Amy Hsieh

This is really neat but it’s not a design tool as much as it’s a design _production_ tool. The practice of design is mostly about what comes before production. There’s no doubt in my mind that all parts of software production will become automated very soon. Writing code, making web pages, putting pieces of a design system together etc. And that’s fine. I think few people actually enjoy this kind of production work. Wouldn’t it be better if we spent our precious time in life on what is more meaningful?! At the core, the practice of design is methodical; like architecture, not like art. In a nutshell: We find constraints, form comprehension of the whole and propose solutions that honor those constraints. First after that do we enter some form of production phase, usually prototypes first, learn about some constraints that were hidden before, loop back, prototype and then build the production-grade “final” artifact. These last few tasks are quickly losing value because AI tools can do it much faster (not yet better though) than humans. It’s simply just what has the best RoI for a business. Some companies and individuals will continue to spend human time on certain parts of the “production line” as a market differentiator, but it will cost them a relatively high price compared to competitors. Anyhow, I still haven’t seen a tool better than Figma that supports the actually-interesting part of the design process. I wouldn’t be surprised if Figma focused their products on that, maybe separating “products for production” of “products for ideation & exploration.” The latter would obviously still leverage AI, but not to do the work for me but rather to support my efforts the way a therapist helps me live a better life (not living my life for me.)

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

2026 90/365 past days fade into dates. today is growing. future days spell out one of fav quote「在我有生之日，做一個真誠的人，不放棄對生活的熱愛和執著，在有限的時空裡，過無限廣大的日子。」——三毛《雨季不再來》

when software had a soul there was a moment around 2005 when using a Mac felt like touching something alive. the dock bounced. the genie effect swooped. exposé scattered your windows like cards on a table. none of it was strictly necessary. all of it felt like someone cared – not about metrics, but about the feeling of using a machine. software back then had texture. it had a philosophy. you could feel the person behind it. someone made a decision to make that icon beautiful, to animate that transition just so, to write that error message with a little warmth. apps had personalities. some were weird. some were over-designed in ways that would make a modern PM flinch. but they were alive. the web was the same. personal sites were genuinely personal. blogs felt like letters. forums had regulars. you knew who made what. the internet had neighborhoods, and each one felt different. nothing was optimized for scale. things were made by people who loved what they were making. somewhere along the way, we traded all of that for growth. A/B tests flattened the edges. design systems standardized the personality out. everything got faster, smoother, more consistent – and somehow less interesting. the quirks were removed because they didn't test well. the warmth got cut because it wasn't measurable. we optimized our way into a world of things that work perfectly and feel like nothing. now every app looks the same. every interface follows the same patterns. every product speaks in the same calm, frictionless voice, siloed in their own little islands. the humanity got rounded off. and then came AI agents. and the speed got inhuman. now you can generate an entire product in an afternoon. ship a feature before lunch. spin up ten variations before anyone's had their coffee. the gap from idea to code is basically zero. which sounds incredible. and it is. but there's a catch. when making things are too easy, the slop comes for free too. mediocre things don't look obviously bad – they look fine. they work. they ship. they pass review. and now there are infinite of them. the internet is filling up with software that functions but means nothing. interfaces that are correct but feel dead. products made by agents, reviewed by no one, shipped into the void. this is the thing that keeps me up at night. not that AI will replace people who care. but that it will drown them out. here's what I still believe: the best things are made by people who couldn't help themselves. someone who lost sleep over an icon. who rewrote the same line of copy twelve times. who added an animation nobody asked for because it made the thing feel right. that obsession – that's not inefficiency. that's the whole point. AI doesn't make that irrelevant. it actually makes it rarer and more valuable. taste is not a markdown skill. caring is not a parameter. the weird, specific, "soul" thing you put into something – that can't be programmed into existence. the path forward isn't to make more slop faster. it's to finally give people with real vision the tools to make the thing they always imagined but couldn't build alone. the designer who had the idea but couldn't code. the kid who saw something nobody else saw. the person who cared too much about something most people wouldn't notice. if we get this right, we don't get a faster factory. we get a renaissance. more strange, personal, opinionated software made by teams of people who care and mean it. that's still possible. but only if the people who care get the space and tools to actually express themselves – and don't just hand the wheel to the agent and walk away.

The loudest story about AI is a lonely one. One person with an army of chatbots. Other humans are friction. That gets the future wrong. The best things aren’t built alone. In a moment of change, we want to remind the world (and ourselves) what Notion stands for: — Think Together

@bartdecrem thank u!!!

@AmyHsieh16 Looks super fun. Pls make it work on iPhone:)

a word tetris game fun project :3 go take that no.1 spot hahah!!! (pc only) try not to panic lol lettris-game.vercel.app

Prediction: In the AI age, taste will become even more important. When anyone can make anything, the big differentiator is what you choose to make. paulgraham.com/taste.html

made my first @openclaw automation tonight (slack + X + Notion) was just testing… i'm hooked!!! i named 2 agents after my favorite foods bagel and cereal😆

@budapp would love to try!

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@Codie_Sanchez 🤓 amywhere.app

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@floguo @lennysan aww thank you!! that really means a lot 🥹

@AmyHsieh16 @lennysan so creative : ) sharing this w my friends who love lenny!

search podcast moments by topic using @lennysan 's open-source transcript lennys-podcast-moments.vercel.app there're always moments in podcasts i want to come back to — to reflect on or connect to what i'm building first time building with open-source data. interesting, learned a lot!

@dubadubabee @lennysan ☺️🙏

@AmyHsieh16 @lennysan So cool!

@HodlerChief @lennysan love this take!! that’s something i’m excited to explore more🙌

@AmyHsieh16 @lennysan Nice one. Next step: make the insights concrete based on own experience and context, then people could get more out of it than just listening.

@ArnovitzZevi @lennysan thank you!!☺️ the cursor effect was a fun experiment!

@AmyHsieh16 @lennysan The UI! and the cursor effect is 👨‍🍳💋

@darshenpatel @lennysan appreciate it! 🙏

@AmyHsieh16 @lennysan 🔥use case.

@UXRohit @lennysan thank u!!!

@AmyHsieh16 @lennysan Kooooooollllllll

@lennysan thanks for sharing, Lenny!! had a lot of fun building on top of the transcripts🙌

🔥🔥🔥