Ankur Sethi 2 weeks ago

I used a local LLM to analyze my journal entries

In 2025, I wrote 162 journal entries totaling 193,761 words. In December, as the year came to a close and I found myself in a reflective mood, I wondered if I could use an LLM to comb through these entries and extract useful insights. I’d had good luck extracting structured data from web pages using Claude, so I knew this was a task LLMs were good at. But there was a problem: I write about sensitive topics in my journal entries, and I don’t want to share them with the big LLM providers. Most of them have at least a thirty-day data retention policy, even if you call their models using their APIs, and that makes me uncomfortable. Worse, all of them have safety and abuse detection systems that get triggered if you talk about certain mental health issues. This can lead to account bans or human review of your conversations. I didn’t want my account to get banned, and the very idea of a stranger across the world reading my journal mortifies me. So I decided to use a local LLM running on my MacBook for this experiment. Writing the code was surprisingly easy. It took me a few evenings of work—and a lot of yelling at Claude Code—to build a pipeline of Python scripts that would extract structured JSON from my journal entries. I then turned that data into boring-but-serviceable visualizations. This was a fun side-project, but the data I extracted didn’t quite lead me to any new insights. That’s why I consider this a failed experiment. The output of my pipeline only confirmed what I already knew about my year. Besides, I didn’t have the hardware to run the larger models, so some of the more interesting analyses I wanted to run were plagued with hallucinations. Despite how it turned out, I’m writing about this experiment because I want to try it again in December 2026. I’m hoping I won’t repeat my mistakes again. Selfishly, I’m also hoping that somebody who knows how to use LLMs for data extraction tasks will find this article and suggest improvements to my workflow. I’ve pushed my data extraction and visualization scripts to GitHub. It’s mostly LLM-generated slop, but it works. The most interesting and useful parts are probably the prompts . Now let’s look at some graphs. I ran 12 different analyses on my journal, but I’m only including the output from 6 of them here. Most of the others produced nonsensical results or were difficult to visualize. For privacy, I’m not using any real names in these graphs. Here’s how I divided time between my hobbies through the year: Here are my most mentioned hobbies: This one is media I engaged with. There isn’t a lot of data for this one: How many mental health issues I complained about each day across the year: How many physical health issues I complained about each day across the year: The big events of 2025: The communities I spent most of my time with: Top mentioned people throughout the year: I ran all these analyses on my MacBook Pro with an M4 Pro and 48GB RAM. This hardware can just barely manage to run some of the more useful open-weights models, as long as I don’t run anything else. For running the models, I used Apple’s package . Picking a model took me longer than putting together the data extraction scripts. People on /r/LocalLlama had a lot of strong opinions, but there was no clear “best” model when I ran this experiment. I just had to try out a bunch of them and evaluate their outputs myself. If I had more time and faster hardware, I might have looked into building a small-scale LLM eval for this task. But for this scenario, I picked a few popular models, ran them on a subset of my journal entries, and picked one based on vibes. This project finally gave me an excuse to learn all the technical terms around LLMs. What’s quantization ? What does the number of parameters do? What does it mean when a model has , , , or in its name? What is a reasoning model ? What’s MoE ? What are active parameters? This was fun, even if my knowledge will be obsolete in six months. In the beginning, I ran all my scripts with Qwen 2.5 Instruct 32b at 8-bit quantization as the model. This fit in my RAM with just enough room left over for a browser, text editor, and terminal. But Qwen 2.5 didn’t produce the best output and hallucinated quite a bit, so I ran my final analyses using Llama-3.3 70B Instruct at 3bit quantization. This could just about fit in my RAM if I quit every other app and increased the amount of GPU RAM a process was allowed to use . While quickly iterating on my Python code, I used a tiny model: Qwen 3 4b Instruct quantized to 4bits. A major reason this experiment didn’t yield useful insights was that I didn’t know what questions to ask the LLM. I couldn’t do a qualitative analysis of my writing—the kind of analysis a therapist might be able to do—because I’m not a trained psychologist. Even if I could figure out the right prompts, I wouldn’t want to do this kind of work with an LLM. The potential for harm is too great, and the cost of mistakes is too high. With a few exceptions, I limited myself to extracting quantitative data only. From each journal entry, I extracted the following information: None of the models was as accurate as I had hoped at extracting this data. In many cases, I noticed hallucinations and examples from my system prompt leaking into the output, which I had to clean up afterwards. Qwen 2.5 was particularly susceptible to this. Some of the analyses (e.g. list of new people I met) produced nonsensical results, but that wasn’t really the fault of the models. They were all operating on a single journal entry at a time, so they had no sense of the larger context of my life. I couldn’t run all my journal entries through the LLM at once. I didn’t have that kind of RAM and the models didn’t have that kind of context window. I had to run the analysis one journal entry at a time. Even then, my computer choked on some of the larger entries, and I had to write my scripts in a way that I could run partial analyses or continue failed analyses. Trying to extract all the information listed above in one pass produced low-quality output. I had to split my analysis into multiple prompts and run them one at a time. Surprisingly, none of the models I tried had an issue with the instruction . Even the really tiny models had no problems following the instruction. Some of them occasionally threw in a Markdown fenced code block, but it was easy enough to strip using a regex. My prompts were divided into two parts: The task-specific prompts included detailed instructions and examples that made the structure of the JSON output clear. Every model followed the JSON schema mentioned in the prompt, and I rarely ever ran into JSON parsing issues. But the one issue I never managed to fix was the examples from the prompts leaking into the extracted output. Every model insisted that I had “dinner with Sarah” several times last year, even though I don’t know anybody by that name. This name came from an example that formed part of one of my prompts. I just had to make sure the examples I used stood out—e.g., using names of people I didn’t know at all or movies I hadn’t watched—so I could filter them out using plain old Python code afterwards. Here’s what my prompt looked like: To this prompt, I appended task-specific prompts. Here’s the prompt for extracting health issues mentioned in an entry: You can find all the prompts in the GitHub repository . The collected output from all the entries looked something like this: Since my model could only look at one journal entry at a time, it would sometimes refer to the same health issue, gratitude item, location, or travel destination using different synonyms. For example, “exhaustion” and “fatigue” should refer to the same health issue, but they would appear in the output as two different issues. My first attempt at de-duplicating these synonyms was to keep a running tally of unique terms discovered during each analysis and append them to the end of the prompt for each subsequent entry. Something like this: But this quickly led to some really strange hallucinations. I still don’t understand why. This list of terms wasn’t even that long, maybe 15-20 unique terms for each analysis. My second attempt at solving this was a separate normalization pass for each analysis. After an analysis finished running, I extracted a unique list of terms from its output file and collected them into a prompt. Then asked the LLM to produce a mapping to de-duplicate the terms. This is what the prompt looked like: There were better ways to do this than using an LLM. But you know what happens when all you have is a hammer? Yep, exactly. The normalization step was inefficient, but it did its job. This was the last piece of the puzzle. With all the extraction scripts and their normalization passes working correctly, I left my MacBook running the pipeline of scripts all day. I’ve never seen an M-series MacBook get this hot. I was worried that I’d damage my hardware somehow, but it all worked out fine. There was nothing special about this step. I just decided on a list of visualizations for the data I’d extracted, then asked Claude to write some code to generate them for me. Tweak, rinse, repeat until done. I’m underwhelmed by the results of this experiment. I didn’t quite learn anything new or interesting from the output, at least nothing I didn’t already know. This was only partly because of LLM limitations. I believe I didn’t quite know what questions to ask in the first place. What was I hoping to discover? What kinds of patterns was I looking for? What was the goal of the experiment besides producing pretty graphs? I went into the project with a cool new piece of tech to try out, but skipped the important up-front human-powered thinking work required to extract good insights from data. I neglected to sit down and design a set of initial questions I wanted to answer and assumptions I wanted to test before writing the code. Just goes to show that no amount of generative AI magic will produce good results unless you can define what success looks like. Maybe this year I’ll learn more about data analysis and visualization and run this experiment again in December to see if I can go any further. I did learn one thing from all of this: if you have access to state-of-the-art language models and know the right set of questions to ask, you can process your unstructured data to find needles in some truly massive haystacks. This allows you analyze datasets that would take human reviewers months to comb through. A great example is how the NYT monitors hundreds of podcasts every day using LLMs. For now, I’m putting a pin in this experiment. Let’s try again in December. List of things I was grateful for, if any List of hobbies or side-projects mentioned List of locations mentioned List of media mentioned (including books, movies, games, or music) A boolean answer to whether it was a good or bad day for my mental health List of mental health issues mentioned, if any A boolean answer to whether it was a good or bad day for my physical health List of physical health issues mentioned, if any List of things I was proud of, if any List of social activities mentioned Travel destinations mentioned, if any List of friends, family members, or acquaintances mentioned List of new people I met that day, if any A “core” prompt that was common across analyses Task-specific prompts for each analysis

JSON Python AI Data Analysis

▲ 0

0 views

Ankur Sethi 3 weeks ago

The only correct recipe for making chai

All my friends have their own personal recipes for making chai. I love my friends, so it hurts me to say that they’re wrong. My friends are, unfortunately, wrong about chai. I’m still coming to terms with this upsetting fact, but I’ll live. What follows is the only correct recipe for making chai. The only correct choice of tea leaves is Tata Tea Gold. Keep it in an airtight jar. Shake it up a bit so there’s an even mix of smaller grains and whole tea leaves. The smaller grains make for a stronger chai and they tend to settle at the bottom, so take that into account when measuring. You need full-cream milk for this recipe. Amul Gold is a good choice. I buy the tetrapacks because they survive in the fridge for longer, but the plastic bags work as well. According to the pack, Amul Gold has 6% fat. If you can’t find Amul Gold, try to find an equivalent milk. For a basic chai, you only need tea leaves, water, sugar, and milk. But we don’t want to make a basic chai, do we? No. So we’re going to add some elaichi (green cardamom) and saunf (fennel). Try to find fresh spices, if you can. I don’t have recommendations for specific brands here because most of them are fine. I learned the hard way that you get two kinds of saunf in the supermarket: green and brown. Green saunf tastes sweet and fresh, almost like a dessert. The brown saunf has a stronger flavor but is also bitter. We want the green saunf. Sometimes you find old elaichi at the store that’s gone a bit brown. Don’t buy that. Your elaichi should be green in color, just like the saunf. This recipe makes three cups of chai. Why three? Because that’s how much chai I drink every day. You can adjust this recipe to make more or fewer cups, as long as you keep all the ratios the same. Dig out your mortar and pestle from the drawer it has been languishing in. Add six pods of elaichi—two for each cup. Add half a tablespoon of saunf. You can use a bit more of both these spices if you want a more flavorful chai. Grind the spices into a semi-powdery mix. You don’t have to turn it into a fine powder, just grind them enough so that the flavors come through. Put two cups of water in a saucepan and add the spice mix. Put it on a high flame until boiling. When the water is boiling, reduce the flame to medium. Add three dessert spoons full of tea leaves to the boiling water. A dessert spoon is slightly smaller than a tablespoon. If all you have is a tablespoon, try about 3/4 tablespoons of tea leaves for each cup. Then add the same amount of sugar. You can adjust the amount of sugar based on how sweet you want your chai, but if you don’t add enough sugar the flavors won’t come through. Allow the mixture to boil on the stove for about 3-4 minutes. Then add a cup of milk. At this stage you should add a tiny bit of extra milk to account for the water evaporating, otherwise you won’t have three full cups of chai. About 1/5 of a cup should be enough, but I’ve been known to add a bit more to make the chai richer. Stir the mixture a bit to ensure everything is properly mixed together, then allow it to sit on the stove until the milk boils over. This next step is crucial. It will make or break your chai. I swear it’s not superstition. When the milk boils over, turn the stove to simmer. Allow it to settle back down into the pan. Then turn it up to medium heat again until it boils over once more. Repeat one more time. The milk should boil over and settle down three times total. Your chai is ready! Use a strainer to strain it into cups and enjoy. Should you eat a Parle-G with your chai? Maybe a Rusk? I have strong opinions on this matter but I’m running out of time, so I’ll leave that decision up to you.

Tutorial Culture Entertainment

▲ 0

0 views

Ankur Sethi 3 weeks ago

Write quickly, edit lightly, prefer rewrites, publish with flaws

Over two years of consistent writing and publishing, I’ve internalized a few lessons for producing satisfying—if not necessarily “good”—work: I covered similar ground previously in Writing without a plan . This post builds on the same idea. If I want to see the shape of the idea I’m trying to communicate in my writing, I must get it down on paper as quickly as possible. This is similar to how painters lay down underdrawings on canvas before applying paint. I can’t judge the quality of my idea unless I finish this underdrawing. Without this basic sketch to guide me, I might end up writing the wrong thing altogether. More than once, I’ve slaved away at a long blog post for days, only to realize that my core thesis was bunk. Writing quickly allows me to see the idea in its entirety before I waste time and energy refining it. How do I define quickly ? For blog posts like this one, I try to produce a first draft in about 45 minutes. For longer pieces, I take about the same time but work in broad strokes and make heavy use of placeholders. It’s easy to edit the life and vitality out of a piece by over-editing it. I’ve done it many times. I’m prone to spending hours upon hours polishing the same few paragraphs in a work, complicating my sentences by attaching a hundred sub-clauses, burying important ideas under mountains of caveats, turning direct writing into purple prose, and inflating my word counts to planetary proportions. Light edits to a first draft improve my writing. If I keep going, I reach a point of diminishing returns where every new edit feels like busywork. And then, if I keep going some more, I start making the writing worse rather than better. Spending too much time editing puts me in a mental state that’s similar to semantic satiation , but at the scale of a full essay or story. The words in front of my eyes begin to lose their meaning, ideas become muddled, and I can no longer tell if anything I’ve written makes sense at all. At that point, I have no choice but to walk away from the work and come back to it another day. It’s no fun. I try to spend a little more time editing than I do writing, but only a little. I’ve learned to recognize that if editing a draft takes me significantly longer than it took me to write it, there’s probably something wrong with the piece. If editing takes too long, it’s better to throw it away and redo from start . If it’s taking too long to edit, rewrite. By writing quickly, I’ve convinced my brain that rewriting something wholesale is cheap and easy. It’s profitable and practical for me to write out a single idea multiple times, exploring it from different angles, finding new insight and depth every time I take a fresh stab at it. If writing a first draft takes 45 minutes, making multiple attempts at the same idea is no big deal. If it takes four hours, I’m more likely to go with my first attempt. Spending too much time on first drafts is a good way for me to get married to bad ideas. I wrote this very blog post three times because I couldn’t quite capture what I wanted to say in the first two drafts. The content of the post changed entirely with every new attempt, but the core ideas remained the same. No piece of writing is ever perfect. If I keep looking, I can find flaws in every single piece of writing I’ve ever published. I find it a waste of time to keep refining my work once it reaches the good enough stage. If I’ve communicated my ideas clearly and haven’t misrepresented any facts, I can allow a few clumsy sentences or a bad opening paragraph to slide. Even as I publish imperfect work, I try to look back at my past writing, notice the mistakes I keep repeating, and try to do better next time. I find that publishing a lot of bad work and learning from each mistake is a better way to learn and grow compared to writing a small number of “perfect” pieces. By working quickly, I’ve been able to produce a lot of bad-to-mediocre writing, but I feel satisfied. As I keep saying, finding joy in the work I do is more important to me than producing something extraordinary. I’d rather write a hundred bad essays with gleeful abandon than slave over a single perfect manuscript. There’s joy in finishing something, closing the book on it, calling it a day, and moving on. There’s joy in trying out different styles, voices, subjects, ideas, personalities. There’s joy in knowing that there will always be a next thing to write, and the next, and the next. When I’m stuck writing something that’s not fun to work on, I find a certain consolation in knowing that I’ll be done soon. That my sloppy writing process means I’m allowed to finish my piece quickly, put it out into the world, and move on to something more enjoyable. Now you’ve reached the end of this post, and I don’t quite know how to leave you with a solid kicker. Instead of doing a good job, I’ll end with this Ray Bradbury quote that I copied off somebody’s blog: Don’t think. Thinking is the enemy of creativity. It’s self-conscious and anything self-conscious is lousy. You can’t “try” to do things. You simply “must” do things. Perfect. I’ve never liked thinking anyway. Write quickly Edit lightly Prefer rewriting to editing Publish with flaws

Writing Career

▲ 0

0 views

Ankur Sethi 1 months ago

Generative AI and the era of increased gatekeeping

Generative AI models can create text, images, code, and music faster and in larger quantities than our ability to absorb them. Before ChatGPT was introduced to the world in November 2022, producing a piece of media took longer than consuming it. In 2026, the equation has been turned on its head. If your job—thus far—involved curating or evaluating the work of other humans, this is a problem. Generative AI is bad news for teachers and professors, editors at magazines and publishing companies, maintainers of open-source projects, academics doing peer-review or replicating studies, and anyone else who must review the work of their peers in order to give them feedback or as quality control. If you have a job that fits this description, then you’ve probably been inundated with a deluge of low-quality AI-generated content in the past few months. It only takes a few minutes for somebody to “write” a short story using an LLM; it takes a human hours to read and evaluate it. In response to the increasing burden on curators, organizations are tightening the rules around how they handle submissions. Some are taking the moderate stance of asking AI-generated submissions to be identified and cleaned up prior to submission, but many are banning outside contributions altogether. For example: Other organizations are placing strict restriction on number of submissions and making submission rules more stringent: This is a net negative for society. Organizations lose out on potentially good contributions, people early in their careers lose out on a chance to get feedback from experienced professionals, and the rest of us lose because fewer good works make their way into publications and the commons. I see three possible futures ahead of us. First: the novelty of using ChatGPT to produce work and throw it over the wall without reading it wears off. It becomes a social faux pas to submit AI-generated work for publication without extensively vetting and editing it. Enough people are named and shamed that new social norms around the use of generative AI emerge. Our societies adapt so that putting your name on a work without verifying its quality is an act that destroys your reputation. Second: we come up with methods to prove that you have in fact done the work you claim to have done. Like proof of work in cryptography 1 , but for humans. Submitting anything without proof of work becomes an automatic rejection. I can’t imagine what this would look like, though. More importantly, I can’t imagine that we will collectively agree to put ourselves through the indignity of being judged by an algorithm. But hey points to everything look at the world we’ve made. Society has a high tolerance for algorithmically inflicted indignities. Third: we enter a new era of gatekeeping, in which most of us can no longer fix a bug in our favorite open-source projects, submit stories to literary magazines, apply for public job postings, or get peer-review on our papers. Unless you’re a well-known name, or you know somebody who knows somebody, or you can get somebody to vouch for the veracity of your work, you’re considered a nonentity. An era of eroding trust, where anything created by a stranger you don’t personally know is considered suspect. An era of increased gatekeeping that only allows some of us to publish, and the rest of us perish. Personally, I think we’ll land on a combination of the three possible outcomes. Some organizations will name and shame, some will ask for proof of work, and yet others will step up their gatekeeping. And who knows, there’s probably a secret fourth option that I haven’t thought of. I’ve never been great at predicting the future. That said, I remain optimistic 2 about our ability to handle this situation. I believe people are generally nice and just want to help, even the ones sending 5,000 line vibecoded pull requests to open-source projects. Our societies are still adjusting to a strange new technology, and the social norms around its use have not been written yet. Until we collectively figure out how to behave reasonably, we might see slightly increased gatekeeping, but my hunch is that it’ll be temporary. I believe we’ll eventually get to a point where we all learn to be editors and reviewers and slush-pile readers of our own AI-generated work. That’s an interesting future to consider: one in which generative AI has turned us all into more discerning readers. Cryptography, not cryptocurrency. The crypto-bros have given perfectly reasonable mathematical techniques a bad name, so I feel it’s important to mention that here. ↩ Sloptimistic? Ha. ↩ arXiv Changes Rules After Getting Spammed With AI-Generated ‘Research’ Papers Curl ending bug bounty program after flood of AI slop reports Sci-fi magazine ‘Clarkesworld’ stops submissions after a rush of AI-made stories : NPR (although this was a temporary measure) Flood of AI-Written Fiction Shuts Down Clarkesworld Submissions – Black Gate (this, too, was a temporary measure) CVPR 2025 Changes ICLR 2026: Submissions, LLM Disclosures, and the Peer Review Shuffle Fearful of AI-generated grant proposals, NIH limits scientists to six applications per year Cryptography, not cryptocurrency. The crypto-bros have given perfectly reasonable mathematical techniques a bad name, so I feel it’s important to mention that here. ↩ Sloptimistic? Ha. ↩

Career AI

▲ 0

0 views

Ankur Sethi 1 months ago

Pushing the smallest possible change to production

I wrote this post as an exercise during a meeting of IndieWebClub Bangalore . During my first week of work with a new client, I like to push a very small, almost-insignificant change into production. Something that makes zero difference to the org’s product, but allows me to learn how things are done in the new environment I’m going to be working in. If my client already has a working webapp, this change could be as simple as fixing a typo. If they don’t, I might build a tiny “Hello world!” app using a framework of their choice and make it available at a URL that’s accessible to everyone within the company (or at least everyone who is involved in the project I’m working on). This exercise helps me figure out everything I need to navigate the workplace and be productive within its constraints. It’s better than any amount of documentation, meetings, or one-on-ones. Doing this work after I’ve already spent weeks building out features is frustrating. When I’m in the middle of solving a problem, I want to iterate fast and get my work in front of users and managers as quickly as possible. I like to go into meetings and stand-up calls with working prototypes that people can play with on their own computers, not with vague promises of code that kinda-sorta works on my own machine. This work also brings me in contact with a variety of people from across the organization, which is always helpful. I like being able to reach out to my co-workers when I’m stuck. As an independent contractor, I can only do that if I put in the effort to build relationships with my team. I also like to have a sense of camaraderie with my co-workers. I want to see my co-workers as more than just names on a Slack channel, which is only possible if I actually talk to them. Pushing the smallest possible change into production helps me do all this and sets the tone for a fruitful working relationship. Plus, it’s always satisfying to end your first week of work at a new workplace with something tangible to show for it. Where is the source code hosted? How do I get access to it? Who will give me access? How do I build and run the software on my dev machine? Is there documentation? Is there somebody who can guide me through the process? What does the version control strategy look like? What workflows am I expected to follow? Are there special conventions for naming branches? Does the codebase have automated tests? Is there a CI server? What’s the process for getting a change merged? Should I open a PR and wait for a code review? How long do code reviews typically take? Who reviews my code? Is there a staging server? When does staging get merged into production? How can I provision new servers if I need them? Who will help me do that? Are there any third-party services in play? What providers does the org use for auth, CDN, media transformation, LLMs? How do I get access to these services? Alternatively, how do I mock them in development?

DevOps Web Development Career

▲ 0

2 views

Ankur Sethi 1 months ago

I'm not listening to full albums anymore

In the last few years, I’ve lost my appetite for discovering new music. The main reason is that I’ve focused on listening to full albums for most of the last decade, but increasingly I find it irritating and anxiety-inducing to listen to albums from start to finish. I’ve been reading/watching music reviews online since my early twenties. In this time, I’ve also been part of a number of music communities online. One defining characteristic of music discussions online—at least in the Anglophone world—is that they’re centered entirely around the album. To most critics and “serious” music fans, the album seems to be a single, indivisible unit of music that must always be considered as a whole. Five minutes with a search engine will dredge up hundreds of blog posts and op-eds touting the benefits of listening to complete albums rather than individual tracks. Some people almost attach morality to the act of listening to an album. If you’re not listening to full albums from start to end, in a darkened room, with your eyes closed, with noise-cancelling headphones, then do you even respect the music? Are you even a true fan? In my mid-twenties, I wanted to be a musician (I still do, but not as intensely). So when I started taking music more seriously myself, I focused my listening mainly on albums. This wouldn’t have been such a terrible thing had it not been for the fact that I didn’t enjoy listening to full albums. At all. Never had, never will. I grew up in an era of cassette tapes. When I was a teenager, my parents would take me to the local Planet M once a month, where I would be allowed to buy exactly one album on tape. My family couldn't afford CDs because CDs cost ₹400-500, whereas cassette tapes cost ₹100-150. So even when most of the world had moved on to CDs, I was still listening to all my music on tape. When you're listening to albums on tape, you're not really skipping around the tracklist. Sure, it’s possible to skip tracks on a cassette tape. That’s what the rewind and fast-forward buttons are for. But it’s not easy. You have to know precisely how long to rewind and/or fast-forward so you land where you want to. If you don’t get it right the first time, it becomes a frustrating back-and-forth dance between the rewind and fast-forward buttons until you manage to find the exact spot you need to be. Kind of like parallel parking in a tight spot. Listening to most of my music on tape, I should’ve grown up to be the sort of adult who enjoys listening to albums, right? But that's not what happened. The moment I discovered I could record my favorite tracks from their original tapes onto blanks, I stopped listening to full albums altogether. I had discovered the joys of making mixtapes, and there was no going back. When my family finally bought a computer, and I discovered how to download MP3s from the internet, I completely gave up on listening to full albums or even downloading them illegally. Freed from the constraints of linear analog media, I began collecting individual MP3s, making playlists, and curating music for myself. This changed in my twenties. As I started participating in music communities, going to gigs and festivals, and running a music blog, I also started forcing myself to listen to albums. That’s how all the pros did it, after all, and didn’t I want to be a pro? However, even when I was listening to nearly a hundred new albums each year, they didn’t quite make sense to me. They still don’t. To me, they just seem like a convenient packaging for a collection of music. Outside of some loose themes and sonic similarities that hold an album together, I don’t see why a certain set of tracks placed in a certain sequence makes for a better listening experience than a slightly altered set of tracks in a slightly altered sequence. There’s a lot of talk about the artistic intent that goes into curating and sequencing an album. But when artists play their music live, they often curate setlists by mixing and matching tracks from several different albums. DJs go even further, curating their mixes from tracks by many different artists, genres, and eras. If musicians themselves don’t constrain themselves to the album format, why must I? Sure, there are some artists who have turned the album into an art form . There are albums out there that are designed to be one unified, cohesive experience. But those albums are exceedingly rare. I’m willing to bet less than one in a hundred albums is designed to be listened to as a unit. Most albums are just collections of tracks that an artist made in a certain time period, or which share a common theme or sound. Albums also seem to me a modern invention, one that came about because of the technical limitations of recording media rather than a human tendency for enjoying a certain amount of music at a time. An LP is about 40-50 minutes long , not because that’s a magic number but because that’s how much music a vinyl record can hold. That’s why so many rock albums are still around 45-50 minutes long to this day. Rock music rose to prominence in the heyday of the vinyl record. On the other hand, hip-hop rose to prominence around the time audio CDs became more common, which is why many rap albums are a bit longer at around 60-70 minutes . The length of an album has little to do with something inherent in the genre or user preferences and everything to do with the technical limitations of the media it's distributed on. And now that streaming music has become more common, we see many artists breaking the mold . Some artists release albums that last several hours , while others only ever release singles. Unless an artist is planning to release physical versions of their music, they’re no longer constrained to the album format. So after more than a decade of forcing myself to listen to albums, I too am releasing myself from the constraint of album-centric listening. Starting this year, I'm going to listen to music in the way I enjoy: by seeking out individual tracks that move me, and arranging them into curated playlists for myself and my friends. To discover new music, I'm listening to more singles, curated playlists, and radio stations. I'm reading Bandcamp editorials and diving deep into obscure tags. I'm allowing myself to open an artist's Spotify page and click around on whatever tracks catch my fancy. I'm even allowing myself to listen to albums on shuffle, something I’ve already done with Audrey Hobert’s Who’s the Clown over this last week. I'm hoping that by freeing myself to listen to music in the way I want will allow me to discover a lot more this year.

Music Media Culture

▲ 0

1 views

Ankur Sethi 2 months ago

Tech predictions for 2026, presented without nuance, context, or evidence

I wrote this post as an exercise during a meeting of IndieWebClub Bangalore . I’m writing these in order, from most likely to happen to least likely. GTA6 becomes the biggest game release of the year, breaking all previous sales records on launch day. LLMs become a load-bearing part of all developer workflows. A little less than half of all commits on Github in 2026 are generated using coding assistants. A new open-world 3D Mario game is announced in the summer and released in the holiday season. The Playstation 6 is not announced. Apple turns the Mac Studio into a dedicated computer for running LLMs and machine-learning models, including support for rack mounting and disgusting amounts of RAM. MLX becomes the most widely supported method of running open-weights LLMs. Hardware to run local LLMs becomes more affordable, and multiple startups make it even easier with “it just works” boxes you can buy and stash in a corner of your office. This massively cuts into OpenAI and Anthropic’s revenues. The launch of the new Steam Machine, along with multiple new SteamOS-based handhelds, finally pushes Linux market-share in the Steam hardware survey to about 5%. Folding phones make up for at least 20% of all new smartphone sales. Apple releases a folding iPhone. The most egregious design and accessibility sins caused by Liquid Glass are slowly rolled back over the course of the year. Spotify launches fully AI generated podcasts. The product is discontinued after massive public backlash. A new, better version of Siri is released. It actually works as advertised. The new, improved version of Siri is immediately compromised via prompt injection attacks within 4-6 weeks of release. Multiple world governments—led by the EU—prohibit the use of American software and cloud services for government work. Apple and Microsoft ship competent local LLMs built into Windows and macOS. Apple and Google are forced to allow side-loading on all their devices after a massive antitrust lawsuit brought on by a coalition of tech companies and advocacy organizations. The change is region-locked to the EU, US, Japan, South Korea, and China. Microsoft buys OpenAI.

AI Hardware Gaming

▲ 0

0 views

Ankur Sethi 2 months ago

Getting a Gemini API key is an exercise in frustration

Last week, I started working on a new side-project. It’s a standard React app partly made up of run-of-the-mill CRUD views—a perfect fit for LLM-assisted programming. I reasoned that if I could get an LLM to quickly write the boring code for me, I’d have more time to focus on the interesting problems I wanted to solve. I've pretty much settled on Claude Code as my coding assistant of choice, but I'd been hearing great things about Google's Gemini 3 Pro. Despite my aversion to Google products, I decided to try it out on my new codebase. I already had Gemini CLI installed, but that only gave me access to Gemini 2.5 with rate limits. I wanted to try out Gemini 3 Pro, and I wanted to avoid being rate limited. I had some spare cash to burn on this experiment, so I went looking for ways to pay for a Gemini Pro plan, if such a thing existed. Thus began my grand adventure in trying to give Google my money. The name “Gemini” is so overloaded that it barely means anything. Based on the context, Gemini could refer to: To make things even more confusing, Google has at least three different products just for agentic coding: Gemini Code Assist (Gemini CLI is a part of this suite of products), Jules , and Antigravity . And then there’s a bunch of other GenAI stuff that is powered by Gemini but doesn’t have the word Gemini in the name: Vertex AI Platform , Google AI Studio , NotebookLM , and who knows what else. I just wanted to plug my credit card information into a form and get access to a coding assistant. Instead, I was dunked into an alphabet soup of products that all seemed to do similar things and, crucially, didn’t have any giant “Buy Now!” buttons for me to click. In contrast, both Anthropic and OpenAI have two primary ways you can access their products: via their consumer offerings at claude.ai and chatgpt.com respectively, or via API credits that you can buy through their respective developer consoles . In each case, there is a form field where you can plug in your credit card details, and a big, friendly “Buy Now!” button to click. After half an hour of searching the web, I did the obvious thing and asked the free version of Gemini (the chatbot, not one of those other Geminis) what to do: How do I pay for the pro version of Gemini so i can use it in the terminal for writing code? I specifically want to use the Gemini 3 Pro model. It thought for a suspiciously long time and told me that Gemini 3 Pro required a developer API key to use. Since the new model is still in preview, it's not yet available on any of the consumer plans. When I asked follow up questions about pricing, it told me that "Something went wrong”. Which translates to: we broke something, but we won’t tell you how to fix it. So I asked Claude for help. Between the two LLMs, I was able to figure out how to create an API key for the Gemini I wanted. Google AI Studio is supposed to be the all-in-one dashboard for Google’s generative AI models. This is where you can experiment with model parameters, manage API keys, view logs, and manage billing for your projects. I logged into Google AI Studio and created a new API key . This part was pretty straightforward: I followed the on-screen instructions and had a fresh new key housed under a project in a few seconds. I then verified that my key was working with Gemini CLI. It worked! Now all that was left to do was to purchase some API credits. Back in Google AI Studio, I saw a link titled “Set up billing” next to my key. It looked promising, so I clicked it. That’s where the fun really began. The “Set up billing” link kicked me out of Google AI Studio and into Google Cloud Console, and my heart sank. Every time I’ve logged into Google Cloud Console or AWS, I’ve wasted hours upon hours reading outdated documentation, gazing in despair at graphs that make no sense, going around in circles from dashboard to dashboard, and feeling a strong desire to attain freedom from this mortal coil. Turns out I can’t just put $100 into my Gemini account. Instead, I must first create a Billing Account. After I've done that, I must associate it with a project. Then I’m allowed to add a payment method to the Billing Account. And then , if I’m lucky, my API key will turn into a paid API key with Gemini Pro privileges. So I did the thing. The whole song and dance. Including the mandatory two-factor OTP verification that every Indian credit card requires. At the end of the process, I was greeted with a popup telling me I had to verify my payment method before I’d be allowed to use it. Wait. Didn’t I just verify my payment method? When I entered the OTP from my bank? Nope, turns out Google hungers for more data. Who'd have thunk it? To verify my payment method for reals , I had to send Google a picture of my government-issued ID and the credit card I’d just associated with my Billing Account. I had to ensure all the numbers on my credit card were redacted by manually placing black bars on top of them in an image editor, leaving only my name and the last four digits of the credit card number visible. This felt unnecessarily intrusive. But by this point, I was too deep in the process to quit. I was invested. I needed my Gemini 3 Pro, and I was willing to pay any price. The upload form for the government ID rejected my upload twice before it finally accepted it. It was the same exact ID every single time, just in different file formats. It wanted a PNG file. Not a JPG file, nor a PDF file, but a PNG file. Did the upload form mention that in the instructions? Of course not. After jumping through all these hoops, I received an email from Google telling me that my verification will be completed in a few days. A few days ? Nothing to do but wait, I suppose. At this point, I closed all my open Cloud Console tabs and went back to work. But when I was fifteen minutes into writing some code by hand like a Neanderthal, I received a second email from Google telling me that my verification was complete. So for the tenth time that day, I navigated to AI Studio. For the tenth time I clicked "Set up billing" on the page listing my API keys. For the tenth time I was told that my project wasn't associated with a billing account. For the tenth time I associated the project with my new billing account. And finally, after doing all of this, the “Quota tier” column on the page listing my API keys said “Tier 1” instead of “Set up billing”. Wait, Tier 1? Did that mean there were other tiers? What were tiers, anyway? Was I already on the best tier? Or maybe I was on the worst one? Not important. The important part was that I had my API key and I'd managed to convince Google to charge me for it. I went back to the Gemini CLI, ran the command, and turned on the "Enable experimental features" option. I ran the command, which told me that Gemini 3 Pro was now available. Success? Not yet. When I tried sending a message to the LLM, it failed with this 403 error: Is that JSON inside a string inside JSON? Yes. Yes it is. To figure out if my key was even working, I tried calling the Gemini API from JavaScript, reproducing the basic example from Google’s own documentation . No dice. I ran into the exact same error. I then tried talking to Gemini 3 Pro using the Playground inside Google AI Studio. It showed me a toast message saying The chat transcript said At this point I gave up and walked away from my computer. It was already 8pm. I’d been trying to get things to work since 5pm. I needed to eat dinner, play Clair Obscur , and go to bed. I had no more time to waste and no more fucks to give. Just as I was getting into bed, I received an email from Google with this subject line: Your Google Cloud and APIs billing account XXXXXX-XXXXXX-XXXXXX is in good standing at this time. With the message inside saying: Based on the information you provided and further analysis by Google, we have reinstated your billing account XXXXXX-XXXXXX-XXXXXX. Your account is in good standing, and you should now have full access to your account and related Project(s) and Service(s). I have no idea what any of this means, but Gemini 3 Pro started working correctly after I received this email. It worked in the Playground, directly by calling the API from JavaScript, and with Gemini CLI. Problem solved, I guess. Until Google mysteriously decides that my account is no longer in good standing. This was such a frustrating experience that I still haven't tried using Gemini with my new codebase, nearly a week after I made all those sacrifices to the Gods of Billing Account. I understand why the process for getting a Gemini API key is so convoluted. It’s designed for large organizations, not an individual developers trying to get work done; it serves the bureaucracy, not the people doing the work; it’s designed for maximum compliance with government regulations, not for efficiency or productivity. Google doesn’t want my money unless I’m an organization that employs ten thousand people. In contrast to Google, Anthropic and OpenAI are much smaller and much more nimble. They’re able to make the process of setting up a developer account quick and easy for those of us who just want to get things done. Unlike Google, they haven’t yet become complacent. They need to compete for developer mindshare if they are to survive a decade into the future. Maybe they'll add the same level of bureaucracy to their processes as they become larger, but for now they're fairly easy to deal with. I’m still going to try using Gemini 3 Pro with Gemini CLI as my coding assistant, but I’ll probably cap the experiment to a month. Unless Gemini 3 Pro is a massive improvement over its competitors, I’ll stick to using tools built by organizations that want me as a customer. The chatbot available at gemini.google.com . The mobile app that lets you use the same Gemini chatbot on your iPhone or Android . The voice assistant on Android phones. The AI features built into Google Workspace , Firebase, Colab, BigQuery, and other Google products. Gemini CLI, an agentic coding tool for your terminal that works the same way as Claude Code or OpenAI Codex. The Gemini Code Assist suite of products, which includes extensions for various IDEs, a GitHub app, and Gemini CLI. The underlying LLM powering all these products. Probably three more products by the time I finish writing this blog post.

AI JavaScript API JSON

▲ 0

1 views

Ankur Sethi 2 months ago

My first online shopping experience, at the tender age of twelve

I wrote the first draft of this post as an exercise during a meeting of IndieWebClub Bangalore . When I was eleven or twelve, a friend at school told me that video games like Age of Empires were built using a language called C. When pressed for details, he had nothing more to tell me. What did he mean by language? Was it like English? How could you use a language to make things move around on the computer screen? He didn’t know. His older brother had informed him about the existence of C and its relationship with video games, but he had inquired no further. I was desperate to learn C, whatever it was. Only, I had no idea who to ask or how to go about learning this "language". I could've searched the web, but this was 2002. If I was lucky, I was allowed to use the dial-up for an hour every week. Search engines were bad and my search skills were rudimentary. The web was not yet filled with thousands upon thousands of freely available programming tutorials. And in any case, it was far more important to download the latest Linkin’ Park MP3. Luckily, Dad knew somebody who worked at the National Institute for Science Communication and Policy Research . They had just published a series of computer education books targeted towards an increasing population of new computer users. My dad had mentioned to him that I was interested in computers, so one day he handed him a giant brown envelope containing two books: a book about the basics of Microsoft PowerPoint, and a slim volume called The C Adventure . The C Adventure only covered the very basics of C: variables, conditionals, loops, and functions. It didn't cover structs, pointers, macros, splitting programs into multiple files, build systems, or anything else that would allow me to build the kind of real-world programs I wanted to build. But that didn't matter. The universe had heard my plea. I finally knew what C was. I could even write a bit of it! They were simple programs that ran in the terminal, but at the time I felt drunk with power. I was one step closer to building Age of Empires . I could do anything with a computer, anything at all. The only limit was myself . But The C Adventure wasn't enough. If I wanted to build games, there was a lot more I’d need to learn: reading and writing files, connecting to the internet, opening windows, rendering 3D graphics, playing sound, writing game AI, and who knew what else. But once again, I didn't know where to find more learning resources. The government had come to my aid once, but I couldn't rely on obscure government departments to come to my aid every time. I had to take matters into my own hands. And I did just that, this time finding my salvation in the free market. I had no idea where to buy programming books in Delhi, but I reasoned that it might be possible to buy them on the internet. I’d visited American e-commerce websites. Might there be Indian equivalents? I’d seen TV and newspaper ads for a website called Baazee.com. The ads said something about buying and selling. Perhaps this was where I’d find my next C book? One evening, during my one weekly hour of parentally approved and monitored internet session, I typed Baazee.com into the Internet Explorer 6 URL bar and began my search. A few minutes of searching led me to a product called "101 Programming eBooks". It was allegedly a CD containing, well, 101 programming ebooks. The seller had good reviews, and the product description looked compelling so, with an excess of hope, I clicked the buy button. At the end of the checkout process, the website asked for my credit card details, and that’s where I realized my whole endeavor had been doomed from the very start. The problem was that my parents didn't have a credit card at the time, and no amount of convincing on my part would induce them to get one. They’d heard too many horror stories of people getting deep into credit card debt and losing their homes. The newspapers were filled with stories of scams and frauds on the nascent internet, most of which involved stealing people’s credit card details and using them to run up huge bills. But teenage Ankur needed to learn C. It was a life and death situation, couldn't they see? I would not allow my parents’ stubborn disapproval of predatory American financial instruments to stand between myself and Age of Empires . So I did what anyone in my place would have done: I begged my Dad to get a credit card, just this once, just for this one purchase. I threw a tantrum. I cried until I ran out of tears. But nope, it was all in vain. Computers and money were not allowed to mix, not in our household. No credit cards, full stop. Dejected, I went back to the computer to close all my Internet Explorer 6 windows, when the universe once again chose to smile upon me. The person selling "101 Programming Ebooks" had left their email address on the product description page! Dad urged me to write to them and figure out if we could pay for the CD with cash or cheque. So I sent an email, and the seller responded with his phone number. He lived in Delhi, not very far from where my family lived. He said we could pick up the CD from his address and pay him in cash. Oh sweet joy! Oh divine providence! I wanted to go meet the seller myself, and do so immediately. But Dad was more cautious. He first talked to the seller on the phone to make sure he was a real person. He asked him a bunch of questions. When he was satisfied that nothing shady was going on, he went to pick up the CD himself. He might have taken a friend or co-worker with him. He’d read enough scary stories about the internet in the newspapers, and he did not want to appear on page seven or whatever of Times of India . The seller was just a college kid, still in his early twenties. He was pirating ebooks, burning them to CDs, and selling them online out of his bedroom for some extra cash. My dad was impressed by the entrepreneurial spirit on display, but neither him nor I understood that selling pirated ebooks was illegal. It didn’t matter, though. Everything that had to do with computers and the internet in India was illegal in the 2000s, so nobody cared. One joyous evening Dad returned home with the CD. It came in an unmarked white paper envelope, with the words “101 Programming Ebooks” scrawled on the disc in permanent marker. I inserted the disc into the family computer and found it contained exactly what had been advertised: a collection of technical ebooks sorted into directories, mostly published by O’Reilly, in PDF and CHM formats. In fact, there were a lot more than just 101 ebooks in there! My first online purchase had turned out to be incredibly satisfying. The Baazee.com pirate had underpromised and overdelivered. I spent a lot of time reading the books on that CD. I don’t remember if I ever read any single one of them cover to cover, but I remember dipping in and out of tens of them, picking up something at random whenever the fancy struck me. I remember learning a bit of Perl and writing some simple programs. I remember trying to learn Java but being turned off by . I remember spending hours reading a book about XML but having no clue why I would want to use it. Could I use it to build Age of Empires ? No? Then I didn't care. I never ended up building my own version of Age of Empires , but I did go on to use some of the books in the collection to learn and use C (and some C++) profitably for many small projects. Later, when I was in college I even learned some Objective-C, and made a bit of money building an iPad game for a small marketing agency. So technically I’ve been paid to build a video game, and technically some part of it was built with C. Success? Let’s call it a success. While no single book on the “101 Programming Ebooks” CD changed my life, the collection gave me a vast buffet of tools and technologies to sample from. It expanded my mind and allowed me to see the full spectrum of possibilities in a computer career. Looking back at that event 23 years later, the only book I can remember clearly is the Camel Book , but I’m sure there were many more in that collection that I used to occupy slow evenings. I sometimes wonder where that college kid is now, the one who was selling pirated ebooks out of his bedroom. Did he go on to start his own tech company? Did he move to America, as so many people in tech do? Or does he still live somewhere in Delhi, ripping Hindi TV shows off Amazon Prime and helping people jailbreak their Nintendo Switches? Wherever he is, I hope he’s done well for himself. I am forever grateful for “101 Programming Ebooks” and the wild-west internet of the 2000s.

C++ Perl Programming Java

▲ 0

0 views

Ankur Sethi 2 months ago

Using LLMs for web search

I have two main use-cases for LLMs: writing code and searching the web. There's a lot of discussion online about LLM-assisted programming but very little about LLM-assisted search, so here are some unstructured thoughts about just that. OpenAI calls their web search feature ChatGPT Deep Research , Google has Gemini Deep Research , and Anthropic just uses the word Research . Regardless of the label, all these products work the same way: I like this feature a lot , and I lean on it heavily when I'm looking for high-quality long-form writing by actual human beings on a topic that's new to me. I use Kagi for most of my search queries, but it helps to turn to an LLM when I'm completely unfamiliar with a topic, when I'm not even sure what keywords to search for. Or, sometimes, when I just don't feel like sifting through thousands of words of SEO content to find a few good results. I rarely ask LLMs factual questions because I don’t trust the answers they return. Unless I have a way to verify their output, I consider anything an LLM says to be a hallucination. This is doubly true when I’m learning something new. If I'm new to Rust, how can I be sure that the Rust best practices Claude is telling me about are truly the best of all Rust practices? I find it much easier to trust LLM-generated output when it cites web pages I can read myself. This lets me verify that the information comes from an entity I can trust, an entity that’s (hopefully) a real person or institution with real expertise. Grounding the LLM in web search also means I'm likely to find more up to date information that's not in its training data yet (though, in my experience, this is not always guaranteed). Whenever I get curious about something these days, I write down a detailed question, submit it to Claude, and go off to get some work done while it churns away browsing hundreds of web pages looking for answers. Sometimes I end up with multiple browser tabs with a different search query running in each of them. For most questions, Claude is able to write me a detailed report with citations in five to ten minutes, and it looks at about a hundred pages in the process. Sometimes it decides to spend a lot more time browsing, reading multiple hundreds of web pages. For one question, it churned away for almost seventeen minutes and looked at five hundred and fifty sources to generate its report. I don’t actually care for the report Claude produces at the end of its research process. I almost never read it. It’s a a whole lot of LLM slop: unreadable prose, needlessly verbose, often misrepresenting the very sources it quotes. I only care about the links it finds, which are usually entirely different from what I get out of Kagi. My workflow is to skim the report to get an idea of its general structure, open all the links in new tabs, and close the report itself. I wish there was a mode where the "report" could just be a list of useful links, though I suppose I could accomplish that with some clever prompting. The web pages Claude surfaces always surprise me. I can’t tell what search index it uses, what search keywords it uses under the hood, or how it decides what links are worth clicking. Whatever the secret sauce is, I regularly end up on web pages that I'd never be able to find through a regular search engine: personal websites that were last updated 20 years ago, columns from long-defunct magazines, ancient Blogger and LiveJournal blogs, pages hidden deep inside some megcorporation’s labyrinthian support website, lecture notes hosted on university websites, PDFs from exposed directories, and other unexpected wonders from a web that search engines try their best to hide away. Sometimes Claude links to pages that aren’t even online anymore! How is it able to cite these pages if it can't actually read them? Unclear. I often have to pull up a cached version of such pages using the Internet Archive. For example, one of the reports it produced linked to On Outliners and Outline Processing and Getting Started Blogging with Drummer , both of which no longer exist on the web. I can't tell whether any major LLM providers take their web search products seriously (outside of Perplexity, which is not technically an LLM provider). I certainly haven’t seen any new changes made to them since they were introduced, and nobody seems to talk about them very much. For me, though, web search is one of the main reasons I use LLMs at all. That's partly why I'm giving Anthropic that $20 every month. I have a long wishlist of features I want to see in LLM-powered search products: Maybe Kagi Assistant will grow into this in the future? Maybe I should try using Perplexity? I've had meh experiences with both these products, and I'm not sure whether they can compete with the quality of results ChatGPT/Claude/Gemini surface. Anyway, yeah. I like LLM-powered search. You enter a prompt, just like in any other LLM workflow. The LLM may ask you a clarifying question (for some reason it’s always a single clarifying question, never more than that). The LLM searches the web for pages matching your prompt using a traditional search engine. It uses the web pages it found to generate a long report, citing its sources wherever it makes a claim within the text of the report. Stop hiding away web search inside a menu! Let me directly click “New search” in the same way I click “New chat” or "Code" in the Claude sidebar. Let me edit the LLM's research plan before it starts searching (Gemini lets me do this to some degree). Let me edit the keywords the LLM will use to start its research process. Or, let me ask it to automatically refine those keywords before it begins. What if the LLM finds new information online that re-contextualizes my original query? In those cases, allow it to interrupt its research process and ask for clarifications. Let me look at the raw search results for each keyword the LLM searched for. Add a mode where the LLM picks the best search results for me and only returns a list of links, like a traditional search engine. Let me use “lenses” in the same way I use them on Kagi . Allow me to place limits on my sources (e.g social media, personal blogs, news websites, or academic journals). Let me uprank/downrank/ban certain sources in the same way Kagi allows .

Rust AI

▲ 0

0 views

Ankur Sethi 3 months ago

Weeknote 2025-W45: Text message bankruptcy

Once again, did zero work on the novel this week. Didn't work on the comic for Indie Comix Fest either. Blame my broken brain. Ironically, I wrote and published a blog post about my relationship with work . Whatever you're thinking right now, stop thinking it immediately. Does emotional work count as work? Because I did a lot of that this week. I spent many of my days reading, taking notes, and writing in my journal. I have little to show for it in terms of output, but I feel more at peace today than I did at the end of last week. I mentioned in my last weeknote that I wanted to carve out more time for myself. I'm happy to report that I managed to do just that on most days this week, spending much of my time on things that make me happy and content. As I write this, I feel a lot more like myself. The orange cat I mentioned last week had to have his tail amputated because of necrosis. It's a terrible shame, but the doctors said it was the only way he could have a life free of pain and infection. He's still recovering at the vet's, but he should be back home this coming week. I'm declaring text message bankruptcy. If you've texted me in the past few weeks and haven't received a response, I apologize profusely. Abandon all hope at this point, because I'm not likely to respond back. I gifted Tanvi a MagiClick AI Button as a late birthday present. The previous version of the MagiClick button was sold as a programmable macropad powered by an ESP32. This new version is sold as a sort of AI assistant, and comes with a pre-installed firmware that allows you to talk to an LLM served by XiaoZhi AI using your voice. The control panel allows you to chose between a couple of popular Chinese open-weights models, tweak a bunch of parameters, and set a custom system prompt. The default prompt tells the LLM to act like an AI girlfriend who is a "biker chick". All I'm going to say is that some choices were made. Regardless, the button is fun to play with and can presumably be used to control something like Home Assistant. I finally met fellow weeknoter Nats and her partner Rudy on Saturday. We spent an entire evening talking about stationery, Hobonichi Techos, Sakura Microns, gothic literature, Rust, LLM assisted programming, and bare-chested Stalin on a horse. My new therapist had to cancel our appointment on Monday because she broke her ankle. I know I sound selfish as I say this, but I was incredibly upset when I received the cancellation message. I'd been feeling awful the previous week and really needed the support. I couldn't have been more grateful to her when she rescheduled our appointment for later in the week, despite still being in pain from her injury. She didn't have to do it, and I hadn't asked for a fresh appointment. She did it anyway, not just for me but for her other clients as well. I'm filled with gratitude, even as I understand that working while sick is unsustainable in the long run. (Didn't I already say I feel selfish? That absolves me from all guilt. Them's the rules.) Finished playing A Short Hike Finished watching Superstore Started watching Nobody Wants This Still reading Fingersmith and All About Love . Sigh. Notes Against Note-Taking Systems by Sasha Chapin How to forget what you read by Oliver Burkeman Learning By Writing by Holden Karnofsky Why Write? by Patrick Grafton-Cardwell Rank and File by Robert Minto How I use AI (Oct 2025) by Ben Stolovitz

Writing Rust

▲ 0

0 views

Ankur Sethi 3 months ago

The solace of work

I like my work and I like working. Would I say I love my work? I'm not sure. Maybe? I don't know what it means to love something that's not alive. In any case, the distinction is not important. As far back as I can remember, I've enjoyed working and looked forward to my workdays. I count myself lucky in that sense. I've somehow stumbled into work that I find meaningful. To me, writing code or fiction or essays does not feel significantly different from playing a video game or watching a TV show. It's all play and it's all work and it's all really the same to me. Of course, there are always some parts of any kind of work that are frustrating. I lose motivation when I'm forced to work on something I believe has no value. Work that's not challenging enough can be fun in small doses, but starts to feel depressing after a while. Work with unreasonable deadlines or constantly changing requirements is usually a recipe for burnout (though even this nightmare scenario can sometimes present a satisfying exercise in creative problem solving under extreme constraints). And self-directed work, such as this blog post, always feels more meaningful compared to work I do for other people in exchange for money. The idea of separating my work from the rest of my life—the idea of leaving my work at work—makes no sense to me. My work and life have always been deeply intertwined. I've met some of my closest friends through work. Work gave me the financial freedom to move out of my parents' house and start a new life here in Bangalore, where I met many of the people I love most dearly today. Traveling for work allowed me to experience new places, new cultures, new ways of being. How do I pull out the one strand marked "work" from the rich braid of my life and cast it aside, even temporarily? It's impossible. Work, life, play, love—they're all the same thing. Lately work has been a source of comfort. My life has been tumultuous this year, but the pleasures of work have remained constant. With all the emotional ups and downs, there has always been blog posts to write, software to build, plot outlines to wrangle, and poetry to scribble in the back of notebooks. It's a powerful feeling. Come heartbreak or contentment or grief or celebration, I have always been able to come back to the work. The notebook and pen, terminal and editor, have always been ready for me, waiting for me to put my mark on their canvas. They have been my constants, my anchors in a turbulent sea. They have been my lighthouses, always guiding me back to myself. I like my work. You might go as far as to say that I love my work. But most importantly, I'm grateful that it exists, that it is never-ending, that it waits for me every morning. I'm grateful that I can lose myself in it whenever I want and, eventually, hopefully, if I do it right, find myself.

Writing Career

▲ 0

0 views

Ankur Sethi 3 months ago

Weeknote 2025-W44: Oklahoma smash

I haven't worked on the novel in two weeks. The week I was traveling for Diwali was going to be a wash anyway, but my inability to work during this post-Diwali week caught me by surprise. A combination of social commitments, mild sickness, physical exhaustion, and poor mental health kept me away from my desk. Itihas led the session for this week's IndieWebClub , where they talked about digital gardens and showed off their note-taking and digital gardening setup. As expected, the discussion veered off into note-taking methods, PKM, non-linear thinking and writing, and the joys of hypertext. I realized during the session that I prefer to think in neat, linear narratives and do not wish to start a digital garden (even though I'm fascinated by other people's gardens). At Writers Club, we used the submission guidelines for the Journal of Imaginary Research to write abstracts for fictional papers. My abstract was a mix of sci-fi with a subtle horror element. After two weeks of zero creative work, writing something and calling it done felt good. I've grown to enjoy writing using writing prompts. They challenge myself to think in different ways, and they're especially fun to do in a group setting. I want to do a lot more of this! I'm helping a friend write a comic for Indie Comix Fest . The submission deadline is next week, so I'm not sure if we'll make it. But we're going to try our darndest. I've been struggling with mental health lately, especially when it comes to intimate relationships and close friendships (aren't they the same?) To help me work through these (long-standing) issues, I've started going to a new therapist. The first session on Monday was promising and left me feeling hopeful, and I'm looking forward to the next one. I've been finding it difficult to carve out time for myself, which is probably a contributing factor to my poor state of mind. In the coming few weeks I will attempt to find my way back to myself. I've also been finding it difficult to set priorities for myself. I've been taking time away from my most important tasks and spending them on trivialities instead. The problem is exacerbated by my sleep cycle being completely out of whack. A good recipe for burnout and resentment. The silver lining in all of this was a lovely dinner date with somebody I have come to care about deeply. Guerilla Diner makes the best Oklahoma smash, don't @ me. For the last three years, an orange cat has made a home in the common areas of my apartment building. Sometime this week, he came back badly injured, with deep wounds across his body. The family on the ground floor—who take care of him and feed him—found him yowling outside their window, demanding to be fed but otherwise unable to move without pain. We tried for a few days to get him into a crate so we could take him to the vet, but he refused to let anybody come near him. Close to midnight on Thursday, my friend Shruti finally lured him into the crate and we drove him to the vet. I'm happy to report that he is pain-free and on his way to recovery! The PSF has withdrawn a $1.5 million proposal to US government grant program by Loren Crary for the Python Software Foundation It's hard to join the Indie Web by Pablo Enoc What writers really do when they write by George Saunders Hacking India’s largest automaker: Tata Motors by Eaton Zveare How to think in writing by Henrik Karlsson

Python Culture Writing

▲ 0

0 views

Ankur Sethi 4 months ago

Everything is index cards

I wrote this post as an exercise during a meeting of IndieWebClub Bangalore. Much of the knowledge-management software we use today is modeled after stacks of index cards. Here is a growing list of applications that are directly or indirectly digital recreations of processes and techniques commonly used with index cards in the analog world. I'll keep appending to this list. Image credit: https://wiki.xxiivv.com/site/hypertalk.html More information: https://en.wikipedia.org/wiki/HyperCard Image credit: https://www.atlassian.com/blog/trello/trello-manage-team More information: https://trello.com Image credit: https://zapier.com/blog/google-keep-vs-evernote/ More information: https://keep.google.com Image credit: https://startupstash.com/tools/scrivener/ More information: https://www.literatureandlatte.com Screenshot from https://zettelkasten.sorenbjornstad.com/ More information: https://tiddlywiki.com

Web Development HTML

▲ 0

0 views

Ankur Sethi 4 months ago

Weeknote 2025-W42: Magical and terrifying

Wrote this weeknote on an airplane flying to Delhi. Magical and terrifying. Zero progress on the novel this week. Instead, I spent most of my time rushing to doctors' appointments, preparing for my Delhi trip, and collapsed facedown on my bed after driving across the city for three hours straight. Next week is likely to be a wash, too. I should probably check my calendar before I set writing goals. I'm hoping to at least get some reading done. Looking forward to finishing Fingersmith and moving on to Close to the Machine: Technophilia and Its Discontents . I'm mildly allergic to eucalyptus and English plantain. The allergies clog my nose when I'm outdoors, which makes me susceptible to upper respiratory infections. On my allergist's recommendation, I've been taking supplements, regularly washing out my nose with a saline nasal spray, and using a nasal decongestant at night. Thanks to these treatments, I haven't been sick as sick this year as I usually get. Last month, the allergist recommended that I start immunotherapy, which could fix my upper respiratory issues once and for all. So for the past few days, I've been putting a tiny drop of custom-made medication under my tongue right after I wake up. I can't tell if it's doing anything, because it feels like nothing. To scratch my programming itch, I've started learning Clojure. I'm treating it the same way people treat Wordle or the daily crossword: an entertaining way to give my brain a vigorous workout. I'm not expecting to use it for production projects, at least not in the immediate future. I wasted too many hours reading tech news last week, which is something I only do when I'm trying to hide from negative emotions. It's not surprising. I've had a tumultuous few months. But thankfully I'm doing better this weekend, and I've set up an appointment with a therapist for when I get back home to Bangalore. I don't like being in Delhi. It've never felt at home there. But I'm looking forward to seeing my brother and parents after a while, and probably some cousins I haven't seen in over a decade. Many good links this week (maybe one of these will convince Azan to switch). Switch to Jujutsu already: a tutorial by Stavros Korokithakis. Let help you keep a clean commit history by Paul Smith. Jujutsu Megamerges and by Chris Krycho Two pieces on generative AI and creative friction. choosing friction by Jenny A cartoonist's review of AI art by The Oatmeal I'm not sure I agree fully with these essays on syntax highlighting, but they did get me to pay close attention to a part of my workflow I rarely think about. I am sorry, but everyone is getting syntax highlighting wrong by Niki Tonsky Syntax highlighting is a waste of an information channel by Hillel Wayne

Programming Clojure

▲ 0

0 views

Ankur Sethi 4 months ago

Just read

I wrote this post as an exercise during a meeting of IndieWebClub Bangalore . I started this year with a goal to read thirty-six books. Towards the end of last year I was easily averaging two to three books a month, so thirty-six seemed like an achievable goal. I clearly overestimated myself, because I've only read ten books so far in 2025. In fact, this might end up being my worst year of reading since I started tracking my books on Goodreads in 2013. So what happened this year that prevented me from reading more? It wasn't a lack of time, energy, or interest. I didn't take up a new hobby, start a challenging job, or get into a new relationship. I didn't travel more than usual, nor did I fall sick more often than I usually do. What went wrong was that I kept getting in my own way. I turned reading into a chore by insisting on taking detailed notes on everything I read. Instead of relaxing on my couch with a good book and a hot beverage, I read at my desk with a pencil in hand, underlining interesting passages and scribbling in the margins. For some books that I considered particularly important, I typed up extensive notes. This changed my relationship with reading. Instead of something I did for pleasure in my free time, it started feeling like work. Instead of something I could pick up and put down whenever I wanted, books became sacred objects that could only be approached at specific times in specific parts of my house. I started avoiding reading, which bred guilt, which bred resentment. Today, reading has gone from being a normal part of my daily life to something I do once or twice a week, something I have to put on my todo list and check off like a chore. So I've been trying something new: I'm reading without taking notes, highlighting important lines, or even trying to remember everything I read. If I encounter something important, I re-read it or stop to reflect for a minute. But at no point do I turn my reading time into a study session. I'm finding this surprisingly difficult to do. I've become so used to extracting maximum value out of my books that to just read them for pleasure feels like a radical, uncomfortable act. Without that pencil in my hand, I'm actively anxious while reading. But I keep reminding myself to just read . To not try to remember and catalog every little bit of information I come across. Books are not resources to be mined, but meals to be enjoyed. If something is important, it will come back to me. If it's not, no amount of highlighting or note-taking will make it stick. I'm hoping by allowing myself to just read, I can change my relationship to reading and go back to enjoying it like I used to. The number of books I finish this year is not even important. I just want reading to once again become something I do as a matter of course in my daily life, not as a sacred act that requires complex rituals. I miss reading for pleasure. I'm hoping I can find that joy once again.

Books Writing

▲ 0

0 views

Ankur Sethi 4 months ago

Weeknote 2025-W41: Starfish on the floor

What do novelists do between getting a promising idea for a story and starting to type out their first lines? Is there a planning phase of some sort? Do they sit at their desks and just think really hard ? Do they draw diagrams? Meditate? Go on walks? Consult the oracle? I’ve spent a lot of time learning about the writing processes of a whole bunch of authors, but I still have no clue what happens during the pre-writing process. For my own novel, I’m spending my time researching, thinking, planning, and frantically scribbling notes to myself. Sometimes I starfish on the floor of my home office in despair. I’m not sure if this is valuable work or plain old procrastination. There is no way to tell. That said, I’ve made satisfactory progress this week. The broad strokes of my plot are now clear to me, though I still have to iron out the specifics. I vaguely understand the mysteries behind the fantastic elements of my story world. I have a deeper understanding of my themes, and with it my protagonist’s psyche. Next up, I need to figure out my main cast outside of the protagonist, outline some important climactic events, and finish reading the seventy three Wikipedia tabs I’ve opened in my browser. I’m hoping to draw up an outline for at least the first chapter by the end of the upcoming week. If I’m good and kind and brave, I might even be able to outline the first two or three chapters. I've finally found a home for all my long-form writing in Ulysses . I'd prefer to use Scrivener for this work, but I don't want to deal with its archaic and unreliable cloud sync . I briefly considered iA Writer , which is my default Markdown editor on all my computers, but it doesn't have the tools to manage projects that contain more than two or three of files. I will not be evaluating any other writing apps. I'm done with that life. I’m going back to therapy. I recently had an insight about an issue that had stumped me for years, and I need to work through it with a professional. I haven’t been able to make time for Hades 2 . In general, games have taken a backset in my life this year. That’s fine, I suppose? Different seasons and all that. I don’t see myself putting significant time into a game again for a few months at least. I’m itching to write code again. I haven’t worked on a significant programming project since March. Ugh. I’ve started tracking my habits using Streaks. It’s silly, but watching that little star appear in the app motivates me to keep journaling and cleaning the litter boxes every day. Just goes to show I’m so easily manipulated, I can do it to myself. I'm grateful for all the new friends I've made this year at IndieWebClub Bangalore and other local events. I'm also grateful for all the old friends I've reconnected with. If you're reading this, know that I love you! Why do LLMs freak out over the seahorse emoji? by Theia Vogel Beyond credibility by Mandy Brown Liquid Glass Is Cracked, and Usability Suffers in iOS 26 by Raluca Budiu Secondhand embarrassment by Robin Sloan

Writing Career

▲ 0

0 views