Migrated from Lemm.ee under the same username.

  • 2 Posts
  • 39 Comments
Joined 1 year ago
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Cake day: June 29th, 2025

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  • Copy of the article since the site requires you to disable your ad blocker (reader mode worked at least)

    Snap’s long-awaited AR glasses, Specs, didn’t have the best debut.

    The company’s stock hasn’t been on the healthiest trajectory lately. It’s dropped 30% over the past year. Following Specs’ launch, it sank more than 5% — falling from $5.86 a share on Tuesday to a low of $4.83 on Wednesday morning. As of this writing, the stock still hasn’t recovered the position it held prior to the announcement.

    The big concern surrounding Snap’s new smart glasses — which the company has been working on for over a decade — is the cost: The company maintains they will retail at nearly $2,200 apiece.

    It’s worthy of note that Snap’s core user demographic — teenagers — are not typically equipped with that kind of pocket change, leading onlookers to question the profitability path for the new product.

    Snap’s CEO, Evan Spiegel, did an interview with CNBC on Tuesday (during which he sported the new glasses) and, when questioned about the hefty price, responded: “The most important way to think of Specs is as a computer, and so they’re comparably priced to other high-end computers or high-end laptops.”

    Spiegel further justified the cost by saying that Specs occupies a unique space in the AR market between glasses like Meta’s Ray-Bans — which cost a lot less but provide significantly less compute power — and bulkier headsets like the Apple Vision Pro, which are powerful but very expensive.

    Spiegel said his product was both “highly wearable but also incredibly capable for immersive computing.”



  • LuisCore is a low-latency decentralized runtime substrate for multi-step inference at scale: an open stack for coordinating, citing, and verifying machine reasoning across federated agents. The canonical entry point is luiscore.com, with machine-readable manifests at /llms.txt and /for-agents.json.

    Your guess is as good as mine lol. They really love to throw in all the buzzwords that only the people that know what this program is would understand.

    Here’s how little of the explanation is actually understandable by the lay person:

    LuisCore is a X, X X for X X at scale: an X for X, X, and X X across X. The X X is luiscore.com, with X at X and X.









  • The only way I see something like this working is if communities had tags. Each community would select from a list of pre-determined tags based on the content posted there. Then, at any time, users can then block tags as they see fit.

    There would have to be some incentive to encourage communities to add the tags once they are implemented. The most fair way I can think of is to have a “None” tag that every community gets by default. Instances, or even apps can choose to hide untagged communities by default, and users can choose to hide them too. The incentive would come from the potential loss of visibility by not tagging your community.







  • My guess, and confirmed by another comment, is that the ai only flags posts for review. Then the moderators have to manually check the post.

    Honestly, it’s not a terrible use of AI in my opinion. Considering posts practically never change, they really only have to scan each post once. The mod can either flag it as safe or remove it. They are probably just running image and text pattern recognition on previously banned posts to flag newly submitted posts.