Tag Archives: chatgpt

[2026 kcdc] 6 ways you’re using chatgpt wrong (and how to fix it)

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Speaker: Fred Deichler

See live blog table of contents for more posts

Off topic title changed from ScrumMaster to delivery excellent lead. The later is about multiple streams/programms(

Survey

  • Almost everyone uses AI at work
  • Most people use 2+ tools
  • A good number include a tool their employer doesn’t pay for

Item #1: The keyboard isn’t a tool; it’s a toll

  • Filters words before send them.
  • Verbally get possibly unimportant context
  • Speaker works from home using a boom mic. Tried turning headset off and just talking. Felt weird at first
  • Vomit prompt by spewing out everything thinking
  • Voice is faster than fingers on keyboard
  • The fix isn’t speed though; it’s removing the toll

Item #2: <missed the title of this because I was busy unsuccessfully trying to find a copy of the deck online>

  • Context window expanded
  • Don’t want to keep using old prompt because AI can do more. Ex: You can use an MCP server vs upload everything.
  • Spend 15 minutes a month improving your interactions
  • Hit stop if getting wrong answer so stop wasting tokens

Item #3 – You keep finding the answer; but never keep it

  • Don’t save prompts; write skills
  • When get what you need; ask AI to write you the skill
  • Using skills can use less tokens if it give it deterministic things like a python script

Item #4 – Your company bought you AI but you are choosing to use something else

  • Carrying risk
  • Exposing company secrets as training day
  • Violating HR policy
  • Missing opportunity to give company feedback to invest in better tools
  • The company AI isn’t customized the same as your home one

Item #5: Still just googling

  • Partner with AI instead of just asking questions
  • Frame what you want vs asking it. State the problem with context
  • Bring up the real constraint
  • Go back and forth
  • Tell AI what you don’t like about the answer
  • Think of as “we”

Item #6: Late to the party

  • Bring it raw thoughts vs brining something done and ask for cleanup
  • Workbench, not a stylist

Item #7: One setting to rule them alll

  • Choose right model and effort level for each tasks
  • Using same settings is burning money
  • Two dials: model and effort. Not one gear
  • Subagents thrive on lower level models

My take

Content was good, but I feel like I only got half the experience due to the slides. The slides were significantly unreadable (ex: red on black/black on red is hard to read. Same for light gray and black. Only the white on black or really big red on black was readable.). He said multiple times that will share slides after. Which is better than nothing but I want them while listening to presentation. That’s the time I’m dedicating to it and when I want to be able to follow those details. Otherwise why have so much on the slides. I looked for the deck in case it was online. Didn’t find it but did find a video with the same title. https://www.youtube.com/watch?v=KMS-YtWDAAg Not the same mistakes (which makes sense since he said he is covering 6 or 10). But that video would have been perfectly readable on a screen. The covering 6 and having us vote on the other 2 was fun.

mutation testing from a five year old (according to chatgpt)

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I asked ChatGPT to explain mutation testing to a five year old to see what it would say. I got this back:

Alright, imagine you have a big puzzle made of Lego blocks. Each block represents a piece of code that makes up a computer program. Now, let’s say we want to make sure that our puzzle is put together correctly so that our program works properly.

Mutation testing is like having a mischievous Lego wizard who comes and changes some of the pieces in our puzzle to see if we notice. He might swap a blue block for a red one, or turn a block upside down. After he’s done, we carefully check our puzzle again to see if it still looks right and if our program still works.

If we can still solve the puzzle and our program still works even with the changes the wizard made, then we know our puzzle – or our program – is really strong. But if we can’t solve the puzzle or our program stops working, then we know there’s a problem and we need to fix it. So, mutation testing helps us make sure our programs are really robust and can handle unexpected changes.

It’s a good example of how ChatGPT is an aide and not a solver. First paragraph is good. In the second part, I love the mischievous Lego wizard who comes and changes some of the pieces to see if we notice. It’s a good premise. We see if our program still works with the changes.

The third paragraph leads us astray. If we notice the change, that is good. If the program stops working it is also good, means we noticed the change. Mutation testing is about having good tests that can detect change. Not a robust program with unnecessary code that behaves the same way when changed.

[uberconf 2023] Practical AI Tools for Java Developers

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Speaker: Ken Kousen

@kenkousen

For more, see the table of contents

Prompt Engineering

  • Tools are improving fast, might not be needed as job
  • Suggest context (ex: “pretend you are”)
  • Give example of what you want

Chat GPT

  • Free version is GPT 3.5 Turbo (improved performance over original 3.5)
  • $20/month for GPT 4. Can make 25 requests in a three hour block.
  • Have not noticed quality control over plugins.
  • Plugins change rapidly.
  • Apologizes when you correct it.
  • Warning about pasting your company’s code in
  • Trained thru summer 2021
  • Can’t read files on local file system (Bard can). Can read link but doesn’t know it can
  • Often wrong if you ask it about whether can do something. Like talking to toddler; says want thinks want to know.
  • Temperature – tweaks creativity vs precision
  • REST API docs
  • REST API: cookbook has examples
  • Must give credit card to call REST APIs. Pennies are for 1000 tokens (about 750 words). Charged for both input and output words. Also limits on context (amount GPT remembers). Not expensive if don’t use it much. Ken’s bill has been pennies and too low to be sent a bill.
  • REST API JSON response says how many tokens used. Can also see graph when log into account
  • Had it make multiple choice questions on a topic

Chat GPT Code Interpreter

  • Code Interpreter beta feature.
  • Need to explicitly enable under settings.
  • From OpenAI, not third party
  • ex: can convert Groovy to Kotlin DSL for Gradle

DALL-E

  • First popular text to image generation tool
  • A generation behind text/GPT.
  • Stable Diffusion free, but behind on quality
  • Prefers MidJourney, more realistic

Whisper

  • Audio to text
  • Takes audio or video and writes transcription.
  • Free (unless use REST API)
  • Mac Whisper – $20 on time fee for larger models. Good for transcribing videos of talks. Slow first time. After that (including other videos, fast. [caching?]
  • Creates .srt file (Subtitles)

Claude.AI

  • Free beta
  • Only available in US and UK
  • Can hold 100K tokens. ex: can summarize a novel
  • Quality comparable to ChatGPT 3.5, but not as good as 4.0
  • Can upload many file types
  • Harder to get back to previous conversations than ChatGPT. Need to click on “A” icon on top to see them
  • Doesn’t do image

Bard

  • Can upload answers to Google docs on Ken’s personal account, but not business account
  • Used to be able to answer who Venkat is but can’t anymore.

Llama 2

  • Meta announced today
  • Pretrained language model
  • Free unless large company (aka: competitors)

Descript

  • Transcribes and edits video
  • Can give instructions – ex: shorten gaps in video, remove filler words
  • If don’t move around much, will make it look like you are looking at camera
  • Can give text and select a voice. With 30 minute sample, can train on your voice

Canva

  • Can describe presentation want and Canva makes a draft
  • Can choose theme from list of choices
  • Magic eraser – brush over part of image don’t want and replaces with background nearby
  • Beats Sync – line of slide transition to beats of music
  • Magic Write – like GPT 3.5
  • Magic Design – give own image and make presentation around that

GitHub Copilot

  • Virtual pair programmer
  • Plugins for VSCode and IntelliJ
  • If hesitate, suggests code
  • Can’t agree to part of suggestion. Need to accept it all or delete
  • Guesses right a lot because knows what have done before in a training class
  • Always looks plausible because trained on own code. Need to look carefully
  • Next generation is GitHub CopilotX. Only available via wait list. VS Code only at this point, can use for pull requests.
  • GitHub Next – tools in a variety of states – https://githubnext.com. “Visualizing a Codebase” runs as github action to see packages

IntelliJ AI Assistant

  • Not much documentation on how it works. Only one blog post
  • In Ultimate, not Community
  • In beta edition
  • Can highlight code and ask to explain it
  • If don’t like suggestion, can request it suggests something else and get more choices
  • Can write commit message for you
  • Find issues with code when know language well
  • Helps in language know less well because it knows the API/syntax
  • Good for nuisance tasks that would take a lot of time

YouTube Summary

  • Get summary or transcript of video
  • Free
  • Up to 20 minute video

My take

I was doing my interview with the Build Propulsion Lab so was a few minutes late. It was a full room so my seat was on the floor. Luckily, the room had a large aisle so I could sit near the front instead of in the very back! And the carpet was comfy.

As far as Ken’s actual talk, it was great. I liked the overview of a bunch of tools and seeing the REST APIs for calling OpenAI. Great breath of topics and fun examples! I learned a lot including some tools I hadn’t heard of. And some very cool functionality!