Chiming in here to say that while yes, often AI/LLMs will tend to agree with you, I have also definitely had many (high context) conversations where the AI/LLM disagreed strongly with me. The danger is in people not having a parallel thread running in their mind while using these systems about 'how agreeable is it being with me right now?' as a meta-axis along which to evaluate the information.
> You don't really need to work for a company anymore, because a solo dev can absolutely build crazy things
Don't conflate what is theoretically vs. realistically possible. In the real world, successful companies have moats from data, patents/IP, network effects, and so forth. Just because you can develop something in 1/100th the time doesn't make it instantly feasible to build a new business around. Look around the tech industry today.. plenty of companies that could be disrupted by spry AI-powered buidlers, but they are not (owing to these lock-in effects).
I understand that standing up memory fabrication plants is no small feat, but how much of this is due to memory fabricators' patent moat? To what degree is this caused by IP barriers vs. the difficulty in actually manufacturing the things? If it's the case that even older-generation / process-node RAM is also going up in value, aren't those 'easier' to produce?
Agreed, and this is exactly what we see happening. Your posts back then were prescient ... there's literally now 'Copilot for Excel' and 'Claude for Excel' etc. But what do you propose the people/commons can still do at this stage to redistribute the inherent power found in RL data loops to a more stable equilibria of sharing participants?
Great question and there’s two steps in my opinion:
First is to become as free as possible from lock in and own your own data. The best way to do this is the self host your own technology.
This is really not possible for the majority of people though.
So practically I always suggest that you have multiple providers for services, don’t pool your data any one place (other than your own place) and own your backups. This is basic stuff that we’ve been teaching since the 90s and still very applicable today.
The harder and more impactful thing is to then create community owned technology that is outside of the commerce model.
So for example imagine that instead of FAANG running the world, the largest tech and data orgs would look more like wikimedia foundation, Annas archive, scihub, Graphene, Linux etc…. and more generally that technology and governance are open and not bound to commerce/taxation/coercion based organizations.
Ultimately we need to create a democratic-technology movement such that capitalists don’t monopolize technology, which is currently the trend. This is not some kind of simple thing by the way, this is revolutionary economics is what I’m talking about.
My suggestion is to read Post-Scarcity Anarchism by Murray Bookchin
Thanks dang for compiling this. I suspect the Nov 2018 resurgence was due to Google publishing BERT [0] around that time? The release of OpenAI’s GPT-1 [1] was earlier that year in June, so unlikely that. Of course Jan 2023 needs no explanation… And now in 2026 things are at a fever pitch.
Interesting to trace these 10yr old AI posts from then to the present moment. The other one with a similar vintage would be “Should AI Be Open” [2] from Dec 2015, which is fascinating to juxtapose against the recent public battles.
+1 for One Sec, a fantastic app, if one has the patience to wire it up using Apple’s first-party Shortcuts app (which is probably the main reason most normies aren’t going to use it). Really helped curb my Instagram usage down from about ~15 minutes per day to around ~5 minutes/day at most, and often now a few days go by without me checking the app at all. It is remarkable how much a 4, 6, or 10 second wait will just cause me to say “nah, forget it, I don’t care anymore”. Like, how much of a dumb ape am I?
There is a nice book by Jonathan Haidt about this called The Happiness Hypothesis.
He uses a "Elephant and Rider" psychological metaphor to describe the internal battle between our rational mind (the rider) and our emotional, intuitive drives (the elephant).
Very cool project. Is this similar to the Apple Watch ‘mindful minutes’ breathing feature? I assume it’s based on the same research as is cited in this project’s repo?
Thank you for your appreciation. I don't have an Apple Watch so it's hard to say. There isn't a ton of research on the subject — no Big Pharma sponsorship money in a breathing technique that you can't patent — so I suspect it's the same research.
Give the app a try and share your feedback. Happy Breathing :)
Mechinterp in general is just completely undervalued right now (and agreed Anthropic's team is doing the most rigorous work, now accompanied by Goodfire). They're doing the closest work to neuroscience's in vivo 'thought-tracing', which is just the most wild science fiction sort of thing to be working on, and yet I feel the average person has no idea this sort of work is happening. When combined with the idea of the 'universal subspace hypothesis' (explored under the paper of the same name), you really start to bridge the gap from engineering to something more philosophical and spiritual. But I digress...
Ya, super interesting research area the authors explored of basically trying to answer the question: "Is there a canonical/intrinsic way that concepts/representations/information are 'stored' in the universe/reality?".
They tested that by performing "spectral analysis of over 1100 models - including 500 Mistral-7B LoRAs, 500 Vision Transformers, and 50 LLaMA-8B models ... by applying spectral decomposition techniques to the weight matrices of various architectures", and concluding that "deep neural networks trained across diverse tasks exhibit remarkably similar low-dimensional parametric subspaces", showing that "neural networks systematically converge to shared spectral subspaces regardless of initialization, task, or domain".
Not just philosophically interesting but also has practical implications for being smarter about how to reuse models, model merging, developing more sustainable training and inference algos, etc.
Does anyone else use the 'smells' as a sort of 'game' to ensure you properly go through a given LLM output (of any kind, be it document, presentation, code, etc) and 'make it your own' by eliminating them? I have a high bar for sharing content so I always do a rigorous pass to eliminate the em dashes, the contrastive negations, the 'quietly', and any other extraneous verbosity, and find that it helps me just really thoroughly polish it up.
This reminds me to a video essay I saw a few months back. Guy explained that one of the metrics he evaluates game quality with, is how much the game manages to hide its game engine.
I think it makes a lot of sense, it's essentially an inversion. Another related thing I recall is hearing game creators talk in terms of "gimmicks" and "scenarios". Anything that gives a structure, a framework to operate within, I'd say is always useful. This is much the same.
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