Amazon status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of Amazon reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by Amazon users through our website.

• Website Down (48%)
• Errors (28%)
• Sign in (24%)

Live Outage Map

The most recent Amazon outage reports came from the following cities:

City	Problem Type	Report Time
Salt Lake City	Errors	22 hours ago
Lake Butler	Website Down	1 day ago
Annecy	Website Down	2 days ago
Frankfurt am Main	Website Down	2 days ago
Bridgeport	Sign in	2 days ago
Rochester	Errors	3 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

Amazon Issues Reports

Latest outage, problems and issue reports in social media:

• 
  FO𝕏 YOU (@F0XYOU) reported 6 minutes ago

  @MEGAMAGAMONSTER @amazon Yup. Completely unreliable. Any time a company grows too big for its britches, the quality goes down the drain. The delivery people rarely bother to even ring the doorbell as if they have never used one before. Mostly they either knock or just leave the package.

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  Anchanix (@Anchanix) reported 8 minutes ago

  📉 THE AI TRADE WOBBLED Alphabet beat on earnings and still fell more than 6% as capex guidance spooked investors. Nasdaq 100 weakness led by chip names dragged crypto down with it late last week. Microsoft, Meta, Apple and Amazon all report this week.

• 
  Pyramus (@beerswithboggs) reported 10 minutes ago

  @hyghner @CHI2COL @Acyn Okay we will slow it down for you - NYC will now enter the low margin grocery business. They will operate at huge losses because their entire goal is to provide artificially cheap groceries, not run a functional business. The only way they can do this is because they will just take taxpayer money to run their money losing business. Still with us? Okay. After several years of running this money furnace, they will accomplish multiple things: 1) they will implode small local grocery stores who can’t compete with their artificially low prices and 2) nyc consumers expectations will be that their groceries should be artificially cheap indefinitely. So they will bankrupt local small business owners - killing jobs and putting nyc entrepreneurs out of business while also creating a monster because consumers will now expect and depend on the city to offer groceries indefinitely at artificially cheap prices. So the city will be left with 2 choices: either discontinue the program that is actively driving local businesses to bankruptcy, destroying value and lighting taxpayer money on fire or double down and commit ever more taxpayer funds to the ever growing money hole. As I said, if Amazon came along and dropped prices 30% below sustainable margins to drive local businesses out of business you’d say it was cruel but because it’s the government - not even using their own money but taxpayer money - you clap and say “they are just helping people”

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  Shhhhhhhhh You Know Who? (@gatesisavirus) reported 10 minutes ago

  @zoeunashamed Oh,well i can tell you in my honest fashion,i worked for Amazon delivering parcels 3 years ago,from day one,we was told to be careful in parts of Seaham,i expect this comment from the manager was mainly about theft,im not trying to worry you,but im a person that always reads the bottom line.Seaham was a mining town thats never recovered from the pit closures,hardly anybody has a job,like most of the North East,but it a cheaper place to live and if you fit in in you fit in Not hitting on you or nothing either,but maybe when i get back down South maybe i can meet up with you and disclose further? Im always happy to meet up with like minded people,so yes,you recognise we have a migrant problem across Europe.....Our lives are getting tougher,mainly financially because of immigrants,theres no escaping the truth of that It amazes me how some unelected bureaucrats got away with this? Afterall, theyre meant to be a UNION not a GOVERNMENT They knew all them years ago,that it could go wrong My question is,where was plan 'B' to reverse it?

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  GHØST (@Ghost_RC_) reported 16 minutes ago

  @JMDucksworth When I was in college I had an "Amazon business" as my "side hustle" lol How it works is that sellers list a product, they'll usually also input the amount they have in stock in there. Amazon will then automatically lower the number based on the amount of purchases. Thing is sellers can input whatever they want. Sometimes (at least back in my day) they'd put incorrect stocks on purpose for various reasons. It can't be anything too crazy different but there's some leeway. Other times, it could be a warehouse/logistics problem. Let's say if one of the items was defective and the buyer sent it back and now it's sitting in the warehouse. That could be the last one lol, and it's just an error. Or a warehouse worker steals it lol From my experience, which admittedly is a little over a decade ago, Amazon hates this but there isn't much they can do

• 
  Ricky Ho (@rickyho_1989) reported 17 minutes ago

  The chart is visually powerful, but economically incomplete because every arrow is treated as though it represents the same type and degree of risk. It does not. An equity investment, a cloud-purchase commitment, a capacity reservation, a supplier warrant, a revenue-sharing arrangement and a debt guarantee may all create interconnected exposure, but they have very different consequences for cash flow, solvency and revenue quality. The correct conclusion is therefore not that the entire AI ecosystem is fraudulent or that every dollar of reported demand is circular. The more defensible conclusion is that AI infrastructure is increasingly being financed through a reflexive system in which suppliers fund customers, customers commit to buying from suppliers, infrastructure providers borrow against those commitments and rising private-market valuations make it easier to raise the next round of capital. Circular financing is not automatically problematic. In a market where demand is expanding rapidly, advanced chips are scarce and the infrastructure required to operate them costs tens of billions of dollars, strategic financing can solve a genuine coordination problem. The company developing the model may have the technological capability and future revenue opportunity but lack the capital required to build infrastructure today, while the chipmaker or cloud provider has the balance sheet, equipment and strategic incentive to accelerate that buildout. Financing and purchase commitments can therefore bring forward productive capacity that would otherwise arrive too slowly. OpenAI itself describes compute, distribution and capital as the three requirements for scaling AI, and its February 2026 financing included $30 billion from $NVDA, $50 billion from $AMZN and $30 billion from SoftBank alongside new infrastructure partnerships. The risk begins when capital supplied by the ecosystem becomes difficult to distinguish economically from demand generated by the ecosystem. A supplier investing in a customer does not automatically invalidate the revenue that follows, provided real equipment is delivered, the customer receives economic value and the obligation is collectible. However, it does weaken the evidentiary value of the order. An order funded from internally generated cash flow tells investors that end customers have already monetized sufficient demand to pay for the infrastructure. An order funded by the supplier’s equity investment, debt guarantee or credit support tells investors that the market expects sufficient demand to emerge later. Both can eventually become profitable, but they represent different levels of commercial validation. That distinction is central to understanding the current AI cycle. The most important question is not whether Nvidia invests in a company that buys Nvidia chips. The more important question is where the money ultimately originates. If enterprises and consumers are paying enough for AI products to finance model development, cloud consumption and hardware depreciation, then the capital flows are merely accelerating a legitimate adoption cycle. If the AI laboratories are paying their infrastructure bills primarily with money raised from the same companies supplying that infrastructure, then the industry may be temporarily financing its own revenue while waiting for external monetization to catch up. The ecosystem is also not equally fragile at every layer. $MSFT, $GOOG, Amazon and $META can fund enormous AI investments from established businesses spanning enterprise software, advertising, commerce and cloud computing. Even if returns on individual AI projects disappoint, these companies possess diversified operating cash flows and investment-grade balance sheets. OpenAI, Anthropic and other frontier laboratories sit in a more vulnerable position because their compute obligations can grow faster than their current revenue and because they rely much more heavily on external capital. The greatest financial fragility may sit one layer below them in highly leveraged infrastructure providers that borrow heavily to build data centers against contracts concentrated among only a handful of AI customers. $CRWV illustrates both the strategic logic and the financial risk. Nvidia invested $2 billion in CoreWeave in January 2026, while the companies agreed to collaborate on more than five gigawatts of AI-factory capacity by 2030. CoreWeave has disclosed that all GPUs used in its infrastructure are Nvidia GPUs as a result of its customer obligations, while Nvidia also has contractual access to residual unsold CoreWeave capacity under a multibillion-dollar agreement. At the same time, CoreWeave historically generated a very large share of its revenue from Microsoft, with Microsoft accounting for approximately 67% of revenue in the third quarter of 2025, and the company has funded growth through substantial amounts of secured and unsecured debt, including bonds issued at interest rates around 9%. None of that proves the demand is artificial. CoreWeave provides real computing capacity, Microsoft and other customers use that capacity and Nvidia delivers physical hardware with genuine economic utility. However, the structure concentrates several risks inside the same network. If model demand weakens, the AI laboratory may reduce cloud consumption; the infrastructure provider then faces lower utilization while retaining debt, lease and power obligations; Nvidia loses future chip demand and may also suffer losses on its equity investment or contractual exposure. The same shock therefore travels through revenue, asset values and credit simultaneously. The largest risk to Nvidia is not necessarily the direct loss on its investments. Nvidia reported fiscal 2026 revenue of $215.9 billion, while its April 2026 filing disclosed approximately $27 billion of investment commitments and $18.6 billion invested in private companies and infrastructure funds. Those sums are meaningful but manageable relative to Nvidia’s earnings power and market capitalization. The more serious risk would arise if strategic financing has brought forward several years of GPU purchases that end-user AI revenue cannot ultimately support. In that scenario, Nvidia would not merely impair an investment; it could experience a decline in future orders precisely when customers are attempting to repair their balance sheets. This is why the circularity debate should focus less on accounting and more on economic substance. Equity investments are recorded separately from product revenue, and genuine hardware deliveries do not become fraudulent merely because the supplier owns part of the customer. There is no public evidence that the current AI partnerships are equivalent to sham round-trip transactions. Nevertheless, economic circularity can exist without accounting fraud. Revenue may be properly recognized under accounting standards while still depending on capital provided by related ecosystem participants rather than independently generated customer cash flow. The late-1990s telecommunications comparison is useful, but only when applied precisely. The internet was real, bandwidth demand did grow and the infrastructure eventually became essential. Investors nevertheless lost enormous amounts of money because the industry built too much capacity too quickly, financed weak customers and assumed that exponential traffic growth would automatically produce attractive returns on capital. When bandwidth prices collapsed, heavily leveraged carriers cut equipment spending, excess fiber remained underutilized and losses propagated back toward equipment suppliers and creditors. Some telecom transactions went considerably further and crossed into improper accounting. The SEC found that Qwest entered capacity swaps in which it bought capacity it did not need while counterparties bought capacity from Qwest, sometimes supported by concealed agreements that undermined the claimed revenue recognition. That is materially different from Nvidia selling functional GPUs to an operating data center or Microsoft selling genuine cloud capacity to an AI developer. The historical lesson is therefore not that every reciprocal commercial relationship is fraudulent. It is that genuine technological demand can coexist with excessive financing, distorted incentives and, in the weakest cases, attempts to manufacture revenue. There is another important difference between fiber and AI infrastructure. Fiber can remain physically usable for decades, but individual semiconductor generations depreciate technologically much faster. A data center built around today’s accelerators may face newer chips offering substantially better performance per watt and lower cost per token before the original equipment has earned its required return. This creates a risk that the economic life of the hardware is shorter than the financing period attached to it. However, obsolescence is not binary. Older GPUs can migrate from frontier training toward inference, fine-tuning, research and lower-cost workloads, while power connections, cooling systems, land and data-center shells can retain value even after the computing equipment is replaced. The question is therefore whether utilization and pricing decline faster than the owner can depreciate the equipment and refinance the liabilities. A facility operating at high utilization can generate attractive cash flow even if the hardware becomes second-generation technology. A facility operating below capacity while rental prices fall and interest expense remains fixed can destroy equity very quickly. The largest vulnerability is not technological obsolescence by itself; it is technological obsolescence combined with leverage, customer concentration and long-dated take-or-pay obligations. Microsoft and OpenAI also show that interconnectedness can evolve rather than remain permanently circular. Microsoft remains a major OpenAI shareholder and reported an approximately 27% ownership interest on an as-converted basis, while Azure continues to play a central infrastructure role. However, the partnership was amended in April 2026 to make Microsoft’s intellectual-property license non-exclusive, allow OpenAI to serve products across other cloud providers and remove Microsoft’s payment of a revenue share to OpenAI, while OpenAI’s revenue-sharing payments to Microsoft continue through 2030 subject to a cap. This diversification reduces OpenAI’s dependence on one supplier, but it also spreads the ecosystem’s exposure across more cloud providers and chipmakers rather than eliminating it. Supporters are therefore correct that many of these deals constitute a rational form of industrial coordination. Building frontier AI infrastructure requires aligning semiconductor production, networking, data-center construction, electricity supply, model development and customer distribution several years before demand is fully visible. Traditional spot-market purchasing cannot efficiently coordinate a buildout of that scale. Long-term commitments, strategic equity and co-investment can lower financing costs, secure scarce capacity and distribute risk among parties that benefit from the ecosystem’s growth. Critics are equally correct that this structure can weaken price discovery. A supplier that owns equity in its customer may accept commercial terms that an independent supplier would reject. A cloud company with a large investment in an AI laboratory may continue providing capacity because protecting the equity value becomes part of the decision. An infrastructure provider may build against commitments from counterparties whose ability to pay depends on future fundraising rather than current cash generation. The danger is not simply that participants make irrational decisions. It is that individually rational decisions, each designed to protect an existing investment or strategic relationship, collectively sustain uneconomic capacity for longer than an independent market would. The most revealing metric is therefore not announced investment or contracted backlog in isolation. Investors need to determine how much of the backlog is supported by investment-grade counterparties, how much is cancellable, whether the contracts contain take-or-pay protection, whether the customer has generated the cash independently, whether equipment can be redeployed and whether the infrastructure owner’s debt maturity is shorter than the expected monetization period. A $20 billion commitment from a cash-rich hyperscaler is fundamentally different from a $20 billion commitment by a loss-making laboratory whose ability to pay depends on raising its next $50 billion funding round. Cash conversion will become increasingly important. Reuters estimates that investment by the major hyperscalers is rising considerably faster than operating cash flow, with approximately $534 billion of incremental capital expenditure expected by 2027 against roughly $340 billion of incremental operating cash flow. That does not by itself imply overinvestment because infrastructure spending is front-loaded while revenue arrives later, but it raises the burden of proof. The longer capital expenditure grows faster than operating cash generation, the more the investment case depends on future AI revenue rather than demonstrated current economics. The bullish case remains credible because end demand is already broadening. AI is moving beyond model training into coding, advertising, search, enterprise automation, scientific research and persistent inference, while newer reasoning and agentic workloads can consume substantially more compute than conventional chatbot interactions. If falling token costs expand usage faster than efficiency reduces the compute required per task, the industry can grow into much of the infrastructure currently being built. Under that outcome, circular financing will be remembered as the bridge that allowed the supply chain to scale ahead of demand. The bearish case is not that nobody uses AI. The bearish case is that AI becomes ubiquitous while the financial returns on today’s infrastructure remain poor. The internet transformed the world, but many companies that financed its initial infrastructure still failed. Technological importance does not guarantee that every participant earns its cost of capital. AI models may become extraordinarily valuable while competition pushes model prices down, open-source systems compress margins, hardware improves faster than assets depreciate and the largest share of the economic profit accrues to a narrower group of platforms than today’s investment boom assumes. My view is that AI is not a repetition of the telecom bubble in the simplistic sense that demand is imaginary or the technology lacks utility. The demand is real, the productivity potential is substantial and the leading hyperscalers possess far stronger balance sheets than the speculative telecommunications carriers of the late 1990s. However, parts of the financing structure are increasingly reminiscent of vendor-financed infrastructure booms, particularly where suppliers provide equity, guarantees or capacity commitments to entities that then become major purchasers of the suppliers’ products. The circularity itself will not necessarily end the AI boom. It will determine where the losses concentrate if the boom slows. I remain structurally bullish on AI infrastructure, but the network of reciprocal investments argues for greater selectivity rather than blanket enthusiasm. The strongest positions remain businesses with technological bottlenecks, pricing power, diversified customers and substantial internally generated cash flow. The most vulnerable positions are highly leveraged infrastructure providers with concentrated customers, fixed power and lease commitments, rapidly depreciating hardware and counterparties whose purchasing power depends on continuing access to capital markets. The warning signs would be declining GPU utilization, falling rental prices that cannot be offset by lower hardware costs, repeated contract renegotiations, customers delaying deployments, suppliers providing progressively larger guarantees to preserve orders and AI laboratories raising capital primarily to meet existing infrastructure obligations rather than funding new growth. The most constructive signals would be the opposite: enterprise AI revenue expanding rapidly, inference utilization broadening, customer concentration falling, free cash flow improving and infrastructure commitments increasingly being paid from operating revenue rather than supplier-backed financing. The sharpest conclusion is that the AI ecosystem is not currently a fraudulent circle, but it is becoming a financially reflexive one. Reflexivity magnifies the upside while capital is abundant, asset prices are rising and demand exceeds supply. It also magnifies the downside when one of those conditions reverses. A real technological revolution can still produce an investment bubble around parts of its infrastructure. The internet proved both propositions simultaneously. AI may do the same.

• 
  unstoppable Hendrix (@___RISkY24) reported 20 minutes ago

  @amazon y’all are terrible. It takes a month for a customer to get their money back. I will never in my life shop with yall as long I’m living on this earth @JeffBezos horrible *** company

• 
  Trev⁵ (@lil_trevis) reported 21 minutes ago

  @thehdroom ah ive had problems ordering from barnes was hoping for amazon, thank you!

• 
  Katy Madelynn (@Katy_Bair) reported 23 minutes ago

  @PokemonCenterUS @amazon What an actual joke. “900+ bought in the last month.” Yeah, to scalpers with bots. I have never been able to pre-order anything because of this. It’s there but always gives me an error and says the quantity I want isn’t available (which is 1) or it’s out of stock, and then the pre-order listing still stays. Get your **** together man.

• 
  Breaking News Jack. (@jackmeridan) reported 26 minutes ago

  @Polymarket @amazon a solution to your overheating human problem.

• 
  Krystal (@krys_withak) reported 27 minutes ago

  My problem is I’ll buy bottoms from ALO , Lulu, Vuori etc, but I refuse to pay $70+ for a sports bra. That **** is coming from Amazon. So the chances of you seeing me in a matching set is almost a zero lol

• 
  J-Dub©️ (@DeLoachJW) reported 28 minutes ago

  @GlennJacobsTN The issue is the following not the tool. She could have easily followed by trailing in a car; Apple AirTag; any one of GPS devices avail from Amazon; turning on “Find My Friends”; etc. Your logic is similar to “gangbanger shoots someone so my mom‘s gun must be confiscated.“

• 
  Monster 🇺🇸 (@MEGAMAGAMONSTER) reported 31 minutes ago

  @Angry_American0 @amazon Yessir. Every time I have an issue these days I’m on the phone for 15 minutes before I get an actual human.

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  Eric S.w.a.n.SUN (@LuckyTPUB) reported 32 minutes ago

  @ArtofCShelton @sorrowen no and here is also why he is undercutting his prices even on his old books just to sell them through amazon meaning that the value of the books is lost for the so called collectors. Something people have pointed out prior but its obviously worse how with this deal more views but will they be return customers. Judging from issue 1 of Isom people did not come back.

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  餅BSD/餅ヴァッシュ/餅ゲゲゲ ✨️ (@gegeroooo) reported 32 minutes ago

  I've spent hours sanding down plastic kit pieces so they'll be ready to paint tomorrow. HOPEFULLY Amazon will be on time and deliver everything early morning so I can quickly get this job done!! 💪💥

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  Denis B. (@denbvk) reported 32 minutes ago

  @QuinnyPig I think the problem with “shipping to customers quickly” is that you cannot ship anything substantial. Like Amazon ui which doesn’t look like it’s from 2005.

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  Michael Shourd (@MichaelShourd) reported 35 minutes ago

  @Dungeon00X @ymnis_v1 You could still play your digital games. And PSN was down because of Amazon. Xbox is down because of Xbox

• 
  haitham (@theonlyhaitham) reported 37 minutes ago

  Most brands launch a new product at zero reviews. The smart ones never do. DRMTLGY launched a new SPF variant with ads that open on the original's 4,610 reviews and Best Seller badge. The new SKU inherits trust it never earned. On Amazon you can build this into the catalog itself: launch the new flavor as a child variation under your best seller. Shared review pool from day one, and your launch creative leads with the number that already converts. The tell you got it wrong: your new variant sits at single digit reviews while your hero holds hundreds. That's a structure problem, not a marketing problem.

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  Rem Klem (@PowrWhit92214) reported 39 minutes ago

  @preterniadotcom Seriously, always some problem with Amazon. It never fails.

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  cheese (@El_Cheesius) reported 44 minutes ago

  @LadyLoveLDN @AmazonHelp @sophiemeaden Oh yeah I had a missing delivery for my dad's Xmas present, which I then needed to ship on. Trying to chase down the order was just AI slop

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  Alec Zakhary (@AlecZakhary) reported 44 minutes ago

  We made an AI the third member of our two-person Amazon business. Its first useful contribution was telling us not to launch anything. Three products entered our research funnel. All three came back NO-GO. That “no” may already have saved us more than the system cost to build. We are developers entering a physical-goods business we don’t yet understand. We didn’t need another chatbot. We needed a teammate who could research markets, process documents, track agreements, and remember why an idea was rejected months later. We had already tried building a personal work hub from nine Markdown files. After one month, it contained zero entries. The problem was not the file format. Recording work simply required more effort than retrieving it was worth. So we kept the files and changed the interface. In two days, we built Vera. She lives in our shared Telegram group and private chats. Her memory is a *** repository that both founders and the agent can commit to. During the first week, Vera: — produced 8 research reports, the largest around 50 KB — recorded 26 decisions with rejected alternatives — converted an XLSX pricing model into an analysis — delivered the team’s Monday briefing automatically The entire infrastructure is 1,388 lines of code. But the most important design decision wasn’t technical: Vera is organized around decisions, not tasks. Every decision records: — what we decided — what we rejected and why — the risks — what would make us reconsider A completed task tells you what happened. A durable decision prevents the team from paying for the same mistake twice. This matters more than the eight reports. Three months from now, we won’t need another AI summary of the same product niche. We’ll need the original verdict, the evidence behind it, and the condition under which that verdict should change. The system also failed in very real ways. One 36 KB analysis became a PDF containing little more than “Done, the report has been saved.” Another finished 59 KB report disappeared after a *** conflict and redeploy. Both failures changed the architecture. Conversation and execution are now separate. Long jobs checkpoint their progress and resume after interruption. That is the difference I keep seeing between agent demos and agent products: A demo proves the model can complete a task once. A product preserves the result, remembers why it matters, and survives being interrupted. We also made one embarrassingly predictable mistake: we spent a day building messaging infrastructure before checking what already existed. We should have bought the transport and built only the differentiated parts—shared team memory and resumable work. The system is still early and deliberately narrow: two allowed users, internal tools only, nothing sensitive. But it already feels less like a chatbot and more like a colleague who remembers the evidence behind every “yes” and every “no.” Which part should I unpack next: the decision memory, the disappearing 59 KB report, or the PDF that contained no report?

• 
  Mackay Bell (@mackaybell) reported 45 minutes ago

  @TexelElf @DougTenNapel The internet doesn't work like that. It's extremely easy and fast to skip past the "glut of terrible." Most of the time won't even notice it. This was the same argument about self-publishing. No one just logs onto Amazon and looks at every book. They search for subjects they like and can quickly guess if it seems like something they might be interested and in two seconds make a decision. If they aren't sure they can read a sample or check the reviews. Anyone who can't find good stuff to fill their hours is an idiot. Sure, highly promoted stuff will get much more visuality. But if you're looking for good original stuff, simply follow the thousands of people who make good recommendations and you can find it instantly. The best stuff always surfaces. It's happened over and over.

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  DK (@DK_DK_005) reported 48 minutes ago

  @amazonIN @AmazonHelp Your automated response is unacceptable. The issue is NOT resolved. My return is stuck because your systemConnect me to a live chat support agent or supervisor immediately to fix

• 
  Kenny (@kennyGdyn) reported 50 minutes ago

  @JanS34343806 @ClownWorld Amazon box was already there he came down dropped a package n took the other. Hopefully just delivered wrong n fixing his mistake but who knows

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  Department Of Deportations (@DoDeportations) reported 55 minutes ago

  Crazy how quickly anthropic went from “do no evil” to “we are the sole legislature of morality”. Also the most locked down and useless model, by far. I’ve gotten better results from the Amazon chatbot than I’ve gotten from Claude.

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  Matthew Turner (@MbtHawk) reported 56 minutes ago

  @michaelpatron0 Honestly we are looking at doing the same thing. Launching everywhere but Amazon.....part of the issue is how long cash gets ******* with FBA. Ship times are brutal + reserve time.

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  Amazon Help (@AmazonHelp) reported 57 minutes ago

  @prakash_cul Please copy the link and access it from a different browser. Make sure to delete all cache, cookies, history from device. Logout and login to Amazon account and try to access the link, it will redirect you to Amazon app, fill the required details, so that our team can check and assist you further. You will receive the response in 6 to 12 hours via email. - Gayathri

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  slappy (@slappyonx) reported 59 minutes ago

  @beffjezos Oh no knowledge will be free instead of $400 through beat up copy resellers on amazon that's terrible

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  Stephanie M (@MowlamSM) reported 60 minutes ago

  @MEGAMAGAMONSTER @amazon It's crazy, the price goes up and the quality goes down. Ever since covid it has been crap.

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  Angry American (@Angry_American0) reported 1 hour ago

  @MEGAMAGAMONSTER @amazon I have yet to run into that issue, the worst Amazon has done was damage my gate trying to deliver a package at night. If it’s a reoccurring problem, message them and talk to a higher up. They could maybe compensate you for it.