HiveGPT

ChatGPT Vs. AI Agents (5 ways AI agents unseat ChatGPT) A new world where AI doesn't just respond, but takes action. Here are 5 ways AI Agents are reshaping our interaction with AI: 1: Autonomous decision-making: AI Agents operate independently, making decisions without constant human input. ↳ Example: While ChatGPT helps draft marketing emails, an AI Agent manages entire email campaigns - segmenting audiences, scheduling sends, and optimizing content based on real-time engagement data. 2: Outcome driven: ↳ AI Agents are designed to achieve specific objectives and optimize outcomes. Example: ChatGPT can analyze financial data, but an AI Agent can analyze data, make investment recommendations, and continuously rebalance portfolios to meet predefined goals. 3: Adaptive learning: AI Agents learn from experiences and adapt strategies based on feedback. ↳ Example: ChatGPT suggests social media post ideas, but an AI Agent generates posts, monitors engagement, learns from performance, and evolves its content strategy to maximize audience interaction over time. 4: Seamless integration: AI Agents integrate with multiple systems and APIs to perform complex tasks. ↳ Example: ChatGPT interprets financial reports, while an AI Agent pulls data from various sources, generates comprehensive reports, identifies trends, and triggers actions in other systems.. 5: Industry specialization: AI Agents can be tailored for specific industries with deep, specialized knowledge. ↳ Example: ChatGPT provides general advice on regulatory compliance, but a specialized AI Agent monitors all company communications, flags potential issues in real-time, suggests corrective actions, and updates its rule set as regulations change. ……………………………………………………………….. AI Agents all the way: + They analyze data and take action 24/7, constantly refining their strategies. + They integrate with and orchestrate multiple systems to perform complex workflows. + They ensure consistent yet personalized experiences across all business operations. The era of passive AI assistance is ending. The future is data-driven decision-making that goes beyond mere suggestions to actual implementation. #aiagents #B2BMarketing

2024 = year for single-agent (ChatGPT) to multi-agent AI systems. Here's is it's MASSIVE impact on marketing: While single agents are powerful, multi-agent collaboration offers MAX efficiency and innovation. …………………………………………………………………………………………   Here are the 5 game changers for marketing:   1) Single-agent simplicity vs. multi-agent complexity: Single agents like GPT-4 can handle tasks individually. But multi-agent systems, like GPT-4o + HiveGPT working together, offer 10X capabilities.   Example: One AI agent creates social media content. Another creates 100s of landing pages personalized to the individual, not just account or industry level.   2) Enhanced user experience: A single-agent interface simplifies user interaction, but multi-agent systems deliver a dynamic experience for each individual. Example: Different agents can do real-time data analysis, content creation, and audience targeting.   3) Modularity and scalability: Multi-agent systems are scalable and flexible. Example: New marketing tools or strategies can be recommended and integrated easily, like adding new roles in a beehive.   4) Cost efficiency: Multi-agent systems can be cost-effective by distributing tasks. Example: Using smaller models like GPT-4 for routine tasks and GPT-4o for critical decisions optimizes costs.   5) Accelerated problem-solving: Collaborative AI agents solve problems faster and more efficiently. Example: In event marketing, one agent manages attendee data, another personalizes communication, and a third optimizes event ops logistics, working like a coordinated swarm.   The future of AI in marketing lies in multi-agent systems. Like a beehive thrives through teamwork, marketing teams will achieve great success by leveraging multiple AI agent collaboration.   Ready to level up with AI agents? …………………………………………………………………….. HiveGPT is AI Agents for personalized b2b marketing, at scale.

Forget everything you know about app UI (The 5 Ways Ahead) A new world where app interfaces aren't fixed but dynamically personalized for each user. Thanks to AI, this will be a reality very soon. Watch the video (below) to learn more from Sundar Pichai, Google CEO. ................................................................................. Here are the 5 ways how AI will transform UI: 1. 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀: AI will tailor app interfaces to meet individual user needs, enhancing usability and satisfaction. Example: A finance app can highlight relevant features like budgeting tools for some users, while others might see investment tracking as the primary focus. 2. 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗮𝗱𝗷𝘂𝘀𝘁𝗺𝗲𝗻𝘁𝘀: UI elements will adjust in real-time based on user interactions and preferences, creating a seamless experience. Example: An e-commerce app can rearrange its layout to emphasize preferred product categories based on past behavior. 3. 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆: AI-driven UIs can adapt to accommodate users with different abilities, making technology more inclusive. Example: Apps can automatically adjust text size, color contrast, and navigation options for users with visual impairments. 4. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: AI can predict user needs and present the most relevant information or tools preemptively. Example: A project management app might prioritize upcoming deadlines and tasks based on user activity patterns. 5. 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁: AI can learn from user feedback and continuously optimize the UI for better performance and user satisfaction. Example: A customer service app can refine its interface to streamline common queries and support requests based on usage data. ………………………………………………………………… 𝗧𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗮𝗽𝗽 𝗨𝗜𝘀 𝗶𝘀 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗮𝗻𝗱 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱, 𝗱𝗿𝗶𝘃𝗲𝗻 𝗯𝘆 𝗔𝗜. Entrepreneurs and developers who embrace this shift will lead the way in creating extraordinary user experiences. #AI #Personalization #UI A repost from 3 months ago, this became even more true!!

ChatGPT came first. But you cannot miss what’s next. AI agents. Explained best by Andrew Ng (see video). The rundown? ChatGPT = single prompt, single output LLMs today are primarily used in a non-agentic workflow. Prompt provided. Answer generated. Then your job is to follow up. What’s next? Agentic Workflows (AI Agents). Agentic AI is iterative and can think and revise its output. More effective. Superior performance. Way more powerful. ………………………………………………. AI Agents at play: 1. 𝗠𝘂𝗹𝘁𝗶𝗢𝗻: 𝗦𝗶𝗻𝗴𝗹𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘁𝗼𝗼𝗹. Learn a task and complete it autonomously. Example: Schedule a meeting. 2. 𝗛𝗶𝘃𝗲𝗚𝗣𝗧: 𝗠𝘂𝗹𝘁𝗶 𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 Agents multi-task and collaborate to achieve goal. Example: Full mkt campaign in minutes not months. The flow: - Upload creative brief. - Many AI agents get to work. - Build entire mkt campaign- content, landing pages, events. AI Agents= individuals, teams, humanity, empowered! P.S. Are you looking forward to future of AI?

Hurricane #Milton made landfall, with 110 MPH winds Devastating, but luckily not a category 5, as it was feared to be. Meteorologists Are Using AI to Forecast Hurricane Milton and Other Storms. See the video below to understand the effects of these storms and the flooding caused as a result. We pray for everyone's safety. #AIforGood

"Chatbots, copilots, and AI agents, what's the difference?" (a quick guide to understanding AI technology today) I get asked this constantly. So here's the scoop: 1. Chatbots: The FAQ Masters • Excellent at data collection • Can assist in sales situations • Handle customer support and FAQs 2. Copilots: Your Creative Sidekicks • Think ChatGPT or Microsoft Copilot • Translate and tackle complex questions • Provide feedback and build frameworks • Generate content and offer suggestions • Create blogs based on provided research 3. AI Agents: Your Decision-Making Powerhouse • 10X of current ChatGPT • Perform tasks on your behalf • Prompt other agents as needed • Adapt to situations based on learning • Make decisions to achieve specific outcomes • Collaborate with other agents to complete work Key takeaway: All three have their place in various workflows. Your choice depends on what you're trying to achieve. P.S. What AI assistant do you use the most?

Marketing is not moving at the speed of the market (The formula = Bees) The future of marketing and AI is bees. Not literal bees, but AI agents working like a hive. Each agent specializes in a task, collaborating seamlessly to execute projects and campaigns. Imagine uploading a campaign brief and triggering a swarm of AI agents: - Research agents digging for insights - SEO expert agents optimizing for visibility - Paid media specialist agents maximizing ROI - Copywriter agents crafting compelling messages - Planner agents strategizing for maximum impact - Social media agents tailoring content for platforms All working in unison, delivering campaigns in days, not months. Nature knows best. The Hive is the framework. Embrace the power of AI agents, and watch your marketing actually keep up with the speed of the market. ----------------------------------------------------- Best place to start here --> HiveGPT

2025: Marketing agencies will lose 50% of current billable hours. Sam Altman's statement: "95% of what marketers use agencies, strategists, and creative professionals for today will easily, nearly instantly and at almost no cost be handled by AI." ……………………………………………… Already, most agencies can’t charge same billable hours for the same tasks. “Use ChatGPT, get it done faster and for less”, is what customers are demanding. ……………………………………………… Next steps: Team up with AI companies and products. 1. AI technology partner: Create mutually beneficial agreements with AI companies that serve your market. 2. Custom solutions: Co-develop custom solutions for your market, working closely with AI partner. 3. GTM next: Got to market with a combo of services (billable hours) + product licensing. ……………………………………………… Your biggest asset = trusted partner of your customers. Trust is key but so is efficiency and saving $$$. Wait and Watch OR Ride the Wave! Ready? HiveGPT is leveling up marketing agencies. AI agents for B2B marketing campaigns. Let’s team up!

Is AI synthetic data just glorified hallucination?   (The thin line between innovation and illusion)   A new world where AI creates its own training data for various industries is emerging.   But is this synthetic data a breakthrough or just high-tech make-believe?   What are we dealing with?   1. AI Hallucinations: When AI confidently generates false or nonsensical information. Example: A banking chatbot insisting that a savings account offers 50% annual interest.   2. Synthetic Data: Artificially generated data mimicking real-world information. Example: Creating millions of fake customer profiles to train marketing segmentation models.   The case for synthetic data as a reliable resource:   1. Unlimited scale: Synthetic data can be generated in massive quantities, filling gaps in real-world datasets. Example: Generating diverse credit histories to improve loan approval algorithms in banking.   2. Privacy protection: It allows for training on sensitive data without exposing real individuals. Example: Marketers using synthetic customer behavior data to develop personalization strategies without compromising real customer privacy.   The case against synthetic data as glorified hallucination:   1. Potential for amplified bias: Synthetic data might reproduce and exacerbate biases present in seed data. Example: A credit scoring model trained on synthetic data unfairly penalizing certain demographic groups.   2. Disconnect from reality: Synthetic data might miss crucial real-world nuances and edge cases. Example: Marketing AI trained on synthetic data failing to predict actual consumer behavior during unexpected events like economic downturns. The line between valuable synthetic data and misleading hallucination in banking and marketing is blurry.   So, the question remains: Is AI-generated synthetic data a revolutionary tool for advancing financial services and marketing strategies, or are we building campaigns and credit models on a foundation of digital mirages?   What's your take? Can we trust AI to create its own reality for training in these high-stakes industries? P.S. Check out Gartner's take below on the future of synthetic data.

Reduced loan underwriting time by 50%   (Check out the 4 AI Agents that made it happen) Loan approvals happening in half the time! With improved accuracy and a better customer experience. Not a distant future – it's happening right now. We've just helped a European bank customer achieve these results using a team of specialized AI agents. The impact? Dramatic improvements in efficiency, compliance, and decision-making. Here's how we did it: 1: Loan Origination Agent -- Powered by Azure AI services using GPT-4 and GPT OMNI -- Streamlined application process with instant, customized checklists for complex loans -- Result: Reduced errors and sped up approvals for hundreds of daily applicants 2: Loan Underwriting Agent -- Utilizes RAG Azure AI Services and OpenAI ADA embeddings -- Retrieves key data from lending history, real-time market data, and regulatory guidelines -- Helps loan officers deliver accurate risk assessments on high-value loans Ensures compliance and improves decision-making 3: Loan Audit and Compliance Agent -- Fine-tuned with T5 and LoRa -- Continuously reviews past decisions and flags anomalies -- Keeps the bank compliant with evolving regulations -- Minimizes computational costs 4: Loan Self-Reflection and Optimizing Agent (my fav) -- Leverages Codex and Autogen -- Learns from past underwriting decisions -- Makes the entire process smarter and more efficient over time .................................................................................. The bank is already seeing tangible improvements in processing times and accuracy while maintaining robust compliance. Will share more detailed results after the quarter closes. Which of these AI Agents could have the biggest impact on your underwriting process? #AIinBanking #FinTech #AIAgents