the blog of founder Arvid. I mistakenly remembered it as Aman Sanger's Blog and marveled at the feeling of being intellectually dominated. As an intellectual admirer who is completely powerless against smart brains, I couldn't help but pay homage to it.
is the bottleneck driving the rapid advancement of AI. Cursor is their attempt to solve the former, aiming to free up more energy, resources, and talent to focus on the latter.
Aman's various ideas
Podcast interview
I'll have time later to listen:
Latent Space: Cursor.so: The AI-first Code Editor — with Aman Sanger of Anysphere
https://castbox.fm/episode/id5327327-id626397008Lex Fridman Podcast: #447 – Cursor Team: Future of Programming with AI
https://castbox.fm/episode/id3102321-id742377735
Analysis of Llama-2: Llama-2 is expensive
https://www.cursor.com/blog/llama-inference
Recommendations for choosing between Llama-2 and GPT-3.5
, it may not be worth deviating from OpenAI's API.
Comparison of price vs latency
Llama-2 is not suitable for generation-heavy tasks
If you need to generate a large amount of content (completion-heavy workloads), it is recommended to prioritize GPT-3.5, as it has significant advantages in terms of price and latency. Llama-2 is more suitable for prompt-heavy tasks
(Although this may not seem intuitive at first, Aman explains the reasons in the article). Batch processing jobs are required（batch processing jobs）。 For example, classification tasks or scenarios requiring minimal generation. Additionally, Llama-2 performs better in the following situations: Special cases of fine-tuning (Finetuning)
Expansion, without involving specific comparisons of fine-tuning.
Key performance comparison
, further demonstrating the superiority of its performance.
Chart: GPT-3.5 outperforms Llama-2 across various benchmark tests.
Details on cost and computing resources
this is the minimum hardware requirement to run Llama at 16-bit precision.
Cost of generating tokens
Under similar latency, the cost of generating tokens for Llama is higher than that of GPT-3.5. Offers competitive pricing, but this can lead to unacceptably high delays. Cost of prompt Tokens
or more.
Summary
If the task is generation-heavy and cost-sensitive, GPT-3.5 is a better choice. Conversely, if the task is prompt-heavy or requires batch processing, Llama-2 may offer better cost-effectiveness. The final choice still needs to be balanced based on specific use cases.
Other technical insights
High cost of 4-bit weight quantization（2023-08）
Aman pointed out that the cost of 4-bit weight quantization is higher than that of 16-bit. This view challenges many people's intuitive understanding of the cost benefits of low-bit weight quantization, implying that a deeper evaluation of its cost-effectiveness is needed.
Limitations of Flash Attention（2023-05）
He believes that Flash Attention does not provide significant help when generating tokens. This observation may influence the application strategies of some currently popular optimization techniques.
Llama-1's demand for multi-query attention（2023-05）
He suggests that Llama-1 needs to adopt the multi-query attention (multi-query attention) mechanism to improve performance. This provides an important direction for further optimizing Llama-1.
The potential of Instruction Finetuning is underestimated（2023-04）
Aman emphasized the value of instruction fine-tuning, believing that this method has great potential in practical applications but has not yet received enough attention.
Various books Aman read
After reading, he was deeply moved and even worked 100 hours per week for the next month.
An unforgettable opening.
"When Breath Becomes Air" - Paul Kalanithi
Read to tears. Also read with tears.
Ted Chiang is one of Aman's favorite writers.
The ghostwriters of "Shoe Dog" and "Open" are both very excellent.
"The Three-Body Problem" - Cixin Liu
"Foundation Trilogy" - Isaac Asimov
He read this book during a cold Cambridge winter night, and winter didn't seem so bad.
A beautiful memory from his high school years.
The only book he has read multiple times.
There is a longer book list📖, refer to https://www.amansanger.com/books