Do you ever catch yourself avoiding things you need to do? Sure you, do: we all do it. In episode 9 of Traction Heroes, Harry and I discuss what to do about it. The conversation took off when Harry read a fragment from Oliver Burkeman’s book, Meditations for Mortals. I won’t cite the entire passage here, but this gives you a taste: It can be alarming to realize just how much of life gets shaped by what we’re actively trying to avoid. We talk about not getting around to things as if it were merely a failure of organization or a will. But often the truth is that we invest plenty of energy in making sure that we never get around to them. … The more you organize your life around not addressing things that make you anxious, the more likely they are to develop into serious problems. And even if they don’t, the longer you fail to confront them, the more unhappy time you spend being scared of what might be lurking in the places you don’t want to go. The irony, of course, is that we put off uncomfortable tasks because they make us anxious. But putting them off ultimately makes us more anxious. As Harry reminded us, “bad news doesn’t get better over time.” He also proposed a helpful framing: that facts are friendly. That is, even though knowing the truth might make us uncomfortable, knowing is better than not knowing. We discussed practical steps to gain traction: Ask yourself, what am I pretending not to know? Deep down, you know there’s more to the situation than you’ve let on; acknowledging the elephant in the room to move forward. Plan around the last responsible moment. Some events have fixed time windows; understand by when you must decide. Rewrite the narrative using the non-violent communication lens: separate your observations from interpretations, feelings, and needs. As always, I got lots of value from this conversation with Harry. But this one you can’t think about; it’s about doing. And doing is hard when the mind doesn’t want to face facts. Traction Heroes episode 9: Procrastination

In week 18 of the humanities crash course, I read five stories from One Thousand and One Nights, a collection of Middle Eastern folktales that have influenced lots of other stories. Keeping with the theme, I also saw one of the most influential movies based on these stories. Readings An influential collection of Middle Eastern folk tales compiled during the Islamic Golden Age. The framing device is brutally misogynistic: a sultan learns that his wife is unfaithful, so he executes her. He decides all women are the same, so he marries a new bride every day and has her executed the following day. Sheherazade asks her father, the vizier, to offer her in marriage to the sultan. The vizier is reluctant: they both know the wives’ fate. But Sheherazade has a clever plan: she starts a new story for the sultan every night but leaves it in a cliffhanger. Curious for the outcome, the sultan stays her execution to the next day. In this way, Sheherazade spares the lives of other maidens of the land. Of the many stories in the book, I read five recommended by Gioia: The Fisherman and the Jinni: a poor fisherman unwittingly unleashes a murderous jinni from a bottle, but tricks him back into the bottle by outwitting him. The Three Apples: an ancient murder mystery (again, centered on the murder of an innocent woman); the “solution” involves more unjust death (at least by our standards.) Sinbad the Sailor: a series of seven fantastical voyages involving monsters, magic, and stolen treasures; one of the voyages closely parallels the Cyclops episode from the Odyssey. Ali Baba and the Forty Thieves: another story of murder and ill-gotten treasure; a poor man discovers where a band of thieve stashes their loot and steals from them. Aladdin: a poor boy discovers a magic lamp that makes him wealthy and powerful, allowing him to marry a princess. These have been re-told in numerous guises. As often happens in these cases, the originals are much darker and bloodier than their spawn. These aren’t Disney versions, for sure. Audiovisual Music: Highlights from Tchaikovsky’s famous ballets plus Rimsky-Korsakov’s Sheherazade. I’d heard the ballets, but not the Rimsky-Korsakov. This piece reminded me of Paul Smith’s music for Disney’s 20,000 LEAGUES UNDER THE SEA (1954). Arts: Gioia recommended aboriginal Australian art. I’d seen works in this style, but hadn’t paid attention. This tradtion has a particular (and gorgeous) style that expresses strong connections to the land. I was surprised to learn about recent developments in this tradition. Cinema: Alexander Korba’s THE THIEF OF BAGDAD (1940), one of the many films ispired by the One Thousand and One Nights. While it now looks dated, this film was a special effects breakthrough. As an early example of Technicolor, it also features an over-the-top palette, much like it’s near-contemporary, THE WIZARD OF OZ. Reflections One can’t do justice to One Thousand and One Nights by only reading five stories. But the ones I read dealt with poor people being unfairly granted wealth and power. Escapist fantasies tend to stand the test of time. The “heroes” in the stories deserved as much comeuppance as the “villains.” For example, in Ali Baba and the Forty Thieves), one of the heroes commits a mass killing of the “bad guys” while they were unable to react. Not only does this go unpunished; it’s celebrated. The people who told these stories had moral standards different from our own. I also learned several stories — including some of the most famous, such as Ali Baba and the Forty Thieves and Aladdin — were not part of the original collection. Instead, they were added by a French translator in the 18th Century. This was frustrating, as they weren’t present in the collection I bought; I had to seek them out separately. So, this week, I’ve been pondering questions of authorship and derivation. We don’t know who originated these stories. Like the aboriginal Australian art, the stories in the One Thousand and One Nights emerged — and belong to — a people more than an individual author or artist. And yet, they’ve inspired other works, such as THE THIEF OF BAGDAD — which inspired Disney’s ALADDIN. (The latter “borrows” liberally from the former.) Is it any wonder I heard Rimsky-Korsakov in the 20k score? At this point, I assume at least some cross-pollination — after all, Rimsky-Korsakov himself was inspired by the One Thousand and One Nights. This is how art has always evolved: artists build on what’s come before. In some cases, the inspiration is obvious. In others, it’s more nebulous. Did Odysseus inspire Simbad? Or did they both retell older stories? The process changed in the 20th Century. With strong copyright laws, stories become intellectual property. Disney may build on the One Thousand and One Nights stories, but we can’t build on Disney’s stories. And it’s changing again with large language models. It will be interesting to see how these new tools allow us to retell old stories in new ways. At a minimum, they’re causing us to reevaluate our approach to IP. Notes on Note-taking A realization: my Obsidian knowledge repository is better suited to reflecting on text than other media. I can try to write down my impressions of the beautiful aboriginal art and Rimsky-Korsakov’s music. But words articulate concepts, not feelings — even when trying to articulate feelings. So I end up reflecting on abstract ideas such as authorship and derivation rather than the nature of the works. It’s a limitation of my current note-taking system, and one I can’t do much about. Perhaps ChatGPT can help by letting me riff on pictures and sounds? But there, too, communication happens through language. Up Next Gioia recommends the Bhagavad Gita, the Rule of St. Benedict, and the first two books of Saint Augustine’s Confessions. This will be my first time with any of them. Again, there’s a YouTube playlist for the videos I’m sharing here. I’m also sharing these posts via Substack if you’d like to subscribe and comment. See you next week!

The dream is running GraphRAG with locally-hosted LLMs. And at least for now, the dream is on hold for me. In case you missed it, GraphRAG is a way of getting more useful results with LLMs by working with data you provide (in addition to whatever they’ve trained on.) The system uses LLMs to build a knowledge graph from documents you provide and then uses those graphs to power RAG queries. This opens lots of possibilities. For information architecture work, it lets you ask useful questions of your own content. I’ve written about my experiments in that scenario. In that case, I used OpenAI’s models to power Microsoft’s GraphRAG application. But I’m especially excited about the possibilities for personal knowledge management. Imagine an LLM tuned to and focused on your personal notes, journals, calendars, etc. That’s primarily why I’m dreaming of GraphRAG powered by local models. There are several reasons why local models would be preferable. For one, there’s the cost: GraphRAG indexing runs are expensive. There’s also a privacy angle. Yes, I’ve told OpenAI I don’t want them to train their models using my data, but some of this stuff is extremely personal and I’m not comfortable with it leaving my computer at all. But an even larger concern is dependency. I’m building a lifelong thinking assistant. (An amanuensis, as I outlined in Duly Noted.) It’s risky to delegate such a central part of this system to a party that could turn off the spigot at any time. So I’ve been experimenting with graphrag using local models. There are good news and bad news. Before I tell you about them, let me explain my setup. I’m using a 16” 2023 M2 Max MacBook Pro with 32GB of RAM. It’s not an entry-level machine, but not a monster either. I’m using ollama to run local models. I’ve tried around half a dozen at this point and have successfully set up one automated (non-GraphRAG) workflow using mistral-small3.1. GraphRAG is extremely flexible. There are dozens of parameters to configure, including different LLMs for each step in the process. Off-the-shelf, its prompts are optimized specifically for GPT-4-turbo; other models require tweaking. Indexing runs (where the model converts texts to knowledge graphs) can take a long time. So tweaks are time-consuming. I’ve had a go at it several times, but given up after a bit. I don’t have much free time these days, and most experiments have unsuccessfully ended with failed (and long!) indexing runs. But a few things have changed in recent weeks: GraphRAG itself keeps evolving There are now more powerful small local models that run better within my machine’s limitations ChatGPT o3 came out That last one may sound like a non-sequitur. Aren’t I trying to get away from cloud-hosted models for this use case? Well, yes — but in this case, I’m not using o3 to power GraphRAG. Instead, I’m using it to help me debug failed runs. While certainly nothing like AGI, as some have claimed, o3 has proven to be excellent for dealing with the sort of tech-related issues that would’ve sent me off to Stack Overflow in the past. Debugging GraphRAG runs is one such task. I’ve been feeding o3 logfiles after each run, and it’s recommended helpful tweaks. It’s the single most important factor in my recent progress. Yes, there’s been some progress: yesterday, after many tries, I finally got two local models to successfully complete an indexing run. Mind you, that doesn’t mean I can yet successfully query GraphRAG. But finishing the indexing run without issues is progress. That’s the good news. Alas, the indexing run took around thirty-six hours to process nineteen relatively short Markdown files. To put that in perspective, the same indexing run using cloud-hosted models would likely have taken under ten minutes. My machine also ran at full throttle the whole time. (It’s the first time I’ve felt an M-series Mac get hot.) The reduced processing speed isn’t just because the models themselves are slower: it’s also due to my machine’s limitations. After analyzing the log files, ChatGPT suggested reducing the number of concurrent API calls. The successful run specified just one call at a time for both models. The upshot is that even though the indexing run finished successfully, this process is impractical for real-world use. My PKM has thousands of Markdown files. ChatGPT keeps suggesting further tweaks, but progress is frustratingly slow when cycles are measured in days. I’ve considered upgrading to a MBP with more RAM or increasing the number of concurrent processes to find the upper threshold for my machine. But based on these results, I suspect improvements will be marginal given the amount of data I’m looking to process. So that’s the bad news. For now, I’ll keep working with local models for other uses (such as OCRing handwritten notes; the workflow I alluded to above. More on that soon!) And of course, I’ll continue experimenting with cloud-based models for other use cases. In any case, I’ll share what I learn here.

There’s a lot of turbulence in the world. What is the source of the turbulence? And how can we navigate skillfully? These questions were on my mind as I met with Harry to record episode 8 of the Traction Heroes podcast. My (at least partial) answer to the first question is that there’s a general lack of systems literacy in the world. Most people aren’t aware of the high degree of complexity that characterizes highly intertwingled systems such as modern economies. As a result, they opt for simplistic interventions that often do more harm than good. At least that was my hypothesis. I was keen to hear Harry’s thoughts — and he didn’t disappoint. My prompt was the following passage from Donella Meadows’s classic Thinking in Systems: A Primer (emphasis in the original): Ever since the Industrial Revolution, Western society has benefited from science, logic, and reductionism over intuition and holism. Psychologically and politically we would much rather assume that the cause of a problem is “out there,” rather than “in here.” It’s almost irresistible to blame something or someone else, to shift responsibility away from ourselves, and to look for the control knob, the product, the pill, the technical fix that will make a problem go away. Serious problems have been solved by focusing on external agents—preventing smallpox, increasing food production, moving large weights and many people rapidly over long distances. Because they are embedded in larger systems, however, some of our “solutions” have created further problems. And some problems, those most rooted in the internal structure of complex systems, the real messes, have refused to go away. Hunger, poverty, environmental degradation, economic instability, unemployment, chronic disease, drug addiction, and war, for example, persist in spite of the analytical ability and technical brilliance that have been directed toward eradicating them. No one deliberately creates those problems, no one wants them to persist, but they persist nonetheless. That is because they are intrinsically systems problems—undesirable behaviors characteristic of the system structures that produce them. They will yield only as we reclaim our intuition, stop casting blame, see the system as the source of its own problems, and find the courage and wisdom to restructure it. Of course, the broader context was (and is) on my mind. But we’re all enmeshed in complex systems in our day-to-day lives. It behooves us to ponder whether the causes of problems are really “out there” — or whether, as Harry suggested, we need to be more introspective. Traction Heroes ep. 8: Quagmires