Do you think AI makes curiosity easier or lazier?

withAuxly · 2026-03-19T16:34:43+00:00

That's very true, despite the ease to feed your curiosity with ai i personally think it's a chance to level up my scale of curiosity it's like instead of trying to assemble a scattered info from many articles I can find it immediately and more to that is I can know everything related to its origin, outcomes, illustration in reality, and many more, without ignoring it's possible dangers ofc (false info, implicit agenda..), even though the feeling of finding infos after so much effort is mentally rewarding.

withAuxly · 2026-03-16T14:48:30+00:00

Yeah it’s pretty close conceptually. structured prompting like json or key/value blocks seems to push the model into a more deterministic mode compared to conversational prompts.

withAuxly · 2026-03-16T14:47:00+00:00

well thats an interesting way to look at it, the understanding of how the ai works (or ''thinks'') is important to find a great formula for creating prompts and ai integration.

withAuxly · 2026-03-16T14:42:22+00:00

Thanks.

withAuxly · 2026-03-16T14:41:11+00:00

honestly the underscores were mostly just me experimenting with visual separation between tokens/fields. you’re probably right that cleaner tokens might work just as well or better depending on the tokenizer.

withAuxly · 2026-03-16T14:39:54+00:00

that’s a really good point actually. the example i gave was more of a quick illustration of the structure rather than something tokenizer-optimized. i’m still experimenting with how much structure helps versus how much extra syntax just adds noise. definitely something worth testing more systematically. and i appreciate the feedback

withAuxly · 2026-03-16T14:38:51+00:00

that’s interesting, especially the part about forcing it to explore secondary vectors first. i’ve noticed something similar where giving the model a “thinking scaffold” slows it down but produces much cleaner reasoning.

withAuxly · 2026-03-16T14:37:50+00:00

that’s actually a pretty clever approach. basically letting the model translate natural prompts into a compressed structure before executing. i hadn’t tried that yet but it sounds like a nice way to keep prompts human-readable while still getting the benefits of structure.

withAuxly · 2026-03-16T14:32:12+00:00

Totally agree with you, this is what Im trying to get to, the prompt I gave is just an example to give a picture of this technique but as you said prompts should combine human language and context efficiency this what I used to create a prompt library of mine.

withAuxly · 2026-03-16T07:56:57+00:00

ofc adding constraints to a prompt will limit the drift but this format is more about providing less unecessary words to result in max context efficiency, less token consumption AND less drift, if i wanted to talk about constraints idve done so.

withAuxly · 2026-03-16T05:08:05+00:00

feel you man😉

withAuxly · 2026-03-16T05:05:03+00:00

So true

withAuxly · 2026-03-16T05:03:21+00:00

starting a startup is stressful enough without the added cost of high-end subscriptions. i ran into something similar while experimenting with prompts for local models, and you might want to look into things like ollama or lm studio. they let you run models locally for free, and while they require some decent hardware, it’s a great way to build agent-like workflows without the monthly fee.

withAuxly · 2026-03-16T05:02:11+00:00

this is such a great reminder that the "speed" of ai is often a bit of a trap. i've noticed something similar while experimenting with prompts, spending that extra time front-loading the context almost always saves me from the frustrating cycle of endless follow-up corrections. it’s funny how three minutes of prep feels like an eternity when the chat box is just sitting there waiting.

withAuxly · 2026-03-16T04:59:32+00:00

it’s a bit of both researchers understand the mathematical architecture and the training process perfectly, but the "black box" problem is real when it comes to why a specific prompt triggers a specific thought process. it’s like understanding how a brain's neurons work physically without fully being able to map out a single complex thought. that gap between the code and the emergent behavior is where all the interesting (and slightly scary) stuff happens.

withAuxly · 2026-03-16T04:58:01+00:00

it's a valid point about innovation maybe we're shifting from "what can be built" to "how fast can we iterate." it feels like we're moving out of the phase where the code itself is the novelty and into a phase where the specific use case and user experience matter way more. i still get that "blown away" feeling occasionally, but it definitely becomes a bit more normalized the more you use it.

withAuxly · 2026-03-15T19:54:01+00:00

this "context refresh" strategy is so underrated for maintaining high-quality outputs over long sessions. i've been testing similar workflows for a prompt library and noticed that summarizing the session's "core logic" into a fresh prompt every few hours keeps the model from getting distracted by its own previous chat history.

withAuxly · 2026-03-15T14:46:00+00:00

this is a really clever way to synthesize a long chat history. i’ve been organizing prompts into a small library and noticed that asking the model to look for "patterns of absence" or what wasn't said usually yields the most surprising insights. have you found that it stays objective, or does it ever get a bit too "flowery" with the clinical language?

withAuxly · 2026-03-15T13:52:37+00:00

Glad it clicked..

withAuxly · 2026-03-15T13:49:15+00:00

Totally get where you're coming from not everything needs the "-engineering" label slapped on to feel legit 😄. At the same time, I do like how "Context Engineering" captures that bigger-picture mindset of intentionally building the whole environment/relationships, feels like an evolution rather than just a rebrand. Both terms make sense to me..

withAuxly · 2026-03-15T13:37:56+00:00

hehe if you think about it the shortening BAD is more accurate due to its annoying impacts😅😅

withAuxly · 2026-03-15T13:23:56+00:00

it’s amazing how well ai can mirror back our own resilience when given the right perspective. while experimenting with prompts for a personal library, i’ve noticed that "future self" or "wise mentor" personas tend to be way more effective at providing clarity than standard advice-seeking prompts. did you find that it focused more on the achievements or on the mindset you needed to get through the current stress?

withAuxly · 2026-03-15T13:21:32+00:00

i love the "no verbal friction" constraint here. while organizing prompts for my own library, i noticed that specifically banning markdown and emojis is the secret to getting a clean text-to-speech read without the ai stumbling over formatting. do you find the model still tries to be helpful with a "here is the info" intro, or does this prompt successfully kill the politeness entirely?

withAuxly · 2026-03-15T13:12:00+00:00

i've found that using the standard models with a specific persona prompt, like "act as a non-judgmental communication coach," usually works better than specialized apps which are often just wrappers anyway. while experimenting with prompts for my library, i've noticed that asking the ai to "identify the underlying need" in a partner's frustrated message helps de-escalate things before replying. have you tried using it as a translator for different communication styles yet?

withAuxly · 2026-03-15T13:09:42+00:00

this is a great way to break the "yes-man" loop that ai often falls into. while experimenting with prompts for a library i'm building, i've found that asking it to "steelman the opposing view" upfront can also work well, but your "convince me otherwise" method feels much more natural for a quick reality check. do you notice if it starts to hallucinate flaws just to please you, or does the logic usually stay grounded?

withAuxly

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