Found some quiet time today to apply what I’ve been learning from AI generated images created by pros and bookmarked on X.
Finally getting the results I actually want. Had much fun in my little creative bubble last 2 hrs. 🖼️👇
To get good animations from an AI you need to get good at telling it what you want:
- "stagger this list of items"
- "make this animation direction-aware"
- "spacial consistency", "crossfade", "layout animation",
I made a motion vocabulary for this:
https://t.co/ExAxpr31no
Traces are the lifeblood of Continual Learning. We need to find useful signals across every deployed agent
So every team doing CL will build/integrate world class infra for observability as a foundation to capture and mine that data at scale
And teams mining that data will build Evals/Environments, collect examples for distillation, build efficient verifiers for their tasks, and organize information into long term memory + context stores
Observability Infra/Products + good Data Mining will underpin a lot of work in improving agents over longer time horizons than see before
This goes without saying, but @lateinteraction deserves an enormous amount of credit for all the projects I have been involved in over the past 18 months. So much so that it is difficult to put into words lest I get choked up.
I'm incredibly excited to continue collaborating on our research projects, and to test them at scale while I am interning @PrimeIntellect
Is there a repository with dataset where all tool calling failure messages per models are documented?.
e.g. MALFORMED_TOOL_FORMAT getting this from the latest preview model of google aka Gemini.
Is there any massive dump?. Want to optimize a prompt just dealing with failure cases. Any leads to dataset are appreciated.
This sidewalk from home to metro had no trees last year and use to be quite bland and dirty. Just planting trees and basic cleanliness makes all the difference.
spent my 11-hour flight back from europe working on a very long report. started as a slack message but morphed into a several pages long doc. wifi was as shitty as it gets. after finally making it home i realized that the computer had forcefully restarted. opened slack: draft was gone :(
hail mary: claude pls save me, no clue how but pls try
it checked APFS snapshots, time machine, slack indexeddb, write-ahead logs, service worker / http caches, local storage, app logs, hibernation image... nothing. all gone
but then... it realized i have alfred installed. so it checked the clipboard snapshots alfred keeps in sqlite. sad news: alfred clipboard memory gets deleted after 24h. aggressive retention policy. however! when sqlite runs DELETE, nothing gets actually deleted. it only marks pages as reusable, but it doesn't override the physical bytes. so claude decided to do a raw-scan of the db, reverse eng alfred data format, figure out the portion containing the timestamp, stitched everything back together across overflow pages... and handed me the exact final version of my report, the last one i cmd+C'd
all this, in a single shot
... day 200 of "what if you had an elite hacker you can ask anything to"
If you’ve been hearing about RLMs (or more recently Dynamic Workflows) and wondering what the fuss is all about, start here.
This guy casually used predict-rlm, an open-source project to run RLMs, coupled with GEPA to smash SOTA on the App-World benchmark.
@_svs_ they don't plant saplings, at-least 3-4 feet tall with leaves and small branches in place when planted on roadside. Still aren't mature trees yet. This is next to Doddakalsandra metro station towards Mantri Serenity.
The AI ponzi scheme goes like this:
Everyone is generating all these long ass docs and then passing them off for others to read
Then the person receiving is like, wtf this is way too long, and hands that into an AI to read and summarize
Then they are generating a long ass response back
and this cycle goes like that forever. and we call this work now 😅
The token lords watch this from their towers nodding and grinning.