https://newsletter.pragmaticengineer.com/ https://substack.com/@garymarcus
Plus I subscribe to updates from Python packages like Langchain and PydanticAI to see what they're up to, since that's usually reflective of what's happening in broader industry.
I'm not on X anymore so I can't follow many of the folks directly, but some of them (like Simon Willison) also post on BlueSky and Mastodon, fortunately. Some folks like Sebastian Raschka and Chip Huyen also post on LinkedIn. Kind of all over, but eventually, I see a good amount of what's happening.
This time the expert systems generate themselves over vast fields of everything by reading Facebook, Reddit and the rest of the internet that they can find.
On that rather shaky basis the next "leap" in AI will be around 2070. Perhaps that one will create AGI or something that looks more like it.
I'm currently not using AI or LLMs in any of my day-to-day work.
Yeah it's not a fad, but I think it's really not as useful to me right now as the hype seems to suggest
I'm going to keep an eye on developments, but I'm not using it for my day to day either. I'm just not seeing the same value other people seem to be seeing right now and I'm not going to exhaust myself trying to keep up
One day Claude is the best. Next is Cursor. People are switching tools every two weeks trying to keep up
Not for me thanks. Especially not with how inconsistent the quality and output still are
btw here's a link to the karpathy videos https://karpathy.ai/zero-to-hero.html
edit: i use claude and chatgpt day to day to help with simple things like regex, a replacement for google search in same cases, and self contained classes, functions, and other small discreet blocks of code.
If I pay for a tool that includes access to frontier models, then they'll keep the models up to date over time for me, let me use models from multiple providers, and the tool is carefully designed around the capabilities and limitations of the models it works with. On the other hand I can't really use the powerful model the tool works with for other applications or write my own.
If I pay for models, then I can only really use it with that manufacturers tools or tools that aren't optimised for the model but allow you to bring your own keys, and if the model provider I'm paying falls behind then I'm tied in for the duration of the contract. The big advantage is that there is a lot of innovation in tooling happening at the moment and you can avoid being locked out of that or having to pay many times for access to the same frontier models accross multiple different tools.
It's hard to make money out of dev tools, but if you tie in a service people are prepared to pay hundreds of dollars a month for, then suddenly it looks easier to make money out of an otherwise unsaleable IDE plugin that accesses that service.
I think Jetbrains does it better with a full BYOM for models, including ollama. And I think if you go for ollama, you only need to pay for the IDE license, not for the AI add-on but don't quote me on that.
Since I'm not bringing my own API key, it's in their best interest to either throttle my usage by slowing it down or subtly downgrading me to a smaller LLM behind the scenes.
AFAICT the best strategy would have been to completely tune out AI for the last ~3 years:
- AI has not meaningfully improved productivity (unless you’re doing something super basic like react and were already bad at it). If you are using AI in a transformative way, that looks different today than it did 6 months ago. - AI has not stolen jobs (end of ZIRP did that) - The field changes so fast that you could completely tune out, and at any moment become up-to-date because the news from 3 months ago is irrelevant.
I don’t get where this meme that “you have to keep up” comes from.
You have agency. You can get off the treadmill. You will be fine.
It is very likely my employer will use my AI apathy as an excuse to include me in the next round of layoffs, compared to my coworkers that are very AI enthusiastic
Imagine a software developer who refuses to use IDEs, or any kind of editor beyond sed, or version control, or some other essential tool. AI is soon similar, except in rare niche cases.
One professor thought syntax highlighting was a distraction.
Lots of colleagues used vim/helix instead of IDEs.
I haven't met anyone who refused version control from an intelligent standpoint.
The most reasonable objection to AI from people who don't hate it are:
I just don't know how it could help me; it's not as skilled as me at my job, and I'm already doing fine.The rest: if they are just as productive as others, I would not care one bit. Tool use as a metric is just bad.
It sort of hints at one reason:
> The most common errors of misunderstanding are either underestimation (“it’s all hype that will blow over”) or overestimation (“I don’t need programmers anymore”). These patterns are rooted in a lack of a solid understanding of the technology and how it is evolving over time.
So if you don't at least find some middle ground between those two poles, you will make uninformed choices.
But I agree: It is safe to ignore AI for now.
I do sense that some people attach to AI because of a fundamental anxiety that it might transform society quickly and detrimentally, because that's part of the hype speech ("it will murder us all, it will make us all unemployed, it will turn us into slaves, maybe you can ride the dragon, and maybe you must").
---
> AI has not meaningfully improved productivity
This is contended.
As the article says, we are in one of the most polluted information environments.
People will say "It's absolutely useless" and "It has fundamentally changed my life."
So neither extreme can be taken at face value as representative; they're samples of a murky picture.
> The field changes so fast that you could completely tune out
It's not that fast, in my opinion. Last big steps:
- Transformer architecture (2017)
- Larger models with greater performance (2020-)
- Chain of thought (research in 2022, commercial breakthrough in 2024)
- Agents (since forever, but 2022 for GPT-based agentic frameworks)
But most of the hype is just people trying to make commercial success on a few cornerstone breakthroughs.
In one to two years, maybe we can add one more major advancement.
Despite the feeling that it's a fast-moving field, most of the differences in actual models over the last years are in degree and not kind, and the majority of ongoing work is in tooling and integrations, which you can probably keep up with as it seems useful for your work. Remembering that it's a model of text and is ungrounded goes a long way to discerning what kinds of work it's useful for (where verification of output is either straightforward or unnecessary), and what kinds of work it's not useful for.
- Someone made a slightly different tool for using LLMs (may or may not be useful depending on whether existing tools meet your needs)
- Someone made a model that is incrementally better at something, beating the previous state-of-the-art by a few % points on one benchmark or another (interesting to keep an eye on, but remember that this happens all the time and this new model will be outdated in a few months - probably no one will care about Kimi-K2 or GPT 4.1 by next January)
I think most people can comfortably ignore that kind of news and it wouldn’t matter.
On the other hand, some LLM news is:
- Someone figured out how to give a model entirely new capabilities.
Examples: RL and chain of thought. Coding agents that actually sort of work now. Computer Use. True end-to-end multimodal modals. Intelligent tool use.
Most people probably should be paying attention to those developments (and trying to look forward to what’s coming next). But the big capability leaps are rare and exciting enough that a cursory skim of HN posts with >500 points should keep you up-to-date.
I’d argue that, as with other tech skills, the best way to develop your understanding of LLMs and their capabilities is not through blogs or videos etc. It’s to build something. Experience for yourself what the tools are capable of, what does and doesn’t work, what is directly useful to your own work, etc.
A lot of people are feeling HN is saturated with AI posts whether it is how MCP is like USB-C (repeated so much you know it is NPCs) or how outraged people are that their sh1t fanfics are being hoovered up to train AI.
This piece is not “news”, it’s a summary which is tepid at best, I wish people had some better judgement about what they vote up.
Last week I showed some colleagues how to do some basic things with Claude Code and they were like "wow, I didn't even know this existed". Bro, what are you even doing.
There is definitely a lot of hype and the lunatics on Linkedin are having a blast, but to put it mildly I don't think it's a bad investment to experiment a bit with what's possible with the SOTA.
It is ridiculously cool, but I think anybody developer who is out of the loop could easily get back into the loop at any moment without having to stay caught up most of the time.
The problem I see is that a lot of people are grossly misaligned with the state of the art, and it does take a bit of experimentation to understand how to work with an LLM. Even basic stuff like how to work with context isn't immediately obvious.
The trouble is that the advice in the post will have very little impact on "understanding how LLMs work". The number of people who talk about LLMs daily but have never run an LLM local, and certainly never "opened it up to mess around" is very large.
A fun weekend exercise that anyone can do is to implement speculative decoding[0] using local LLMs. You'll learn a lot more about how LLMs work than reading every blog/twitter stream mentioned there.
0. https://research.google/blog/looking-back-at-speculative-dec...
That’s a nice way to put it, made me chuckle. :)
I also have not experienced the post's claim that: "Generative AI has been the fastest moving technology I have seen in my lifetime." I can't speak for the author, but I've been in this field from when "SVMs are the new hotness and neural networks are a joke!" to the entire explosion of deep learning, and insane number of DL frameworks around the 20-teens, all within a decade (remember implementing restricted Boltzmann machines and pre-training?). Similarly I saw "don't use JS for anything other than enhancing the UX" to single page webapps being the standard in the same timeframe.
Unless someone's aim is to be on that list of "High signal" people, it's far better to just keep your head down until you actually need these solutions. As an example, I left webdev work around the time of backbone.js, one of the first attempts at front end MVC for single pages apps. Then the great React/Angular wars began, and I just ignored it. A decade later I was working with a webdev team and learned React in a few days, very glad I did not stress about "keeping up" during the period of non-stop changing. Another example is just 5 years ago everyone was trying to learn how to implement LSTMs from scratch... only to have that model essentially become obsolete with the rise of transformers.
Multiple times over my career I've learned lesson that moving fast is another way of saying immature. One would find more success learning about the GLM (or god forbid understanding to identify survival analysis problems) and all of it's still under appreciated uses for day-to-day problem solving (old does not imply obsolete) than learning the "prompt hack of the week".
The minutiae of how next token prediction works is rarely appreciated by lay people. They don't care about dot products, or embeddings, or any of it. There's basically no advantage to explaining how that part works since most people won't understand, retain, or appreciate it.
Happily canceled my GPT Plus this week; personally not gonna feed that beast any longer! As well it can not generate maps (create road trip travel maps showing distance between locations to share with friends, a creek tubing map route & etc) at all like Gemini can for free.
This would increase employment ceteris paribus. That's like saying inventing new programming languages is bad because they're too productive.
> This would increase employment ceteris paribus.
This might be true, but if it is, the one "doing the job ten use[d] to" would not actually being doing the same kind of work at all, and so therefore might not be the same people or even same kind of people. Even if we do Jevons ourselves out of this situation, it might still spell employment disaster for mid level coders, while increasing employment for product managers.
Is this not obvious?
Why do people hide behind this ridiculous analogy: "That's right, there are no carpenters or lumberjacks anymore because power tools were invented"
???
I mean sure the analogy is catchy and makes surface level sense, but can your brain do some analysis outside the context of an analogy??? It makes no sense that all of AI can be completely characterized by an analogy that isn't even accurate yet people delusionally just regurgitate the analogy most fitting with the fantasy reality they prefer.
Are you saying that when the sewing machine was invented, it would be in the employees interest to not learn how to use it? Or when the computer was invented, it’s not in the employees interest to learn how to use it?
Even if you are a software engineer and are fired / laid off / pushed out of the industry because of AI, knowing how to use AI, its risks, etc is still helpful. It’s a skill you can hopefully use in your next career, whatever you pivot to. Unless you pivot to manual labor.
Thinking otherwise is shortsighted.
So You and all other people like to save money are going to continue spend the same thousands on such a resource when AI can do what they do in a few minutes or more for WAY LESS? UX Design was supposedly a growing field ... not at anymore! Definitely one can do the same thing in that field that 10 did.
Further, future mobile AI devices will pull the information and put it all on the lock screen of your AI device visualizing the data in a fun new way. Technology that makes things simpler and more magical get adopted yet visits to websites will significantly decline.
For federal workers who have lost their jobs they are feeling this pain competing for jobs against each other and now AI. It will only get worse for designers because it's now cheaper and faster to use AI to design logos, sites, apps to even including do vibe coding for the front end development to possibly the backend but that's not my specialty yet no doubt I vibe coded front-ends.
It's fine to go do other things with your precious time instead.
Unless you are building an AI startup furiously searching for PMF before your runway expires, I don't see the urgency.
What is an "AI startup"? If you add a chatbot do your product, are you an "AI startup"? Does "startup" require having no moat? Can you be an established stable business that loses everything to a startup that leapfrogs your tech, AltaVista?
I've also got some gems from Microsofts Build talks, specifically whenever Scott Hanselman and Mark Russinovich get together, e.g.: https://www.youtube.com/watch?v=KIFDVOXMNDc
Isn't the whole promise of AI tools that they just work?
What skill am I missing out on learning, exactly, by not using them right now? Prompt Engineering?
I think I'm a reasonably good communicator in both voice and text, so what skill am I failing to train by not using LLMs right now?
No, not at all. Like with pretty much any development tool, you need to get proficient with them.
>what skill am I missing out on
At this point, it seems like pretty much all of them related to generative AI. But, the most recent of them that I'll point at is: tooling tooling tooling, and prompting. But the specific answer (to answer your "exactly") is going to depend on you and what problems you are solving. That's why on tries not to fall behind, so you can see how to use tooling in a rapidly evolving landscape, for your exact circumstances.
>I think I'm a reasonably good communicator in both voice and text, so what skill am I failing to train by not using LLMs right now?
You know how to achieve something you will use different words with different people? You don't talk to your spouse the same way you talk to your parents or your children or your friends or your coworkers, right? You understand that if you are familiar with someone you speak to them differently if you want to achieve something, yes?
none of this stuff is complicated, and the models themselves have been basically the same since GPT-2 was released years ago
pulling the covers back so hard and so fast is going to be shocking for some.
To make it more concrete you can try and build something yourself. Grab a small model off of hugging face that you can run locally. Then put a rest API in front of it so you can make a request with curl, send in some text, and get back in the response what the llm returned. Now in the API prepend some text to what came on the request ( this is your system prompt ) like "you are an expert programmer, be brief and concise when answering the following". Now add a session to your API and include the past 5 requests from the same user along with the new one when passing to the llm. Update your prepended text (the system prompt) with "consider the first 5 requests/responses when formulating your response to the question". you can see where this is going, all of the tools and agents are some combination of the above and/or even adding more than one model.
At the end of the day, everyone has a LLM at the core predicting and outputting the next most likely string of characters that would follow from an input string of characters.
https://x.com/rowancheung - Rowan Cheung: “Daily” updates and insider access
No, I don't think I do. Been working great for me so far.
Progress is like a bus. You can just get on board at any time. You’re not going to fall behind. And staying up to date doesn't keep you “ahead” of anyone.
Doing things is what gets you ahead, and if you don’t feel like doing something right now, don’t worry about, do something later, and you’ll be ahead of people who aren’t doing anything at that moment.
I have a list in X for AI; it's the best source of information overall on the subject, although some podcasts or RSS feeds directly from the long-form writers would be quite close. (If one is a researcher themselves, then of course it's a must to follow the paper feeds, not commentary or secondary references.)
I'd add https://epoch.ai to the list, on podcasts at least Dwarkesh Patel; on blogs Peter Wildeford (a superforecaster), @omarsar0 aka elvis from DAIR in X, also many researchers directly although some of them like roon or @tszzl are more entertaining than informative.
The point about polluted information environment resonates on me; in general but especially with AI. You get a very incomplete and strange understanding by following something like NYT who seem to concentrate more on politics than technology itself.
Of course there are adjacent areas of ML or AI where the sources would be completely different, say protein or genomics models, or weather models, or research on diffusion, image generation etc. The field is nowadays so large and active that it's hard to grasp everything that is happening on the surface level.
Do you _have_ to follow? Of course not, people over here are just typically curious and willing to follow groundbreaking technological advancements. In some cases like in software development I'd also say just skipping AI is destructive to the career in the long term, although there one can take a tools approach instead of trying to keep track of every announcement. (My work is such that I'm expected to keep track of the whole thing on a general level.)
There's far too much noise, churn, and indecision at this stage to get any productive value out of riding the bleeding edge.
If it's actually revolutionary, you'll hear about it on HN.
In a sense it may be more efficient to ignore the current state for a while and jump on later.
It’s also interesting that at the heart of the skill set evolving around efficient LLM use, is communication. Engineers and technical people have been the first to admit for decades that they struggle with effective communication. Now everyone’s an expert orator, capable of describing in phenomenal detail to the LLM what they need from it?
Not worrying about all this feels so much better!
This week I saw a video of a robot assembling PCBs at lightning speed. This reminded me a lot of an LLM coding for us, but there are still numerous people in the production line to design, oversee and manage this kind of production. Software engineering is changing but not going.
muglug•4h ago
dvfjsdhgfv•4h ago
krat0sprakhar•4h ago
bzmrgonz•4h ago