maglite77

Does anyone have links to "how-to's" for Linux in the enterprise? Password/MFA management, central app management, the equivalent of group policy, device management, etc.? IME, these are the areas in the enterprise where Windows "wins" with the IT teams, but it's been a long time since I've been close to the rollout side of the house. TIA

Something I'm surprised this article didn't touch on which is driving many organizations to be conservative in "how much" AI they release for a given product: prompt-jacking and data privacy.

I, like many others in the tech world, am working with companies to build out similar features. 99% percent of the time, data protection teams and legal are looking for ways to _remove_ areas where users can supply prompts / define open-ended behavior. Why? Because there is no 100% guarantee that the LLM will not behave in a manner that will undermine your product / leak data / make your product look terrible - and that lack of a guarantee makes both the afore-mentioned offices very, very nervous (coupled with a lack of understanding of the technical aspects involved).

The example of reading emails from the article is another type of behavior that usually gets an immediate "nope", as it involves sending customer data to the LLM service - and that requires all kinds of gymnastics to a data protection agreement and GDPR considerations. It may be fine for smaller startups, but the larger companies / enterprises are not down with it for initial delivery of AI features.

Just tested on an older (2015) Intel UHD 620 GPU (integrated), and surprisingly all the demos except one hit ~45FPS and up. The first one - Big Forest - struggled to get ~10fps, and was extremely laggy.

Meh...the phrase "deep dive" has been around forever, not sure what about that phrase specifically gives "AI generated" vibes? For me, I just read through those and focus on the content quality of the article - "verbal seasoning" doesn't phase me at all.

Noting that Azure Blob storage supports e-tag / optimistic controls as well (via If-Match conditions)[1], how does this differ? Or is it the same feature?

[1]: https://learn.microsoft.com/en-us/azure/storage/blobs/concur...

Previous MSFT employee of about 7 years (left about 3 years ago) - this was totally not true. In fact, we were encouraged to make non-competing contributions and I worked with several peers that had very successful open source projects.

While I'll agree the "eye candy updates" may not be there (apart from marketing-driven AI additions and visual updates), there are some big features that aren't visible until you enter the enterprise space. For example, Purview Information Protection [1] and Data Classification integration [2] make data protection, audits/compliance a no-brainer, and are _extremely_ compelling arguments for an integrated suite at the CISO level.

(The downside of course is this is a single-source stack, which can be a risk in of itself)

I have no real background in Libre (apart from using it, which I enjoy), but from cursory searches, there doesn't appear to be equivalent features available (very happy to be wrong here FWIW). Are there alternatives in this space?

[1]: https://learn.microsoft.com/en-us/purview/information-protec... [2]: https://learn.microsoft.com/en-us/purview/data-classificatio...

It's been ages, but this use of unions reminds me of the VARIANT structure definition from Windows OA/COM:

[1]: https://docs.microsoft.com/en-us/windows/win32/api/oaidl/ns-...

Always have been. See: https://en.wikipedia.org/wiki/Terms_of_service

Disclosure: Engineer at Microsoft

There is an excellent Network Academy video on YouTube [1] that covers some of the internal implementations of the layer-4 load balancers (in the context of HA for NVA's).

[1]: https://youtu.be/oYJBrWcOXfY

Many of these concerns seem to not be limited to Javascript at all - especially once you're sending/receiving data between systems. In Python for example, when receiving JSON/XML payloads you have the same defensive parsing layer at the point of entry, but humans can still mess that up and monkey patch a class that breaks assumptions elsewhere without warning. Did I miss something in the larger argument?

One area I would argue benefits greatly from this type of formal treatment is the specification of business rules in software. I can be quite insightful to formally spell out requirements from the business, and then apply converse/inverse/etc. analysis to drive out missing cases.

Admittedly, most of us can do this in our head quickly (for easy cases), but I find the formal evidence lends itself well to more complex scenarios.

[Disclosure: I work for Microsoft]

One thing to keep in mind for Azure SQL: you are getting a guaranteed SLA [1], business continuity and full point-in-time backups (up to 30 days) [2] for that price. To compare effectively you would need to price out a comparable Always-On cluster, domain controllers, SAN storage (for replication), and backup solution + storage.

[1]: https://azure.microsoft.com/en-us/support/legal/sla/sql-data... [2]: https://azure.microsoft.com/en-us/documentation/articles/sql...

Also, remember that even building a "simple" web app (comprised of a browser (client), web server, and database) counts as a distributed system. Concerns over event ordering, lack of global time, and data consistency models are all alive and well even within boring LOB systems.

To put it another way, distributed system does not necessarily imply a massive research cluster connected via MPI.