The approach I’ve been taking lately with general AI development:
1. Define the work.
2. When working in a legacy code base provide good examples of where we want to go with the migration and the expectation of the outcome.
3. Tell it about what support tools you have, lint, build, tests, etc.
4. Select a very specific scenario to modify first and have it write tests for the scenario.
5. Manually read and tweak the tests, ensure they’re testing what you want, and they cover all you require. The tests help guardrail the actual code changes.
6. Depending upon how full the context is, I may create a new chat and then pull in the test, the defined work, and any related files and ask it to implement based upon the data provided.
This general approach has worked well for most situations so far. I’m positive it could be improved so any suggestions are welcome.
OpenTelemetry (OTel) and the OpenTelemetry Protocol (OTLP) are immensely powerful tools. The ability to emit telemetry data from any source, coupled with a receiver that can sample, filter, pipe, and potentially reshape the data to suit any need, is a game changer. This flexibility revolutionizes how we approach observability and monitoring across diverse systems.
With things like this, you realize how short life is, and sometimes how brittle it can be. I often forget that the people that built the foundation of what we use every day are still with us, and we have a short time to be able to show them appreciation before we say thank you to someone who has gone, if you know of someone that has dedicated time to something that you use, show your appreciation this week. It might be the last time that you have the opportunity to make that human contact.
As an American and working with a lot of really smart people over my career in IT, I don’t see this happening. I find most people I’ve come across suffer from imposter syndrome.
Those I know outside IT work, get paid and spend time with family. I don’t really see any superiority.
That’s just my perspective on a small part of the world. Have a great weekend all!
I wonder if this study is accidentally measuring people’s perception of their potential. I struggle a fair amount with public speaking; it’s a phobia for me, but I also know that I am capable of overcoming it if I prioritize it. So maybe I would seem to think I’m better than I am if the methodology accidentally conflates my belief about my potential with my belief about my current state?
Maybe not even with their own potential but others'. For example, nearly everyone feels that their kids are special high performers in the US, or at least hope they are. We can't all be above average. I've heard that the Japanese have a different way of viewing it - that they hope for average since that's logically what they will get, but still push for fulfilling one's full potential.
This happens due to the fact that working in IT what you see instead is that individuals they deem inferior are not hired. Racism is a very big think in both Asian and American Culture. In systems such as the University of Texas we see outcomes for Hispanics that receive degrees in technology. They just don't get jobs. The results of it are easily translated over for other ethnicities with the notable exception of African Americans which Texas surprisingly has very few African Americans and even less with a bachelors degree or higher.
It tells us a lot firstly that there is a limit to the amount of minorities that are hired every year and that it's artificially set by a group of people systemically due to varying biases, often a sense of superiority. I could list the two groups that actively participate in this but doing so is pointless.
This has helped me immensely. Nothing crazy, do 5 rounds of squats at your pace, 1 minute a part. Or sit-ups, push-ups, hollow-mans, etc. it wakes me up, and makes me want to work. I’m tired after, but that’s getting better as I get more into shape. Best of luck to all!
1. Define the work.
2. When working in a legacy code base provide good examples of where we want to go with the migration and the expectation of the outcome.
3. Tell it about what support tools you have, lint, build, tests, etc.
4. Select a very specific scenario to modify first and have it write tests for the scenario.
5. Manually read and tweak the tests, ensure they’re testing what you want, and they cover all you require. The tests help guardrail the actual code changes.
6. Depending upon how full the context is, I may create a new chat and then pull in the test, the defined work, and any related files and ask it to implement based upon the data provided.
This general approach has worked well for most situations so far. I’m positive it could be improved so any suggestions are welcome.