For curious developers 🧠
I built "The Anatomy of an LLM", an interactive explainer showing how text becomes tokens, vectors, attention, transformer blocks, and finally generated text.
https://t.co/fgCeZuQwJf
0.1 + 0.2 =?
It’s not 0.3, yes you heard that right.
To know more this and how many problems this can create in real industries, and how this can be resolved checkout the video here :
https://t.co/LVGWzcreEU
0.1 + 0.2 =?
It’s not 0.3, yes you heard that right.
To know more this and how many problems this can create in real industries, and how this can be resolved checkout the video here :
https://t.co/LVGWzcreEU
You might believe you should spend less time thinking about code because of AI.
I strongly disagree! We’re watching this play out live where tons of AI generated code becomes a liability.
At the end of the day, an engineer needs to be responsible / on call for code that gets shipped to production. If you don’t understand the system you’re trying to debug, you’re probably going to have a bad time.
Yes, AI can help with all of this, if you set up the proper systems. You can have agents triage prod logs, look at errors, etc. You can speed up parts of the investigation, but an engineer needs to make the call. There might be serious customer or financial implications from that change.
I expect the trend continue for trimming dependencies, vendoring code so you can modify it directly, preferring simpler systems with fewer abstractions, and spending waaaay more time thinking about system design and code maintenance.
I’ve said this before, but it’s a great time to get familiar with CS fundamentals and some of the history behind what great software looks like. Many parts will be different in the coming years as AI progresses, but also a lot more than people realize will stay the same.