The UI is a bit confusing to me in what I have to pick one of before I can search for a book. Also, the search bar could not find the book I wanted to pick which was published in 1971.
I only see a few articles listed compared to the amount of new AI and ML articles that come out every day. Are most of them being filtered out of your feed and what are the criteria for them being excluded?
Good question. There's filtering built in to keep noise down: reddit and hacker news posts need 50+ upvotes/points to make it through, and HN also has to match AI keywords. github only pulls the top 10 trending repos per language. arxiv and huggingface papers come through with less filtering since they're already curated by those communities.
there's also deduplication so the same link from multiple sources only shows once. the tradeoff is fewer posts but (hopefully) higher signal. I'm still working through happy to tune the thresholds if it feels too aggressive. The tradeoff is fewer posts but (hopefully) higher signal. User submissions bypass all that though, if you submit something it goes straight to the feed. So, I'm hoping folks find it useful enough to start sharing links they come across.
I'm still tweaking the approach for this, and the thresholds I picked are absolutely arbitrary, so I'm open to any feedback that can improve it!
It would be great if the results table had a column for whether or not the software had a public web endpoint to actually run the software without installing it locally yourself.
No, I don't think you are going to find such a tool. For one thing, there is a subculture of people who write and vlog about AI tools, and sometimes they offer affliate weblinks with discount codes for them. It would be too much trouble for anyone to identify all those people and scrape all their content daily.
Expert systems don't work better than LLMs - they are very brittle and completely fall apart when they encounter a novel situation outside the scenarios programmed into their if-then rules.
Do you have any references that further explain what MIND and the "Last Economy" concepts are? Also, any references on "valuation tension" or "expectation saturation" as I do not understand what you are trying to measure?
Thanks for the question - in brief, I'm trying to gather opinions as to whether M.I.N.D. (see below) is truly an effective metric to evaluate: "if AI capabilities keep improving and diffusing, how well positioned is this entity to capture second-order value from that process?".
M.I.N.D. / "Last Economy"
The "Last Economy" framing comes from Emad Mostaque's book of the same name and is a way of thinking about where long-run value concentrates when intelligence becomes abundant. M.I.N.D. is the operationalization of that idea from the book and positioned as a better "yardstick" than current metrics like GDP or other traditional, scarcity-oriented financial metrics. For background on the broader thesis, Emad has written and spoken about it publicly here: https://ii.inc/web/the-last-economy. [It's a quick read for those familiar with the AI space and IMHO an important and relatively accessible read for anyone planning to live in the future].
At a high level he outlines:
- Material: control over scarce physical inputs that AI depends on (energy, fabs, supply chains, hardware)
- Intelligence: leverage over computation, models, or inference at scale
- Network: data, ecosystems, distribution, or flywheels that compound usage
- Diversification: exposure across multiple AI value paths rather than a single bet
The specific choice to multiply the dimensions (rather than add them) is also from his formulation: it encodes the assumption that missing one leg meaningfully caps long-run alignment. That assumption is very much up for debate, but the better an entity (country, company, person etc.) can score along the dimensions the better prepared they are for the Last Economy future governed by more physical than metabolic processes, and the ability to convert energy into computation.
I do want to stress that this chart is my interpretation, not an official formulation.
Valuation tension / expectation saturation
I'm not trying to introduce a standard valuation metric here, and there isn't a single reference I'd point to. The idea is closer to a sentiment / expectation proxy than intrinsic value.
Concretely, I'm asking: how optimistic does current pricing appear relative to a longer-horizon narrative based on how well a company may thrive or suffer in The Last Economy scenario?
To keep it interpretable, I approximate that using:
- a relative long-term opportunity estimate (2030 horizon, directionally based on a creative, scenario driven process)
- divided by price position within the 52-week range as a proxy for how much optimism or skepticism is already expressed
It's intentionally blunt and debatable. I'm treating it as a secondary axis — useful for highlighting where narratives feel "fully priced" versus where they don't — not as a valuation model.
I realise there is a lot of context underlying my question. Thanks for your patience and interest.
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