Stealth

Founder

Ideavault

We founded Ideavault with a clear mission: to make investing easy and rewarding for everyone. Our approach is simple yet powerful. By using advanced AI technology and being transparent in everything we do, we're changing the way people invest and helping them build long-term wealth. Our goal is to make investing accessible and a gratifying investing experience for individuals across the board

Co-Founder

KARL MAYER

Worked at KM.ON, the digital innovation arm of Karl Mayer

Skills: Docker | Kubernetes | Istio | Kafka | OPA | OPAL | Policy Management | Cloud Technologies

Worked extensively with Kubernetes, Docker, Helm Chart, OPA, and OPAL technologies. Developed authorization checks using Open Policy Agent (OPA) for KM.ON.

Created and implemented policies using OPAL to streamline KM.ON's operations. Contributed to enhancing KM.ON's policy management processes and ensuring real-time implementation.

Played a key role in securing KM.ON's operations by designing and implementing authorization automated policies. Acquired valuable insights into cloud-native architectures

Software Engineer (Cloud Team)

The University of Edinburgh

NICE Programme 2023: Participated in a prestigious entrepreneurship and innovation program focused on developing entrepreneurial and intercultural competence skills. 

Partners: University College Dublin, the University of Amsterdam, the University of Edinburgh

Key Skills Gained: Product Management. Acquired valuable skills in product management, including ideation, design, and implementation of sustainable fashion solutions. Developed expertise in project coordination, team collaboration, and strategic decision-making.

Project: Collaborated with a diverse team on "The Thrift Revolution: Making Sustainability Fashionable," a patchwork clothing project. Led product management efforts, overseeing the transformation of old clothes into fashionable and eco-friendly designs, while ensuring accessibility and customer satisfaction.

Entrepreneurship | Product Management

University College Dublin

Under Professor Mark Keane, University College Dublin 

• Responsible for researching explainable AI, with a focus on counterfactual explanations as post-hoc justifications for AI system decisions.
• Responsible for understanding semi-factuals, an underexplored area in AI, and summarizing recent breakthroughs that could serve as a foundation for future algorithmic developments.

Machine Learning

United Nations

The Millennium Fellowship is a semester-long leadership development program, a collaboration between the United Nations Academic Impact and Millennium Campus Network (MCN), in which we are required to work with leaders from all over the world to achieve the United Nations SDG's. The United Nations Academic Impact and MCN will help us elevate our leadership skills to help us make a positive change on the society.

United Nations Millenium Fellow 2022 

Cathay Pacific

Team Akash HKU – Selected as the Top 6 Finalists out of 100+ teams

•Our Team focused on the theme of Greener Together and came up with a solution of accurately predicting CO2 emissions to help provide a guilt-free- travel option to passengers.
• We created a Machine Learning model to predict CO2 emissions per passenger based on several factors including weather conditions, weight, aircraft age etc.
• Our one-click solution could simply be integrated into the current system without requiring the need to develop something new from scratch. We proudly came in the top 6 finalists in the competition.

Cathay Hackathon 2022

HKU Business School

• Involved in Case Research Training and Analyzing Regional Businesses relevant to Digital Economy

• In-Depth Study and analysis of business helping to understand how Digital Innovation works in practice

• Required to write and develop a case study suitable for undergraduate students that will be published in Asia Case Research Centre (ACRC)

Student Research Assistant: ACRC Case Writing

MIT Hong Kong Innovation Node

MIT Entrepreneurship and FinTech Integrator (MEFTI) 2022

•MIT Entrepreneurship and FinTech Integrator, is a Startup program for students from MIT and Hong Kong, to tackle real-world industry challenges.
•It involved intensive brainstorming sessions, key industry leader talks, mentoring sessions and our team "Metasurance" consisting of Hong Kong and MIT students had 'Prudential' as our client.
•Our team proposed the idea for providing insurance in the Metaverse for people who are interested in the Metaverse or buying NFT's based on the level of risk.
•Our team achieved the Runner-Up position, securing 2nd place in the competition. We were awarded and felicitated by the Chairman of the FinTech Association of Hong Kong.

Product Management

DocPro.com

Full-Stack Development · HTML · CSS · React Native · Twig · Node.js

• Helped making the UI more appealing and attractive to improve the customer experience by making changes on the DocPro website.
• Collaborated with TechNine developers and worked with Software Development Team for creating new platform for DocPro company.
• Worked on Data Analytics including Google Analytics 4 and GTM to help increase the traffic and drive up the conversions, thus serving as a link between the DocPro's Tech Team and DocPro’s management team.

Software Developer

If you’re tired of feeling behind, confused, or stuck when it comes to AI, you’re exactly who I create for.

Most people fall into one of two categories when it comes to AI:

1️⃣ Curious but disconnected — want to learn, don’t know where to start.
2️⃣ Overwhelmed and burnt out — too many tools, trends, and no clarity.

So they pause… and put it off.

But ignoring AI today is like ignoring the internet in 2001.
The truth? AI is not optional anymore. And avoiding it won’t make it go away. 

The real solution? 
Start small. Stay consistent. Learn fast. Apply faster.

That’s where I come in.

I simplify AI for students, working professionals, and founders.

 I focus on:
• GenAI & Agentic AI
• AI Tools for Careers & Education
• Startup Execution & Product Strategy

As a startup founder and AI strategist, I work at the intersection of GenAI, agentic systems, product strategy, and execution. My background includes a Computer Science degree from the University of Hong Kong ($100K+ scholarship), building startups across borders, and creating content to help people.

If you want to use AI, not just read about it —> follow me.
Reach out: rahul@scholarbaniya.info

3 ways how most AI systems are built.

I’ve explained each one step-by-step.

1) 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽)
1. 𝗦𝗲𝘁 𝘁𝗮𝘀𝗸 – Decide what problem the model should solve.
2. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗱𝗮𝘁𝗮 – Gather lots of examples.
3. 𝗣𝗿𝗲𝗽𝗮𝗿𝗲 𝗱𝗮𝘁𝗮 – Clean and label it so the model learns correctly.
4. 𝗕𝘂𝗶𝗹𝗱 𝗶𝗻𝗱𝗲𝘅 – Make data searchable with embeddings.
5. 𝗧𝗿𝗮𝗶𝗻 𝗺𝗼𝗱𝗲𝗹 – Teach the model using the prepared data.
6. 𝗗𝗲𝗽𝗹𝗼𝘆 – Put the trained model into use.
7. 𝗚𝗲𝘁 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 – Model answers queries based on training.
8. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 – Measure performance and retrain if needed.
_________________________________________________

2) 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 (𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽)
1. 𝗦𝗲𝘁 𝗴𝗼𝗮𝗹 – Give the agent a clear objective.
2. 𝗣𝗶𝗰𝗸 𝗟𝗟𝗠 – Use a language model as the agent’s brain.
3. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗼𝗼𝗹𝘀 & 𝗔𝗣𝗜𝘀 – Link it to calendars, browsers, databases, etc.
4. 𝗦𝗲𝗮𝗿𝗰𝗵 & 𝗳𝗲𝘁𝗰𝗵 – Agent can look up info or call external services.
5. 𝗣𝗹𝗮𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 – Breaks goal into steps, loops until complete.
6. 𝗗𝗲𝗰𝗶𝗱𝗲 𝗮𝗰𝘁𝗶𝗼𝗻𝘀 – Chooses next steps without human input.
7. 𝗘𝘅𝗲𝗰𝘂𝘁𝗲 𝘁𝗮𝘀𝗸𝘀 – Sends emails, runs scripts, calls APIs.
8. 𝗟𝗲𝗮𝗿𝗻 & 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 – Adjusts based on outcomes over time.
_______________________________________________

3) 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 (𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽)
RAG = 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻. Agentic RAG combines agentic behavior with fast retrieval of grounded information.

1. 𝗦𝗲𝘁 𝗴𝗼𝗮𝗹 – Define the task clearly.
2. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗲 𝗱𝗮𝘁𝗮 – Pull relevant docs or knowledge from databases.
3. 𝗩𝗲𝗰𝘁𝗼𝗿 𝘀𝗲𝗮𝗿𝗰𝗵 / 𝗔𝗣𝗜 𝗰𝗮𝗹𝗹𝘀 – Use embeddings and search indexes to find exact, relevant facts.
4. 𝗗𝗲𝘀𝗶𝗴𝗻 𝗺𝘂𝗹𝘁𝗶-𝘀𝘁𝗲𝗽 𝗽𝗿𝗼𝗰𝗲𝘀𝘀: Agent plans steps using the retrieved knowledge.
5. 𝗟𝗼𝗼𝗽 𝗹𝗼𝗴𝗶𝗰 – Retrieve → reason → act → verify.
6. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗾𝘂𝗲𝗿𝘆 𝗔𝗣𝗜𝘀: Carry out actions (call services, update DB).
7. 𝗖𝗵𝗲𝗰𝗸 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 – Verify answers against facts for accuracy.
8. 𝗥𝗲𝗳𝗿𝗲𝘀𝗵 𝗺𝗲𝗺𝗼𝗿𝘆 – Save outcomes into memory or vector DB so future tasks use updated info.
9. 𝗔𝗱𝗮𝗽𝘁 – System improves over time with stored results.
_____________________________________________

✅ Which to pick?
• Use 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 when task is narrow, stable, and must be highly reliable (e.g., image recognition in a controlled domain).
• Use 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 when automation of multi-step work is needed (scheduling, orchestration, admin tasks).
• Use 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 when you need both action and factually grounded responses drawn from up-to-date data (customer support, codebase assistant, enterprise knowledge work).

✅ Repost this for others in your network.

Rahul Agarwal

6 design patterns used in AI agents.

I've broken down each in simple steps.

1. 𝗦𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 (𝗕𝗹𝘂𝗲)
• 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀:
The query moves through agents one after the other.
• 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: You ask a question → First agent processes it → Passes to next agent → And so on until the final answer comes out.
• 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Email Drafting: Idea → Draft → Proofread → Format → Finalize.
________________________________________________
2. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 (𝗢𝗿𝗮𝗻𝗴𝗲)
• 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀:
The query is divided into smaller parts and sent to different agents 𝗮𝘁 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘁𝗶𝗺𝗲.
• 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: You want information about a company → One agent searches the web, another checks a database → Both results are combined into one output.
• 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Fast research tasks.
________________________________________________
3. 𝗥𝗼𝘂𝘁𝗲𝗿 (𝗚𝗿𝗲𝗲𝗻)
• 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀:
One main agent (the “router”) decides which specialized agent should handle the query.
• 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: You contact customer service → It sends general questions to a 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗔𝗴𝗲𝗻𝘁, billing issues to a 𝗕𝗶𝗹𝗹𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁, and tech problems to 𝗧𝗲𝗰𝗵 𝗦𝘂𝗽𝗽𝗼𝗿𝘁.
• 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Customer service systems.
________________________________________________
4. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 (𝗥𝗲𝗱)
• 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀:
Agents are connected in a network and can pass information back and forth, not just in one direction. A “Coordinator Agent” manages this process.
• 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: You ask for software → Builder Agent writes it → Reviewer Agent reviews it → Sends back corrections to Builder → Loop continues until output is correct.
• 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Complex problem-solving like debugging or simulations.
________________________________________________
5. 𝗔𝘂𝘁𝗼 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 (𝗣𝘂𝗿𝗽𝗹𝗲)
• 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀:
The query is split into tasks, and multiple agents (like coder, documenting, or test generator agents) work together to build something new.
• 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: You want a program → The “Coding Agent” writes it, the “Documentation Agent” explains it, and the “Generator” polishes the result.
• 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Writing software with explanations.
________________________________________________
6. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗴𝗲𝗻𝘁𝘀 (𝗬𝗲𝗹𝗹𝗼𝘄)
• 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀:
Agents can work 𝗶𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁𝗹𝘆 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁 𝘄𝗶𝘁𝗵 𝗲𝗮𝗰𝗵 𝗼𝘁𝗵𝗲𝗿 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁 𝘂𝘀𝗲𝗿 𝗶𝗻𝗽𝘂𝘁.
• 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: You ask two agents to plan a trip → Agent 1 books flights, Agent 2 books hotels → They coordinate on their own and return the full plan.
• 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲: Personal assistants that complete tasks automatically.
________________________________________________

These multiple agent design patterns offer flexible ways to build powerful AI systems, each suited for different types of tasks.

✅ Repost for others in your network who can benefit from this.

How Multi-Agent AI systems actually work?

Explained in a very simple way. Read below:

-> 𝗧𝗵𝗲 𝗠𝗮𝗶𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁

The main 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 is the 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿.
It has several capabilities:
• 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 – Stores knowledge or past responses.
• 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 – Learns from results and user interactions.
• 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 – Breaks down the query into smaller tasks.
• 𝗠𝗲𝗺𝗼𝗿𝘆 – Remembers past interactions, results, and context.
• 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 – Makes logical decisions about what steps to take.
• 𝗧𝗼𝗼𝗹𝘀 – Connects to external APIs, functions, or services when needed.

This agent delegates tasks to 𝗔𝗴𝗲𝗻𝘁 1 and 𝗔𝗴𝗲𝗻𝘁 2.

-> 𝗔𝗴𝗲𝗻𝘁 1
• Connected to an 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿 → a vector database that allows it to 𝘀𝗲𝗮𝗿𝗰𝗵 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘀𝘁𝗼𝗿𝗲𝗱 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 efficiently.
• Has its own 𝗠𝗲𝗺𝗼𝗿𝘆 (for context/history).
• Has 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 ability (to decide what to fetch or compute).

Think of 𝗔𝗴𝗲𝗻𝘁 1 as the 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲𝗿 + 𝗽𝗹𝗮𝗻𝗻𝗲𝗿.

-> 𝗔𝗴𝗲𝗻𝘁 2
• Connected to an 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿 (𝗦𝗲𝗮𝗿𝗰𝗵) → this one interacts with 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝘀𝗲𝗮𝗿𝗰𝗵 𝗲𝗻𝗴𝗶𝗻𝗲𝘀 𝗼𝗿 𝘀𝗲𝗻𝘀𝗼𝗿𝘆 𝗱𝗮𝘁𝗮 (real-time info).
• Has 𝗠𝗲𝗺𝗼𝗿𝘆 (to keep track of what it found).
• Has 𝗦𝗲𝗻𝘀𝗼𝗿𝘆 𝗗𝗮𝘁𝗮 (ability to process information from outside sources like live search, signals, or real-world sensors).

Think of 𝗔𝗴𝗲𝗻𝘁 2 as the 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝘀𝗲𝗮𝗿𝗰𝗵 + 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗱𝗮𝘁𝗮 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁.

𝗛𝗼𝘄 𝗧𝗵𝗲𝘆 𝗪𝗼𝗿𝗸 𝗧𝗼𝗴𝗲𝘁𝗵𝗲𝗿

1. The 𝗤𝘂𝗲𝗿𝘆 is received by the main 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁.
2. It decides:
• If the query needs 𝘀𝘁𝗼𝗿𝗲𝗱 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 → it asks 𝗔𝗴𝗲𝗻𝘁 1 (𝗩𝗲𝗰𝘁𝗼𝗿 𝗱𝗯).
• If the query needs 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝘀𝗲𝗮𝗿𝗰𝗵/𝘀𝗲𝗻𝘀𝗼𝗿𝘆 𝗶𝗻𝗳𝗼 → it asks 𝗔𝗴𝗲𝗻𝘁 2 (𝗦𝗲𝗮𝗿𝗰𝗵).
3. Both agents send their processed information back to the main agent.
4. The 𝗺𝗮𝗶𝗻 𝗮𝗴𝗲𝗻𝘁 𝗿𝗲𝗮𝘀𝗼𝗻𝘀, 𝗽𝗹𝗮𝗻𝘀, 𝗮𝗻𝗱 𝗰𝗼𝗺𝗯𝗶𝗻𝗲𝘀 𝗿𝗲𝘀𝘂𝗹𝘁𝘀.
5. The system generates the 𝗳𝗶𝗻𝗮𝗹 𝗢𝘂𝘁𝗽𝘂𝘁 for the user.

✅ In short:
You ask → Main AI Agent plans → Specialist AI Agents fetch/process info → Main AI Agent reasons & combines → You get the result.

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Enough talking about the benefits of AI.

But when all does AI go wrong?

Let’s take a look at some of the key ways AI can fail:

1️⃣ 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗙𝗮𝗶𝗹𝘂𝗿𝗲𝘀
• 𝗠𝗮𝗸𝗲𝘀 𝘂𝗽 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 confidently.
• Focuses on the 𝘄𝗿𝗼𝗻𝗴 𝘁𝗮𝘀𝗸.
• 𝗥𝗲𝗽𝗲𝗮𝘁𝘀 without progress.
• 𝗔𝗰𝘁𝘀 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁 but is 𝘄𝗿𝗼𝗻𝗴.
• 𝗠𝗶𝘀𝘂𝘀𝗲𝘀 tools.

2️⃣ 𝗦𝘆𝘀𝘁𝗲𝗺 𝗙𝗮𝗶𝗹𝘂𝗿𝗲𝘀
• 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗰𝗹𝗮𝘀𝗵 with each other.
• 𝗙𝗼𝗿𝗴𝗲𝘁𝘀 𝗶𝗻𝗳𝗼 after long conversations.
• 𝗙𝗿𝗲𝗲𝘇𝗲𝘀 𝗼𝗿 𝘀𝗹𝗼𝘄𝘀 𝗱𝗼𝘄𝗻.
• 𝗠𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝘀 requests.

3️⃣ 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗙𝗮𝗶𝗹𝘂𝗿𝗲𝘀
• 𝗙𝗼𝗿𝗴𝗲𝘁𝘀 details.
• 𝗪𝗼𝗻’𝘁 𝗲𝘀𝗰𝗮𝗹𝗮𝘁𝗲 to a human.
• 𝗚𝗲𝘁𝘀 𝘁𝗿𝗶𝗰𝗸𝗲𝗱 by hidden commands.

4️⃣ 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗙𝗮𝗶𝗹𝘂𝗿𝗲𝘀
• 𝗖𝗿𝗮𝘀𝗵𝗲𝘀 when connected to real systems.
• 𝗪𝗿𝗼𝗻𝗴 𝘀𝗲𝘁𝘁𝗶𝗻𝗴𝘀 cause breakdowns.
• 𝗡𝗲𝘄 𝗔𝗜 doesn’t work with older systems.
• 𝗘𝘅𝗽𝗼𝘀𝗲𝗱 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗳𝗹𝗮𝘄𝘀 let hackers in.

These are common failures in AI systems, affecting how they interact, think, and function in real-world applications.

Would you like to add any?

Be careful of these issues by recognizing the limitations of AI.

✅ Repost for others in your network who can benefit from this.

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