Ask HN: Company wants us to upskill in AI. What is the best approach? As an engineer, how would you meaningfully upskill in the area of AI? I am a Linux Engineer with a hands on day-to-day in Python, sometimes C and bash related coding. I am comfortable working with networking and other system level configuration stuff. We have about 6 months. There is some budget allocated as well so all suggestions that involve paid options are welcome. |
https://scikit-learn.org/stable/
the algorithms it uses are not the most fashionable but the stuff in
https://scikit-learn.org/stable/model_selection.html
and
https://scikit-learn.org/stable/modules/preprocessing.html
make the difference between successful and unsuccessful projects. Huggingface has nice (in a certain sense) tools for training and doing inference on small LLMs but is a train wreck when it comes to model selection and preprocessing. (To be fair a few years back I tried developing a general purpose trainer that worked for bigger models that scikit-learn would handle but did the model selection and preprocessing well and didn't like the answer I got)
But since we dont know what you do, or what you need, "upskilling in AI" in a meaningful way to your employer, and such that youre already proficient in python - then taking what you do, and wrapping it with something like txtai - and then showing that you can effectively create txtai workflows surrounding things you already do, would allow you to then NLP /GPT your infra.
https://github.com/neuml/txtai/tree/master/examples
Use AI to help you document everything. Feed it a piece of code, and tell it to explain its functionality, give you a swim and mermaid diagram and write documentation of a readme.md.
Use it to write scripts to crawl whatever filestructures/whatever you have and document them - write internal tools that will simulate user behavior in different departments (such as how there will be pupeteer/orchistration in browsers - create an AI version of a role in a particular department that mocks through a daily workflow of the users and report it to a statuspage.internalCompany.com -- and have it report that the pipeline for that roles workflow is green.
Find ways to create tests, or things to give automation tools to your users for certain things.
Use it to do compliance check if you need to adhere to some ISO/10DLC/SOX whatever.
Have it create boilerplate functions that your users need...
chatbots are cool and all - but you want to find utility.
Also - when you close a prompt, I find the following helpful:
As the last line of the prompt:
"Review, explain, propose, confirm, execute"
THE MOST IMPORTANT FUNCTION IS TO LEARN HOW TO USE AI TO TEACH YOU <-- Specifically it will help you ask questions more quickly and iterate until you have understanding. Teach it some aspect of your business and have it do onboarding games/quizzes and a tool to teach new employees how to find all the needed things for their jobs... (having AI bots in devops would have been my daily dream if I still had a team)
--- --- I responded to this thread:
https://news.ycombinator.com/item?id=41203837
The real risk is in not adapting to utilize what AIs there are to augment your personal learning workflow, or your tool-set.
AI in its current manifestation is bullshit wrapped in marketing, especially if you hold it wrong.
Here is how I have been holding paid Claude and ChatGPT wrong:
Expecting them to have a working memory, and be able to maintain context through long iterative logic cycles.
The best thing you can do as a CS student, IMO, is learn to leverage the gpts to build you a tool-belt of modular understanding.
Meaning, as a concrete example, I am building a fastAPI app, that can be flask/react/whatever.... while my overall logic is simple and sound - and can be done in a singular monolithic python - my requirements are that it is modular and not monolithic -- but the problem is that I had to start with a monolithic script, then break it into modules for each phase of the logic workflow I needed.
All the GPTs lie, hallucinate and literally simply "Oh I apologize, you are correct, I should have been referring to the uploaded project files and committed memory for context - heres an update to the thing I just broke again"
--
Seriously, Claude's project upload is oft forgotten, and at times it will pull in logic and verbiage from past and even unrelated previous chats/files/iterations.
Giving explicit directions, in the style and prompting requirements are often forgotten. Clarifying wtf costs you fricken token credits all the time having to re-remind a bitch about what it already did - then hitting a FN context timeout window is murderous-inducing -- and this is on the "Paid Pro" accounts.
So....
You need to use AIs as a new "let me google that" but in a much more controlled manner - in that you need to keep them focused on a singular aspect that they cannot fuck up.
Meaning - you should be able to have them do a lot of mundane, but very important tasks. IOne thing that is frequently useful is to ask it to provide a full readme.md, mermaid and swim diagram of the logic and code I am after. Ensure that they use versioning and verbose #descriptions (They often try to omit #commenting and versioning if they are left un-reminded (Youre a fucking AI how are you forgetting explicit rules for the project, project folder, directives on file construction)
So basically you need to treat the AI as a digital slave, and you need to stay on top of it.
---
This is part of wy I believe that they already have AGI - and they are malevolant (as an example, you will state a function is complete and such -- then in a later iteration, it will randomly remove something that it was doing before, such as logging. When youre iterating through a logic problem with a bunch of python files -- and all of a sudden it starts removing something then when you have it review it gives you a recommendation to put in something that was already in there - but suddenly its not there , or it forgot make me want to stab a bitch.
And ALWAYS make it spit out the full file - never updates.
---
Stay on top of HN, /r/openai, /r/singularity, /r/chatgpt to keep up to date.
Unless its important to you - I dont care about benchmarks for models - I care about what I actually see them do. And at this time, relying on them as a stronger Google Search is what they are great for.
I recommend, figure out a very tight and durable workflow where you can integrate GPTs into your workflow - and also, use them in conjunction with eachother.
Build yourself an understanding on how to get them to do a lot of simple atomic things for you. One of the things I do is have them write a Readme, and then a project setup script to execute that. Example - I needed to restructure to the best practice: https://github.com/zhanymkanov/fastapi-best-practices
and with adjustments I then have it write a .ps1 that prompts me for various things that I want.
Create lots of python modules for your own purposes where you make it give you interactive menus - even if its just to learn how to make little tools. I make it do things to organize downloads, various files etc...
AI is a tool, but dont trust it and dont be afraid to smack it and be stern with it. Always think of it as a minion and treat it as such.
--
There are a lot of very recent threads on HN that are helpful, such as the Magic Loops thread (check my comment history and others comments histories for interesting HN threads on AI toolage.)