I’ve seen people say that agents will make high level programming languages obsolete. Like why should Python et al exist if LLMs could just write Rust, C or even Assembly?
The complaint in data science 15 years ago was “I came here to do machine learning but all I do is data cleaning.” In AI it’s “I came here work with LLMs but all I do is auth and context management.” The most effective people see the shit work as the leverage for huge outcomes
Excited to share that this week I joined Vercel as a Forward Deployed Engineer! Vercel is building the future of agentic infrastructure, and I can’t wait to work with some of the largest organizations in the world as they transform their organizations with AI.
If you think AI generated posts on LinkedIn are bad, wait till you see AI generated spreadsheets with all hard coded values
Data engineers to software engineers who are all excited about context pipelines. (AI engineering is a data engineering shaped problem) #dataBA
the data influencer to AI influencer pipeline
I never got a dopamine hit or flow state in coding. Maybe I never dug deep enough, could be a personal failing. But I do get a dopamine hit from a beautifully designed process, a boring and functional architecture. Basically I was AI native before it was cool
For every technology, there is a phase where you shouldn’t be allowed to post about it, but you are the most inclined to post about it. Many such cases today in AI
I think some people are missing the point of these “my agentic harness migrated this library to rust” posts. These are interesting benchmarks precisely because they are a known, difficult, solvable problem that has objective grading criteria (i.e. the test suite and “it compiles and runs!)
I’ve seen a lot of skepticism about AI similar to the famous “you can see the computer revolution everywhere but in the productivity statistics.” Everyone is being told to adopt AI at work, but to what end? Where are the outcomes? This paper by Ethan Mollick has an answer:
