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Cake day: June 23rd, 2023

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  • Earl Turlet@lemmy.ziptoTechnology@lemmy.worldThe ruthless forking of Terraform
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    10 months ago

    Hashicorp recently changed the license of Terraform and its other core products from MPL to BSL, restricting commercial use and preventing competitors from offering services based on the code. While this makes business sense for the now-public Hashicorp, it upset many users who saw it as undermining the open source nature of the projects. In response, the OpenTF project was launched to fork Terraform and maintain it under a truly open source license. While Terraform is not as likely to cause vendor lock-in as databases, its dominance as a developer tool could be impacted by this change and emerging alternatives. Interestingly, the video ends by humorously discouraging viewers from supporting the OpenTF project in opposition to Hashicorp’s licensing change.

    Via Kagi universal summarizer






  • FWIW, the AI features are not used to provide search results; they are all on-demand and triggered by the user (via Quick Answer, or Universal Summarizer, or the “discuss this site” feature).

    The founder is well aware of the problems with AI and that is taken into account when deciding how to use it in Kagi.

    See this link: https://blog.kagi.com/kagi-ai-search#philosophy

    Generative AI is a hot topic, but the technology still has flaws. Critics of AI warn that “[AI] will degrade our science and debase our ethics by incorporating into our technology a fundamentally flawed conception of language and knowledge”.

    From an information retrieval point of view, relevant to our context of a search engine, we should acknowledge the two main limitations of the current generation of AI.

    Large language models (LLMs) should not be blindly trusted to provide factual information accurately. They have a significant risk of generating incorrect information or fabricating details (confabulating). This can easily mislead people who are not approaching LLMs pragmatically. (This is a product of auto-regressive nature of these models where the output is predicted one token at a time, and once it strays away from the “correct” path, for which the probablity grows exponentially with the length of the output, it is “doomed” to the end of output, without the ability to plan ahead or correct itself).

    LLMs are not intelligent in the human sense. They have no understanding of the actual physical world. They do not have their own genuine opinions, emotions, or sense of self. We must avoid attributing human-like qualities to these systems or thinking of them as having human-level abilities. They are limited AI technologies. (In a way, they are similar to how a wheel can get us from point A to point B, sometimes much more efficiently than human body can, but it lacks the ability to plan and the agility of human body to get us everywhere a human body can)

    These limitations required us to pause and reflect on the impact on search experience, before incoporating this new technology for our customers. As a result, we came up with an AI integration philosophy that is guided by these principles:

    AI should be used in closed, defined context relevant to search (don’t make a therapist inside the search engine, for example) AI should be used to enhance the search experience, not to create it or replace it (similar to how we use JavaScript in Kagi, where search still works perfectly fine when JS is disabled in the browser) AI should be used to the extent that it enhances our humanity, not diminish it (AI should be used to support users, not replace them)


  • $25 is a lot per month but it is saving me a lot of time and helping me to find better results so I find it worth it.

    I justify the cost by relating it to how it helps me at work. I believe Kagi makes me more effective; my boss(es… :( ) and peers notice, and that translates to better performance evaluations and raises. I don’t hide my usage of it from my team, but I don’t think they realize how much of an advantage it gives me. Once you get the rankings and lenses tuned to your workflow, it’s amazing how it lets you cut through the nonsense of the internet.


  • I’ve been using it for about a year and a half, on the unlimited plan. I pay for the year up front for the discount. There’s no way I’m willingly going to stop using Kagi. I’m a developer and perform about 2500 searches a month.

    The ability to adjust the ranking of domains and the lenses save me a ton of time. No other engine comes close to the productivity.

    You can easily talk to the developers and founder, too. I’ve had many of my suggestions actually implemented. It’s great when you pay for the service and they are in it for you, not your data.




  • I use Hurl. Everything is just a text file:

    POST https://example.org/api/tests
    {
        "id": "4568",
        "evaluate": true
    }
    
    HTTP 200
    [Asserts]
    header "X-Frame-Options" == "SAMEORIGIN"
    jsonpath "$.status" == "RUNNING"    # Check the status code
    jsonpath "$.tests" count == 25      # Check the number of items
    jsonpath "$.id" matches /\d{4}/     # Check the format of the id