there is always an aspect of every job that is performative - even small companies. I like to call this perception management. a lot of any job is effectively communicating what you're doing. a lot of effective communication is also not just saying what you're doing, but also how you deliver the information. people are more likely to listen when you communicate things in a more positive tone, make the information concise in a bottom-line up-front style, use a deeper voice (told to me by my wife and women colleagues), and pace the information in a way that lets people ask follow up questions iff needed. no one should _have_ to do all this, but it does change people's perception of how competent you are. I've seen both sides of this coin - amazing engineers that get no promo because they can't communicate, and mediocre engineers that get promoted quickly due to their ability to communicate. I'd almost even argue that this is how should be - as you climb the corporate ladder, communication becomes a lot more important than technical skills and ability

to your point about 1:1s: if you're not getting anything out of your 1:1s, that's a skill issue and is on you IMHO. even when I had bad managers, I was able to effectively communicate my needs, goals, updates, thoughts, as well as give feedback back; in doing so, I've been able to turn horrible manager-team dynamics into a positive experiences. and I'd always argue it came down to the fact that the people perceive you directly correlates with how serious they'll take your word

at the same time, I can empathize with the idea that some middle managers are just bodies that get in the way - everyone's had their fair share of that. but if you're actually good at your job and communicating , you should almost always be able to get around them when it's really necessary

EDIT: and this is coming from a person who is and will always want to stay as an IC engineer

> “We programmers are currently living through the devaluation of our craft”

my interpretation of what the author means by devaluation is the general trend that we’re seeing in LLMs

The theory that I hear from investors is as LLMs generally improve, there will exist a day where a LLMs default code output, coupled with continued hardware speeds, will become _good enough_ for the majority of companies - even if the code looks like crap and is 100x slower than it needs to be

This doesn’t mean there won’t be a few companies that still need SWEs to drop down and do engineering, but tbh, the majority of companies today just need a basic web app - and we’ve commoditized web app dev tools to oblivion. I’d even go as far to argue that what most programmers do today isn’t engineering, it’s gluing together an ecosystem of tooling and or API’s.

Real engineering seems to happen outside of work on open source projects, at the mav 7 on specialized teams, or at niche deeply technical startups

EDIT: I’m not saying this is good or bad, but I’m just making the observation that there is a trend towards devaluing this work in the economy for the majority of people, and I generally empathize with people who just want stability and to raise a family within reasonable means

I like to explain this whole hallucination problem by stating that LLMs are 2 different machines working together. one half of the machine is all the knowledge it was trained on, and you can think of this knowledge as an enormous classic tree you learn in CS classes; and each node in this tree is a token. the other half of the machine is a program that walks through this enormous tree and prints the token it's on

when you think of it like this, 3 things become immediately obvious

1. LLMs are a totally deterministic machine

2. you can make them seem smart by randomizing the walk through the knowledge tree

3. hallucinations are a side effect of trying to randomize the knowledge tree walk

I find it interesting that LLM companies are trying to fix such a fundamental problem by training the model to always guess the correct path. the problem I see with this approach is that 2 people can enter the same input text, but want 2 different outputs. if there isn't always a _correct path_ then you can't really fix the problem.

the only 2 options you have to “improve” things is prune and or add better data to the knowledge tree, or you’re trying the make the program that walks the knowledge tree take better paths.

the prune/add data approach is slightly better because it’s improving the quality of the token output. but the downside is you quickly realize that you need a fire hose of new human data to keep improving - but much of the data out there is starting to be generated by the LLMs - which leads to this inbreeding effect where the model gets worse

the 2nd approach feels less ideal because it will slow down the process of generating tokens.

all of this to say, from this point on, it’s just hacks, ducktape, and bandaids

the high-level facts are

1.) unemployment and number of available jobs is bad right now, and inflation never got back down to 2% after covid. So Powell made the announcement to lower interest rates. this effect will raise inflation, but create more jobs - which is the correct and more important thing to focus on right now

on top of this, tariffs are making things worse for the average american. based on what powell is saying, the current estimates claim the tariff's alongside the planned increase in inflation will lead to about a 20% increase in prices for the average consumer, but this one time 20% increase is better than having no jobs!

2.) the government has been overestimating the amount of available jobs for 10+ years. A large part of why this is happening is because of the gig economy

an example of what I mean is if you sign up to be an uber driver, uber registers you, the driver, as its own company with US government. this kind of thing is fine for uber, but the government doesn't count you becoming an uber driver as 1 new job - they were counting it as roughly 7 newly available jobs. this is because each new company created in the US roughly brings on 7 employees. larger private financial institutions were correcting for this, but the department of labor statistics hasn't corrected for this. this means banks and private institutions have had better data than the government on the job market for years and were calculating that in to the stock market

3.) to add another layer of confusion, the government calculates the unemployment rate by counting the number of US citizens that file for unemployment checks, but many people found it easier/faster to get a gig economy job in between full time jobs - rather than waiting a 1+ month(s) to get on unemployment checks. this means that the number of people who are unemployed is way higher/worse than what the government is reporting. what this means is that method used to count available jobs AND unemployment are wildly wrong - there are less available jobs and more people unemployed by about 5-7x what was reported this summer.

On top of that, if you look at states where there are stricter/more requirements to become an uber driver, it actually shows the unemployment rate in those states is much higher than expected. the avg unemployment rate amongst these states are probably more accurate to how bad the unemployment situation is in the US overall

4.) the current US administration has fired a lot of employees, which has led to even worse labor statistics/estimates compared to previous years

5.) trump specifically has actually caused a lot of confusion for the average person trying to understand this year's US economic status because we use to have quarterly checkins in June, but as of the past 2 years we've been doing it in July. The way the government tracks important economic indicators starts with the US gov announcing their initial stats, but these numbers often over estimate; so the US gov will often have a large correction the following month

trump this year has been making claims like, "this is the best GDP we've seen in July of recent years!" but of course it's the best because he is intentionally doing the comparison wrong

to ELI5 what I mean, June 2024 had the over estimates stats and the US government would correct them in July 2024. but now in 2025, July is the month with over estimate, and August will be the month we correct the estimates

what we should be doing is comparing august 2025's GDP with July of 2024's GDP. doing so would show you that GDP is not better, but essentially stagnant

trump and his administration are intentionally not doing the comparison correctly for better sounding headlines

[0] dept of labor statistic report- https://www.bls.gov/news.release/pdf/empsit.pdf

[1] Deep Dive: The US Jobs Market Is Much Weaker Than it Appears - https://www.financialsense.com/blog/20854/deep-dive-us-jobs-...

EDIT - typos

If you're curious about lit and like longer form content - I recommend watching the [0] http 203 video that talks about lit element and other tools like it

[0] - https://www.youtube.com/watch?v=uCHZJy2n8Qs

for the record, I've been bullish on the tooling from the beginning

My dev-tooling AI journey has been chatGPT -> vscode + copilot -> early cursor adopter -> early claude + cursor adopter -> cursor agent with claude -> and now claude code

I've also spent a lot of time trying out self-hosted LLMs such as couple version of Qwen coder 2.5/3 32B, as well as deepseek 30B - and talking to them through the vscode continue.dev extension

My personal feelings are that the AI coding/tooling industry has seen a major plateau in usefulness as soon as agents became apart of the tooling. The reality is coding is a highly precise task, and LLMs down to the very core of the model architecture are not precise in the way coding needs them to be. and it's not that I don't think we won't one day see coding agents, but I think it will take a deep and complete bottom up kind of change and an possibly an entirely new model architecture to get us to what people imagine a coding agent is

I've accepted to just use claude w/ cursor and to be done with experimenting. the agent tooling just slows my engineering team down

I think the worst part about this dev tooling space is the comment sections on these kinds of articles is completely useless. it's either AI hype bots just saying non-sense, or the most mid an obvious takes that you here everywhere else. I've genuinely have become frustrated with all this vague advice and how the AI dev community talks about this domain space. there is no science, data, or reason as to why these things fail or how to improve it

I think anyone who tries to take this domain space seriously knows that there's limit to all this tooling, we're probably not going to see anything group breaking for a while, and there doesn't exist a person, outside the AI researchers at the the big AI companies, that could tell ya how to actually improve the performance of a coding agent

I think that famous vibe-code reddit post said it best

"what's the point of using these tools if I still need a software engineer to actually build it when I'm done prototyping"

quanta published an article that talked about a physics lab asking chatGPT to help come up with a way to perform an experiment, and chatGPT _magically_ came up with an answer worth pursuing. but what actually happened was chatGPT was referencing papers that basically went unread from lesser famous labs/researchers

this is amazing that chatGPT can do something like that, but `referencing data` != `deriving theorems` and the person posting this shouldn't just claim "chatGPT derived a better bound" in a proof, and should first do a really thorough check if it's possible this information could've just ended up in the training data

if I just need a basic fact or specific detail from an article, and being wrong has no real world consequences, I'll probably just gamble it and take the AI's word for it most of the time. Otherwise I'm going to double check with an article/credible source

if anything, I think aimode from google has made it easier to find direct sources for what I need. A lot of the times, I am using AI for "tip of the tongue" type searches. I'll list a lot of information related to what I am trying to find, and the aimode does a great job of hunting it down for me

ultimately though, I do think some old aspects of google search are dying - some good, some bad.

Pros: don't fee the need to sift through blog spam, I don't need to scroll past paid search results, I can avoid the BS part of an article where someone goes through their entire life story before the actual content (I'm talking things like cooking website)

Cons: Google is definitely going to add ads to this tool at some point, some indie creators on the internet will have a harder time getting their name out.

my key takeaway from all this is that people will only stop at your site if they think your site will have something to offer that the AI can't offer. and this isn't new. people have been steeling blog content and turning into videos for ever. people will steel paid tutorials and release the content for free on a personal site. people will basically take content from site-X and repost in a more consumable format on site-Y. and this kind of theft is so obvious and no one liked seeing the same thing reposted a 1000 times. I think this long term is a win

Crossover - https://www.codeweavers.com/crossover?gad_source=1&gbraid=0A...

These 2 scenarios don’t have any commonality. You’re comparing how AI is being used in two different types of “social games” that have wildly different stakes. The stakes make this incomparable.

Dating apps are about fostering human connection. AI posing as a person is the opposite of human connection

Meanwhile, not having a job for the vast majority people means you can’t eat. The system shouldn’t punish people for using a tool that helps them find work to survive