Sixty editions in, and we’re just getting started. Whether you’ve been with us since the beginning or just joined, we’re glad to have you here as we explore the latest in AI—without the fluff.

This week, we’re diving deep into vCons + Generative AI, a powerful combo that’s redefining conversation intelligence. We’re also tackling a pressing ethical issue: AI accent neutralization and its unintended consequences. And of course, we’ve got the latest tools, trends, and insights to keep you ahead of the curve.

Let’s get into it.

The Latest in AI

A Look into the Heart of AI

Featured Innovation
vCons + Generative AI: The Future of Conversation Intelligence

Virtualized Conversations (vCons) provide a powerful standard for storing and managing conversations, ensuring compliance, trust, and interoperability across systems. But on their own, vCons are just structured data.

The real power comes when you combine vCons with Generative AI.

vCons act as the memory layer - capturing and structuring conversations, transcripts, speaker data, metadata, and compliance details. Generative AI is the intelligence layer - analyzing those conversations, extracting insights, and automating workflows at scale.

Together, vCons and Generative AI allow businesses to:
✔ Store conversations in a portable, structured format that ensures compliance and data security
✔ Analyze those conversations using AI to extract real business value
✔ Process millions of conversations efficiently, unlocking insights that were previously hidden
✔ Extend existing platforms without expensive system overhauls

This combination is already being used at scale in contact centers, automotive business development centers (BDCs), and AI-driven customer engagement platforms. Companies like STROLID and Five9 are proving that when vCons and Generative AI work together, businesses gain unmatched visibility into customer interactions.

Beyond Transcription: What AI Can Do with vCons

For years, AI-powered conversation analysis has focused on three core areas: transcription, sentiment analysis, and summarization. While useful, these are just the beginning.

By applying Generative AI to vCons, businesses can unlock deeper, more advanced insights, including:

• Speaker analytics – Identifying who spoke when, how long they spoke, and detecting interruptions or silences

• Emotion detection – Going beyond sentiment to analyze tone, stress levels, and emotional cues in voice conversations

• Topic modeling – Automatically grouping conversations by subject matter to identify trends across customer interactions

• Real-time compliance monitoring – Detecting and flagging conversations that contain regulatory violations or required disclosures

• Conversational intent tracking – Mapping the full customer journey across multiple touchpoints and predicting future behavior

• Fraud detection and risk scoring – Analyzing conversation patterns to detect fraud attempts or identify high-risk interactions

• Automated coaching and training insights – Identifying where customer service agents struggle and providing real-time feedback

With vCons handling the storage and structure of conversation data, Generative AI can focus on making sense of it at scale. This allows businesses to turn conversations into actionable intelligence rather than just raw data.

Memory Alone Isn’t Enough - Trust Matters Too

While AI memory is critical for learning from past interactions, it’s only useful if the memory is trustworthy.

Jeff Pulver recently wrote about this in his article, AI That Remembers but Can’t Be Trusted: Why AI Trust Will Define the Next Era. He highlights a growing concern - what happens when AI memory itself is unreliable or manipulated?

• If an AI financial advisor is trained on altered transaction data, its decisions could be dangerous.

• If an AI healthcare system has access to incorrect patient records, the consequences could be life-threatening.

• If an AI-powered legal system misremembers past rulings, justice could be compromised.

This is where SCITT (Supply Chain Integrity, Transparency, and Trust) comes into play.

SCITT ensures that vCons are tamper-proof, verifiable, and cryptographically secured. This guarantees that AI-generated insights are based on authentic, unaltered conversation records.

With vCons for structured memory, Generative AI for deep analysis, and SCITT for trust, businesses can confidently leverage AI-driven conversation intelligence.

Industries That Will Benefit First

Some industries won’t just benefit from this approach - they will require it to remain compliant and competitive.

💰 Finance – AI-driven fraud detection, lending, and trading must be built on verified transaction records.

🏥 Healthcare – AI-assisted diagnoses must rely on trusted patient data.

⚖️ Legal – AI-generated case research and contract automation must be protected from data corruption.

🏛️ Government & Policy – AI-driven public decision-making must be transparent and auditable.

For these industries, vCons provide the memory, Generative AI extracts the insights, and SCITT ensures the memory can be trusted.

AI is Shifting from Memory to Accountability

We’re entering a new phase where AI needs more than just intelligence – it needs accountability.

The next big question in AI won’t be: “Does it remember?” It will be: “Can we trust what it remembers?”

Companies that only focus on storing data will fall behind those that prioritize trust, insights, and real-world applications.

Where Do You Stand?

• Are you thinking about AI trust and accountability, or just memory?

• Will your industry require trusted AI frameworks sooner than you expect?

• How are you integrating vCons, Generative AI, and SCITT into your AI strategy?

The future of AI isn’t just about processing conversations. It’s about ensuring that what AI learns and remembers can be trusted.

Ethical Considerations & Real-World Impact 
The Silent Erasure: How AI Accent Neutralization Reinforces Linguistic Bias

Here’s one for the books. Companies are rolling out AI-powered accent modification to “improve” customer service, but what they’re really doing is enforcing the idea that some accents aren’t good enough. Workers in call centers, many already under heavy pressure, are now being told their natural voices need fixing. This appears to be about far more than just about making conversations smoother and could be a direct hit to confidence, job satisfaction, and identity.

And here’s where it gets even messier, scammers are going to love this. Fraud call centers are already a massive problem, and one of the few ways people spot them is by recognizing certain accents. AI-driven voice modification wipes out that warning sign, making scams even harder to detect. A tool meant to build trust in customer interactions could just as easily help fraudsters sound more legitimate, tricking even the most cautious targets.

The bigger issue is how this shifts responsibility. Instead of encouraging people to understand different accents, companies are forcing workers to conform. Meanwhile, scammers get a free upgrade, removing one of the few red flags that protect people from fraud. AI should be used to make communication better without erasing identity or making deception easier.

Tool of the Week: AnythingLLM

AnythingLLM – A Powerful, Open-Source AI Assistant

🔧 Tool of the Week: AnythingLLM – A Private, All-in-One AI Assistant

If you’ve been looking for an AI tool that can handle chatbots, document analysis, web searches, and automation – all while keeping your data private – AnythingLLM is worth checking out. It’s an open-source, self-hosted AI assistant developed by Mintplex Labs, and it’s quickly gained traction in the AI community, racking up over 25,000 stars on GitHub.

Unlike cloud-based AI services that store your data on external servers, AnythingLLM runs locally, meaning your information stays where it belongs – on your machine. Whether you’re a developer, researcher, educator, or business user, it’s a solid option for setting up a ChatGPT-style assistant without privacy trade-offs.

What Can AnythingLLM Do?

At its core, AnythingLLM is designed to be versatile and modular. Here’s a quick rundown of its key features:

🔀 Multiple AI Models – Use different large language models (LLMs) for different tasks.
📑 Chat With Documents – Upload PDFs, Word files, or text docs and ask questions about them.
🤖 AI Agents – Automate tasks like web searches, document summaries, and data visualization.
🔐 Privacy-Focused – Runs locally with built-in storage, so your data stays secure.
⚡ Cross-Platform – Works on Mac, Windows, Linux, and even Docker for cloud deployments.

It’s like having your own custom AI assistant, but without the concerns of third-party data collection.

How It Works

AnythingLLM is structured around three types of AI models, allowing users to fine-tune their setup:

✅ System LLM – The default AI model for general interactions.
✅ Workspace LLM – Assign specific models to different projects.
✅ Agent LLM – Dedicated models for AI-powered automation.

This flexibility makes it easy to switch between local and cloud-based models, depending on your needs. Want to run a fully offline AI chatbot? Done. Need a hybrid setup that taps into OpenAI for certain tasks? No problem.

AI Agents: Automating Tedious Tasks

One of the most useful aspects of AnythingLLM is its built-in AI agents, which help automate a range of everyday tasks:

📝 Document Analysis – “What are the key points of this contract?”
📊 Data Visualization – “Can you graph y=mx+b where m=10 and b=0?”
🔍 Web Search – “Find the latest trends in AI ethics.”
📂 File Management – “List all documents in this workspace.”

If you’ve ever wished ChatGPT could interact with your own files and workflows, this is exactly what these agents do.

Who Should Use AnythingLLM?

This tool is great for:

📚 Researchers & Educators – Summarize studies, analyze academic papers, and organize research.
🏢 Businesses & Teams – Build internal AI chatbots for knowledge management.
🔧 Developers & Engineers – Integrate AI into workflows without sending data to external APIs.
📰 Content Creators – Generate articles, edit documents, and automate research.

It’s especially useful for anyone who wants AI-powered assistance but doesn’t want to rely on third-party services.

Why We Like It

• It’s self-hosted, so you control your data.

• Works out of the box but is customizable for power users.

• Supports multiple AI models, both local and cloud-based.

• It’s free and open-source.

If you’re interested in trying it out, you can find it here:
🔗 GitHub Repo

Have you used AnythingLLM, or are you considering it? Let us know [email protected] / [email protected] 

Rico's Roundup

Critical Insights and Curated Content from Rico

Skeptics Corner
AI Cops and Robo-Layoffs: The Rico Prophecy Comes True

Well folks, here we go. It wasn't too long ago, perhaps a year or two, that we talked about AI policing video games, speech, and online communities. And here we are now - AI isn’t just moderating toxic lobbies in Call of Duty; it’s being used as a tool to police federal workers and even decide who stays employed in government jobs. And no, I am not joking.

First, let’s talk about the AI-powered government overhaul happening under the Department of Government Efficiency (DOGE) - yes, that’s also real and not to be confused with the DOGE cryptocurrency. Led by none other than Elon Musk, the push to integrate AI into government processes is a bold move aimed at streamlining operations, cutting costs, and delivering better services. Sounds great on paper, right? But like any big tech shift, it’s a double-edged sword, and the consequences of these decisions will be ripple effects that may not be realized for months, but could be catastrophic for American’s in every corner of the U.S..

Efficiency vs. Employment

On one hand, we know that AI can crunch data at lightning speed, automate mind-numbing paperwork, and even answer citizen inquiries through chatbots, freeing up human workers for more complex tasks. That’s the good part. The not-so-good part? Mass layoffs. We’re already seeing thousands of government workers being replaced by software that never needs a lunch break. If there’s no solid plan for retraining and transitioning these displaced employees, we’re looking at a serious workforce crisis and major drain on the unemployment system, as they turn up the rhetoric around what Musk thinks about federal workers.

Bias, Transparency & The "Black Box" Problem

AI in government also raises massive ethical concerns. Algorithms trained on biased data can unintentionally reinforce discrimination in hiring, law enforcement, and social services which we have seen time and time again the past two years with developing LLM’s and generative AI models, etc.. And then there’s the "black box" issue, which is that many AI-driven decisions are difficult to trace or challenge. Imagine being denied a permit, job, or benefit with no clear explanation because “the AI said so.” That’s not just frustrating, it’s completely plausible in this scenario and dangerous for accountability.

Privacy & Security Nightmares

The government is now collecting and analyzing vast amounts of personal data using AI. That means one major hack or data leak could expose millions. And let’s not forget tools like AutoRIF (Reduction In Force), an AI system used to automate employee terminations (yep, that exists). AI handling sensitive information needs to be locked down with airtight security, or the fallout could be catastrophic.

Winning (or Losing) Public Trust

AI in government can only succeed if people trust it. That means transparency about how it works, what data it uses, and safeguards against abuse. Without that, skepticism will kill public buy-in before AI even gets a chance to prove its value, which could be masked by the marketing campaign currently underway of convincing the public at large that “all” federal workers are a part of fraud, waste and abuse. Let’s face it: Incentivizing the population with a potential $5,000 check from 'DOGE Savings,' while ramping up emotional rhetoric against all federal workers, excites the masses so much that nobody wants to ask the tough questions or care about the details (or fallout in the months to come).

Final Take

Bringing AI into government is inevitable, and it has the potential to revolutionize public services. But if it’s rolled out carelessly (or recklessly), we’ll be dealing with more problems than solutions. Efficiency is great, but not at the cost of fairness, accountability, and security.

And here’s the real question: Would you want your job, your career, your livelihood left to an AI’s decision-making? Can it truly determine your relevance to your company or agency? If not, why should we trust it to make those calls for thousands of government workers?

We would love to hear your take on this issue, so if you are interested, hit us up on X.com or LinkedIn.

Mike's Musings

AI Insights
Agents Without the Hype

The world is obsessed with AI agents right now. Everywhere you look: YouTube, blog posts, Twitter - there’s endless hype about autonomous AI systems.

But here’s the thing: some of the biggest tech companies, like Apple and Amazon, are struggling to ship effective AI-powered features.

• Apple had to pull back Apple Intelligence because it was hallucinating while summarizing content.

• Amazon still hasn’t integrated AI smoothly into Alexa due to reliability issues.

So, if AI agents are supposedly revolutionary, why can’t the biggest tech giants make them work?

The Hard Truth About AI Agents

Most of the AI agent demos you see online are just that… demos.

They look cool, they hint at the future, but when you actually try to deploy them at scale, they break down.

Why? Because building reliable AI agents is incredibly hard.

In this guide, I’ll cut through the noise and show you how to build effective AI systems - ones that actually work in production - based on two years of hands-on experience building AI solutions for clients.

What Even Is an AI Agent?

Before we talk about how to build them, we need to agree on what they actually are.

And that’s tricky, because there’s no single definition.

AI Workflows vs. AI Agents

According to Anthropic, one of the leading AI companies, there’s a crucial distinction:

• Workflows → LLMs are used in predefined sequences, following a structured flow.

• Agents → LLMs make dynamic decisions, choosing their own process and tool usage.

Most of what people call “AI agents” today?
They’re just workflows with an LLM call baked in. And that’s fine - because in most real-world cases, workflows are more reliable.

When (and When Not) to Use AI Agents

Most applications do not need true AI agents.

Anthropic sums it up perfectly:

Translation?
A well-designed LLM workflow can outperform complex AI agents 99% of the time.

Before you build, ask yourself:

✅ Can I solve this problem with a simple LLM-enhanced workflow?
✅ Do I really need autonomous decision-making?
✅ Will adding complexity make it more reliable or just harder to control?

If you hesitate on any of these, stick with workflows.

How to Build AI Systems That Actually Work

If you want practical, reliable AI, focus on solid engineering principles.

Choose the Right Tools

• If you can code → Use Python, TypeScript, or JavaScript.

• If you can’t code → Use Make.com, n8n.io (our favorite), or Flowise.

It’s not about the tools - it’s about how you structure your AI system.

Core AI System Patterns

No matter what platform you use, these are the six essential AI system designs.

1️⃣ Augmented LLMs

Every AI system starts as just an API call to an LLM. But you can enhance it using three techniques:

✅ Retrieval – Pull external knowledge from databases (RAG, vector databases).
✅ Tools – Call APIs (e.g., get weather, fetch tracking updates).
✅ Memory – Store past interactions for context.

These three enhancements make LLM applications significantly more useful.

2️⃣ Prompt Chaining

Instead of dumping everything into one LLM call, break it into multiple logical steps.

Example: Writing a blog post →

1️⃣ Research ideas
2️⃣ Generate an outline
3️⃣ Write section-by-section

This keeps each step focused and reduces hallucinations.

Here’s an example of one of the workflows Rico and I wrote that has multiple LLM interactions and steps:

When it comes to interacting with LLMs, small, atomic steps wins the day!

3️⃣ Routing

If your AI needs to handle multiple user requests, add routing.

Example: Customer support chatbot →

• “Where’s my order?” → Route to an order lookup workflow.

• “How do I return an item?” → Route to the returns workflow.

This keeps your AI system scalable and modular.

4️⃣ Parallelization

Instead of waiting for sequential API calls, run them in parallel.

Example: Evaluating AI-generated text →

✅ Accuracy check
✅ Safety check
✅ Prompt injection check

Running all three simultaneously speeds up processing.

5️⃣ Orchestrator-Worker Pattern

This is a more agent-like approach:

1️⃣ An orchestrator LLM decides what steps to take.
2️⃣ Worker modules execute the steps.

Example: A customer support AI might:

• Retrieve order data

• Check the knowledge base

• Fetch shipping updates

It’s more flexible than a hardcoded workflow, but still structured and predictable.

6️⃣ Evaluator-Optimizer

Loop AI responses through an evaluation step to improve quality.

Example: AI writing a blog post →

1️⃣ AI writes the draft
2️⃣ A second AI reviews for quality
3️⃣ AI improves based on feedback

This reduces hallucinations and improves accuracy over time.

What Real AI Agents Look Like

A true AI agent follows a looped decision-making process:

1️⃣ LLM chooses an action
2️⃣ It executes the action
3️⃣ It evaluates the result
4️⃣ If needed, it loops back and tries again

This makes agents powerful but also unpredictable.

Example: Devin, the AI software engineer

• Autonomously writes and debugs code.

• Works in loops, iterating on its own.

• Success rate? ~20% → Not ready for real-world production.

This is why true AI agents struggle at scale.

Final Tips for Developers

✅ 1. Be Wary of Agent Frameworks

They add unnecessary complexity. Learn to build from scratch.

✅ 2. Prioritize Deterministic Workflows

Start small and reliable, expand later.

✅ 3. Expect Chaos When Scaling

Demos aren’t reality - real users break things.

✅ 4. Implement AI Testing from Day One

If you tweak a system prompt, do you know for sure it improved things? If not, you need evaluation metrics.

✅ 5. Add Guardrails

Before sending AI output to users, have another AI check it.

Even Amazon’s chatbot failed this - it claimed to be human, then proceeded to write Python code on demand.

The Bottom Line

AI agents sound exciting, but workflows deliver results.

If you want to build reliable AI systems:
1️⃣ Start simple
2️⃣ Use deterministic workflows
3️⃣ Only add agentic behavior if truly necessary

Let’s cut through the hype and focus on what actually works.

What is your approach to AI strategy? Let’s connect and compare notes [email protected].

Mike's Favorites

Sesame AI | Bringing the Computer to Life

I found this tonight and wanted to test it out, the conversational style of this agent is really nice. It was so lifelike it kind of threw me off (uncanny valley style)

Revolutionize Your Dealership with Cutting-Edge AI Solutions by Shannon Neilson

Shannon Neilson nails it with this piece on AI in automotive. 🚗💡 Her breakdown of Divideo.ai and EvoAuto.ai shows how dealerships can turn customer reviews into engaging videos and automate key tasks… giving them a real edge.

What I love most? Shannon’s human-first approach to AI. It’s not about replacing people; it’s about enhancing what great teams already do. A must-read for anyone looking to stay ahead!

What kind of wins and learnings are you having with AI this week? Let me know: [email protected].

Must-Read Articles

Latest Podcast Episode of Artificial Antics

• Listen on:

  • Apple

  • Spotify

  • All other major podcast networks

Connect & Share

• Have a unique AI story or innovation? Share with us on X.com or LinkedIn.

• Collaborate with us: Mike [email protected] or Rico [email protected].

Stay Updated

• Subscribe on YouTube for more AI Bytes.

• Follow on LinkedIn for insights.

• Catch every podcast episode on streaming platforms.

• Utilize the same tools the guys use on the podcast with ElevenLabs & HeyGen

• Have a friend, co-worker, or AI enthusiast you think would benefit from reading our newsletter? Refer a friend through our new referral link below!

Thank You!

Thanks to our listeners and followers! Continue to explore AI with us. More at Artificial Antics (antics.tv).

Quote of the week: "AI is everywhere, it seems omnipotent, but people are still taking time to get used to it. Like other technologies, AI is a double-edged sword." - Li Qiang