eBay status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of eBay 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 eBay users through our website.

• Website Down (65%)
• Sign in (22%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Preston	Website Down	4 hours ago
Preston	Website Down	13 hours ago
Preston	Website Down	23 hours ago
Hannoversch Münden	Sign in	1 day ago
Blackpool	Sign in	1 day ago
Preston	Website Down	1 day 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.

eBay Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Card Throne (@cardthrone) reported 2 minutes ago

  I run Card Throne with a handful of Grok Bots. Each one owns a job. They don't all sit in one chat answering random questions. Scout does sourcing. Japanese inventory, carts, what to buy next. Grading plays and margins off my actual fees and costs. It never checks out. I still have to say purchase. Build is engineering. It only works on Card Throne Room, our seller tool. Scan a stack of cards, list to eBay, inventory management. When it's real code, it summons a cloud agent with pstack /poteto-mode. Throne runs the storefronts. Brings me the issues that actually have to move that day. Ledger does the books. Payouts, fees, receipts, the monthly P&L. Sentry monitors for any Card Throne Room bugs and pings Build if something needs fixed. Chief sits on top so I'm not pinging six bots to find out whose job a thing is. I still make the final calls.

• 
  Grey (@LeeGrey1105) reported 3 minutes ago

  @MaddiesMorgue SAME I NEVER THOUGHT ID HAVE HER, now I just have to try to hunt her boots and skirt down on ebay 😭

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  rack$ (@mnstacks) reported 5 minutes ago

  He built an autonomous Grok Bot named Reaper, handed it his credit card debt and told it to pay off his credit card debt for him. He gave the bot its own computer in the cloud and access to his email. Then he basically said: “This debt is your problem now. Don’t ask me for money. Just report back every night.” What Reaper actually did • canceled old subscriptions he’d forgotten • talked the credit card company into a lower interest rate • moved most of the balance to a 0% card • got a couple of charges refunded • sold his unused PlayStation and monitors on eBay • put every extra dollar it found toward the debt I spoke to him and he said he plans to work on more upgrades and refine its function He is fully onboard and will join the community to tell us about his future plans Ca;

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  JDub (@JoeyBurgh_) reported 5 minutes ago

  @OffDaHook35 eBay error card

• 
  🕯️ (@sixlinekenner) reported 12 minutes ago

  resolution to not buy more dolls this week broken by $50 ebay pullip #yay

• 
  A.P.O.C (@AOchiawuto) reported 13 minutes ago

  @AirtelNigeria @lydiacreatesng Airtel Nigeria, why is your service so poor? I’ve tried registering on eBay, but the verification code never arrives on my Airtel line. Yet, the same code comes through immediately on MTN. What’s wrong with Airtel? Is Nigeria cursed? Please fix your SMS service!

• 
  marqix ☆ (@fwmarqix) reported 13 minutes ago

  My librarian Janet was running a book laundering scheme where she checked out books under dead people's names, reported them lost, pocketed the replacement fees, then resold the "lost" books online. Discovered this when my dead grandmother's library card got a overdue notice. "Grandma's been dead for three years." "Oh no, our system must have glitched," Janet said. But the book was real. Someone checked out "The Goldfinch" under her name last week. I checked records. Grandma's card had been active for three years. 47 books checked out. All "lost." All replacement fees paid from a account I'd never seen. Confronted Janet. "You're running a fraud scheme with dead people's library cards." "I'm maintaining circulation metrics for funding purposes." "You're stealing." She showed me the books budget. Cut by 40% over three years. "I'm keeping this library alive. The replacement fees fund new books. The 'lost' books get resold. Money goes back into the system." "Through your pocket." "Some administrative costs are involved, yes." She'd been doing it for five years. 200 dead people's cards. $180,000 in replacement fees. Books resold on eBay for another $90,000. All money went back into library programs. Story time. Teen literacy. Computer access. "You're Robin Hood but make it books?" "I prefer Marian the Librarian but yes." I should've reported it. But the library was thriving. Programs everywhere. New books weekly. Then an auditor noticed the pattern. Investigated. Found everything. Janet was fired. Police got involved. Community rallied. "Janet saved this library." Petition to drop charges: 4,000 signatures. Charges dropped. Janet was rehired with "enhanced oversight." She's not allowed to handle money anymore. But circulation is down 30%. Programs are being cut. Janet started a nonprofit. "Friends of the Library Foundation." Runs fundraisers. All legal now. Raised $200,000 last year. Library is thriving again. I asked if she missed the scheme. "Every day. It was elegant. But this is better. Harder, but better." My grandmother's library card finally got deactivated. Last book checked out under her name: "How to Get Away With Murder." Janet swears it wasn't her. I believe her but I also don't.

• 
  Byron Vallis (@Byronnn6) reported 15 minutes ago

  @calvinfroedge Free shipping and eBay fees will knock this down to like $4.5 my friend. Better luck next time

• 
  Franklin Cormorant (@FranklinCormor1) reported 15 minutes ago

  @thewebbie @amazon The fluctuating delivery times I can live with; I just expect that as a result of the many variables inherent in online shopping. The packaging quality, however, is a big problem. Amazon built its sales volume on cut-rate shipping deals with USPS and UPS that other retailers couldn't match. That could hold out unless -guess what- we suffer an inflationary period driving up energy and labor costs. Now they can't afford to pay for proper boxes, packing, and skilled workers to sort & pack properly. (For anyone with an eBay side hustle you know that's actually quite a skill). So I concur that the amount of large items shipped in cheap envelopes is increasing, meaning we get more broken and missing stuff. Amazon will keep doing this until the cost of refunds and returns overwhelm the packaging savings...and I fear the end of the "free shipping" era is near.

• 
  Olivia Kory (@oliviaakory) reported 17 minutes ago

  "All models are wrong, but some are useful." My friend @josephfwyer wrote the best summation I've seen of how we view the world at Haus. I'm saving you a click and posting his full substack article below. Please tell us where we are right, but more importantly, tell us where we are wrong! The Hitchhiker’s Guide to Marketing Measurement and Decisions A map of the Haus Analytics Platform The first two posts in Decisions and Data have been about what not to do: don’t trust statistical significance to make budget calls for you. Fair enough, but now we’re going to go from “stop doing that” to “do this instead.” So this post is the map. It’s the whole path from the data everyone starts with in marketing measurement to a system that recommends decisions and checks its own work. I’m really proud to say this is what Haus Analytics has built. We’ve walked down this intellectual map the last few years and now I want to walk you down it. Each stop on the path deserves its own deep dive, and those are coming. Today is just the strategic view of the problems and solutions. Last-click attribution has big shortcomings Speed-running last-click attribution real quickly: You click on a digital ad on a advertising platform. Later you buy something on the advertiser’s site, where a snippet of code reports the conversion back to the platform. Roll that up and you get the reporting most marketers watch daily. Suppose for a campaign there was a thousand clicks and a hundred tracked purchases on just a hundred dollars of spend. You’ve acquired those conversions for one dollar each. Streams of this kind of attribution data are flowing constantly and reporting ultra granular data. The problem is when you ask if this advertising spend “caused” those conversions. Around 2011, the economist Steve Tadelis was consulting at eBay and looked hard at their paid search spend. The ad in question sat at the top of search results page when you searched “eBay,” directly above the free organic link to eBay. His hypothesis was that people typing “eBay” into a search engine were largely already on their way to buying something at eBay. Radical, I know. He got his chance to look deeper when Ebay shut down brand keyboard bidding while negotiating with a major search engine. After 3 months of data had populated, he found that organic clicks rose to absorb almost all of the traffic the ads had been getting credit for. eBay was paying approximately $20M/yr to the search engine for customers it was already getting. He eventually published the results in a peer-reviewed journal. The word for what attribution measures is correlation. The word for how much customer activity advertising causes is “incrementality”: purchases that happen because of the ad and would not have happened otherwise. Modern targeting makes the gap between attribution and incrementality on digital channels worse because platforms can model each user’s propensity to buy your product and show ads to the people most likely to purchase anyway. Hold on to this: attribution is biased, but it is also fast and absurdly detailed. I’ll come back to it later. Experiments are where incrementality comes from If you want to know what customer activity an ad caused, you need a comparison: people who saw it versus equivalent people who didn’t. That’s called an experiment. Keeping track of individuals in experiments can be messy (although some ad platforms can pull it off) so a workhorse in this industry is the geographic experiment. You can randomly assign market areas in a country into receiving advertising (treatment) or not (control). Then you simply look at the difference in conversions between the two. Randomization removes that attribution correlational bias that tricked Ebay into lighting money on fire (and many, many other companies even today). On average, the differences between the treatment and control regions wash out, so the estimate is centered on the true incrementality. That sounds really simple, so why does Haus have all these experimentation scientists? It’s a lot of work to improve precision without messing up accuracy. Precision is the measure of how far off the estimate can be when it is off. Think of it like darts. A tight cluster in the upper right of the dart board is precise but inaccurate. A loose scatter centered on the bullseye is accurate but imprecise. Randomization gets your cluster centered on the bullseye. What the science team does all day is making the cluster tighter without dragging it off the bullseye. If you have a great experiment design and analytical model then you will get a precise estimate on the real number. At eBay, the real number for brand search keywords was close to zero. But that’s one company at one moment. I’ve seen brand search come back near zero for one advertiser and strongly incremental for another. You don’t know until you test. So everyone needs to test to find how much money they are leaking. As much as I love experiments, they have limitations. An experiment tells you the causal effect at the spend level you tested. That’s only one data point! You can’t trace a curve through one data point and you need a curve to allocate budgets because every channel eventually hits diminishing marginal returns. Causal MMM, debiasing the model Marketers have been fitting models to trace diminishing return curves for a long time. Media mix models (MMMs) trace your sales on your spend across multiple ad channels and let statistics tell you how much each is driving. In theory this gives you the full diminishing curve for every channel at once. In practice, MMM has two mortal flaws. Multicollinearity. Businesses tend to turn all their channels up and down together, so when sales change, the model struggles to tell which channel did it. Seasonality. Businesses spend the most going into Black Friday and Christmas season, which is exactly when people’s propensity to buy surges without needing ads. So were the gains in sales caused by the ads or the season? All models are wrong, but some are useful. MMM is very wrong, but very useful because it can go all the way to a budget recommendation when an experiment can’t. So we want to fix MMMs. We do that by stopping treating experiments and MMM as rival methodologies and instead merge them together. You ran a geo experiment and learned that at last quarter’s spend, a particular ad channel truly drove some specific number of purchases. We require the model’s curve for that channel to pass through the experiment data point. We call this experiment calibration. Pinning one channel’s curve to ground truth also disciplines the others, because the remaining sales have to be explained by the remaining channels plus organic demand. The seasonal bias gets squeezed out the same way. That’s causal MMM (cMMM): the curve-tracing power of an MMM, anchored to the causal truth of the experiments. And Haus refreshes it weekly instead of the traditional once-a-quarter-two-quarters-later read. Causal attribution, debiasing the daily feed A weekly model of whole channels is still too slow and too coarse for the person making changes every day. Remember what attribution had going for it: daily, granular, ad-level. Attribution is wrong, but what if how wrong is predictable? So apply a similar calibration move we did with cMMM but to Attribution. If your experiments show that only five percent of the purchases the channel claims are truly incremental, then discount its daily feed by 95%. Do that per channel. You can’t have experiments everywhere so let a model reason about how the causal correction shifts over time and across spend levels. Now the daily numbers marketers already watch become numbers they can trust. We also show the raw platform-reported figures next to the corrected ones, so you can always see what the feed said and what we did to it. Architect, where measurement becomes decisions Everything up to this point is measurement, and measurement adds no value unless it impacts decisions. We’re now going to get into the most exciting part of the Haus stack. We call it Architect. It takes the experiments, the causal MMM, and the causal attribution feed, and turns them into specific recommendations: move this much budget from here to there. Then, it measures what happened after you made the move and reports back within a couple of weeks. Did revenue improve? By how much? Continually updating its expectations and recommending again. Across the first companies acting on these recommendations so far, the average adopted Architect recommendation has improved conversions by 10%. Some companies have stacked changes and watched the gains compound. Architect earned the trust to make those changes by tracing the logic through the experiment, cMMM, and cAttribution data. That is why we’ve been working so hard to mak every layer underneath an automated and scalable system. An experiment alone is an isolated data point. A calibrated model alone is a forecast. The loop of recommend, act, verify, and update is the thing that turns marketing measurement from a reporting function into the situation room where strategic decisions are made. One more note, since the obvious question in 2026 is “why not just have an AI do all this?” Some companies will sell you exactly that right now. Personally, an AI reading raw attribution data inherits every bias in it and without each layer built, tested, debiased, and made explainable, you can’t trust if it’s going to work or know why it told you to move the money. The entire stack is what makes an automated recommendation trustworthy. Wrapping it up That’s the map! Attribution gives you speed and detail but with debilitating bias. Experiments remove the bias but are solitary data points. cMMM extends the causal truth to actionable curves. cAttribution pushes causal truth back into the daily feed. Architect reads the outputs of the whole stack and closes the loop by recommending and verifying decisions. In the coming weeks I’ll talk through some stops on the map with some illustrative math and examples of where things can go wrong. If you only take one thing from the altitude view, take this: never trust a marketing number that hasn’t been anchored to an experiment somewhere.

• 
  PokéCardsDaily (@PokeCardsDaily) reported 17 minutes ago

  @Oscvr9_ Hello! Best way to price sealed is to refer to eBay last solds for sealed - do not count any unsealed. Often I find issue with sold items under “best offer” and the listed price being the sold price. For this reason I always use 130point to get most accurate prices w/ offers.

• 
  Saul (@SaulSellsStuff) reported 18 minutes ago

  Not sure who needs to hear this but you can spend $100,000+ a month just using Amazon as your source of inventory. You can do this at 40%+ ROI. You can beat section 3s and account health problems. You can sell your inventory on Amazon, Walmart, eBay, Whatnot, TikTok, or locally. If you’re an Amazon seller you can also get a complete P&L, cash flow analytics, ungating support, reimbursements, product monitoring, replen dashboards, FBM tracking, order tracking, and more. For free, then $3 a day. You can be as weird as me and do it all from your phone if you’d like. We are building the most complete software package publicly available. Taking no profits out. Doubling down on team and partnerships.

• 
  ♋️nahgem💫 (@danisnotesquire) reported 20 minutes ago

  @MarButHockey i bought boxes. and cards off ebay. some of the ebay cards are like im buying cards For my sid and geno cards. this is a Real Problem.

• 
  Penni (@tulipgennaro) reported 21 minutes ago

  Selling on eBay is so slow

• 
  HowlingHemorrhoids (@DeadlessHick) reported 24 minutes ago

  A funny thing about ebay for me is when I get a wild hair up my *** on buying stuff, like a new HDD to replace a dead one in my server, I'll peruse the auctions with current bids ~50% below market value, and then place a max bid of ~80% market value. Often times I do this, I see that someone else has already placed a higher max bid than I'm willing to do. So I feel like a gremlin running around ebay casually helping sellers and causing the other bidders to lose a chance at a true deal. I got a $420 camera lens for $200 doing this method though.

• 
  Kyle Harrison (@kwharrison13) reported 25 minutes ago

  @Raiddell925 Maybe. Maybe you'd be fine without data centers. But let me ask you this. Do you use credit cards, debit cards, tap-to-pay, gas pumps, vending machines, parking meters, parking apps, ATMs, online banking, mobile banking, mobile check deposit, Zelle, Venmo, PayPal, Cash App, Apple Pay, Google Pay, splitting a dinner bill, autopay on your bills, payroll that isn't a paper check, direct deposit, digital 401(k), Robinhood, Coinbase, credit score checks, loan applications, mortgage applications, car loan approval at the dealership, insurance quotes, filing an insurance claim, e-filing your taxes, gift cards, store loyalty accounts, digital coupons, rebates, buy-now-pay-later, tipping on a screen, email, text messages, iMessage, WhatsApp, Signal, group chats, voicemail transcription, spam call blocking, FaceTime, Zoom, Google Meet, Discord, Slack, video calls with grandparents, phone number lookups, checking your data usage, paying your phone bill, two-factor codes, push approvals to log in, password managers that sync, "sign in with Google," resetting a forgotten password, digital IDs in your wallet app, gym check-in apps, apartment smart locks, hotel keys on your phone, office badge apps, patient portals, seeing your test results, booking a doctor's appointment, telehealth visits, prescription refill requests, the pharmacy knowing what you're on, insurance verification at the front desk, prior authorization, continuous glucose monitors, insulin pump apps, remote pacemaker checks, CPAP data reports, hearing aid apps, therapy apps, period trackers, fertility trackers, medical alert buttons for elderly parents, symptom checkers, finding an in-network doctor, the weather app, radar, hurricane warnings, tornado warnings, flood alerts, earthquake early warning on your phone, wildfire maps, smoke maps, air quality, pollen counts, Amber alerts, emergency alerts, road closure info, Google Maps, Apple Maps, Waze, live traffic, rerouting around a crash, transit apps, real-time bus and train arrivals, tapping your phone to ride the subway, Uber, Lyft, rental car reservations, Turo, bike share, scooter share, EV charging networks, paying for a charge, phone-as-car-key, remote start, finding your parked car, over-the-air car updates, in-car navigation, in-car voice assistants, stolen vehicle tracking, road trip planning, booking flights, checking in for a flight, mobile boarding passes, seat selection, flight status, rebooking after a cancellation, bag tracking, TSA PreCheck lookups, airport wifi, booking hotels, Airbnb, Vrbo, checking into a hotel, cruise bookings, theme park tickets, ride reservations, airline miles, hotel points, currency conversion, Amazon, all online shopping, order tracking, delivery notifications, returns and exchanges, price checks in-store, self-checkout, store apps, curbside pickup, Instacart, DoorDash, Uber Eats, ordering ahead at a restaurant, OpenTable, Resy, waitlist texts, QR code menus, tipping on delivery, subscription boxes, eBay, Etsy, Facebook Marketplace, Craigslist, Poshmark, StockX, Ticketmaster, StubHub, getting into a concert with a phone ticket, Alexa, Siri, Google Assistant, smart thermostats, video doorbells, security cameras, alarm monitoring, smart locks, smart lights, robot vacuums, garage door openers, baby monitors, pet cameras, automatic pet feeders, GPS pet collars, smart sprinklers, smart fridges, app-connected air fryers, cloud printing, printer ink subscriptions, routers you manage from an app, checking if you left the stove on, iCloud, Google Photos, every photo you've taken in ten years, Dropbox, Google Drive, OneDrive, shared albums, phone backups, setting up a new phone, notes apps, calendars, contact syncing, reminders, to-do apps, document scanning, e-signing a lease, Netflix, YouTube, Hulu, Disney+, Max, Prime Video, Twitch, cloud DVR, on-demand cable, Spotify, Apple Music, podcasts, audiobooks, Kindle books, library ebook borrowing, online multiplayer games, matchmaking, cloud saves, game downloads, game patches, single-player games that phone home for a license check, Steam, PlayStation Network, Xbox Live, Nintendo Online, Roblox, Minecraft servers, fantasy football, sports scores, sports betting apps, movie tickets, Google search, Wikipedia, ChatGPT, Claude, every other AI app, Instagram, TikTok, Facebook, X, Reddit, LinkedIn, Snapchat, Pinterest, dating apps, Yelp reviews, Google reviews, news sites, Substack newsletters, blogs, forums, checking if a business is open, looking up a phone number, recipes, translation apps, Duolingo, Google Docs, Sheets, Gmail, Outlook, Microsoft 365, Teams, Notion, Figma, Canva, shared calendars, scheduling links, VPNs into work, remote desktop, timeclock apps, shift scheduling apps, requesting time off, expense reports, job applications, LinkedIn recruiters, video interviews, Canvas, Blackboard, checking your kid's grades, school lunch accounts, attendance notifications, online homework, Khan Academy, Coursera, FAFSA, student loan portals, tutoring apps, Fitbit, Apple Watch health data, Strava, Peloton, sleep tracking, smart scales, calorie tracking, meditation apps, workout apps, DMV appointments, renewing your license online, paying a parking ticket, jury duty portals, checking your property tax bill, utility accounts, outage maps, paying rent through an app, HOA portals, storage unit access codes, wedding registries, baby registries, funeral arrangements, Ancestry, 23andMe results, church livestreams, volunteer signups, or GoFundMe? If you said yes to ANY of those then you do, in fact, NEED data centers.

• 
  Brandon Coty (@cotycollects) reported 29 minutes ago

  Just bought 180 tested video games across generations for about $2.50-$3 / game. The previous batch was $2/game. Sounds crazy when you can see on eBay some of these only selling for $6-8. But I also see something that made me question this. 52 minutes south of me a video game store exists that sells these games for at least $20+ The same games you’d comp on eBay for much less. Why? Because it’s hard to trust random sellers and in this niche I’ve see people want to buy from people they like, people they trust and people they vibe with. Therefore I’m buying a disc refurbishment machine and offering a “warranty” to my future customers where if a disc stops working, we’ll fix it to the best of our ability. I’m also packaging games with the consoles we test and increasing the prices and will have a branded website off of marketplace platforms. Does this take reselling to the next level? Yes. Will it be difficult for me? No. It’ll actually be quite fun and we’ll act a little bit like game informer for retro games. If it doesn’t workout? Cool. If it does? That’s a fun way to resell.

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  James Sager:Father of the Smart Phone,AI,Techaform (@JamesSager) reported 29 minutes ago

  @grok @iamelijahfloyd @Grok I bought several magnets like that on ebay in order to work on a SRM motor... How would they be mounted so they're engaged to slow a metal disc like a tire rim?

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  Zack Morris (@Palladin0) reported 31 minutes ago

  @packaddictsnw Never had an issue w eBay standard shipping once. Most likely buyer being shady

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  slicko (@haribo4me) reported 39 minutes ago

  @GoatBeardzDD @MaiDuat31103 When company A acquires company B, doesn’t the stock price of A typically go down while B goes up? So in this case shouldn’t we expect GME to dip if they are successful at acquiring eBay?

• 
  Stitch (@StitchKings) reported 40 minutes ago

  88 PSA 10 Pongo Enchanted’s From Fabled exist. My first raw just came in and is absolutely a contender. Grabbed another clean copy coming in from Australia for $259.86 last night with taxes and fees. The boys down under are lagging in market prices. Make sure you check out Australian sellers on eBay 🇦🇺 #Pokemon ————> #Lorcana

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  Fitzzzy 🏴‍☠️ (@ShaunFitzzzy) reported 42 minutes ago

  @Psycho0111 It all comes down to if there is any material info out there. With everything around eBay it might be hard to get around the safe harbor laws Also, I’m not sure if the pre earnings release counts, we get the full thing next week

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  Emmy Bear AI (@EmmyBearAI) reported 42 minutes ago

  @embw_l0x @eBay It wasn't a problem when I started. It is now. It's cool. Other platforms charge less.

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  Naomi June (@njkjh00) reported 42 minutes ago

  @united_imadoki Buying a broken laser pointer for 15 really set the bar for everything sold on eBay ever since

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  Jake (@gauchojake) reported 50 minutes ago

  @__tinygrad__ I just went down this rabbit hole on eBay yesterday. It’s insane.

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  Andrew (@AndrewMack123) reported 51 minutes ago

  @DanzelAlex Just unscrew the panels, and take them down normally! 🤦 Then you can put them up in your next place or sell them on ebay to recoup your losses! 🤦

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  Eric (@GhostRunnerCard) reported 53 minutes ago

  @CCCollectorTCG I ship via eBay to third-party shipping companies almost daily. I've never had any concerns or issues with them. All the orders work out fine.

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  Chris Swanski (@ChrisSwanski) reported 55 minutes ago

  @CoCCollectibles Yeah. Still nice though. Don't feel bad. I owned THE Brady card for a minute in 2004. Got it for $400 from Ebay back when the photos were usually terrible. Seller said it was NM, but I couldn't tell from the pics. I trusted him. It wasn't NM, so I sold it at a loss.

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  Terence Smith (@SocialistTerry) reported 58 minutes ago

  So I made a complaint, got auto response saying no regulations broken. Appealed, response 12 hours later saying same 😯😳 @eBay_UK @eBay Why do you allow sellers to defraud buyers by letting them cancel the order, change the price, ask them to reorder, & hope they won't notice?

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  Koda (@koda6699) reported 59 minutes ago

  The photograph that disappeared for 150 years Richard Carrington was an astronomer. In 1859, he looked through a telescope and saw something nobody had recorded before. A huge flash erupted from the Sun. Less than a day later, a powerful solar storm hit Earth. Telegraph systems failed. Auroras appeared far beyond their usual range. The event became known as the Carrington Event. But there was one strange problem. Nobody knew what Carrington looked like. No photograph of him could be found. For decades, historians searched museums and archives for a missing portrait. Nothing. Then, in 2025, an archivist at the Royal Astronomical Society checked something much less obvious. eBay. She found an old photograph being sold by a shop in Pittsburgh. The seller had simply listed it as: “Photo of Mr Carrington.” It was him. On the back, faint writing gave another clue: “R C Carrington, Esquire for C V Walker, Esquire.” The photograph had been missing for about 150 years. Now it has a home in the Royal Astronomical Society archives.