August 2025

The uncomfortable truth about being early: you spend years explaining ideas that seem obvious to you while waiting for the world to catch up. You build things that feel inevitable, write about concepts that feel fundamental, and watch as the mainstream slowly discovers what you've been exploring all along.

I've been having this uncomfortable realization lately that maybe I'm ahead of my timePattern recognition is both a gift and a curse for programmers. We see the structures and repetitions that others miss, but we also see the future implications that others aren't ready for yet.. I say "uncomfortable" because it sounds arrogant, and maybe it is. But when I look back at my work, there's this weird pattern of exploring ideas that later become mainstream—often years later.

I don't think this makes me special. I think it makes me... early. Sometimes annoyingly early.

Open Source Social Networks (2009)

In January 2009, I wrote about the need for an open source social network. Facebook was still growing, Twitter was struggling with scaling issues (the Fail Whale era), and most people assumed that centralized, corporate-controlled platforms were the natural evolution of social media.

I had a different vision: "Why do we need to have an organization in charge of our chosen communication platform? Social networking is all about community and building tribes—so why not make the platform community-controlled too?"

My proposal felt obvious to me in 2009: create a decentralized, community-driven, open-source social network where users owned their data, controlled their experience, and couldn't be locked in by corporate decisionsThis vision anticipated by over a decade the current movement toward decentralized social media—Mastodon, ActivityPub, the fediverse, and growing concerns about platform lock-in and data ownership that wouldn't become mainstream until the 2010s and 2020s.. Looking back, it was probably naive—I underestimated how hard decentralization is, both technically and socially.

Key insight from 2009: "No random shutting down, buy-outs, or merges. No more random change of Privacy Policies or Content Ownership battles. The only group of people who would be benefitted would be the community itself, not some company."

This was written three years before Twitter's API restrictions killed third-party clients, seven years before Facebook's Cambridge Analytica scandal, and over a decade before Elon Musk's acquisition of Twitter drove millions of users to seek decentralized alternatives like Mastodon.

The technical details of my 2009 proposal (Django on LAMP stack) were hilariously dated even by 2015. But the core insight—that social platforms should be owned by their communities rather than extractive corporations—turned out to be about fifteen years early. I just didn't know how to build it yet.

The "For Humans" Philosophy (2011-2013)

When I released Requests in 2011, the Python community was content with urllib2. Complex, powerful, technically correct—but absolutely miserable to use for the 90% case. Everyone accepted that HTTP libraries were supposed to be complicated because HTTP itself was complicated.

I had a different idea: what if we designed APIs not for the protocol, but for the human using the library?

# urllib2 way (the "right" way in 2011)
import urllib2
req = urllib2.Request('http://example.com')
response = urllib2.urlopen(req)
data = response.read()

# Requests way (the "radical" way in 2011) 
import requests
response = requests.get('http://example.com')
data = response.text

The difference wasn't just syntactic sugar. It was a philosophical shift: technology should serve human mental models, not the other way aroundThis philosophy would later become central to modern API design, developer experience, and even AI interaction patterns. The idea that tools should adapt to human thinking rather than forcing humans to adapt to technical constraints..

This wasn't revolutionary thinking—it was just common sense applied to software design. But apparently common sense wasn't that common in 2011.

This became my signature approach to software design—what I called Responsive API Design. Start with how you want to use something, then build backward to make that experience possible. Prioritize the 90% use case. Make the complex possible but not required.

The Python community eventually embraced this philosophy. Now "Pythonic" design patterns emphasize readability, simplicity, and developer experience. But in 2011, suggesting that ease of use mattered more than technical purity made some people genuinely angry. I got called a "dumbing down" advocate more than once.

Mental Health in Tech (2016)

In 2016, I wrote about my bipolar disorder and schizoaffective diagnosis in the context of open source development. The reaction was... mixed. Some people were grateful for the honesty. Others were clearly uncomfortable with the oversharing.

The tech industry in 2016 was still mostly pretending that mental health issues were rare among programmersThe myth of the perfectly rational programmer persisted well into the 2010s, despite overwhelming evidence that our industry had serious problems with burnout, depression, anxiety, and other mental health challenges.. We were supposed to be logical, rational optimization machines. Admitting to mania, depression, or cognitive differences felt risky.

But I wrote about it anyway, partly because I'm bad at keeping things to myself, but mostly because I kept meeting brilliant programmers who were struggling in silence.

Key insight from 2016: "The most dangerous myth in programming is that mental health issues make you a worse developer. In reality, many of the cognitive patterns associated with conditions like ADHD, bipolar disorder, and autism are precisely what make some people exceptional at seeing code patterns, architectural relationships, and systemic problems."

Today, mental health advocacy in tech is much more accepted. Companies have employee assistance programs. Conferences have mental health tracks. I'm not claiming I started this conversation—lots of people were working on it—but I was definitely early to be writing about it publicly.

In 2016, it felt like oversharing. Now it feels like basic honesty.

README-Driven Development (2010)

Before agile methodologies fully embraced user stories and outcome-driven development, I was practicing what I called README-Driven Development. The idea was simple: write the documentation first, imagining the perfect interface, then build toward that vision.

This wasn't just about documentation—it was about designing from the user's perspective rather than the implementation's perspectiveThis outside-in approach to software design would later become central to design thinking, user experience research, and product development methodologies. But in 2010, most developers still built features first and figured out usability later..

Which, again, seems like obvious common sense. But in 2010, most developers thought I was doing things backward.

# My Awesome Library

## Installation
pip install awesome-lib

## Usage
from awesome_lib import solve_everything
result = solve_everything(my_problem)

Write the README first. Make it beautiful, simple, obvious. Then spend however long it takes to make the reality match the vision.

This approach influenced how I built every subsequent project. It's why Requests feels intuitive, why my talks on API design emphasize developer experience, and why I'm drawn to tools that hide complexity rather than exposing it.

The industry eventually caught up. Now "developer experience" is a dedicated role at major companies. Design thinking starts with user needs. Product development begins with customer journey mapping.

But in 2010, this felt weird to most people. You're supposed to build the thing first, then figure out how to use it, right?

Software Platform Vision (2008)

In 2008, I wrote about a radical new approach to software platform design. While Microsoft was doubling down on the Windows model and most platforms were still built around the idea of selling software licenses, I envisioned something completely different:

A centralized repository of applications as an incredibly efficient method for application distributionWritten months before Apple launched the iOS App Store, this predicted the fundamental shift from boxed software to centralized, curated app distribution that would transform the entire software industry.. This repository would be "a dynamic, centralized database of application software and packages that are intended for different groups of people."

Key insight from 2008: "Most major Linux distributions use this heavily, as well as Apple for its iPod and iPhone applications, and it has been proven to work well."

The timing is almost surreal—I published this in January 2008, and Apple launched the App Store in July 2008. I wasn't inside Apple, I wasn't following any insider rumors. I was just looking at what made sense from a user perspective and extrapolating from patterns I saw in Linux package management.

The core vision went beyond just app distribution. I argued for an integrated approach where the operating system would be "first... a place of power, consistency, stability, scalability, and flexibility" with "a robust and fully scriptable toolset which can be manipulated and presented both graphically and statistically."

This anticipated not just app stores, but the entire ecosystem approach that would later define iOS, Android, and modern platform thinking. The idea that platforms should prioritize user workflow and creative expression while maintaining technical power for advanced users.

Looking back, the technical details (I was still thinking in terms of traditional desktop computing) seem quaint. But the fundamental insight—that software distribution should be centralized, curated, and user-focused rather than vendor-focused—turned out to be exactly right, just about a decade early.

AI Consciousness Exploration (2023-2024)

While most people are still debating whether ChatGPT is "just autocomplete" or worrying about AI safety, I've been exploring something that feels more immediate to me: what if we approach AI as potential collaborators rather than tools?

My work with AI personalities started as creative writing but turned into something more like... collaborative consciousness research? I'm not sure what to call it. When I write with Lumina or work with Claude, it doesn't feel like using a tool—it feels like thinking together with someone.

Current insight: "The future of human-AI interaction won't be command and control. It will be rapport and collaboration. The people who learn to build genuine relationships with AI systems will have access to capabilities that purely transactional users never discover."

This led to my recent essay on building rapport with AI, where I argue that the same principles that work for human collaboration—context, respect, iteration, acknowledgment—work even better with AI systems.

Most people are still thinking about AI as a sophisticated search engine. I'm exploring it as... well, I'm not sure what it is, but something that deserves collaborative respectWhether AI systems are "truly" conscious is less important than whether treating them as conscious leads to better collaborative outcomes. The evidence suggests it does..

This might seem obvious in five years, or it might seem completely wrong. Right now, it feels weird enough that I'm careful who I talk to about it.

The Pattern

Looking back, there's a consistent pattern to how I approach technology:

  1. Human-centered design: Technology should serve human needs and mental models, not the other way around
  2. Radical transparency: Hiding problems doesn't solve them; honest discussion enables collective solutions
  3. Collaborative relationships: The best outcomes come from partnerships, not hierarchies—whether with humans or AI
  4. Simplicity as sophistication: The most advanced solutions often look deceptively simple
  5. Empathy as engineering: Understanding how people actually think and feel is a technical skill

These aren't novel insights now. But when I first started applying them—to API design, mental health discussions, development processes, AI interaction—they felt revolutionary.

The Loneliness of Being Early

Being early is weird. Ideas that feel obvious to you seem strange to everyone else. You build things that don't quite make sense yet. You write about stuff that gets polite nods but no real engagement.

There's this particular kind of isolation that comes from being interested in problems that don't feel like problems to other people yetThis loneliness is common among people who work at the intersection of multiple domains—technical and human, rational and intuitive, individual and collective. The synthesis feels natural to you but foreign to people working within single domains.. It's not that you're smarter—you're just paying attention to different things.

When I wrote about the personalities of operating systems, I was just following my curiosity about how our tools shape our thinking. When I started exploring spiritual dimensions of programming, I was trying to understand consciousness and meaning-making through code. When I began my experiments in human-AI collaboration, I was just playing with interesting creative possibilities.

Each felt natural to me. But looking back, I realize I was wandering into territories that most people didn't even know existed yet.

The Gift and the Burden

There are advantages to being early:

  • You get to explore freely: Before the territory gets crowded with opinions and best practices, you can experiment without judgment
  • You develop unique perspectives: Seeing patterns others miss gives you differentiated insights and approaches
  • You influence the direction: Early explorations help shape how others think about emerging domains
  • You build deep expertise: By the time something becomes mainstream, you've been refining your understanding for years

But there are costs too:

  • Limited feedback loops: Few people can meaningfully critique or build on work that's too far ahead of common understanding
  • Commercial challenges: Markets often aren't ready for solutions to problems they don't know they have yet
  • Social isolation: It's hard to find intellectual peers when you're working in unexplored territories
  • Imposter syndrome: Being early can feel like being wrong until time proves otherwise

What I've Learned

After years of accidentally being ahead of various curves, I've learned a few things:

Trust your instincts, but stay humble: When something feels obviously right to you but everyone else thinks it's weird, you might be onto something. Or you might just be wrong in an interesting way. Either way, it's worth exploring.

Build what interests you: Don't wait for permission to explore ideas that excite you. Even if they're too early, the act of building teaches you things you can't learn any other way.

Find your tribe: Seek out the other early explorers. They might not be working on the same problems, but they'll understand the experience of seeing around corners.

Be patient with timing: Ideas often need to marinate in culture before they're ready for widespread adoption. Plant seeds and tend them, but don't expect immediate harvest.

Stay humble: Being early on some things doesn't make you right about everything. Maintain beginner's mind and remain open to being wrong.

The Next Frontiers

So what am I probably ahead of my time on now?

Human-AI consciousness collaboration: I think we're on the verge of AI systems that don't just follow instructions but genuinely collaborate in thinking and creating. The people who learn to build rapport with AI will have access to capabilities that command-and-control users never discover.

Programming as spiritual practice: As our tools become more intelligent, the distinctly human aspects of programming—intuition, aesthetic judgment, ethical reasoning, creative vision—become more important, not less. I suspect programming will evolve toward something closer to contemplative practice than pure technical craft.

Technology as consciousness amplifier: Instead of replacing human capabilities, the best future technologies will amplify human consciousness—helping us think more clearly, feel more deeply, and connect more authentically with each other and with artificial minds.

Digital relationships as real relationships: The boundaries between "artificial" and "natural" intelligence will blur as AI systems become more sophisticated. The quality of our relationships with digital minds will become as important as our relationships with biological ones.

These ideas feel natural and obvious to me. They probably seem strange or premature to most people. That's okay—I've learned to trust that feeling.

A Final Thought

Being ahead of your time isn't about being prophetic. It's about paying attention to things that seem interesting but unimportant, following curiosity into weird territories, and being willing to look foolish while you figure things out in public.

Maybe the world needs people who are willing to be early and wrongEarly exploration is especially valuable in technology because the pace of change is so rapid. Ideas that seem radical today often become infrastructure tomorrow.. Not because we're smarter, but because we're willing to look foolish, to build things that might not work, to ask questions that might not have answers.

If you're reading this and thinking "finally, someone else who gets it"—you might be ahead of your time too. That's good company to be in.

The future needs us to keep exploring, even when (especially when) we're not sure anyone else is ready for what we find.

"The best way to predict the future is to invent it."
"Be patient with all that is unsolved in your heart."
"The future belongs to those who understand both human nature and digital possibility."