This is meant to be an ongoing list of very short reviews as I read, watch, and listen to new things over time. I’ll include a brief backlog for the past below this. Started this in August 2026, so ongoing from this month. And at some point as this goes on maybe I’ll re-arrange the page to make it structured more nicely.
\nFor now, instead of a 1-to-5-star ⭐ rating, I’ll use 1-5 ramen bowls 🍜 for how much I liked it, and 1-5 pieces of fried shrimp 🍤 for how much it disagreed with me as I’m allergic to shrimp.
\n\nA Deadly Education (2020) by Naomi Novik
\nPersonal Rating: 🍜🍜🍜🍜 | 🍤🍤
\nWhat I liked most 🍜: In games and real life where magic is interactive and used, mana woes are a serious issue, and this story captured it well to the point where it was like mana management and economics. I’m assuming, as I have very little knowledge of economics as a subject.
\nWhy my personal rating isn’t higher 🍤: I think academics, as the domain of educators, has a bad or unfair stereotype or a perception of needing to be confusing sometimes. Even for advanced topics, there’s always a way to explain things simply according to the audience (which includes equal peers because we often don’t have the exact same contextual knowledge as each other on every relevant topic), in my opinion. So I wasn’t a huge fan of the choice to leave some of the more complicated academic magic aspects less explained.
\nThe Book That Wouldn’t Burn (2024) by Mark Lawrence
\nPersonal Rating: 🍜🍜🍜🍜🍜 | 🍤
\nWhat I liked most 🍜: The details of the worldbuilding reminded me of Tower of God which was maybe the most expansive fantasy world I’ve ever read about, so that nostalgic feeling was a biased plus for me. This book wasn’t at the scale of a long-running story like that, but the initial feeling was similar I mean. Aside from that and the consistent quality of the writing, some very cute moments were what I liked most.
\nWhy my personal rating isn’t higher 🍤: In my opinion some scenes didn’t carry the amount of emotion they could’ve, but it seemed almost intentional. Just my opinion anyway, was still an enjoyable story.
\n\n\nFirst Peoples (2015) PBS
\nPersonal Rating: 🍜🍜🍜🍜 | 🍤🍤
\nWhat I liked most 🍜: The amount of in-depth information covered and the heavy inclusion of the researchers working in the various domains, letting them tell the stories alongside the narrator which also let them show their passion for their work and their own storytelling ability. (at least I’m assuming it’s their own storytelling ability, I guess prep and production would’ve helped too, but a couple of them spoke with a vigor and clearness that couldn’t have come from production aid alone)
\nWhy my personal rating isn’t higher 🍤: They covered a lot of info, at one or two points it was clear even as a layman to the field that they had to skim details with the amount included. So more visualizations, supporting on-screen text, that kind of stuff would’ve helped in my opinion. I’m sure that would’ve also been more appreciated by those who prefer reading over listening or second-language english speakers without a good available translation. For example, there were a couple of researchers using dice and cards to explain their point who could’ve used some post-recording help, or the frequently used visualization of global migrations could’ve been better with at least some labelling.
\nMy Octopus Teacher (2020) Off The Fence, The Sea Change Project
\nPersonal Rating: 🍜🍜🍜🍜 | 🍤🍤
\nWhat I liked most 🍜: I don’t think I’d like being a reviewer as a job, especially for cases like this where it’s so clear how much time, effort, energy, and care went into making something. It was a story told in a soothing and calm way, while still feeling journalistic in a research-oriented way.
\nWhy my personal rating isn’t higher 🍤: It would’ve been nice to learn more of his research prep and how that compared to the experience in practice, and similarly it would’ve been convenient to learn more about the general line between observation and interference with regards to the ethics of it all.
\nOur Planet II (2023) Silverback Films, Worldwide Fund for Nature, dist. by Netflix
\nPersonal Rating: 🍜🍜🍜🍜 | 🍤
\nWhat I liked most 🍜: Lots of interesting stuff, aesthetic and vibrant colours good for TV displays but also pleasing to watch, and calm and soothingly told. The gentoo penguins were my favourite animal protagonists.
\nWhy my personal rating isn’t higher 🍤: It revolved around seasons and migrations, so it felt sort of like highlight reels over several animal stories, and maybe it would’ve been nice to spend more time with each one.
\nW: Two Worlds Apart (2016) written by Song Jae-jeong
\nPersonal Rating: 🍜🍜🍜🍜 | 🍤 🍤 🍤
\nWhat I liked most 🍜: The story, premise, and thought behind it was really cool and felt special. Or it felt creative in a way that clearly gave a “high-ranked manhwa of it’s time” kind of feeling. And I liked the romantic comedy bits of it the most. The plot also moved quickly but didn’t feel rushed, the effect was more like it was difficult to predict episode by episode.
\nWhy my personal rating isn’t higher 🍤: Just a matter of personal preference. I’m not a huge fan of thrillers in general, and since this was mixed genres I was hoping for more of the sweet light-hearted romantic comedy style scenes but it leaned to thriller more throughout. And I also just feel like it’s premise was better suited to that style than the thriller stuff.
\n*These categories below are more likely to just be videos, posts, or papers I really want to mention here since these are short.
\nLasso: Large Language Model-based User Simulator for Cross-Domain Recommendation by Yue Chen et al. (2025)
\nAn interesting way of combining LLMs with traditional recommendation methods in a way that competes or competed with state-of-the-art models at the time. Where “cross-domain” recommendations means, for example, “a user likes these books, would they like this movie?” they evaluated using the Amazon reviews dataset on the books->movies, books->music, and movies->music tasks, seeing a 2% to 10% improvement under various conditions. They also evaluated using the Kwai e-commerce platform’s data on a live-stream interaction -> product purchasing recommendation task, compared against their live system, and saw about a 2% improvement. The first use of LLMs for CDR to their knowledge, and further validates the quality of LLM recommendations.
\nA brief imperfect backlog of top 3s that come to mind for each category, without much if anything in reviews here, since reviews when the experience is fresh would be best. Maybe a short note.
\nSiddhartha by Hermann Hesse
\nThe Alchemist by Paulo Coelho
\nLife of Pi by Yann Martel
\nCreativity Inc. by Ed Catmull
\nPirate Hunters by Robert Kurson
\nThe Buccaneers of America by Alexandre Exquemelin
\nRatatouille by Pixar (story: Brad Bird, Jan Pinkava, Jim Capobianco)
\nYour Name by Makoto Shinkai
\nNinotchka by Melchior Lengyel
\nFriends
\nHannibal
\nHow I Met Your Mother
\nRomance is a Bonus Book by Jung Hyun-jung
\nMoving by Kang Full
\nLove Alarm based on Cho Kye-young’s Webtoon
\nTower of God by SIU
\nAnnarasumanara by Ha Il-Kwon
\nThe Breaker by Kamaro
\nFor considerate convenience, I’ll add my imperfect one-line descriptions to these papers, since this is a fiction author site.
\nA Contextual-Bandit Approach to Personalized News Article Recommendation by Wei Chu et al. of Yahoo! Labs (2010)
\nA foundational approach to recommendation algorithms that contributed to eventual systems like Netflix’s interesting personalized thumbnail selection: a feature, rationale, and observed effect that seems applicable to digital book covers though not yet implemented by major platforms to my knowledge.
\nGenerative Adversarial Nets by Goodfellow et al. (2014)
\nA paired approach to generating data by training one model to generate data alongside the training of another that judges its output.
\nI’m only one person with an imperfect memory and limited perspective so disclaimer on this comment, but I think this one was a community-level breakthrough point for AI.
\nThe subject of generative models today seems to be viewed as the enemy of creators, but at the time of this paper, the datasets needed for aiding critical problems in other domains were insufficient, so data generation carried a heavy social good. Similarly today, there are many domains important to society, like healthcare, industrial manufacturing, economics, etc. that can/do benefit from the advances in generative AI. Whether or not its use in creative spaces is misuse of well-intended advances, I’d rather not take a public position on. My personal choice is that I don’t use it to generate my writing, especially in fiction. Otherwise, like images, video, audio, if I can afford to support a capable human, that’s the option I choose. And so far the only exception to that has been the digital voices for my audiobook which are appropriately labeled.
\nCollege Admissions and the Stability of Marriage by Gale and Shapley (1962)
\nThe core of their nobel prize winning work that provided a critical matching algorithm for many domains.
\nHaikyu!! by Haruichi Furudate
\nNaruto by Masahi Kishimoto
\nYour Lie in April by Naoshi Arakawa
\nThese are from my childhood, and not recent consumption.
\nKids shows: Spongebob Squarepants by Stephen Hillenburg, Stanley by Andrew Griffin, The Powerpuff Girls by Craig McCracken
\nEarly-Teens shows: Suite Life of Zack & Cody / on Deck by Danny Kallis and Jim Geoghan, Avatar: The Last Airbender by Michael Dante DiMartino and Bryan Konietzko (technically a kids show, but I think it’s crossover, and this to me is certainly a kids show that could be appreciated by childless adults), iCarly by Dan Schneider
\nEarly Kidlit / Fairy tales: Jack and the Beanstalk, The Lion and the Mouse, Goldilocks and the Three Bears
\nMostly from my youth. If I could spare the time, energy, and priority to it as an adult, I’d lean towards tactical games like D&D, World of Warships, Teamfight Tactics/Dota Autochess, League of Legends, or plain Chess. And with games in general, it’s more about the social aspect and the time with friends for me, (unless it’s short with a really nice story) which is why I’m also a fan of jackbox couch/party games, and board games like Codenames.
\nMegaman X
\nPokemon Pearl
\nRunescape
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