IMHO, don't, don't keep up. Just like "best practices in prompt engineering", these are just temporary workaround for current limitations, and they're bound to disappear quickly. Unless you really need the extra performance right now, just wait until models get you this performance out of the box instead of investing into learning something that'll be obsolete in months.
I agree with your conclusion not to sweat all these features too much, but only because they're not hard at all to understand on demand once you realize that they all boil down to a small handful of ways to manipulate model context.
But context engineering very much not going anywhere as a discipline. Bigger and better models will by no means make it obsolete. In fact, raw model capability is pretty clearly leveling off into the top of an S-curve, and most real-world performance gains over the last year have been precisely because of innovations on how to better leverage context.
My point is that there'll be some layer doing that for you. We already have LLMs writing plans for another LLM to execute, and many other such orchestrations, to reduce the constraints on the actual human input. Those implementing this layer need to develop this context engineering; those simply using LLM-based products do not, as it'll be done for them somewhat transparently, eventually. Similar to how not every software engineer needs to be a compiler expert to run a program.
I agree with this take. Models and the tooling around them are both in flux. I d rather not spend time learning something in detail for these companies to then pull the plug chasing next-big-thing.
Well, have some understanding: the good folks need to produce something, since their main product is not delivering the much yearned for era of joblessness yet. It's not for you, it's signalling their investors - see, we're not burning your cash paying a bunch of PhDs to tweak the model weights without visible results. We are actually building products. With a huge and willing A/B testing base.
Agree — it's a big downside as a user to have more and more of these provider-specific features. More to learn, more to configure, more to get locked into.
Of course this is why the model providers keep shipping new ones; without them their product is a commodity.
If I were to say "Claude Skills can be seen as a particular productization of a system prompt" would I be wrong?
From a technical perspective, it seems like unnecessary complexity in a way. Of course I recognize there are lot of product decisions that seem to layer on 'unnecessary' abstractions but still have utility.
In terms of connecting with customers, it seems sensible, under the assumption that Anthropic is triaging customer feedback well and leading to where they want to go (even if they don't know it yet).
Update: a sibling comment just wrote something quite similar: "All these things are designed to create lock in for companies. They don’t really fundamentally add to the functionality of LLMs." I think I agree.
All these things are designed to create lock in for companies. They don’t really fundamentally add to the functionality of LLMs. Devs should focus on working directly with model generate apis and not using all the decoration.
Me? I love some lock in. Give me the coolest stuff and I'll be your customer forever. I do not care about trying to be my own AI company. I'd feel the same about OpenAI if they got me first... but they didn't. I am team Anthropic.
Joking aside, I ask Claude how to uses Claude... all the time! Sometimes I ask ChatGTP about Claude. It actually doesn't work well because they don't imbue these AI tools with any special knowledge about how they work, they seem to rely on public documentation which usually lags behind the breakneck pace of these feature-releases.
"Recursion" is a word that shows up a lot in the rants of people in AI psychosis (believe they turned the chatbot into god, or believe the chatbot revealed themselves to be god.)
Thats the start of the singularity. The changes will keep accelerating and less and less people will be able to keep up until only the AIs themselves know how to use.
I don’t think these are things to keep up with. Those would be actual fundamental advances in the transformer architecture and core elements around it.
This stuff is like front end devs building fad add-ons which call into those core elements and falsely market themselves as fundamental advancements.
Yes and as we rely on AI to help us choose our tools... the phenomena feels very different, don't you think? Human thinking, writing, talking, etc is becoming less important in this feedback loop seems to me.
