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.

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.

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 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.

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

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.

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.

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.

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...

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.

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.

"Last month I connected my Obsidian vault to AI. The questions changed completely:

Instead of "Write me something new" I ask "What have I already discovered?""

yep

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"

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.

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

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

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.

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

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

> AI has access to the entire vault

Yes, consumption. Also consider who or what you are feeding too.

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

For example, collecting the thoughts, questions, aspirations, etc. of "AI" users. Much of this appears to be personal or confidential hence the recent OpenAI anouncement about turning over "private chats" to plaintiffs' counsel in copyright infringement litigation

https://sites.google.com/view/elizaarchaeology/blog/3-weizen...

The original "AI chatbot" from the 1960's, "ELIZA", was not "trained on" (it did not consume) much data at all in today's terms. Yet it could solicit larger amounts of data from users who anthropomorphised the computer

If data collection is a goal, as it is with today's Silicon Valley companies, then this is arguably a very effective hack

The "AI" only needs to seem realistic to such individuals

"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?