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It is not a question if, but ‚when‘ we will see what we can call AGI. The ‚when‘ can be answered if we know the ‚how‘. The ‚how‘ we know if the ‚when‘ is answered by ‚today‘.

  1. > … what human intelligence is because intelligence is best described as a side-effect of consciousness …

    Is "human intelligence" and "intelligence" equal?

    And: How to become conscious before being intelligent?

    Or: If intelligence is a side-effect, how often this side-effect can't be observed?

    Xor: What if an intelligent being denies being conscious?

  2. "low hanging" is relative. At least from my perspective. A significant part of my work involves cleaning up structured and unstructured data.

    An example: More than ten years ago a friend of mine was fascinated by the german edition of the book "A Cultural History of Physics" by Károly Simonyi. He scanned the book (600+ pages) and created a PDF (nearly) same layout.

    Against my advice he used Adobe tools for it instead of creating an epub or something like DocBook.

    The PDF looks great, but the text inside is impossible to use as training data for a small LLM. The lines from the two columns are mixed and a lot of spaces are randomly placed (makes it particularly difficult because mathematical formulas often appear in the text itself).

    After many attempts (with RegEx and LLMs), I gave up and rendered each page and had a large LLM extract the text.

  3. > This is actually a clever way to distinguish if the browser supports XSLT or not. Actual content is XHTML in https://xslt.rip/index.xsl

    I agree it is a clever way. But it also shows exactly how hard it is to use XML and XSLT in a "proper way": Formal everything is fine to do it in this way (except the server is sending 'content-type: application/xml' for the /index.xsl, it should be 'application/xslt+xml').

    Almost all implementations in XML and XSLT that I have seen in my career showed a nearly complete lack of understanding of how they were intended to be used and how they should work together. Starting with completely pointless key/value XMLs (I'm looking at you, Apple and Nokia), through call-template orgies (IBM), to ‘yet-another-element-open/-close’ implementations (almost every in-house application development in PHP, JAVA or .NET).

    I started using XSLT before the first specification had been published. Initially, I only used it in the browser. Years later, I was able to use XSLT to create XSDs and modify them at runtime.

  4. Thnx for sharing!

    With Safari (standard and tech preview) the rendering looks strange (at least). The root sign does not have a strait line at the top (for many fonts) and at least the partial derivative is not rendered as italic (for all fonts).

  5. I agree with you one hundred percent.

    But: Interestingly, the behavior of LLMs in different contexts is also the subject of scientific research.

  6. Is that you? The same guy with the comment "hahahhahaha"[1] on "Women dating safety app 'Tea' breached, users' IDs posted to 4chan"[2]?

    [1] https://www.hackerneue.com/item?id=44689366

    [2] https://www.hackerneue.com/item?id=44684373

  7. > Finally, companies may have reached the ceiling of what can be achieved with prompting alone. Some want models that know their vocabulary, their tone, their taxonomy, and their compliance rules.

    Together with speed and const, this is from my point of view this is the only "case" for the return of fine-tuning here. And this can be managed by context management.

    With growing context sizes, first RAG replaced fine-tuning and later even RAG was replaced by just a good-enough prompt preparation for more and more usage pattern.

    Sure, speed and costs are important drivers. But like with FPGAs vs. CPUs or GPUs, the development costs and delivery time for high-performance solutions, eliminate the benefit most the time.

  8. You are right. And the initial question is also meaningless as there is no difference between these two:

      {"project": "MAML"}
    
      {project: "MAML"}
    
    A parser is not able to decide if '"project"' equals 'project'.
  9. Hmm, that's just "another link from someone who uses HN as a bookmark service". User started this self-service at the end of last year. With now 60+ submissions in the last two weeks. We can expect more interesting topics.
  10. Also not abstract. To be ‘abstract’, a language must combine several objects ‘under one umbrella’. Depicting different things in the same notation is the opposite of abstract. YACCANL (Yet Another Cargo-Cult Abstraction Non-Markup Language).
  11. > (although the license seems proprietary?)

    Hm, "BSD 3-Clause License" is seems really proprietary to you?

    But you are right: do the personal license in many(most?) Verilog files[1] overrules the LICENSE file[2] of a repo?

    [1] https://github.com/chili-chips-ba/wireguard-fpga/blob/main/1...

    [2] https://github.com/chili-chips-ba/wireguard-fpga/blob/main/L...

  12. Databricks offers an engineering, management, ETL (focussed on data) platform as SaaS/PaaS. As far as I can see, this is what the revenue comes from.

    n8n offers an orchestration, integration, ETL platform as SaaS/PaaS (plus self-hosted).

    For many medium sized and enterprise companies such a service is as important as the data platform from Databricks. From my personal experience in a small EU-wide enterprise in finance sector, the budget for the data platform is in the lower 7digits/year. For automation and workflows multiple solutions in place. Some are outdated for years (on mainframes) or with an EoL in the next years (due to retirements in-house or at the datacenter provider).

    Since decades such companies externalize their own business logic – formally running on homegrown software – to SaaS vendors. Established workflows are quite often not able to integrate these kind of "modern" integration. REST? GraphQL? WebSocket? We speak SOAP and FTP!

    So, from my point of view n8n is a valid solution in a growing market.

  13. > They are SaaS and not consumer products. They have no side gig like amazon or google - they have a single product in a tight market.

    Like Databricks with no consumer products, no side gig, single product, but 100 billion valuation[1]?

    [1] https://www.forbes.com/sites/sasirekhasubramanian/2025/10/07...

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