mbriggs retweeted
The difference between having a tool and being a tool is subtle these days and it’s easy to accidentally build yourself into being a tool. Don’t be a tool.
mbriggs retweeted
Initial iPhone Duo score: 9/10
Why? Same reason Gruber and Nilay are slagging on it:
1. It's an iPad mini you can carry everywhere
2. It eliminates any need to carry an iPhone
I have wanted to go iPhone-free for a decade. Calling it the worst iPhone but best iPad is awesome!
Vibecoders, 𝗜'𝗺 𝗯𝗲𝗴𝗴𝗶𝗻𝗴 𝘆𝗼𝘂, add these two lines to your AGENTS.md:
"Cut taglines, helper text and secondary labels. If a screen needs them to make sense, fix the layout."
"Make the best decision that will elevate all three: the user experience (UX), developer experience (DX), and agent experience (AX) always, without breaking anything."
mbriggs retweeted
ok I think the main thing we've learned this month is that computer programs get faster whenever they're ported to another programming language, and all we need to do is keep porting back and forth until every program only needs one CPU cycle to finish
mbriggs retweeted
Want my advice on becoming a better builder, a better orchestrator of agents, a better engineer?
Build intentional boundaries into your life. Take breaks. Keep a sabbath. Make room for rest and time away from screens.
Protect your time to disconnect as ruthlessly as you protect your time to focus.
You need both to do good work.
how do you trust your agents' work more? copy this post/link and give it to your @bot or agent!
1. codebase improvements: this is just good software engineering - better architecture, stronger types and invariants, clear boundaries. agents love to copy what exists and take the shortest path, so make the easy thing the right thing. categorically eliminate problems through code and you won't need to think about it again.
2. static analysis: lint rules, tests, CI, verification (eg TLA+). very token efficient ways to catch issues ahead of time.
3. prose: comments, rules, AGENTS.md, skills - anything that can't be enforced and requires agent judgment. use it minimally, but do use /create-verification-skill.
4. human review: the hardest to scale. at the rate agents land code, it's pretty much impossible to thoroughly review every PR, so spend that effort on the highest leverage point, designing an environment where agents do the right thing by default.
this forms an inverse pyramid: you want most of your effort at the top, least at the bottom. codebase improvements are deterministic, making it more token efficient, and lets you sleep more soundly at night.
install github.com/cursor/plugins/tr… and create a new Project:
1. look through my slack/chat messages and code review comments where i've given feedback on PRs, code, architecture
2. use /recall on my past transcripts to learn how i steer agents
3. /poteto-mode use /correct and /architect on past commits to find the most common pitfalls agents fall into, then propose a plan to rearchitect the code i own so it's more agent friendly. answer open questions with prototyping rather than defer to me, and back the plan with real data.
4. before you begin, use /sharpen + /in-your-own-words and confirm this prompt with me
i love writing. i never let agents write for me. whether it's docs, or tweets, or shitposts, or even prompts (okay maybe that one's harder as agents manage other agents)
there's a simple reason for that: i still strongly believe that writing is thinking. it's a skill i hope to never give up. it's also important to me that whatever i say is always authentic, mistakes and all. my favorite posts are the ones that i just full send without any editing
i suspect it also helps with prompting. being able to clearly express what i want, and knowing the vocabulary i need to be able to direct my agents does seem to make for more effective usage of agents. in the same way that engineers have an advantage when we prompt agents to write code, because we are intimately familiar with the words that make up the discipline
knowing the right word incantations to get an agent to produce a desired goal almost feels like spellcasting. as models get more capable, and that input box looks more and more like a magic box that can output anything, words become the interface between you and the genie
there may come a time when we give up the keyboard in exchange for brain-computer interfaces. but until then, i'm writing every word.
mbriggs retweeted
Its amazing seeing so many different pieces of software copying the visual style of Superlogical (at highly varying degrees of quality lol). It's a really good sign we're directionally doing the right thing, since generally speaking only really good patterns get copied widely.
mbriggs retweeted
It's crazy that there's a tool out there that's become vital to they way I do my job, parent my kids, and make major life decisions; and yet we barely spend more on it than AI
mbriggs retweeted
Ok, now I understand why people want to get rid of unit tests. Apparently they overrely on mocks and there aren't proper boundaries between the different parts of the system. If that's the case, go ahead! You should have gotten rid of those before AI. :D
mbriggs retweeted
there used to be a natural limit to how much you could huff your own farts, but now you can build factories to fart for you and huff until your mind can fold space
mbriggs retweeted
I'm a bit confused by all the people buying and/or installing skills blindly.
A skill is not like an app. You can't just download it, click around, and get value out of it.
They are more like extensions of your own thinking process. You have to actually learn how to use them.
mbriggs retweeted
poteto's cursor profile is public. She's used 1.5T tokens in the last 30 days.
Assuming an avg rate of $8/M that's $384k/day or $11m. In the last 30 days. Per year thats $132M.
Let's assume Cursor has some insane deal with Anthropic and gets 90% off. That comes out to $38k/day or $1.1M in Sept/Oct.
$13m will get me 26 sassy staff-level all stars for $500k/yr
Readers added context they thought people might want to know
The Cursor profile at cursor.com/@lauren shows 1.5T tokens used. Cost estimates at $8/M do not account for prompt caching common in agent coding, where cache reads are billed at ~10% of input rates per analyses of Anthropic pricing. This results in lower effective costs.
cursor.com/@lauren chudi.dev/blog/claude-pr… carlosaragon.online/blog/anthropic…

















