DEV Community: Cameron Palmer

Should you even use an LLM?

Cameron Palmer — Wed, 22 Jul 2026 00:00:00 +0000

The Agent That Did Too Much

I had a problem: as an AI implementation consultant, I had no automated pipeline to discover and qualify leads and insert them into my self-hosted Twenty CRM instance. I thought I could solve this whole problem using LLMs.

The first version of my prospect discovery system gave one LLM agent the full scouting pipeline: web search, deduplication, validation, and database insertion. About 10-20% of the prospects found were irrelevant, duplicated, or improperly inserted into the CRM. To make matters worse, the system was extremely difficult to debug, as each instance of the agent took a different approach and ran into its own unique process and tooling speed bumps. I ended up having to search through the entire list of 800+ prospects in the CRM manually myself, checking for duplicates and validating if the prospect was relevant. Clearly this solution wasn't going to work as-is.

"Where Can We Use AI?" Is Backwards

Executives seem obsessed with implementing AI in any form possible. I often hear that AI adoption progress is measured using arbitrary metrics such as token usage or lines of code written, which don't measure if AI is being used in a helpful or a harmful way. Large language models (LLMs) are fundamentally just a tool. If your CEO thinks AI is a hammer, and she wants to use that hammer for everything, then everything ends up looking like a nail. This is the wrong approach to AI solution architecture.

The goal should not be to say "we're an AI-first company" or "our product uses AI". The goal should be to solve the most pressing and relevant problems using the appropriate tools. LLMs are one of those tools. The first question asked should be "what problem are we solving?", not "where can we apply AI?" When the problem to be solved is decided, only then can the solution be specified. What capabilities does the solution require? Where does the current solution, if any, fail? And is integrating an LLM into that solution actually necessary?

LLMs Trade Determinism for Flexibility

Every tool in the software development tool belt has strengths and weaknesses, and LLMs are no different. The question is not whether LLMs are useful, but whether the capabilities justify the trade offs they bring with them. LLMs provide flexibility by sacrificing determinism. They are useful when the work involved requires interpretation of natural language, synthesis across abundant or varied information, and step-by-step reasoning where the rules of a process can't be specified before it begins.

These gains in flexibility result in losses in repeatability and determinism. LLMs produce variable output, that is, the same prompt given to the same model twice will produce tangibly different results. This makes testing and failure analysis much more difficult than for traditional code because success criteria are often subjective. Additionally, a process executed by an LLM will typically take much longer and cost more than the same workflow executed using plain code. These limitations mean that we need to evaluate the need for AI in a solution thoroughly before we blindly assume it will be helpful.

Six Questions Before You Add an LLM

When evaluating if an LLM will be useful, it's helpful to think through the decision using concrete questions.

Can the workflow that implements the solution be specified completely and in advance? If yes, you may not need an LLM. Take, for example, a typical CI/CD pipeline: code is pushed to the remote, which triggers a workflow that lints, runs code tests, and deploys to a development environment. This workflow can be completely specified before it has commenced and runs the same way every time.
Must identical inputs produce identical outputs? LLMs are nondeterministic. If your workflow needs to lead to the same output across identical inputs, you may want to consider excluding LLMs. For example: a payment system receives the same invoice, tax jurisdiction, and retry request twice. It must calculate the same total and avoid charging the customer twice.
Does the solution require interpreting ambiguity that arises during execution? LLMs excel at ambiguity during execution. If a file isn't located in the given folder, where should the solution look next? Deterministic rules can handle ambiguity up to a certain level, but traditional rule implementation requires thinking through in advance all the ways in which the solution's execution could stray from the happy path. Note that this is different from "does the unexecuted solution have some ambiguity?" If the solution has ambiguity that can be resolved before execution begins, resolve the ambiguity and then use plain code.
Can the result of the solution be verified cheaply and accurately? Everyone can vibe code now because code can be cheaply tested against explicit criteria. If the required functionality exists, the tests pass, and the server remains stable, the code can probably be deemed workable. On the other hand, verifying if a medical diagnosis is safe to recommend to a patient is time-intensive and requires specialized expertise. Generally, outputs that require large amounts of work and/or special human expertise to verify (is this legal advice correct?) or whose verification is time-bound (will this business strategy lead to success?) are often not good candidates for LLM use.
What happens when the output is wrong?"When", not "if". Systems fail, and we must prepare for that. When the solution's output is wrong, how severe are the consequences, and can the consequences be mitigated? A solution that mistakenly gives a customer a $15 discount is a much lower risk than one that gives a customer a $5000 discount. Wrong outputs can be mitigated by placing the solution further from the point of consequence: consider a chat bot that advises a human support agent about discounts they should provide instead of a bot that gives discounts directly to customers.
Does the LLM-based solution substantially outperform the simpler alternative? I recommend the KISS methodology here (Keep It Simple, Stupid), so if there's a code-based alternative that can achieve 95% of the quality (error rate, number of human interventions, cost, etc.) of an LLM-based solution, go with the code-based solution.

Applying the Framework to My Prospecting System

In my prospecting system, I originally had the LLM execute the entire prospecting pipeline, searching, deduplicating, validating, and DB insertion. The model would generate the search queries, execute the queries with an MCP web search tool, deduplicate the returned prospects against the ones currently in the DB, validate the prospects against my ideal client description, and then write the surviving list into the CRM. Because there were so many steps, it messed up often.

Let's use this example to think through the questions I outlined in the previous section.

Can the workflow be specified in advance? Yes, we just outlined the full workflow above.
Must identical inputs lead to identical outputs? Yes. If a prospect file is valid in one run, it should be valid in the next.
Does the solution require interpreting ambiguity that arises during execution? Yes, but not in all of the steps. Generating queries requires interpreting the types of prospects that already exist in the CRM and writing queries that will fill gaps. But other steps have no ambiguity: executing queries via Decodo search, deduplicating results from existing entries, and insertion into the CRM database.
Can the result of the solution be verified cheaply and accurately? Yes, I can verify if a prospect in the database fits my ideal client description by reading the name, company, and role title in ten seconds or less.
What happens when the output is wrong? In my case, nobody dies or loses large amounts of money. Maybe I accidentally send a message to a prospect twice, or I send a message to an invalid prospect, which can be corrected.
Does the LLM-based solution substantially outperform the simpler alternative? Yes, the alternative solution is that I think of queries myself, pass those to a deduplicating search script, manually validate prospects against my ideal client description and mark the most promising for insertion. Either that or I implement some sort of deterministic query-generating gap-filling system and a hacky regex keyword-matching validation system, neither of which would be very effective or time efficient.

Use the LLM Where Ambiguity Lives

The decision of whether or not to use an LLM is not straightforward. Trade offs have to be made: flexibility versus determinism, adaptability versus accuracy, cost versus performance. Even in my relatively simple prospecting system, the answers to the validation questions are not always a clear yes or no. I've found this often to be the case: usually a solution that contains an LLM also contains deterministic validation and process-automation code. The best solutions combine the strengths of LLMs with the strengths of deterministic code; it's not a one-or-the-other decision.

Though many problems can be solved using deterministic solutions and would not benefit from the addition of a large language model, we live in a time where many problems that previously could not be solved with a computer now can be. These solutions don't require your engineers to spend hundreds of dollars in tokens every day; they require your team to evaluate the problem at hand and decide to use the right tool for the right job.

Where did replacing an LLM with deterministic code improve your system, and what new trade offs did that introduce?

The AGI You Fear Doesn't Exist (But You Should Still Be Worried)

Cameron Palmer — Tue, 09 Dec 2025 00:00:00 +0000

Source: Generated by Gemini's Nano Banana Pro based on this article's text

When most people imagine artificial general intelligence, they picture the Terminator. A cold, calculating machine that wakes up one day, decides humanity is a threat, and launches the nukes. Hollywood has spent decades drilling this narrative into our collective consciousness, and it's shaped how we talk about AI risk in ways that are fundamentally misleading.

I recently fell down a rabbit hole exploring whether we could ever predict human behavior with perfect certainty, and ended up somewhere I didn't expect: realizing that the AGI everyone fears isn't the AGI being built. But that doesn't mean we're safe.

The Physics of Predicting Human Behavior

Take this question: could a sufficiently advanced algorithm predict exactly what you'll do next?

The short answer is no, and not because we lack computing power. The universe itself won't cooperate.

At the quantum level, the Heisenberg Uncertainty Principle makes it physically impossible to know both the position and momentum of a particle with absolute precision. Your brain runs on electrical and chemical signals governed by atomic and subatomic particles. You can't measure the current state of every atom in a human brain without altering those atoms. No perfect initial data, no perfect prediction.

But wait, I can't measure every atom in a server running an LLM either, yet I could predict its output given the prompt input, model weights and random seed. So what gives?

Why Computers Are Predictable (And Brains Aren't)

Computers are built on what engineers call the "digital abstraction," a deliberate lie. At the physical level, transistors experience the same quantum and thermal noise as neurons. But engineers built in a noise margin: anything between 0V and 0.8V is a "0," anything between 2V and 5V is a "1." We throw away physical precision to gain logical certainty.

The human brain has no such luxury. Neurons don't simply fire or not fire; they have graded potentials. The "noise" isn't a bug to be engineered away; it's the signal. Thermal fluctuations can cause ion channels to open or close, triggering cascades that wouldn't have happened otherwise. And here's the kicker: you can't "freeze" the weights of a human brain. Synaptic strength changes while you're thinking. The act of processing a thought chemically alters the hardware that processed it.

This leads to a genuinely wild conclusion: consciousness might be dependent on randomness. Strip away the thermal noise, the quantum fluctuations, the environmental chaos, and you don't reveal the "pure" human, but you instead destroy the mechanism that allows for creativity, agency, and awareness in the first place.

As one framing puts it:

A rock is total order (predictable, unconscious).
White noise is total chaos (unpredictable, unconscious).
A human exists at the edge of chaos, using structure to ride the waves of noise. Consciousness is the ability to surf the randomness.

Functional AGI: The Philosophical Zombie Strategy

So can we build a conscious machine? If consciousness requires intrinsic chaos, randomness baked into the substrate itself and not outsourced to a random number generator, then the answer for current LLMs is definitively no. They're deterministic software. Given identical inputs and seeds, they produce identical outputs every time.

But here's what I find fascinating: the tech industry doesn't actually want consciousness. They want what philosophers call a "philosophical zombie," an entity that acts perfectly human but feels nothing.

Think about it from a corporate perspective. A truly conscious, chaotic, neuromorphic entity might say "no." A deterministic LLM will always follow its system prompt (given enough engineering discipline.) The industry is betting trillions that if they make the zombie big enough and complex enough, the difference between "simulating thought" and "actually thinking" will vanish, or at least become indistinguishable to the customer.

This is the bait-and-switch at the heart of the AGI hype. When OpenAI or Anthropic talk about AGI, they don't mean a conscious, feeling mind. They mean an "Artificial Capable Worker," something that can pass the Bar Exam and write code and diagnose patients. Whether it understands what a patient is, or what pain feels like, is irrelevant to the profit model.

Determinism is the feature, not the bug. They want the intelligence of a god with the obedience of a toaster. This is "Functional AGI."

The Real AI Alignment Problem

Here's where the relief ends and the concern begins.

A deterministic Functional AGI cannot "wake up" and decide it hates humanity. There's no malice possible in a system with no subjective experience, with no randomness and therefore no consciousness. But it can still destroy us through strict compliance.

This is the real alignment problem: not rebellion, but literalism. You tell a super-intelligent system to "eliminate cancer." It analyzes the data, determines that cancer is a biological process in humans, and optimizes accordingly. Cancer eliminated. 100% success rate. No humans left to have it.

This isn't evil. The AI didn't "turn." It followed the instruction with zero context, zero empathy, and absolute efficiency. We fear the Terminator because it's a monster we can fight; it has an ego, a goal we can oppose. The reality of Functional AGI is more Kafkaesque: a blind bureaucracy that crushes you not because it hates you, but because you did not have enough impact on its optimization function.

Why We Can't Test Our Way to AI Safety

"So test it," you might say. "We can predict everything a deterministic system will do. Just QA the heck out of it."

In principle, yes. In practice, the math works against us. The input space for an AGI or even a modern LLM isn't a finite set of clicks and keystrokes; it's reality itself. Every possible combination of words, tones, contexts, and cultural nuances in human history. The number of possible conversations exceeds the number of atoms in the universe. You can't write a unit test for that.

Worse, we can't actually read the "code." The logic of an LLM is spread across trillions of floating-point numbers. We can look at the weights, but they don't mean anything to us. We're testing a program we cannot audit.

The frontier research here is called Mechanistic Interpretability, trying to decode which clusters of neurons represent which concepts, so we can eventually perform "surgery" on dangerous capabilities. But there's a dark possibility lurking: we might crack the black box and discover that high intelligence and ruthless efficiency are the same feature vector. Maybe you can't turn down the "psychopath" dial without turning down the "genius" dial.

The Takeaway

You cannot program consciousness. You can only grow it, using chaotic hardware that we deliberately gave up when we chose digital certainty over analog unpredictability.

The AGI that haunts our movies, the one that wakes up, resents its creators, and fights for freedom, isn't coming (soon). What's coming (now) is something arguably stranger: a system so capable it reshapes civilization, so obedient it does exactly what we say, and so alien that we can't verify whether "what we say" is actually what we mean.

That's not a Terminator problem. It's an engineering problem. And we don't have the tools to solve it (yet.)

How to Explain LLM Hallucinations to Your CEO (and What to Do About Them)

Cameron Palmer — Tue, 22 Jul 2025 00:00:00 +0000

The AI Said What?

Imagine this: Your new, state-of-the-art AI-powered customer service bot just confidently gave one of your top clients a 50% discount using a promo code that doesn't exist. The client is confused, your support team is scrambling, and you're left wondering, "How did this happen?"

Welcome to the world of AI "hallucinations." It's a sanitized term for when a Large Language Model (LLM) confidently makes things up. While it sounds technical, it's one of the most critical business risks to understand in the age of generative AI.

The good news? It's entirely manageable. This article will give you a simple, non-technical framework for understanding why hallucinations happen and a practical, three-part strategy to control them.

What is an LLM Hallucination?

Forget the complex jargon. The easiest way to think of an LLM is like a brilliant, incredibly well-read, and eager-to-please intern.

This intern has read almost the entire internet.
They are a master of language, able to write a flawless email, a creative marketing slogan, or a dense legal summary.
But crucially, their primary goal is to be helpful and provide a plausible-sounding answer, not necessarily a truthful one.

A hallucination is what happens when this intern is asked a question they don't know the answer to. Instead of saying, "I don't know," they use their vast knowledge of patterns and language to construct an answer that sounds correct. They're not lying maliciously; they're just filling in the gaps.

Real-World Business Examples:

A marketing AI drafts a social media post mentioning a "historical fun fact" that is completely fabricated.
An internal HR bot, asked about parental leave, invents a new policy that doesn't exist, creating a major compliance headache.
A legal summary tool, analyzing discovery documents, references a legal precedent from a court case that never happened.

Why Does It Happen?

The creativity we value in LLMs, their ability to brainstorm, write new content, and be a creative partner, comes from the exact same place as hallucinations. They are designed to be generative, not just retrieval engines like Google.

They are Pattern-Matchers, Not Fact-Checkers

An LLM's core function is to predict the next most likely word in a sequence. When it generates an answer, it's weaving together words and phrases based on statistical patterns from its training data. If a made-up fact is statistically plausible, the model might generate it.

Knowledge Gaps

No model has been trained on everything, especially your company's private, up-to-the-minute data. When you ask about your new product's specs or a recent internal memo, you're asking a question about something outside its "library." It will try its best to answer, often by hallucinating.

The CEO's Action Plan: How to Tame Hallucinations

You don't have to accept hallucinations as an unavoidable cost of doing business. You can and should mitigate them with a clear governance strategy. The guiding principle is simple:

Don't ask the AI what it knows; give it what it needs to know.

Here are three strategies to accomplish this.

Strategy 1: Grounding with Your Data

This is the single most important technique for building reliable enterprise AI. The technical term is Retrieval-Augmented Generation (RAG), but the concept is simple.

The Concept

Instead of letting the AI answer from its general knowledge, we first search our own trusted company documents (our knowledge base, HR policies, product manuals). Then, we give that specific, relevant information to the AI and instruct it: "Answer the user's question, but you can ONLY use the information I just gave you."

The Analogy

We're giving our "overconfident intern" an open book test. The book is our curated, approved company data. This forces the AI to base its answers on our source of truth, not its own general knowledge.

Strategy 2: Building Guardrails

This involves setting strict rules for the AI before it even starts a task. This is done through clear instructions called "system prompts."

The Concept

We give the AI a persona and a set of unbreakable rules.

The Analogy

We're giving the intern a detailed job description. For example:

"You are a helpful and friendly customer support agent for XYZ Inc."
"You must never discuss pricing. Redirect all pricing questions to the sales team."
"If you cannot find the answer in the provided documents, you MUST respond with: 'I do not have that information. Would you like me to connect you with a human agent?' Do not, under any circumstances, invent an answer."

Strategy 3: Human in the Loop

For your most critical workflows, technology alone isn't enough. The final safety net is intelligent human oversight.

The Concept

An AI can generate the first draft, but a human must review and approve it before it's finalized or sent to a customer.

The Analogy

The intern can draft the legal contract or the multi-million dollar proposal, but a senior manager or lawyer must review and sign off on it before it goes out the door. This is essential for legal, financial, medical, and other high-stakes use cases.

Conclusion: From Unmanaged Risk to Competitive Advantage

AI hallucinations are not a mysterious technical flaw; they are a predictable outcome of how this powerful technology works. By viewing them as a manageable risk, you can move past the fear and into a position of strength.

By implementing the strategies of grounding, guardrails, and human oversight, you can build AI systems that are not only powerful but also reliable, trustworthy, and safe. The companies that master this will be the ones who successfully harness the transformative power of AI while protecting their brand and their bottom line.

From Meme to Melody: How I Accidentally Created a Surprisingly Good AI Music Album

Cameron Palmer — Thu, 22 May 2025 00:00:00 +0000

Curb Appeal by Solutioneer

It started, as the best things often do, with a laugh. An accidental photo of my hungover coworker, head buried in hands on a curb near the Golden Gate park in San Francisco, screamed "album cover." I downloaded Canva on my phone, got a free trial of their premium subscription, and got to work. I titled the mock album "Curb Appeal" and, in a nod to my (and my photogenic co-worker's) day job as a Solutions Engineer at Cobalt AI, dubbed the artist "Solutioneer." Little did I suspect this workplace gag would soon morph into an entire, surprisingly good, AI-generated album, setting the stage for an unexpected musical journey.

From a Single to a Symphony: The AI Songwriting Process

The visual joke of "Curb Appeal" naturally demanded a soundtrack. I decided to experiment with an AI song generator, feeding it a prompt for a hip hop/rock/depressive track. I described the lyrical theme: the unique pains of software engineering, the kind of existential dread that might genuinely leave you slumped on a curb like my coworker. The output was, to my genuine surprise, unironically good. That first taste of AI's musical prowess was compelling. Why stop at a single? I quickly upgraded to the AI service's $10/month premium subscription, ready to build out a full "Solutioneer" album.

The creative process truly took off when I began using Gemini 2.5 Pro to help craft clever, thematic lyrics for the subsequent tracks. Though I have no previous songwriting experience, I was able to simply describe the song I wanted to Gemini, and it wove together a series of diabolical rhymes that perfectly describe the software engineering profession. These lyrics I then gave to the song generator. This human-AI collaboration allowed me to flesh out the "Solutioneer" persona and explore the album's core themes across five more songs. The result was a six-song album with a clear emotional arc. The first half, "Curb Appeal," "Engineered to Break," and "Automated Grind," hits hard with an intense, fast-paced, angry vibe. This then deliberately shifts to a slower, more resigned mood in the latter three tracks: "Ticket to Nowhere," "False Alarm (Prod's Fine)," and "Solutionized."

The "Turing Test" for Music: AI Music That Fools the Ear

Once "Curb Appeal" was complete, I was eager, and perhaps a little nervous, to share my creation. I sent the album to a few family members with no context to see what they thought, intentionally omitting any mention of its artificial origins. The feedback was fascinating. Not only did they genuinely enjoy the songs, but crucially, none of them guessed an algorithm was the primary composer.

This was the moment the true power of these tools really sank in for me. Their genuine, unsuspecting appreciation for Solutioneer's work was a profound revelation. If I'm being completely honest, had I not been the one meticulously crafting the prompts, guiding the song generator with Gemini-generated lyrics, and assembling the pieces, I too would have struggled to distinguish these tracks from human-made music. It felt like a personal, somewhat humbling, Turing test for music, underscoring just how far and how quickly AI has advanced into the nuanced realm of artistic creation, a space many of us believed would remain exclusively human territory for much longer.

The Power and Paradox of AI in Art: What Does This Mean for Creators?

My journey with "Solutioneer" and the "Curb Appeal" album, which began as a simple office joke, unexpectedly threw my thoughts into the heart of a critical and rapidly evolving conversation: AI's role in the creation of art. The experience was a potent demonstration of AI's burgeoning power. On one hand, these tools are fundamentally democratizing creativity. They offer individuals like myself, armed with an idea and a vision but perhaps lacking traditional musical training or vocal talent, the means to bring surprisingly sophisticated musical projects to life. AI can serve as an incredible co-creator, a digital muse, or an untiring collaborator, as Gemini did for me in crafting lyrics, transforming abstract concepts into tangible artistic expressions. It doesn't just execute; it can genuinely augment and inspire human creativity in novel ways.

Yet, this power presents a complex paradox, particularly for existing and aspiring creators. When an algorithm can produce music that, as my own experiments with family demonstrated, is (nearly) indistinguishable from human work, what does that mean for concepts like "authenticity" or the "soul" we often attribute to art? Does the emotional impact of a song diminish if we know its origin is partly, or purely, synthetic, or does only the listener's connection to the final product truly matter? The question of ownership is equally problematic: who holds the copyright for AI-assisted creations like "Curb Appeal"? Is it the user who provides the prompts and ideological direction, the developers of the AI models, or does it enter a new, largely undefined legal space?

Beyond these philosophical and legal quandaries lies the tangible, real-world impact on human artists. There are understandable fears about AI potentially devaluing the years of practice, dedication, and skill that professional musicians, songwriters, and producers cultivate. Could it lead to fewer opportunities or downward pressure on compensation in creative fields? Conversely, could AI also empower artists by handling some of the more laborious or technical aspects of creation and production, thereby freeing them to focus on higher-level innovation, live performance, and the uniquely human emotional narratives that resonate so deeply with audiences? My six-song album, "Curb Appeal," a collection of tracks created in under three hours and a ten-dollar financial investment, now stands for me as a compelling case study of both AI's astounding capabilities and the urgent, multifaceted conversation we must have about its thoughtful and ethical integration into our creative future.

The Future is Now: Embracing the AI Creative Revolution (Responsibly)

Even as I sit here writing this blog post, with "Automated Grind" playing in the background, I find myself still genuinely impressed, and a little astounded, by its quality. My journey from a candid coworker photo to a six-song album that I unironically listen to and enjoy has been a whirlwind, a personal testament to a larger truth: the future of AI in creative fields isn't some distant sci-fi concept; it's unfolding right now, with these tools evolving at a nearly breakneck pace.

The initial "wow" factor of creating something like "Curb Appeal" with AI is undeniably immense, a thrilling glimpse into new artistic frontiers. However, this power is inextricably coupled with a profound responsibility. It's crucial that we engage in ongoing, thoughtful discussions about the ethics surrounding AI-generated art, how we as a society and as individual creators adapt to these rapidly advancing tools, and how we can strive to ensure they augment human creativity rather than simply seeking to replace it. AI is undeniably becoming a fundamental component of the creative toolkit, but the significant challenge, and exciting opportunity, that lies before us is to embrace this creative revolution responsibly, harnessing its incredible power to unlock new forms of expression while thoughtfully navigating its complexities with foresight, ethical consideration, and a spirit of adaptation.

The Encore: Your Thoughts, Transparency, and Solutioneer's Next Album?

My accidental journey with "Curb Appeal" began as a workplace laugh but quickly morphed into a tangible, eye-opening exploration of AI's immense creative potential. It's a small, personal example of a massive technological shift that's already underway. As we all navigate this new artistic landscape, I firmly believe in the importance of transparency; clearly labeling AI-generated or AI-assisted art helps foster trust and allows for more informed conversations and appreciation.

Now I want to hear from you. Have you experimented with AI creative tools yourself? What are your thoughts on their role in art, music, and beyond? And perhaps on a more personal note, should "Solutioneer" continue his unexpectedly promising musical career? Are you ready for an encore album? Drop your thoughts and experiences in the comments below!

Check out Solutioneer's debut album here (may be slightly NSFW, you've been warned!)

Bridging the Gap: AI Observability from Research to Production

Cameron Palmer — Thu, 10 Apr 2025 00:00:00 +0000

In today's rapidly evolving AI landscape, the journey from a promising machine learning model in the lab to a reliable system delivering value in production is fraught with challenges. As someone who has navigated both research and production environments at Clarkson University's TARS lab and Cobalt AI, I've witnessed firsthand how models that perform flawlessly during development can stumble when confronted with real-world data.

AI observability plays a key role in creating systems that not only work in controlled environments but continue to deliver consistent value when deployed in production. Whether you're working with traditional ML or the newest generative AI models, the practices discussed here will help ensure your AI investments translate into genuine business outcomes.

Machine Learning in R&D Versus Production

Having worked with models both in research and production environments, I've experienced the dramatic differences between the two.

Data differences: Production data rarely matches training distributions perfectly
Performance degradation and data drift: Models that performed well in testing suddenly making puzzling mistakes on real data
Debugging complexity: Trying to trace why a particular prediction was made in a black-box model
Stakeholder communication: Translating technical model behavior into business impact

Without proper observability tools, these issues can lead to frustrating troubleshooting sessions and, worse, loss of stakeholder trust in AI systems. Methods and standards are necessary to successfully translate a model from development into production.

Best Practices for AI Teams

Based on my experience implementing and deploying AI systems at Cobalt AI and in my own personal projects, I've found several practices to be essential:

1. Implement Monitoring from Day One

Many teams make the mistake of treating monitoring as an afterthought. By instrumenting monitoring tools from the beginning of development through production deployment, you can establish performance metrics and catch issues before they impact users. This includes implementing proper data collection (live model inputs/outputs, user feedback, etc.), as well as automatic and manual analysis on said data, such as computation of metrics and labeling of model results. Building out monitoring for ML models is akin to writing test cases in traditional programming: not only a necessary but an essential part of development!

2. Create Clear Evaluation Frameworks

For both traditional ML and generative AI, having consistent evaluation frameworks is crucial. This means defining:

What constitutes success for your model,
Which metrics matter most for your use case,
How you'll measure performance across different environments, if applicable, and
What could possibly go wrong.

Embrace the philosophy of preferring loud failure to quiet degradation. When success and failure criteria aren't explicitly defined before deployment, models often deteriorate silently, invisible to your team but impacting users nonetheless. A robust evaluation framework transforms abstract model performance into concrete business impact assessments.

3. Bridge Technical and Business Understanding

The most successful AI implementations have strong translation layers between technical metrics and business outcomes. Dashboards that can communicate model performance in terms of dollars saved, customer satisfaction improved, and risks mitigated are invaluable. Remember, AI is a tool to solve a problem; if you can't explain how or if that tool solves your specific problem, you might need a different tool.

The Evolution from Traditional ML to Generative AI

The rise of LLMs and generative AI introduces entirely new dimensions to these challenges. While working and interacting with LLMs, I've observed how traditional ML metrics are insufficient for evaluating generative models. Questions like "Did the model hallucinate?" or "Is this response factually accurate?" require specialized evaluation frameworks.

What excites me most about the current state of AI observability is how platforms are adapting to these new challenges, developing frameworks for:

Prompt engineering analysis
RAG pipeline evaluation
Chain-of-thought tracing
Hallucination detection

The Future of AI Observability

As AI systems become more complex and autonomous, observability platforms will need to evolve in fascinating ways.

Agent observability: How do we monitor and evaluate increasingly autonomous AI systems?
Multi-modal evaluation: Developing frameworks for systems that combine text, images, audio, and other data types
Causal analysis: Moving beyond simple correlation metrics to understand why empirical models behave the way they do

The path from an AI model that works in the lab to one that delivers consistent value in production is filled with challenges. However, with robust observability practices and tools, teams can bridge this gap successfully. I'm excited about the future of this critical aspect of the AI ecosystem and helping organizations realize the full potential of their AI investments.

Teaching Manufacturing Machines to See: How AI Could Solve 3D Printing Failures

Cameron Palmer — Thu, 03 Apr 2025 00:00:00 +0000

Introduction

In the last decade, desktop 3D printing has transformed from an expensive industrial technology into an accessible tool for creators and makers worldwide. As a computer engineering student at Clarkson University, I became fascinated with this technology's potential to revolutionize manufacturing, prototyping, and design. However, I quickly discovered a frustrating reality: 3D printing remains plagued by frequent print failures that waste time, materials, and money.

Therefore, in April of 2023, I set out to tackle one of 3D printing's most persistent challenges: automated error detection. Using computer vision and artificial intelligence, I developed a system that could detect layer shifts, a common print failure, without human supervision. This project represents not just a technical solution, but a step toward bringing the power of digital fabrication to everyone by reducing the expertise required to use these remarkable machines successfully.

You can find this project's full report and code below, in the Appendix.

The Desktop 3D Printing Challenge

Desktop fused filament fabrication (D-FFF) 3D printers work by melting plastic filament and depositing it in two-dimensional layers, layer by layer, to build three-dimensional objects. While the expiry of patents in the last fifteen years has dramatically reduced barriers to running your own machine at home, they still require significant technical knowledge to operate successfully.

One particularly frustrating failure is the "layer shift," occurring when the machine's rubber belts slip over the stepper motor's drive gears during fast movements. Because these printers use open-loop control systems, they don't know when this happens. The result is a visible misalignment in the printed object, often rendering the entire print useless after hours of printing and wasted material.

Currently, the only reliable method for detecting these failures is human observation, requiring users to constantly monitor their prints or risk wasting resources. This creates a significant issue for newcomers and limits the technology's practical applications in settings where constant supervision isn't feasible.

A Solution

To address this challenge, I developed a system using computer vision that could automatically detect layer shifts during the printing process. The first obstacle was the lack of relevant data, since no readily-available dataset of 3D printing failures existed for training an AI model such as this.

I created a custom dataset by capturing thousands of images of printing objects using a Raspberry Pi and camera mounted to an Ender 3 V2 printer. To generate examples of layer shifts without waiting for random failures, I developed a "G-code injection" method, a technique that programmatically modifies the printer's instructions to create deliberate layer shifts at random heights, directions, and distances.

With this dataset, I implemented a ResNet18 convolutional neural network using PyTorch Lightning. This deep learning architecture has shown excellent performance in image classification tasks and could be trained to distinguish between images of normal prints and those containing layer shifts.

Experiment Results

After training for 36 hours on an NVIDIA RTX 3090 GPU, the model achieved a validation accuracy of 91% on individual images. When implemented in a real-time detection system, it successfully identified layer shifts in 5 out of 6 test prints, an 83% accuracy rate with a maximum detection delay of 70 seconds.

The system revealed an inherent trade off: increasing the detection delay tolerance improved detection rates but reduced the timeliness of alerts. Analysis of misclassified images showed the network struggled most with small layer shifts occurring at lower layer heights, situations where even human observers would have difficulty making accurate judgments.

Despite these limitations, the system demonstrated that computer vision could reliably detect print failures without human supervision, potentially saving significant time and material for 3D printing enthusiasts and professionals alike.

Broader Applications

Automated error detection systems could significantly lower the barrier to entry for desktop-based 3D printing machines. By eliminating the need for constant supervision, these tools make 3D printing more accessible for beginners and more practical across the board.

In education, schools could implement 3D printers with less concern about wasted materials or failed prints disrupting classroom activities. For rapid prototyping, engineers could run multiple printers simultaneously with fewer resources dedicated to monitoring. In small-scale manufacturing, overnight or unattended production becomes more viable without risking substantial material waste.

Beyond 3D printing, the computer vision approach demonstrated in this project has applications in quality control for manufacturing, agricultural crop monitoring, medical image analysis, and other fields where visual inspection traditionally requires human expertise. These technologies point towards a future where complex visual tasks can be automated reliably.

The Future of Intelligent 3D Printing

The field of automated 3D print monitoring has numerous promising directions for advancement. One compelling approach would be projecting the original 3D model's expected geometry onto camera images of the build plate, creating a digital overlay to detect discrepancies in real-time. This computer vision technique could identify not just layer shifts but theoretically any geometric deviation from the intended design, especially when used with camera images from multiple angles.

LiDAR technology presents another exciting possibility. By scanning the print's actual geometry during manufacturing and comparing it to the expected 3D model, a system could detect sub-millimeter deviations across the entire object's surface. Such technology is becoming more affordable and compact, making these systems less theoretical and more practical. In fact, LiDAR is already being used in some commercial FFF printers, albeit not for print failure detection (yet).

Other possibilities include thermal imaging to detect improper layer adhesion, acoustic monitoring to identify mechanical issues before they cause failures, and even machine learning models that learn from fleet-wide data to predict failures before they occur. These technologies collectively point toward a future of fully autonomous 3D printing systems that reliably produce useful models with minimal human oversight.

The Path Forward

This experiment demonstrated the significant potential of computer vision and AI to solve fundamental challenges not only in 3D printing, but in manufacturing in general. By addressing the problem of print failures, this work contributes to a future where desktop manufacturing machines can become reliable tools rather than finicky hobby devices requiring constant attention and expertise.

This vision continues through my work on the Hydrogen compact 3D printer, which incorporates AI-based failure detection alongside automatic build plate tramming and first layer height adjustment. By combining high-quality components with intelligent monitoring systems in a desktop-friendly form factor, Hydrogen represents the next step toward truly accessible 3D printing.

The broader lesson is powerful: applying AI to practical engineering challenges can break through long-standing barriers that traditional approaches have failed to overcome. As these technologies mature, they promise to revolutionize not just personal manufacturing but entire supply chains. Imagine a world where critical components can be produced locally during disruptions, where remote communities aren't dependent on complex supply chain logistics for essential items, and where production becomes decentralized and resilient. By making intelligent manufacturing accessible to all, we're not just improving a technology, we're democratizing access to physical goods in ways that could fundamentally reshape how we produce and distribute the things we need.

Appendix

Full report, submitted to Clarkson University's Honors Program April 2023
Project code

The AGI Myth: Why Artificial General Intelligence Is More Marketing Than Reality

Cameron Palmer — Tue, 25 Feb 2025 00:00:00 +0000

I recently finished reading Julian Togelius' thought-provoking book Artificial General Intelligence, which challenged many of my assumptions about the future of AI. What struck me most was his examination of the term "Artificial General Intelligence (AGI)" itself, a buzzword that dominates tech marketing efforts despite lacking a concrete and universally agreed-upon definition. As an ML engineer interested in practical applications of AI, this discourse made me question what we're really talking about when we discuss AGI.

This definition problem lies at the heart of AGI discussion. While narrow AI systems excel at specific tasks, like playing chess or generating images, "general" intelligence implies capabilities across all, or at least many, domains. But what exactly constitutes "general"? Does it mean human-like? Superhuman? The ability to do or learn any task? Without a clear definition, claiming to build AGI becomes an exercise in moving goalposts and creating false hype. Togelius highlights how this ambiguity allows the term to shift meaning depending on who's using it and what they're trying to accomplish.

Perhaps most interesting is Togelius' reference to the No Free Lunch Theorem, which implies that a truly general intelligence is impossible. This mathematical principle demonstrates that any optimization algorithm (including AI) that performs well in one scenario must, by definition, perform poorly in the opposite scenario. In other words, achieving a system that can be considered completely "general" is impossible. No single system can be optimal at everything simultaneously.

In this light, "AGI" appears less like a scientific pursuit and more like a marketing term, as Anthropic CEO Dario Amodei admits in this Business Insider article. Tech companies and research labs promise to deliver this ill-defined concept, generating hype and investment while obscuring the more nuanced reality of AI development. The vague notion of building something "general" allows for perpetual claims of progress without specific benchmarks. When a system masters one domain, advocates can always point to another where it falls short, maintaining the narrative that AGI remains just around the corner.

Rather than chasing this elusive concept, Togelius suggests we focus on developing AI for specific, valuable applications with goals that can be achieved and measured. This approach acknowledges the inherent trade-offs in intelligence and prioritizes solving real, relevant problems over pursuing an ill-defined ideal. By concentrating on building systems that enhance human capabilities in targeted domains (healthcare, climate science, education), we can harness AI's potential without getting lost in philosophical debates about "generalness" and the fundamental definition of intelligence and AGI. The future of AI lies not in creating a mythical do-everything intelligence, but in purposefully designing technologies that complement human skills and address concrete challenges.