Comment by _pdp_

This is also why it's perfectly fine to wait out this AI hype and see what sticks afterward. It probably won't cost too much time to catch up, because at that point everyone who knows what they're doing only learned that a month or two ago anyway.

Counterpoint to these two posts: a journeyman used to have to make his own tools. He could easily have bought them, or his master could have made them. Making your own tools gives you vastly greater skills when using the tools. So I know how fast AI agents and model APIs are evolving, but I’m writing them anyway. Every break in my career has been someone telling me it’s impossible and then me doing it anyway. If you use an agent framework, you really have no idea how artificially constrained you. You’re so constrained, and yet you are oblivious to it.

On the “wasting three months” remark (GP), if it’s a key value proposition, just do it. Don’t wait. If it’s not a key value prop, then don’t do it at all. Often times what I’ve built has been better tailored to our product than what AWS built.

Note that even many of those "long knowledge" things people learned are today obsolete, but people that follow them just haven't figured it out yet. See how many of those object oriented design patters just look very silly the minute you use immutable data structures, and have access to functional programming constructs in your language. And nowadays most do. Many seminal books on how to program in the early 2000s, especially those covering "pure" OO, look quite silly today.

Bumping for interest too. Would love to hear what you believe is correlated to success.

Yeah, that will be the make it to brake it moment because if it’s too essential, it will be implemented but if it’s not, it may become a competitive advantage

These days it seems like training yourself into a specialty that provides steadyish income for a year before someone obliterates your professional/corporate/field’s scaffolding with AI and you have to start over is kind of a win. Doesn’t it feel like a win? Look at the efficiency!

One example is that there used to be a whole complex apparatus around getting models to do chain of thought reasoning, e.g., LangChain. Now that is built in as reasoning and they are heavily trained to do it. Same with structured outputs and tool calls — you used to have to do a bunch of stuff to get models to produce valid JSON in the shape you want, now it’s built in and again, they are specifically trained around it. It used to be you would have to go find all relevant context up front and give it to the model. Now agent loops can dynamically figure out what they need and make the tool calls to retrieve it. Etc etc.

If we expand this to 3 years, the single biggest shift that totally changed LLM development is the increase in size of context windows from 4,000 to 16,000 to 128,000 to 256,000.

When we were at 4,000 and 16,000 context windows, a lot of effort was spent on nailing down text splitting, chunking, and reduction.

For all intents and purposes, the size of current context windows obviates all of that work.

What else changed?

- Multimodal LLMs - Text extraction from PDFs was a major issue for rag/document intelligence. A lot of time was wasted trying to figure out custom text extraction strategies for documents. Now, you can just feed the image of a PDF page into an LLM and get back a better transcription.

- Reduced emphasis on vector search. People have found that for most purposes, having an agent grep your documents is cheaper and better than using a more complex rag pipeline. Boris Cherny created a stir when he talked about claude code doing it that way[0]

https://news.ycombinator.com/item?id=43163011#43164253

I'm amazed at this question and the responses you're getting.

These last few years, I've noticed that the tone around AI on HN changes quite a bit by waking time zone.

EU waking hours have comments that seem disconnected from genAI. And, while the US hours show a lot of resistance, it's more fear than a feeling that the tools are worthless.

It's really puzzling to me. This is the first time I noticed such a disconnect in the community about what the reality of things are.

To answer your question personally, genAI has changed the way I code drastically about every 6 months in the last two years. The subtle capability differences change what sorts of problems I can offload. The tasks I can trust them with get larger and larger.

It started with better autocomplete, and now, well, agents are writing new features as I write this comment.

On the foundational level, test time compute(reasoning), heavy RL post training, 1M+ plus context length etc.

On the application layer, connecting with sandboxes/VM's is one of the biggest shifts. (Cloudfares codemode etc). Giving an llm a sandbox unlocks on the fly computation, calculations, RPA, anything really.

MCP's, or rather standardized function calling is another one.

Also, local llm's are becoming almost viable because of better and better distillation, relying on quick web search for facts etc.

Not the LLMs. The APIs got more capabilities such as tool/function calling, explicit caching etc.

We started putting them in image and video models and now image and video models are insane.

I think the next period of high and rapid growth will be in media (image, video, sound, 3D), not text.

It's much harder to adapt LLMs to solving business use cases with text. Each problem is niche, you have to custom tailor the solution, and the tooling is crude.

The media use cases, by contrast, are low hanging fruit and result in 10,000x speedups and cost reductions almost immediately. The models are pure magic.

I think more companies would be wise to ignore text for now and focus on visual domain problems.

Nano Banana has so much more utility than agents. And there are so many low hanging fruit ways to make lots of money.

Don't sleep on image and video. That's where the growth salient is.

For a JS dev, it’s just another Tuesday

If you’ve spent any time with the vertex LLM apis you wouldn’t be so enthusiastic about using Google’s platform (I say this as someone who prefers GCP to aws for compute and networking).