August 2025 – From Pixels to Particles

Beyond the AI Boost: The Human Frontier of Mastery

Based on “AI is a Floor Raiser, not a Ceiling Raiser”

Excerpt:
When AI tools deliver instant scaffolding and context‑aware answers, beginners and side‑projecters can sprint past the usual startup slog. But no shortcut replaces the mountain‑high effort needed for true mastery and dark horse novelty.

I first stumbled across Elroy Bot’s incisive piece on AI’s new role in learning and product development while wrestling with a gnarly bug in my side project. Within minutes, I had a working patch—courtesy of an AI assistant—but the real insight hit me afterward: AI didn’t conquer the problem; it simply handed me a ladder to climb the first few rungs.

In the article, the author frames AI as a “floor raiser”—a force that lifts novices and busy managers to basic proficiency at blinding speed. Yet, when it comes to reaching the ceiling of deep expertise or crafting truly novel works, AI still lags behind.

Why the Floor Rises Faster

Personalized On‑Demand Coaching: Instead of scouring StackOverflow for a snippet, AI answers your question in context, at your level. You start coding frameworks or understanding new concepts in hours, not weeks.
Automating the Mundane: Boilerplate code, rote research, and template tasks get handled by AI, freeing you to focus on the pieces that actually matter.
Bridging Gaps in Resources: AI tailors explanations to your background—no more hunting for that one tutorial that links your existing skills to the new framework you’re tackling.

“For engineering managers and side‑projecters, AI is the difference between a product that never existed and one that ships in days.”

Why the Ceiling Isn’t Coming Down

Despite these boosts, mastering a large legacy codebase or producing a blockbuster-quality creative work still demands:

Deep Context: AI doesn’t grasp your business’s ten-year-old quirks or proprietary requirements.
Novelty & Creativity: Audiences sniff out derivative content; true originality still springs from human intuition.
Ethical and Critical Judgment: Complex or controversial subjects require source vetting and nuanced reasoning—areas where AI’s training data can mislead.

Balancing the Ecosystem

The ripple effects are already visible:

Teams lean on AI to prototype faster, shifting headcount from boilerplate work to high‑value innovation.
Training programs must evolve: pairing AI‑powered tutoring with hands‑on mentorship to prevent skill atrophy.
Organizations that overinvest in AI floor-raising without nurturing their human “ceiling climbers” risk plateauing at mediocrity.

AI may give you the ladder, but only your creativity, judgment, and perseverance will carry you to the summit. Use these tools to clear the base camp—then keep climbing toward true mastery, where human insight still reigns supreme.

Systems Thinking & the Bitter Lesson: Building Adaptable AI Workflows

In “Learning the Bitter Lesson,” Lance Martin reminds us that in AI—and really in any complex system—the simplest, most flexible designs often win out over time. As a systems thinker, I can’t help but see this as more than just an AI engineering memo; it’s a blueprint for how we build resilient, adaptable organizations and workflows.

Why Less Structure Feels Paradoxically More Robust
I remember the first time we tried to optimize our team’s editorial pipeline. We had checklists, rigid approval stages, and dozens of micro-processes—each put in place with good intentions. Yet every time our underlying software or staffing shifted, the whole thing groaned under its own weight. It felt eerily similar to Martin’s early “orchestrator-worker” setup: clever on paper, but brittle when real-world conditions changed.

Martin’s shift—from hardcoded workflows to multi-agent systems, and finally to a “gather context, then write in one shot” approach—mirrors exactly what many of us have lived through. You add structure because you need it: constrained compute, unreliable tools, or just the desire for predictability. Then, slowly, that structure calcifies into a bottleneck. As tool-calling got more reliable and context windows expanded, his pipeline’s parallelism became a liability. The cure? Remove the scaffolding.

Seeing the Forest Through the Trees
Here’s the systems-thinking nugget: every piece of scaffolding you bolt onto a process is a bet on the current state of your environment. When you assume tool-calling will be flaky, you build manual checks; when you assume parallelism is the fastest path, you partition tasks. But every bet has an expiration date. The real power comes from designing systems whose assumptions you can peel away like old wallpaper, rather than being forced to rip out the entire house.

In practical terms, that means:

Mapping Your Assumptions: List out “why does this exist?” for every major component. Is it there because we needed it six months ago, or because we still need it today?
Modular “Kill Switches”: Build in feature flags or toggles that let you disable old components without massive rewrites. If your confidence in a new tool goes up, you should be able to flip a switch and remove the old guardrails.
Feedback Loops Over Checklists: Instead of imagining every exception, focus on rapid feedback. Let the system fail fast, learn, and self-correct, rather than trying to anticipate every edge case.

From Code to Culture
At some point, this philosophy goes beyond architecture diagrams and hits your team culture. When we start asking, “What can we remove today?” we encourage experimentation. We signal that it’s OK to replace yesterday’s best practice with today’s innovation. And maybe most importantly, we break the inertia that says “if it ain’t broke, don’t fix it.” Because in a world where model capabilities double every few months, “not broken” is just the lull before old code bites you in production.

Your Next Steps

Inventory Your Bottlenecks: Take ten minutes tomorrow to jot down areas where your team or tech feels sluggish. Are any of those due to legacy workarounds?
Prototype the “One-Shot” Mindset: Pick a small project—maybe a weekly report or simple dashboard—and see if you can move from multi-step pipelines to single-pass generation.
Celebrate the Removals: Host a mini “structure cleanup” retro. Reward anyone who finds and dismantles an outdated process or piece of code.

When you peel back the layers, “Learning the Bitter Lesson” isn’t just about neural nets and giant GPUs—it’s about embracing change as the only constant. By thinking in systems, you’ll recognize that the paths we carve today must remain flexible for tomorrow’s terrain. And in that flexibility lies true resilience.

If you’d like to dive deeper into the original ideas, I encourage you to check out Learning the Bitter Lesson by Lance Martin—an essential read for anyone building the next generation of AI-driven systems.

From Cron Chaos to Centralized Calm

I’ve spent countless evenings hunting down why one of my dozen automated processes failed—digging through server A’s logs, then B’s, then wondering if timezone math on machine C silently swallowed a reminder. If that sounds familiar, check out the original article now: Replacing cron jobs with a centralized task scheduler. It flipped my whole mindset.

Instead of treating each cron script as a black box, the author models every future action as a row in one ScheduledTasks table. Think of it: every job you’d ever schedule lives in a single, queryable place. Because each task records when it’s due, its priority, retries left, and even an expiration window, you immediately know:

What went wrong? Was the row created? Did the status flip to “EXECUTING”?
When did it fail? Timestamps are part of the schema.
Can I retry it? Built-in retry logic based on expectedExecutionTimeInMinutes handles stuck tasks automatically.

And because the table uses deterministic IDs for editable tasks—upserting instead of piling on duplicates—your reminder for “Event X at 3 PM” never spawns two competing jobs if the event gets rescheduled. It just updates the one, single record.

Applying This to Your Own Stack

Model work as data: Start by designing a simple table (or collection) that captures every scheduled action: due time, status, payload, retries, and expiration.
Use one poller, many workers: Replace your multiple cron scripts with a single poller that enqueues due tasks into your favorite queue (SQS, RabbitMQ, etc.), then let specialized consumers pick up and execute.
Unify logging & monitoring: With everything funneled through one scheduler, you gain a centralized dashboard—no more jumping across machines to trace a failure.

By embracing this pattern, I went from juggling eight Node scripts across three servers to maintaining one tiny service. When something breaks now, I head straight to the ScheduledTasks table, filter by status or timestamp, and—boom—I’ve got my starting point. No more haystack.

Ride the AI Wave: Strategic Integration Over Litigation

Combined Strategic View – Forward-Looking Angle (Rooted in Bo Sacks’ Facts)

In his newsletter BoSacks Speaks Out: Notes from the Algorithmic Frontline, veteran editor Bo Sacks lays out a stark reality: AI has already ingested decades of Pulitzer-winning journalism without compensation; Judge Alsup’s ruling against Anthropic offers only a narrow copyright reprieve; Getty Images is pioneering revenue-sharing for AI-trained image datasets; and niche print titles like Monocle, Air Mail, and Delayed Gratification thrive even as legacy printers and binderies collapse. These are the hard facts on the ground.

These facts point to a stark choice: fight the tide or ride it. Relentlessly suing OpenAI or Anthropic over scraped archives may score headlines, but it won’t keep pace with machine learning’s breakneck advance—and it diverts precious resources from innovation. Instead, forward-thinking publishers should turn Bo Sacks’ own evidence into a blueprint for growth:

1. Automate & Accelerate

Archive Mining: Apply AI to sift your own backfiles—precisely the content under dispute—to surface timeless stories worth republishing or expanding.
Bite-Sized Briefs: Convert long features into “5-minute reads” or multimedia snippets for mobile audiences, mirroring slow-print curation but optimized for screens.

2. Elevate Craft with AI

Instant Fact-Checks: Use AI assistants that cross-verify claims on the fly, speeding up verification without sacrificing accuracy.
Rapid Design Mockups: Integrate AI-powered layout previews to iterate cover and spread designs in minutes, recapturing the precision Bo Sacks mourns in lost binderies.

3. Data-Informed Revenue

Smart Pricing: Leverage real-time engagement signals to adjust sponsorship and ad rates dynamically—echoing Getty’s revenue-share ethos but tailored to your audience.
Segmented Offers: Use simple clustering techniques to distinguish your premium-print devotees from casual readers, then craft subscription tiers and perks that drive loyalty and lifetime value.

Why this matters: The tools Bo Sacks warns are “already at home” in our archives have upended every stage of publishing—from discovery and design to distribution and monetization. Legal victories may buy time, but strategic integration of AI buys relevance. By running small pilots, measuring impact on both costs and engagement, and retiring manual processes that no longer move the needle, publishers can turn today’s adversary into tomorrow’s catalyst—and deliver the richer, more personalized journalism readers are hungry for.

Reignite Your Niche Magazine: Blending Timeless Marketing with Smart Digital Tactics

I just dove into Tom Goodwin’s provocative piece, “We’ve forgotten how to market. So, how should today’s playbook look?”—which I first spotted in BoSack’s newsletter (highly recommend subscribing if you haven’t!)—and it got me thinking: what if you ran a niche magazine or a specialist news outlet—how would you apply his six-pronged revival plan to your world?

1. Reclaim the Classics

When you’re covering, say, indie architecture or artisanal food, you already know your audience’s quirks. But have you written down your positioning statement lately? Dust off that 4-Ps playbook:

Segmentation: Beyond “interested in craft beer,” drill into motivations—collectors hunting rare brews, home-brewers, industry pros.
Proposition & Consistency: If your magazine promises “deep dives into brewing’s alpine terroir,” every newsletter, cover story, and Instagram Reel must echo that core promise.

By re-centering these fundamentals, you build a loyal, identifiable readership that transcends fickle click metrics.

2. Pick & Polish the New Tools

Goodwin isn’t anti-tech—he just wants us to be choosy. For your niche title:

Retargeting with Purpose: Don’t just chase “abandoned carts.” Use web analytics to spot readers who read three long-form essays in a session—serve them a webinar invite or premium newsletter upsell.
Lookalike Audiences: Model your highest-value subscribers (annual-plan renewers) and find similar prospects on social platforms. But keep it tight: a narrow +5% lookalike is better than broad +20%.
Creative Testing, Lightly: A/B test subject lines for your subscription emails, but only iterate on those that truly shift conversion by more than 10%.

The key? Only invest in tech that measurably deepens engagement or loyalty, not vanity metrics.

3. Define Your Success on Your Terms

Are your quarterly targets all about “lowering cost-per-click” or about “growing paid circulation by 15%”? Maybe you want to be known for live-streaming expert panels on emerging tech, even if that doesn’t spike immediate ad revenue. Clarify:

Short-Term: Boost open rates on your weekend roundup from 25% to 35%.
Long-Term: Cultivate a community around exclusive member-only Slack channels where your most passionate readers network.

When you know what you really care about, you can filter out the noise.

4. Fuse Old & New Playbooks

Put the two in dialogue:

Classic Insight: A paid subscriber is 5× more valuable than an ad-only reader.
Modern Tactic: Use cohort analysis (new tool!) to see which first-month issue topics yield the highest 6-month renewal rates.

You might discover that long investigative features drive retention more than listicles—and then double down on those premium stories.

5. Reimagine Your Canvas

Today’s screens are gorgeous and interactive. For a specialized news org:

Interactive Infographics: Instead of a static pie chart on artisanal cheese markets, build a click-through journey that lets readers explore each region’s unique strains.
Audio Supplements: Embed 2-minute mini-podcasts in your articles—think “soundscape of a New England dairy farm” alongside your written feature.

These high-quality, sensory-rich experiences align with Goodwin’s call for distinctive, creative work that stands out in a sea of bland ads.

6. Persuade the Powers That Be

This is often the toughest: convincing your board or investors that a six-month brand campaign—say, a “Founders Series” profiling craft producers—matters even if it doesn’t drive clicks immediately. Build your case by:

Benchmarking Success: Show how other niche titles (e.g., a gourmet-pizza newsletter) saw a 20% lift in subscriptions six months after launching a video miniseries.
Hybrid KPIs: Combine quantitative (subscription growth) with qualitative (Net Promoter Score, reader surveys on “how memorable was last month’s cover?”).

Frame it as “investment” rather than “cost,” and lean on your deep knowledge of your audience’s values.

Wrapping Up

If you’re running a niche magazine or specialist news outlet, Tom Goodwin’s rallying cry isn’t just about ditching hollow metrics—it’s about owning your unique space with conviction. Rediscover the bedrock of segmentation and consistency, wield modern tools to enrich—not distract—and champion the long game to stakeholders.

“Do we want to hide behind spreadsheets… or do we want to make work that we feel proud of?”

That question feels especially urgent when your brand is small but mighty. Here’s to bold, big-idea marketing in the specialist press—thanks again to BoSack’s newsletter for pointing me to this gem.

Daily Links: Friday, Aug 1st, 2025

In my latest blog post, I dive into some cool tools and concepts that can seriously boost your productivity and understanding of technology. Discover how the Routinery app turns your intentions into actions, and get the scoop on whether you should build or buy your data pipelines. Plus, I explore Meta’s vision for personal superintelligence and introduce a handy tool to spot untested code changes.

Routinery Habit Tracker & Builder – Intention to Action: Turn your intentions into meaningful actions with Routinery, the routine app and habit tracker that helps you build lasting routines. Discover tools to improve your daily life.
Build vs. buy data pipeline: How to decide: Build vs. buy data pipelines is a key decision. Explore pros, cons, and when each option fits best.
Personal Superintelligence: Explore Meta’s vision of personal superintelligence, where AI empowers individuals to achieve their goals, create, connect, and lead fulfilling lives. Insights from Mark Zuckerberg on the future of AI and human empowerment.
grodowski/undercover: undercover warns about methods, classes and blocks that were changed without tests, to help you easily find untested code and reduce the number of bugs. It does so by analysing data from git diffs, code structure and SimpleCov coverage reports –