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Amazon Issues Reports

Latest outage, problems and issue reports in social media:

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  Ben Podraza (@benCBai) reported 6 minutes ago

  @GangaramRo5087 I suspect so. I expect they will start putting export controls on the most advanced models. I won't use Chinese models, just like I won't sell on Amazon. I don't do business with people after I witness them doing unethical things to their business partners. Chinese products are cheap, but I wouldn't give them a look at my codebase. They steal everything that is not bolted down. I am not interested in having to constantly monitor and manipulate the people I work with. In China, that kind of surveillance and control is engrained in them at birth. I find it distracting, annoying and counterproductive. They are, and will continue to, dump their models because, like electric vehicles, once you buy AI from them, you are committing to buying everything else from them too - e.g. insurance, banking, security...naval bases. The most important consideration, however, is that the world is going to get divided up. I am not going to build a business around something I am not going to have access to. China is going to make a significant move on the US at some point. Pretty sure the only reason they have not attacked Taiwan are those nuclear subs we have creeping around. But they are waging other kinds of war against us (e.g. psyops, economic, etc.), and it is only going to get more tense, until we complete this divorce. Which AI a country uses will be a function of whether they lean to an autocracy or democracy, not the capabilities, or even cost, of the models.

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  Haytham Kenway (@HaythamKenway99) reported 7 minutes ago

  @AmazonHelp Yea, no one is ever going to contact me. And your Indian customer service people transferred me to someone in the Philippines. Their answer was to tell me I needed to jump through hoops (send a picture to prove they sent the wrong item before they will consider sending me the right item.) in order to fix Amazon screwing up an order multiple times. And when I clicked your link it took me to a customer service chat.

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  Sumit Chander (@SumitPathipaka) reported 9 minutes ago

  @AmazonHelp I have done enough escalation and issue reporting, if U have any service commitment you start doing your job! I am confident there would be no better resolution,I decide to avoid further frustration due to your lousy services.Dont ask me to do anything more, U start doing the job

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  Olli_757 (@Olli757) reported 14 minutes ago

  Sadly coming to the last part of @alexwestco 's book series. Its been great to be able to follow along with his adventure of building a business, trying countless things to keep improving it, constantly iterating with the product and organising his employees and team setup etc It really brought up memories in me of when I built an ecommerce business 10 years ago, selling phone holders for the car. (Next to my fulltime job as a pilot) It ended up being the #1 phone holder on Amazon in all EU marketplaces. Started from scratch and built a brand around it. It ended up doing over 1M in revenue at its peak. Then like Alex, got overwhelmed and built systems around it with freelancers. By the time I it was all automated and running like clockwork, it was already on the way down. So many parallels with Alex's story. I tried so many things: other products, other channels, shopify store. But nothing ever worked so well as my first product, it was a home run for the start and downhill after that. It lasted for about 3-4 years total until the Chinese suppliers caught up and sold direct on Amazon. Alex might be correct: nobody knows what they are doing and everyone is just trying their best. And once in a while, you hit a goldmine.

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  DC Schmöka (@smoeka) reported 15 minutes ago

  The AI Infrastructure Trade vs. the 1999–2001 Telecom Build-Out: A Structural Autopsy and Forward Test (Revised) Abstract The telecom analogy is roughly half correct, and the correct half is not the part most people cite. The fatal mechanics of 1999–2002 were four: (1) supply built massively ahead of demand on a falsified growth statistic; (2) a commodity product whose scarcity premium was destroyed by a supply-side architectural technology (DWDM) that multiplied capacity per dollar faster than demand grew; (3) leveraged, mono-line owners of that commodity capacity; and (4) vendor financing that manufactured the appearance of end demand. Mechanism (1) is absent today — demand queues for supply, and the best-attested evidence for that comes from the most hostile possible witness. Mechanisms (2), (3), and (4) are all present in identifiable, tiered form: the memory-architecture shift now underway is the true DWDM analog, a leveraged neocloud tier plays the Global Crossing role, and — critically — the frontier labs themselves are the closest structural heirs of the 1999 carriers: enormous committed capex against a product layer being deliberately commoditized, funded substantially by their own vendors. The deepest break from 1999 remains vertical integration: the largest infrastructure builders also own the application and distribution layers where internet value ultimately accrued, so if value migrates up the stack again — and it will — it largely migrates within the same balance sheets. The correct decomposition is therefore not "AI infra = telecom" but a four-tier map: a carrier tier (neoclouds and commitment-heavy labs), a Cisco tier (Nvidia and the scarcity-premium complex), a hybrid tier with no historical precedent (integrated hyperscalers), and a deflation-exporting actor with no 1999 analog at all (China). One feature is genuinely worse than 1999: asset life. Overbuilt fiber waited twenty years and eventually carried the cloud; 60–70% of today's capex sits in silicon that depreciates toward scrap in three to six years inside twenty-year shells. There is no patient-capital redemption arc — the trade must be right on timing, not merely direction. And unlike the blank-slate version of this question, the timing is now datable: the observables that resolve the analogy cluster in a window centered on H1–H2 2027. I. What Actually Killed Telecom (Getting the History Precise) The popular memory — "they overbuilt fiber" — misses the mechanism. Three things compounded. The demand statistic was false. WorldCom's claim that internet traffic was doubling every 100 days became industry gospel; actual traffic doubled roughly once per year. Capacity was sized to a fabricated exponent. Technology deflated the product faster than demand could absorb it. DWDM multiplied the capacity of already-installed fiber by 40–100x within a few years. Supply grew as the product of route-miles × wavelengths × modulation gains; price per bit collapsed >90%. Since long-haul bandwidth between two cities is a perfect commodity, there was no pricing power anywhere, and even four years post-crash, 85–95% of 1990s fiber remained dark. The essential lesson: the scarcity premium wasn't destroyed by demand weakness or price wars — it was destroyed by an architectural change inside the supply side. The owners were leveraged mono-lines, financed by their vendors. The carriers had no other business, junk-rated balance sheets, and Lucent/Nortel lending customers the money to buy the equipment. When prices collapsed, the debt didn't: WorldCom, Global Crossing, 360networks, PSINet. Cisco — the "picks and shovels" name everyone cites — never faced solvency risk; its revenue recovered and its multiple never did. That distinction, solvency destruction versus multiple destruction, is the most useful single lens for today. The final act: the fiber eventually got lit — by Google, the cloud, streaming — and the value went to the application layer riding near-free bandwidth. The assets were right; the capital structures, the timing, and above all the ownership were wrong. The surplus went to whoever consumed the commodity, not whoever sold it. II. What Checks Out Today The true DWDM analog is live, and it is not price cuts. Cost per token at fixed capability falls roughly an order of magnitude per year, but aggregate deflation numbers obscure the mechanism that matters. The structural event is the memory-architecture shift: the demonstration that decode-phase inference is bandwidth-bound rather than FLOPs-bound and can be served from cheap-memory tiers (LPDDR, GDDR7, capacity-optimized ASICs) instead of HBM — with Nvidia itself validating the design point by dropping HBM for GDDR7 on its prefill-optimized Rubin CPX, and Qualcomm's AI200 and d-Matrix occupying the same space. If cheap-memory architectures capture a meaningful share of inference silicon by 2028, the assets holding scarcity rents — HBM, CoWoS, leading-edge premiums — get repriced the way lit-fiber scarcity was repriced in 2001: not because demand fell, but because a technical change multiplied effective capacity per dollar. When the monopolist copies the disruption, the disruption is real. Vendor/circular financing has returned at genuine scale. Nvidia's up-to-$100B OpenAI investment immediately revived circularity concerns, and 2026 analyses put the interlocking commitment web north of $800 billion, running chip maker → AI lab → cloud provider → back to chips, with the same firms on multiple sides. UBS estimates the OpenAI–Nvidia arrangement alone could represent up to ~13% of Nvidia's projected 2026 revenue. Oracle's ~$300B OpenAI contract requires delivering 4.5GW of capacity to a customer that loses about $1.22 per $1 earned. Lucent–Nortel with more zeroes and stronger intermediaries — the incentive distortion is identical in kind. The capex/revenue gap is telecom-shaped. 2026 hyperscaler capex runs ~$725B, roughly 75% (~$545B) AI-specific, against combined frontier-lab revenue on the order of $70–90B annualized — Anthropic at a reported ~$47B run-rate by mid-May 2026 versus OpenAI's ~$25B. Coverage ratio: ~0.15–0.2x. Revenue growing 3–10x annually against capex growing ~60% closes this gap arithmetically — but only if sustained through 2027–28, which is exactly what telecom bulls assumed and never got. A narrative statistic anchors the build. "Compute demand growing exponentially" is today's traffic-doubling claim — more verifiable than WorldCom's fiction, but carrying the same sleight: token volume growing exponentially at collapsing token prices is compatible with disappointing revenue per unit of installed capex. Volume statistics are not revenue statistics. Asset life cuts worse than 1999. Burry estimates $176B of understated depreciation 2026–28; Amazon shortened a subset of server lives to five years citing AI's pace while Meta extended to 5.5 years — identical hardware, opposite conclusions. Nadella himself said he didn't want to be "stuck with four or five years of depreciation on one generation". The structural point: in telecom, the long-lived asset (trenched fiber, 20+ years) dominated the capex and the short-lived electronics were the minority. Today the ratio is inverted — the twenty-year shell is the minority cost, and 60–70% of the spend sits in silicon on a 3-to-6-year economic clock. Overbuilt fiber could wait for demand; overbuilt GPUs cannot. The redemption arc that eventually vindicated the fiber build is structurally unavailable here unless the cascading-workload thesis (training → inference → batch) genuinely extends economic life — an empirical question the used-GPU resale market will answer. III. What Is Different — In Descending Order of Importance 1. Vertical integration of infrastructure and application layers. In 1999, the fiber owners and the value capturers were different companies, and the migration of value up the stack was fatal to the former. Today Microsoft, Google, Meta, and Amazon own the compute, the models or stakes in them, the distribution (Office, Search, Instagram, AWS), and the customer relationships. If AI value accrues to applications, they are the applications. This remains the single strongest reason the analogy fails at the index level even where it succeeds at the tier level. The unhedged 1999 exposure lives only outside this integrated core. 2. Demand rigidity — attested by the most hostile witness available. Cloud GPUs are sold out and power, not customers, binds supply. But sold-out claims from sellers are exactly what 1998 produced too. The higher-quality evidence is behavioral and adversarial: the most capex-averse, restraint-ideological frontier operator in the world — DeepSeek's Liang Wenfeng, on the record in a closed-door setting with no promotional incentive — exhibits rigid compute demand and cannot procure enough. In 2000, marginal bandwidth demand was substantially fake (carriers swapping capacity with each other); in 2026, the marginal buyer who ideologically refuses to overspend still queues. This is the strongest anti-telecom datapoint in the entire comparison. Its caveat is temporal: it describes the pre-2027 supply regime, not the one that follows the multi-gigawatt energization wave. 3. Buyer solvency — real but eroding. The 1999 builders were junk-rated startups; today's core builders generate several hundred billion in non-AI operating cash flow. But capex now runs 45–57% of revenue and exceeds internal cash generation, with $108B of debt raised in 2025 and ~$1.5T projected, and Amazon's free cash flow is projected to turn negative. Equity-funded is becoming debt-funded mid-cycle — the classic late-stage marker. 4. Application-layer revenue exists now — but the model layer is being commoditized from within. In March 2000, application-layer internet revenue was a rounding error; today model-layer revenue is real, enterprise-weighted, and growing at unprecedented rates — over 500 companies spending $1M+ annually at Anthropic, OpenAI's enterprise mix past 40%. The complication: the model layer's own leading cost-innovator is deliberately commoditizing it — open-source releases, cost-plus pricing, an explicit "no windfall profits at the model layer" doctrine. Commoditization by ideology, not just competition. Revenue existing at a layer does not mean value pools there. 5. China is a deflation actor with no 1999 analog. The telecom bubble contained no state-scale parallel ecosystem committed to collapsing the product's unit price. DeepSeek's efficiency exports plus the domestic silicon stack (Ascend-class accelerators, an independent compiler layer) are exactly that — a permanent, exogenous accelerant of commoditization at both the model layer and, on a longer clock, the silicon layer. This shortens every timeline in the bear case and none in the bull case. 6. Compute is more fungible than fiber but obsoletes faster. A GPU reprices instantly across a global market of workloads; a transatlantic cable competes only with its neighbors. Fungibility softens overbuild in space; annual silicon cadence hardens it in time. Telecom failed in space; AI, if it fails, fails in time. IV. The Tier Map — Where the Analogy Bites and Where It Breaks Even in the bull case, the value-migration half of the thesis likely repeats: surplus flows to whoever owns the customer and workflow layer, and to end users as consumer surplus — not to sellers of raw flops, and not necessarily to sellers of raw tokens either. Map the 1999 roles precisely: The carrier tier (solvency risk) has two occupants. First, the leveraged neoclouds and single-customer compute landlords: collateral depreciating faster than debt amortizes, interest expense consuming a quarter of revenue at the weakest names, market rental rates for prior-generation GPUs already down sharply, funding gaps requiring perpetual issuance, covenant pressure building into 2027, and their best long-term customers — the hyperscalers — incentivized to internalize the very capacity they currently rent. Second, and less obviously: the commitment-heavy frontier labs. A company carrying hundreds of billions in infrastructure obligations, funded substantially by its own vendors, selling into a product layer being commoditized by a competitor's ideology, with deeply negative unit economics, is Global Crossing's shape regardless of its brand recognition. This is where the original thesis — "value was derived through totally different players" — applies with more force than the integrated-hyperscaler counterargument admits: the layer anchoring the demand side of the entire buildout may itself be the layer value migrates through rather than to. Enterprise-weighted, lighter-commitment labs are less carrier-shaped; the category risk is shared. The Cisco tier (multiple risk, not solvency risk). Nvidia's ~75% gross margin is the largest arbitrage in the system, and the fast attack channel is not ASIC market share — it is the memory-architecture shift repricing the scarcity assumptions embedded in consensus. Nvidia's revenue does not need to fall; under a bifurcated-inference scenario it merely disappoints against expectations calibrated to permanent HBM-centric scarcity, which at current multiples is sufficient. Broadcom's ~$10.8B quarterly AI revenue and the hyperscalers' motivation to escape a 75%-margin single supplier is the slow channel running in parallel. This tier extends beyond Nvidia to the whole scarcity-premium complex — HBM, CoWoS, advanced packaging — where genuine rents exist now (sold-out books, take-or-pay contracts) but carry an architectural expiry risk in 2027–28. The reconciliation of "real supercycle" and "real disruption" is temporal, not logical: both are true on different clocks. The hybrid tier (no precedent). Integrated hyperscalers are simultaneously the overbuilders and the Amazon/Google of the next act. Downside is capped at multiple compression plus write-downs — painful, not existential — unless the debt migration of item III.3 runs much further. The tier with no 1999 seat at all. China's parallel ecosystem, which holds no Western scarcity rents, suffers nothing from their compression, and structurally benefits from every leg of the commoditization it exports. V. Goalposts and Catalysts (The Falsifiable Part — Now Dated) The blank-slate version of this question required open-ended monitoring. It no longer does: the observables cluster in a window centered on H1–H2 2027, and the analogy will be substantially resolved inside it. Revenue coverage ratio. Annualized end-market AI revenue ÷ annual AI capex, currently ~0.15–0.2x. Crossing ~0.5x by end-2027 largely kills the bear case; stalling below ~0.25x while capex grows confirms it. Telecom never closed its version. Hyperscaler capex language, Q4 2026–Q2 2027. The specific, falsifiable call on the table: Microsoft signals capex deceleration first as internal AI infrastructure stands up, Google follows. The tell is any migration from "capacity-constrained" to "capacity-matched" phrasing on earnings calls. This is simultaneously the neocloud tier's demand-side death warrant, since hyperscalers are their long-term customers. Decode-tier silicon adoption. The earliest tripwire for scarcity-premium compression: Qualcomm AI200 shipment volumes, Rubin CPX mix within Nvidia's own lineup, d-Matrix deployments, or any hyperscaler disclosing a cheap-memory inference fleet at scale. This replaces the cruder "ASIC share crosses 30%" milestone — the architectural channel fires earlier and needs no market-share threshold. Memory price inflection, H1 2027. DRAM supply additions (M15X, Boise, CXMT ramp) converge with algorithmic demand-side efficiency shocks to put the memory-cycle top on an internal supply clock that can hit AI-infrastructure equities before any demand event occurs. Watch the rate-of-change of contract price increases, not the level. Price×volume test at the model layer. The first quarter where a major lab's revenue growth decelerates below ~50% YoY while per-token prices keep falling is the deflation-outrunning-elasticity signal. Until then, elasticity is winning. The marginal price of compute. Neocloud spot and renewal rates for current-generation GPUs — the only honest utilization proxy in a market where hyperscalers disclose none. Prior-generation rates have already fallen sharply; the signal is current-generation rates sliding while new capacity energizes. Depreciation convergence and resale reality. Whether Microsoft/Meta/Google follow Amazon toward shorter lives, and whether used A100/H100 values hold near the ~95% resale levels CoreWeave has claimed. The used-GPU market settles the Burry debate empirically and, with it, the cascading-lifecycle defense of the entire asset-life problem. Credit-market tells, 2027 window. GPU-collateralized and data-center securitization spreads, single-customer landlord CDS, and the first covenant breach or failed refinancing at a leveraged neocloud. Credit prices the Global Crossing moment before equity does; the balance-sheet arithmetic at the weakest names puts the pressure window in 2027. Circularity stress test. The reported stalling of Nvidia's $100B OpenAI tranche in early 2026 is a live experiment: if lab-tier purchases require continuous vendor equity support to continue, demand is partly manufactured; if they continue without it, demand is organic. Extended to the tier level: any commitment-heavy lab renegotiating, deferring, or reselling contracted capacity is the carrier-tier confirmation signal. China's domestic-silicon deadline, ~Q3 2027. The self-imposed clock for domestic-ecosystem viability. Proof accelerates global silicon-layer commoditization and validates the parallel-ecosystem tier; failure extends Western scarcity rents by years. Alongside it, FY2026 enterprise-revenue disclosures test whether the commoditized-model-layer doctrine is compatible with a business at all. The power flip. Today power scarcity throttles supply and protects pricing across every tier. The 2027–28 multi-gigawatt energization cohort delivers capacity in lumps; the cycle's top is approximately the moment the binding constraint stops being megawatts and becomes customers. The first earnings call to say so marks it. OpenAI's IPO. The confidential S-1 was filed June 8, 2026. Its pricing and aftermarket are the cycle's sentiment referendum — and given the tier reclassification above, also the market's first full-information verdict on whether a commitment-heavy lab is a platform or a carrier. The regime-change branch: continuous learning. The lowest-probability, highest-consequence signpost. Models that learn continuously in deployment would restructure the training/inference compute mix, obsolete today's chokepoint map in both directions, and retire the telecom analogy entirely — the correct reference class would become electrification, a technology whose infrastructure overbuilds were absorbed because the demand curve itself kept changing shape. No 1999 observer faced an equivalent branch; it belongs on the dashboard precisely because it invalidates the dashboard. VI. Verdict The analogy holds at the tier level, not the sector level, and it holds for two tiers rather than one. The carrier role — leveraged owners of commoditizing capacity, vendor-financed, structurally unable to survive the deflation of their own product — is occupied jointly by the neocloud/compute-landlord complex and by the commitment-heavy frontier labs, and for that combined tier the 1999 script applies with full force, on a credit-market clock centered on 2027. The Cisco role belongs to Nvidia and the broader scarcity-premium complex: real rents, real revenue, and an architectural expiry risk — the memory shift is this cycle's DWDM, and it attacks consensus assumptions before it attacks income statements. The integrated hyperscalers occupy a hybrid position with no historical precedent, capped at multiple-compression downside because they pre-own the layer value migrates toward — which is simultaneously the strongest single objection to the original thesis and the reason the analogy cannot cash out at the index level. And China occupies a seat that did not exist in 1999: a deflation exporter that compresses every timeline in the comparison. Two features make this cycle structurally less forgiving than telecom: the inversion of asset life (the short-lived component now dominates the capex) and the mid-cycle migration from equity to debt funding. One feature makes it structurally more forgiving: demand that is rigid enough to be attested by adversarial witnesses, not just interested sellers. Which of these dominates is no longer a matter of standing debate — it is a dated empirical question whose principal observables (capex guidance language, the memory inflection, neocloud credit, decode-tier adoption, the coverage ratio) all report between late 2026 and the end of 2027. The discipline, accordingly, is not to answer "bubble or not" — the question the analogy keeps forcing — but to hold the tier map, watch the dated tripwires, and let the divergence between tiers do the work that the binary question cannot. Methodology This paper was produced through a deliberate two-phase protocol designed to separate independent reasoning from accumulated context, thereby controlling for confirmation bias in both directions. Phase one was conducted under an explicit blank-slate constraint: the telecom-analogy question was analyzed de novo, with no reference to prior research threads, using only first-principles decomposition of the 1996–2002 telecom cycle (demand fabrication, DWDM-driven scarcity destruction, mono-line leverage, vendor financing) and freshly sourced 2026 market data — hyperscaler capex, frontier-lab revenue run-rates, depreciation disclosures, circular-financing structures, and credit-market conditions. This phase produced the initial four-mechanism framework, the three-tier map, and an undated goalpost dashboard, and constitutes the "AI infrastructure valuations versus telecom bubble valuations" analysis referenced throughout — it is the opening section of this same research thread, not a separate document. Phase two lifted the blank-slate constraint and cross-examined the independent result against two prior deep-research threads conducted separately: the Deep Technical Analysis of AI Infrastructure Thesis (a verification-driven stress test of the pseudonymous "Big Boss" memory-architecture and neocloud theses, including the bifurcated-inference scenario framework, the hyperscaler capex-deceleration call, and the H1 2027 memory-inflection base case) and the DeepSeek Wenfeng Article Analysis (a primary-source analysis of Liang Wenfeng's leaked closed-door transcript, including the model-layer commoditization doctrine, the rigid-demand behavioral evidence, the China parallel-ecosystem thesis, and the fifteen-claim falsifiable signpost tracker). The cross-examination was conducted as an audit rather than a merge: each element of the blank-slate verdict was tested for contradiction, reinforcement, or refinement against the prior work, with changes admitted only where the prior threads supplied either a mechanism the independent analysis lacked (the memory shift as the true DWDM analog), evidence of higher quality than the independent sourcing (adversarial demand attestation), or dated specificity where the independent analysis was open-ended (the 2027 catalyst cluster). Internal contradictions between the prior threads themselves — notably the memory-supercycle longs versus the memory-architecture disruption thesis — were surfaced explicitly and resolved temporally rather than suppressed. The principal methodological virtue of this sequencing is that the blank-slate phase could not inherit the prior threads' conclusions, so every point of convergence between the two phases (the tier-level rather than sector-level validity of the analogy, the Cisco/carrier distinction, the centrality of the 2027 window) counts as independent replication rather than repetition; the principal limitation is that both phases were conducted by the same analyst-and-model pairing, so convergence controls for context contamination but not for shared analytical priors.

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  Shane Aalam 🇮🇳 (@mShaneHu) reported 18 minutes ago

  @AmazonHelp Dear you here also not trying to understand the problem? If understand please refund my amount.

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  Alexandre Cappucci (@ALEXANDRE121975) reported 19 minutes ago

  @premium Why does X prevent users who have subscribed to one of the 3 Premium plans from opting out, and or from easily changing their plan ? Unlike sites like Amazon, X has made it so that people who pay for a premium subscription can no longer delete it without changing their payment methods through their bank. This is an unfair and illegal practice under commercial law. Mercie to rectify this major problem, and finally to allow subscribers of a Premium plan to easily unsubscribe. For my part, i have been trying for weeks, without success, to unsubscribe from the plan i subscribed to, without having to change my credit card. Is that normal ? Absolutely not !

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  Devutopia (@D_Raval) reported 19 minutes ago

  Another day, another Burnham con trick. 20% off business rates for pubs, clubs and music venues. Average saving: about £1,100. Sounds like help for the high street. Except it isn’t the high street. It’s hospitality only, on top of a rate relief scheme Reeves already announced last November. Burnham’s added a top-up and put his name on the whole thing. Meanwhile the businesses actually holding your high street together, pharmacies closing at record rates, independent shops, get nothing here. And it’s funded by a warehouse tax for “taxing Amazon.” Except look who actually owns the UK’s biggest warehouses. Not just Amazon. Tesco. Lidl. Next. M&S. John Lewis. Sports Direct. This isn’t a tax on Amazon. It’s a tax on the supply chains of the same high street names he says he’s protecting. The Warehousing Association is already warning it’ll feed straight into prices. One industry group says it risks pushing distribution overseas. So the “cost of living” fix for your pint is funded by a mechanism that may push up the cost of your shopping. Same trick, same self-defeating con.

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  Shawn Dedalus .·. 🇺🇲 ⚓ (@ShawnDevDedalus) reported 22 minutes ago

  @eloffd Some how, they came up with the idea that blaming American companies for their problems and trying to ruin their lives was the great idea. All major European international banks maintain a physical and digital server presence or utilize US-based cloud infrastructure to run their operations. They rely heavily on American technology and infrastructure companies for daily life... Microsoft, Google, and Amazon, among others. It is also known that 74% of Europe's publicly listed companies depend on U.S. cloud and software platforms to operate. What would happen if we get tired of this **** and leave or simply start charging them more to recover all the money they constantly take away from doing their crazy EU crazy lawsuits. There are limits and it could happen.

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  Jimmy Kiberu (@KiberuJimmy) reported 25 minutes ago

  @EnjoyItraw @amazon Pedestrian response! Read and rebutt the issues.

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  Andy Coleman (@AndyColema86904) reported 30 minutes ago

  @HawleyMO Shut the USPS down, Amazon could do it cheaper and better, just like UPS and Fedex. Also rural people dont need mail every day, use email like normals. Also we have a post office in Palau, a foreign country , how much is that costing ?

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  Rachit Sharma (@RachitS92154694) reported 32 minutes ago

  @amazon @AmazonHelp @amazonIN On 20 July, I contacted Amazon again. I was told they were unable to process the refund because the refund/payment option was not showing in their system, and I was asked to contact them again on 23 July. I waited again, hoping the issue would finally be resolved. (5/5)

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  Vivek Giri (@VGoswami90635) reported 32 minutes ago

  Still no any resolution provide by @amazon and @amazonIN Your support team said that issue will be resolved on 22nd July but today is 23rd July and still I am waiting for refund my entire amount. Order Number: 404-6614807-3843527

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  Karl Mehta (@karlmehta) reported 32 minutes ago

  Jeff Bezos explains the thesis behind his $41B AI company Prometheus: an LLM can read a thousand books on gymnastics and still be a terrible gymnast. "Prometheus is building a set of tools that's designed to empower engineers to really invent and build much, much faster." "Today there's a kind of a dream build cycle. You know, dream of something. And then depending on how complicated it is, it may take a few years to ten years before you're really producing it at rate and manufacturing it." "All of civilizational wealth is driven by invention. Six thousand years ago, somebody invented the plow and we all got wealthier." "It can't be done with traditional, large language models. They have a place, but they aren't trained with the right data to be able to do detailed engineering." "If I read a thousand books on how to be a great gymnast, I would still be a terrible gymnast. And it's because it needs a different kind of training data." "When you go to actually design real physical objects, it's very complicated and you can't do it just by reading about it. And so we're building a model that is very good at doing engineering." The biggest labs are still fighting over text. Bezos is betting the next moat is training data for the physical world, and that whoever owns it compresses the invention cycle itself. Whether engineering judgment can be trained at all is the one open question. - Jeff Bezos (@JeffBezos), Amazon founder and co-CEO of Prometheus, at VivaTech 2026 (Associated Press live).

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  Fozzy Trooper (@TrooperFozzy) reported 33 minutes ago

  @AmazonHelp Do you even realise the ridiculousness of this? This is being posted on a comment that is about the same problem only happening yesterday. This is a proven ongoing problem that absolutely nothing gets done about.