Welcome to this month’s playlist: Rock Anthem Sing Along. December doesn’t need more holiday music. It needs permission to feel communal without being sentimental. A room full of people singing together because it feels good.

This is a holiday playlist without the holiday music.

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This is a soundtrack for that kind of night. Where you and your friends are talking over each other, refilling drinks, and someone inevitably shouts a lyric from across the room because they couldn’t help themselves. Or you find yourself lost in thought because the next song brought up a memory from 10 or 15 years ago.

Sing-along rock. Zero pretense and maximum joy. For any season.

I’ve been working on this playlist for a few months. It started with an idea of making a holiday playlist that didn’t have any holiday songs. And this was the result. It’s not quite karaoke. It’s not quite a dance party. It’s some other, in-between thing.

Where to Listen

Use any of the links below, and be sure to save it to your profile. Then you can say, “Hey, Alexa/Siri, play the Rock Anthem Sing Along playlist!” Each playlist releases on the full moon. A small ritual of rhythm and reflection. The next is January 3rd.

Listen to my monthly playlist Rock Anthem Sing Along on any of these services:

🎧 Spotify | 🎧 Apple Music | 🎧 YouTube Music | 🎧 Amazon Music

The Songs

These are the songs I’ve shouted in cars, at concerts, and with friends on nights that didn’t need a reason to be good. There’s a kind of magic in everyone remembering the same lyric at the same time.

These are songs meant to be lived in — yelled, misheard, and shouted again.

Rock Anthem Sing-Along – December 2025

1. Sabotage — Beastie Boys

2. Smooth Criminal — Alien Ant Farm

3. This Ain’t a Scene, It’s an Arms Race — Fall Out Boy

4. All Star — Smash Mouth

5. Bullet With Butterfly Wings — The Smashing Pumpkins

6. Misery Business — Paramore

7. Mr. Brightside — The Killers

8. The Middle — Jimmy Eat World

9. Are You Gonna Be My Girl — Jet

10. Monkey Wrench — Foo Fighters

11. Rebel Yell — Billy Idol

12. Hey Ya! — Outkast

13. Kiss Off — Violent Femmes

14. Mrs. Robinson — The Lemonheads

15. What I Got — Sublime

16. Whiskey in the Jar — Metallica

17. Bulls on Parade — Rage Against the Machine

18. Minority — Green Day

19. Here It Goes Again — OK Go

20. Loser — Beck

21. Teenagers — My Chemical Romance

22. Undone (The Sweater Song) — Weezer

I hope all 22 tracks are all familiar, help you feel the energy, and tempt you to sing.

🐇🕳️ The Rabbit Hole

I’ve been a hobby musician for the last 35 years. I’ve been a guitarist. Played the piano. Made some electronic music. And during COVID I learned to DJ.

A good mix has emotional continuity. About making each track feel like it steps naturally into the next. I used some harmonic mixing theory quite extensively when I sequenced this December playlist. You should feel every transition lift the energy a little bit. It’s subtle, but gives the mix this smooth, upward pull.

Harmonic mixing was first sketched in the 80s and later refined into what DJs now use as the Camelot Wheel — a simpler way to understand which keys lift or anchor a transition. Spotify even embeds it into the Mix feature. You can find more info on the Camelot Wheel and harmonic mixing on Mixed In Key’s Website, and a full tutorial on this How to Use the Camelot Wheel for Harmonic Mixing YouTube video.

Here are a few amazing sets from some of my favorite DJs. They are all cohesive, but in their own way. The first one from Diplo at space is especially amazing.

Why I Love Making These

A good playlist is a small emotional journey. My October playlist tried to capture energy; My November playlist was serene. For December, I wanted something communal. The songs are maybe the opposite of holiday music, but somehow still perfect for this (or any) season. Turn it on at your next gathering. Or even when you’re doing something ordinary and want the room to feel alive for a few minutes.

Happy listening, and happy singing.

- Rob Allegar

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I’m a lifelong builder and advisor exploring what happens when technology stops behaving like a tool and starts acting like a collaborator. In this newsletter, I explore the space between ideas and execution, and help people build things that matter. roballegar.com

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Thanks for reading. If you enjoyed this piece, hit ♥ or share it with someone else who, like me, also sings along to Whiskey in the Jar as if they know the words.

My previous post on roles showed how including a role in your prompt can get you better AI responses. But a role only gets you halfway there — because it still leaves the model to decide how that role should act. That’s what this post is about: helping you close the gap between do the task and understand my intent.

With this approach, you’ll get the results you want faster, with fewer iterations, and more aligned to your specific needs.

🎧 Prefer to listen? Here is the 9-minute audio version, also on Spotify, and Apple.

Roles Contain Multitudes

Here’s the secret: you want to create a character, not just name a role.

There’s a scene in Good Will Hunting where Robin Williams’ character shares his perspective on love, learning, and life. Here is a link to the full monologue — one of my favorites in any movie. It’s emotional, cinematic, and full of amazing dichotomies. And it perfectly illustrates the difference between a role — Williams playing a psychologist — and a character — Williams playing a lonely psychologist, stuck in the past, and still grieving over his beloved wife who he lost to cancer years ago.

It’s the character, not the role, that determines the performance.

The script’s words may have been the same no matter which actor played the part, but Williams’ delivery and character choices change everything. He could have played that monologue with frustration or authority, but he chose empathy, gentleness, and love. And the entire meaning shifted.

A role tells the model who to be.
A character tells the model how to behave.

And once you see that distinction, the next leap becomes obvious. Roles shape identity. Character shapes interpretation. Try this: Imagine a gardener. Don’t think too long — what’s the first thing that came to mind?

• Your grandmother spraying blue Miracle Gro on her summer tomatoes?

• Alain Baraton1, the head gardener at Versailles since 1982?

• A biotech researcher studying plant pathogens?

They’re all gardeners. But if you asked each of them to, “describe the perfect garden,” you’d get three entirely different — and entirely valid — answers.

If a gardener contains this much variation, imagine the variance inside roles like CISO or CEO — where interpretation isn’t aesthetic, it’s operational.

And this is the heart of the problem: a single role can hold wildly different lived experiences, instincts, and values. Walt Whitman captured that truth in 1855, long before large language models existed:

Do I contradict myself?
Very well then I contradict myself,
(I am large, I contain multitudes.)

— Leaves of Grass, Project Gutenberg Release #1322

Roles contain multitudes. And when you’re leading teams or making decisions, “multitudes” is another word for “variance”, and variance slows everything down. Your job is to reduce variance, not manufacture it.

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Why Defining the Right Character Matters

If you’re leading teams, programs, or product decisions, the “average” version of a role is never the one you want. But before you can nudge the model beyond the average, you need to think about which direction you want. Professional? Creative? Warm? Urgent? Critical? Here are a few examples in some professional roles:

• A program manager might sound operational and disciplined, or diplomatic and coalition-building, or ruthlessly focused on delivering outcomes that matter.

• A product manager could be visionary and expansive, or analytical and exacting, or a cool-eyed realist who surfaces the risks no one else wants to say out loud.

• A compliance officer could offer feedback as calm reassurance, or as firm guardrails, or as a direct, sleeves-rolled partnership that keeps you out of trouble.

• A CISO might lead with policy and governance, or with threat modeling and live-fire scenarios, or with the clarity of what keeps the company off the front page.

• A regulatory advisor could be cautious, or pragmatic, or forward-leaning in a way that balances innovation and scrutiny without decreasing quality or risk posture.

• A CEO might be a bold storyteller shaping hearts and minds, or a steady operator, or a strategist who cuts straight to risk, ROI, and leverage.

Once you’ve decided what kind of behavior you need, you can tell the model how to interpret your specific role. And you can get much better results for your specific task.

Character Notes: How You Direct the Role

That’s where character notes come in: they narrow the interpretation so the model behaves like the specific version of the role you intended. They fill in the human texture that roles alone can’t supply — the judgment, taste, and emotional intelligence that make the response feel crafted instead of computed. Here are a few examples:

Character notes help you steer the response towards your intent.

Without character notes:

You are a creative director. 

Review this product launch email.

With character notes:

You are the creative director for the most successful marketing company in the country, and a former New York Times copy editor. 

You’re expressive and artistic, known for award-winning campaigns, but you’re also a perfectionist who notices rhythm, tone, and nuance. You communicate with the precision and tone that senior stakeholders admire. 

Review this product launch email.

Same role. Same task. Completely different result.

How to Add Character Notes that Actually Work

You don’t need much. A couple of sentences is usually enough. As long as what you’re providing is directionally aligned to your intent, it should be helpful. There are essentially three types of things you can include. Think of these as character cues:

1. Background – What is this character’s history? Where were they before?

2. Temperament – How do they approach the world? How should they engage?

3. Values – What does “good” look like? What other qualities are important?

Example for product work:

You are a senior PM who started your career in operations. You’re direct, structured, and allergic to fluff. You care about clarity, feasibility, and real-world constraints.  

Rewrite this roadmap summary for executive review.

Example for strategy work:

You are a CEO who built and sold two companies. You value fact-based decisions, crisp thinking, and an understanding of tradeoffs. You're framing a decision memo for the board. 

Review this memo for narrative, risk, and executive usefulness.

The Bottom Line

Character notes affect the tone, perspective, and emotional calibration of the result. They make AI feel directed instead of instructed, and shift the model from completing a task to inhabiting a point of view. And that’s where the quality jump happens.

This is especially useful if your work requires clarity, speed, or alignment across teams, which is most modern leadership work.

Once you start adding character notes, you’ll notice it immediately. The answers will sound sharper, more confident, and more aligned with your intent.

Before you close this tab, try one line. Use whatever you’re working on right now:

You are a [role] with [character traits]. Here’s what we’re working on …

One sentence. Ten seconds. A noticeably better response — multitudes better. And once you feel the difference, you won’t go back.

This is the third post in my AI Superpowers series, where you’ll find some techniques that make AI feel a little more human, and a lot more useful.

Next in the series: Using roles to build a creative brainstorming buddy.

Rob Allegar

I’m a lifelong builder and advisor exploring what happens when technology stops behaving like a tool and starts acting like a collaborator. In this newsletter, I explore the space between ideas and execution, and help people build things that matter. roballegar.com

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Please share this with someone who contradicts themselves and contains multitudes.

If you want better answers, you first need to find an expert.

🎧 Prefer to listen? Here is the 4-minute audio version, also on Spotify, and Apple.

Who Do You Think You Are?

You’re probably using AI for text-related tasks. Things like, summarize this meeting, draft an email, or analyze this report. Those will get you a response, but most of the time the results are… average. Here’s why:

• Non-technical reason: Telling AI what to do is like hiring a verb. It completes the action, but it doesn’t know why it’s doing it or what good looks like. It does the job, without any judgment.

• Technical reason: Most chat bots encode text into mathematical vectors, calculate, and then decode it back into words. So when I say “average,” I mean it1—the model picks something near the statistical center of all possible responses. (If this sentence excites you, see NVIDIA Isn’t the Company You Think It Is).

So how do you move AI from average to intentional? Start by giving it a role.

Every Prompt Needs a Protagonist

You’ll get better AI results when you ask it to assume a role. Roles shift tone, structure, and focus toward your intent, and the right one will guide results without extra instruction.

You may not want a financial analyst reviewing a product-launch email—but you might. Either way, AI will step into the role. Just include the role name before your request. So instead of “Review this product launch email” try:

You are a creative director. 

Review this product launch email. 

That single role cue—creative director—will transform a flat summary into something stylistically aware and strategically sharp. The role could be yours, a coworker’s, or one you wish existed on your team.

Adding a role shapes the result’s personality.

Why this matters: Adding a role is like using a lens—it focuses the response and aligns it to your expectations. A role tells the model what to notice, what to ignore, and how to decide what ‘good’ means. Giving AI a role doesn’t just change the words; it changes the reasoning behind them.

The Bottom Line

Roles make AI feel less like a search and more like a colleague. For teams, that means faster drafts more aligned to stakeholder expectations. For creators or analysts, that means content that is closer to your intent on the first try, with fewer iterations.

Once you find a few that fit your workflow—the Analyst, the Editor, the Board Advisor, the Mirror—you can reuse them anywhere. Each becomes an extension of your collective intelligence. Before you close this window, test it once:

You are a [role]. Here’s what we’re working on…

It doesn’t need to be fancy. Just enough to give it a point of view. Then watch what happens.

This is the second idea in my AI Superpowers series. Over the next few weeks you’ll find some techniques that make AI feel a little more human, and a lot more useful.

Next in the series: Character Notes: How to teach AI to act the part.

Rob Allegar

I’m a lifelong builder and advisor exploring what happens when technology stops behaving like a tool and starts acting like a collaborator. In this newsletter, I explore the space between ideas and execution, and help people build things that matter. roballegar.com

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Please share this with someone who dreams of hiring parts of speech.

🎧 Prefer to listen? I read this article on the voiceover (above), on Spotify, and on Apple

The Best Kind of Mornings

I’ve experienced these mornings from time to time, across a variety of activities. Running weekend errands without the kids. Heading to work a bit earlier than normal when everything—even the early morning sky—has a different energy. Spending time on a crossword puzzle snuggled next to someone I love. Feeling productive before everyone else wakes up. The morning hours before a vacation or road trip.

These mornings of calm help maintain inner confidence for the rest of the day.

Maybe you’ve had these mornings too? If so, I’d love to hear in the comments what activity you found yourself doing and what your morning energy was like.

Where to Listen

Use any of the links below, and be sure to save it to your profile. Then you can say, “Hey, Alexa/Siri, play the November Falling playlist!” I release each playlist on the full moon. A small ritual of rhythm and reflection. The next one arrives December 4th.

Listen to my monthly playlist November Falling on any of these services:

🎧 Spotify | 🎧 Apple Music | 🎧 YouTube Music | 🎧 Amazon Music

Why Even Make a Playlist?

Someone I respect emailed after reading last month’s October Voltage playlist post: “Is it business or a hobby?” he asked. It was a fair question. Why spend time on this if I should be building market gravity and attracting buyers? The simple answer: I make playlists for people anyway, so I decided to share them.

My playlists may not change the world, but I hope they add a little light to yours.

I have a few friends who panic when asked, “What should we listen to?” I’ve seen actual beads of sweat form if no one suggests anything quickly. Often the tension they feel comes from trying to come up with a “right” song or band or mood. There is so much music to pick from! How can all of it be reduced to one choice right now?

So if you ever find yourself faced with this situation, here’s a trick that can help.

First, do what Douglas Adams recommends: Don’t Panic1. Then, do a quick scan of your body. Try and notice what you’re feeling. Are you happy? Frustrated? Anxious? Sad? Next, try and think of a song that you love that matches that mood. It’s OK if you’ve heard it a million times, or maybe played it earlier in the day. Once you have a mood and a song, just start a radio and let it play. Give it a few songs.

Here’s the real trick: once the music starts, notice when you notice it. Are you in the middle of a conversation and suddenly you’re singing along to the chorus? Or maybe you’re doing the dishes and you notice the song feels “wrong” or starts to irritate you. Either way—good feedback! Either keep on grooving, or skip the song! And if you skip more than twice, it’s time for a new playlist starter. Just try again.

I think you’ll find this approach will make picking songs a bit easier. You may not feel like you’re getting any better at it, but at least you won’t panic.

— Rob Allegar

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I’m a lifelong builder and advisor exploring what happens when technology stops behaving like a tool and starts acting like a collaborator. In this newsletter, I explore the space between ideas and execution, and help people build things that matter. roballegar.com

Thanks for reading. If you enjoyed this piece, hit ♥ or share it with someone else who, like me, likes to drink room temperature coffee in the mornings.

Share Rob Allegar

🎧 Prefer to listen? I read this article on the voiceover (above), on Spotify, and on Apple (Disclaimer: I own NVIDIA stock. I am not providing financial advice.)

The ~2,000 people in the audience weren’t gamers—they represented nearly every corner of modern industry. On my left, an executive selling on-chip cooling; on my right, a supply chain consultant. There were financial managers too, and logistics leaders, policy experts, communications execs, construction managers, robotics engineers, and IT folks—they were all there, and they all had something at stake.

More than once, I had goosebumps thinking about what comes next.

In this essay I focus on the ideas that didn’t make headlines but will matter most. I’ve organized it into three acts—Act I: The Shift in Identity, Act II: The Shift in Utility, and Act III: The Shift in Scale. But first, a short preamble to set the stage.

Preamble: The Engines of Intelligence

Most people know NVIDIA for its graphics cards—the chips that make games beautiful and animations realistic. But those same chips, called Graphics Processing Units or GPUs, have quickly become the engines of modern intelligence.

A GPU is built for parallel processing. While a Central Processing Unit or CPU handles a few tasks at a time, a GPU performs thousands—tiny math calculations that render 3D objects or simulate light and motion. The GPU’s design also happens to be the perfect solution for another frontier problem: teaching machines to learn.

The same geometry that renders realistic dragons and race cars can also map relationships between words, images, and ideas.

The turning point came in 2017, when a group of Google researchers published Attention is All You Need and changed everything. This research paper introduced the concept of a transformer, a technique that turns text (or any input) into tokens, encodes those tokens into mathematical vectors, performs lots of linear algebra, then decodes the result into output—language, images, code, or whatever. The paper’s breakthrough was that the transformer math produced better results if it was parallelized2. And parallelization was a task for which the GPU was purpose built.

So while the GPU began as a chip used to push pixels, after 2017 it quickly became the go-to chip for processing tokens. And once researchers realized the transformer / token technique could also solve a variety of difficult (and previously impossible) problems—demand for GPUs exploded. NVIDIA, already the dominant GPU supplier, found itself at the center of an entirely new market.

In short: GPUs made intelligence scalable. And that is where this story begins.

Once intelligence became scalable, the question was no longer what can GPUs do? but what else do you need to make them useful? Or perhaps: now what?

Act I: The Shift in Identity

NVIDIA isn’t a hardware company anymore—it’s become the backbone of modern computing. Since 2017, the year Google’s transformer paper changed the trajectory of AI, NVIDIA has reshaped itself from a chip designer into the central infrastructure provider for the age of intelligence. It now occupies a role similar to the one AT&T once played for early 20th century telecommunications: it’s the connective tissue catalyzing growth across entire industries.

Like AT&T, NVIDIA’s power comes from alignment—mirroring its customers’ ambitions, and evolving itself each time a new market emerged. Those shifts aren’t cosmetic; they mark new audiences, new business models, and new leverage.

NVIDIA’s real product is computation itself.

Across NVIDIA’s annual 10-K filings from 2011 to 20253, each year’s opening sentence showed the company’s evolving identity—from “GPU inventor” to “visual computing leader,” to “accelerated computing platform,” to today’s “full-stack computing infrastructure company.”

NVIDIA’s origin story may have been the GPU, but today the company’s real product is all of the necessary infrastructure to access computation at an unheard of scale.

💎 This is the Treasure of Our Company

Early in the keynote (timecode 00:08:17), Jensen paused on a slide showing several of NVIDIA’s CUDA-X libraries—quantum, weather, imaging, and several more.

“Our software is the treasure of our company,” he said. “Each one of these libraries opened new markets for us.” - Jensen Huang

It was a quick moment, easy to miss, but it captured a decade-long transformation in a single line: NVIDIA, once a company that sold silicon, now sells capability.

When NVIDIA first released CUDA in 2007, developers finally had a powerful (albeit complex) way to program GPUs. Twelve years later, CUDA-X was released, which abstracted away that complexity. Although CUDA-X is built on CUDA, CUDA-X transformed the GPU from a chip into a platform—one that almost anyone can use. Today there are ~400 CUDA-X libraries across a variety of fields.

Hardware is replaceable; software ecosystems aren’t.

This is the genius of Jensen’s “treasure” metaphor. Hardware is replaceable; software ecosystems aren’t. So when semiconductor leaders like TSMC, Samsung, and ASML adopt CUDA-X libraries like CuLitho, they’re wiring their businesses into NVIDIA’s software layer. And because that software stays compatible across GPU generations, the relationship compounds over time.

NVIDIA has achieved what Microsoft once did for PCs: it now provides software libraries in CUDA-X that amplify the value of the NVIDIA hardware beneath it. Every industry that adopts CUDA-X becomes an NVIDIA customer, and requires NVIDIA hardware for their application to run.

NVIDIA’s real asset isn’t chips or code; it’s the ecosystem itself.

Act II: The Shift in Utility

So what happens when we have an ecosystem of hardware and software powering advanced computing within every industry? Well, this modern computing infrastructure—once built to augment intelligence can now be used to augment effort. We will no longer teach machines to think; we will teach them to work.

But before we change the nature of a machine’s utility, we need new tools. In particular, we need digital worlds—digital twins—that mirror ours.

💡 It’s a Simulation, Not an Animation

Early in the keynote, Jensen played a three-minute montage (timecode 00:11:05) which, on the surface, seemed like a Pixar short. There was dramatic weather, intricate objects, realistic light, fabric fluttering, an engine exploding into its parts, and lots of robots doing things. Logos of Amazon, BMW, General Atomics and others flashed across the screen too. When the video ended, Jensen said something profound.

“Everything you saw was a simulation. There was no art. No animation. This is the beauty of mathematics.” - Jensen Huang

The audience was remarkably quiet. It looked like artistry, but it was physics and math. The miracle wasn’t how it looked, but how it was made.

Here is the difference: animations are designed; simulations are calculated. Models replace creative shortcuts with equations. Models help drive decisions and reduce risk. And models can be super useful when you need to train a variety of robots.

This video from March 2025 shows off some of NVIDIA’s modeling tech to do just that. Essentially, the software enables companies to train simulated robots inside simulated worlds. Then, robotic prototypes work out of the box, as if they had already been trained to handle a variety of real-world situations. Because they had.

"We’re working with Disney research on an entirely new framework and simulation platform … for the robot to learn how to be a good robot inside a physically-aware, physically-based environment.” - Jensen Huang

Jensen even demonstrated this on stage. More than an hour after the, “everything you saw was a simulation” comment, he showed something from Disney. It’s essentially a pet Star Wars robot walking and interacting with the world (timecode 01:33:28). As you watch, notice how it stumbles on the uneven terrain and recovers—an amazing skill it learned. If you were Disney, and planned to sell millions of pet robots, wouldn’t you want them to have experienced a huge variety of conditions first?

💡 AI is Work, Not Intelligence

Jensen was just wrapping up a discussion on AI as the New Industrial Revolution when he said (timecode 00:36:19):

“But AI is not a tool. AI is work. That is the profound difference. AI is in fact workers that can actually use tools.” - Jensen Huang

He wasn’t being poetic. He meant it literally. For decades, we’ve thought of AI as a tool that thinks—a system that analyzes, predicts, or advises. But the next generation of AI doesn’t just think; it acts. It designs, moves, builds, drives, and negotiates. It doesn’t simulate work—it does the work. And this isn’t just robots either, it’s AI in general: AI as the commander; AI as the director; AI as the supervisor.

This is what Jensen meant by the “new industrial revolution.” AI is becoming an operating class within the economy—performing cognitive and physical labor side-by-side with humans. The GPU, once an engine of images, is now an engine of effort.

And with more workers, we’ll need to think about scale differently.

Act III: The Shift in Scale

We’re entering an era where scale multiplies work. What started as parallel computation has become parallel production. At this scale, AI isn’t just a tool or a worker—it’s infrastructure that builds more of itself.

💡 Robots Using Robots to Build Robots

Jensen showed a short video covering American reindustrialization (timecode 01:28:50). In the video, Foxconn and Siemens demonstrated a digital twin of an NVIDIA factory that allowed engineers to optimize the mechanical, electrical, and plumbing layout for robotic automation. The same digital twin of the factory was used to train and simulate robots (as I described in Act II). But here is what blew my mind:

“That’s the future of manufacturing,” he said. “The factory is essentially a robot that’s orchestrating robots to build things that are robotic.” - Jensen Huang

That’s right. The factory itself is a robot that uses robots to build robots. When the entire facility is a single, programmable organism, efficiency becomes a software problem, not a human one. The limiting factors are no longer hands or hours—they’re energy, bandwidth, and imagination. And maybe logistics.

The implication also means manufacturing can become mobile. When the factory is software, it can be replicated anywhere power and connectivity exist. Perhaps it could even replicate itself. Geography no longer defines production; compute does. This is true scale; when production produces production.

💡 The AI Factory

There’s another kind of scale coming too. For decades, we’ve measured computing progress in floating point operations per second (FLOPS) or instructions per second (IPS), but we are soon likely to start measuring tokens per second. If you remember from the preamble, tokens connect inputs and outputs to the underlying math. And tokens are the new atoms of digital work. Jensen said (timecode 00:33:12),

“Tokens [are] … the computational unit … of artificial intelligence. You can tokenize almost anything … words, images, video, 3-D structures, chemicals, proteins, genes … anything with structure … anything with information content.” - Jensen Huang

Once you can tokenize something, AI can learn and translate inputs to outputs, and respond and generate. So what’s possible today with text will be possible with robotic motion and action and behavior. Under the covers, it’s essentially the same math.

But now imagine a factory that accepts input and spits out tokens. This was the real ah-ha moment for me. Jensen said (timecode 00:40:13),

“We need a new type of system and I call it an AI factory”, he said. “Its purpose is designed to produce tokens that are as valuable as possible.” - Jensen Huang

He said cloud computing is the general computer of the past. A traditional data center stores or retrieves information; an AI factory manufactures it. Every prompt, image, or simulation is an act of production. Compute becomes supply chain. And the measure of efficiency is no longer energy per bit; it’s energy per token.

We have reached the industrialization of compute.

If data was the new oil, tokens are the refined fuel—and NVIDIA just cornered the market on refineries.

🐇🕳️ The Rabbit Hole

If you’d like to dig a bit deeper, I’ve included some of my favorite videos on large language models, transformers, and the attention mechanism in the footnotes4. These are some of the best videos I’ve ever found on these three topics.

Jensen’s keynote from CES 2025 is pretty remarkable too, but I especially liked this short video at the end. If you watch it, you’ll see some of the things I described in the essay. There is some stuff I didn’t cover too, like the modularity NVIDIA is building in to the Blackwell chip, that not only makes it easier to manufacture in a variety of configurations, but also easier to scale.

While writing this article, I discovered Google Gemini can summarize and extract content from YouTube video links. Here are a few fun prompts to try if you find yourself in a similar situation. (Note: they didn’t work as well on ChatGPT).

💡 Prompt Idea: Analyze this video

This prompt will analyze a video and provide you a list of major thematic elements. Feel free to adjust if you want a different analysis (e.g., major thesis).

Please analyze this video and summarize the content into major themes. https://youtu.be/dQw4w9WgXcQ

💡 Prompt Idea: Content timestamps

This prompt will provide you a list of timestamps where your topic is referenced.

Please provide me a list of timestamps as formatted URLs where the topic of clear communication is discussed. https://youtu.be/dQw4w9WgXcQ

Enjoy!

— Rob Allegar

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I’m a lifelong builder and advisor exploring what happens when technology stops behaving like a tool and starts acting like a collaborator. In this newsletter, I explore the space between ideas and execution, and help people build things that matter. roballegar.com

Thanks for reading. If you enjoyed this piece, hit ♥ or share it with someone else who is already worried that robots are going to take over the world.

Share Rob Allegar

🎧 Prefer to listen? I read this article on the voiceover (above), on Spotify, and on Apple.

Why This Matters

You’re wasting time and money if you treat AI1 like search. Every prompt, conversation, and half-useful output burns compute, tokens, and attention.

This is the first installment in my AI Superpowers series, and it focuses on the foundational shift you need to make today: stop treating AI like a search engine, and start treating it like a collaborator.

Except where noted, I built the included images myself using AI prompts, a little Photoshop, and lots of trial and error. Like everything else in these essays, they’re part of how I explore the intersection of technology, creativity, and culture.

The Interface Illusion

For thirty years, the web has trained us to think inside a box. A search box, that is. Type a few words, press Enter, get results. It’s clean, fast, and universal.

When generative AI arrived, designers borrowed that same search interface. ChatGPT, Gemini, Claude, Copilot—all look like upgraded search bars. The design feels familiar—and that’s the mistake. It’s one big reason so many people are disappointed with their AI results.

By giving two completely different technologies the same interface, we blurred the line between retrieval and creation. The box stayed the same. The behavior underneath changed completely, and couldn’t be more different.

We built two different kinds of tools and hid them behind the same box.

Understanding why it’s important to treat search boxes and AI input boxes differently requires a look under the hood.

Retrieval vs. Generation

Search engines, like Google or Bing, are essentially gigantic card catalogs. You ask a question, they scan a vast index of existing things they’ve seen before—websites, PDFs, images, source code, videos, etc.—and return a list of references. Essentially, the relevant things and where they can be found. Their strength is recall and ranking.

Generative AI models are different. They don’t look up answers; they compose answers. From scratch—every single time. Really. Each word in the response is a prediction. It’s the most likely continuation of the result, given everything that came before.2 That streaming text isn’t slow retrieval—it’s real-time, on-the-fly generation. I demonstrated this behavior in a previous Substack post, showing how even a simple three-word prompt leads to a unique result every time.

AI doesn’t search; it predicts.

Once the first few words are predicted3, the model loops again, and asks: what’s next most likely, given what came before? And then it loops again. And again. Each loop builds on the last until the response forms. And just like that—statistics becomes language.

To see how much that text box design choice matters, I made a cartoon (with ChatGPT and a little Photoshop) that illustrates what happens when two totally different tools hide behind the same interface. This took longer than I’d care to admit.

So if the text boxes look the same, what separates good AI use from just another Google query? Here’s the answer in one word: context.

AI Superpower #1: Think in Context, Not Questions

We open an AI chat and start typing a question. That’s what decades of search engines have taught us to do—treat the text box as a place to ask. But the AI text box isn’t for questions; it’s for adding context.

Every message you send—and every one the AI generates—shapes the model’s context. And this context shapes the overall interaction—what the model thinks about the topic you’re discussing. The context makes the model’s responses coherent and relevant. So use the AI text box as a way to share your context— your intent, your goals, your ideas, your constraints, your outcomes. The better you do this, the better your results.

Your results improve in proportion to the context you give.

The AI chat window is more than an interaction history—it’s actually the entire context for that particular thread. Unlike a search query, which forgets you instantly4, this chat window stores every message that you and the model exchange, providing source material and patterns that shape how the model behaves.

Context isn’t a prompt trick; it’s the environment that influences how the model responds. The clearer the environment, the smarter the model appears. Think of context like a notebook that fills itself as you talk—each message layers tone, ideas, and intent on top of the last.

What that “notebook” really represents is the model’s working memory—what it remembers, summarizes, and uses to predict what comes next.

How Context Actually Works

Every chat thread has its own context window—the model’s short-term working memory. It holds your messages, its replies, and the invisible logic connecting them. That history makes the conversation feel coherent, even “attentive.” But that memory isn’t infinite. Knowing how the context window fills and forgets, and being intentional about how you use it, will help you get better results.

✅ Each conversation has its own memory. Isolate tasks in separate chats. Keep related work in one thread. Start fresh for new projects.

✅ The whole conversation counts. The model rereads everything each time it replies. Long threads cost more and can drift in tone.

✅ Every turn adds weight. Be concise. Every word consumes finite context budget and compute resources. Long prompts and verbose outputs fill it faster.

✅ Memory is finite. When the window fills, older details fade, sometimes without warning. Restate key instructions in long chats.

The context window is a budget—spend it wisely.

A few years ago, context windows were tiny. Only a few pages of text. Today, they can swallow whole reports, transcripts, or codebases.

The Expanding Memory of Machines

The chart below from epoch.ai5 shows how rapidly context window capacity has expanded—a reminder that this ‘working memory’ isn’t fixed. Larger windows allow richer collaboration and longer reasoning chains—but they can also raise costs.

Once you understand how a model remembers, you can decide what’s worth remembering. The next step is teaching it how to think—defining roles, tone, and sequence so its reasoning matches yours. That’s where we’ll go next week in AI Superpower #2: Think in Roles, Not Rules.

🐇🕳️ Try This Next (Rabbit Hole Prompts)

Here are some fun prompts to try that will help you dive a bit deeper into this topic. You can copy and paste any of these as-is into new or existing chats. If you find anything useful or particularly interesting, let me know in the comments!

Each of these prompts explores a different dimension of the context window—what it remembers, what it drops, and how those trade-offs shape your results.

💡Prompt #1: How Big is Your Context Window?

This prompt will give you a deeper understanding of context window capacity. It’ll work on any conversation, but you’ll get more interesting results on longer threads.

Analyze this thread’s context usage. Estimate input/output tokens so far, what you think was summarized or dropped, and where additional context would most help quality.

💡Prompt #2: How Much Did it Cost?

This prompt will help you understand how costs are correlated with context window use. It works best on conversations that have had some back-and-forth, especially so where you’ve shared things like documents or long blocks of text.

Estimate total tokens and a cost range using typical API pricing for comparable models. Show assumptions.

Every interaction has a price. Long prompts, repeated uploads, and extended histories all drive up token count.

🏢 Executive Note: Poor context management quietly erodes margins. Repeated uploads of the same material, redundant chats, or overly long prompts can double or triple model usage costs without a noticeable improvement in quality.

💡Prompt #3: Who’s Really in Charge?

This prompt analyzes your conversation so far and estimates how much of the response tone and structure comes from your phrasing and guidance vs. the model’s pre-built training.

Analyze our conversation so far. How much of your generated output appears to be influenced by my specific phrasing, ideas, or tone versus your prior training? Provide your best estimate with a short rubric (tone, structure, facts, examples) and describe the results in plain language.

The better your framing and context the more the system reflects your thinking instead of its statistical defaults. Try it for yourself. Open a new chat and ask a fact-based question, like, “Why does a cat’s tail flick?” followed by this prompt. You should see that the model’s response was nearly 100% influenced by its training.

🎨 Creative note: I’ll dive in to this topic more in the coming weeks, and will be sure to share lots of tips, prompts, and other ways to get better outputs! It’s especially useful when you’re trying to use AI to invent something entirely new in the world.

💡Bonus Prompt: Context Window Meta Prompt

Ready to get meta? Try one more experiment that asks your AI to design its own context tests.

Give me 6 non-obvious, single-paste experiments to help me understand how your context window behaves. Each idea should show something new about how memory, truncation, tone, or drift work in long chats. For each, provide a short and catchy title, what I’ll see (1 line), a copy-paste prompt, and the token cost (low / medium / high). Keep it concise and practical.

This prompt will generate its own mini-lab of experiments, which should be different from model to model and tool to tool. You may learn as much from how it interprets the assignment as from the prompts it creates for you. Happy hunting!

Looking Ahead

I sometimes wonder how future generations will characterize this era, this golden age—the year 2025, when AI first became widespread. What will people think, say, 100 years from now, as they cast their learned gaze backwards through the century that is our future, towards a time which, for them, will be as archaic as 1925 is for us?

Will they regret how much we underestimated AI’s impact on the world? Or how much or how little we governed its use? Or how much energy and water and surface area of the earth we dedicated to the data centers that powered early versions?

One thing I am certain they will regret: that humans built the most transformative technology in generations—AI—and made it look like a search engine.

Putting It Into Practice and Avoiding Context Waste

This mental shift—from retrieval to prediction, from questions to context—is the first step. Applying it is where you unlock leverage.

If you’re a business leader, mismanaged context windows quietly drain productivity and budget. Every redundant upload or unfocused thread compounds costs. Teaching your team how to think about context and how to use it correctly will add value and reduce AI’s drag on your margin.

If you’re an individual, try the prompts above. Maybe provide a bit more context in an existing thread and see if the model immediately improves tone, accuracy, and relevance. You may find some threads are better restarted, if the context has drifted.

I help leadership teams audit their AI workflows—identifying where context mismanagement is leaking time and budget—and design systems that turn hidden waste into measurable advantage. If you’re ready to optimize your organization’s use of context book an introductory call ↗.

— Rob Allegar

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I’m a lifelong builder and advisor exploring what happens when technology stops behaving like a tool and starts acting like a collaborator. In this newsletter, I explore the space between ideas and execution, and help people build things that matter. roballegar.com

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In This Article

You’ll learn why designing AI guardrails is the next great leadership skill.

The next wave of AI will behave less like a tool and more like a collaborator—and sometimes, a chaotic one. This essay explores what happens when predictability disappears from our systems and is replaced with intentional uncertainty:

• The Broken Deal: Our 250-year relationship with predictable machines is ending.

• Control vs. Chaos: A tale of two AIs that explains the new age of uncertainty.

• Randomness is the new Operating System: What 100 AI-generated cat images reveal about the future of business predictability.

• Leading with Intent: A new leadership framework for designing guardrails in a world where technology makes its own decisions.

  ---

The End of Our Deal With Technology

The next decade of technology leadership will be defined by one idea: our tools will no longer be predictable. So what happens when our systems stop following our rules?

For 250 years, we’ve had a simple deal with our machines: we provide the thinking, they provide the work. From the steam engine to the spreadsheet, our tools have always obeyed us. When they failed, it was because our instructions were flawed.

That deal—humans commanding, machines obeying—is ending. And we’re completely unprepared for what comes next.

A lot of attention has gone to the jobs AI might replace. CEOs like Dario Amodei (Anthropic),1 Jim Farley (Ford),2 and Sam Altman (OpenAI)3 have all weighed in. Altman’s view is perhaps the most dire, suggesting that “… entire classes of jobs will go away”.

But some roles will remain, and new kinds of work will emerge.

In the post-AI world, the most valuable leadership skill will be managing within uncertainty, not managing towards outcomes.

The Age of Uncertainty

Today, most people meet AI through a text box: you type in a prompt, it replies. That model works for curiosity and creativity. But in operations, safety, or any high-consequence domain where decisions carry real-world risk, AI won’t live in a chat window—it will live “behind the scenes”, running automatically on relevant data.

Now imagine leading a company where systems’ inputs aren’t visible, and their results can’t be reliably reproduced. That’s the world emerging around us. That’s reality when AI is used to make (or suggest) decisions based on what it observes.

The CEO of NVIDIA, Jensen Huang, put it best in his 2024 interview at the World Government Summit in Dubai: “It is our job to create computing technology such that nobody has to program.”4

Systems that aren’t programmed don’t follow rules—they navigate a sea of probabilistic outcomes. They will think and act better than humans, sense and interact with the world on their own, and they won’t wait for permission. They’ll observe, decide, and act—sometimes in ways we can’t predict.

These systems will exist because of probability, not certainty. Therefore their behavior will be unpredictable.

Let’s make this shift tangible with two examples: one about cars, and one about cats.

Which is the Better Driver—Control or Chaos?

Imagine two self-driving cars: Control and Chaos. They look identical, and are built in the same factory from the same parts. But at their core, each is a different species of machine.

• Control is a library of rules. It follows code meticulously written and tested by human engineers. Every decision can be traced back to a specific instruction: “Yield to the car on your right at 4-way stop.” It is a marvel of deterministic logic.

• Chaos is a garden of experience. It taught itself to drive over millions of simulated lifetimes, making more mistakes than all human drivers combined—and learning from each one.

Both work flawlessly most of the time. The difference only becomes visible at the edge of failure. When Control crashes, the situation is tragic but traceable. When Chaos crashes, what is the cause? There is no line of code to inspect; no specific rule to update. We can evaluate the inputs and the outcomes—but not the reasoning behind them5. There isn’t a way to ask why it crashed, because it just knows how to drive.

Failures in AI systems may be errors of judgment, not bugs.

Have you ever wanted to see Einstein racing Feynman in futuristic race cars? I did, so I asked Sora 2 to create it for me. Try it for yourself, and share your video in the comments. Let’s see how many variations we get! (Here is a direct link to Sora if the video can’t load for you).

Herding Cats: One Input, One Hundred Outputs

This shift from deterministic to probabilistic logic isn’t just for complex systems (like cars)—it touches everything. To make it visible, I asked ChatGPT to write Python code to generate one hundred images using the same three-word prompt: a cute cat.

The results, arranged in the mosaic below, are a visual study in the beauty of randomness: One input. One hundred outcomes. Each image was born from the same three word input, yet none are identical.

Randomness is the new operating system.

At first glance, the randomness feels harmless—maybe even charming. But the same randomness that makes each cat photo distinct will soon affect hiring models, trading systems, and autonomous processes in every industry.

Measuring Difference and Detecting Outliers

Even measuring how much these images differ is complex.

To illustrate, I analyzed the 100 images in two ways. One method grouped the cats by what the AI thought it was showing (semantic similarity), revealing a few distinct “concepts” of a cat. Another method grouped them by their raw visual data (hue, saturation, and value), revealing the micro-randomness that makes each image unique.

The key insight is this: if one of these 100 outputs had been a dangerous outlier—say, a critical component designed outside of safety tolerances—one analysis might spot it while another would miss it entirely. Detecting unpredictable failures requires a new set of tools, because the failures themselves will be unpredictable.

We now have systems whose default state is variation, not repetition. Prepare for the unexpected.

And here’s another challenge: choose any single image from the mosaic and try to regenerate it exactly. The probability of reproducing it on demand is close to zero.

If you’d like to experiment, the full Python code that generated and analyzed these images is on Github: github.com/liminalblinkconsulting/substack-cat-variations↗.

Design Guardrails, Not Guarantees

The impact of AI’s randomness isn’t a technical problem to solve; it’s an opportunity to build smarter ways to manage your business.

Every process that depends on repeatability—compliance, quality control, hiring, finance—will need new guardrails: frameworks that tolerate variation without letting chaos leak into your margins.

This is the real leadership challenge. Managing within uncertainty. Turning probabilistic behavior into operational confidence.

Companies that adapt first will use variability as a design constraint, not a failure.

So start the conversation inside your own company. Ask:

• When AI is applied, can you determine outliers in its outputs?

• How do you know the decisions are within tolerance—and who defines these tolerances?

• If it makes a mistake, will you know before your customers do?

If you’d like a sparring partner to help your team think through uncertainty, design guardrails, and turn ambiguity into action, let’s talk↗.

— Rob Allegar, Liminal Blink Consulting

I’m a builder, advisor, and lifelong tinkerer exploring what happens when technology stops behaving like a tool and starts acting like a collaborator. In Liminal Blink, I explore the space between ideas and execution, helping people build things that matter. roballegar.com

Footnotes

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What’s Inside This Week

1. My Monthly music playlist for October 2025, called October Voltage.

2. Playlists links for Spotify (recommended), Apple Music, YouTube, and Amazon.

3. A Personal Story of Embarrassment and Prizes is about a time when I got free music even though I wasn’t paying attention.

4. The Rabbit Hole is where I keep my footnotes. Where do you keep yours?

Monthly Playlist: October Voltage

This month’s playlist is pretty energetic—full of songs that get me moving and grooving. I’ve named it October Voltage. It’s perfect as background music for chores, driving, impromptu dance parties, or making out. There is a pretty wide mix of genres on here, and lots of stuff you probably haven't heard before.

Where to Listen

You can use any of the links below to listen, but I’d recommend using Spotify1 if you can. They have a new “mix” feature for playlists (in beta), and I used it to smooth out a lot of the transitions. It unfortunately doesn’t work on all their supported devices yet, but it’s a good start! Just make sure you tap the “mix” button on the app’s playlist screen and it should work!

Listen to my monthly playlist October Voltage on any of these services:
🎧 Spotify | Apple Music | YouTube Music | Amazon Music

A Personal Story of Embarrassment and Prizes

What’s the most embarrassing thing you’ve ever said to a personal hero? One of mine was in 2001, uttered into a terrible Nokia-ish phone, while stuck in traffic. It was just a few words, and I still remember the single-sound response I got back in return.

To understand why a single sound had such a big impact on me, you have to understand that in the Washington D.C. radio landscape of the early 2000s, rock music didn’t matter at all. It was so unloved that only one rock station - DC 101 - was even in the top 10. This was the most mainstream station too, the one that played the most radio friendly and most popular songs. From bands everyone knew and liked, like the Foo Fighters,2 Metallica, and Alice in Chains.

Baltimore’s 98 Rock was around too, but it played a strange mix of classic rock and prog metal. They’d play Deep Purple’s Smoke on the Water,3 but then the DJ would play a 4-minute Dreamtheater song. It was … weird. There was also 94.7, which called itself “The Arrow”. It played 1970’s radio and album hits, like Hendrix’s All Along the Watchtower (but never Dylan’s original), along with lots of visits to Hotel California.

And then there was 99.1 WHFS. HFS (as it called itself) had everything you’d expect from a low market share indie progressive rock station in one of the country’s largest metro areas - an edgy logo, annual festivals, DJs with invented names4, and music you couldn’t hear anywhere else. It was the only station I knew that might play The Impression That I Get, Bigmouth Strikes Again, Come Out and Play, and Firestarter in the same hour.

Listening to WHFS felt “underground” in the best sense. It carried a weird ethos too, like it was a college station run by dropouts who didn’t care much for school—or for anyone or anything else, really. It was remarkable that HFS existed when it did in the way that it did, especially when most of the area didn’t care for its music.

The early 2000s was a different time for music. Before streaming. Before digital. Before the Internet. If you wanted to listen to music in the car or at work, you either brought it with you, or you listened to the radio. And carrying around stacks of heavy, fragile plastic, in the form of CDs and tapes, got really old really fast. So everyone listened to the radio. And that made radio DJs gods. They were revered! They had access to ALL the music! They could play whatever they wanted whenever they wanted. Especially on HFS, which, as far as I could tell, seemed to have only one rule: don’t play music that sucks.

HFS had this weekly evening segment called New Music Mondays5 where the DJ would play a song not yet in rotation. Some songs were forgettable, some were instant hits, and some were deep cuts from well known bands. But the real draw was the call in. If you got through you got a free CD. And what’s better than free swag from the coolest radio station in town?

One night in 2001, as I started my 75-minute nightly commute home, I called in to New Music Monday. I worked in Tyson’s Corner, VA at the time, which during dark weekday evenings around the winter holidays, felt like the sliding puzzle game Rush Hour6. It was just you alone in your little car, frozen in place, while all of the other vehicles moved around you in bursts of commotion, freeing up just enough space for you to move forward a few feet. It was miserable.

My mobile phone at the time was a foldable gray plastic oval with a blue-glow keypad and a screen that wasn’t much bigger than a calculator watch7. Calls sounded like they’d been transmitted from a haunted seaside location—full of static and wind and ghost sounds. You had to hold the phone to your face too; there was no speakerphone, no Bluetooth, no mercy.

That night, as I inched forward in traffic, one hand on the wheel, one on the phone, I called the station. In one ear, the phone’s inaccurate yet somehow recognizable rendition of a phone ringing; in the other, a new song from an unfamiliar artist. I do my best to pay attention to both, and also the other, barely moving cars around me. Safety was not my concern, because of the gridlock, but boy I wanted that CD.

Then—the sound. A click, a hiss, a faint electronic woosh.8 If you’ve ever called in to a radio station, you’ve heard it too. It the sound that means someone at the station picked up, and you were connected.9

“HFS!” A man’s voice. Hollow. Distant. “What’s your name?” 
“My name?”
“Yeah—you called in to New Music Monday! What’s your name?”
“Uh ... Rob.”
“Rob, what did you think of the song?”

I wasn’t prepared for this question. At all. I’m not sure why. I’d heard the segment before. But the question still took me by surprise. I had nothing to say! I was barely listening to the song! I didn’t answer for a few beats. It felt like forever. But I’m nothing if not confident. So I started talking.

“You know what I liked,” I said—half a question, half a stall. “The words…” I paused, flailing for sophistication. “The lyrics. They… they really matched the music.”

I remember the feelings: pride, accomplishment, and a serene conviction that I had just uttered the most profound observation on music ever spoken aloud. That’s how confident I was in my answer. With all of the chaos going on around me at that moment, I felt like I’d crushed it. Wow, was I wrong.

“Ffft,” the DJ responded. A pause. Then a quick, “… yeah.” with a half-laugh.

The “Ffft” sound he made is what I remember most. It was a scoffing sound. A sound you might hear in middle school, just after you asked a girl who was way out of your league to the 8th grade dance. A sound you might hear from your friend, just after you made an over-confident boast that both of you knew wasn’t possible. A sound that, when coming from one of your DJ idols at the most counter-culture radio station within 100 miles, was definitely not a compliment. A sound that, to my ears, was cool-kid shorthand for, “I heard what you said, but your response was so stupid and insignificant that to form any verbal response at all would be a waste of my life. So instead, I will exhale the breath I was reserving, as it is no longer needed for sound.”

Time slowed down. The traffic vanished. I felt less like Neo dodging bullets in the Matrix10, and more like I was 12 years old and said something dumb in front of the whole class.

“Well … Rob? Please stay on the line so we can get your info!”

I still think about that moment. But now almost 30 years later, having processed all the shame I felt over the last three decades, I think about the DJ. What kind of day was he having? Maybe he’d been waiting all week for New Music Monday. Maybe he regretted choosing my call—the one guy whose answer managed to be both confident and useless. Or maybe he was delighted. Maybe my stupid comment made good radio. Or maybe, just maybe, he agreed with me. Either way, I eventually got my free CD.

This memory captures the spirit of what I loved about ‘HFS—it was a little awkward, a little weird, and they definitely didn’t care. This month’s playlist is a tribute to that feeling. It’s full of artists who follow their own path and music that, I hope you’ll agree, at least follows HFS’ rule: it definitely doesn’t suck.

This essay is part of my ongoing studio log exploring how technology, creativity, and culture can help us live, love, and work better — one memory, one thought, one sound at a time.

Yours from the studio,

Rob

The Rabbit Hole

The footnotes below are probably an amazing waste of your time…

4

[READ]: One fan’s nostalgia for WHFS DJ Weasel and his love of obscure songs

No Such Thing As Was

Remembering Frantic Fridays

Much of what we endure in the present softens and comes to a mild glow through the wide lens of nostalgia. It's more rare when something vital and urgent in the far past retains its charge in the present. Joyce Carol Oates says that "blood is memory without language." When a recollection stirs us, language vainly tries to translate. So my dilemma: how d…

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15 years ago · 3 likes · 3 comments · Joe Bonomo