For those who aren't aware, Cursor sports one of the best LLM harnesses for coding. The app itself is annoying to use compared to their CLI counterparts, but the harness is widely recognized as the best in the business, or very close. Buying that harness makes a lot of sense considering the cash Musk invested in Grok. He's clearly trying to play with the big boys and grab a chunk of the LLM-assisted dev market.
I thought that was what everybody was doing all day since LLMs came out. I certainly spend most of my free time asking questions, pulling refs, parsing research papers and brainstorming. As models become better, it'd become way more rewarding and detailed. Opus 4.8 on high is particularly great at teaching you physics/math fundamentals and other technical subjects, as long as you always ask for the references to read.
There's a reason why this tech is called disruptive.
The same phenomenon can be observed on Reddit. You'll see a lot of knee-jerk reactions to anything that looks AI, as in 'Thanks ChatGPT' or 'AI slop' top comment, and at the same time you'll see entire subs raving about any new AI advance, or massive upvotes for somebody's vibe-coded project - because it's just... good.
Like others have said, we're becoming more polarized, partly because of the nature of social media (anybody can share anything, anybody can comment), and partly because of the effects of said media on the human brain. It'll only get worse/more amplified as we go forward.
If you have suggestions for good Discord servers, please share. The bullshittery is coming out of my ears. I don't quite know where to turn to anymore. There's HN. Reddit is getting a bit crazy, all other social media is a pass for me.
Many of us tested 27B and 35B side by side, and the dense model is significantly smarter. It indeed is slower, but 35B makes a lot of mistakes 27B doesn't.
Bonus points for MD being readable even when it's not parsed. More bonus points for Sublime Text displaying it in plain text and still looking great. Good enough++
Same as top comment, have spent a lot of time on local models. IMHO, qwen3.5 is the very first model that is actually usable for serious work, ever - and I've tried them all. The 35B 3B is very smart. It understands things no other local model I've ever used does, it's that good. The 9B runs on my slow Mac, and it's also very 'smart'. I can say with confidence that 2026 is the year of the local model, at last.
I used to use LLMs to 'clean up' my own writings, and in the end I agree with the author here: it doesn't really help. The reader will have this impression of 'too perfect', and will have a diminished feeling of value, of honesty. I think we would benefit from a standardized way of signaling text and content that is exclusively human. Say, some sort of logo that says 'genuine', 'untouched by the hand of AI'. I'll be thinking about a way to do this.
The tech debt this title speaks of only applies if humans have to deal with it. Tech debt is an assumption made on the grounds that humans are still programming and AI does not evolve. It's the opposite of reality.
There is one thing everybody forgets when making such predictions: companies don't stand still. Nvidia and every other tech business is constantly exploring new options, taking over competitors, buying startups with novel technologies etc... Nvidia is no slouch in that regard, and their recent quasi-acquisition of Groq is just one example of this. So, when attempting at making predictions, we're looking at a moving target, not systems set in stone. If the people at the helm are smart (and they are), you can expect lots of action and ups and downs - especially in the AI sphere.
My personal opinion, having witnessed first hand nearly 40 years of tech evolution, is that this AI revolution is different. We're at the very beginning of a true paradigm shift: the commoditization of intelligence. If that's not enough to make people think twice before betting against it, I don't know what is. And it's not just computing that is going to change. Everything is about to change, for better or worse.
I had the same idea. I think this is very useful. As it is it does look like a proof-of-concept, and that's OK. I'd develop this as a book recommendation site and simply link to the books on Amazon or your preferred book source. Collect cash on referrals. Good stuff!
We've been through many technological revolutions, in computing alone, through the past 50 years. The rate of progress of LLMs and AI in general over the past 2 years alone makes me think that this may be unwarranted worry and akin to premature optimization. Also, it seems to be rooted in a slightly out of date, human understanding of the tech/complexity debt problem. I don't really buy it. Yes complexity will increase as a result of LLM use. Yes eventually code will be hard to understand. That's a given, but there's no turning back. Let that sink in: AI will never be as limited as it is today. It can only get better. We will never go back to a pre-LLM world, unless we obliterate all technology by some catastrophy. Today we can already grok nearly any codebase of any complexity, get models to write fantastic documentation and explain the finer points to nearly anybody. Next year we might not even need to generate any docs, the model built in the codebase will answer any question about it, and will semi-autonomously conduct feature upgrades or more.
Staying realistic, we can say with some confidence that within the next 6-12 months alone, there are good reasons to believe that local, open source models will equate their bigger cloud cousins in coding ability, or get very close. Within the next year or two, we will quite probably see GPT6 and Sonnet 5.0 come out, dwarfing all the models that came before. With this, there is a high probability that any comprehension or technical debt accumulated over the past year or more will be rendered completely irrelevant.
The benefits given by any development made until then, even sloppy, should more than make up for the downside caused by tech debt or any kind of overly high complexity problem. Even if I'm dead wrong, and we hit a ceiling to LLM's ability to grok huge/complex codebases, it is unlikely to appear within the next few months. Additionally, behind closed doors the progress made is nothing short of astounding. Recent research at Stanford might quite simply change all of these naysayers' mind.
Racket has a very nice built-in debugger in its DrRacket editor, with thread visuals and all. Too bad nobody uses DrRacket, or Racket anymore. Admittedly, even with the best debugger, finding the cause of runtime errors has always been a pain. Hence everybody's moving towards statically compiled, strongly typed languages.