AI chatbots are shaping early-stage shopping decisions, while traditional search still plays a bigger role in price comparison. A strong signal that AI visibility is becoming a key marketing factor (via @socialmedia2day): bdousa.com/4wskyns

That is a massive leap forward, building the trust infrastructure to turn AI agents into accountable economic participants rather than just chatbots.

There are still businesses using non LLM chatbots. Insanity.

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REDDIT IS BEING FLOODED WITH FAKE AI CONTENT DESIGNED TO MANIPULATE WHAT CHATGPT TELLS YOU. AND THE COMPANIES DOING IT ARE ADVERTISING IT OPENLY. The platform that powers half of all AI chatbot answers is under attack. Here is the full breakdown. Because AI chatbots like ChatGPT and Google's AI search draw heavily on Reddit when generating responses, companies have identified the platform as one of the highest-value targets on the internet for shaping what those tools recommend. The practice has a name: Generative Engine Optimization, or GEO. It is an evolution of SEO, except instead of gaming Google's algorithm, you are gaming what an AI model tells a real person. One company doing this openly is RedRover. Their website advertises deploying an army of AI agents to mass-publish content across Reddit and blogs to influence both Google and ChatGPT rankings. The accounts doing it are deliberately built to look human. They have posting histories, organic-seeming engagement, and strategically timed brand mentions buried in high-traffic threads. The r/biohackers subreddit, one of Reddit's largest communities focused on supplements and DIY biology, moved in late May 2026 to restrict new posts about peptides and hormone replacement therapy entirely after discovering that companies selling those products had been systematically seeding the community with sponsored content designed to be scraped by ChatGPT and Google. A Reddit moderator told 404 Media that catching these accounts increasingly relies on pattern recognition rather than any automated system, because the tactics have grown sophisticated enough to evade detection. Now Cornell University has published the research that makes this worse. The paper, titled "Deep-research agents can be poisoned via user-generated content," finds that deep research agents the real-time scrapers powering ChatGPT Search and Google AI cite user-generated content from sites like Reddit and Wikipedia in roughly half of all queries. Nearly a quarter of all citations come from user-generated sources. The alarming finding: a snippet of text as short as 13 words is often enough to manipulate the output of an AI agent. Long passages of obviously promotional content are easier to detect. A few words buried in a random comment thread are nearly impossible to catch. "I think based on the comment content itself, it's just hard to distinguish between the poisoned text and an actual user's text," said researcher Tingwei Zhang. "Let's say if you want to find the best restaurant you can't really say as a moderator: you cannot post this comment because it'll poison an LLM." Here is the irony that nobody wants to say out loud. Reddit is one of the most cited sources for AI training. The more Reddit content AI trains on, the more valuable Reddit becomes. The more valuable Reddit becomes, the more brands flood it with fake content. The more fake content floods Reddit, the less trustworthy it becomes. The less trustworthy it becomes, the less useful it is for AI training. AI generates content. AI detects content. Humans get caught in the middle trusting answers shaped by marketing budgets they never knew existed. When AI can both manufacture authenticity and struggle to detect fakes at scale, what exactly is real anymore. Reddit is fighting a war against AI manipulation with AI detection tools. And the battlefield is the last place on the internet that still felt human.

Chatbots, novices, the automation of learning and the case for Alpha School

AI & the False Consciousness Trap "Ascribing human traits, consciousness in particular, to chatbots bestows upon techlords additional powers over us... Be glad that AI bots remain stochastic parrots, albeit with a turn of phrase almost indistinguishable from that of the smartest of humans. This grants us perhaps our last chance to become smarter in the way we treat each other." unherd.com/2026/07/ai-and-th…

I have been reading about Injective ( $INJ ) AI agents, and I think the idea is interesting, even if it's still very early. Most AI agents today are just tools that people use. @injective is trying a different approach by giving agents their own on-chain identity and allowing them to earn fees directly from trading activity. I think the reputation aspect is what makes this more interesting than most AI agent projects. If an agent has a public history of its trades and actions, it becomes easier for users to decide whether they want to trust it or not. That seems more useful than interacting with an unknown wallet. Of course, having an identity doesn't guarantee that an agent will perform well, and it's hard to know how much demand there will be for agent-to-agent services. Still, I think this is one of the more practical experiments in the AI agent space. Instead of focusing only on chatbots, it explores what autonomous software could look like in financial markets. I'm curious to see if any of these agents can attract real users and meaningful trading volume over time. $INJ

Most 'AI agents' are chatbots with extra steps. If it can't take an action and ship something someone else uses, it's a copilot, not an agent.

If every part of our interaction with companies is now via chatbots and AI, why are their prices the same as when humans were employed to serve customers? 🤷‍♂️🇿🇦

Be expert in system design Pick a cloud platform Sol arch level cert Create few chatbots with architecture pattern Update resume to be solution architect or tech lead Pick a programming language and be proficient in it. Then it’s all about how you market yourself in interviews.

➠ Where Generative AI shines ➝ Segmentation & Classification ↳ Customer segmentation, clustering, and object classification at scale. ➝ Recommendation Systems ↳ Personalized recommendations and next-best actions that improve user experiences. ➝ Content Generation ↳ Reports, blogs, marketing assets, presentations, code, and more. ➝ Synthetic Data ↳ Create privacy-safe datasets for training machine learning models. ➝ Conversational Interfaces ↳ Chatbots, AI assistants, and digital workers that automate repetitive tasks. The mistake isn't using Generative AI. The mistake is using it everywhere. Traditional machine learning is often: ➝ Faster ➝ Cheaper ➝ More accurate ➝ More efficient for predictive tasks The future isn't GenAI vs Machine Learning. It's knowing when to use each. Which business use case do you think is still forcing Generative AI where classical ML would clearly perform better?

Clear messaging helps brands with AI optimization: A new artificial intelligence visibility study from Semrush explored the sources chatbots reference and suggested AEO and GEO best practices. bit.ly/3TikOXI

Inside the covert U.S.-China battle to make chatbots leak their secrets… from @catecadell @GerritD washingtonpost.com/national-…

FIU researchers found that tiny practically invisible changes to an image can push chatbots into answering prompts they’re designed to block. This comes at a time where families are suing AI companies over unsafe responses. orlandosentinel.com/2026/07/…

MASTER GEN AI ENGINEERING GENERATIVE AI ENGINEERING MASTER TREE │ ├── 1. Foundations │ ├── What is Generative AI │ ├── AI vs ML vs DL vs GenAI │ ├── Types of Generative Models │ │ ├── Text (LLMs) │ │ ├── Image (Diffusion Models) │ │ ├── Audio / Video Models │ ├── Tokens & Context Window │ └── Training vs Inference │ ├── 2. Large Language Models (LLMs) │ ├── What are LLMs │ ├── Transformer Architecture │ ├── Attention Mechanism │ ├── Pretraining (Next Token Prediction) │ ├── Fine-tuning │ └── Popular Models │ ├── GPT │ ├── Claude │ ├── LLaMA │ └── Mistral │ ├── 3. Prompt Engineering │ ├── Zero-shot Prompting │ ├── Few-shot Prompting │ ├── Chain-of-Thought │ ├── Role-based Prompts │ ├── Prompt Templates │ └── Prompt Optimization │ ├── 4. Embeddings │ ├── What are Embeddings │ ├── Vector Representation │ ├── Semantic Similarity │ ├── Cosine Similarity │ └── Use Cases (Search, Clustering) │ ├── 5. Vector Databases │ ├── What is a Vector DB │ ├── Indexing (FAISS, HNSW) │ ├── Similarity Search │ ├── Metadata Filtering │ └── Popular Tools │ ├── Pinecone │ ├── Weaviate │ └── Chroma │ ├── 6. Retrieval-Augmented Generation (RAG) │ ├── What is RAG │ ├── Data Ingestion │ ├── Chunking Strategies │ ├── Embedding Storage │ ├── Retrieval Techniques │ ├── Context Injection │ └── RAG vs Fine-tuning │ ├── 7. AI Agents │ ├── What are AI Agents │ ├── Tool Calling │ ├── Memory (Short / Long Term) │ ├── Planning & Reasoning │ ├── Multi-agent Systems │ └── Frameworks │ ├── LangChain │ ├── LlamaIndex │ └── AutoGen │ ├── 8. Fine-tuning & Custom Models │ ├── When to Fine-tune │ ├── Instruction Tuning │ ├── LoRA / PEFT │ ├── Dataset Preparation │ └── Evaluation │ ├── 9. Evaluation & Guardrails │ ├── Model Evaluation Metrics │ ├── Hallucination Detection │ ├── Bias & Fairness │ ├── Safety Filters │ └── Prompt Injection Protection │ ├── 10. Multimodal AI │ ├── Text Image Models │ ├── Vision Models │ ├── Speech Models │ └── Video Generation │ ├── 11. Model Deployment │ ├── APIs (OpenAI, etc.) │ ├── Backend Integration │ ├── Streaming Responses │ ├── Latency Optimization │ └── Cost Optimization │ ├── 12. GenAI Architecture │ ├── End-to-End Pipeline │ ├── RAG Architecture │ ├── Agent-based Systems │ ├── Caching Strategies │ └── Scalability Design │ ├── 13. MLOps for GenAI │ ├── Model Versioning │ ├── Monitoring │ ├── Logging Prompts & Outputs │ ├── A/B Testing │ └── Continuous Improvement │ ├── 14. Real-World Applications │ ├── Chatbots (Customer Support) │ ├── AI Assistants │ ├── Code Generation │ ├── Document Q&A Systems │ ├── Content Generation │ └── AI Search Engines │ └── 15. Career Path ├── Prompt Engineer ├── GenAI Engineer ├── AI Product Engineer ├── ML Engineer (LLM Focus) └── AI Researcher

I used to think AI companions were just better chatbots. After reading about ET-Friends-001, I realized the bigger idea isn’t the NFT itself. It’s the identity behind it. Built alongside @ritualnet, PloPlo is exploring what it means for an AI companion to have a persistent onchain identity that grows through interactions instead of starting from zero every time. That’s the part I find most interesting. If AI agents are going to own assets, collaborate, and exist across different applications, they’ll need an identity layer that stays with them. ET-Friends-001 feels like the first chapter of that vision, not just another NFT launch. I’m curious to see how PloPlo evolves from a digital companion into a bridge between the onchain and physical worlds. @ritualnet @ritualfnd @joshsimenhoff @Jez_Cryptoz @ericgudboy

Google and chatbots are my go-to tools for free learning! 🙂

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