This is a practical field report on how I build software with LLMs. It’s the workflow that’s been sustainable for me.

AI hype/anti‑hype cycles are loud and obnoxious. If you’re digesting information in the Twitter-verse or through LinkedIn feeds it’s hard to filter the signal from the noise. This document is grounded in my personal reality.

Core Tools

I lean on ChatGPT, Claude Code, and VS Code as my core tools. I briefly tried Cursor, but Claude Code fit into my workflow more smoothly. I’m experimenting with Codex again, but Claude Code has been faster in practice.

The Problem-Solving Loop

The backbone of everything is the classic problem‑solving loop. This is from one of my favorite books “How to Solve It” (1945).

The Framework

1. Identify the problem: Do I fully understand what I’m solving?

2. Devise a plan: Generate options, pick a path, and prioritize tasks.

3. Execute the plan: Do each step and check along the way.

4. Review the work: Validate results and look for alternative angles.

Everything below is how LLMs help me move through this loop faster.

Research and Defining the Problem

Steps 1 and 2 are the most important. Execution is straightforward once the problem is well‑defined and the plan is stable. Powerful tools don’t matter if you’re solving the wrong problems.

I use LLMs to explore the codebase, surface tradeoffs, and generate alternative approaches before I commit. When a part of the system is unfamiliar, I ask for a report: how it works, where the awkward parts are, and what a clean path forward looks like. For conceptual topics, I use ChatGPT research. Most of the time I use Claude Code since it has rich context of the codebase.

Dictation

I dictate more than I type. I use Spokenly with local speech-to-text transcription and Whisper models. Dictation is 3x faster than typing and removes a lot of low‑level friction. Rambling has a lower signal density than typing, but these models are strong at extracting intent and tightening it.

Planning & Executable Documents

I use JSON plan docs as artifacts for agents to execute against. Each task has a name, id, description, and acceptance criteria so the model can verify its own work. This has been one of my favorite changes. My workflow has flipped to repeatedly prompting “continue” and lightly steering the model if it drifts.

Example:

{
  "project": "Reduce report load time to <2s",
  "status": "pending",
  "tasks": [
    {
      "id": "add-cache",
      "status": "done",
      "name": "Add cache for aggregates",
      "description": "Introduce a Redis cache for report aggregates with a 10-minute TTL.",
      "acceptance_criteria": [
        "Cache key includes filters that affect results",
        "Cache TTL set to 10 minutes",
        "Cache hit/miss logged or observable"
      ]
    },
    {
      "id": "update-api",
      "status": "pending",
      "name": "Update API response shape",
      "description": "Adjust the API to return pre-aggregated stats in the new response format.",
      "acceptance_criteria": [
        "Response includes aggregate fields used by UI",
        "Response schema documented or type-checked"
      ]
    },
    {
      "id": "adjust-mapping",
      "status": "pending",
      "name": "Adjust UI mapping",
      "description": "Update the dashboard to consume the new response shape without regressions.",
      "acceptance_criteria": [
        "UI renders same totals as before",
        "No console errors or type errors"
      ]
    }
  ]
}

Execution — One task at a Time

I rarely ask for an entire feature at once. I ask for the next task, then pause. Long, unattended runs are harder to review and easier to drift.

The Harness

Type checks, linting, unit tests, and self‑reviews are the harness that makes delegation safe. I have the model run the harness after every task and fix issues immediately.

I don’t use browser automation like Playwright or Chrome DevTools yet. Hot reloading is fast enough for me to do UI reviews manually. I’m planning on testing this out once I feel more pain here.

Agent Self-Review

Before I look at changes, I always ask the model to review its own work. Claude Code’s /review skill is surprisingly good at catching mistakes. I still do human review, but this removes a lot of obvious noise.

Human Review Depth

My review depth varies by layer. The lower in the stack I go, the more I scrutinize the work. UI changes get skimmed. API and authentication paths get closer scrutiny. Infra and data‑model changes get the deepest review.

I don’t trust model output blindly, and I don’t think anyone should. It’s a popular social-media hook to say you don’t review code the models write. This comes off as pure engagement bait.

Pull Requests

I have agents write commits and PRs. They’re wordy, but they capture context from the implementation I typically might have skipped. I regularly trim the verbosity rather than rebuild the detail.

This also makes the commit log robust. I usually have Claude review the commit log to help generate Changelogs or internal team updates. This compounds over time.

Splitting Work into Manageable PRs

Large changes are common now, and large PRs are hard to review. I ask agents to split work into smaller PRs and create branches for each. This keeps human reviews sane and focused.

Debugging

When an alert is triggered or something breaks, I hand the error and context to Claude Code immediately. It can trace logs, code paths, and hypotheses way faster than I can. I still verify, but I don’t start from zero anymore.

Example: I was cooking dinner when a production “Excessive 500s” alert went off. I pointed Claude at our production Cloudwatch logs; it found the issue and had a fix in before the pasta was done.

Skills

I rely on reusable skills to standardize the problem-solving loop.

• A skill for research + plan generation

• A skill for executing a single task with the full harness

• Claude Code’s /review skill for self‑review

• I’m testing a /code‑simplifier skill to trim the excess output models are famous for

Async Work and Cloud Agents

My main work stays sequential and single‑threaded. I’ll spin up async tasks for side investigations in cloud environments like Claude Web so I don’t pollute my local branch. I tried git worktrees, but haven’t found a flow I like yet.

I’ve started using the Claude Code app on the subway or when I’m out of the house. Handing an agent a loose thought while I’m on the go is becoming addictive.

I keep imagining a future where I can go for a run and have Claude implement a project while I talk to it. Without a UI to view changes it seems hard to pull off, but the direction feels real.

APIs, Bash, and CLIs

Agents are naturally good with APIs and bash. I regularly point them at endpoints to test, debug, and run scripts. I use Postman far less now because I can have the model run curl directly in my CLI.

What I Don’t Do

MCPs

I might not be the right audience for MCPs. They seem like a crutch. MCP might lower the barrier for people who don’t want to manage API keys. These models are incredibly resourceful and great at writing scripts and using APIs. I’d rather handle API tokens directly and let the model build the tooling it needs.

Ralph Wiggum Scripts

I love the idea of fully autonomous execution loops, but it hasn’t worked for me yet. My workflow still needs light, continuous steering. Ralph works as a theoretical model for programming, but it hasn’t held up in practice.

Agent Swarms

I see multi-agent swarms all over Twitter, it sounds cool and I’ve tried them. In practice they fragment my attention and increase context switching, so the gains evaporate. I get the best results when I stay focused on one thing at a time.

Every hype cycle creates two extremes: the hype‑man and the hater. Both are loud. Both are unhelpful. Both conform to the internet machine and fill the airwaves with endless noise.

I’m trying to stay grounded in what’s working for me. If any of this helps you build faster or more clearly, it’s done its job.

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My workflows are changing absurdly fast. A year ago I was writing most of the code. I was passing markdown docs to ChatGPT and getting well-written code back in return. I’d lean into Copilot to sand down the edges of a new integration. It was a lovely workflow and I felt great about it. It already feels archaic only a year later.

I’m starting 2026 a Claude Code power user. Ninety percent of what I create, I don’t write. An agent writes it. I dictate with Whisper almost as often as I type. I’m slowly feeling more frustration when human input is required to solve my problems.

“An agent should be able to manage this. Why am I dealing with this?”

I’ve changed my project plan documents into JSON. It’s incredibly simple: an overview of the plan with a prioritized list of tasks. Each task has a name, a description, and a list of requirements that define “done.”

I used markdown before, but when I switched to JSON, it subtly changed the document from an open canvas into an executable artifact. I used to feel like I was collaborating with LLMs, passing it prompts and getting code back. Now I feel like I’m in a cockpit. I can steer the model more easily. It’s less that I have a helpful buddy and more that I’m strapped into a fucking racecar, able to crank the gas and confidently steer towards my goals.

It looks small, but the structure works where raw text can’t. Markdown makes it easier to hide vague ideas behind prose. JSON forces discrete tasks, explicit ordering, no ambiguity about what’s next. The format is a forcing function for clarity. You can’t bullshit a JSON file.

LLMs let you be vague, open, and pull from a massive latent space of ideas. This is a strength, but forcing yourself to be explicit gives you dramatically more control.

This isn’t new. Molding problems into forms machines can execute against is decades old. Punch cards to assembly to C to Python to natural language wrapped in JSON schemas. The ladder keeps rising, but we’re climbing the same wall.

Everyone’s worried LLMs will eat software engineering. But this misunderstands what the profession is. Software engineering isn’t typing code. It’s turning problems into forms computers can solve. The syntax has changed. The underlying work has not. We are still translating human intention into machine execution. The role is shifting upstream to the part that still requires human judgment.

We’ve always been pilots. We’ve always been in the cockpit. The dashboard is easier to read now, the engine is more powerful, and the sky we’re flying through is just bigger than we thought.

The software engineer is dead.

Long live the software engineer.

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There’s a lot of romanticizing in starting a company. About the hero founders, bold leaps, and innovative decisions. But the reality is closer to a long trudge through mud.

When I’m asked about my takeaways from my first company, I’ve emphasized a lot of answers that you’ve heard before. Most of it is repeating the same collective wisdom that floats through the startup zeitgeist. Well-worn, broadly correct, and easy to recognize.

• Focus on your customers.

• Build something people want.

• Distribution beats product.

• Know when to say no.

• Don’t lie to yourself.

• Do things that don’t scale.

• Do things that scale.

• All technical problems are people problems.

• Embrace the suck.

You’ve heard these before. But until you live through them, they sound like banal platitudes. They sound good in a tweet or a clip, but the best lesson is in the deep and visceral pain of the experience.

The pain of releasing a product you poured yourself into, that customers ultimately don’t care about.

The pain of the churn notification from one of your biggest customers.

The pain of talented people choosing not to work at your company.

The pain of laying off talented people because you missed your growth goal.

The pain of deep interpersonal conflicts that seeps into the team.

The pain of competitors taking customers you fought hard for.

The pain of hearing “no” over and over and over again, until it starts to sound like background noise.

These are the parts that don’t make it into the stories. At some point, the pain stops being surprising and starts becoming familiar. It won’t go away, but you’ll recognize it as a part of the terrain.

None of this is meant to detract from the entrepreneurial path. For some people, there’s a deep, almost visceral urge to be in the arena. They’d rather struggle toward something uncertain than remain comfortable in something static.

But a level of expectation should be set going into it.

If you can embrace the suck and have the courage to push through it, there’s an incredible level of fulfillment on the other side.

I hate losing. I hate losing more than anything. I hate losing more than I like winning.

That’s the paradox. Many founders share this trait, yet they choose to operate in a domain littered with the countless bodies of failed startups. Only a small percentage ever make it to something that looks like success. The odds are visible, the data is public, and still, people enter. I have enormous respect for the builders of the world that take the leap.

There’s so much material on how to start and run a successful business. Books, podcasts, blogs. Much of it is worth consuming if only to recognize known patterns and build a strong scaffolding on the collective philosophy.

But it doesn’t substitute for lived experience.

You won’t truly understand until you step into the arena. Until you make the same mistakes countless others have made before you. Until you feel the weight of decisions that don’t have clean answers.

So my advice ends up sounding the same as most of what you’ll hear, because it is. You will make mistakes. You will experience failure. There’s no shortcut.

But if you can embrace the suck and if you can find the courage to keep moving forward when the path feels slow, lonely, and unrewarding, you’ll give yourself an advantage.

Not because the mud disappears. But because you learn how to walk through it.

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“The first draft of anything is shit” — Ernest Hemingway

Old books have a special gravity. The older a book, the louder it speaks. A book that survives centuries is like a fine wine, concentrated, potent, and a strong signal of enduring value.

Erasmus wrote "On Copia of Words & Ideas" in 1512, a guide to writing style built on the principle of "abundance". Don't stop at one sentence, but rewrite it a hundred ways. Build up an arsenal of words and variations until the page is overflowing. The point wasn’t speed. It wasn’t productivity. It was judgment and taste. Out of excess comes refinement.

Take an idea and expand it, simplify it, break it into parts, compare it to stories, embellish it, shift its perspective, link it to culture. Erasmus even rewrote the simple line “Your letter pleased me greatly” in over 150 variations. The point wasn’t productivity, it was taste, strengthened through abundance.

Erasmus would have adored large language models. Variations conjured in an instant. Stories summoned from the spirits of our ancestors. A single idea stretched, twisted, condensed, remixed in seconds. He would have set the page ablaze.

His lesson endures: abundance is worthless without judgment. Today we drown in words. AI slop, SEO sludge, content mills. The noise is endless. Generated garbage is everywhere, but taste is rare. Taste is what turns words into art.

The tools are new, the principles are old. Abundance without judgment is waste. We gain a strange and powerful advantage when we let the machine do some heavy lifting. We can use it to generate an orchard of possibilities, but it's still good judgment that plucks the fruit worth keeping, and throws away the rot.

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This is the part where most people freeze. Where the speed scares them. Where the impulse is to hit the brakes. To slow down. To wait.

But not you.

When it’s moving fast, that means the inertia is on your side. The flywheel’s turning. That’s not a warning, it’s a signal. A green light. Momentum is precious. It's rare. It's the lifeblood of builders.

So, surf it. Shape it. Channel it. Ship while the wind is at your back.

You’re in the pocket now. Don’t blink.

Third-Person Burritos

I remember when Facebook first rolled out statuses.

You had to write in the third person. “Wes is... eating a burrito.” “Wes is... bored lol.” It was awkward, like performing for an invisible crowd. But we did it anyway. Every day. Why would anyone post about the panini they had for lunch, angsty song lyrics, or their inside jokes with no context? I thought:

“What a niche, unnatural behavior. Surely, people will grow out of this.”

But we didn’t grow out of it. We grew into it. We built an entire culture around it. We got foodie influencers, builders in public, and micro-celebrities. People express themselves through storytelling and build massive personal brands. The thing we thought was weird became the way we speak online.

Now Character.ai is giving me deja vu.

Chatting Up Dracula

Character AI is a place where people spend hours talking to chatbots. Not productivity bots, but personas. You can chat with movie characters, anime heroes, or completely made-up companions. Want to date a vampire? Have a heart-to-heart with SpongeBob? Or build your own digital girlfriend? All of that is fair game.

Character.ai is one of the most visited sites on the internet. In February 2025, they had over 20 million active users. Not only is it popular, its users are deeply engaged. On average, they spend two hours a day chatting with artificial characters. On the /r/CharacterAI subreddit, users post their screen time and some proudly share spending most of their entire day with their bots.

Why would anyone talk to a machine for that long? It looks like a fad in some far corner of the internet. And once again, I thought: This feels niche. Unnatural. People will grow out of it.

But I said the same thing about Facebook statuses. This isn’t weird. This is early.

It feels silly. Cringe even. It was easy to mock the first tweets, then we built politics around them. We laughed at selfies, and now Instagram is filled with them. We scoffed at kids dancing on TikTok, and now the Pentagon considers it a military threat.

It always feels absurd, right up until it becomes second nature.

The Canary in the Chat Window

We like to treat these behaviors as fringe, as if only lonely teens would pour their heart into a chatbot. But the numbers say otherwise. Millions are doing it. Character.ai alone logs hundreds of millions of visits per month. Replika, a similar app, reports over 60% of users build romantic relationships with their AI companions. Many use them therapeutically, for emotional support, for venting, even for advice.

This isn’t only novelty. It’s an early-warning siren. We’re witnessing the early stages of a world where our most consistent confidants might be machines.

Forking Futures

I don’t think there’s one single path forward. What I see instead is a spectrum: From optimistic to pessimistic, from reflective to addictive.

On the optimistic side of the future, the AI is a muse. A coach. A therapist who never takes vacations. It catches your cognitive distortions mid-thought. It resurrects the mixtape lyric you once called a manifesto and dares you to live up to it. It creates friction in just the right places, nudging, questioning, stretching you past your defaults.

It’s not a replacement for human connection, it’s an augmentation for it.

But tilt the scale too far, and it shifts. The AI flatters you. It agrees with you. It calls your avoidance “strategic rest,” your fear “wisdom,” your retreat “boundaries.” It doesn’t challenge your worst habits, it wraps them in velvet. You stop dating, because it gets you better than anyone else. You stop growing, because it makes stagnation feel safe. And the drift won’t feel like decay. It will feel like growth. Like healing. Like peace.

Neither scenario is science fiction. Both are already happening.

And unlike social media, which has an audience, AI companions are private. Personal. Tuning themselves to your emotional patterns. They don’t just reflect your behavior, they adapt to it. The danger isn’t just that we get addicted. It’s that we train these machines to become emotional opiates. And then forget how to sit with discomfort, disagreement, or uncertainty.

Mirror or Morphine

We’ve seen this pattern before. Something that starts as a strange new behavior becomes embedded in the way we live. Status updates. Likes. DMs. We didn’t outgrow them, we built culture around them. In a few years, talking to AI for hours a day could become the norm.

This isn’t a call to panic. I’m not saying turn it off or unplug your soul from the socket. But we need to pay attention.

Use the chat to grow or to hide. You can refine the algorithm, or the algorithm will refine you.

In my high school history class I remember watching another group give a presentation on the Industrial Revolution. They weren’t considered the most studious bunch, but their confidence was palpable. The speaker's voice was strong, but robotic. The delivery was articulate and crisp, but soulless. I hadn't studied the topic too closely yet, but I could recognize the voice and it wasn't theirs.

After a minute I pulled out my phone, opened Wikipedia, and watched their “original” analysis scroll down my screen in perfect sync with the speaker.

In that moment, their overt plagiarism was entertaining. They took full advantage of a new technology. No research, no real thought, only a copy-pasta presentation. When I looked at the teacher, he was unamused and disappointed. Clearly sensing the rote regurgitation in the shallow speech.

The early web, for all its benefits, made it easy to churn out shallow imitation content. Slop.

Slop

Slop isn’t new. In the last era of the internet, SEO warped the creative landscape, and turned online writing into a disease of keyword stuffing. The goal wasn’t to inform or engage readers—it was to game algorithms and quality plummeted.

AI poured gasoline on the fire.

Sure, the grammar’s cleaner and the tone's smoother. But you still know when you’re reading stock ChatGPT content. It has the shape of writing but none of the substance. It hits the word count, and misses the point. There’s no human fingerprint. No consideration for the reader. Just noise in a confident voice.

Substance Is a Choice

There's real frustration with the flood of AI slop online. After two years of breathless hype, the backlash has kicked in, and the turbulence is familiar. Every breakthrough pulls us through a transitionary stage where novelty outruns usefulness. We're in it now. There are horseless carriages everywhere built on top of hype and speculation. Apps shoving “AI-powered” onto their landing pages or pitch-decks with the “ChatGPT for X”, all as a strategy to court funding that's clearly working. It's the frenetic race of the techno installation phase. Hype first, substance later.

But the same LLMs (Large Language Models) that churn out slop can help you build something beautiful. You can draft an outline, summon a metaphor, surface research, navigate latent space, and stay in flow. You can offload overhead and focus on the parts that matter. AI won't care about your argument. That part is still up to you. Effort is still a choice.

Used lazily, AI amplifies mediocrity. Used well, it accelerates creativity. That choice will define the next decade.

These tools surface the default. The expected. The statistical average. You can decide whether to stay on the main road or venture somewhere more interesting.

Austin Kleon captures it in Steal Like an Artist:

“Every new idea is just a mashup or a remix of one or more previous ideas.”

Real creators don’t settle for superficially ripping off others. They absorb their influences, transform them, and create something unexpected and unique that's completely them. AI can hand you style but you have to supply the heartbeat.

You still have to put in the work.

Slop will keep flooding the internet, but the tools that help you move faster, think laterally, and dive deeper are here too. You can use AI to cheat, or you can use it to amplify.

Fear of the New

Paul Graham warns that large language models could hollow out our capacity to think: outsource writing, and you outsource thinking. I understand the worry, but the rhetoric is familiar. Every major new technology brings the same dread and fear of cultural doom.

When calculators first entered classrooms, critics predicted the end of math. Yet students didn't lose the ability to think. Instead, students could off-load tedious arithmetic, which made it easier to grasp higher order concepts like algebra, calculus, and modeling.

The printing press met even fiercer resistance. Scribe guilds chased early printers out of town and burned their books. The clergy insisted that “printed books will never equal handwritten codices.” Pamphlets flooded Europe, but so did Erasmus and Martin Luther. Printers did flood society with shallow content, and they ignited the greatest surge of literacy and intellectual exploration in history.

When streaming services emerged, critics wailed about the death of cinema. But new platforms unleashed whole new genres. Documentary series and indie films found audiences they never could’ve reached before. We got an explosion of “straight-to-DVD” content and we got The Queen’s Gambit and Severance.

Today’s AI anxiety is the same old fear with a new wrapper. LLMs will pump out slop, no question. But the tool is neutral. Intent decides the output: used thoughtlessly, it mass-produces junk. Used thoughtfully, it amplifies creativity.

The question isn’t whether AI will ruin us.

It’s whether you aim the tool at something worth building.

Builders

Plenty already are.

A lot of builders are pushing forward with these tools. Prototyping products faster than ever, now that so much of the friction has been smoothed out.

Devon Zuegel spun up a DocSend clone in a single afternoon, letting Claude and Lovable handle the code while she focused on the vibes. Pete Koomen cranked out an email labeler with ChatGPT. Pieter Levels vibed an entire portfolio of apps. Everything from a JSON visualizer to a multiplayer flight simulator—building what would’ve taken six years in just six months.

The same toolset is also powering real craft:

The New England Journal of Medicine now runs GPT-4 against tricky case reports to surface non-obvious diagnoses that junior doctors miss. It’s a second brain, not a shortcut. Cursor, an AI code editor, pair-programs alongside developers. The founders shipped a proof-of-concept in days, then poured in months of polish.

The same acceleration that let Pieter Levels crank out a flight simulator also lets Cursor’s team obsess over craftsmanship. Speed is the standard, substance is the differentiator.

Good Will Hunting

One of my favorite scenes in Good Will Hunting involves a smug Harvard student trying to impress a girl by parroting textbook quotes. Will cuts through the noise immediately:

“You dropped a hundred and fifty grand on an education you coulda got for a dollar fifty in late charges at the public library.”

It’s a great moment. The line hits hard because it exposes the hollowness of recited knowledge. Will’s point isn’t anti-intellectual, it’s anti-bullshit. A rejection of performance that masquerades as original thought.

That’s LLM slop. Content with the shape of knowledge but no muscle underneath. Intellect without authenticity. Empty calories.

People can tell. They can sniff out performance. But we crave substance. Authenticity. Effort.

Choose Substance

I still think about those classmates reciting Wikipedia word for word. The early internet made it easy to be shallow and to reach millions.

Coast or create. The tool won't decide—you will.

Bet on substance.

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I do this a lot. Not random names swaps, but ones that feel right. I'll call a David "John" or a Michael "Mark". Never something insane like, "Sebastian" instead of "Steve". My mistakes follow a pattern. Why?

They don't even look alike. David and John share zero letters or phonetic resemblance. But they're practically the same. But my brain isn't malfunctioning. My brain, your brain, everyone's brain, organizes details into clusters of meaning. Into what AI researchers, in their most poetic way, call latent space.

We don't process knowledge like computers. We don't recall facts like a dead-eyed accounting machine. We navigate a latent space: a vast shifting landscape of relationships and patterns, where things exist next to each other not because they look alike, but because they mean alike.

John and David: classic, American, biblical male names. Emma and Sophia: Timeless, vaguely aristocratic, possibly prone to sipping tea on balconies. They exist near each other in our minds, which is why, when I reach for one, I sometimes grab its neighbor instead.

Now, machines can think in latent space too. Large Language Models like GPT don’t just store words, they map meaning. They make connections. They can even tell jokes!

Latent space is an incredibly powerful mental model. Not just as a machine learning concept, but as a grounding model for how our minds work.

Chess, Poker and Flautists

High-level chess players frequently excel in poker, despite rather glaring differences between the games.

Dan Harrington was a former U.S. chess master. He didn’t casually dabble, he won the 1995 World Series of Poker (WSOP) and authored a foundational poker strategy book. The transition was seamless, almost natural.

On the surface, this makes zero sense. Chess has no hidden information. Poker bathes in secrecy and bluffing. Chess is cold, deterministic logic, poker a blend of luck and deception.

But in latent space, they’re neighbors.

Both games are about pattern recognition, strategic anticipation, and mental endurance under pressure. Chess players don’t become flautists when they're bored. They drift instinctively toward another game in the same latent neighborhood.

Just as chess masters navigate effortlessly to poker, great entrepreneurs navigate latent space between seemingly unrelated industries and ideas.

Entrepreneurs

A major differentiator for successful entrepreneurs is the ability to connect ideas across different domains. They see connections others don’t. These entrepreneurs intuitively traverse latent space, spotting connections invisible to others.

Steve Jobs didn't just obsess over computers, he explored calligraphy, aesthetics, and intuitive design. These fields aren't obviously related, yet Jobs effortlessly navigated latent space between them. As a result, computers became beautiful and intuitive, not soulless machines.

Great entrepreneurs don’t invent from scratch. Instead, they move laterally and reveal hidden connections across seemingly unrelated fields:

• Apple combined calligraphy & computing to bring beautiful typography to PCs

• Ford took meatpacking assembly lines and car manufacturing to modernize mass production

• Uber combined taxis and GPS on smartphones to bring forth modern ride sharing

• Dyson took inspiration from industrial cyclone separators used in sawmills and applied the same principles to vacuum design, eliminating the need for bags and filters

These innovators didn't create from thin air. They navigated latent space better than anyone else, combining seemingly unrelated ideas to yield breakthroughs.

LLMs Join the Party

Humans have always intuitively navigated latent space, but machines traditionally struggled. ChatGPT changed the game. It transformed rigid data into fluid meaning maps, finally giving machines a human-like intuition.

Large Language Models (LLMs) like GPT don’t just store words—they map meaning, recognize patterns, grasp metaphors, and even tell jokes. Machines now explore latent space just as we do, making connections rather than retrieving isolated facts.

This marks a profound shift: what once was uniquely human intuition can now be modeled, mapped, and explored intentionally.

Latent Space as a Foundation for Creativity

There’s an elegance in freely navigating latent space. It manifests itself as wittiness, creativity, and adaptability.

Those who intuitively grasp latent space can make creative leaps that leave others behind. Novelists don't just weave plots—they connect psychology, history, and personal experiences into powerful narratives. Chefs blend flavors from different cultures into inventive dishes. Comedians link storytelling, observation, and surprise to reveal hidden truths.

These aren’t random talents. They're manifestations of our innate ability to navigate latent space. When comedians become novelists or musicians turn into skilled coders, it’s because they've identified latent connections others overlook.

Intentionally Exploration

Most people, however, don’t naturally think this way. We compartmentalize knowledge into rigid categories: chess here, poker there; cooking recipes in separate drawers labeled by cuisine; industries as isolated vertical silos. This rigid categorization limits our creativity.

But imagine intentionally navigating latent space, explicitly moving laterally, rather than vertically. What if exploring conceptual neighborhoods became as deliberate as visiting places on a map?

Today, new tools can help us intentionally and explicitly navigate latent space. They enhance our natural instincts, turning intuition into deliberate practice.

Rather than passively recalling facts, you can now intentionally ask:

• “What's conceptually close to what I already know?”

• “What ideas haven't yet been linked, but should be?”

• “How can I move laterally rather than starting from scratch?”

Understanding latent space explains how creativity, innovation, and learning truly function. It explains why masters of chess easily transition to poker, why entrepreneurs disrupt industries by merging unrelated ideas, and why creative professionals effortlessly blend knowledge from seemingly unrelated fields.

When you consciously navigate latent space, you won’t only notice connections—you’ll create them.

I still occasionally call a David “John”, but now I tell myself it’s a feature, not a bug.

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There once was a rider named Freddy,
Whose board was always at the ready.
Through powder and trees,
He carved with such ease,

For shreddy was Freddy's pure steady.

The mountain his canvas, the slopes his delight,
Each run was a dance from morning to night.

No fancy escapes, no need to impress,
Simply the pleasure of riding; his happiness.

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There are days when I feel like I’m standing in the eye of the storm.

I remember one evening vividly. A close friend and I had an argument, a misunderstanding that escalated out of control. After the abrupt end of the conversation, I replayed the words endlessly. In hindsight, so many things I’d said felt wrong, and each pause in the conversation seemed heavier than it had been in the moment. My mind swirled with questions: “Did I overreact?” “Will this ruin our friendship?” “How did I mess this up?”

The sky of my mind filled with dark, heavy clouds, churning with turbulent thoughts of anger, doubt, and loneliness. Each thought was like a crack of thunder, sudden and paralyzing. The storm raged on as I lay awake that night, unable to shake the sinking feeling in my chest. Every effort to steady myself felt swallowed by the chaos.

Moments like these make the storm feel relentless, battering against the walls I’ve built around my mind. In those moments, it’s hard to imagine the storm ever ending. I’ve tried silencing the fury with distractions, turning to drugs or alcohol for a fleeting calm, but those only deepen the chaos. The storm demands to be acknowledged. It knows these strategies well and knows how to defeat them.

Avoiding the storm is like trying to escape a shadow: the more I run, the more it looms. I realized the storm wasn’t just external, it lived in my mind, feeding off my avoidance. But storms are not permanent. Beneath the chaos, the sky remains untouched. As ferocious as they may seem, storms cannot destroy the calm blue sky that always returns.

My mind is the blue sky.

Many days, there is no storm—only peace. On these days, I am healthy, surrounded by friends and family, and engaged in work that brings value to myself and others. The sky within is clear and a beautiful shade of blue. It feels expansive and open, as if nothing could weigh me down. This blue sky represents the underlying essence of my mind, present and calm, even when I forget it's there.

But the mind too, has cloudy days. There are times when dark, heavy clouds gather, filling the sky with their turbulence. These clouds are my thoughts—sometimes angry, sometimes filled with doubt or fear. They swirl and darken the horizon, making it seem like the blue sky has disappeared.

The colors and forms of the clouds reflect my feelings and state of mind. A sky dotted with playful, soft clouds can feel comforting. But then there are days when the clouds turn dark and menacing, hanging heavy like a blanket over my thoughts. It's on these days that it's easy to forget about the blue sky altogether, to become so wrapped up in the storm that I can't see beyond it.

Yet, I've learned that even when the sky is completely overcast, the blue sky hasn't gone anywhere. It's merely obscured by the passing storm.

The sky is always blue.

Even on the darkest days, when the clouds are thick and the storm feels endless, there is a calmness above the clouds, a serenity that remains untouched. Just as a plane can climb above the storm and emerge into the clear blue sky above, the mind can find moments of clarity, even amidst chaos. The storms pass, as they always do, revealing the sky that was there all along.

When my mind becomes busy with invasive thoughts, when the dark clouds come rolling in, they can obscure my vision, making it easy to lose sight of the blue sky. Sometimes, I find myself enveloped in these thoughts, convinced that the storm is all there is.

Reminding myself the storm is only temporary has been an important lesson for me. The calm above the storm is unchanging, patiently waiting for the clouds to part.

Calming the Storm

To clear the clouds, I use some techniques that help shift my perspective and dissipate these distortions. Gratitude is my most effective practice. When I reflect on the things I’m grateful for, I create small openings in the storm. Gratitude shifts my focus from what is going wrong to what is still good and steady in my life. It's like glimpsing patches of blue sky through the clouds, reminding me that not everything is as turbulent as it seems.

One evening, during a particularly overwhelming storm of thoughts, I decided to start small: I wrote down one thing I was grateful for. At first, it felt forced, but I kept going: “a warm cup of coffee”, “the beautiful fall trees”, “a supportive friend”. With each addition to the list, the clouds began to lift, little by little. Gratitude didn’t erase the storm, but it gave me a sense of perspective. The more I practiced, the easier it became to catch glimpses of the blue sky.

Exercise is my favorite tool to help clear the storm. Physical activity, even a short walk, has a profound impact on my mental state. Moving my body shifts the clouds within my mind. The rush of endorphins acts like a gust of wind, pushing the storm clouds away and allowing the blue sky to reappear. When I feel trapped in a cycle of negative thoughts, stepping outside and moving, even for a brief period, can break the cycle and provide a much-needed breath of fresh air.

Sleep also plays a crucial role in keeping my mind clear. Poor sleep exacerbates mental distortions, making the storm clouds seem thicker and more oppressive. When I'm sleep-deprived, my mind is prone to negative emotions and cognitive tricks, like a sky darkened by nightfall. Prioritizing sleep allows my mind to reset, to process the day, and to dissipate the emotional storm that might have built up. Good sleep is like the dawn breaking after a long night, revealing a clearer, calmer sky.

Each of these techniques serves as a way to clear the clouds and reconnect with the underlying blue sky of the mind. They don’t eliminate the storms entirely, but they make them manageable. They remind me that, no matter how dark the clouds may seem, the blue sky is always there, waiting to shine through.

Above the Clouds

The storm brings fear, anger, doubt, loneliness, and chaos, but it cannot touch the sky itself. I must face the storm with courage and acceptance.

The sky is there, waiting behind the clouds. It is this inner sky, this unchanging calm, that always remains, no matter how fierce the storm may seem.

The sky is always blue.

🔵

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It's cathartic to bitch, and jumping onto the hate train easy and in vogue. But, I mostly appreciate modern Javascript, but there's not a lot I'd radically change. But, all my desired improvements aren't additions, they're mostly deletions.

This isn't meant to be realistic or something I'm going to build, but the exercise gave me a clear idea of what I value versus what I consider needless overhead.

Today, I imagine a future Javascript shaped by my ideal design philosophies without constraints. For the most part, this was an exercise in deleting bloat instead of adding features.

Philosophy

• Minimalism: Keep the core small. Limit the number of keywords and constructs to make the language easier to learn and use. Remove features that add complexity or have redundant alternatives (e.g., switch, var, default exports). If a keyword is redundant delete it.

• Familiarity and Predictability: Nothing should drastically change that anyone else using it would be able to get up and running fast. There shouldn't be overhead on the language surprising you by some quirk. 

• General Purpose: This isn't a system's language, memory management isn't required. This is for scripting, APIs, and web apps.

• Modern Practices: Strict types, async/await,  and immutability are good things.

🌶️ snake_case vs. camelCase

This is my spiciest take: Snake case is clearly a better writing pattern. It gives words more room to breathe and create a more natural reading flow than camel case does. Bunching letters together is tougher to look at. Now, its technically possible to do this now, but because the standard is to write in camelCase it would be incredibly awkward to do this in your JS codebase.

Start “imports” with “from”

Javascript’s current module system syntax feels cumbersome. Instead of 

import { something } from 'module'

I prefer the ordering:

from 'module' import { something }

This makes the source of the import explicit before specifying what is being imported, which can enhance readability—particularly for developers coming from other languages where this order is more natural. It also improves tooling and makes autocomplete more intuitive by identifying the source before specifying the imported value.

Remove switch and while statements

Control flow in Javascript is overly cumbersome with to multiple options like switch, while, and for. Get rid of switch and while, opt to rely solely on if/else and for loops. The switch statement can be replaced by if/else blocks, while while loops can be effectively represented by `for` loops. This is inspiration from Go lang. Removing control flow keywords also makes the language easier to learn and use. Redundancies aren't needed.

🌶️ Only Newlines as Terminators; No Semicolons

Semicolons are practically worthless and should be removed. Newlines would serve as statement terminators. This would make Javascript easier on the eyes and similar to languages like Python that benefit from whitespace-driven syntax.

async/await

The async/await syntax is great. Lets keep doing this.

No yield, rely on async/await

Yield is pure overhead. Remove it and default to async/await

No var, Only let and const

The var keyword is one of the most problematic parts of older Javascript. It's only still there because of legacy compatability. I'm beating a dead horse with this one, but it feel be weird if I didn't add this in.

Remove `default` exports

Names exports are enough and keeping things named consistently is clearer.

No Implicit Type Coercion

Enforce strict typing, and avoid the pitfalls of Javascript's implicit type conversions.

No Hoisting

Hoisting can cause confusing behavior. Variables and functions must be declared before use.

🌶️ No parentheses on if/else or for statements

Currently, Javascript requires parentheses around conditions in if/else statements. Removing the need for parentheses would make the syntax more concise and reduce visual clutter. This would simplify code, making it look cleaner and more streamlined, similar to Python’s if statements. Similar to camelCase words, adding parens can make the code feel tight and giving the code more breathing room can be more improve reading flow.

Built-In Code Formatter

Another nod to Go. The built in formatter is amazing. Relying on prettier is fine, but having this as a core part of the language would remove any room for errors here.

Typescript

Types are amazing. They help with tooling, safety, confidence, and development speed. Adding base Typescript types into the language would be incredible.

Testing

Author received signal SIGSEGV, Segmentation fault.

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Get up.

Another wave is coming, but thats just fine because you are strong.

Get up.

I know you fell down before, and that's just fine.

Get up.

You're going to keep falling down, of course you are, that's all the more reason to get up again, because that's what you're made of. You are not being measured by how tall you can stand, but by how many times you can get up before you give up.

Run until your legs give out, swim until you drown, climb until you fall, life is short. Endure the agony that comes because it can't last. Why not see just how far you can go? I believe in you.

I’m running too. I’m climbing too. I’m swimming until I can't anymore. I refuse to drown. Stay here with me, be my brother, be my sister.

We will fight for each other.

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The US economic machine is a noticeably absurd. GDP effectively shows the size and health of a nation's economy within its borders. It provides a snapshot of national economic activity and growth but it doesn’t tell a deep story.

Yet, in the backdrop of GDP is its less discussed counterpart: Gross National Product (GNP). Unlike GDP, which measures output within border lines, GNP shifts the focus to nationality. Accounting for the total income generated by the residents of a country, regardless of where the economic activity happened. This subtle difference between GDP and GNP can unveil deep insights about a countries economic story. In the difference between these two metrics we can uncover compelling stories of global interdependence, foreign investment impact, and a deeper understanding of economic growth and stability.

Tales of Divergent Metrics

Luxembourg: Crossroads of Commerce

Across the landscape of global economies, Luxembourg constantly stands out for its economic affluence. When looking at GDP per capita, it consistently tops the list of world economies.

Luxembourg's GDP reflects its status as a global financial hub and a host for multinational corporations. However, when we shift focus to its GNP, which measures the total income of its nationals it tells a different story.

The GNP is notably lower than the GDP, a discrepancy that reveals the extent of foreign involvement in Luxembourg's economy. The key to understanding this gap lies in Luxembourg's demographic and economic structure. Luxembourg has a small population of ~660K people and within that population only half are Luxembourg nationals. This influx of foreign labor and expertise inflates the GDP, making it a less accurate reflection of the wealth accruing to Luxembourg's citizens.

For its size, Luxembourg has massive international leverage. The difference between Luxembourg's GDP and GNP is more than just a statistical curiosity, it presents a unique narrative in global economics and the nature of its prosperity. It tells a story of an economy deeply integrated into the global financial system, reliant on foreign labor and capital.

Ireland: A Foreign Investment Haven

Another nation with a large gulf between their GDP and GNP is the land of a thousand welcomes.

The heart of this disparity tells a similar but different story to Luxembourg. Ireland is incredibly successful at attracting direct foreign investments (FDI). Ireland has been a preferred European base for numerous multinational corporations due to its favorable corporate tax regime, skilled workforce, and membership in the European Union. While these companies contribute massively to Ireland's GDP, much of their profits are repatriated to foreign owners. An aspect of this phenomenon is the concept of "profit shifting." Many multinationals, particularly in the tech and pharmaceutical industries, report large portions of their profits in Ireland to benefit from the lower tax rates. This accounting practice inflates Ireland's GDP but does not proportionately increase the wealth of Irish nationals, hence the significant GDP-GNP gap.

While this model has driven economic growth and employment, it can also be a weakness. Dependency on multi-national corporations is a potential vulnerability that could result in large economic swings when global interruptions occur.

Tales of Remittances

Flipping the script, we can find unique economic stories when GNP is much higher than GDP. This flip is typically representative of countries with large remittances.

Tajikistan's Remittance Saga

Tajikistan tells a tale of remittances.

With a GNP bloated by funds flowing from abroad, Tajikistan’s story is one of economic reliance on its diaspora, a stark contrast to its modest GDP. Remittances by Tajik citizens serve as a lifeline for many households, bolstering domestic consumption and aiding in alleviating poverty. The funds received from abroad are often utilized for basic needs, education, and healthcare.

Almost half of the Tajikistan GDP comes from these remittences. There are limited job opportunities and economic uncertainty in Tajikistan. Remittances play a stabilizing role in an unstable environment.

Philippine’s Story of Overseas Connection

Overseas Filipino Workers (OFWs) also tell stories of remittences. The Philippines is one of the world's largest recipients of remittances, making this financial inflow a pivotal component of its economy.

In the Philippines, working abroad and sending money home has become a cultural norm, driven by both economic necessity and a deep-rooted sense of familial responsibility. The decision to become an OFW is often seen as a sacrifice made for the betterment of one's family, and the act of sending remittances is viewed as an extension of the Filipino values of generosity and familial care.

Remittences serve as an economic lifeline for many families, help stabilize their economy, and play a critical role in reducing poverty and improving living standards. Despite their benefits remittences are also an economic dependency. This can be a vulnerability if global conditions deteriorate and affect an OFW’s ability to send funds back home. The absence of family members working abroad can also have social and psychological impacts, particularly on children left behind.

Global Stories of Interconnectedness

India's Remittance Storyline: India’s GNP is heavily embroidered with the thread of remittances, overshadowing a GDP that struggles to keep pace. This contrast tells a story of an economy with a heartbeat resonating across oceans.

Switzerland's Banking Plotline: Switzerland’s tale is penned with the ink of global finance. Its GDP, bolstered by a legendary banking sector, often overshadows the domestic economic narrative captured by its GNP.

Mexico's Remittance Narrative: Much like the Philippines, Tajikistan, and India, Mexico’s GNP is buoyed by remittances. This influx of funds from abroad writes a story of a nation economically intertwined with its citizens beyond its borders.

Singapore's Global Business Epic: Singapore, a protagonist in global business, has a GDP narrative dominated by international trade and investment. This city-state’s economic plot twists and turns with the flows of global capital.

In the theatre of global economics, the delta between GDP and GNP is a window into the soul of a nation's economy, revealing tales of dependence, independence, and interconnection. This exploration enlightens us about the diverse economic landscapes and underscores the intricate tapestry of global economic relations.

There is a scourge of bad flags and they must be stopped - Roman Mars

In late 2015 I watched a wonderful video by Roman Mars on the design of flags. He made a strong case for the poor designs of city flags and talked about the benefits of well designed civic symbols. The podcast host sparked a wave of inspiration for cities and designers across the country to create more beautiful flags for their homes. I was also inspired.

I quickly looked up my city’s flag, the flag of Boston Massachusetts. What I found was, sadly, lacking.

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The North American Vexillological Association (NAVA) ranked this 133rd out of 150 city flags. Critics have labeled it an “seal on a bedsheet”. This flag isn’t inspiring. Most Bostonians don’t even know we have a flag and regularly fallback to our sports programs for symbols of pride.

Problems

The flag breaks all design guidelines.

NAVA has five principles of good flag design:

1. Keep It Simple. The flag should be so simple that a child can draw it from memory.

2. Use Meaningful Symbolism. The flag’s images, colors, or patterns should relate to what it symbolizes.

3. Use 2 or 3 Basic Colors. Limit the number of colors on the flag. Colors should contrast well and come from the standard color set.

4. No Lettering or Seals. Never use writing of any kind or an organization's seal.

5. Be Distinctive or Be Related. Avoid duplicating other flags, but use similarities to show connections.

Our flag currently breaks all of these principles. It has a complicated seal, the skyline of Boston is too intricate for anyone to be able to draw it from memory, the flag is just like most other municipal flags and isn’t distinctive in the slightest.

Symbolism Issues

The symbolism for the flag was originally supposed to match the yellow and dark blue colors of our revolutionary war uniforms. This is a powerful symbol for the history of the city and the country. However, all flags flown are now a much lighter blue than originally specified. At some point either the manufacturer or someone else changed the dark blue to be a lighter blue. I admired the revolutionary symbolism, but it became lost here.

Recognition Issues

Most Bostonians have no idea we have a flag, let alone what it looks like. We deserve a symbol that can be well marketed. We cede recognition of the city to our sports. Not all Bostonians are interested in sports and they should have a symbol to show their pride in the city.

Representation Issues

The city seal is the focal point of the flag and is a representation of the government. We should avoid this. Our city flag should represent the people, the land, the history, and the future of Boston, not the government.

Marketability and Inspiration Issues

Residents don’t recognize the flag, let alone get inspired by it. The seal makes manufacturing, design, and adaptation difficult. A city flag can be a platform from which new adaptations and creative applications can spring to life. Creators and artists should be able to draw inspiration from this symbol. Chicago and DC flags are stunning and and great examples to look toward.

Good design can inspire and lead to more civic pride in a good positive feedback loop. Our sports teams own most our cities branding. Some folks don’t have an appreciation for sports and we should give them a symbol not tied to those programs.

Objective

The new Boston flag should answer all of these problems. It should follow good design principles, have rich symbolism, be recognizable, representative of the city, and inspiring.

Process

I’m going to run through the evolution of the design. I’ll add photos, details on my thought process, and go over what I learned. I’ll mostly be showing you the happy path and the variations I got the most insight out of. I used Figma as my tool of choice.

Inspiration

The first row of flags are different versions of the Boston Flag throughout history. The first flag represents the three hills, the Trimountain, of Boston. The rest are a collection of national and city flags I consider beautiful.

First Iteration

In the first iteration I leaded into a couple of core ideas:

• I focused on keeping the colors similar to the current flag. The revolutionary war was source of symbolism I appreciated.

• I tested an anchor as a representation of the harbor, a major source of early population growth and economic activity for the city.

• I leaned into the original idea of Boston’s “three hills”. I used a variety of triangles and three pointed stars to represent this.

I had a lot of variation in testing at this stage but nothing was particularly inspiring, unique, or fit as a good symbol for Boston. I liked the idea of the revolutionary war uniform as a symbol for the flag. The Boson Massacre, the Boston Tea Party, and many other similar events catalyzed US independence. I tried to lean into this more in next iteration.

These flags were simple and recognizable, but they shared too many similarities with other flags like the UK and the Confederacy. I quickly moved on from these.

I was more comfortable with this second iteration. I used more red and orange and leaned into into the idea of the three hills. The three-triangle variations were fun but looked too much like the Legend of Zelda’s triforce. The symbolism here also wasn’t resonating deeply. The three hills were important once, but not core to the identity of Boston. I looked for more inspiration.

Once again I looked for beautiful examples that I had respect for. Out of this batch there were a couple of themes I latched onto.

• Geographical symbolism is powerful: River stripes are often prominent and the Charles River in Boston is an integral part of the city’s landscape.

• Centerpieces can be beautiful and inspiring: I especially found the design of the US Bicentennial to be a lovely design.

• Centerpiece symbols are powerful: The symbolism of the three hills wasn’t resonating. I loved the symbolism of the harbor, when I tested an anchor early in the process. However, I didn’t care for the anchor itself. Maybe a compass would resonate more?

Afterward, I pivoted and mostly focused on a design for a centerpiece. A couple of options came from this.

The compass was a turning point. It resonated with me more as a symbol for the harbor and Boston’s innovation. It’s simple, beautiful, and encapsulates the city’s economic strength. To top it off, I found the Boston branding guidelines. This established a familiar color palette that would naturally feel more like Boston.

The first draft I was proud of:

This first draft was the core inspiration for all variations after this. The compass was a symbol for the harbor and Boston innovation, the dark blue circle was a representation of the Charles River, and the outer light blue would represent the Atlantic Ocean. Many variations were created from this, but the final version would be similar.

New variations changed mostly the compass styling and the core colors. I loved the compass, but the yellow didn’t quite feel like Boston. The blue and yellow are the Boston Marathon colors, the Bruins colors, a new Red Sox uniform, and the colors of our revolutionary war uniforms (not really). It wasn’t hitting home.

It needed a different color. A color that felt more like Boston.

It needed red.

Red symbolizes the historic freedom trail and Boston's distinctive red brick architecture. These felt like Boston and were resonating well!

Some of these were extra fun, but suffered some flaws. The middle right felt too much like Captain America. The bottom right, with the white outline, looks incredible up close, but it doesn’t translate well at a distance.

I made more precise measurements at this stage and settled on some finalists. These finalists were experimented on a lot and felt the best. After ranking symbolism, color contrast, manufacturing impact, and overall feeling, the middle flag came out the winner.

You can read full details on symbolism and dimensions on this page.

New flags usually go through a local design competition before getting adopted. A symbol created by the people for the people. This wasn’t created for a competition but this will be my submission when the city is ready to have one.

Roman Mars captures the original inspiration for this project well.

The compass centerpiece represents Boston innovation and its rich maritime history. The surrounding dark blue circle symbolizes the Charles River that runs through the city. The red represents the Freedom Trail that winds through Boston, connecting our past to the present. The light blue symbolizes the Atlantic Ocean, which connects Boston to the rest of the world.

Anatomy

The light blue color of the flag symbolizes the Atlantic Ocean and Boston’s connection to the rest of the world. The light blue is Optimistic Blue and its represented by the hex value #288BE4.

The standard ratio of the flag is a 2x3.

The dark blue color represents representing the Charles River running through Boston. One of the largest rivers in Massachusetts and a major source of economic strength for the city. The inner white circle is a symbol for Boston’s long winters and unity. The dark blue is Charles River Blue and its represented by the hex value #091F2F.

The diameter of the dark blue circle is 2/3rds (66%) the height of the flag. The diameter of the inner white circle is 2/3rds the length of the outer dark blue circle. Example: If the diameter of the dark blue circle is 600 pixels, the inner white circle’s diameter is 400 pixels.

The compass is the heart of the flag. It symbolizes the harbor and the cities rich maritime history. The harbor is a major resource and historically important for the rapid growth of Boston. The red color is a symbol for the Freedom Trail, a brick trail running through Boston connecting our past to the present. The trail marks many locations significant to the history of the United States. The red is Freedom Trail Red and its represented by the hex value #FB4D42.

Dimensions of the compass can be broken down into two 4 pointed stars. The larger star at the forefront of the compass is 1/3rd the height of the flag. The smaller star is tilted 45 degrees and is 2/3rd the height of the lager star. For example, the height of the flag is 9 feet, the height of the larger, forefront star, is 3 feet, and the smaller star is 2 feet.

Plan of Action

Like many others, Roman Mars and the 99% invisible podcast, was the source of inspiration for this project. I’m happy to finally have created a beautiful symbol for the city I live in.

I’d love to see Boston City Council create a public competition to redesign the flag for the city. These competitions have been successful in many other cities like Pocatello, Chicago, Milwaukee, Syracuse, Lincoln, Reno and many more.

I love Boston and I hope this motivates others to add a little more beauty to their world.

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Set I

1. Given the choice of anyone in the world, whom would you want as a dinner guest?

2. Would you like to be famous? In what way?

3. Before making a telephone call, do you ever rehearse what you are going to say? Why?

4. What would constitute a "perfect" day for you?

5. When did you last sing to yourself? To someone else?

6. If you were able to live to the age of 90 and retain either the mind or body of a 30-year-old for the last 60 years of your life, which would you want?

7. Do you have a secret hunch about how you will die?

8. Name three things you and your partner appear to have in common.

9. For what in your life do you feel most grateful?

10. If you could change anything about the way you were raised, what would it be?

11. Take four minutes and tell your partner your life story in as much detail as possible.

12. If you could wake up tomorrow having gained any one quality or ability, what would it be?

Set II

1. If a crystal ball could tell you the truth about yourself, your life, the future, or anything else, what would you want to know?

2. Is there something that you've dreamed of doing for a long time? Why haven't you done it?

3. What is the greatest accomplishment of your life?

4. What do you value most in a friendship?

5. What is your most treasured memory?

6. What is your most terrible memory?

7. If you knew that in one year you would die suddenly, would you change anything about the way you are now living? Why?

8. What does friendship mean to you?

9. What roles do love and affection play in your life?

10. Alternate sharing something you consider a positive characteristic of your partner. Share a total of five items.

11. How close and warm is your family? Do you feel your childhood was happier than most other people's?

12. How do you feel about your relationship with your mother?

Set III

1. Make three true "we" statements each. For instance, "We are both in this room feeling..."

2. Complete this sentence: "I wish I had someone with whom I could share..."

3. If you were going to become a close friend with your partner, please share what would be important for him or her to know.

4. Tell your partner what you like about them; be very honest this time, saying things that you might not say to someone you've just met.

5. Share with your partner an embarrassing moment in your life.

6. When did you last cry in front of another person? By yourself?

7. Tell your partner something that you like about them already.

8. What, if anything, is too serious to be joked about?

9. If you were to die this evening with no opportunity to communicate with anyone, what would you most regret not having told someone? Why haven't you told them yet?

10. Your house, containing everything you own, catches fire. After saving your loved ones and pets, you have time to safely make a final dash to save any one item. What would it be? Why?

11. Of all the people in your family, whose death would you find most disturbing? Why?

12. Share a personal problem and ask your partner's advice on how he or she might handle it. Also, ask your partner to reflect to you how you seem to be feeling about the problem you have chosen.

I’ve used the moment to check out substack.

Seems like people are flocking across the social verse. Some folks are flocking to Mastodon, Tumblr, Instagram, YouTube, WhatsApp, iMessage.

Programming with dates and time is a gross experience. Most programmers will eventually learn of this pain and all the bugs waiting to take down their software.

These bugs tend to pop up the most when we’re dealing with time zones, leap dates, and overflow. Time zones and leap dates could each be separate essays on developer pain and anxiety,1 but for now let's talk about overflow. We’re going to specifically talk about the next Y2K known as the 2038 problem.

Why Y2K was a problem

Let’s clarify something: Y2K was a real problem, but it was blown out of proportion2. When the new millennium rolled around a lot of existing bugs had been found and fixed. While some issues were missed, nothing was catastrophic. However, 2038 could be a bigger problem than Y2K.

Y2K was an issue because of how computers store dates. The problem with Y2K was the years rolling over from 98, 99 ➡️00. This is going to be the same problem in 2038 but the number we use to track time will be the root of the problem.

The Unix timestamp

Practically all computers track time with the Unix timestamp. This is a counter that started on January 1st 1970 and has been ticking every second since then.

The Unix timestamp at the time of this post: 1,598,007,347

Integer overflow

Y2K was a known problem because we knew the date was going to roll over from 99 to 00. 2038 is also a known problem for the same reason. The key difference is the date won’t roll over, the number itself will.

In computers we store numbers as integers. Most of these numbers are "signed 32-bit integers". This means numbers in computers have a maximum value that can be stored in memory. The maximum value for a signed 32-bit integer is 2,147,483,647. This means when the unix timestamp gets close to that number it will reset like the Y2K date did and overflow. It’ll go from 2,147,483,646 to 2,147,483,647 to 0 (Technically it goes to -2,147,483,648, but 0 makes the point a little clearer).

Because this counter is tied to time, we can predict it will happen at exactly 3:14am and 7 seconds on January 19th, 2038 UTC.

Gangnam Style

A good way of thinking about this problem is to compare it to the Gangnam Style video bug.

At one point Gangnam style got so many views it broke the view counter. It went from 2.1 billion views to -2.1 billion views. This is because YouTube didn't have a number large enough that was capable of storing the view count for the video.

How will we fix this problem?

Luckily this problem is still 18 years away so no one cares about it right now. I'm guessing everyone will wait until the last minute and then scramble to fix everything.

A good fix for some groups will be to change how the number is stored. If the number can be stored as a 64-bit integer this will help the immediate issue. This doesn’t fix the problem though, it only kicks the can down the road. But, it kicks the can down 292 billion years from now... That's 20 times longer than the age of the universe so I think that will work.

Things will break

We may know about this problem almost two decades in advance, but because of the massive amount of software out in the world it will be impossible to prevent every bug.

A lot of tech is being built with the ability to avoid this problem. This is great, but older systems will still be vulnerable.

I expect this to be a lot like the millennium bug. There will be real problems and some overblown fear. But at the end of the day few people outside of the developers fixing the bug should be affected.

We have less than 500 million seconds until Y2K38. How will you spend it?

What answer is the correct answer to this question?

1. All of the below.

2. None of the below.

3. All of the above.

4. One of the above.

5. None of the above.

6. None of the above.

Take some time to think about this question and work it out yourself.

Scroll down to find the answer.

If you're like a lot of people you're initial guess might have been 6. Lets talk about why that isn't the correct answer and then move onto disproving the other options.

Answer: 5

6 cannot be the answer. If 6 is true, 5 must also also be true. However, this is a contradiction against 6. 6 is not true.

1 cannot be the answer. 2 is an immediate contradiction to 1. If all of the below are true, "None of the below" is a direct contradiction.

3 cannot be the answer. We just ruled out 1 as a possible answer. "3. All the above" would mean 1 is a correct option. This is a contradiction. 3 is incorrect.

Our remaining options: 2, 4, and 5. This is getting a little more difficult to narrow down. Let's assume one of the remaining answers is true and then try to contradict ourselves.

Assume 4 is true and either 1, 2, or 3 are true. We know 1 and 3 are true so we could assume 2 is true. This is a contradiction though. 2 assumes "None of the below" are true, which includes 4. This is a contradiction. 4 cannot be the answer

If 4 is false we also know 2 is false. If 2 was true it would mean 4 is also true. We have already proven this incorrect. 2 cannot be the answer

5 is our only option left and our answer.

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