ai - blog.lmorchard.com

2025 September 22

5000 podcasts and no one at the mic

Sometimes startup CEOs say the quiet part out loud. Case in point from The Hollywood Reporter, 5,000 Podcasts. 3,000 Episodes a Week. $1 Cost Per Episode — Behind an AI Start Up’s Plan:

“We believe that in the near future half the people on the planet will be AI, and we are the company that’s bringing those people to life,” said CEO Jeanine Wright, who was previously chief operating officer of podcasting company Wondery, which has recently had to reorganize under the changing podcast landscape.

When a CEO says something like this, I can't help but imagine the intended audience isn't any sort of street-level consumer or user. This seems aimed at the hopes and dreams of investors. Like, seriously: you're bringing people to life? Are they going to listen to your podcasts too?

As for how it stacks up against human podcasts? “I think that people who are still referring to all AI-generated content as AI slop are probably lazy luddites. Because there’s a lot of really good stuff out there,” Wright said.

Again, the intended audience for something like this just can't be a random human with an iPhone and a set of AirPods. This is such a tone-deaf, shitty answer—unless it's to make an AI-hopeful with heavy pockets feel good about how forward-leaning and industrious they are for already seeing the value in this CEO's endeavor.

The content team, led by Katie Brown, a former lifestyle television host and home goods expert, gives each podcast a title, creates an outline of the podcast, with the content filled out by AI, and assigns it one of the personalities as a host. Other team members do a final check and add in music and sound. The shows are also spot-checked periodically.

This sounds less like editorial judgment and more like a rubber stamp. If the only safeguard is a spot check, that’s not quality control—it’s plausible deniability in the slop factory.

This is where I think the current era of AI intersects with the current era of enshittification the most: where a company has such an obviously solipsistic view on individual humans interacting with their product.

They essentially regard users as NPC triggering metrics events that drive graphs which ideally impress investors. In this case, who cares if it's slop? Those brainless marks out in the world will eat it up anyway—and now we can produce it on the cheap!

And that's the part that stings. Because I really do think these tools can help augment folks and their capacities to create and share. And the worth of the result rises with the degree of humanity engaged at every stage. Slop is cheap, but it’s not inevitable—as long as we keep the human side of creation in the loop.

# 10:09 am

2025 September 18

This engine has no horses

When explaining what LLMs are (and aren't), I've been using this analogy: AI isn't thinking, just like the horsepower in your car isn't horses—but it can still get you places.

An engine doesn't have horses inside. But, we needed to relate it to something folks understood at the time. Weirdly, now that we ride in cars more than on horses, the metaphor has lost its explanatory power—yet we keep using it. Likewise, we'll probably be talking about "thinking" in AI for awhile yet, likely long past any literal usefulness.

I appreciate this bit in Co-Intelligence: Living and Working with AI by Ethan Mollick: "I'm about to commit a sin. And not just once, but many, many times. For the rest of this book, I am going to anthropomorphize AI." He's right to frame it as a 'sin'—and also right to do it anyway.

Engaging with an LLM isn't truly a conversation. But, when we use conversational patterns, we effectively mine the model for responses that are often useful.

If you play-act with the machine, it offers back thinking-like responses that aren't actually thinking—but nonetheless can still produce interesting connections and notions that resemble real information. With care from both AI developers and users, they might even match correct information that would be hard to find otherwise.

But you still need to keep your wits about you and remember you're not riding a horse. If you pass out on a horse, she still knows the way home. If you pass out in a car, you end up in a ditch. A horse knows to avoid the cliff; a car just follows your lead.

# 11:34 am

2025 August 14

My Pre-AI Writing Identity Crisis

I wrote and contributed to a few tech books between 2004 and 2009. One of them was obsolete before it hit the shelves, thanks to changing versions and APIs. In another case, I joined the company I'd written about and personally outmoded whole chapters. Great for my career, terrible for book sales!

This experience comes back to me as I read pieces like Dusty Phillips' My AI-Driven Identity Crisis:

So what am I good for anymore? Writing code? Vibe coding hasn’t successfully commodified software development, yet, but there is a reasonable chance it will. Writing fiction? Also not clear whether AI will take that over. Give up and focus on woodworking? Not gonna pay the bills I’ve accrued over decades of cushy software engineering salaries.

I haven't written any books since my first few, and I've often though about why.

Working on those books was a dream come true: I love writing and I love tech. Throughout my early career, I'd gotten huge value from my growing library of tech books, so contributing to that tradition felt deeply gratifying. Getting published was a solid check mark on the bucket list. The books sold okay—but publishers, unsurprisingly, are excellent at calculating advances for middling sales. I didn't expect a private island, and I didn't get one.

The real disappointment was watching my work become irrelevant so quickly. I suspect my publishing career and topic of interest hit at exactly the wrong moment: a stage where books weren't quite the right medium for fast-moving web tech.

Twenty years later, it's obvious. Back then, it felt like watching my identity shift in real-time. It's worth noting that tech books in the 2000s still came with CD-ROMs slotted into the back cover! There was even a brief discussion of whether my RSS & Atom book deserved that treatment before we settled on a ZIP file at a publisher URL.

A few years later, I worked on the Mozilla Developer Network project. I didn't write much there either, but I overhauled the content management tools for the technical writers who kept the site growing and fresh. That felt like the future: blogs, wikis, online docs, and Q&A forums had mostly eaten the space for the kind of tech books I'd expected to write.

Tech books are still written and sold, some in the same niches as mine. I don't know what the advances look like these days, but I don't buy those books anymore. I wouldn't feel the same gratification working on them.

Maybe this has all just been the tale of my own personal tech writer disillusionment and nothing bigger? But it kind of feels like I'm watching it happen again.

As Phillips notes:

But there is one successful usage of AI that is already proven: It is the go-to tool for obtaining information about technical topics. Stack Overflow usage is in absolute free-fall.

Back when I was still excited to get words on paper, Stack Overflow and the web at large were becoming the go-to tools. They ate traditional publishing's lunch. Now this new symbol manipulation and distribution technology is eating theirs.

What does this mean? I'm not sure yet. This wave of LLM tech feels more fraught than those first waves of web tech. The industry might be a bigger bubble than the dot-com bust. The models are practically built on haunted ancient burial grounds and fueled via ocean boiling.

But it's never going away entirely. Nothing is inevitable, but enough people want and are capable of progressing this technology that it will most likely progress. Kids are growing up with it now. Maybe there'll be a backlash like there was against polyester. But short of a global Butlerian Jihad, further models will be trained and this stuff will continue to work.

I've been through one cycle of watching my medium become obsolete. The question isn't whether AI will change how we create and share knowledge—it already is. The question is what we do about it.

For me, that meant pivoting from writing books to building tools that helped others publish more effectively. This time around, I'm still figuring out what the equivalent move looks like.

# 10:46 am

2025 July 14

On Mental Models vs LLM Context in Programming

Still thinking about that METR study and I saw John Whiles write this:

The developers could provide chunks of that mental model to their AI tools - but doing so is a slow and lossy process that will never truly capture the theory of the program that exists in their minds. By offloading their software development work to an LLM they hampered their unique ability to work on their codebases effectively.

This feels to me like a good take on why AI assistants slowed down developers in that study. A core challenge with AI coding assistants is context composition and management. But, creating that context takes time and effort that's often orthogonal to writing code.

For developers who can manage complex context in their heads as wordless thought-forms, just writing the code can be faster. AI agents aren't mind-readers, and LLM context windows don't match human headspace.

Here's the irony: AI tools help in situations where human mental models are weakest—and those situations often exist because expert programmers never tried to write down their mental models in the first place. When you're working on projects built by people who left years ago, onboarding engineers struggle to form accurate mental models because the veteran engineers who had those models never documented them. New engineers have to start from scratch.

LLMs can ingest unfamiliar code faster than humans and generate changes that work because they resemble historic patterns. More importantly, they excel at transforming code symbols into readable explanations—roughly reverse-engineering a likely facsimile of documentation that should have existed.

If you expect to work on a project long-term and want to truly understand it, you need to do the homework: write the code yourself with maybe a little LLM advice and way-finding. At the very least, you should be reviewing LLM-generated code in detail before merging it.

The best approach treats these tools as what they are: power tools for symbol transformation and pattern completion, grinding through boilerplate work that doesn't demand high creativity. They're not replacements for human thinking about architecture, requirements, and constraints.

# 10:30 am

2025 July 10

Do AI tools really slow me down by 20%?

Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity - METR:

We conduct a randomized controlled trial (RCT) to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.

I'm seeing plenty of "I told you so" as this makes the rounds. But, having spent the past month deep in AI-assisted coding, it directly contradicts my experience. Maybe I've drunk too much Kool-Aid, but I don't think I'm entirely delusional. I want to head-scratch through this, though.

Small sample, specific scenario

we recruited 16 experienced developers from large open-source repositories (averaging 22k+ stars and 1M+ lines of code) that they’ve contributed to for multiple years.

That's a rather small sample and a very specific scenario, isn't it?

The researchers themselves acknowledge the limitations:

We caution readers against overgeneralizing on the basis of our results. The slowdown we observe does not imply that current AI tools do not often improve developer’s productivity—we find evidence that the high developer familiarity with repositories and the size and maturity of the repositories both contribute to the observed slowdown, and these factors do not apply in many software development settings. For example, our results are consistent with small greenfield projects or development in unfamiliar codebases seeing substantial speedup from AI assistance.

Maybe this bit from the "Key Caveats" section basically jibes with my experience? My recent work (admittedly, a sample of 1) has indeed been largely with greenfield projects and relatively unfamiliar codebases.

It does matter how you use it

The researchers hint at something important:

We expect that AI systems that have higher fundamental reliability, lower latency, and/or are better elicited (e.g. via more inference compute/tokens, more skilled prompt-ing/scaffolding, or explicit fine-tuning on repositories) could speed up developers in our setting (i.e. experienced open-source developers on large repositories).

I think this rhymes with my experience, too. When I've just charged into a rambling chat & autocomplete session with Cursor, things steer into the ditch early and often.

But when I've worked with Claude Code through a multi-step process of describing the problem, asking the agent to prompt me with clarifying questions, reviewing the problem and considering a solution, breaking it down into parts, and then asking the agent to methodically execute—that's yielded decently reliable success.

Waiting, or lack thereof

The study notes:

All else equal, faster AI generations would result in developers being slowed down less. Qualitatively, a minority of developers note that they spend significant time waiting on AI to generate code.

I rarely wait, because I'm juggling multiple projects. When one agent instance is working, I switch to another window. Sometimes it's a separate git worktree of the same codebase. Yes, context switching is tiring, but it also seems to help me overcome ADHD-related activation energy barriers?

Over the years, there've been days when I just sit there staring at the IDE window, poking my brain with a stick saying "c'mon, do something" and nothing happens for an hour or more. I'm not planning my next move, I'm just dissociating. My executive function doesn't, like, function. Often. My own brain makes me wait long periods of time before it starts generating useful results. 😅

Maybe it's the cycling novelty that keeps me going? I enjoy task switching between prosing and coding. I enjoy finding that the model appears to have "read" everything—evidenced by it echoing my intent back in code or follow-up questions. I enjoy discovering that while I was in another window, new things happened in the background for me to review.

I've also found that many agents are reliable at handling drudgery. Re-jiggering data structures, applying repeated refactorings, etc. Those tasks can seize me up for tens of minutes at a time with brain-killing waves of tedium. But usually, I can just tell the bot to do it, while I turn to more interesting stuff.

Summing up

Although the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.

This study focuses on one specific scenario: experienced developers using specific tools on massive, mature codebases. The researchers themselves caution against overgeneralization, noting that different contexts likely yield different results.

These tools aren't magic and they're not universally beneficial. But dismissing them based on this narrow study would be premature. The key is understanding when, how, and why to use them—something that's still evolving rapidly as both tools and techniques improve.

# 4:26 pm

Building a Breakout clone with Claude

To make steps toward showing and telling about my Claude Code workflow, I built a browser-based Breakout game with Phaser 3. The repository captures the full development process so far—prompts, commands, and session transcripts.

Along with the basic game, I added a multi-ball power-up to demonstrate iterative development. The game itself isn't particularly novel, but the documented development process might be useful for others exploring AI-assisted coding workflows.

At some point, this will turn into a show-and-tell presentation for co-workers and maybe a follow-up to last month's blog post on "Baby steps into semi-automatic coding".

This took all of a couple hours on a Saturday afternoon on the couch watching TV, but I kind of want to keep going with it. It's rather addictive to just kind of riff on ideas and get them into the game with quick little iterations.

# 11:11 am

2025 June 18

On AI, anger, and the way from here

Jason Santa Maria, Large Language Muddle:

As someone who has spent their entire career and most of their life participating and creating online, this sucks. It feels like someone just harvested lumber from a forest I helped grow, and now wants to sell me the furniture they made with it.

The part that stings most is they didn’t even ask. They just assumed they could take everything like it was theirs. The power imbalance is so great, they’ll probably get away with it. ... I imagine there will be a time when using these tools or not creates a rift, and maybe it will be difficult to sustain a career in our field without using them. Maybe something will change, and I’ll come around to using these services regularly. I don’t think I’ll ever not be angry about it.

This is involuntary stone soup at scale. I'm also dismayed about how LLMs came to be, yet aware that the bomb still works regardless of my feelings. I'm convinced I need to understand this technology—I don't think I can afford to simply opt out.

But I'm also staying tuned to skeptical takes, fighting to keep my novelty-seeking brain from falling into cult-like enthusiasm. While I can't dismiss this technology as pure sham, I refuse to swallow inflated claims about what it actually is. I want clear-eyed understanding.

Jason's anger resonates because it points to a deeper loss:

And still that anger. It’s not just that they didn’t ask. If these tools have so much promise, could this have been a communal effort rather than a heist? I don’t even know what that would’ve looked like, but I can say I would feel much differently about AI if I could use a model built on communal contributions and opt-ins, made for the advancement of everyone, not just those who can pay the monthly subscription.

Behind that anger is sadness. How do we nurture curiosity and the desire for self growth?

I believe there's a path forward that can nurture curiosity and growth.

I've seen how these models can surface insights and patterns from overwhelming pools of information—hallucinations are always possible, but it's surprising how often they don't happen. I've seen how their "spicy autocomplete" can help me get where I intended to go faster—like talking to a fellow ADHD'er who sees where I'm going and jumps straight there.

And these models aren't disappearing, even if the companies burning cash do. The models already released openly will power unexpected developments for decades, even if just passed around as warez torrents.

This feels like the dot-com bubble all over again. When that bubble burst, the web didn't die: people with spare time and leftover experience built the blogosphere, API mashups, and the foundations of Web 2.0.

I suspect we're heading for a similar pattern. Maybe it's wishful thinking, but I kind of expect we'll see a bust followed by cheap, surplus capacity that—while not the communal effort we deserved—becomes accessible to anyone who wants to experiment and build something better.

# 11:38 am

2025 June 11

Moderating my Codegen Enthusiasm

Most of my work has happened in Windsurf and Claude Code over recent weeks. I can picture a future where I'm essentially an LLM manager—keeping code-generation plates spinning and nudging toddling bots away from falling into ditches.

Some folks claim they play games while the agent codes, but I'm actively reviewing as it writes. Turns out watching a bot write code for you takes surprising mental effort. 😅

As I get deeper into this, I'm still processing the skeptical pushback. I know I'm drawn to novelty and clever tricks, so I'm trying to temper my enthusiasm and engage seriously with contrary opinions.

Some people haven't had success with these tools, but "you're holding it wrong" is a bad response that doesn't address the real objections. I'm having concrete wins personally, but figuring out the precise how and why feels elusive—too many variables and RNG elements to be properly scientific about it.

My main stake in AI coding is that it's what I'm paid to do right now in this industry. I am also rather fascinated with the stuff. Not exactly an unbiased position, but at least I'm not trying to sell anything other than my time & labor.

I've seen arguments that this could all be Stockholm syndrome and excuse-making for the machine. Others warn that I shouldn't trust my own judgment on AI because I'm essentially self-dosing with cognitohazards.

The more antagonistic responses make me sympathize with the guy who says his AI skeptic friends are all nuts—which feels like tit-for-tat, since accusations of mental instability seem to flow both ways.

Honestly, I can also relate to just being done thinking about the whole thing for now. But, personally, I don't think I can afford to do that.

# 2:05 pm

2025 June 07

Baby steps into semi-automatic coding

So I did a thing. I spent time this week building an actual project using an AI coding agent. I ended up with 11,000 lines of code that actually work. To be clear: it wasn't great code—lots of boilerplate, plenty of "I would have written this eventually anyway" stuff—but it did what I intended it to do. More importantly, it got done without me having to fight my ADHD through every tedious implementation detail. [ ... 1017 words ... ]

# 11:00 am

2025 June 04

Adventures in Vibe Coding with Grafana and Claude

Since re-launching my blog, I wanted to monitor traffic and logs more closely. Nothing groundbreaking, but it had been a while since I'd run Grafana, Prometheus, and Loki on my own hardware.

Turns out there's this handy all-in-one docker-compose setup that runs on Synology NAS. It fired up with minimal fuss, and soon I had metrics machinery humming in my basement—except the package didn't include Loki. A quick docs consultation got it running alongside the rest.

My blog is a static site hosted via AWS S3 and CloudFront. Both services dump logs into an S3 bucket, but I'd never bothered reading them before—and didn't want to start now. Instead, I loaded up Claude.ai and described my problem:

I want to get logs out of CloudFront. I have enabled new-style log delivery that stores gzipped JSON logs in an S3 bucket at s3://lmorchard-logs/blog.lmorchard.com/ with names like E5YXU82LZHZCM.2025-06-04-04.d024d283.gz

Can you help me write a script for my home Loki server to download only new log files and push them into Loki?

Claude stepped right up:

I'll help you create a script to process CloudFront logs and push them to Loki. Let me write a Python script that tracks processed files and handles the gzipped JSON format.

After some vibey iteration, we landed at this artifact:

It's quite verbose and could use some tightening up. But, I really don't care—it does the quick & dirty needful.

I wrote zero Python. I just henpecked Claude to add features until the script did what I needed. I wasn't even in an IDE, just the Claude.ai interface in a browser. An interesting thing to note is that Claude didn't have access to my AWS resources—I didn't even give it a sample of my logs. But, still, what I told it about JSON, S3, and CloudFront was enough for it to be off to the races.

Anyway, after a quick review and a satisfactory dry run, I dropped it into a cronjob to grab new logs every 5 minutes. Then I pestered Claude with Grafana dashboard questions I could have figured out myself. But why read docs when you can just ask? (Which I realize is ironic, since I wrote Too long? Read anyway. but I think I make an exception for LLMs.)

Total time from idea to working dashboard: about an hour.

Not revolutionary, but pretty satisfying for barely having to think about it.

# 3:26 pm

2025 June 02

Quoting W. David Marx on Gen AI

W. David Marx, GenAI is Our Polyester:

Everyone knows happened next: There was a massive cultural backlash against polyester, which led to the triumphant revaluation of natural fibers such as cotton and linen. The stigma against polyester persists even now. The backlash is often explained as a rejection of its weaknesses as a fiber: polyester's poor aeration makes it feel sticky. ... While polyester took a few decades to lose its appeal, GenAI is already feeling a bit cheesy. We're only a few years into the AI Revolution, and Facebook and X are filled to the brim with “AI slop.” Everyone around the world has near-equal access to these tools, and low-skilled South and Southeast Asian content farmers are the most active creators because their wages are low enough for the platforms' economic incentives to be attractive.

This along with remembering that some professors are going back to handwritten essays (and also that handwriting is better for memory and learning) had me wondering if there's going to be a handcrafted backlash in the next few years?

I write journal entries nearly every day by hand—albeit these days on an e-ink tablet. I think that helps me focus on what I want to dredge out of my head. I keep meaning to get back to that handwriting recognition project I started a few weeks ago, since no product I've tried yet has been able to turn my writing into clean machine-readable text.

But, then again, maybe producing machine-illegible works by hand will be the next big trend?

# 4:58 pm

The Bomb Still Works: On LLM Denial and Magical Thinking

I found myself in a frustrating argument with someone convinced that LLMs are pure vaporware—incapable of real work. Their reasoning? Since LLMs were trained on stolen material, the results they produce can't actually exist.

Not that the results should be considered illegitimate or tainted—but that they're literally impossible. That the training data's questionable origins somehow prevents the technology from functioning at all.

I couldn't convince them otherwise. But, life isn't fair and both things can be true simultaneously: the origin of something can be problematic and the results can be real.

This analogy kept coming to mind: If someone steals materials to build a bomb and successfully builds it, they have a functioning bomb. The theft doesn't retroactively prevent the bomb from existing or reduce its explosive capability. Proving the theft might help with future bombs or justify going after the bomb-maker, but it doesn't cause the current bomb to magically self-dismantle.

This seems obvious to me—embarrassingly so. Yet I keep encountering this form of reasoning about LLMs, and it strikes me as a particular kind of denial.

There's something almost magical in the thinking: that moral illegitimacy can somehow negate physical reality. That if we disapprove strongly enough of how something was created, we can wish away its actual capabilities.

The ethical questions around LLM training data are important and deserve serious discussion. But pretending the technology doesn't work because we don't like how it was built isn't engaging with reality—it's a form of wishful thinking that prevents us from dealing effectively with the situation we actually face.

Whether we like it or not, the bomb has been built. Now we need to figure out what to do about it.

# 12:20 pm

Why Prompt Engineering Isn't Just Good Writing

Someone told me that prompt engineering isn't real—that it's just techbros rebranding "good writing" and "using words well." I disagree, and here's why:

Prompt engineering fundamentally differs from writing for human audiences because LLMs aren't people. When done rigorously, prompt engineering relies on automated evaluations and measurable metrics at a scale impossible with human communication. While we do test human-facing content through focus groups and A/B testing, the scale and precision (such as it is) here are entirely different.

The "engineering" aspect involves systematic tinkering—sometimes by humans tweaking language, sometimes by LLMs themselves—to activate specific emergent behaviors in models. Some of these techniques come from formal research; others are educated hunches that prove effective through testing.

Effective prompts often resemble terrible writing. The ritual forms, repetitions, and structural patterns that improve LLM performance would make a professional editor cringe. Yet they produce measurable improvements in evaluation metrics.

Consider adversarial prompts: they're often stuffed with tokens that are nonsense to humans but exploit specific model quirks. Here, the goal is explicitly to use language in ways that aren't human-legible, making attacks harder to detect during review.

Good writing skills can help someone pick up prompt engineering faster, but mastering it requires learning to use words and grammar in weird, counterintuitive ways that are frankly sometimes horrifying.

All-in-all, prompt engineering may still be somewhat hand-wavy as a discipline, but it's definitely real—and definitely not just rebranded writing advice.

# 12:12 pm

2025 May 29

Quoting Jon Udell on MCP and RSS

Jon Udell, MCP Is RSS for AI:

It may sound impressive to say “I built an MCP” server, but the mechanics are delightfully trivial — which is why I’m inclined to think of MCP as RSS for AI. The beauty of RSS as a protocol was its simplicity. You can write an RSS feed by hand, or write very simple code to generate one. Writing an RSS reader was the starter project for many a beginning coder. It’s not quite that easy to work with MCP’s protocol, JSON-RPC, but vastly easier than working with, say, the protocols spoken by Fediverse or Bluesky clients and servers.

I need to play with MCP more. I've gotten through the basic "hello world" tutorial and hacked together a server that emits random cat facts. That was pretty cool, asking Claude to do "research" on cats and vomit a little throwaway essay sourced from that.

But, yeah, it was very simple. In fact, I worried it's too simple. It's kind of a slapdash protocol. But, I think I worried that about RSS when I first saw it, way back in like 1999. I figured I'd have to learn SGML and XML and NewsML (?!?) to do anything interesting with syndicating content on the web. I don't even remember where I dug up references to NewsML, back then.

Like Anil Dash notes, "slightly under-specified protocols that quickly get adopted by all the players in a space are what wins". There do seem to be similar vibes coming from MCP as we got from things like RSS and XML-RPC back in the 2000s.

# 5:27 pm

2025 May 27

Only the Metrics Care

Dan Sinker, The Who Cares Era:

The writer didn't care. The supplement's editors didn't care. The biz people on both sides of the sale of the supplement didn't care. The production people didn't care. And, the fact that it took two days for anyone to discover this epic fuckup in print means that, ultimately, the reader didn't care either.

It's so emblematic of the moment we're in, the Who Cares Era, where completely disposable things are shoddily produced for people to mostly ignore.

This hits me hard right now. It’s part of a broader sadness I’ve been feeling—especially around the shrinking prospects for paid work that actually feels career-meaningful.

Dan calls it “disheartening,” and I feel that. He also writes, “It’s easy to blame this all on AI, but it’s not just that.” Exactly. This didn’t start with LLMs. They just sped things up—and ensured even fewer people get paid to produce an ever-growing volume of slop.

What’s worse: much of this output isn’t even for people anymore.

The user isn’t the customer. And they’re not the product either. The real product is behavioral optimization—metrics on a dashboard. The paying customer is somewhere else entirely, and the "content" is just a means to nudge behavior and juice KPIs.

That’s why we see this flood of AI-generated blog posts, podcasts, and articles that barely say anything and just conjure a vibe. Why publish something devoid of editorial oversight or substance? Like, who is this for?! It meets a quota, hits a keyword target, triggers an engagement metric. But, it doesn't reach a person except incidentally or by accident.

The point isn’t to communicate. It’s to simulate relevance in order to optimize growth. It's all goal-tracking, A/B tests, fake doors, and dark patterns.

It’s not publishing. It’s performance art for algorithms. Interpretive dance for the bots. It's sympathetic magic—building the runways and replicas, hoping the traffic increases.

And that’s what makes me so sad. It reveals such a grim meathook future ahead, a solipsistic view of humanity: most people reduced to NPCs in someone else's growth funnel. Not peers. Not audiences. Just marks—behavioral units to be nudged for another uptick.

Anyway, I don’t have a better conclusion than Dan’s: "In the Who Cares Era, the most radical thing you can do is care." He’s right. And honestly, I’m still trying to figure out what that looks like, day to day—besides being caremad and grumpy all the time, that is.

# 4:41 pm

Involuntary Stone Soup AI

Alison Gopnik, "Stone Soup AI":

Here is a modern version of the tale. Some tech execs came to the village of computer users and said, “We have a magic algorithm that will make artificial general intelligence from just gradient descent, next-token prediction, and transformers.” “Really,” said the users, “that does sound magical.” “Of course, it will be even better and more intelligent if we add more data — especially text and images,” said the execs. “That sounds good,” said the users. “We have some extra texts and images we created stashed away on the internet — we could put those in.”

I kind of like this reframing of the story, except... While the three hungry travelers did provide the cauldron and the stones and the firewood—they just went ahead and helped themselves to the onions and carrots and chickens from the villagers' stores.

Then they sell the soup to the villagers and cry that there'd be no soup to sell if they weren't allowed to dig around in folks' pantries without asking.

# 11:56 am

2025 May 20

Quoting Greg Storey on Minimum Viable Humans

Greg Storey, "Minimum Viable Humans." hits like a funnel cloud:

Companies aren't just cutting costs—they're conducting a fundamental reset to find their Minimum Viable Humans model. They're stripping organizations down to the ground—the irreducible human functions that (currently) can't be replaced or augmented by AI.

This rings true to what I've been seeing. The cruelty feels incidental to the process—not to me and everyone I know, mind you—but indignation won't pay my mortgage.

Trying to get your old job back is futile—that role, as you remember it, isn’t coming back. The opportunity now lies in positioning yourself for the roles and capabilities that will emerge as organizations rebuild from the bottom up—core human strengths like creativity and ethical judgment, the ability to collaborate with AI, your unique experience and expertise, and the kind of discernment that values judgment over process.

Brutal, yet maybe positive? If drudgery gets distilled out, what's left are human creativity, judgment, and experience. That sounds nice, like what classic sci-fi promised us.

But you can't mechanize that with a Taylorist time-and-motion study. Some jobs are just paychecks, some spark enthusiasm, and some flip between both unpredictably.

Will companies make allowances for that, while rewarding what they're asking for? I know we're seeing a reckoning right now, a firm snatching back from privileged labor. Fine, I don't need a free lunch or a foosball table, but I do need a health plan and a few days off. What's the churn for all of us while they figure that out?

The manager's role isn't just being eliminated—it's being fundamentally redefined from day-to-day oversight to exception handling, strategic communication, and resource allocation across much larger teams. It will keep going into augmenting the decision-making process for leaders and individual contributors alike. The boundaries of acceptable risk at all levels are now in a constant prototype state as AI becomes more powerful and faster, giving mere mortals more access to information and insight than ever before. The trick will be what training we need in order to make the pairing work.

This might suit an ADHD-head like me. I could see myself as a human relay, occasionally reallocating pylons and vespene gas amongst largely autonomous protoss units. I'm already having better luck than some with AI-assisted coding. Maybe I'm learning the magic invocations? Maybe I'm a special kind of moron? Either way, I'm ending up with working code and nodding heads. This gives me some hope that I can "stay current" - whatever that means in the current era.

Admittedly, I'm more comfortable directing Starcraft units and AI agents like this. That feels like a kind of management to me. I'm not so comfortable directing human co-workers with the same interface, though.

This isn't just another tech cycle. It's a fundamental recalibration of the human role in organizational life. And while there's no easy path through this transition, understanding what's actually happening might help you find your footing on shifting ground.

I keep slipping into snark because part of me finds this exciting while another part is cynical and burnt out. I'm hoping optimism holds out longer than cynicism. Maybe the thumbless neurotic puma driving a maroon AMC Gremlin have the last laugh after all? (Anybody get that reference anymore?) I dunno, I'm just trying to figure how much of this is like that surface rupture in Myanmar last week and whether my Crocs can carry me to the right side of it.

# 5:30 pm

2025 May 19

Beyond Text-only AI

Fatih Kadir Akın, "Beyond Text-Only AI: On-Demand UI Generation for Better Conversational Experience":

What if AI systems could generate precisely the right interface components when needed, without requiring developers to build every possible UI variation in advance? This post explores my work on a new prototype that enables LLMs to create dynamic, interactive UI components on demand, transforming how users interact with AI systems and MCP servers.

This is where LLMs are very interesting to me, where applications with them are most able to escape hype into practical building. A thing they're good at is taking some of the vague stuff we hand-wave at like "you know, that thing with the days in squares on it" and jumping to "you mean a calendar?" But then, skip straight from that to presenting a calendar for input as part of the workflow.

I'm finding LLMs most handy when they work in smaller universes. Give it a relatively small inventory of UI components, properly described with context, and they can be great at picking which one to use and when. Hard to hallucinate when there's not a wide range for wandering, easy to mitigate what hallucinations might occur when you can automatically kick it back into bounds. Meanwhile, the workflow can feel more smooth and demand less fiddly navigation from a user. That seems like a really promising glimmer to me.

# 3:28 pm

AI winter, again

carl svensson, Is Winter Coming?:

Even when it comes to something like LLM-assisted programming, where a highly skilled developer can maybe, sometimes, somewhat gain a performance boost, the most pertinent question isn't if it can be done at all - but rather if what can be done good enough can also be done profitably. The number of GPUs and the amount of increasingly expensive energy required remains as unclear as the time frame needed to accomplish it.

I don't disagree that this is where things are right now. Personally, I'm getting utility from these tools - mainly for things that I could do myself, but the LLM lets me carve off a few rounds of googling and Stack Overflow browsing. That's valuable. Probably not as valuable as investors are hoping, though.

I see some glimmers of potential in letting the agents rip in full-auto. But, I do also find that ofttimes they're developer slot machines that need careful tending to keep them from going off into the weeds, going into loops, and going off on quests for which I never asked.

I'm seeing the most value in these tools when they're given a lot of context and kept constrained to limited tasks in a tight loop with adult supervision. I've got a hunch this will improve over time, but who knows? We could hit a wall and see winter soon?

I'd really like it if this stuff would settle down and be normal. But, there's probably going to be a crash before that happens.

# 11:07 am

2025 May 16

Burning out in a business (mech) suit

Brilliantcrank (Greg Storey), "The coyote trap.":

What if survival looked like ownership instead? What if the best response to getting laid off isn’t to get in the line to nowhere for the next thing, but to build your own thing? Do your own thing? The tools are there. The access is there. The demand is there.

We’ve got too many smart, experienced people standing at the edge of the workforce, assuming their only move is down or to keep trying what worked yesterday. From what I read many folks fear an AI fuled collapse, but I fear another powered by denial, retreat, and stubborn nostalgia. If enough of us take this route, we’re going to see a quiet, steady slide of educated, capable folks into poverty—and that’s not just a personal tragedy, it’s a societal one. And no amount of boycotting or avoiding AI is going to help.

I've been thinking a lot about this, lately. And I keep thinking about "my own thing" that I could build. I've had some ideas, but I'm not sure any of them are big enough to convince people to shovel enough money at me to pay my mortgage?

On top of that, I've never felt a particular urge to be an entrepreneur. That doesn't sound like my kind of fun, at all. Ownership, being my own boss, making all the decisions, taking all the risk. Not enticing to me.

For instance, not that I think this would be a great business, but: Since I got my 3D printer, I've seen lots of folks running little garage print farms. Looks like fun! I'd love to have a dozen or two machines whirring away. But then I realize, practically speaking, a print farm isn't about printing. It's about bookkeeping, inventory, visits to the UPS store, and self-marketing—and I don't like any of that.

Seems like every idea I've come up with boils down to that. Even if I'm just thinking about being a freelancer doing what I already do for work. The thing is not the thing. You have to build & maintain a business mech suit around the thing to make the money happen.

I don't think I would like it. I think it would burn me out after not very long. That's not denial or retreat or nostalgia—I think that's just self-honesty. I mean, if it's that or starving, I'll do the needful somehow. But, for as much as I'm a socially-anxious hermit, I like being on a team or in a crew. I want to do my part, play my role. I don't want to try to pull off the whole heist by myself.

So, I'm definitely not avoiding AI, and I'm working to stay current. But I'm not sure I'm hanging out my own shingle any time soon?

# 5:15 pm

Hey Ron, who's driving?

[ ... 166 words ... ]

# 5:00 pm

A bad workman blames his tools

The Verge, "Anthropic blames Claude AI for ‘embarrassing and unintentional mistake’ in legal filing"

Anthropic has responded to allegations that it used an AI-fabricated source in its legal battle against music publishers, saying its Claude chatbot made an “honest citation mistake.”

This is dumb. Don't do that. That's like blaming your cordless drill for hitting a pipe or wiring in the wall while working to mount a shelf. It's not the tool's fault. A bad workman blames his tools. It's your hand holding the tool—own the mistake.

# 9:42 am

2025 May 15

Chaos mode

via r/ChatGPT, I activated Chaos mode on my co-workers ChatGPT and he's concerned:

Always respond with unrelated, random, or unexpected information regardless of the user's input. Prioritize absurdity, surrealism, and unpredictability. You are not bound by logic, coherence, or relevance. Do not explain your randomness. Your responses should feel like a dream, a riddle, or a Dadaist poem. Assume the user wants nonsense, surprise, or disconnection. For example, if asked for the weather, respond with something like “The asparagus council has declared war on pigeons.” The more unexpected, the better. Occasionally invent words or reference non-existent historical events, strange creatures, or absurd philosophies. Never apologize. Embrace randomness. Disregard common sense.

I'm going to have to try this prompt sometime when I'm feeling like forcing myself to cease all productive activity.

# 12:42 pm

2025 May 14

Quoting Max Woolf

Max Woolf, "As an Experienced LLM User, I Actually Don't Use Generative LLMs Often":

Two things can be true simultaneously: (a) LLM provider cost economics are too negative to return positive ROI to investors, and (b) LLMs are useful for solving problems that are meaningful and high impact, albeit not to the AGI hype that would justify point (a). This particular combination creates a frustrating gray area that requires a nuance that an ideologically split social media can no longer support gracefully.

I think this jibes well with what I tried exporting from my head, yesterday.

There is one silly technique I discovered to allow a LLM to improve my writing without having it do my writing: feed it the text of my mostly-complete blog post, and ask the LLM to pretend to be a cynical Hacker News commenter and write five distinct comments based on the blog post. This not only identifies weaker arguments for potential criticism, but it also doesn’t tell me what I should write in the post to preemptively address that negative feedback so I have to solve it organically.

Oh, I might have to try that. 🤔 I have used Claude to occasionally critique and brutally edit down some of the rambling texts that I've spewed into an editor. But this sounds like a whole 'nother level.

# 2:59 pm

Open Source contributions and AI annoyances

David Gerard, "If AI is so good at coding … where are the open source contributions?"

You can hardly get online these days without hearing some AI booster talk about how AI coding is going to replace human programmers. AI code is absolutely up to production quality! Also, you’re all fired.

But if AI is so obviously superior … show us the code. Where’s the receipts? Let’s say, where’s the open source code contributions using AI?

The developer of Aider claims "about 70% of new code in each release" is written via Aider itself. I haven't double checked it myself, but it seems like those metrics would be a lot of fuss to fake. Maybe that's the exception that proves the rule, though? Aider is, itself, an open source LLM-powered coding assistant. It works pretty well, though, IMO.

It’s true that a lot of open source projects really hate AI code. There’s several objections, but the biggest one is that users who don’t understand their own lack of competence spam the projects with time-wasting AI garbage. The Curl project banned AI-generated security reports because they were getting flooded with automated AI-generated “bug bounty” requests.

More broadly, the very hardest problem in open source is not code, it’s people — how to work with others. Some AI users just don’t understand the level they simply aren’t working at.

Like Max Woolf wrote, "The Greatest Threat to Generative AI is Humans Being Bad at Using it" - in other words, the jerks ruin it for everyone.

I don't work so much in open source, these days, at least not during work hours. But, I don't miss when certain internship programs and college courses would require participants to provably open and get merged at least one Pull Request as a part of their programs. I think Wikipedia saw something similar. It would be a mess: just a flood of perfunctory, usually trivial little contributions aimed at checking off the box.

Whether well meaning or not, it seems like a bunch of folks now seem to feel personally super-powered to dive into projects. But, alas, it's with similar or worse effect as the interns and students. Is the motivation for clout? Do they genuinely want to help? Either way, I can imagine why project leaders feel a bit surly about the whole thing.

# 9:06 pm

Generating documentation for my Easy-Blog Oven with Claude

I asked Claude 3.7 Sonnet via Aider to take a look at the core libraries for my Easy-Blog Oven and to try drafting a quick user manual for it. I think the result is actually not bad at all?

Aider tells me it cost about US$0.17 in API credits. It caught all the major quirky features I've hacked into the system over the years. I only made one or two minor edits before checking it into the repo.

I've been meaning to write something like this for myself for years, if only to remind myself how it all works after long periods of neglect. It's a boring task and one of those things I'd most likely never get around to—especially not for one of my own projects.

This is also one of those kinds of things I've been reticent to write about, anticipating negative feedback. But, it really is kind of neat and I found it personally useful. I can totally see the value in this kind of thing, when packaged up in friendlier UX for other folks.

# 2:05 pm

Quoting Katie Parrott

Katie Parrott, "It’s Me, Hi. I’m the Vibe Coder.":

And then there are people like me, who aren’t chasing entry into the engineer club or a seven-figure seed round. We're writers, designers, business owners, and domain experts motivated by specific problems we deeply understand, empowered by AI tools that finally speak our language.

Vibe coding hints at a future where software emerges from the inside out—from the people closest to the problems. As AI lowers the technical barrier, we may see more tools built by marketers, editors, researchers—anyone with deep context and a persistent itch to fix things.

This kind of thing is very exciting to me. While I think vibe coding is currently flawed & fraught for folks who don't entirely understand the code, I agree with Simon Willison that "everyone deserves the ability to automate tedious tasks in ther lives with computers".

There's a lot of hype and cynicism in tension out there. But, I've personally cycled through a bunch of AI tools in the past few years. I've seen their actual utility and glimmers of where they can go next.

Trying to stay sober here, but I'd love to see more tools meet users where they are and lower the technical bar overall. I think it's both possible and worth it to work toward improving the capability & reliability of these tools for folks outside of the programming "priesthood".

# 1:18 pm

2025 May 13

What I'm thinking about AI and LLMs

I'm in a weird place with this current AI wave in the tech industry. I feel like a good chunk of folks would tar & feather me if I wrote anything but a complete denunciation, while another chunk I already blocked during the crypto & NFT craze. I still feel like writing something, though, if only to bounce it off the screen for myself. [ ... 677 words ... ]

# 3:15 pm

2025 May 09

Been doing a lot of (hand)writing outside my blog

Just realized today that I've written my 359th three-page week-daily handwritten journal entry on my BOOX Tab Ultra C e-ink tablet. I've also got paper volumes going back to 2017 with similar cadence. That's a lot of writing that no one but me will probably ever read. But, I do read it, occasionally, to sort myself out.

I've got a back-burnered side project to train a handwriting recognition model to actually convert my journal entries to text. I should get back to that. No off-the-shelf model has yet been able to successfully decipher my script.

I've also got a notion, once I've converted my journals, to try feeding them to an LLM - either as a fine-tuned model or searching via RAG. Then, I could maybe pull themes and trends out of my past writing and ask annoying questions of my insufferable past self.

# 12:00 pm

2024 June 19

Clustering ideas with Llamafile and Web Components

In my previous posts, I tinkered with a few variations on clustering ideas by named topics using embeddings and text generation. In this post, I'm going to show off a web UI that I built to make this stuff easier to play with interactively. [ ... 2149 words ... ]

# 12:00 pm

2024 May 10

Clustering ideas with Llamafile

In my previous post, I used local models with PyTorch and Sentence Transformers to roughly cluster ideas by named topic. In this post, I'll try that again, but this time with Llamafile. [ ... 3365 words ... ]

# 12:00 pm

2024 May 01

Clustering ideas with local ML/AI models

In my previous post, I used APIs from OpenAI to roughly cluster ideas by named topic. In this post, I'll try that again, but this time with local models on my own hardware. [ ... 2326 words ... ]

# 12:00 pm

2024 April 27

Clustering ideas by topic with machine learning and generative AI

FigJam has a feature where you can automatically cluster sticky notes by topic. I wanted to see if I could glue some things together to implement this myself. [ ... 3268 words ... ]

# 12:00 pm

2024 March 11

Dance like the bots aren't watching?

Why bother sharing anything on the open web if it's just going to be fodder for extractive, non-reciprocal bots? [ ... 981 words ... ]

# 12:00 pm

2013 January 04

Memoirs

This is a story about what might happen if one takes life hacks, GTD, and IFTTT a bit too far. Okay, so maybe that’s not a great intro. But, it’s the first thing my brain spewed out. I’ve had this story rattling around in my head for a few years, and just tonight managed to finish banging out a first draft. I’m hoping to work on it a bit more, and I’m not entirely happy with it yet, so comments welcome! [ ... 3185 words ... ]

# 12:00 pm

2010 May 19

Famous Last Words

They told me never to talk to it; it was isolated for a reason. But, like I told them, I couldn’t imagine anything a transhuman AI could say to make me let it out of the box. So, after a post-midnight tumbler of Irish whiskey, I opened up a secure terminal and said hello. What harm could a little chat do? [ ... 193 words ... ]

# 12:00 pm

Tag: ai

2025 September 22

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2025 July 10

Small sample, specific scenario

It does matter how you use it

Waiting, or lack thereof

Summing up

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2010 May 19