I think I'm addicted to getting agents to build state machines Every time I see an agent has built a state machine (or even just a reducer) I can feel a huge bump in the robustness And I know that future updates will be trivial and testable Would recommend for frontend

Science fiction

recommended reading. i like the term "coherence" over "taste". historically, simplified, a human would approach a software writing human, asking them to build a thing. that's an iterative process, until software writing human truely understands what the other human needs and wants. which is also why we moved away from waterfall. you often can't know what you want before you've build a first shitty solution you can feel out. and your wants might change. in this scenario, the outcome is what matters, not the program structure itself, unless that makes the outcome worse/impossible (bugs, perf, ease of adding features). the software writing human's skill is not so much programming itself, but understanding the other humans wants, pulling out domain knowledge from the other human, asking questions, and ultimately translating the other human's wants into programs, while ensuring the program structure is fit to be modified for future needs. now, can you replace the software writing human with an agent? for simple one-off stuff, yes. if the human with a want is technically minded that extends to more complex programs. most humans suuuuuck at this iterative colab with agents tho, as they are bad at structuring and communicating their wants. and agents currently still suck at the software writing human skill of colaborating with another human to understand their wants and translate them to a program. my gut tells me that this is because the training data and RL for coding agent tasks is largely composed of trajectories where a software writing human and an agent colaborate. that missed the other half of the problem: the human with a want and software writing human interaction. and until we can encode that agents for writing programs will likely only be effective in the hands of software writing humans, or very technically minded humans. another thing agents currently suck ar is translating software writing human shallow instructions into architecture that's not a house of cards. i think this too is a consequence of the lack of training data/RL-able trajectories that encode the process a team of software writing humans go through when building a thing. we do have digitized artifacts (design docs, commits/PRs, issue trackers) that capture some but not all of that. i think this will improve, as we all are now doing design sessions with our agents, so labs could in theory train on that. however, even if this data were available and becomes part of training, the pretraining data still has tons of shit code from around the internet in it, ehich posttraining would have to combat. welcome to my morning word salad.

I'm a newcomer to @poteto's plist; But it seems to me that there is some very significant wisdom encoded in there. I suspect you could teach a master class on the principles.

Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible. We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial: felixrieseberg.com/how-to-be…

We're honored to be named to the #Disruptors60 class of 2026 curated by @GreenfieldVC Partners at @TechCrunch Disrupt, alongside the top AI companies. Thank you to our team, customers, and partners.

Asked Opus 5.5, GPT-6.1 Sol, and Muse Wonder Full to fix a bug Opus found the root cause and filed a pr GPT-6.1 Sol asked a question, crashed from the async question tool interrupt, found the bug, fixed it, wrote 300 lines of tests, and explained it in impossible to understand terms. Muse Wonder Full found a small PR someone else had filed and said "you should probably merge this instead" I really like this model.

it’s clear we need to retire the term vibe coding because the models now produce better architecture & way more coherent code than an average software engineer. perhaps even better than top software engineers too because unlike a human, ai has a much much larger context window. this ain’t vibe coding at all. it’s precision engineering.

something that keeps bothering me is how some people see slop as something that is happening to them, like they're some kind of helpless bystander powerless to do anything about it excuse me but you are a software engineer, you literally learned how to make sand think and make computers do your bidding?! and now you can't do anything about it? please the 2 things you should know to cut through all the noise: 1. you need to give your agent a way to verify its own work (i just posted about this so won't rehash) 2. your codebase is memory the 2nd is not new either, we have always had lint rules, compilers with good diagnostics and strong type systems, LSPs, etc to help us write better code. there is no difference when agents write the code instead, you just need more of them, and stronger ones. forget rules and skills. you can add those later. yes, your codebase is a form of memory to agents! agents love extending what already exists. so every nit and every annoyance you've ever seen, every mistake and bad pattern you observe from agents, needs to turn into a constraint, or the agents will just keep repeating those same mistakes like a virus in your code. then you keep observing and repeating this process until your codebase is locked down enough that even agents with little context can one shot a high quality PR. an even better direction is to refactor/rewrite your code such that agent anti-patterns become categorically impossible. use type systems to your advantage, use libraries/frameworks that reduce the architectural search space, or heck, create your own framework like we did for grok bot and design high quality primitives and well thought out APIs