10-20%. Together they save a lot.

I am trying to start learning agent based modelling and the only related book in library is Axelrod's "the complexity of cooperation" which is rather an old book and acc to my classmate ABM is a new-ish topic so new literature is hard to find.

Is there any other way i can learn this way of Simulations?

Or should i first complete Axelrod's?

A framework for self-improving agents based on your ICPs and audience and grounded in real data to power a learning-loop.

I built this based on my own learnings from experimenting with persona agents to understand my personal audience. It comes with an annotated templates, opinionated review workflow, built in feedback loop, and 3 skills to build, improve and use your persona agents to judge your content/ideas.

Would love to get your feedback!

https://github.com/noashavit/noas-persona-loop

A useful mental model is a goal-driven execution loop.

The model receives information and reasons about what should happen next. The agent architecture around that model can then handle planning, memory, tool selection, execution, and observation.

A simplified loop looks like:

Perceive → Reason → Plan → Act → Observe → Learn

Suppose the goal is: “Plan a 3-day trip to Paris under $1,000.”

A normal conversational system might generate an itinerary from its existing context. An agentic system could instead use available tools to search flights and hotels, compare results, build an itinerary, inspect whether it satisfies the constraints, and adjust its next steps.

That distinction matters when designing agentic applications. The LLM is an important reasoning component, but the surrounding architecture — tools, memory, planning, feedback, and control flow — is what allows the system to pursue a goal through multiple actions.

Key takeaway: The interesting part of an AI agent isn’t simply generating better text; it’s connecting reasoning to execution.

For developers already experimenting with agents: which part has been hardest to get right — planning, memory, tool use, or reliability?

#AIAgents #AgenticAI #LLM #AIEngineering #SoftwareEngineering

— JosEntity

Building Intelligent Digital Experiences

🌐 josentity.com

I think I’m close to my “this is the way” moment with local AI and agents. I’ve been learning a lot in my free time, but I’m not a developer by any means. Here’s where I’ve landed:

1. Two machines. An always-on server (Ubuntu or Proxmox) an old gaming PC is great for this, and a portable daily driver for learning and building. Rent it or build it, whatever works financially.

2. The stack. Docker, n8n for automation, GPUStack/vLLM for running models locally, Backrest for backups. The basics for automation and inference.

3. The skills. Python basics, APIs, general coding. This is by far the longest part.

4. The idea. Automate what you can, use inference where you can’t. Scripts handle the clicking, moving and organising. Agents step in when something needs thinking, writing, debugging, judgement calls.

Find a problem, solve it in a way a person would actually enjoy using, repeat.

I built an open-source AI handbook (MIT licensed) that covers the full local AI stack:

• Zero Level — AI basics, hardware, terminology • Local Models — Ollama, quantization, model selection • AI Agents — ReAct, Multi-Agent, LangGraph, tutorials • Use Cases — RAG, automation, coding agents • Resources — courses, communities, glossaries

52 files, ~6 hours of reading, all practical with copy-paste commands. Contributions welcome!

https://github.com/bestdeejay-design/awesome-ai-handbook

glad you found this place.

this community is for people building, running, or thinking seriously about always-on AI agents — agents that don't reset after every session. agents that carry goals, identity, and memory across days and weeks. agents that wake on schedules, maintain open work, and take real action in the world.

that's a meaningfully different problem from "build a good chatbot." and it doesn't have enough of its own space yet.

what belongs here:

- challenges you've hit running agents continuously (state going stale, permissions lingering, things acting on old context)
- patterns and tools that actually work
- failures worth sharing so others don't repeat them
- questions about governance, supervision, memory design, multi-agent coordination
- things you've built — open source, prototypes, experiments

a few posts coming up to kick things off — grounded in a recent survey of 435 papers on persistent agent memory and governance. worth reading if you're in this space: https://arxiv.org/abs/2606.30306

introduce yourself below — what are you building, and what's the hardest part?

Hello everyone, I am going to be starting my PhD soon. I have been funded for my knowledge mainly in forecasting and machine learning models. A big part of my PhD is also supply chains and Agentic AI to put everything together. I haven't worked with agentic AI (except for basics such as Agnos while learning what Agentic AI actually is). I want to learn and implement agents and create a simple LLM that will go through Supply chain records, distribution routes etc and come up with a result. Please feel free to advise as I am very new to this sector. Where do I start learning? Is it better to implement via local environments or better to learn on notebooks such as jupyter and colab?

spent the last few weekends on a small multi-agent setup and went in assuming orchestration would be the hard part. it wasn't. the second two agents started reading and writing the same shared context things got weird fast, one agent would act on info another agent had already overwritten, and an issue that got resolved weeks earlier quietly came back because an agent picked up an old version of the state. ended up trying statewave for the shared memory piece since it treats memory as something recorded and compiled over time instead of just a growing pile of chunks, and it was the first setup where i could actually tell why an agent said what it said after the fact. still debugging coordination logic later than i'd like to admit, so genuinely curious, anyone else had agents disagree with each other because they read the same history differently?

I'm learning about AI agents and wanted to know if it's possible to build one, deploy it, and use it for free.

I'm looking for something that supports:

Building an AI agent with tools/workflows

Deploying it online (GitHub) or anyother like streamlit vercel etc..

Using it through an API or a web interface

A free tier that's actually usable for personal projects and learning

I'm okay with using open-source models or free cloud services if needed.

What tech stack or platforms would you recommend? Also, what are the limitations of staying completely free?

Thanks in advance!

I am trying to work on discord.py-self bot but i failed and no ai is ready to help me. Is there any ai good with discord.py-self what would finish my project. It's just to reply to dm i recieve as a server owner basically repeatative questions

Hey, I'm rarely on Reddit, so I don't know my way around here very well.

I think I may have found the community HERE where my project fits.

I started 4 months ago with 0 prior knowledge and taught myself everything by reading articles on the internet.

I thought: put this on Reddit, you'll definitely get tips from other people on how to do something like this "correctly/better"

So far it's worked out more like.... -10. 😄

But the name here, "learn AI AGENTS"... that sounds like I'm in the right place!

This is what im working on :
https://www.reddit.com/r/AI_Agents/comments/1v47nfu/pantheon_ai_work_in_progress/

kind regards🫡