this is pure f*cking gold
andrej karpathy landed at anthropic 14 weeks ago
about that "two anthropic seniors made his loop 1000x better with graph engineering" line - i can't find a source for it anywhere, so i'm not passing it off as fact
what is public and checkable:
anthropic's own cookbook on building knowledge graphs, plus karpathy's autoresearch loop that ran 700 experiments in 2 days and surfaced 20 optimizations on its own
i wired the graph approach into my own setup
the difference showed up on the very first reply
claude skipped the stock answer and actually reasoned through the problem
start with the cookbook, the full write-up is below
New TIL on using Blender with coding agents on macOS: https://t.co/GvMDfD926E
GPT-6 Astra (medium):
> Use the already install /Applications/Blender to render a scene of a pelican riding a bicycle
> OK add a background and a lot of flair
> OK make it a whole lot better
If your entire development environment can run AI locally,
what happens to the "you need the cloud" argument?
Microsoft is now pushing a developer-focused Windows setup around high-memory machines and local AI workloads
Everyone is celebrating GPT-6 Astra
But as a software engineer, when u see:
- Better code
- Better reasoning
- Better agents
- Better security
4 major models. 3 days.
AI progress is accelerating
AGI feels closer
And software engineers? You should be worried
So, I finally tested GPT-6 Astra.
2 real repos, 105 bugs, find and fix what you can. Max effort:
GPT-6 Astra: 48/105
Fable 5.1: 43/105
GPT-5.6 Sol: 42/105
Gemini 3.8 Flash: 20/105
And it's crazy efficient. Look at the time and cost 🧵
Got access to GPT-6 Astra. Want to see some pelicans? Yeah you want to see some pelicans... here's a grid comparing Astra to GPT-5.6 Sol, Terra, and Luna https://t.co/LJi3XOpYpJ
Sam Altman (CEO of Open AI):
"You no longer need to write prompts."
In just 38 minutes, he explains how to use ChatGpt at a level that most people can't even imagine.
It's talk he gave to stanford students. A friend sent me the recording last night.
After watching it, I realized I was only taking advantage of about 15% of what this tool can really do.
Watch it in full and then read the guide l leave below on how to create a system that prompts itself.
Research papers every LLM engineer must read:
- Attention Is All You Need
- BERT
- GPT-3: Language Models are Few-Shot Learners
- Scaling Laws for Neural Language Models
- Chinchilla
- InstructGPT
- Chain-of-Thought Prompting
- Retrieval-Augmented Generation
- LoRA: Low-Rank Adaptation
- LLaMA
- FlashAttention
- DPO: Direct Preference Optimization