this is the “chronological newsfeed to auto curated newsfeed moment” but for ai/anthropic … _great_
Readit Newsdrudolph914 commented on Anthropic drops flagship safety pledge time.com/7380854/exclusiv... · Posted by u/cwwc
drudolph914 commented on 2026 will be my year of the Linux desktop xeiaso.net/notes/2026/yea... · Posted by u/todsacerdoti
They're going from Microsoft to... Linux. From bad to worse. Just use macOS and get on with your life.
tbf mac is starting to get pretty bad too
drudolph914 commented on I don't care how well your "AI" works fokus.cool/2025/11/25/i-d... · Posted by u/todsacerdoti
I always thought years of experience in a language was a silly job requirement. LLMs allow me to write Rust code as a total Rust beginner and allows me to create a valuable SaaS while most experienced Rust developer never built anything that made $1 outside of their work. I wouldn't say devaluation, my programming experience definitely helps with debugging. LLMs eliminate boilerplate, not engineering judgement and product decisions.
I think when the author says
> “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
Great, another feature I need to figure out how to turn off
Indeed, I successfully use LLMs for research, and they're an improvement because old-school search isn't very reliable either.
But as to the 80-20 tradeoff on other tasks, the problem isn't that the tool is wrong 20% of the time, but that it's not trustworthy 100% of the time. I have to check the work. Maybe that's still valuable, but just how valuable that is depends on many factors, some of which are very domain-dependent and others are completely subjective. We're talking about replacing one style with another that is much better in some respects and much worse in others. If, on the whole, it was better in almost all cases, that would be one thing (and make the investment safer), but reports suggest it isn't.
I've yet to try an LLM to learn a new codebase, and I have no doubt it will help a lot, but while that is undoubtedly a very expensive task, it's also not a very frequent one. It could maybe save me a week per year, amortised. That's not nothing (and I will certainly give it a try next time I need to learn a new codebase), but it's also not a game-changer.
80-20 is also a gracious ratio, my experience it’s more like 65-35
drudolph914 commented on Why language models hallucinate openai.com/index/why-lang... · Posted by u/simianwords
I am an educator alongside being an engineer, so I've had to think about how to explain this topic to people in ways that give them some kind of intuition/insight. I don't have a good take for non-stem people, but I think I have a better explanation for people who are CS adjacent
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
You are utterly wrong, and you can easily read about it and find the process behind all of the numbers. The current administration just hates facts that go against their narrative and so are installing yes-men to give the "facts" they want them to give.
there is a lot of half correct and half in-correct information in this thread. it might be worth reading some of the articles im linking
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
drudolph914 commented on Lit: a library for building fast, lightweight web components lit.dev... · Posted by u/merqurio
I love lit. I've been using lit in production since 2020 and I have never looked back. There is so much to say about lit, but I think the biggest win is that it's built on a very stable foundation. Building apps on top of native web components coupled with all the modern QoL features with Lit allows me to without the fear of some new framework/update coming along to the ecosystem - which in the FE world means the last X years worth of code becomes outdated. Native web components are a stable feature in all browsers and I can just focus on building - more engineering teams need to give it a try
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
drudolph914 commented on A staff engineer's journey with Claude Code sanity.io/blog/first-atte... · Posted by u/kmelve
to throw my hat into the ring, I am in no way shy about using the AI tooling and I like using it, but I am happy we're finally seeing people talk about AI that matches with my personal reality with the tools.
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"
drudolph914 commented on Claim: GPT-5-pro can prove new interesting mathematics twitter.com/SebastienBube... · Posted by u/marcuschong
interesting if true, but this isn't the first time we heard of something like this
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