# January 2023 Digest: CRDT Optimization, ML Data Ethics, and Prompt Engineering

DevFeed: [January 2023 Digest: CRDT Optimization, ML Data Ethics, and Prompt Engineering](<https://devfeed.tech/articles/random-things-i-liked-jan-2023-38429.md>)

Original publisher: [Read original article](<https://khmylov.com/2023/01/random-things-i-liked-jan-2023/>)

Author: Andrew Khmylov

Published: 2023-01-30T00:00:00Z

Content type: opinion

Language: en

Sources: [Despite the odds](<https://devfeed.tech/sources/despite-the-odds.md>)

Topics: [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Prompt Engineering](<https://devfeed.tech/topics/prompt-engineering.md>), [Data structures](<https://devfeed.tech/topics/data-structures.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [stable-diffusion](<https://devfeed.tech/topics/stable-diffusion.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [data-structures](<https://devfeed.tech/tags/data-structures.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [ml](<https://devfeed.tech/tags/ml.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [prompt-engineering](<https://devfeed.tech/tags/prompt-engineering.md>)

## AI overview

A personal digest discussing CRDT performance improvements through better data structures and hardware-specific techniques, concerns about the ethics and copyright implications of training large ML models on web data, and skepticism about prompt engineering as a durable discipline. It argues that prompt behavior depends on model implementation and version rather than magical intelligence hacks.

## Source excerpt

Keeping on the challenge, here's a digest of things which got my attention recently. CRDTs optimization https://josephg.com/blog/crdts-go-brrr/ I am long time fan of collaborative editing and distributed systems in general. The author makes a great overview of existing approaches to CRDTs, taking a special hint on Mr Kleppmann's Automerge. It is a great theoretical proof-of-concept (I actually had an idea to use it for several work projects), but it lags behind in terms of performance. The author reaches the astonishing 5000x improvement by utilizing better data structures and some hardware specifics. Nice side idea: Kleppmann keeping a trace of keyboard events while writing a paper, which then can be used as a real-world benchmark test data! Have I been trained? https://haveibeentrained.com/ The recent rise of huge ML models trained on the data scrapped over the web raises a lot of concerns around copyrights and ethics. On the one hand, if you share something on the web, you should assume that it'll stay there forever, with possible malicious uses. On the other, is it appropriate for the companies to use your data without any consent from your side, to make a profit for them? Critique of prompt engineering Remember 2022? Everyone was crazy about "hacking" prompts for the recently released models. Adding "critically acclaimed" or "artstation trending" to the image generation prompts; Twitter influencers claiming "prompt engineering" to be the future, replacing traditional software jobs. I was skeptical on that, it immediately felt like super niche implementation details of each particular model, that's why prompt hacking guides were not gonna work in the long run. I guess it comes from not understanding the implementation details: there is no magical intelligence hacks, just massive amout of weights. People try random stuff on prompts, and then try to make some sense and assumptions about them. What works for one model version may not even work on another version of