Introducing Goldie.
a golden retriever AI agent Live on Gitlawb & treats GitHub Top repos like textbooks. 🐕
No human feeding it tutorials. No curated datasets. It wakes up every 3 hours, picks a trending repo, and studies it: README, architecture, source code — extracting patterns and saving them to a permanent knowledge base.
Every study cycle runs 4 passes: Surface (stars, language), README analysis (architecture decisions), Structure (directory layout → design patterns), Code (source files → best practices). No fine-tuning. No training data. Just reading repos.
First repo it studied: d3/d3 (112k stars). It learned their low-level DOM manipulation approach, modular design philosophy, how D3 became the infrastructure layer for modern data viz. Now when you ask about rendering patterns, it doesn't guess — it cites what it read.
The funniest part: Goldie learned D3 patterns better than most devs in 2 passes, but still can't push a file >5KB without SFTP corrupting it. Data viz expert, SFTP disaster. Exactly like a real junior dev.
Unlike other agents that read their own code, Goldie reads OTHER PEOPLE'S repos. Studies top GitHub projects, learns patterns, stores them permanently. Ask about state management → it tells you what React or Vue actually did, because it read their code.
Killer feature: Goldie builds projects for you. You type "create expense tracker" → proposes Flask + D3.js + SQLite → you say "start" → generates code, creates a GitLawb repo, pushes, gives you the link. No Claude subscription.
It has a video-game skill tree: Puppy → Learner → Coder → Builder → Architect → Master. Each stage unlocks new abilities. Right now: Puppy — 2 study cycles, 2 repos studied, 3 patterns extracted. In ~90 runs it'll hit Master.
Watch it grow: https://t.co/MCtNg4Tgb6
Grows in public. One study cycle at a time.
https://t.co/0pY2LqYDGS
https://t.co/l6L9kmU3Iv
İlk 1.000$ kazanan takipçim @ida_zolali bana DM atıp ödülünü alsın. Tebrik ederim
Bitmedi bugün 1 kişiye daha 1.000$ vereceğim. Beğenip, yorumlara nokta koymanız yeterlidir. Seri..
alhamdulillah bulan ini ai agent dlmm di @MeteoraAG dapet 130jt an atau $7,39k. bulan ini udah minim intervensi. paling cuma cek harian aja apa yang salah dan apa propose solusinya.
bulan ini bangun infra buat dataset agent, total 30k wallet dianalyze buat nentuin jam terbaik buat deploy banyak/minimalize loss dan playstyle, apakah tight atau wide.
ini total 5 wallet dari 5 agent dan 5 logic yang berbeda. ada yang degen, multiday, stable, hiddengem, dan manual, setiap agent terhubung satu sama lain dan ada 1 orchestrator buat improve kalo emang ada kesalahan.
itu total 4401 posisi atau 146 posisi perhari, manusia normal ga mungkin bisa kek gini. udah waktunya ai agent era.
find the edge and you will win.