mike not impossible retweeted
The new normal:
Dev: I built a new multi-writer distributed-queue transactional zero-proof foo system.
Me: How does it work?
Dev: I don't know yet
mike not impossible retweeted
I asked Opus 5.5 to make a video about whether or not it was conscious. The ending of this video is insane.
mike not impossible retweeted
"I'm so done with this shit" I say as I continue to responsibly do my job to the best of my ability.
mike not impossible retweeted
"Crush displays fragments of its own source code in realtime on an ASCII grid."
crush by @andreasgysin
mike not impossible retweeted
It's really cool how every project has 1 file with a comment at the top that says "don't try to refactor any of this code or the whole platform will fall apart"
mike not impossible retweeted
Boss: how long will it take?
Me: “2 weeks.”
Reality: The only thing that actually takes 2 weeks is realizing the estimate was wrong.
mike not impossible retweeted
amazing..
a solo wizard made this ultra creative ASCII looking game.. so unique
mike not impossible retweeted
✨ New samwho visual essay o'clock! ✨
Reservoir sampling is one of my favourite algorithms. It allows you to sample from a set without knowing the size of that set.
I assume no prior knowledge, and use no math notation. Just beautiful visuals.
Let me know what you think! ❤️
mike not impossible retweeted
Replying to @evilrabbit_
"AI is gonna take your job...to the next level." ©
mike not impossible retweeted
Noticing myself adopting a certain rhythm in AI-assisted coding (i.e. code I actually and professionally care about, contrast to vibe code).
1. Stuff everything relevant into context (this can take a while in big projects. If the project is small enough just stuff everything e.g. `files-to-prompt . -e ts -e tsx -e css -e md --cxml --ignore node_modules -o prompt.xml`)
2. Describe the next single, concrete incremental change we're trying to implement. Don't ask for code, ask for a few high-level approaches, pros/cons. There's almost always a few ways to do thing and the LLM's judgement is not always great. Optionally make concrete.
3. Pick one approach, ask for first draft code.
4. Review / learning phase: (Manually...) pull up all the API docs in a side browser of functions I haven't called before or I am less familiar with, ask for explanations, clarifications, changes, wind back and try a different approach.
6. Test.
7. Git commit.
Ask for suggestions on what we could implement next. Repeat.
Something like this feels more along the lines of the inner loop of AI-assisted development. The emphasis is on keeping a very tight leash on this new over-eager junior intern savant with encyclopedic knowledge of software, but who also bullshits you all the time, has an over-abundance of courage and shows little to no taste for good code. And emphasis on being slow, defensive, careful, paranoid, and on always taking the inline learning opportunity, not delegating. Many of these stages are clunky and manual and aren't made explicit or super well supported yet in existing tools. We're still very early and so much can still be done on the UI/UX of AI assisted coding.
mike not impossible retweeted
🎯 main takeaways from the accomplished people I follow who are taking AI assisted coding seriously
by seriously I mean they don't simply throw the AI a prompt and expect it to build a whole complicated software...
...and at the same time they don't simply ignore the whole phenomenon
🔑 takeaways:
>> context is king - your AI coding assistant is only as good as the context you give it
>> human not only needs to stay in the loop, but the human needs to be the LEAD. AI can significantly amplify your productivity but it can't (yet) make the important decisions that require true originality and creativity
>> LEARN as you go along. don't use the AI as an excuse to not understand what you're doing and build your knowledge
>> be precise with what you change. make ONE change at a time. test the change when you're done to verify
all of these tips have proven to be useful in my own experience. still learning to hold true to them in practice
Noticing myself adopting a certain rhythm in AI-assisted coding (i.e. code I actually and professionally care about, contrast to vibe code).
1. Stuff everything relevant into context (this can take a while in big projects. If the project is small enough just stuff everything e.g. `files-to-prompt . -e ts -e tsx -e css -e md --cxml --ignore node_modules -o prompt.xml`)
2. Describe the next single, concrete incremental change we're trying to implement. Don't ask for code, ask for a few high-level approaches, pros/cons. There's almost always a few ways to do thing and the LLM's judgement is not always great. Optionally make concrete.
3. Pick one approach, ask for first draft code.
4. Review / learning phase: (Manually...) pull up all the API docs in a side browser of functions I haven't called before or I am less familiar with, ask for explanations, clarifications, changes, wind back and try a different approach.
6. Test.
7. Git commit.
Ask for suggestions on what we could implement next. Repeat.
Something like this feels more along the lines of the inner loop of AI-assisted development. The emphasis is on keeping a very tight leash on this new over-eager junior intern savant with encyclopedic knowledge of software, but who also bullshits you all the time, has an over-abundance of courage and shows little to no taste for good code. And emphasis on being slow, defensive, careful, paranoid, and on always taking the inline learning opportunity, not delegating. Many of these stages are clunky and manual and aren't made explicit or super well supported yet in existing tools. We're still very early and so much can still be done on the UI/UX of AI assisted coding.
mike not impossible retweeted
My experience using LLMs for coding suggests the future of software engineering looks more like science than waterfall: quick experiments, hypothesis-driven iterations, and rapid learning from failures.
When experiments are cheap, innovation accelerates dramatically.
mike not impossible retweeted
1. Make it work (AI)
2. Make it right (human)
3. Make it fast (AI)
4. Make sure it still works (human)
mike not impossible retweeted
Making cookies in zero gravity isn't easy. The dough must have correct composition to form cookies.





















