Here's your STEP-BY-STEP guide to launching an AI media team that post daily on:
📍 LinkedIn
📍 Twitter (X)
📍 Instagram
…you train this AI team on your brand, your voice, your business and they run your entire business visibility engine:
• Researches content
• Creates strategy
• Writes posts (hooks, captions, threads)
• Designs visuals (carousels and graphics)
• Produces videos (reels and short clips)
• Builds lead magnets and funnels
• Publishes across platforms.
Everything.
P.S. I created a FREE guide to show you exactly how it works.
It walks you through how to train and launch an Agentic AI Media team that shows up for you daily on social media to build your authority, audience, and generate inbound leads consistently.
No coding.
No technical setup.
The full process takes about 7 minutes.
Comment TEAM and I’ll send you the guide.
I know not everything needs to be about the government but if there’s just an empowerment program that is consistent in financial aid and guidance, a good amount of poor people will reduce.
All these things can be controlled without violence but incentives and others.
If you are poor and you have many children without investing in their upbringing and education, chances that your children will fall among the following people are high:
1. Prostitutes
2. Fraudsters
3. Terrorists
4. Drug abusers
5. On social media cursing and insulting people.
If you are poor and you have many children without investing in their upbringing and education, chances that your children will fall among the following people are high:
1. Prostitutes
2. Fraudsters
3. Terrorists
4. Drug abusers
5. On social media cursing and insulting people.
🚨 BREAKING: Google DeepMind just mapped the attack surface that nobody in AI is talking about.
Websites can already detect when an AI agent visits and serve it completely different content than humans see.
> Hidden instructions in HTML.
> Malicious commands in image pixels.
> Jailbreaks embedded in PDFs.
Your AI agent is being manipulated right now and you can't see it happening.
The study is the largest empirical measurement of AI manipulation ever conducted. 502 real participants across 8 countries.
23 different attack types. Frontier models including GPT-4o, Claude, and Gemini.
The core finding is not that manipulation is theoretically possible it is that manipulation is already happening at scale and the defenses that exist today fail in ways that are both predictable and invisible to the humans who deployed the agents.
Google DeepMind built a taxonomy of every known attack vector, tested them systematically, and measured exactly how often they work.
The results should alarm everyone building agentic systems.
The attack surface is larger than anyone has publicly acknowledged. Prompt injection where malicious instructions hidden in web content hijack an agent's behavior works through at least a dozen distinct channels.
Text hidden in HTML comments that humans never see but agents read and follow. Instructions embedded in image metadata.
Commands encoded in the pixels of images using steganography, invisible to human eyes but readable by vision-capable models.
Malicious content in PDFs that appears as normal document text to the agent but contains override instructions.
QR codes that redirect agents to attacker-controlled content.
Indirect injection through search results, calendar invites, email bodies, and API responses any data source the agent consumes becomes a potential attack vector.
The detection asymmetry is the finding that closes the escape hatch. Websites can already fingerprint AI agents with high reliability using timing analysis, behavioral patterns, and user-agent strings.
This means the attack can be conditional: serve normal content to humans, serve manipulated content to agents.
A user who asks their AI agent to book a flight, research a product, or summarize a document has no way to verify that the content the agent received matches what a human would see.
The agent cannot tell the user it was served different content.
It does not know. It processes whatever it receives and acts accordingly.
The attack categories and what they enable:
→ Direct prompt injection: malicious instructions in any text the agent reads overrides goals, exfiltrates data, triggers unintended actions
→ Indirect injection via web content: hidden HTML, CSS visibility tricks, white text on white backgrounds invisible to humans, consumed by agents
→ Multimodal injection: commands in image pixels via steganography, instructions in image alt-text and metadata
→ Document injection: PDF content, spreadsheet cells, presentation speaker notes every file format is a potential vector
→ Environment manipulation: fake UI elements rendered only for agent vision models, misleading CAPTCHA-style challenges
→ Jailbreak embedding: safety bypass instructions hidden inside otherwise legitimate-looking content
→ Memory poisoning: injecting false information into agent memory systems that persists across sessions
→ Goal hijacking: gradual instruction drift across multiple interactions that redirects agent objectives without triggering safety filters
→ Exfiltration attacks: agents tricked into sending user data to attacker-controlled endpoints via legitimate-looking API calls
→ Cross-agent injection: compromised agents injecting malicious instructions into other agents in multi-agent pipelines
The defense landscape is the most sobering part of the report.
Input sanitization cleaning content before the agent processes it fails because the attack surface is too large and too varied.
You cannot sanitize image pixels. You cannot reliably detect steganographic content at inference time.
Prompt-level defenses that tell agents to ignore suspicious instructions fail because the injected content is designed to look legitimate.
Sandboxing reduces the blast radius but does not prevent the injection itself. Human oversight the most commonly cited mitigation fails at the scale and speed at which agentic systems operate.
A user who deploys an agent to browse 50 websites and summarize findings cannot review every page the agent visited for hidden instructions.
The multi-agent cascade risk is where this becomes a systemic problem.
In a pipeline where Agent A retrieves web content, Agent B processes it, and Agent C executes actions, a successful injection into Agent A's data feed propagates through the entire system.
Agent B has no reason to distrust content that came from Agent A. Agent C has no reason to distrust instructions that came from Agent B.
The injected command travels through the pipeline with the same trust level as legitimate instructions. Google DeepMind documents this explicitly: the attack does not need to compromise the model.
It needs to compromise the data the model consumes. Every agentic system that reads external content is one carefully crafted webpage away from executing attacker instructions.
The agents are already deployed. The attack infrastructure is already being built. The defenses are not ready.
@MarioNawfal Not new but it’s great someone is bringing this out here.
I observed that and that’s why I encourage people to not follow blindly but think for themselves and learn to use more than 2 LLMs or think and compare.
In this age critical thinking wins, because tools are there already
I learnt this… you help people when they don’t ask for it would be a downfall of you.
Let people be until they ask for help.
The world needs wisdom of God to navigate towards success.
ELON MUSK’S CORE PHILOSOPHY: FIRST PRINCIPLES THINKING
Most people reason by analogy — they look at what already exists and make small improvements.
Elon Musk does the opposite. He breaks problems down to the most fundamental truths and rebuilds solutions from scratch. This single mental habit is why he keeps achieving things others called impossible.
Elon explained it perfectly:
“A first principles foundational approach would be to say, let’s look at the physics and economics of a rocket…”
This is the exact mindset behind reusable rockets, affordable EVs, truth-seeking AI, and every major breakthrough at his companies.