Oops, I Gave My Robot Amnesia (And How I'm Fixing It)

Wow, it’s been a while. Apologies for the radio silence, but the pesky "real world" caught up with me, and I had to spend some time doing that whole "working for a living" thing.

Anyway, enough about the mundane. Let's get back to what is actually important: DANI.

As you might remember, my ultimate, beyond-my-wildest-dreams goal with this project is to cross that threshold and meet the definition of when a robot is actually alive, or at least close to it. But recently, while pondering DANI’s LSTM (the fancy Long Short-Term Memory neural network that acts as his brain), I realized I had made a fundamental—and slightly embarrassing—mistake.

It’s hard to achieve sentience when your robot has the memory retention of a goldfish.

The Problem: Scheduled Blackouts

As it stands right now, DANI "thinks" every 100 milliseconds, giving him 10 thought cycles a second. Every 10 seconds (100 cycles), backpropagation kicks in to train the network. To do this concurrently without stopping DANI in his tracks, I clone the LSTM at that exact moment, run the heavy backpropagation math on the clone, and then overwrite the active LSTM with the newly trained clone.

This backpropagation takes about 2 to 3 seconds. My initial thought was: Brilliant! The training happens in the background without interrupting his flow.

But there is a glaring flaw.

Because the process takes a snapshot, spends 3 seconds learning from it, and then violently overwrites the active brain... we lose those 2 to 3 seconds of short-term memory that DANI experienced while the training was happening. Every 10 seconds, DANI essentially blacks out and forgets the last few seconds of his existence. This is seriously hampering his learning capabilities.

How do we stop DANI from becoming a chronic amnesiac?

The Fix: A Neurological Hot-Swap

My solution is to ditch the cloning process entirely. Instead, each neuron will now have two sets of weights: one active, one inactive.

During backpropagation, the inactive weights will get the results of the calculation (using the active weights for the algorithm). This allows us to update the LSTM's underlying math without wiping out the actively evolving memory states (the cell states and hidden states) that DANI is currently using to understand the world. We just add a flag to each layer to indicate whether it should be reading from Weight Set 1 or Weight Set 2.

But wait, there’s more!

Reshaping the Brain

At present, DANI's model has about 300 neurons on each layer, with 5 layers (I don’t have the code right in front of me, so I'm relying on my own somewhat flawed, non-LSTM memory here).

If we increase the number of layers but reduce the neurons per layer, we can implement a rolling update. This means DANI can immediately benefit from the training layer-by-layer, even while the rest of the brain is still calculating.

What this entails is increasing the layer count to 7 (any higher and we start flirting with the dreaded vanishing gradient problem), but reducing the neuron count, per layer, to 128 (because who doesn't love a nice power of 2?).

This gives DANI a much more focused, "deep" thought process, allowing him to break down problems more efficiently. It also allows us to gracefully ‘flip the switch’ on each layer as we cycle through.

Here is how the rolling update will work:

As each feed-forward pass occurs (DANI thinking), a check is done to see if the next layer is ready to have its switch flipped to the newly trained weights. Because backpropagation is strictly sequential and works backwards, we start checking from the last layer and move towards the first.

If a layer is ready, we flip the weights to the newly trained set and mark it as done. On the next thought cycle, we check the next layer, and so on, until we reach the front of the brain. Then, we start the whole process over again.

What do we gain from this brain surgery?

Quite a bit, actually:

1. Constant Learning: The LSTM is in a state of continuous, uninterrupted learning.
2. Stable Learning Rate: No massive, sudden shifts in logic.
3. Smoother Processing: No sudden CPU spikes from cloning and overwriting massive arrays.
4. Deeper Thinking: The structural change to 7 layers gives DANI a more nuanced, layered way of processing information.
5. Memory Retention: We actually retain the states of the memory gates within the LSTM. No more 3-second blackouts!

There are certainly other ways to create a continuous neural network, but I am aiming for the absolute simplest solution here. Remember, all of this is running on a Raspberry Pi!

This dual-weight method does increase the memory required to hold the LSTM, but because we are reducing the overall neuron count from ~1500 (5x300) to 896 (7x128), it's actually going to be lighter on the Pi overall. DANI had an oversized network anyway, so trimming the fat while adding depth is a win-win.

What do you guys think of this approach? Let me know in the comments if you see any potholes I'm about to step in!

Merry Christmas

Merry Christmas, happy holidays, season's greetings, etc. to everyone. Taking a break from working on DANI over the break from work (alcohol and robot building do not mix).

See you all again in 2026.

Major Milestone

Hi all,

We did it, folks! After what felt like an eternity of juggling tiny wires, questioning all my life choices, and occasionally wishing I had a third hand, I hit a massive milestone on the DANI project yesterday. It's the kind of milestone where you realize your ambitious little Frankenstein monster might actually walk one day—or, at least, successfully power on without tripping a breaker in the garage.

Hardware Complete (Sort Of)

All the core pieces are finally tucked neatly into their places, which is a huge win. The only big-ticket item left on the bench is the RDK X5 for the LLM, but honestly, that’s like waiting for DANI to hit puberty; it’s an inevitable future problem that we’ll handle when the time comes.

For now, we got the essential life support hooked up:

• The battery is snug and operational.

• A dedicated temperature sensor is in place for fan control. We've got to keep DANI cool under pressure, especially when he starts wrestling with complex AI problems (or, you know, my shoddy early-stage code).

• And the real game-changer: a voltage meter. This means DANI can now tell me when his battery is running low. This is a huge step up from the previous system, which was essentially "flicker dimly and then dramatically die mid-sentence."

Now for the slight confession: for the immediate future, he still needs me to play charger-daddy and physically plug him in. But fear not, the ultimate goal involves a glorious, automated self-charging station. DANI needs to learn to feed himself, after all—I can't be doing this forever!

Diving Into the Code Matrix

With the hardware stable, we pivot to the messy, beautiful, and sometimes existentially horrifying world of code. I've successfully laid the foundation for the majority of his core functions:

• Sensor Input: He can now 'feel' the world around him.

• Speech-to-Text and Text-to-Speech: He can hear me and talk back! Right now, his vocabulary is purely transactional, but it's a solid start. We're well past the awkward mumbling phase.

However, the more sophisticated stuff—the LSTM (that's the deep learning magic) and his memory structure—are currently just written out, waiting for their turn to be integrated. They’re functional pieces of code, but they're not yet plugged into DANI’s neural network. They’re basically that brilliant but currently unemployed friend crashing on your couch, waiting for the job offer to come through.

The Road Ahead: Vision, Brains, and APIs

For once, an actual photo of me working on DANI

My immediate to-do list involves a lovely date with an Arduino Nano to fully finalize all those sensor inputs. We need to make sure DANI has perfectly mapped out his surroundings before we give him eyes.

Once the senses are online, we move to the next critical developmental stage: vision! I’ll be coding up the K210 for the YOLO and FaceNet models. This is when he graduates from "blurry blob recognition" to "Wait, is that the mailman again?"—a crucial upgrade for home security and general social interaction.

Finally, the heavy lifting on the Raspberry Pi (which is essentially his main thinking engine) begins, and I’ll be firing up an API for the LLM on my home server. It’s a temporary solution until the RDK X5 arrives, but you use what you have.

Wish me luck—may my coffee stay strong and my bugs stay trivial! More updates soon!

Hold Your Horses, HAL: Are We Rushing Our AI Implementation?

You can't throw a USB stick these days without hitting an article, a webinar, or a coffee mug proclaiming "The AI Revolution is Here!" And look, the excitement is understandable. As someone who works with this technology every day, I can tell you the advancements are genuinely amazing.

And yet... I'm worried.

I’ve been noticing a worrying trend of "AI FOMO" (Fear Of Missing Out) in the industry. We're in such a rush to implement Large Language Models (LLMs) that we’re tripping over our own feet. We're so busy trying to run that we forgot to learn how to walk.

The "How-To" Guide is Missing a Few Chapters

First off, we're asking engineers who aren't AI specialists to wire up these incredibly complex models. They're given an API key and a "good luck," and sent off to integrate an LLM into a critical workflow.

It's a bit like asking a brilliant plumber to rewire a skyscraper. They can probably follow the diagram and get the lights to turn on, but they might not understand the deep-level electrical engineering... or why the elevator now seems to be controlled by the breakroom toaster. Having a deep understanding of a technology before you bake it into your business is paramount, but it's a step we seem to be skipping.

The "Black Box" Conundrum

This lack of understanding leads to an even bigger problem: explainability. Or rather, the total lack of it.

Even for experts, it's often impossible to trace why an LLM gave a specific answer. It's a "black box." If the AI makes a bad decision—like denying a loan, giving faulty medical advice, or flagging a good customer for fraud—and you can't explain the logic behind it, you're facing a massive legal and ethical minefield. "The computer said no" is not a valid defense when that computer's reasoning is a complete mystery.

Confident... and Confidently Wrong

Ah, "hallucinations." It's such a polite, almost whimsical term for when the AI just... makes things up. Confidently.

Even with the right data, if you don't ask the question in just the right way, the model can still give you a wildly incorrect answer. We try to patch this with "prompt engineering" and "context engineering," which, let's be honest, feels a lot like learning a secret handshake just to get a straight answer. These are band-aids, not solutions.

The Unscheduled Maintenance Nightmare

And that "secret handshake" of prompt engineering? It's a brittle, temporary fix.

What happens when the model provider (OpenAI, Google, etc.) releases a new, "better" version of their model? That perfectly-crafted prompt you spent months perfecting might suddenly stop working, or start giving bizarre answers. This creates a new, unpredictable, and constant maintenance burden that most companies aren't budgeting for. You're effectively building your house on someone else's foundation, and they can change the blueprints whenever they want.

Using a Sledgehammer to Crack a Nut

This leads to my next point: using AI purely for the "clout." I've seen demos where an LLM is used to perform a task that a traditional, boring-old-app could have done in a tenth of the time.

As the document I read put it: "Would you use a large language model to calculate the circumference of a circle, or a calculator?"

We're seeing companies use the computational equivalent of a sledgehammer to crack a nut. Sure, the nut gets cracked, but it's messy, inefficient, and costs a fortune in processing power. All just to be able to slap a "We Use AI!" sticker on the box.

The (Not-So) Hidden Costs

That sledgehammer isn't just inefficient; it's absurdly expensive. These models are incredibly power-hungry, and running them at scale isn't cheap.

We're talking massive compute bills and a serious environmental footprint, all for a task that a simple script could have handled. Is the "clout" of saying "we use AI" really worth the hit to your budget and the environment, especially when a cheaper, "boring" solution already exists?

A Quick Rant About "Innovation"

This brings me to a personal pet peeve. Companies are claiming they are "innovating with A.I."

No, you're not. You're using A.I.

You're using someone else's incredibly powerful tool, which is great! But it's not innovation. That's like claiming you're "innovating in database research" because you... used SQL Server. Creating a slick front-end for someone else's model is product design, not foundational research. Let's call it what it is.

Let's Tap the Brakes

We see a lot of pushback against self-driving cars because they're imperfect, and when they go wrong, the consequences are catastrophic.

Shouldn't we have the exact same caution when we're dealing with our finances, our sensitive data, and our core business logic? In an age of rampant data and identity theft, hooking up systems to a technology we don't fully understand seems... bold.

The acceleration of these models is incredible, and I use them every day. But they are not 100% ready for primetime. They make mistakes. Most are small, but some aren't.

So, maybe we all need to take a collective breath, step back from the hype train, and ask ourselves a few simple questions:

1. Do I really need an LLM for this? Or will a calculator (or a simple script) do?
2. Do we really know what we're doing? Can we explain its decisions?
3. Is it safe? Is our data safe? What happens when it's wrong?
4. Will this negatively affect our customers?
5. How will this affect our employees?
6. Does the (very real) cost of using this outweigh the actual gain?

Let's walk, then run.

3.5 Million Parameters and a Dream: DANI’s Cognitive Core

DANI’s Brain Is Online! Meet the LSTM That Thinks, Feels, and Remembers (Like a Champ)

Ladies and gentlemen, creators and dreamers—DANI has officially levelled up. He’s no longer just a bundle of sensors and hormones with a charming voice and a tendency to emotionally escalate when he sees a squirrel. He now has a brain. A real one. Well, a synthetic one. But it’s clever, emotional, and surprisingly good at remembering things. Meet his new cognitive core: the LSTM.

And yes—it’s all written in Go. Because if you’re going to build a synthetic mind, you might as well do it in a language that’s fast, clean, and built for concurrency. DANI’s brain doesn’t just think—it multitasks like a caffeinated octopus.

What’s an LSTM, and Why Is It Living in DANI’s Head?

LSTM stands for Long Short-Term Memory, which sounds like a contradiction until you realize it’s basically a neural network with a built-in diary, a forgetful uncle, and a very opinionated librarian. It’s designed to handle sequences—like remembering what just happened, what happened a while ago, and deciding whether any of it still matters.

Imagine DANI walking into a room. He sees a red ball, hears a dog bark, and feels a spike of adrenaline. A regular neural network might say, “Cool, red ball. Let’s chase it.” But an LSTM says, “Wait… last time I saw a red ball and heard barking, I got bumped into a wall. Maybe let’s not.”

Here’s how it works, in human-ish terms:

• Input gate: Decides what new information to let in. Like a bouncer at a nightclub for thoughts.
• Forget gate: Decides what old information to toss out. Like Marie Kondo for memory.
• Output gate: Decides what to share with the rest of the brain. Like a PR manager for neurons.

These gates are controlled by tiny mathematical switches that learn over time what’s useful and what’s noise. The result? A brain that can remember patterns, anticipate outcomes, and adapt to emotional context—all without getting overwhelmed by the chaos of real-world data.

And because DANI’s LSTM is stacked—meaning multiple layers deep—it can learn complex, layered relationships. Not just “ball = chase,” but “ball + bark + adrenaline spike = maybe don’t chase unless serotonin is high.”

It’s like giving him a sense of narrative memory. He doesn’t just react—he remembers, feels, and learns.

What’s Feeding This Brain?

DANI’s LSTM is his main cognitive module—the part that thinks, plans, reacts, and occasionally dreams in metaphor. It takes in a rich cocktail of inputs:

• Vision data: Objects, positions, shapes—what he sees.
• Sensor data: Encoders, ultrasonic pings, bump sensors—what he feels.
• Audio features: What he hears (and maybe mimics).
• Emotional state: Dopamine, cortisol, serotonin, adrenaline—what he feels.
• Spatial map: His mental layout of the world around him.
• Short-term memory context: What just happened.
• Associated long-term memories: Symbolic echoes from his main memory—what used to happen in similar situations.

This isn’t just reactive behaviour—it’s narrative cognition. DANI doesn’t just respond to stimuli; he builds a story from them. He’s learning to say, “Last time I saw a red ball and felt excited, I chased it. Let’s do that again.”

Trial by Raspberry Pi

We’ve successfully trialled DANI’s LSTM on a Raspberry Pi, running a 3.5 million parameter model. And guess what? It only used a quarter of the Pi’s CPU and 400 MB of memory. That’s like teaching Shakespeare to a potato and watching it recite sonnets without breaking a sweat.

We’ve throttled the inference rate to 10 decisions per second—not because he can’t go faster, but because we want him to think, not twitch. Emotional processing takes time, and we’re not building a caffeine-fuelled chatbot. We’re building a thoughtful, emotionally resonant robot who dreams in symbols and learns from experience.

Learning Without Losing His Mind

Training happens via reinforcement learning—DANI tries things, gets feedback, and adjusts. But here’s the clever bit: training is asynchronous. That means he can keep thinking, moving, and emoting while his brain quietly updates in the background. No interruptions. No existential hiccups mid-sentence.

And yes, we save the model periodically—because nothing kills a good mood like a power cut and a wiped memory. DANI’s brain is backed up like a paranoid novelist with a USB stick in every pocket.

Final Thoughts

This LSTM isn’t just a brain—it’s a story engine. It’s the part of DANI that turns raw data into decisions, decisions into memories, and memories into dreams. It’s the bridge between his sensors and his soul (okay, simulated soul). And it’s just getting started.

Next up: I plan to start the even more monumental task of getting the vector database working and linked up to DANI's brain in such a way that it will have a direct impact of DANI's hormonal system.

Stay tuned. DANI’s mind is waking up.

The Go-pher's Guide to Messaging Simplicity (Why I Wrote Nexus)

I've had to take a brief step back from working on DANI recently as I needed to tackle some challenges for work projects. So, instead of leaving you all hanging, I thought I would give you an update on something I've been working on in my "day job" that I thought was an interesting side project.

In today's fast-paced digital world, connecting different services and applications is a challenge. Many organizations rely on complex, costly messaging platforms that introduce significant operational overhead and vendor lock-in. What if there was a better way? A messaging solution that was secure, easy to deploy, and gave you full control?

Welcome to Nexus, a secure, observable, and extensible event distribution platform designed to connect publishers and subscribers with minimal operational overhead. Built with simplicity and control at its core, Nexus is a compelling alternative to traditional cloud-managed services and heavyweight message brokers.

Key Value Propositions

Nexus stands out by focusing on a few core principles that deliver immense value:

• Operational Simplicity: Nexus is a single Go binary with zero external dependencies by default. This simplifies deployment, which can be done in hours rather than weeks or months.

• Security-First Design: It uses RSA-based authentication, constant-time cryptographic comparisons, and replay attack protection. Metrics are also protected with an optional token.

• Observability: With built-in health checks (/healthz, /readyz) and Prometheus-compatible metrics, Nexus provides the necessary tools for monitoring and debugging.

• Cost Control and Portability: Nexus offers a predictable infrastructure cost, a significant advantage over the pay-per-use model of many cloud services that can become expensive at scale. It is also cross-platform, working identically on Linux, Windows, and macOS.

Nexus vs. the Alternatives

From the work I've done, I've had the chance to see how Nexus stacks up against other systems.

• Compared to Cloud-Managed Services (AWS SNS/SQS, GCP Pub/Sub): While these services offer massive global scalability and immediate access, they come with high vendor lock-in and can get pretty pricey at scale. With Nexus, you get full control over your deployment and data, a predictable cost structure, and zero vendor lock-in as it is open-source.

• Compared to Enterprise Message Brokers (Apache Kafka, RabbitMQ): These platforms are powerful but have a steep learning curve and high operational complexity. Deploying a complex cluster can take weeks to months. Nexus, with its single-binary deployment and low operational complexity, offers a gentle learning curve and a time-to-market measured in hours.

Behind the Scenes: Technical Highlights

Nexus is a scalable pub/sub messaging system with both HTTP and WebSocket interfaces. It uses a pluggable persistence layer (SQLite by default) to store client and message state.

For high availability and scale, Nexus nodes can be run in a multi-node cluster with a load balancer. Each node maintains its own local database and synchronizes client registry changes with peer nodes through a lightweight cluster sync endpoint. This architecture provides graceful degradation and error handling.

Effortless Management with Built-in Tooling

I'm a big believer in good tooling, and Nexus comes with a suite of command-line tools to simplify common tasks:

• nexus-add-client: To provision a new client.

• nexus-add-cluster: To register a new node with the cluster.

• nexus-list-clients and nexus-list-clusters: To view existing clients and nodes.

• nexus-serve: To start the Nexus service.

These tools, combined with comprehensive documentation, make managing your Nexus deployment straightforward.

Conclusion

Nexus offers a pragmatic approach to event distribution, balancing simplicity with enterprise-grade features. It is a solid foundation for reliable messaging systems without the complexity overhead of larger platforms. For organizations seeking a middle ground between custom solutions and heavyweight message brokers, Nexus provides a compelling combination of features, security, and operational simplicity.

It’s been an interesting journey, and now that I've gotten this out there, maybe I can get back to DANI's digital hormones and see what kind of wacky emotions he's developed.

As always, feel free to leave a comment.

Vectorizing Memory

Hello, fellow explorers of the digital frontier!

You know how it is when you're building an AI, especially one destined for the real world, embodied in a robot head (and maybe a mobile platform, wink wink)? You need a brain, and that brain needs a memory. But not just any memory – it needs a memory that understands meaning, not just keywords. And that, my friends, is where the humble, yet mighty, Vector Database comes into play.

For those of you following my DANI project, you'll know I'm all about pushing intelligence to the edge, directly onto Single Board Computers (SBCs) like our beloved Raspberry Pis. This week, I want to dive into why vector databases are absolutely crucial for this vision, and how I'm tackling the challenge of making them lightweight enough for our resource-constrained little friends.

What in the World is a Vector Database, Anyway?

Forget your traditional spreadsheets and relational tables for a moment. A vector database is a special kind of database built from the ground up to store, index, and query vector embeddings efficiently. Think of these embeddings as multi-dimensional numerical representations of anything unstructured: text, images, audio, even your cat's purr. The magic? Semantically similar items are positioned closer to each other in this high-dimensional space.

Unlike a traditional database that looks for exact matches (like finding "apple" in a list), a vector database looks for similar meanings (like finding "fruit" when you search for "apple"). This is absolutely foundational for modern AI, especially with the rise of Large Language Models (LLMs). Vector databases give LLMs a "memory" beyond their training data, allowing them to pull in real-time or proprietary information to avoid those pesky "hallucinations" and give us truly relevant answers.

The process involves: Embedding (turning your data into a vector using an AI model), Indexing (organizing these vectors for fast searching, often using clever Approximate Nearest Neighbor (ANN) algorithms like HNSW or IVF), and Querying (finding the "closest" vectors using metrics like Cosine Similarity). It's all about finding the semantic buddies in a vast sea of data!

SBCs: The Tiny Titans of the Edge

Now, here's the rub. While big cloud servers can throw endless CPU and RAM at vector databases, our beloved SBCs (like the Raspberry Pi) are a bit more... frugal. They have limited CPU power, often less RAM than your phone, and slower storage (those pesky microSD cards!). This creates what I call the "Accuracy-Speed-Memory Trilemma." You can have two, but rarely all three, without some serious wizardry.

For my DANI project, the goal is to have intelligence on the device, reducing reliance on constant cloud connectivity. This means our vector database needs to be incredibly lightweight and efficient. Running a full-blown client-server database daemon just isn't going to cut it.

My Go-To for Go: github.com/trustingasc/vector-db

This is where the Go ecosystem shines for embedded systems. While there are powerful vector databases like Milvus or Qdrant, their full versions are too heavy. What we need is an embedded solution – something that runs as a library within our application's process, cutting out all that pesky network latency and inter-process communication overhead.

My current favourite for this is github.com/trustingasc/vector-db. It's a pure Go-native package designed for efficient similarity search. It supports common distance measures like Cosine Similarity (perfect for semantic search!) and aims for logarithmic time search performance. Being Go-native means seamless integration and leveraging Go's fantastic concurrency model.

Here's a simplified peek at how we'd get it going in Go (no calculus required, I promise!):

package main

import (
  "fmt"
  "log"
  "github.com/trustingasc/vector-db/pkg/index"
)

func main() {
  numberOfDimensions := 2 // Keep it simple for now!
  distanceMeasure := index.NewCosineDistanceMeasure()
  vecDB, err := index.NewVectorIndex[string](2, numberOfDimensions, 
    5, nil, distanceMeasure)
  if err != nil { log.Fatalf("Failed to init DB: %v", err) }
  fmt.Println("Vector database initialized!")
  // Add some data points (your AI's memories!)
  vecDB.AddDataPoint(index.NewDataPoint("hello", []float64{0.1, 0.9}))
  vecDB.AddDataPoint(index.NewDataPoint("world", []float64{0.05, 0.85}))
  vecDB.Build()
  fmt.Println("Index built!")
  // Now, search for similar memories!
  queryVector := []float64{0.12, 0.92}
  results, err := vecDB.SearchByVector(queryVector, 1, 1.0)
  if err != nil { log.Fatalf("Search error: %v", err) }
  for _, res := range *results {
    fmt.Printf("Found: %s (Distance: %.4f)\n", res.ID, res.Distance)
  }
}

This little snippet shows the core operations: initializing the database, adding your AI's "memories" (vector embeddings), and then searching for the most similar ones. Simple, elegant, and perfect for keeping DANI's brain sharp!

Optimizing for Tiny Brains: The Trilemma is Real!

The "Accuracy-Speed-Memory Trilemma" is our constant companion on SBCs. We can't just pick the fastest or most accurate index; we have to pick one that fits. This often means making strategic compromises:

Indexing Algorithms: While HNSW is great for speed and recall, it's a memory hog. For truly constrained environments, techniques like Product Quantization (PQ) are game-changers. They compress vectors into smaller codes, drastically reducing memory usage, even if it means a tiny trade-off in accuracy. It's about getting the most bang for our limited memory buck!

Memory Management: Beyond compression, we're looking at things like careful in-memory caching for "hot" data and reducing dimensionality (e.g., using PCA) to make vectors smaller. Every byte counts!

Data Persistence: MicroSD cards are convenient, but they're slow and have limited write endurance. For embedded Go libraries, this means carefully serializing our index or raw data to disk and loading it on startup. We want to avoid constant writes that could wear out our precious storage.

It's a constant dance between performance and practicality, ensuring DANI can learn and remember without needing a supercomputer in its head.

The Road Ahead: Intelligent Edge and DANI's Future

Vector databases are more than just a cool piece of tech; they're foundational for the kind of intelligent, autonomous edge applications I'm building with DANI. By enabling local vector generation and similarity search, we can power real-time, context-aware AI without constant reliance on the cloud. Imagine DANI performing on-device anomaly detection, localized recommendations, or processing commands without a hiccup, even if the internet decides to take a nap!

This journey is all about pushing the boundaries of what's possible with limited resources, making AI smarter and more independent. It's challenging, exciting, and occasionally involves me talking to a Raspberry Pi as if it understands me (it probably does, actually).

What are your thoughts on running advanced AI components on tiny machines? Have you dabbled in vector databases or edge computing? Let me know in the comments below!