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Matt Dunn
@mattgdunn
A.I. and automation guy
sydney
Joined March 2009
84 Following
60 Followers
152 Posts
Teachers spend over half their time on admin. Not teaching. AI can fix that. Practical AI for Busy Teachers, early bird pricing with code earlybird-45-ws. https://t.co/XCLEQVRd3v
Most teachers using AI for lesson planning aren't telling anyone.
The risk isn't that they're using it. It's that they're doing it without a framework.
Lesson planning has two layers. AI handles one well. The other is yours.
https://t.co/CZ3UUAporR
#AIinEducation #EdTech
i deleted half my Claude setup last week and every output got BETTER
sounds backwards, but anthropic's own team just explained exactly why it works.
here's the one prompt that tells you what to cut (and you don't even have to paste anything):
this is what happens to everyone...
you get a bad output, so you add a rule to your skills. "be more concise."
next week, another bad output. another rule. "use a casual tone."
but a month later, something else breaks. "always explain technical terms."
you keep stacking, and it feels productive because you're fixing problems as they come up.
but 3 months in, you've got 30 rules piled on top of each other.
some of them contradict each other ("be concise" and "always explain your reasoning" are fighting).
some of them fix problems that the model doesn't even have anymore.
and the model is trying to follow all of them at once, which means it's doing none of them well.
it's like handing a chef a 47-step recipe when they only need 12.
the extra 35 steps slow the chef down, make them second-guess the parts they already know, and the dish comes out worse than if you'd just let them cook.
that's what over-prompting does.
anthropic just published a piece on how they build claude code (the ai coding agent).
their own engineering team found that their scaffolding was making the ai worse
which means your custom instructions are almost certainly doing the same thing.
so here's the actionable move...
instead of manually reading through your setup line by line, just tell claude to audit itself.
if you're in claude's desktop app, claude already has access to your:
claude[.]md (the file where your preferences and rules live), your skills folder (where your reusable instruction files are stored), your context files, everything.
just open claude code/cowork and say this:
—
"read my entire setup before responding. check my claude .md, every skill in my skills folder, every file in my context folder, and any other instruction files you can find.
then go through every rule, instruction, and preference you found. for each one, tell me:
1. is this something you already do by default without being told?
2. does this contradict or conflict with another rule somewhere else in my setup?
3. does this repeat something that's already covered by a different rule or file?
4. does this read like it was added to fix one specific bad output rather than improve outputs overall?
5. is this so vague that you'd interpret it differently every time? (ex: 'be more natural' or 'use a good tone')
then give me a list of everything you'd cut with a one-line reason for each, a list of any conflicts you found between files, and a cleaned up version of my claude.md with the dead weight removed."
—
one message. claude goes and reads your entire setup, audits it, and comes back with exactly what to cut and why.
you don't dig through files, you don't read every rule yourself. it does the whole thing.
once you get the results, don't just blindly delete everything it flags.
here's the process:
1. read what it flagged and why
2. delete the flagged rules
3. run your 3 most common tasks with the trimmed setup
4. did the output stay the same or get better? the deleted rules were dead weight
5. did something specific break? add back just that one rule
the goal is to find the minimum viable setup that gets you the output you want.
your ai setup should be getting simpler over time.
addition by subtraction baby
![itsolelehmann's tweet photo. i deleted half my Claude setup last week and every output got BETTER
sounds backwards, but anthropic's own team just explained exactly why it works.
here's the one prompt that tells you what to cut (and you don't even have to paste anything):
this is what happens to everyone...
you get a bad output, so you add a rule to your skills. "be more concise."
next week, another bad output. another rule. "use a casual tone."
but a month later, something else breaks. "always explain technical terms."
you keep stacking, and it feels productive because you're fixing problems as they come up.
but 3 months in, you've got 30 rules piled on top of each other.
some of them contradict each other ("be concise" and "always explain your reasoning" are fighting).
some of them fix problems that the model doesn't even have anymore.
and the model is trying to follow all of them at once, which means it's doing none of them well.
it's like handing a chef a 47-step recipe when they only need 12.
the extra 35 steps slow the chef down, make them second-guess the parts they already know, and the dish comes out worse than if you'd just let them cook.
that's what over-prompting does.
anthropic just published a piece on how they build claude code (the ai coding agent).
their own engineering team found that their scaffolding was making the ai worse
which means your custom instructions are almost certainly doing the same thing.
so here's the actionable move...
instead of manually reading through your setup line by line, just tell claude to audit itself.
if you're in claude's desktop app, claude already has access to your:
claude[.]md (the file where your preferences and rules live), your skills folder (where your reusable instruction files are stored), your context files, everything.
just open claude code/cowork and say this:
—
"read my entire setup before responding. check my claude .md, every skill in my skills folder, every file in my context folder, and any other instruction files you can find.
then go through every rule, instruction, and preference you found. for each one, tell me:
1. is this something you already do by default without being told?
2. does this contradict or conflict with another rule somewhere else in my setup?
3. does this repeat something that's already covered by a different rule or file?
4. does this read like it was added to fix one specific bad output rather than improve outputs overall?
5. is this so vague that you'd interpret it differently every time? (ex: 'be more natural' or 'use a good tone')
then give me a list of everything you'd cut with a one-line reason for each, a list of any conflicts you found between files, and a cleaned up version of my claude.md with the dead weight removed."
—
one message. claude goes and reads your entire setup, audits it, and comes back with exactly what to cut and why.
you don't dig through files, you don't read every rule yourself. it does the whole thing.
once you get the results, don't just blindly delete everything it flags.
here's the process:
1. read what it flagged and why
2. delete the flagged rules
3. run your 3 most common tasks with the trimmed setup
4. did the output stay the same or get better? the deleted rules were dead weight
5. did something specific break? add back just that one rule
the goal is to find the minimum viable setup that gets you the output you want.
your ai setup should be getting simpler over time.
addition by subtraction baby](https://pbs.twimg.com/media/HEGNR64boAA8x4U.jpg)
@UnitedCupTennis From two maths teachers
@emollick Glad to have a comparator since this question comes up a lot in conversations
This edition's jam-packed with what happened in July in AI including OpenAI's new agents, Microsoft Copilot's vision, Spotify's new chatty DJ and AI paint that cools buildings down (whaaaaaat?)
go to https://t.co/7Gdfxh2cgb
If you weren't concerned about the power of ai before, you should probably check this out. Nuts
https://t.co/dulw9cp2ZL
🚨 Copilot just levelled up: it now has memory + custom instructions.
I’ve built a set of instructions that makes it way more useful.
Comment if you want it.
👇
https://t.co/EKwnPuR33C
#copilot #chatgpt #microsoft365 #aitraining
This video was made with #midjourney Read more in this month's newsletter from Fambirai!
https://t.co/mjBJxiNr93
Check out my latest article: AI Adoption Rates and Key Trends in Construction (Australia vs. Global) https://t.co/hgDecKTmqp via @LinkedIn
i think we are going to initially offer 10 uses per month for chatgpt plus and 2 per month in the free tier, with the intent to scale these up over time.
it probably is worth $1000 a month to some users but i'm excited to see what everyone does with it!
Check out my latest article: Generative AI Adoption in Australia: Trends, Demographics, and Impact https://t.co/pyry4ABRQA via @LinkedIn
Flinders University partners with The Missing Link for AI Copilot training - CRN Australia https://t.co/SKQrfJuhef #ResponsibleAI
The AI-enabled worforce is arriving
https://t.co/JXSLnJBj9X
@MSFTCopilot check out our training that's driving user adoption
https://t.co/9SRK40GAvo
We're rolling out interactive tables and charts along with the ability to add files directly from Google Drive and Microsoft OneDrive into ChatGPT. Available to ChatGPT Plus, Team, and Enterprise users over the coming weeks. https://t.co/Fu2bgMChXt
Microsoft's Copilot for 365 now available to smaller businesses and those wanting to trial with fewer than 300 users. Want support rolling it out? Get in touch with me on [email protected]
#ai #microsoft #copilot #themissinglink
https://t.co/3O56wSJMzx
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