> When I save twenty hours of a client's money and my own time, by telling them that a new software feature they want would be unnecessary if they changed the order of questions their employees ask on the phone, I've done my job well.
I like to explain my work as "do whatever is needed to do as little work as possible".
Being by improving logs, improving architecture, updating logs, pushing responsibilities around or rejecting some features.
I jumped off the boat of llm a little before MCP was a thing, so I thought that the tools were presented as needed by the prompt/context in a way not dissimilar of RAG. Isn't this the standard way?
I've used to commute in inline skates in traffic. It wasn't usual but I had a couple of close encounters.
The was an avenue with bike preference in the leftmost lane, but as cars parked anyway it way like a 1/4 lane.
Once I was completely zoomed out and felt something was off. The car at my right just push the break.
I followed along with a t drag and straighten up to get a clearer vision. A truck was turning left from the middle of the avenue, cutting 2 lanes including mine.
These things always happens in downhill. I had not enough space to brake. I turned left almost, not without almost hitting a woman that was waiting to cross the street 2 steps down the curb, and headed full blast too a cobblestone street. I don't know how I manage to do that without falling.
Yes a less experienced me would have not seen the risk early enough nor had the skills to get that turn. But there are shit you cannot predict.
I think battlestar Galactica must be quoting one of the Eddas. I've only read if it from Borges in Spanish, but Conner the same meaning: "Estas cosas han pasado. Estas cosas también pasarán."
My experience with Gemini in AI studio mirrors what the AI overview shows. An hallucinated libraries and their internal reasoning dialogue reinforcing the hallucination and saying "the user don't know how to search on pipy".
Yesterday I had a very frustrating experience with Gemini flash 2.0.
I was lean an streamlit app that went from poc to production servers as a showcase....
Then came requirements for user registering,and someone tried to bolt in recaptcha by some magic unsafe markdown evaluation.
I haven't touched streamlit in a year so LLM looked like the right tool.
It kept saying some package existed in pipy, even after I said I googled, searched in pipy, followed their link (that get me to a 404) and posted the result of pip install failing.