Isolumi

future software should always behave exactly how you want it to behave. we no longer need to be bounded by how certain designers and devs think how we should interact with software

you can outsource your thinking but you cannot outsource your understanding

The modern computer probably needs to be rebuilt around personal context, not apps. My email, calendar, messages, travel, running recovery, even wine. Yours will look completely different. That’s the beauty of the next paradigm. The computer adapts to the person, quietly enough that life feels lighter, not louder.

i was 30 minutes away from switching to codex… clutch tweet oty

maybe i need to pull off a niantic 🤔

@BenMacLaurin this is going to save me a lot of tokens

Introducing Budge An agent skill to tweak UI without going back-and-forth with AI - ↑↓ to fine-tune the value - ←→ to switch between properties - Enter to copy the prompt to clipboard Works in Claude Code, Codex, Cursor. Fully open source

stepped outside for the first time today and saw ts

@vrloom @NousResearch do you think theres still things OpenClaw does better?

Switched from OpenClaw to Hermes, setting up Hindsight memory now, and it's already blowing my mind. This is light-years ahead of what OpenClaw is doing. @NousResearch 👑

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.

@itsnotryan @Uber congrats ryan!

i'm interning at @Uber this summer in SF :) shoot me a DM if you're also in the Bay Area!

@chesscom "chess"

everyone who replies "chess" will be in our new twitter header next week

@kat_kampf @GoogleAIStudio would love to try early!

We started internal testing some big updates to the @GoogleAIStudio experience today! Coming to you early next year but reply below if you’d like early access in the coming weeks 👀

@OfficialLoganK @GoogleAIStudio i would like to try!

Big upgrade to vibe coding in @GoogleAIStudio lands in Jan, but if you want to test early… 👇🏻

@UofTHacks yo the frontend team is cracked 🔥🔥🔥

Chat are we cooking? uofthacks.com

A new Steph Curry documentary comes out on July 21st. 🔥👀 (via @A24)

Night Night

DUB NATIONNNNN

@pinkwafflecat NBA finals!

@Isolumi What’s the cONTEXT

👨‍🍳👨‍🍳👨‍🍳

@pinkwafflecat LOLLL do you like my out of context tweets!

@Isolumi i still have tweet notifs on for u LMAO

🏊‍♂️🏊‍♂️🏊‍♂️🎉🎉🎉