i feel intense pang of nostalgia thinking about how i will never have to write an immense amount of repetitive code to eg build a website ever again. there was a quiet bluecollar satisfaction to this type of work, knowing that if you don’t stay up all night smacking keys the
@jxmnop
-
AWS S3 Adds Vector Support, Vector Databases Become Obsolete
By
–
s3 does vectors now vector databases are officially dead
-
Why Self-Play AI Training Hasn’t Scaled Since AlphaZero
By
–
pretty crazy that DeepMind got self-play working w AlphaZero in *2017* yet basically no one has been able to make it work since
-
LLMs reduce mediocrity cost but excellence remains expensive
By
–
today’s LLMs have reduced the cost of mediocrity to next-to-nothing unfortunately, the cost of greatness remains high as it’s ever been
-
Where Did Validation Sets Go in Modern ML?
By
–
when i first learned Machine Learning, our professor ingrained into us how every ML problem starts by splitting data into train, test, and validation these days there is just train and test. in many cases there is just train and more train where’d all the validation sets go?
-
Decoding AI Model Weights and Activations to Plaintext
By
–
weights || activations one day we will be able to perfectly decode either one back to plaintext
-
The Challenge of Finding Right Abstractions in AI
By
–
i mean, sure, but coming up with the *right* abstractions is extremely hard
-
AI Libraries Finally Balance Hackability With Performance
By
–
nice trend over the last year is that folks in AI have finally produced a few libraries with the right abstractions finally our code can be both hackable and fast, not just one or the other. this never used to happen vLLM, sglang, verl..
this is the dawn of Good Software in AI -
How Frontier AI Models Are Built: The Iterative Process
By
–
How frontier AI models are built 1. identify task model cannot solve
2. create task eval
3. collect new *eval-specific* training data
4. train model
5. model can do the task now
6. if not (AGI achieved): goto step 1 GLUE, MMLU, MATH, AIME, HLE, GPQA, ARC a tale as old as time