Amazon Outage Map

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Amazon Issues Reports

Latest outage, problems and issue reports in social media:

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  SGB Law (@SGB_LawTX) reported 8 seconds ago

  @MarcWatkinsEsq @amazon They are always delayed. I’ve had the same issue before as well.

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  taxidermylion (@taxidermylion43) reported 48 seconds ago

  @Amb8819 @bdquinn Nah. The price should go up even more. Beef raising is terrible for water supplies, and cheap imported beef from South America is courtesy of clear-cutting the Amazon.

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  Tango247 (@TheTango247) reported 2 minutes ago

  @Boilerman2019 Two issues: 1 - the original propellant is CO2 2- Amazon does not sell the valve stems in singles.

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  Gabe (@GanjoKazooie) reported 9 minutes ago

  @Hyperkin Will the Amazon preorders yet to ship out come with the adapter for the M64 connection issue, or will they come fixed already?

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  Don't Smoke Liberal Zombie **** (@RichC2868021263) reported 10 minutes ago

  @WallStreetApes If they are worth more money, put in two weeks and get that green. Everyone should be beating down their door, great employees that Amazon is underpaying are top tier candidates at small - medium sized business.

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  JenOakie42💫 (@OakieJs) reported 17 minutes ago

  @BrianRoemmele It's good to have breast exam options, and I believe thermography has valid applications. HOWEVER, everyone needs to be aware of the quality of testing. Traveling thermography operations, or buying your own equipment off Amazon is NOT the way to go. Amazon equipment is probably better than nothing, but there is likely a LOT of room for error. I can say this from a personal experience: I went to a traveling thermography appointment with my mother, about 6 years ago. Super nice ladies worked there, they seemed to genuinely care. But, they gave her a clean bill of health. Two months later, the lump she had on her breast began to really concern her as it had begun to hurt by that time. She was promptly diagnosed with the Her2 breast cancer, the size of a half dollar. Highly aggressive, but very treatable nowadays. Use a reputable source, with a stationary facility, and people with experience in the use of that type of medical equipment. That's why I'm also showing the guidelines people should be aware of when a medical professional purchases thermography equipment. Quality matters.

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  Alexander Shulgin (@SandraM82850351) reported 17 minutes ago

  There are plants in the Amazon that took 10,000 years of trial and error for indigenous people to combine in the exact right ratio to unlock DMT ******. Two plants. Out of 80,000 species in the rainforest. One deactivates the enzyme that destroys the other. When asked how they figured it out, the answer is always the same: the plants told us.

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  فاطمة الحَبيب (@FS_l6H) reported 19 minutes ago

  I linked my amazon account years ago to my old number, but I don't have that number anymore, now i'm trying to send books to my kindle but it's such a mess and super confusing so i'll js drop it (my way of solving problems) This is israel ruining my life again god damn israel.

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  Kuldeep Singh Sidhu (@kuldeep_s_s) reported 23 minutes ago

  Can LLMs recommend your next purchase in a single forward pass? A new paper in ACM Transactions on Recommender Systems, from researchers at the National University of Singapore, the University of Science and Technology of China, and Meta AI, says yes - and the trick is rethinking how items are tokenized. The problem: LLM-based recommenders must assign each item an "identifier" so the model can read user history and generate the next item. Today's two dominant designs both break down. Token-sequence identifiers (TIGER, LETTER-style) generate items token by token with beam search - so if the first token of the right item has low probability, it gets pruned early (the local optima issue), and the serial LLM calls make inference painfully slow. Single-token identifiers are fast but cram everything into one embedding, losing either semantic richness or collaborative filtering (CF) signals. The proposed fix: SETRec, a "set identifier" paradigm. Each item becomes an unordered set of continuous tokens - one CF embedding from a pre-trained recommender (e.g., SASRec) projected into the LLM's hidden space, plus N semantic embeddings produced by a single unified autoencoder over SentenceT5 representations of the item's title, description, and category. How it works under the hood: 1. A sparse attention mask removes dependencies between tokens of the same item - each token attends only to previously interacted items, never its siblings. This preserves item-to-item sequence order while making tokens within an item truly order-agnostic, and it cuts batch generation complexity by a full factor of the token dimension count. 2. Query-guided generation uses learnable query vectors, one per information dimension, so the LLM emits all tokens of the next item simultaneously in one call - no autoregression, no beam search. 3. Since freely generated token sets may not correspond to real items, a grounding step scores every catalog item using the collected token corpus as grounding heads, blending CF and semantic scores for ranking. The upgrade - SETRec++ - tackles "token collapse," where naively adding more semantic tokens just yields redundant copies (verified via a Normalized Mutual Information diagnostic). Two self-supervised strategies fix it: a disentangled regularization loss based on the Hilbert-Schmidt Independence Criterion that pushes tokens toward statistical independence, and a token masking mechanism that randomly hides roughly half the semantic tokens during training, forcing every token to carry predictive signal on its own. Results across four datasets (Amazon Toys, Beauty, Sports, and Steam) on T5 and Qwen backbones (1.5B to 7B): consistent wins over strong baselines on full, warm, and cold-start ranking; inference speedups averaging 8–20x over token-sequence methods; cold-start performance that keeps improving as model size scales; and - unlike the vanilla version - monotonic gains as the number of semantic tokens grows. A thoughtful takeaway for anyone building generative recommenders: identifier design isn't a preprocessing detail. Order-agnosticism, multi-dimensional information, and token disentanglement may matter as much as the backbone LLM itself.

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  Terrible Vocabulary (@Steadfastgurl) reported 30 minutes ago

  They did make you step down from CeO of Amazon

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  Dario D'Aversa (@ddaversa) reported 30 minutes ago

  Billions of dollars spent by @amazon on AI & infrastructure, in the name of progress. But we're approaching 2027 and my kids still can't listen to two separate @Spotify tracks on their Echo devices at the same time. Maybe use some tokens to fix this?

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  Ignorenation (@Shyyyyyam) reported 32 minutes ago

  @AmazonHelp what is wrong with you , it just redirecting to the same customer support page ,which is in amazon website, who has no knowledge of to solve this problem .

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  Gary (@Therearenone21) reported 33 minutes ago

  @WallStreetApes Just like the little red wagon when you were 6...now still get to play with them at 36. Amazon much be going bankrupt if their freight volume is that slow.

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  Anil Basheer (@anilb786) reported 37 minutes ago

  @AmazonHelp Pincode 695303 (Perumathura) was suddenly marked unserviceable for Amazon Now. I had 2 successful deliveries to this exact address on Aug 14. Can you please escalate this mapping glitch to the local logistics team to fix the zone boundary?

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  Boyo (@musekeni) reported 39 minutes ago

  @jaynitx And he goes ahead and solves a similar problem. The answer was not cosine. It was Amazon.