DEV Community: Brian Tarbox

Which Part Was Me?

Brian Tarbox — Thu, 24 Sep 2026 13:08:15 +0000

Last week I said I was still working on an answer. I have a partial one. It's smaller than I hoped and bigger than I feared.

Here's the question again, for anyone who skipped the last post. (I saw the analytics. I'm fine. I said it a little too fast.) When a tool does in seconds what took me a decade to learn, which part was me?

I started by making a list. Engineers make lists when they're anxious.

Column One

Column one was the stuff I spent years learning that a model now does as well as I do, or better. It was longer than I wanted. Syntax for a dozen languages, most now niche (Assembler, Fortran, Prolog). The exact incantation that makes a linker stop complaining. API surfaces I memorized like phone numbers. Boilerplate. So much boilerplate. I was proud of all of it. Some of it paid for a house.

A lot of it turns out to have been lookup. Very fast lookup, with good taste in what to look up, but lookup. That's the "smaller than I hoped" part. It stung more than I expected.

Column Two

Column two took longer, because the things in it were harder to name. Knowing which problem is worth solving before anyone writes a line. Looking at a clean, confident architecture diagram and feeling that something is wrong before I can say what. Asking the question nobody in the meeting asked. Knowing when the right answer is "don't build it." None of it survives being written down as a rule.

That's the "bigger than I feared" part.

In college I studied Wittgenstein, who spent a lot of time on a question that sounds dumb until it doesn't. How do you know how to follow a rule? Not the rule itself. The applying of it. No rule tells you when it applies. You'd need another rule for that, and another for that one, and eventually you hit bottom on something that isn't a rule at all. It's practice. It's judgment. It's what's left when the lookup is free.

I think that's the part that was me. It's also, inconveniently, the part I can't put on a slide.

So how did I sort the columns? Mostly by remembering an old bug.

The 2 a.m. Lesson

Years ago I worked on Video On Demand at Motorola. We had a system that would run beautifully, then fall over. Not one server. The whole thing, in a cascade. We'd bring it back up, it would be fine, and then it wasn't. We looked for a pattern in time of day, load, content, the phase of the moon. Nothing.

Eventually I noticed something small. The failure always started on the ninth request of a burst. Not the eighth. Not the tenth. The ninth.

I had no idea why that mattered. I stared at it for longer than I'd like to admit. Then I remembered our servers had eight cores. The ninth request was the first one that had to wait. The first one to block.

That pointed us at locking, specifically fair versus unfair locks in Java. A fair lock serves waiting threads in the order they arrived. An unfair one lets newcomers jump the queue. That meant that the 9th request stayed blocked until the burst of traffic cleared, and by that point it had timed out.

Here's the thing. A model today could explain fair and unfair locking better than I could. Point it at the lock and it would probably fix it before lunch. What I'm less sure it could do is notice the nine. The number wasn't in any single log. It was a pattern across dozens of crashes, and its meaning lived in a hardware spec that had never been written down. Maybe a future model spots it. Even then, someone has to decide the ninth request is worth staring at.

That staring is column two. It's also the part I'd been underpricing for 45 years, because it never showed up in a line count.

The Test

Here's the working distinction I landed on. If I can write it down as a rule, it's column one, or it will be soon. If I can only show it by example, it's column two.

This is not a comfortable test. It means the columns aren't fixed. Things migrate. Every time someone writes down a piece of judgment clearly enough, it moves left. Code review heuristics. Architecture patterns. Security checklists. That's the grief I wrote about last time, and it isn't going away.

But the migration has a floor. Every rule you write needs someone to decide when it applies. Wittgenstein's regress doesn't end with a better rule. It ends in practice, not another rule.

What I'm Doing About It

If column two is the part that was me, I should probably invest in it on purpose. Four things so far.

I review agent output like a teaching case, not a chore. When it's wrong, I ask how I knew. Half the time the answer is context it didn't have. The other half is interesting.

I write down the near misses. Not the rules. The stories. Stories carry judgment in a way checklists don't. That's also how I learned most of it in the first place, usually from someone older telling me what went wrong in 1987.

I pair juniors with failures instead of tutorials. A tutorial teaches column one, and a model will beat them at it by lunch.

I stopped apologizing for the staring. Looking at something until you know what's wrong with it is not slowness. It's the job now. Possibly it always was.

Back to the Buoy

Last time I said nobody catches a drifting buoy, and the rower's job is to hold a steady stroke (I wrote the timing program for a regatta and the finish line buoy got cut and started floating downstream). I'd add one thing. Someone also has to notice the buoy moved. Someone on the bank has to say, "Wait. That's not where the finish was."

That's column two. The model rows beautifully. It still doesn't know the buoy was cut.

So which part was me? Less than I thought. The part that remains is the part that was always doing the real work, hiding behind all that syntax. I'm a little embarrassed it took a machine to show me. I'm also relieved it's there.

Anyway. I'm going to go knead some dough. That's where the column two stuff tends to show up.

I've Been Doing This for 45 Years and I Can't Keep Up

Brian Tarbox — Mon, 21 Sep 2026 13:50:38 +0000

I started in assembly. I have written C on machines that would lose a fight with your toaster. I have lived through the PC, the web, mobile, and the cloud. I have ten patents and a closet full of conference badges.

Last Tuesday I felt like a beginner. I did not enjoy it. I did recognize it.

I've also started noticing a look on other people. It's a lack of affect. Someone asks how you're doing, and you hear "I'm fine" a little too fast. I know that answer. I've given it. Everyone says it. Nobody believes it, including the person saying it.

Let me be clear about what this is not. It is not a post about whether AI takes over the world. Assume every model ships safe, aligned, and polite. I am still tired.

I know tired. I've done crunch. Crunch is a familiar kind of awful. It has a shape, and it ends when the release ships. This has no ship date, so there's no collapse afterward.

The problem is the pace. Something lands on Monday that would have been a research paper two years ago. By Thursday there is a better one. I used to have twenty browser tabs open. Now I have twenty tab groups. I'm adding things to read faster than I can read them.

The bar keeps moving, too. AI made everything faster, so everything is now expected faster. I ship more than I ever have and still end the week feeling behind. "Enough" got redefined and nobody sent a memo.

I've been through paradigm shifts before. They felt like slopes. You could see the hill coming and climb it in a few months of evenings. This one is different because the tool I'm learning is also speeding up the learning. The ground isn't moving. The rate the ground moves is moving.

The Wrong Question

So how does a working technician stay current?

That's the wrong question. The honest answer is you don't, not fully, and anyone who says otherwise is selling a newsletter. The better question is what I'm allowed to let go of.

What I Do Instead

I track the building blocks, not the products. Model names change monthly. The pieces underneath change slowly. How much a model can hold in its head at once (the context window). How it calls outside tools. How it fetches facts it wasn't trained on. How you coordinate several of them. How you tell whether any of it works. Learn those and the next release is a variation, not a surprise.

I picked a depth. I can't be an expert in everything, so I chose a few areas to go deep on and made peace with being a tourist everywhere else. My inner completionist filed a complaint. I overruled it.

I budget for checking the agent. Reviewing what a model hands you is a background process that never quits. Watching one confidently do the wrong thing at 2 a.m. taught me more than any blog post has. It also cost me sleep. So I plan for it like any other cost.

I let myself be bad at things again. Senior people hate this one. We're used to being the person in the room who knows. Being a beginner 45 years in feels like a demotion. It's actually the job. There's a quieter version I like less. When a tool does in seconds what took me a decade to learn, I catch myself wondering which part was me. I'm still working on that answer.

Keep Rowing

In college, my team wrote the timing and scoring software for the Head of the Connecticut Crew Regatta. Then the finish-line buoy got cut, and the finish line started drifting down the river. A rower in the middle of the race shouted to us on the bank, "What do we do?" We shouted back, "Keep rowing!" and recorded several alternative finish times.

AI is that buoy. There is no fixed finish line to sprint toward, and it's drifting.

Which brings me to the trap. It says if the pace is insane, match it. Read everything. Try every release. Give up your evenings, your weekends, and your dignity. You won't stay ahead. The treadmill speeds up as you run, and the bar moves while you're under it. A full sprint buys you burnout on a slightly earlier schedule.

The rower's job was never to catch the buoy. It was to hold a steady stroke. And nobody catches a drifting buoy, so if you're waiting to be caught up before you rest, you'll wait forever.

So here is my heretical position. Take the week off.

Most of what you'd learn that week has a shelf life shorter than the leftovers in your fridge. Some of it gets replaced before you finish the tutorial. Some of it is a dead end that other people found so you don't have to. Skipping it costs you very little.

The durable stuff is different. The building blocks, and hard-won instincts about how these systems fail, keep compounding. You don't need to be online 24/7 to collect them.

I took a real break not long ago. No newsletters, no release notes, no "quick look" at the new model. The world did not end. I came back to find that three of my tab groups had been made irrelevant by events. The universe closed them for me. I have never been so grateful.

Rest isn't wasted time, either. Some of my best architecture ideas arrived while I was kneading dough or staring at a wall. Your brain has to stop taking in new material so it can sort what it already has.

None of this means quitting. It means pacing. The people who last aren't the fastest. They're the ones still here.

Grief and Joy

Under the fear and the FOMO, there is grief. Some of what took us years to learn is getting cheaper. That stings.

There is also joy, the same joy that pulled me into this as a kid. Things are possible now that weren't possible last year. Both feelings are true at once.

And sometimes it's more than tired. If it starts following you into the parts of your life that used to be safe from it, talk to a person. That isn't weakness. It's good engineering. Escalate early.

So if you feel behind, you're not alone. You're probably not even that far behind. You're just paying attention.

Close a few tab groups, or let the universe close them for you. Build something small. Then take the week off.

The buoy will still be drifting when you get back.

None of this means quitting. It means pacing. The people who last are not the fastest ones. They are the ones who are still here.

The Part Nobody Says Out Loud

So if you are feeling behind, you are not alone. You are probably not even that far behind. You are just paying attention. The silence is part of the load. We started a Neurodivergent support group at work and made it a safe space to say you're not fine. That gives people permission to be honest.

Close a few tabs, or let the universe close them for you. Build something small. Then take the week off. It will still be here when you return.

My Books Are Invisible and I Want Them Back

Brian Tarbox — Mon, 22 Jun 2026 14:33:15 +0000

My Books Are Invisible and I Want Them Back

There used to be a thing that happened when someone came to your house. They'd drift over to your bookshelf. They'd tilt their head sideways to read the spines. And then the conversation would start. "Oh, you read Gödel, Escher, Bach?" or "Wait, you have the whole Dresden Files?" or just a long knowing look when they spotted the Ursula Le Guin.

Your bookshelf was a social artifact. It was a portrait of your mind sitting right there in the room.

I've read over 200 books in the last decade. You'd never know it walking into my house. They live on my Kindle. A few more on Audible. The physical shelves have some old favorites and a lot of decorative objects I don't know what to do with. The library I've actually built is invisible.

I wanted to fix that.

The Idea

The premise is simple. Project a digital bookshelf onto a wall. Real wooden shelves with book spines. Your actual books. Tap one and the cover pops up. Leave it running as an ambient display when people come over and let the conversation start the way it used to.

I built it with Claude. One conversation, no prior codebase, starting from scratch. If you want to play with it check out [https://briantarbox.com/bookshelf_display.html]

Step One: The Shelf Itself

The first decision was display style. I wanted realistic wooden shelves, not a flat grid of covers. I wanted it to look like something you'd actually hang on a wall. Claude built it as a single self-contained HTML file. No server. No database. No dependencies to install. Just open it in a browser, go fullscreen, and you have a bookshelf.

The wood grain is CSS. The ambient candlelight glow is CSS. The little flickering flame on the candle between the books is a CSS animation. There's a small plant. There are bookends. It looks like a real shelf.

Book spines have colors derived from the title using a hash function, so the same book always gets the same color. Not random on every reload. The title and author are rendered vertically, the way a real spine reads.

Step Two: Real Books

The default shelf ships with sample titles. I needed my actual library in there.

Goodreads makes this easy. Go to My Books, scroll to Import and Export, click Export Library, download the CSV. It has every book you've ever shelved, with title, author, and ISBN.

I uploaded the CSV to Claude. It parsed all 234 books, formatted them correctly, and baked them directly into the HTML file. The list lives as a JavaScript array in the file itself. My books, my shelf.

Anyone can do this with their own library. Upload your Goodreads export, tell Claude to substitute it for the sample books, done. The whole thing takes about two minutes.

Step Three: Cover Art

Clicking a spine should show the front cover. That meant fetching cover images from somewhere.

This turned out to be the most iterative part of the build. The first version used Open Library exclusively. It found nothing. Not because Open Library is bad, but because the sandbox Claude runs in can't reach external APIs. The browser can, but the testing environment can't. The silent failures looked like the code working fine.

We switched to Google Books as the primary source. Better coverage, no API key needed, handles CORS cleanly. But about half the books still came back with no cover.

The fix was a four-step waterfall:

Google Books by ISBN (most precise, uses the ISBN13 from the Goodreads CSV)
Google Books by cleaned title and author
Open Library by ISBN
Open Library by title search

Step two includes a small but important detail. A lot of my books have series information in the title field from Goodreads. Things like "Cold Days (The Dresden Files, #14)". That parenthetical confuses search APIs. The code strips it before querying. That alone recovered a lot of missing covers.

After that, we added a background prefetch thread. When the page loads, it quietly works through all 234 books three at a time, runs the full waterfall for each one, and caches the result. By the time you tap a book it was prefetched a few seconds ago. The cover is already in browser memory. It appears instantly.

Step Four: The Layout

Four rows of books fit on screen but 234 books across four rows means about 59 per row. The spines were getting cramped.

The solution was horizontal scrolling. One long continuous shelf you drag left and right. Click and drag on a laptop. Swipe on a touchscreen. The whole library is there, sorted alphabetically by author, just like a real bookshelf. A subtle hint at the bottom fades out once you start scrolling.

What It's Made Of

Everything is in one HTML file. The CSS. The JavaScript. The book list. The cover fetching logic. The drag-to-scroll behavior. All of it.

The file is not small. It has 234 book objects with titles, authors, and ISBNs. It has a four-step cover fetching waterfall with error handling at each step. It has a prefetch cache and a concurrency-limited background runner. It has a modal with a spinner and a styled fallback card for when no cover is found. It has drag-to-scroll with touch support.

And it runs fine as a local file in any browser. No build step. No npm install. No deployment. Open it, fullscreen it, project it.

Making It Yours

The book list in the HTML is my library. But the file is just a file.

If you want your own version, export your Goodreads library as a CSV, upload it to Claude (or your favorite AI) and ask it to build the bookshelf. The whole process takes a few minutes and is a learning journey on its own.

Don't use Goodreads? A plain list of titles works too. Use the Edit Book List panel built into the shelf itself. One book per line, Title | Author format. Hit Update Shelf.

The Bigger Point

This project is a good example of what iterative AI-assisted building actually feels like. We didn't spec it out up front. We started with the visual, got the shelf looking right, then added books, then added covers, then discovered the covers weren't working, diagnosed why, fixed the strategy, added prefetching, rethought the layout, added scrolling.

Each step was a conversation. Some steps took one exchange. The cover waterfall took four or five as we worked out what was actually failing and why.

The final artifact is a real piece of software. It has error handling. It has a caching layer. It has graceful fallbacks. It does something genuinely useful.

And when someone comes to my house now, there's a bookshelf on the wall again.

The conversation can start.

Listen to Your Cloud: Co-Developing a CloudTrail Sonifier with an AI Partner

Brian Tarbox — Tue, 14 Apr 2026 18:06:31 +0000

Listen to Your Cloud: Co-Developing a CloudTrail Sonifier with an AI Partner

Back in 2010 I won a Duke's Choice Award at JavaOne for Log4JFugue, a system that converted log4j output into music streams. The core idea was simple: just as an auto mechanic can listen to a car and hear what's wrong, developers should be able to listen to their applications. You'd map your program's key verbs (create, process, destroy, error) to instruments like bass drum, snare, and cymbal crash, count occurrences in one-second buckets, and generate a chord per second. Busy seconds sounded thick. Quiet seconds sounded thin. Errors sounded wrong. You could literally hear your application's health while doing other work. (shoutout to David Koelle, the creator of JFugue, the technology underlying Log4JFugue).

That was sixteen years ago and the project has been on the shelf for a while. Recently I started wondering what it would look like to apply the same concept to AWS CloudTrail logs. Not log4j lines from a single application, but the firehose of API calls across an entire AWS account. I decided to find out, and I decided to do it by co-developing the system with Claude. What followed was one of the most interesting pair-programming sessions I've had, and a real education in what AI-assisted development actually looks like in practice.

Starting from the Idea

I gave Claude the context: JFugue was a Java library for programmatic music creation, Log4JFugue used it to sonify log files, and I wanted something similar for CloudTrail. Could it build a Python program that does a tail-dash-follow on CloudTrail events?

Within a minute I had a complete first version. It mapped AWS services to General MIDI instruments (EC2 got piano, S3 got marimba, IAM got trumpet), classified API actions into pitch ranges by their CRUD nature, added dissonant intervals for error events, and even hashed source IPs to stereo pan positions. The code was well-structured, properly documented, and showed a real understanding of the musical concepts behind the original project. A strong start.

It also didn't work at all.

The Debugging Dance

What followed was a series of increasingly specific problems that we worked through one at a time. The first was boring: AWS credentials weren't configured. Claude walked through the options (aws configure, environment variables, SSO) and noted the minimum IAM policy needed. Fair enough.

The second was more interesting: the program ran fine but produced no sound. This is the classic MIDI trap and I'll admit I should have seen it coming. The original code used the mido library to send MIDI messages, but MIDI messages are just instructions. Without a synthesizer listening on the other end, you get silence. Claude proposed adding a sounddevice backend that synthesized audio directly using numpy waveforms, no MIDI routing required. That was the right call. It also added a --test flag that plays a C major scale on startup so you can verify audio works before waiting for CloudTrail events. Small thing, huge time saver.

Then we hit the CloudTrail delivery delay problem. I was generating events but seeing nothing. Turns out CloudTrail's lookup_events API has a 5 to 15 minute delivery delay from when an event occurs to when it shows up in the API. Our initial 2-minute lookback window was missing everything. Claude widened it to 20 minutes. Problem solved, but this was the kind of thing where my AWS experience (I've been working with CloudTrail for years) and Claude's ability to quickly restructure the code made for a good partnership. I knew the problem, Claude implemented the fix in seconds.

Getting the Music Right

Once events were flowing and audio was working, we moved to the part I actually cared about: making it sound right.

The first version played events sequentially, one note per event. This was fundamentally wrong. In Log4JFugue, the whole point was the chord-per-second model. All events within a one-second window get stacked into a single chord. You hear density. Fifteen API calls in one second produces a thick, rich chord. One lonely DescribeTable produces a single thin tone. The difference is immediately perceptible, and that perceptual bandwidth is the whole reason sonification works.

I explained this to Claude and it restructured the entire architecture around a ChordBucket data class. Events get grouped by timestamp, deduplicated pitches form the chord, and repeated occurrences of the same event drive up velocity instead of adding more notes. This was a substantial rewrite and it got it right on the first pass.

Then came the error sounds. I asked Claude to make errors more prominent. It went for it: minor seconds, tritones ("the devil's interval" as it noted), square wave timbres, noise bursts, a 55 Hz bass rumble, and error chords that rang 50% longer than normal chords. I ran it and nearly fell out of my chair. "Can you dial back the error effect about half? We want people to notice the error without giving them a heart attack." Claude's response: let's make it an alert, not a cardiac event. It dialed everything back, and I asked it to print the actual error messages alongside the musical output. Now you hear the dissonance and can glance over to see "s3.GetObject: AccessDenied" right there in the terminal.

The Timing Problem

The trickiest issue was pacing. After switching to 60-second poll intervals (to stop getting throttled by CloudTrail's API rate limits), we had a new problem: 20 seconds of music followed by 40 seconds of dead silence. The program was playing all the chords as fast as possible, then sleeping until the next poll. That's not ambient monitoring, that's morse code.

The fix was to stretch each chord's duration to fill the entire poll interval. Twenty chords across 60 seconds means each chord sustains for 3 full seconds, flowing directly into the next. This also created a nice emergent property: busy intervals with many events produce rapid-fire chord changes, while quiet intervals produce long sustained drones. The pace of the music now encodes the activity level, not just the chord thickness. That's actually better than what Log4JFugue did.

What I Learned About AI Co-Development

This project took a single extended conversation. The system went from concept to working, audible, properly-paced CloudTrail sonification through maybe a dozen iterations. Some observations from the process:

Claude is an excellent first-draft generator and a fast refactorer. The initial code was structurally sound even if it didn't work out of the box. When I described what needed to change, the changes came fast and were usually right.

Domain knowledge still matters enormously. I knew about the CloudTrail delivery delay, about the chord-per-second model being essential, about MIDI needing a synthesizer. Claude didn't volunteer any of these things. But once I identified the issue, it could fix it faster than I could have.

The back-and-forth is the whole point. This wasn't me typing a prompt and getting a finished product. It was a genuine iterative development process: try it, find what's wrong, describe the problem, get a fix, try again. The pattern is much closer to pair programming than it is to code generation.

And sometimes you have to say "go back." We went down a path trying to eliminate tiny audio gaps between chords by pre-rendering and threading. It added complexity without solving the actual problem (which turned out to be the pacing model, not the gap). I asked Claude to revert to the simpler version and we took a different approach. That's a normal part of development, and it worked fine here too.

If you want to try it yourself, you need boto3, sounddevice, and numpy. Point it at an AWS account with some activity and listen. After a few minutes you'll start to develop an intuition for what "normal" sounds like. And when something goes wrong, you'll hear it. That was the whole point of Log4JFugue, and it turns out the idea translates to the cloud just fine.

The Compiler Never Used Sarcasm: Why AI Feels Unsafe to the Neurodivergent Coder

Brian Tarbox — Mon, 02 Feb 2026 14:20:01 +0000

I have been writing code for 45 years. I started when "memory" was something you counted in bytes, not gigabytes, and when a "bug" was nearly literal.

Over four and a half decades, I have watched languages evolve from Assembly to C, to Java, to Python. But through every iteration, one fundamental truth remained constant: The machine was literal. If I told the computer to do $X$, and it did $Y$, it was because I made a mistake in the instructions. It wasn't because the computer misunderstood my tone, or didn't like my attitude, or was having a bad day.

For a neurodivergent mind programming was more than a career. It was a sanctuary.

But today, as we shift toward Generative AI and Large Language Models (LLMs), that sanctuary is dissolving. We are moving from a world of explicit instruction to a world of implicit persuasion. For the neurodivergent coder, this isn't just a technical pivot; it is the loss of the only language that ever truly made sense to us.

The Black Box Problem

We often refer to neural networks as "Black Boxes" because we don't truly know how they arrive at an answer. But here is the irony: The neurotypical mind is also a Black Box to us.

We flocked to computers because they were transparent boxes. We could see the registers, trace the execution stack, and inspect the variables.

By replacing explicit code with natural language models, we have essentially built a machine that mimics the neurotypical brain: it relies on context, implies rather than states, and is confidently wrong just often enough to make you doubt your own sanity.

The Sanctuary of Syntax

To understand why this shift is so jarring, we have to look at the Theory of Mind.

In psychology, Theory of Mind is the ability to attribute mental states, beliefs, intents, desires, emotions, to oneself and others. It is the ability to understand that what is in my head is different from what is in your head. For many neurodivergent people, this is an exhausting, high-friction process. Navigating a dinner party requires constant, real-time calculation of social signals, subtext, and hidden agendas. If I tell a co-worker that “we’re going out for drinks” is that an implied invitation or just passing on some information? If it’s just information and you say “great, lets go” then you’re being rude and presumptuous. But, if it was an invitation and you say “well, that’s nice” then you’ve been rude and unfriendly. Why can’t people just be clear in what they say?

Coding required zero Theory of Mind.
The compiler has no hidden agenda. It has no "mind" to theorize about. It operates on pure, unadulterated logic.
Human interaction: "I'm fine" (Could mean: I am happy, I am angry, I am tired, or go away).
Computer interaction: return 0; (Means: The function ended successfully).

For 45 years, the IDE (Integrated Development Environment) was a safe space where the rules of social engagement were suspended. The feedback was brutal, but it was honest. A syntax error isn't a judgment of your character; it is a factual statement about a missing semicolon. I recall back in those days thinking “well if you know the semicolon is missing why can’t you just add it?” The answer of course is that the compiler had no theory of mind and didn’t “know” what I wanted.

The Invasion of Ambiguity

Enter Artificial Intelligence.
We are told that "English is the hottest new programming language." We are told to "prompt" the machine. But prompting is not programming. Prompting is negotiating.

When we write a prompt for an LLM, we are suddenly thrust back into the messy world of Grice's Maxims. Paul Grice, a philosopher of language, proposed that effective communication relies on the Cooperative Principle—rules regarding quantity, quality, relation, and manner.

Humans violate these rules constantly. We use sarcasm (violating Quality), we ramble (violating Quantity), and we are passive-aggressive (violating Manner).
Traditional code strictly enforced these maxims. It was succinct, truthful, and relevant. But an LLM? It hallucinates (violating Quality). It gives verbose, flowery explanations when you ask for a boolean (violating Quantity). It requires you to "massage" the input to get the right output.

Suddenly, the "source code" is subject to the same linguistic ambiguity as a casual conversation. We have to guess how the model "feels" about a certain phrasing. We are essentially performing therapy on a matrix of floating-point numbers to get it to write a SQL query.

An example where ambiguity nearly caused an accident
There was an accident when a pilot needed a go-around and told the co-pilot "takeoff power". That is an instruction to set full (takeoff) power to help the plane gain altitude. Unfortunately the co-pilot heard "take off power", which he interpreted as “remove power” so he set the engines to idle. This situation was exacerbated by the fact that all Air Transport communications are done in English, which was not these pilots' primary language. It’s hard to see how guardrails or contextual grounding could have helped here.

The Shannon Limit of Certainty

I actually knew Claude Shannon. We lived in the same town and I was close friends with his daughter in high school and college. We were also both members of the MIT Juggling Club.

For those who only know the name from textbooks, Shannon was the "Father of Information Theory." He was the man who realized that all information could be represented in binary digits—bits. He gave us the fundamental unit of digital certainty.

In Shannon’s world, a "bit" was a measure of the reduction of uncertainty. It was the answer to a yes/no question. It was the mathematical opposite of ambiguity.

When we wrote code in C or Java for the last 45 years, we were living in the house that Shannon built. We were manipulating bits. We were resolving uncertainty. The goal of every line of code was to eliminate noise so that the signal was perfect.

But LLMs operate on a different part of Shannon’s work: The Entropy of English.

Shannon famously estimated the "entropy" (or unpredictability) of written English. He understood that human language is redundant and statistical. This is exactly how modern AI works—it exploits the statistical redundancy of language to predict the next word.

But here is the catch: Prediction is not precision.

By moving from traditional coding to Prompt Engineering, we are trading the Bit (absolute certainty) for the Token (probabilistic likelihood). We are leaving the noise-free channel of the compiler and wading back into the swamp of linguistic entropy—the very swamp Shannon helped us pave over with digital logic.

Determinism vs. Probability: The Anxiety of "Probably"

The deepest friction, however, is mathematical.
For decades, we lived in a Deterministic world.

If P, then Q. This is a binary comfort. It is verifiable. It is reproducible.
AI introduces a Probabilistic world.
P(Q|P)
(The probability of Q, given P).

When I ask an AI to write code, it doesn't "know" the code. It predicts the next most likely token based on a massive dataset. It operates on vibes and statistical likelihoods.

For the neurodivergent thinker who finds comfort in patterns and rigid systems, this is a source of profound anxiety. We are moving from a system that is "Correct or Incorrect" to a system that is "Good Enough."

Old World: You spent 3 hours debugging because the logic was flawed.
New World: You spend 3 hours "prompt engineering",which is really just trying to figure out the magic words to persuade the black box to behave. And then spent another 3 hours validating that the generated code was correct (you did check, right?)

Where Do We Go From Here?

I am not a Luddite. I’m an AWS Hero, AWS Ambassador and have 10 US technical patents. I use AI every day. It is a powerful tool. But I mourn the loss of the binary sanctuary.

We are entering an era where "coding" will look less like architecture and more like diplomacy. It will reward those who are good at linguistic nuance and persuasion, skills that have traditionally favored the neurotypical. On the other hand, intuitive leaps might actually favor the neurodivergent.

For those of us who spent decades finding solace in the absolute truth of a compiler error, we have to learn a new skill. We have to learn to tolerate the ambiguity of the machine, just as we have learned to tolerate the ambiguity of the world.

But I will miss the days when, if I said exactly what I meant, the machine did exactly what I said.

Brian Tarbox holds degrees in Linguistic Philosophy and Cognitive Psychology

Why Resilience Matters

Brian Tarbox — Sat, 06 Apr 2024 20:43:45 +0000

In today's digital landscape, where businesses heavily rely on cloud-based applications to drive their operations, ensuring the resilience and reliability of these systems is of paramount importance. Resilience refers to the ability of an application or system to withstand failures, recover quickly, and maintain continuous availability, even in the face of unexpected events or disruptions.

Achieving resilience is crucial for several reasons. First and foremost, it minimizes the risk of costly downtime, which can lead to significant financial losses, damage to brand reputation, and customer dissatisfaction. Additionally, resilient systems are better equipped to handle unexpected spikes in demand, ensuring that users can access the application or service without interruptions. Furthermore, resilience contributes to overall business continuity, enabling organizations to maintain critical operations and meet their obligations, even during challenging circumstances.

Shared Responsibility Model

When it comes to cloud computing, the concept of the Shared Responsibility Model is fundamental to understanding the division of responsibilities between the cloud provider and the customer. In the case of Amazon Web Services (AWS), the cloud provider is responsible for the security and availability of the underlying cloud infrastructure, including the hardware, software, networking, and facilities that run AWS Cloud services.

On the other hand, customers are accountable for securing and managing their applications and data within the cloud environment. This includes tasks such as configuring security groups, implementing access controls, and ensuring the resilience of their applications through proper design and operational practices.

Embracing Serverless Architecture

One effective way to shift more responsibility to the cloud provider and simplify resilience efforts is by embracing a serverless architecture. Serverless computing allows developers to focus on writing code without worrying about provisioning, scaling, or managing servers. AWS services like AWS Lambda, Amazon API Gateway, and Amazon DynamoDB enable developers to build and run applications without the need for server management, reducing the operational overhead and potential points of failure.

By leveraging serverless services, organizations can offload a significant portion of the infrastructure management responsibilities to AWS, allowing them to concentrate their efforts on application logic and resilience strategies specific to their use cases.

Control Plane vs. Data Plane

When discussing resilience in cloud computing, it's essential to understand the distinction between the control plane and the data plane. The control plane refers to the management and configuration of cloud resources, such as creating, modifying, or deleting instances, load balancers, or databases. The data plane, on the other hand, encompasses the actual data processing and application logic that runs on top of the cloud infrastructure.

While AWS is responsible for the resilience of the control plane, ensuring the availability and reliability of the underlying cloud services, customers are accountable for the resilience of their applications and data within the data plane. This includes implementing strategies for fault tolerance, redundancy, and failover mechanisms to ensure continuous operation in the event of failures or disruptions.

Infrastructure Design

Designing a resilient infrastructure is a critical aspect of building resilient cloud applications. This involves implementing redundancy at various levels, such as networking, storage, and compute resources.

Networking redundancy can be achieved by leveraging multiple Availability Zones (AZs) or even multiple AWS Regions, ensuring that if one AZ or Region experiences an outage, the application can failover to another location. Additionally, services like Amazon Route 53 can be used for DNS failover, automatically routing traffic to healthy endpoints.

Monitoring, logging, and alerting are essential components of a resilient infrastructure. By implementing comprehensive monitoring solutions like Amazon CloudWatch, organizations can proactively detect and respond to potential issues before they escalate into major incidents. Centralized logging and alerting mechanisms help teams quickly identify and troubleshoot problems, minimizing downtime and ensuring timely recovery.

Security is another crucial aspect of resilience. By implementing robust security measures, such as security groups, network access control lists (NACLs), and least-privileged access controls, organizations can mitigate the risk of security breaches, which can lead to significant downtime and data loss.

Application Design

While infrastructure design plays a vital role in resilience, the application itself must also be designed with resilience in mind. Adhering to good design principles, such as loose coupling and high cohesion, can help minimize the impact of failures and enable easier recovery.

Event-driven message passing and queuing systems like Amazon Simple Queue Service (SQS) can act as buffers, allowing applications to ride out transient errors and handle bursts of traffic without disruption. Implementing idempotent operations, where multiple identical requests have the same effect as a single request, can also enhance resilience by ensuring that duplicate requests do not cause unintended consequences.

Adopting a microservices architecture can further contribute to resilience by breaking down applications into smaller, independent components. This approach allows for more granular deployment and scaling, reducing the blast radius of failures and enabling teams to update or replace individual services without impacting the entire application.

Code reviews play a crucial role in ensuring the quality and resilience of the codebase. By involving peers and subject matter experts in the review process, potential issues can be identified and addressed before deployment, reducing the risk of failures and downtime.

Designing for observability is another key aspect of resilient applications. By exposing key metrics and integrating comprehensive monitoring and logging mechanisms, teams can gain valuable insights into the application's behavior, enabling proactive identification and resolution of issues.

Infrastructure as Code (IaC) practices, such as using tools like AWS CloudFormation or Terraform, can significantly enhance resilience by enabling automated deployments, updates, rollbacks, and replacements, reducing the risk of human error and ensuring consistent and repeatable configurations.

Operational Design

Resilience extends beyond the application and infrastructure design; operational practices also play a crucial role in ensuring continuous availability and recovery from failures.

Implementing robust backup and restore strategies is essential for protecting against data loss and enabling rapid recovery in the event of a disaster. Regular testing of backup and restore processes ensures that these mechanisms function as expected when needed.

Maintaining hot, warm, or pilot light standby environments can provide additional layers of resilience, allowing for rapid failover and minimizing downtime during major incidents or planned maintenance activities.

By incorporating these principles and best practices into the design and operation of cloud applications, organizations can significantly enhance the resilience and reliability of their systems, ensuring business continuity and delivering a seamless experience to their customers.

Certification is not enough

Brian Tarbox — Sun, 31 Mar 2024 16:45:27 +0000

Obtaining AWS certifications is a valuable step in validating one's knowledge and skills in cloud computing. However, relying solely on certifications without practical, hands-on experience can be detrimental, especially in a field as dynamic and complex as cloud engineering. The metaphor of becoming a pilot illustrates this point effectively.

There are a number of different licenses, certificates and ratings one can obtain for flying. They include Private Pilot, Instrument Rating, Commercial Single-Engine, Commercial Multi-Engine and Airline Transport Pilot. Personally I am a Private Pilot with an instrument rating. This means I can fly into clouds and navigate solely with the in-cockpit instruments. I'll say that getting this rating was one of the hardest things I've ever done. I got that rating two years after getting my private license.

In those years I had: a bird strike, an engine failure, landing light failure at night, door blow open, and fuel cap dislodge. We used to joke that my call sign was "303EC no I'm not declaring an emergency".

Now, imagine a scenario where an individual obtains all five of those licenses within a month through a flight school that promises a fast-track program. While they may have acquired the theoretical knowledge required for the certifications, they would lack the crucial practical experience of logging hundreds or thousands of hours in the air, handling various weather conditions, and dealing with unexpected situations. Would you feel comfortable entrusting your safety to such a pilot? The answer is likely no (or perhaps Hell No).

Similarly, in the realm of cloud computing, certifications alone do not equip individuals with the practical experience necessary to design, deploy, and maintain robust, scalable, and secure cloud solutions. Hands-on experience is invaluable, as it allows professionals to encounter real-world challenges, troubleshoot issues, and develop problem-solving skills that cannot be fully replicated in a certification exam environment.

A question I like to ask during an interview is "tell me about time when you were shocked by an AWS bill at work or in your personal account". If they've never encountered this there is a fair chance that they don't have much actual experience.

The Value of Learning from Failures and Mistakes
Practical experience not only provides exposure to different technologies and services but also offers opportunities to encounter failures and make mistakes – invaluable learning experiences that cannot be gained from certifications alone.

In the pilot metaphor, a seasoned pilot with thousands of hours of experience has likely faced various situations such as having to cancel or divert flights due to adverse weather conditions, dealing with mechanical issues, or navigating through unexpected airport closures. These experiences, although challenging at the time, contribute to the pilot's ability to make informed decisions, remain calm under pressure, and prioritize safety.

Likewise, in cloud computing, practical experience exposes professionals to a wide range of potential failures and mistakes. For instance, a cloud engineer might encounter scenarios such as misconfigured security groups leading to data breaches, improperly sized resources resulting in performance bottlenecks, or unexpected spikes in costs due to inefficient resource management. While these situations can be frustrating, they provide invaluable learning opportunities that cannot be replicated in a certification exam.

Encountering failures and making mistakes allow cloud professionals to develop critical problem-solving skills, troubleshooting techniques, and a deeper understanding of the intricacies of cloud services. They learn to anticipate potential issues, implement proactive monitoring and alerting mechanisms, and develop contingency plans to mitigate risks.

Moreover, these experiences foster a mindset of continuous improvement and a commitment to adhering to best practices. Cloud professionals who have faced real-world challenges are better equipped to design and implement robust, scalable, and secure cloud solutions that can withstand various failure scenarios.

In conclusion, practical experience in cloud computing is not only about gaining exposure to different technologies and services but also about encountering failures and making mistakes. These real-world challenges provide invaluable lessons that cannot be learned from certifications alone. Just as a seasoned pilot has faced and learned from various adverse situations, a cloud professional who has experienced and overcome failures and mistakes is better equipped to design, deploy, and maintain robust and resilient cloud solutions.

Certifications can validate experience but they are not a substitute for it.

Certification Tips from an AWS Hero

Brian Tarbox — Tue, 26 Mar 2024 20:18:21 +0000

Earning an AWS certification is a great way to validate your cloud skills and advance your career. You should keep in mind that getting a certification isn't enough to get you a job. You need experience to do a job, and the certification should be seen as validating that experience rather than replacing it.

Also, the exams are challenging and require dedicated preparation. Here are some study tips I've used to get both AWS Professional certifications, the Data and Security Specialities and the (now deprecated Alexa specialty)

Take an Initial Practice Exam

Before you start studying, take an initial AWS practice exam to get a baseline score and identify which areas you need to focus on. The practice exams from sources like Tutorials Dojo closely mirror the actual certification exams in terms of question style and difficulty. Your initial score will likely be low, but that's okay - it will show you which domains need more work.

Take a Class from an AWS Expert

While you can self-study using the free resources on the AWS site, many people find it valuable to take a structured course taught by an instructor who is recognized as an AWS expert. Courses from providers like Cloud Academy and Linux Academy provide in-depth coverage of the exam objectives and include hands-on labs. An expert instructor can explain complex topics and share insights from real-world experience. Personally I'm a huge fan an classes by Adrian Cantril, Stephane Maarek and Neal Davis.

Take Notes by Hand

As you go through your training course or other study materials, take notes by hand rather than typing them. The physical act of writing has been shown to improve learning and retention. Use a notebook or loose-leaf paper, and write down key facts, definitions, diagrams, and anything else that will help cement the concepts. I've become a huge fan of the reMarkable tablet for note taking. The reMarkable is just for note taking, you can't check email on it and it has no browser. That is it's strength ... it doesn't allow you to get distracted. Writing long hand is much slower than typing but that gives your brain more time to incorporate the concepts.

Use GenAI if you are confused

Don't be afraid to ask your favorite chat bot (mine is Perplexity.ai) to explain things to you. Sometimes just reading something that is phrased differently can make all the difference in understanding. Try prompts such as "when should I pick Kenesis Firehose rather than Kenesis Data Streams".

Take Practice Tests in Review Mode

One of the most effective ways to study is by taking practice tests in review mode so you get immediate feedback and explanations. Go through each practice question slowly and use both correct and incorrect answers as a learning experience

Analyze Questions You Missed

For any practice test questions you get wrong, go back and analyze why you missed them. Write out in longhand the reasons your incorrect response was wrong, and explain why the right answer is correct. This reinforces the underlying concepts. Don't just skim over missed questions - dig into them deeply.

Also, dig deeply into the questions you get right!

For each of the incorrect answers you will get a reason why the answer is wrong. You can use the factoid in other questions. If an answer is wrong because Service A can't be a source for Source B, you can keep that in mind for all following questions.

Revise your notes

In the US we say study but in the UK they use the term "revise". The key difference is that studying often just means re-reading your notes which is a rather passive activity. Revising by contrast, involves actually re-writing your notes. You might for example have gotten several questions wrong about Kinesis and the corresponding notes are spread out across multiple pages of notes. Taking the time to revisit these notes and gather them together forms a stronger memory map than just re-reading them all.

Review Notes and Repeat

Repeat this cycle of practice testing and reviewing notes until you consistently score above the passing mark. By following this study plan using practice tests, expert-led training, handwritten notes, and focused analysis of missed questions, you'll build the knowledge required to earn your AWS certification. The effort is challenging but extremely rewarding.

Where to take the exam

Keep in mind that these exams can take over three hours. If you opt for a remote testing experience you must

have a completely clear desk
no external monitors
no food or drink
no bathroom breaks - actually you can't even stand up
you are not allowed to speak or even move your mouth - because you might be giving information about the test to a hidden recorder.

If you choose this option put a sign on your door saying no interruptions and hope that your kids and pets can read! Your local library might be a better bet for a venue for a remote exam.

If there is an exam center near you that is often a good option. You are allowed a bathroom break and can bring in water (in a clear, sealed bottle).

Enhancing Data Security with S3 Object Lock

Brian Tarbox — Fri, 22 Mar 2024 12:51:57 +0000

As organizations increasingly store critical data in Amazon S3, the risk of cyber threats such as ransomware attacks escalates. According to SonicWall, there were 1,748 recorded ransomware attempts per customer during the first three quarters of 2021. Furthermore, a report from Positive Technologies states that cybercriminals can penetrate 93 percent of company networks. Ransomware attacks involving S3 data often involve stealing or encrypting the victim's data, holding it hostage until a ransom is paid.

While companies traditionally employ a layered approach to protecting their S3 data, including bucket policies, IAM roles, service control policies, and permission boundaries, these measures may not be sufficient if an attacker gains access to an administrative account. This is where S3 Object Lock comes into play.

Understanding S3 Object Lock

S3 Object Lock is a mechanism that prevents an object version from being deleted or modified. It is only available for versioned objects and does not prevent the creation of new versions. However, it can guarantee that a specific version will not be altered or deleted.

It is important to note that objects in S3 are immutable. When a PUT operation is performed on a non-versioned object, a new object is created with the same key, and the previous object is deleted. With versioned objects, the previous object gets a version tag, and the newly PUT object becomes the current version.

Object Locks can exist for either a specific time period called the "retention period" or indefinitely via a "legal hold." During an object's retention period or when a legal hold is enabled, the object cannot be deleted. A retention period can be extended as needed, and a legal hold can be disabled.

Governance and Compliance Modes

Object Lock operates in two modes: governance and compliance. In governance mode, a user with the s3:BypassGovernanceRetention permission can shorten a retention period, effectively removing the lock. Similarly, a user with the s3:PutObjectLegalHold permission can remove a legal hold.

However, in compliance mode, no one, including AWS, can shorten the retention period. If an Object-Locked object is set to compliance mode with a five-year retention period, that object will remain locked for five years, regardless of any attempts to modify or delete it.

	Governance Mode	Compliance Mode
Change Legal Hold	Requires special permission	Requires special permission
Extend Retention Period	Requires special permission	Requires special permission
Shorten Retention Period	Requires special permission	No one, including AWS, can shorten the retention period

Comparison with Glacier Vault Lock

While S3 Object Lock has similarities to Glacier Vault Lock, their respective use cases differ. Vault Lock is designed to protect a Glacier vault from modification, assuming that the objects stored in the vault are unlikely to be accessed frequently due to their petabyte scale, such as genomic or machine learning workloads.

On the other hand, Object Lock is used to protect data that may be subject to active usage, such as drug trial data, which is heavily used initially and may be required for re-calculation by regulatory bodies like the FDA.

Initiating S3 Object Lock

Object Lock only works on buckets with versioning enabled, and it can only be enabled for a bucket when it is first created. However, AWS can enable Object Lock for an existing bucket upon request.

To lock a large number of objects, lifecycle rules or S3 Batch Operations can be used. Lifecycle rules allow a limited set of operations to be performed on all objects in a bucket or those matching a filter pattern. S3 Batch Operations support Object Lock operations and can perform actions on a list of objects specified in a manifest, which can be a CSV file created manually or via S3 Inventory.

Easing into Object Lock Usage

Before implementing Object Lock, it is essential to understand the usage patterns of your objects. If your objects are short-lived, undergo frequent updates, or are not mission-critical, Object Lock might not be appropriate.

One approach is to start by setting short retention periods and using governance mode on a select group of objects. If applications encounter failures under this regime, an administrator can remove the lock or shorten the retention period until the application's behavior is understood. After a trial period in governance mode, organizations can switch to compliance mode, initially with relatively short retention periods before moving to longer periods.

Conclusion

S3 Object Lock provides robust protection for critical data stored in Amazon S3, helping organizations mitigate the risks of ransomware attacks and data breaches. While not suitable for all objects, it is worth considering for sensitive data such as personal health information (PHI), personally identifiable information (PII), or other business-critical or privacy-sensitive data. By implementing Object Lock, organizations can enhance their data security posture and provide peace of mind to their CISOs and stakeholders.

Running an Inclusive and Engaging AWS User Group

Brian Tarbox — Thu, 21 Mar 2024 22:32:15 +0000

AWS User Groups are a great way for cloud enthusiasts to come together, learn from each other, and grow their skills. However, running a successful user group requires more than just technical know-how. It's essential to create an inclusive environment that welcomes people from all backgrounds and encourages active participation from everyone. Here are some tips to help you run a better AWS User Group:

Attract a Culturally Diverse Set of Speakers

Diversity in speakers not only brings fresh perspectives but also makes your user group more inclusive and welcoming to a broader audience. Actively seek out speakers from underrepresented groups in tech, such as women, people of color, LGBTQ+ individuals, and those with disabilities.

Reach out to local universities, coding bootcamps, and organizations that support diversity in tech to find potential speakers. You can also leverage social media platforms and online communities to connect with a diverse pool of AWS experts and enthusiasts.

When inviting speakers, be mindful of their preferred pronouns and any accessibility needs they may have. Ensure that your event venue is accessible and that you provide accommodations, such as sign language interpreters or captioning, if needed.

Create a Safe Space for All Attendees

Establishing a code of conduct and enforcing it consistently is crucial for creating a safe and welcoming environment for all attendees. Your code of conduct should clearly outline expected behavior, define what constitutes unacceptable conduct, and specify the consequences for violations.

Encourage attendees to report any incidents or concerns they may have, and have a designated team ready to address them promptly and professionally. Consider having a quiet room or area where attendees can take a break if they feel overwhelmed or need a moment of respite.

Encourage Introverts to Engage

While user groups are social events, they can be overwhelming for introverts or those who prefer quieter settings. To ensure their engagement, consider the following strategies:

Provide opportunities for written or online participation, such as Q&A platforms or collaborative note-taking tools.
Encourage small group discussions or breakout sessions where introverts may feel more comfortable contributing.
Avoid putting introverts on the spot by calling on them unexpectedly. Instead, give them time to prepare their thoughts before asking for their input.
Offer virtual attendance options for those who prefer to participate remotely.

Defend Against Zoom Bombing

With the rise of virtual events, user groups must be vigilant against disruptive behavior like Zoom bombing. Here are some preventions:

Require registration and approval for attendees to join the meeting.
Enable the waiting room feature and have a co-organizer monitor and admit attendees. This can be challenging if you don't have a co-organizer.
Disable screen sharing for non-hosts and limit other potentially disruptive features.
Have a co-organizer dedicated to monitoring the chat and removing disruptive participants if necessary.
Be aware of the security options provided by your platform of choice

Manage Meeting Dynamics

Running an engaging and productive meeting requires careful planning and facilitation. Here are some tips:

Set clear expectations and ground rules at the beginning of the meeting, such as respecting others' opinions and avoiding interruptions... unless you actively want people to interject with questions during the talk
Use a structured agenda and timekeeping to ensure that the meeting stays on track and all topics are covered.
Encourage participation by asking open-ended questions and calling on quieter attendees to share their thoughts (without putting them on the spot).
Be mindful of your body language and tone, and ensure that you're not inadvertently favoring or dismissing certain attendees.
Assign a dedicated facilitator or moderator to manage the flow of the meeting and ensure that everyone has a chance to contribute.

Select Engaging Meetup Topics

Choosing the right topics is crucial for keeping your user group engaged and attracting new members. Here are some tips:

Survey your members regularly to understand their interests and pain points.
Stay up-to-date with the latest AWS releases, updates, and industry trends to identify relevant and timely topics.
Alternate between beginner-friendly and advanced topics to cater to attendees with varying skill levels. Some meeting are structured with two talks: an introductory level talk followed by a Deep Dive.
Consider inviting guest speakers from AWS or partner organizations to share their expertise on specific services or use cases. One thing to watch out for here is that AWS speakers can be "too polished" ... warn AWS speakers that your group will want to ask questions during the talk.
Encourage members to suggest topics or volunteer to present on areas they're passionate about or have experience with.

Running a successful AWS User Group requires a combination of technical expertise, organizational skills, and a commitment to creating an inclusive and welcoming environment. By following these tips, you can foster a vibrant community of AWS enthusiasts who feel valued, engaged, and empowered to learn and grow together.

Are LLM's essentially Teenagers?

Brian Tarbox — Thu, 22 Feb 2024 20:27:38 +0000

Introduction

Diving into the world of Large Language Models (LLMs) might feel like trying to have a heart-to-heart with a teenager. Both come with their own unique capabilities and peculiarities in their use of language, sprinkled with moments of baffling decision-making. Imagine trying to untangle the world through the eyes of a teen—full of confidence, sometimes too much, ready to take on complex conversations but occasionally tripping over their own shoelaces. This piece takes a light-hearted yet insightful stroll through the similarities between the mysterious minds of LLMs and the unpredictable nature of teenage behavior.

Similarities

Just like teenagers stepping into the big, wide world without much real-life experience under their belts, Large Language Models (LLMs) navigate the vast digital universe with a blend of overconfidence and, let's say, a vivid imagination. Both are in their formative years, so to speak, learning on the go and sometimes making decisions that leave us scratching our heads. This exploration into their common ground isn't just for fun—it sheds light on the quirks and capabilities of our AI counterparts.

Now, think about the last time you tried to follow the thought process of a teenager. Their decisions are shaped by a cocktail of factors: brain development, peer pressure, and personal experiences, to name a few. It's a puzzle that's tough to solve, mirroring the complexity of understanding how LLMs arrive at their conclusions. Even though feedback can guide them in new directions, peeling back the curtain to reveal the "why" behind their choices often feels like an exercise in guesswork.

Diving into conversations with Large Language Models (LLMs) or teenagers can sometimes feel like talking to someone who's convinced they know exactly where you're coming from—regardless of whether they actually do. Both LLMs and teens can come across as a bit too sure of themselves, often missing the mark on gauging the other person's level of expertise. LLMs, with their impressive language skills, still haven't mastered the art of recognizing who they're chatting with, much like a teenager confidently explaining the internet to a software engineer.

When it comes to learning, LLMs go through a kind of digital "growing up" that's reminiscent of human evolution but at hyper speed. They absorb vast oceans of text to get a grip on human chatter, a process that mirrors the slow, meticulous journey of human language development over millennia. This training is no small feat; it's a massive investment in understanding and mimicking the way we communicate. It highlights not just how LLMs learn to talk the talk but also puts into perspective the incredible journey of human language evolution—showing that both teenagers and AI have a lot of growing up to do, each in their own complex, sometimes overconfident way.

Just as teenagers navigate the tricky waters of growth, guided by the cheers and jeers from their world, Large Language Models (LLMs) and Generative AI learn to refine their digital personas through feedback. It's a bit like how a teen lights up with a well-timed compliment or mulls over a piece of constructive criticism, adjusting their course slightly with each new piece of advice. LLMs, fed on a diet of endless data, tweak their responses and improve their chatter based on the digital applause or boos they receive. This process is akin to a teenager's journey of self-discovery and adaptation, absorbing life's lessons and evolving. Both LLMs and teens show us the power of feedback—not just in shaping AI's ability to communicate, but in reminding us of the timeless act of learning from the responses we gather in day to day communication.

Differences

When it comes to solving a problem, Large Language Models (LLMs) act as digital detectives, sifting through mountains of data, applying intricate computational formulas to sniff out patterns and spit out answers. Their method is all about crunching numbers and matching patterns, which means while they often hit the nail on the head with contextually spot-on replies, figuring out the "why" behind their conclusions is a bit like trying to read tea leaves.

Then there are teenagers, whose approach to problem-solving is as layered as their personalities. Imagine them navigating a maze, where each turn is influenced by a mix of sharp cognitive skills, the social compass set by their peers, and the rich tapestry of their personal experiences. Their decisions emerge from a blend of thought, education, personal growth, and social interaction—making for a problem-solving style that’s holistic and grounded in experience.

While LLMs dissect problems with the precision of a computer algorithm, teenagers tackle them with a depth that comes from living through experiences, feeling every high and low, and learning from the social world around them. This distinction highlights not just the difference in how they arrive at solutions, but the contrast between the logical, pattern-based reasoning of AI and the complex, emotionally rich decision-making of human beings.

Healthy Skepticism of their Output

In the world of advice-giving, both Large Language Models (LLMs) and teenagers hold a unique place. They're like eager helpers, ready to chime in with insights or solutions. However, taking their words as gospel might lead you down a rabbit hole. LLMs, for all their linguistic finesse, sometimes echo the biases and errors marbled throughout their vast training data. It's a bit like getting directions from a well-meaning friend who's never actually been to the place they're describing.

Teenagers, with their boundless energy and fresh perspectives, also come with their own set of disclaimers. Their advice, while often insightful, carries the limitations of their life experiences. It's like they're seeing the world through a kaleidoscope—vibrant and full of potential, yet not always clear or accurate.

Both LLMs and teens share a common trait: a confident exterior that doesn't always match up with the depth of their knowledge. This confidence, while admirable, can sometimes lead us astray, especially when it comes to sifting through the information they provide. LLMs don't always know when they're out of their depth, spinning out answers without the ability to critique their own sources. Teens, influenced by their social circles and their own budding self-assurance, might not always question their conclusions with the rigor needed.

Peeling back the layers to understand why they've landed on a certain piece of advice is another challenge. With LLMs, you're dealing with a black box of algorithms and data; with teenagers, a complex web of thoughts and influences. Both can leave you puzzled, trying to trace the path from question to answer.

Navigating the insights offered by both LLMs and teenagers requires a discerning eye. It's a dance of valuing their input while also recognizing the need for a pinch of skepticism and a healthy dose of follow-up questions.
Making Use Of This Insight
Navigating conversations with Large Language Models (LLMs) and teenagers can sometimes feel like trying to solve a mystery without all the clues. But, just like any good detective, knowing the right questions to ask can make all the difference. Being clear and specific in your queries, such as using prompts like "How did you come up with that?" or "Explain it like I'm 10," can turn a vague answer into a treasure trove of insights. It's about encouraging a deeper dive into their thought processes, whether you're dealing with a sophisticated AI or a savvy teen.

Asking for elaboration with phrases like "Can you tell me more about that?" or "Could you put that another way?" can also work wonders. These techniques don't just apply to extracting more meaningful responses; they're about fostering understanding and clarity, regardless of whether you're interpreting the output of an LLM or decoding the latest teen lingo.

And then there's the lighter side of the comparison—the investment. Training an LLM can be as financially overwhelming as planning for a teenager's college education. It's a humorous but apt analogy that highlights the cost and commitment behind these endeavors. Sometimes, opting for a less intensive route—a smaller AI model or a more affordable educational path—might not just save resources but also turn out to be the smartest choice in the long run. In both scenarios, the key is to weigh the return on investment carefully, reminding us that bigger or more expensive isn't always better.

Conclusion

The comparison between Large Language Models (LLMs) and teenagers isn't just witty banter; it's a gateway to a deeper understanding of the complexities we face when interacting with advanced AI. Recognizing their shared traits—like how they respond to feedback, their sometimes misplaced confidence, and the opaque nature of their decision-making—can equip us with a more layered approach to engaging with LLMs. This perspective helps peel back the curtain on the enigmatic world of artificial intelligence, revealing not just its potential but also its limitations.

Indeed, as we race to keep up with the breakneck pace of AI development, any tool that demystifies our "soon to be robot overlords" is invaluable. By embracing this analogy, we're not just making sense of LLMs; we're paving the way for the creation of ethical standards and effective strategies that harness the power of LLMs across various fields. This not only enhances our grasp of their behavior and skills but also ensures that as we move forward, we do so with a keen awareness of the responsibility that comes with wielding such transformative technology.

Unraveling the Innovations of AWS Caspian, Grover and Time Sync

Brian Tarbox — Wed, 24 Jan 2024 17:28:31 +0000

Caspian, Grover and Time Sync are key features in the march towards "real" serverless.

Introduction to AWS Caspian
Caspian is a pioneering technology developed by AWS, primarily for the Aurora Serverless platform. It represents a paradigm shift in resource allocation and management for serverless databases. The technology is built upon several key innovations:

New Hypervisor Technology
At the heart of Caspian lies a newly developed hypervisor, distinct from traditional ones like AWS's Nitro. Traditional hypervisors allocate a fixed set of resources to an instance. In contrast, the Caspian hypervisor dynamically allocates and reallocates resources based on the database's real-time needs. This flexibility ensures that databases always have the necessary resources, thereby optimizing performance and efficiency.

Advanced Heat Management System
Caspian's heat management system is a cornerstone of its innovation. It oversees the real-time allocation of physical resources, ensuring that databases have access to necessary resources when required. This system is pivotal in managing database migrations between physical hosts with minimal performance impact, allowing for a smooth and efficient scaling process.

Cooperative Oversubscription Technique
A unique aspect of Caspian is its use of cooperative oversubscription. This approach allows each instance to support the maximum memory available on the host. However, physical memory allocation is based on the actual needs of the database running on the instance, not on a predetermined allocation. This technique ensures efficient resource utilization and reduces wastage.

Dynamic Resizing Capability
Perhaps the most striking feature of Caspian is its ability to enable Aurora Serverless databases to resize within milliseconds in response to changing workloads. This capability makes the database highly elastic, catering to the fluctuating demands of modern applications with unprecedented efficiency.

Exploring AWS Grover
While Caspian revolutionizes serverless computing, Grover is transforming the world of database storage and logging. Grover is an internal, optimized distributed storage system for Amazon Aurora. It brings several advancements:

Disaggregated Storage System
Grover introduces a disaggregated storage system, allowing Aurora to decouple the database from its storage. This separation leads to more efficient data handling and processing, enabling Aurora to manage data at scale more effectively.
Innovative Approach to Database Logs
Traditionally, databases log locally. Grover changes this by sending each log entry to a remote system. This system ensures the durability and availability of these logs across multiple Availability Zones, enhancing data reliability and recovery capabilities.
Data Structure Replication
One of the most innovative aspects of Grover is its ability to process the log and replicate the database's internal memory structures on a remote system. These structures can be sent back to the Aurora database as needed, significantly reducing the I/O demands and boosting overall efficiency.
Enhanced Performance and Durability
Grover's architecture offers superior performance, scalability, and durability compared to traditional database systems. It allows Aurora to provide higher throughput and resilience, making it a robust choice for modern applications that demand reliability and speed.

Impact on Cloud Computing
The implications of Caspian and Grover on cloud computing are profound. Caspian reimagines how resources are allocated and managed in a serverless environment. Its dynamic resizing capability ensures that serverless databases can adapt to workload changes swiftly and efficiently. This innovation allows businesses to manage their databases with unprecedented agility and cost-effectiveness.

Grover, on the other hand, revolutionizes data storage and logging for distributed databases. Its approach to handling database logs and the replication of data structures enhances the performance and durability of databases. The technology enables businesses to handle massive amounts of data with improved efficiency and reliability.

Time Sync
Caspian and Grover depend on the ability to safely perform distributed database writes. This requires a globally agreed upon time synchronization ability. In the past such a system was either too expensive or not accurate enough to allow for high through distributed writes.

The AWS Time Sync service is a timekeeping service designed to offer both precision and accuracy in time synchronization across AWS services and instances. It achieves this through a custom chip built into Nitro (of course). These on-board chips run at an incredible precision and because they're part of Nitro they are more efficient than past attempts at synchronization ensuring that the time delivered is consistent and accurate to a few nanoseconds globally.

You may not directly interact with any of these three services but your future applications may well depend on them.

At the core of the service is custom-designed infrastructure integrated with Nitro, including specialized reference clocks and a dedicated time synchronization network. This network distributes the timing pulse directly to each EC2 server, bypassing common sources of variability and ensuring ultra-precise timekeeping.

The latest version of the Time Sync service, as announced, brings time synchronization to within microseconds of UTC. This level of accuracy is pivotal for applications that require ultra-precise time measurements, such as high-frequency trading platforms and scientific experiments.

Conclusion

In conclusion, AWS's introduction of Caspian, Grover, and the Time Sync service represents a monumental stride in the realm of cloud computing, addressing key challenges in serverless computing, database management, and time-sensitive operations. Caspian and Grover, with their dynamic resource allocation, efficient data handling, and scalability, are paving the way for more robust, efficient, and cost-effective cloud solutions. Simultaneously, the Time Sync service not only strengthens AWS's existing offerings but also establishes a foundational component for emerging technologies like quantum computing.