AI's real superpower: consuming, not creating

I think it's ability to consume information is one of the scarier aspects of AI. NSA, other government, and multi-national corporations have years of our individual browsing and consumption patterns. What happens when AI is analyzing all of that information exponentially faster than any human code and communicating with relevant parties for their own benefit, to predict or manipulate behavior, build psychological profiles, identify vulnerabilities, etc.

It's incredibly amusing to me reading some people's comments here critical of AI, that if you didn't know any better, might make you think that AI is a worthless technology.

Models are mediocre solo consumers: they skim, paraphrase and confidently miss the one subtle thing that actually matters. Humans are still better at deciding which three paragraphs in a 40‑page spec are load‑bearing. But as soon as you treat the model as a stochastic code monkey with a compiler, test suite, linter and some static tooling strapped to its back, it suddenly looks a lot more like “creation with a very fast feedback loop” than “consumption at scale”.

The interesting leverage isn’t that AI can read more stuff than you; it’s that you can cheaply instrument your system (tests, properties, contracts, little spec fragments) and then let the model grind through iterations until something passes all of that. That just shifts the hard work back where it’s always been: choosing what to assert about the world. The tokens and the code are the easy part now.

This might make it into this week's https://hackernewsai.com/ newsletter.

> No human could read all of this in a lifetime. AI consumes it in seconds.

And therefore it's impossible to test the accuracy if it's consuming your own data. AI can hallucinate on any data you feed it, and it's been proven that it doesn't summarize, but rather abridges and abbreviates data.

In the authors example

> "What patterns emerge from my last 50 one-on-ones?" AI found that performance issues always preceded tool complaints by 2-3 weeks. I'd never connected those dots.

Maybe that's a pattern from 50 one-on-ones. Or maybe it's only in the first two and the last one.

I'd be wary of using AI to summarize like this and expecting accurate insights

I often see things like this and get a little bit of FOMO because I'd love to see what I can get out of this but I'm just not willing to upload all these private documents of mine to other people's computers where they're likely to be stored for training or advertising purposes.

How are you guys dealing with this risk? I'm sure on this site nobody is naive to the potential harms of tech, but if you're able to articulate how you've figured out that the risk is worth the benefits to you I'd love to hear it. I don't think I'm being to cynical to wait for either local LLMs to get good or for me to be able to afford expensive GPUs for current local LLMs, but maybe I should be time-discounting a bit harder?

I'm happy to elaborate on why I find it dangerous, too, if this is too vague. Just really would like to have a more nuanced opinion here.

AI's real super power is telling you what you want to hear (doubly true since RLHF became the standard).

You can really see the limitations of LLMs when you look at how poorly they do at summarization. They most often just extract a few key quotes from the text, and provide an abbreviated version of the original text (often missing key parts!)

Abbreviation is not summarization. To properly summarized you need to be able to understand higher level abstractions implied in the text. At a fundamental level this is not what LLMs are designed to do. They can interpolate and continue existing text in remarkable and powerful ways, but they aren't capable of getting the "big picture". This is likely related to why they frequently ignore very important passages when "summarizing".

> We're still thinking about AI like it's 2023.

Just a reminder that in 2023 we were all told that AI was on a path of exponential progress. Were this true, you wouldn't need to argue that we're using it "wrong" because the technology would have improved dramatically more than it did from 2021-2023 such that there would be no need to argue that its better, using it "wrong" would still be a massive improvement.

I'm wary about using AI models to generate stuff for me - I still bristle from the time a model told me that "JS Sets are faster than Arrays" and I believed it, until I discovered that it forgot to add the important piece of information: for Arrays containing tens of thousands of elements. Which made me feel stupid.

Still, I find the models to be excellent synthesisers of vast quantities of data on subjects in which I have minimal prior knowledge. For instance, when I wanted to translate some Lorca and Cavafy poems into English I discovered that ChatGPT had excellent knowledge of the poems in their native languages, and the difficulties translators faced when rendering them into English. Once I was able to harness the models to assist me translate a poem, rather than generate a translation for me (every LLM is convinced it's a Poet), I managed to write some reasonable poems that met my personal requirements.

I wrote about the experience here: https://rikverse2020.rikweb.org.uk/blog/adventures-in-poetry...

This is a really cool insight. I'm going to try this in my Obsidian vault as well. What are some of the highest leverage items to add to your vault to start with?

I think meetings is one thing I'm missing out on. How do you put meeting information into your Obsidian? Is it just transcripts?

The article is more about offloading your thinking to the machine than a real usage of what notes is. You may as well make every decision rely on a coin toss.

I take notes for remembrance and relevance (what is interesting for me). But linking concepts is all my thinking. Doing whatever rhe article is prescribing is like sending someone on a tourist trip to take pictures and then bragging that you visited the country. While knowing that some pictures are photoshopped.

At least half of AI's "superpower" in OP's case is the fact that he has everything in Obsidian already. With all of that background context, any tool becomes super valuable in evaluating & guiding future actions.

Still, all credit to him for creating that asset in the first place.

We know from the era of data the power of JOIN. Bring in two different data sources about a thing and you could produce an insight neither of them could have provided alone.

LLMs can be thought as one big stochastic JOIN. The new insight capabilities - thanks to their massive recall - is there. The problem is the stochasticity. They can retrieve stuff from the depths and slap them together but in these use cases we have no clue how relevant their inner ranking results or intermediary representations were. Even with the best read of user intent they can only simulate relevance, not really compute it in a grounded and groundable way.

So I take such automatic insight generation tasks with a massive grain of salt. Their simulation is amusing and feels relevant but so does a fortune teller doing a mostly cold read with some facts sprinkled in.

> → I solve problems faster by finding similar past situations → I make better decisions by accessing forgotten context → I see patterns that were invisible when scattered across time

All of which makes me skeptical of this claim. I have no doubt they feel productive but it might just as well be a part of that simulation, with all the biases, blind spots etc originating from the machine. Which could be worse than not having used the tool. Not having augmented recall is OK, forgetting things are OK - because memory is not a passive reservoir of data but an active reranker of relevance.

LLMs can’t be the final source of insight and wisdom, they are at best sophists, or as Terrence Tao put it more kindly, a mere source of cleverness. In this, they can just as well augment our self-deception capacity, maybe even more than counterbalancing them.

Exercise: whatever amusing insight a machine produces for you, ask for a very strong counter to it. You might be equally amused.

Agree with OP that LLMs are a great tool for this use case. It's made possible because OP diligently created useful input data. Unfortunately OP's conclusion goes against the AI hype machine. If "consuming" is the "superpower" of AI, then the current level of investment/attention would not be justified.

I was in a research math lecture the other day, and the speaker used some obscure technical terminology I didn't know. So I dug out my phone and googled it.

The AI summary at the top was surprisingly good! Of course, the AI isn't doing anything original; instead, it created a summary of whatever written material is already out there. Which is exactly what I wanted.

Wrt temperature/randomization is it not possible for it to create something genuine. Even life there seems to always be some inspiration for things being made. How did Tesla go from Brushed to Brushless AC motors. There was some foundational knowledge of electricity similar to airplanes, the Wright Brothers their airplane seems backwards (like a canard) but still a plane/needs wings. Not something radical like ion engines for lift (much harder).

A guy I work with has been doing this, I watched his tutorial and it was all a bit... overwhelming for me (to think about using such a system), I'm still on pen and paper, heh. Nevertheless - here is his template: https://github.com/kmikeym/obsidian-claude-starter and tutorial: https://www.youtube.com/watch?v=1U32hZYxfcY

Sorry, is this new? Providing the right data to LLMs supercharges them. Yes, I agree. I've been doing this since March 2025 when there was a blog post about using MCP on HN. I'm not the only one who's doing that.

I've written my whole lifestory, the parts I'm willing to share that is, and posted it in Claude. It helped me way better with all kinds of things. It took me 2 days to write without formatting, pretty much how I write all my HN comments (but then 2 days straight: eat, sleep, write).

I've also exported all my notes, but it's too big for the context. That's why I wrote my life story.

From a practical standpoint I think the focus is on context management. Obsidian can help with this (I haven't used it so don't know the details). For code, it means doing things like static and dynamic analysis to see which functions calls what and create a topology of function calls and send that as context, then Claude Code can more easily know what to edit, and it doesn't need to read all the code.

AI's consumption superpower reminds me of birds, flying about eating worms, then flying back to the nest and regurgitating them into baby's mouth, because it's the processed nutrition they provide that valuable; their own consumption is a combination of fractional/temporary

I think this will be a significant thing in the future, but right now I think the reasoning abilities are too limited. It can reasonably approximate a vector database where it can find related things, but I think that success can hide the failure to find important things.

I'd like to be able to point a model at a news story and have it follow every fact and claim back to an origin, (or lack of one). I'm not sure when they will be able to do that, they aren't up to the task yet. Reading the news would be so much different if you could separate the 'we report this happened' from the 'we report that someone else reported this happened"

What is the approach used? It seems everything gets done in context by plain text searches with some agent like Claude code or is there RAG involved? (was the article written by AI? it has that LinkedIn-groove all over the place)

Ironically this article/blog itself is giving off an AI-generated smell as it's tone and cadence seem very similar to LinkedIn posts or rather output of prompts to create LinkedIn posts.

Anyone has a simple setup for this with local LLMs like Mistral that they can share?

I would love to try this out but don’t feel comfortable sharing all my personal notes with a third party.

Can you share some prompt examples you use to try to ensure it doesn't get "lazy" and just cherry pick from here and there?

I have a written novel draft and something like a million words of draft fiction but have struggled with how to get meaningful analytics from it.

I would say the AI consumption aspect was a side effect: the primary goal was to "generate" new stuff. So far, to me, the significant boost is the coding aspect. Still, for the rest of the people, I think you are right: 90% of the benefits come from being an interactive, conversational search on top of the available information that AI can read/consume.

I don't see what's new here. The biggest enterprise usecase for AI is to "consume" the vast amount of internal wiki pages, process documents, policy manuals, code repos, presentations and be able to answer questions.

I do use such an approach and it is actually awesome however only for data I'm sure I don't mind being sold.

Compound the gains again by asking AI to write the questions too!

If we pair this with a wearable ai pendant like plaid or limitless, we can increase the amount and frequency of depositing into our knowledge vault. Op, do you type your thoughts and notes or dictate them?

This is the right approach. I exported my 25k Evernote notes to markdown (I'm using Emacs' Howm mode) and I use Codex CLI to ask questions about my notes. It is great and powerful!

This is akin to using AI as a 'second brain', just getting started with Obsidian, my main challenge is loading it up with every communication trace I have...but haven't given up.

This approach feels like a much more honest use

What is a good way of connecting Obsidian vault to AI?

Digital Information Consumers or Digital Information Copiers or Digital Information Creators

Either way, they are D.I.C.s

Yay, another HN post confidently claiming "everyone’s doing X wrong."

"Everyone’s using AI wrong." Oh, we are? Please, enlightened us thought leader, tell us how we’ve all been doing it wrong this entire time.

"Here’s how most people use AI." No, that’s how you use AI. Can we stop projecting our own habits onto the whole world and calling it insight?

AI-powered solipsistic reassurance?

For fuck's sake, isn't anyone here horrified at how much information on yourself you are willingly funneling into Big Tech with this approach?

Thats why proofreading jobs still exist

How many times have the goal-posts shifted now?

Everyone is justifiably afraid of AI because it's pretty obvious that Claude Opus 4.5 level agents replace developers.

I found that out while working with music models like Suno! I love creating music for my own listening experience as a hobbyist and when I give suno a prompt no matter how well crafted it is the outcome varies from "meh" to "that's good" ... while when I upload semi finished beat I made and prompt it to cover it the results consistently leave me speechless! Could be a bias since the music has a lot of elements I created but this workflow is similar across other generative models for me.

>real superpower: consuming, not creating

Well for most humans that's the more super of the powers too ;)

So I decided to give this a try - I have a `writing` directory/git repo where I store most of my writing, which can be anything from notes on technical subjects (list of useful syntax for a given programming language), to letters to friends, to philosophical ramblings.

I opened Claude Code in the repo and asked it to tell me about myself based on my writing.

Claude's answer overestimated my technical skills (I take notes on stuff I don't know, not on things I know, so it assumed that I had deep expertise in things I'm currently learning, and ignored areas where I do have a fair amount of experience), but the personal side really resonated with me.

Not really surprising that a tool created for surveillance and mass profiling turned out to be pretty good at surveiling and profiling