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Motion
21 posts
Motion
@motion_so
Motion is an AI assistant built by @mosaic_so to prompt motion design and animated videos.
SF 加入时间 Mart 2026
1 关注20 粉丝
@FarzaTV @motion_so visually explain Farzapedia to me and how it works in a 1 minute animation
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This is Farzapedia.
I had an LLM take 2,500 entries from my diary, Apple Notes, and some iMessage convos to create a personal Wikipedia for me.
It made 400 detailed articles for my friends, my startups, research areas, and even my favorite animes and their impact on me complete with backlinks.
But, this Wiki was not built for me! I built it for my agent!
The structure of the wiki files and how it's all backlinked is very easily crawlable by any agent + makes it a truly useful knowledge base.
I can spin up Claude Code on the wiki and starting at index.md (a catalog of all my articles) the agent does a really good job at drilling into the specific pages on my wiki it needs context on when I have a query.
For example, when trying to cook up a new landing page I may ask:
"I'm trying to design this landing page for a new idea I have. Please look into the images and films that inspired me recently and give me ideas for new copy and aesthetics".
In my diary I kept track of everything from: learnings, people, inspo, interesting links, images.
So the agent reads my wiki and pulls up my "Philosophy" articles from notes on a Studio Ghibli documentary, "Competitor" articles with YC companies whose landing pages I screenshotted, and pics of 1970s Beatles merch I saved years ago. And it delivers a great answer.
I built a similar system to this a year ago with RAG but it was ass.
A knowledge base that lets an agent find what it needs via a file system it actually understands just works better.
The most magical thing now is as I add new things to my wiki (articles, images of inspo, meeting notes) the system will likely update 2-3 different articles where it feels that context belongs, or, just creates a new article.
It's like this super genius librarian for your brain that's always filing stuff for your perfectly and also let's you easily query the knowledge for tasks useful to you (ex. design, product, writing, etc) and it never gets tired.
I might spend next week productizing this, if that's of interest to you DM me + tell me your usecase!
Andrej Karpathy@karpathy
Wow, this tweet went very viral! I wanted share a possibly slightly improved version of the tweet in an "idea file". The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the other person's agent customizes & builds it for your specific needs. So here's the idea in a gist format: gist.github.com/karpathy/442a6… You can give this to your agent and it can build you your own LLM wiki and guide you on how to use it etc. It's intentionally kept a little bit abstract/vague because there are so many directions to take this in. And ofc, people can adjust the idea or contribute their own in the Discussion which is cool.
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@steipete @motion_so explain me what's going on with a documentary style video
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"There’s a big wave coming" #theres-a-big-wave-coming" target="_blank" rel="nofollow noopener">mtlynch.io/claude-code-fo…
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@karpathy @motion_so explain this to me in a short explainer video
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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.
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@AnthropicAI @motion_so explain this to me in a short digestible vox style explainer
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New Anthropic research: Emotion concepts and their function in a large language model.
All LLMs sometimes act like they have emotions. But why? We found internal representations of emotion concepts that can drive Claude’s behavior, sometimes in surprising ways.
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@EvanLuthra @motion_so explain how Matthew did this to me like I’m 5
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THIS IS CRAZYY!!!!🤯
A guy just built a $1.8 billion company with two employees. Him and his brother. Using AI.
Matthew Gallagher started Medvi from his house in Los Angeles. Spent $20,000 and two months. AI wrote the code. AI made the website. AI made the ads. AI handled customer service.
First month. 300 customers. Second month. 1,000 more. First full year. $401 million in sales. This year on track for $1.8 billion.
His only hire? His younger brother. That's the entire company.
The New York Times verified the numbers. $65 million profit last year. More than $3 million coming in every single day.
Now compare this. Hims & Hers sells weight loss drugs online. 2,442 employees. $2.4 billion revenue. 5.5% profit margin. This guy is doing nearly the same with two people and triple the margins.
He grew up living in motels and cars. Taught himself to code on a laptop his uncle gave him. Sold samurai swords on eBay as a teenager. Didn't finish college. Moved to LA to become an actor.
Now he's running the fastest growing company nobody has heard of.
When his website broke during a hike he had to sprint home because there was nobody else to fix it. Lost 200 customers in one hour. That's the reality of a two person company doing $1.8 billion.
A VC told him don't raise money. He listened. Zero outside funding. He owns 100% of it.
Two brothers. $20,000. A laptop. And every AI tool they could get their hands on. That's all it took.
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🚨 JUST IN: The US House just gaveled IN AND OUT after the Senate sent over a DHS funding bill that doesn't include ICE or CBP
This means the DHS shutdown will continue until at least next week
The plan is to send President Trump a bill without ICE/CBP, then pass reconciliation including both agencies via simple majority in the Senate
Trump can also likely fund civilian support staff — which are unpaid right now — through executive order, to fill in the gaps through June 1
GET MOVING!
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TBPN is my favorite tech show.
We want them to keep that going and for them to do what they do so well.
I don't expect them to go any easier on us, am sure I'll do my part to help enable that with occasional stupid decisions.
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TBPN has been acquired by OpenAI!
The show is staying the same and we’ll continue to go live at 11am pacific every weekday.
This is a full circle moment for me as I’ve worked with @sama for well over a decade. He funded my first company in 2013. Then helped us fix a serious logjam during a critical funding round a few years later. When I took my second company through YC, he was president at the time, and then when I joined Founders Fund, the first deal I saw in motion was the post-ChatGPT round in late 2022. And as we started growing TBPN last year, he was the very first lab lead to join the show.
Thank you to everyone that has been a part of TBPN until now. The last year has been the most fun and rewarding part of my career and we’re excited to have more resources than ever going forward.
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As sure as night follows day, the Golden State will eat its Golden Geese until there are no more left.
gg
Chamath Palihapitiya@chamath
California is going bankrupt before our eyes.
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@jithingarapati @OpenAIDevs @linear Mosaic Motion video ready!
Watch on Motion: motion.so/share/cf8cdbf1…
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@OpenAIDevs @linear @motion_so I never used linear in my life can you explain me how to use it in a video
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@sundarpichai @motion_so create a documentary-style motion graphics film on Artemis
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And we have liftoff! 🚀 Wishing the best to the Artemis II crew on their incredible journey ahead.
Imagining the amazing view they have, looking down at Earth from space would be a beautiful thing.
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@jithingarapati @demishassabis Mosaic Motion video ready!
Watch on Motion: motion.so/share/5d4c65fe…
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@demishassabis @motion_so make me a video about Demis teasing Gemma 4 :) (documentary style)
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Y Combinator CEO Garry Tan’s advice for startups: “When you’re small, act small”
A lot of founders try to emulate large companies, and will do things like use the same terminology as Microsoft to describe their products. But Garry argues this is a mistake:
“When you’re starting something new, the whole advantage is that you’re a real human being. We are so starved for real, authentic connection that if you can talk to people and say ‘Hey, I’m the CEO. What do you need?’ That’s the most powerful thing.”
Being small lets you offer fanatical customer support. Not only will this win customer trust, but it’ll help you find product/market fit. If you listen to customers, they will tell you what they want.
“The reason why people don’t do this is they think starting a startup is building this incredibly complex machinery… But I encourage you to think about it in a different way. It’s more like throwing a really, really amazing party… You go there, you see a friend, they say ‘Welcome! Let me take your coat. Let me introduce you to your friends.’”
For his first startup, Posterous, Garry and his team aimed to reply to every single customer support email within ten minutes. And if there was a bug, they fixed it on the spot.
Human connection with your customers is really important. Garry cites a study on Usenet that found retention increased from 16% to 26% if someone received a reply to their post on the forum.
As Garry explains:
“A 10% difference in retention is actually the difference between a startup that’s flatlining and one that’s working. The compounding of this is really, really massive… Be small. Be human.”
Video source: @ECorner (2023)
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prompt: "teach me what this thread is talking about"
one-shot. everything editable. done in 10 minutes
Engineering at Meta@Meta_Engineers
We're open-sourcing BOxCrete, a new AI model for the construction industry. Using Bayesian optimization, BOxCrete helps producers rapidly design concrete mixes with domestic materials, bypassing months of lab work. The results from our data center build in Rosemount, MN: 🚀 43% faster time to full structural strength 🛠️ 10% reduction in cracking risk 🇺🇸 100% domestic material usage We are open-sourcing the model and the foundational data to empower producers everywhere. Check out the full technical deep dive on our Engineering blog: go.meta.me/90538a
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@motion_so make a documentary-style animation about the character who says this in Naruto.
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