We got broad and wide access to AI tools maybe a month ago now. AI tools meaning claude code, codex, cursor and a set of other random AI tools.

I use them very often. They've taken a lot of the fun and relaxing parts of my job away and have overall increased my stress. I am on the product side of the business and it feels necessary for me to have 10 new ideas and now the ones with the most ideas will be rewarded, which I am not as good at. Ive tried having the agents identify opportunities for infra improvements and had no good luck there. I haven't tried it for product suggestions but I think it would be poor at that too.

I get sent huge PRs and huge docs now that I wasnt sent before with pressure to accept them as is.

I write code much faster but commit it at the same pace due to reviews taking so long. I still generate single task PRs to keep them reviewable and do my own thorough review before hand. I always have an idea in ny head about how it should work before getting started, and I push the agent to use my approach. The AI tools are good at catching small bugs, like mutating things across threads. I like to use it to generate plans for implementation (that only I and the bots read, I still handwrite docs that are broadly shared and referenced).

Overall, AI has me nervous. Primarily because it does the parts that I like very well and has me spending a higher portion of my job on the things I dont like or find more tiresome.

Maybe OpenAI does things different, but as soon as an OKR around ad performance gets committed to, the experience will degrade. Sure they're not selling data, however they'll almost certainly have a direct response communication where advertisers tell Open AI what and when youve interacted with their products. Ads will be placed and displayed in increasingly more aggressive positions, although it'll start out non intrusive.

Im curious how their targeting will work and how much control they'll give advertisers to start. Will they allow businesses of all sizes? Will they allow advertisers to control how their ads work? I bet Amazon is foaming at the mouth to get their products fed into chat gpt results.

Its awesome that youre feeling better. Its possible, but hard to believe, that its due to nothing but diet changes and if it is, then its hard to imagine that such an extremely specific diet is needed to get the same results.

I use multi armed bandits a lot at work, and I wonder what other algorithms I should look into. Its hard to read the name of an algorithm or category and get a sense of whether or not its applicable. It seems like the general process of "which path out of N options is the best?" can be reframed in many ways depending on contextual details such as how large is N, what's the feedback latency, what are the constraints around evaluation and exploration, etc

* we have a single optimization goal (e.g the metric to minimize or maximize). The hard part isnt defining a optimization goal. The hard part is identifying what stakeholders actually want the tradeoff between different metrics. If you have goal A and goal B, where B is more aggressive than A, then getting agreement on which is better is hard. This is a people problem.

* MAB seems to be a good proof of concept that something that can be optimized but isnt an "end game" optimization path.

* MAB for A/B testing is going to mess up your AB data and make everything more difficult. You should have a treatment that uses the MAB algorithm and a treatment that doesnt.

All of the above is for non contextual MAB. I am currently learning about different MAB algorithms although each of them are pretty similar. The ones ive read about are all effectively linear/logistic regression and tbe differences come from exploration mechanism and how uncertainty is represented. Epsilon greedy has no uncertainty, exploration just happens a fixed percentage of time. UCB is optimistic about uncertainty, and the amount of optimism controls exploration. Thompson sampling uses statistical (beta) distributions about uncertainty, exploration happens less as confidence about a particular set of options increases.

Overall its a fun area to work in that is quite different from your typical CRUD development which is a nice change of pace.

> you’d have to either memorize the entire answer before speaking or come up with a simple pattern you could do while reciting that takes significantly less brainpower

This part i dont understand. Why would coming up with an algorithm (e.g. a simple pattern) and reciting it be impossible? The paper doesnt mention the models coming up with the algorithm at all AFAIK. If the model was able to come up with the pattern required to solve the puzzles and then also execute (e.g. recite) the pattern, then that'd show understanding. However the models didn't. So if the model can answer the same question for small inputs, but not for big inputs, then doesnt that imply the model is not finding a pattern for solving the answer but is more likely pulling from memory? Like, if the model could tell you fibbonaci numbers when n=5 but not when n=10, that'd imply the numbers are memorized and the pattern for generation of numbers is not understood.

This is what I've found it most helpful for. Typically I want an example specific to my scenario and use an LLM to generate the scenario that I ask questions about. It helps me go from understanding a process at a high level, to learning more about what components are involved at a lower level which let's me then go do more research on those components elsewhere.

Do companies like this have a need for SWEs? Are there opportunities for a backend SWE without any background in hardware or biology?

Im imagining a meeting about some project design, and thinking about how it'd go if someone suggested using parentheses to represent nodes of a tree. I imagine it'd get written off quickly. Not because it wouldn't work, but because of the complexity and learning curve involved.

I own DDIA but after a few chapters of how database work behind the scenes, I begin to fall asleep. I have trouble understanding how to apply the knowledge to my work but this seems like a useful thing with a more clear application.

If you create an issue, and solve an issue, indifferent of the issue being real, you'll be credited with solving the issue. It's ridiculous at this scale