Is that "difficult" quote from a separate source? I can't find it in the text of the linked report.
Also, the report says that both red and blue states had both overcounts and undercounts, which is a different picture than "over counts in blue states". It is accurate that more blue states had overcounts and more red states had undercounts.
I realise your question is rhetorical, but the report does offer a possible answer: "The 2020 Census faced many challenges such as
conducting fieldwork during the COVID-19 pandemic.
Other challenges to the 2020 Census included con-
troversy around a proposed citizenship question and
changes in the duration of the Nonresponse Followup
operation." It's plausible that those "challenges" could have been experienced unevenly by state, particularly with how politicised the pandemic and its mitigations were in the US.
Also worth pointing out that when the census can't collect individualized data, they estimate it based on existing government data. The quality of data varies significantly depending on the local government, and pretty much all governments were struggling to figure out the current state of things during the pandemic.
So what? The article describes the political implications of the Census Bureau errors. I cited the Census Bureau release itself so you can cross-check whether the factual assertions in the article are accurate.
> and also implies political causes.
> So it's useful to identify this as not something a journalist or credible outlet would actually put their name to.
> An independent analysis by the Pew Research Center and election data experts calculated exactly how those specific errors altered the final map. If the census had perfectly matched the PES estimates, the seats would have shifted as follows: [table showing apportionment errors cost Texas and Florida 1 seat each, and gave Minnesota, Rhode Island, and New York 1 undeserved seat each.
Fucking up 5 House seats for a decade seems like a firing-worthy offense.
Oh, I see, you are posting the same misleading comment on multiple threads here. I'm sure you are doing so in good faith, so here is a link to my other comment explaining why it is misleading: https://news.ycombinator.com/item?id=49546715
1. Why send ballots until verified.
2. There's no document verification. Anybody can enter someone's name and social security number and get a ballot delivered anywhere.
3. I still don't trust the ballot gets thrown out if there's a mismatch.
In order to be 100% safe, there should be only in person voting with a voter id. Anything else is open to abuse.
You might enjoy Spirit Island, a cooperative game designed explicitly to make that "quarterbacking" strategy infeasible and encourage more interesting player interaction. Players have asymmetrical abilities and power progression, so it's hard to micromanage everyone, and everyone has enough choices to make that even the "I think we can win this turn if..." conversation generally depends on each person making their own reasonable choices rather than one person planning out the whole turn.
However, companies have been using AI as an excuse for layoffs since well before January 2026, which corroborates the study's conclusion. (Source: https://layoffs.fyi/ai-layoffs/) There is certainly an uptick starting 2026, but that could be explained either by AI actually causing more layoffs, or by AI becoming an even better excuse for layoffs.
Most of the comments so far are responding to the first few paragraphs of this article. On reading further, I thought this was actually an unusually balanced take on how to use LLMs in a software org.
I can't help but cringe at the "cost of code is now zero" meme that this article repeats because in my experience the biggest cost of code was always the activities around the code - planning, communicating, reviewing, validating. This author, despite repeating the meme, seems to agree. Their emphasis on writing PR descriptions and specs for humans rhymes with my experience and it sounds like a nicer way to work than chasing some dark factory fever dream.
I also thought the "Two Modes of Working" section was useful. People get wildly different results from coding agents depending on how they use them, but I've not seen a lot of actual guidance on when to do X vs Y.
Personally, I've been using what the author calls "sidekick mode" since last October - before the supposed "AI got good now" threshold - and agree it's a more useful default for an engineer than "delegate mode".
Writings code has been viewed as an expensive part of development for a while. That's why the coding interview script the industry has adopted requires you to to not write any code until you suffenciently planned the whole problem.
Because the assumption is code is expensive to generate. Otherwise why would you put all the upfront effort into all of the planning?Just write some code and iterate on where it goes.
Because you're not just dicking around for funsies? It's fine if nobody uses your shit, but it's not fine if you have actual customers who depend on you.
Maybe this is a paid feature that we're going to charge money for. Or we're going after a new market segment, or trying to take business from a rival. A new feature might have regulatory or legal requirements, somebody needs to understand all that.
At the very least, someone wants this new thing for a reason. You can't just guess, you actually have to talk to people and understand the problem domain lol.
Your not going to know your classes, parameters, how data should actually be laid out in storage or represented in the application until you start writing anyway. It's always an iterative process.
Code being expensive would be one reason to plan, but hardly the only one. Some other reasons: cost of failure (don't leak customer PII), maintenance, unclear requirements, unclear success criteria.
Developers that can't see from A to Z (all steps ahead) are generally not the ones hired for serious roles (or high paying jobs), that's the difference with iterating on the fly.
The post says they wrote a custom harness that orchestrates work between multiple separate model invocations. That is different from running Claude Code (which is a specific existing harness around the Claude models).
The post takes a while to get around to saying that, and could have included more detail besides the workflow diagram and table (which they flag as only "an example of" such a harness), but it does answer the question. It's a different kind of tool because it's a model rather than a harness+model pair.
I wouldn't agree that LLMs are a higher level of abstraction, but I've found they do help me think at a higher level of abstraction, by temporarily outsourcing cognitive load.
With changes like substantial refactors or ambitious feature additions, it's easy to exceed the infamous "seven things I can remember at once":
* the idea for the big change itself
* my reason for making the change
* the relevant components and how they currently work
* the new way they'll fit together after the change
* the messy intermediate state when I'm half finished but still need a working system to get feedback
* edge cases I'm ignoring for now but will have to tackle eventually
* actual code changes
* how I'm going to test this
Good lab notes, specs etc can help, but it's a lot to keep in mind. In practice these often turn into multi person projects, and communication is hard so that often means delay or drift. Having an agent temporarily worry about
* wiring a new parameter through several layers
* writing a test harness for an untested component
* experimentally adding multibyte character support on a branch
frees up my mental bandwidth for the harder parts of the problem.
The main benefit is to defer the concern until I have a mostly working system. Then I come back and review its output, since I'm still responsible for what it delivers, and I want better than "mostly working".
This is what I've found to be very successful for me. My flavour of ADHD has historically made it hard for me to start new projects as I get very stuck on all of the little details from the start, while also thinking about the high level aspects.
Being able to spend my energy on the architectural decisions and validate my understanding before spending time on optimising the internals has actually allowed me to follow through with some of my designs.
Experimentation is then faster. If the data model wasn't good enough, I can actually experiment with it immediately, before we accidentally ship something to production and then have to deal with a very annoying data migration problem. The exact code doesn't matter to begin with when we just want to make sure the data is efficient to decode and is cache friendly.
I recently built a project I had in my mind for 3 years but could never work on because all the individual components were overwhelming. It involved e2e encryption, consensus, p2p networking, CRDTs, and API design. It was very nice to see it come together. The project ended up failing due to some underlying invariant, so it was nice to validate that and finally get it out of my head.
Does it? Claude the chatbot is available for free, and it can write code, but Claude Code is a separate product that as far as I know is only available on paid plans. Source: https://claude.com/product/claude-code
I guess I mixed the two, but I suppose the point still stands because Anthropic has a free Claude chatbot _and_ OpenAI doesn't have a Claude Code product (does it?).
Also, the report says that both red and blue states had both overcounts and undercounts, which is a different picture than "over counts in blue states". It is accurate that more blue states had overcounts and more red states had undercounts.
I realise your question is rhetorical, but the report does offer a possible answer: "The 2020 Census faced many challenges such as conducting fieldwork during the COVID-19 pandemic. Other challenges to the 2020 Census included con- troversy around a proposed citizenship question and changes in the duration of the Nonresponse Followup operation." It's plausible that those "challenges" could have been experienced unevenly by state, particularly with how politicised the pandemic and its mitigations were in the US.
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