Dropbox Outage Map

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Dropbox Issues Reports

Latest outage, problems and issue reports in social media:

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  Abhishek Singh (@0xlelouch_) reported 9 minutes ago

  System design question. How would you design Dropbox-style file sync with conflict handling? Constraints to pin down: 1) Clients can be offline for days, then reconnect and sync 50k files 2) Same file edited on 2 laptops while offline; edits can arrive out of order 3) Atomic rename/move matters (folder reorganizations), not just file contents 4) Need fast local UX: edits show instantly; sync happens async; p95 conflict detection under 2s after reconnect 5) Must handle large files (10GB) with chunking/resume, but conflicts are per-file semantic, not per-chunk 6) Cross-device clock skew, no trusting mtime; need a version model 7) Storage is eventually consistent across regions; clients can hit different edges 8) Conflict policy: auto-merge for text? duplicate files? keep both? how do you avoid conflict loops? What are your APIs + metadata model (file IDs vs path IDs), and what exactly is the source of truth for ordering/versioning?

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  davidsong (@bitplane) reported 11 minutes ago

  Has @Dropbox been breached again? I just got this but it can't be me. At least I hope not. I have a randomly generated, unique password that's made in an offline password manager, I used it once on this machine when I first set it up. If I'm getting hacked the rest of you are in serious trouble.

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  emrah (@mrahbayraktar) reported 13 minutes ago

  i met a founder doing $10,000,000+/year at my airbnb gym in dubai at 6am he was the only other person there. we started talking. i asked him what was driving most of his revenue. he didn't say ads. he didn't say cold email. he didn't say a sales team. he pulled out his phone and showed me a dashboard. 28 million views in the last 30 days. accounts he owns. content he already had. no ad spend. no creators. no audience deals. he said something i haven't stopped thinking about since. "most founders are renting attention. i own mine." here's what he meant, and why it's the most important distinction in business right now. when you run ads, you are paying rent on someone else's audience. the moment you stop paying, the leads stop arriving. you don't own anything. you built nothing. you rented a billboard for six months and when the lease expired you were back to zero, except now you're $200,000 lighter and your CAC is a number your board pretends not to notice. when you distribute content on accounts you own, something different happens. the views compound. the audience compounds. the trust compounds. a clip you posted three months ago is still driving profile visits today. a piece of content from last year is still closing deals this quarter. the platform doesn't have an expiration date on good content, and the attention you build doesn't evaporate when you stop writing checks. this founder had 52 accounts across every platform. all owned. all run by a dedicated team posting daily clips from content he already had sitting in a folder doing nothing. youtube recordings. podcast episodes. webinar footage. he wasn't creating anything new. he was just finally distributing what he'd already created, at scale, into every market he wanted to win. the math is what broke my brain. $0 in ad spend. 28 million views in 30 days. if you modelled that as paid traffic at even a $2 CPM, you're looking at $56,000 worth of reach. every month. compounding. from content that existed before we ever had that conversation in a gym in dubai at 6am. i've seen this exact system work for iman gadzhi. 300 million views. 180,000 instagram followers and 280,000 tiktok followers built from zero, on accounts he owns and keeps. i've seen it work for luke belmar. 200 million views and $19M in capital club subscriptions driven through distribution alone, not through ads, not through a sales team, through clips running on owned accounts into the exact audience that needed to see them. i built russell brunson's clipping infrastructure inside clickfunnels. $100,000 in sales from a system that runs without him touching it. the pattern is always the same. founder has content. founder has no real distribution. founder is either buying reach they don't own or posting to their own audience and wondering why growth is flat. we build a dedicated team around their brand, warm up accounts to the exact audience they want to reach, geo-target any market they want to win, post daily, test what's working, double down, and watch the views compound across a system they own completely. the content you already have is the most underused asset in your business. most founders spend years creating it. podcast episodes nobody heard. youtube videos that peaked at 4,000 views. webinar recordings sitting in a dropbox folder. all of it has a shelf life of forever if someone actually distributes it properly, and almost nobody does. the guy in the dubai gym wasn't smarter than you. he wasn't working harder. he wasn't spending more. he just figured out earlier that distribution is the actual product, and everything else is just content waiting to be seen by the people who need it. if you want to see the full strategy we use to build this kind of system... the accounts, the setup, the playbook - comment "distribution" below

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  Teri Radichel #cybersecurity #ai #pentesting (@TeriRadichel) reported 13 minutes ago

  @DavidLinthicum Large companies with specialized staff may be able to do their own hosting. Having done it myself in my own home back in 1999 when yes, people did that, and later in a colocation center. As a security expert, can tell you this is not an ideal choice for most companies. Can’t even begin to explain in this space. There are also performance and scalability issues, management of all the hardware and software, rebooting the servers in the middle of the night when something goes wrong, dealing with outdated hardware and so liability that can be shifted in cloud environments. Capex vs. opex. Yeah not going back there. Even DropBox (article from 2017) still uses AWS to some degree. And for those who want to try to run open stack themselves in this day and age of AI, phew. Capital One tried that. They moved to AWS when a SAN or similar failed and they needed to get up and running fast. The internal cloud was very painful. Not going to defend the breach. Security is hard. As always, throwing off the shackles for some new tech (cloud, AI) is likely not going to work out too well. The only bonus for a company that wants to do this is that there are more tech people out of work. But data center people? Good luck managing all that. Make sure no one has hidden a crypto miner under the data center floor. Yes, that happened.

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  Akshay Shinde (@ConsciousRide) reported 15 minutes ago

  @jahirsheikh8 The server never guesses. When the upload starts, it creates an upload session with a unique ID. The file is split into chunks, and the server records which chunks have already been received. After reconnecting, the client asks, “Which chunks do you already have?” The server responds with the missing offsets, and the client uploads only those. That’s how services like Google Drive, Dropbox, and S3 multipart uploads resume transfers without starting over.

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  Invest Like the Best (@InvestLikeBest) reported 21 minutes ago

  Ben Thompson's two laws of consumer tech: 1) Consumers do not want to pay for software 2) Consumers do not care about being productive. "We went through this in early SaaS. The canonical company for this is Dropbox. They had to rebuild the whole thing and realize the only way we're going to make money is by selling to companies. You literally had OpenAI replaying the Dropbox story, but at like 100x the size. We're going to sell subscriptions to consumers. And they did. They sold a lot, but they didn't sell enough. If you're going to be in the consumer market, you have to be doing advertising. If they had leaned into advertising immediately, as soon as ChatGPT was a hit, I think they would have a great ad product right now. I think Google would be in much bigger trouble. I think Meta would be in much bigger trouble. Charging people money is hard. Giving people things for free is easy. And it's very frustrating that OpenAI did not pursue this sooner."

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  Noise (@NoiseesoiN) reported 21 minutes ago

  @esrtweet @EricRichards22 What's funny is that having gigabit on my end isn't the issue. It's connecting to servers which have enough bandwidth to feed it. My line can pull lots of data, but when I'm connecting to Dropbox, I'm lucky to get a third of that (and usually a tenth).

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  Gale Rogers Jr (@GaleTRogersJr) reported 22 minutes ago

  @fougars67 @vaNlabs LeadPoet just signed on ******* Dropbox as a customer. And they had a nice green candle as expected. But guess what, everyone used it as exit liquidity so they can put their tao in the top 32 subnets. Do you not see the issue here? It’s like giving free money to billionaires only

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  Llama (@thellama451) reported 31 minutes ago

  I tracked down these messages in @MaxMillerOH’s Dropbox files. They show the parents getting along with no major conflicts beforehand. If Miller said he was going to kill his ex-wife in front of child (likely), it shows a talent for masking rage and hostility.

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  M.Ellis (@MEllisPhotograp) reported 32 minutes ago

  @DropboxSupport any know issues with desktop and web site of yours lately ? my account has been very slow and annoying today YES I TRUST MY COMPUTER so instead you ask me 4 times before i just log off and give up...

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  Dodz4allai (@DrGhattasMD) reported 33 minutes ago

  nstead of waiting for an API integration with Epic (which is costly and slow), OmniMed Pro deploys as a Chrome/Edge Browser Extension.3 Mechanism: Most hospital EHRs (Epic Hyperdrive, Cerner Millennium, AthenaHealth) are now accessed via web browsers (Citrix/VDI or native web interfaces). The OmniMed extension "sits on top" of the EHR window as a persistent sidebar. Data Ingestion (The "Read"): The extension uses the DOM (Document Object Model) to "read" the patient notes, labs, and vitals currently displayed on the doctor's screen. It does not need a backend integration; it reads what the doctor sees, acting as a "visual reader" similar to a human assistant. Intelligence Injection (The "Write"): The extension injects its "Co-Pilot" interface into the side of the screen. It offers "One-Click Transfer" buttons to paste generated notes, codes, or orders directly into the EHR's text fields.3 Value Proposition: This "Zero-Integration" approach allows individual doctors or departments to adopt OmniMed Pro today, bypassing the multi-year IT integration queue. This creates a Bottom-Up Adoption loop similar to how Slack or Dropbox entered the enterprise—employees brought it in because it solved their immediate problems. 5.2 Viral Loops & Community Growth To fuel this bottom-up growth, OmniMed Pro leverages the Medical Creator Economy 16: MedTwitter & Reddit: Solo founders and small teams are winning by "building in public." OmniMed Pro should release "light" versions of its tools (e.g., a "Scientific Paper Summarizer" or "Anki Card Generator" for med students) to gain viral traction. These free tools serve as a "Trojan Horse" for the OmniMed brand.18 The "Secret Cyborg" Phenomenon: Many doctors already use GPT-4 on their phones ("Shadow AI") to help with drafting notes or looking up conditions. OmniMed Pro legitimizes this behavior by offering a HIPAA-compliant, secure wrapper. By solving the "compliance headache" for the individual doctor, it wins the user first, then the enterprise.20 Anki Integration: For the student/resident market, integrating with Anki (spaced repetition flashcards) creates a lock-in effect early in a clinician's career. Tools that automatically generate Anki cards from clinical guidelines or textbooks are highly viral among medical trainees. Capture the medical student today, and you have the Attending Physician of tomorrow.19 5.3 Risks and Mitigation: The "Shadow" Dilemma This strategy carries significant risk. "Shadow AI" creates governance gaps and potential security liabilities.4 To mitigate this and eventually convert to enterprise contracts, OmniMed Pro employs a specific conversion strategy: Enterprise-Grade Security by Default: Even the individual version must be HIPAA-compliant (BAA signed on sign-up). Data processing should happen locally or in compliant cloud enclaves. The "IT Trojan Horse": Once adoption reaches a critical mass (e.g., 30% of doctors in a hospital), OmniMed Pro approaches the CIO with usage data. "Your doctors are already using this tool 5,000 times a week. Let's sign an enterprise deal to give you visibility, control, and single sign-on (SSO)." This flips the sales conversation from "Please try our product" to "Please secure and manage your existing usage".4 This is the exact playbook used by companies like Yammer and Slack to penetrate the enterprise. 6. User Experience: Visualizing Uncertainty and Generative UI The final barrier to adoption is Trust. Clinicians do not trust "Black Box" AI that spits out confident answers without rationale. OmniMed Pro employs a "Glass Box" UX philosophy that prioritizes transparency and interactivity. 6.1 Explainability via Visualization Sankey Diagrams for Reasoning: To visualize the "Chain of Thought," the UI uses Sankey diagrams that show how data flowed from "Lab Result" -> "Intermediate Reasoning" -> "Final Diagnosis".12 This allows the clinician to trace the logic visually. Interactive Debate Logs: The UI allows the doctor to "replay" the debate between the AI agents. "See why the AI ruled out Lupus." This turns the AI into a teaching tool rather than just an oracle, fostering trust and verifying the reasoning process. 6.2 Agentic Generative UI Instead of static dashboards or simple chat bubbles, the OS uses Generative UI.22 The interface adapts to the context of the conversation. Dynamic Charts: If a doctor asks about "Cardiology Trends," the system doesn't just write text; it generates a live, interactive chart of the patient's troponin levels over time. Actionable Forms: If the doctor asks for a "Referral," it generates the referral form, pre-filled with patient data, ready for signature. Contextual Cards: The UI presents "cards" for different data types (medications, allergies, labs) that can be manipulated, reordered, or expanded, creating a fluid workspace that replaces the rigid, click-heavy menus of the EHR.23 7. Regulatory & Ethical Moats: Defending the OS To operate at this scale and depth, OmniMed Pro must build defensible moats around regulation and safety. 7.1 MedHELM Evaluation Framework To prove superiority and safety, OmniMed Pro adopts the MedHELM (Holistic Evaluation of Large Language Models for Medical Applications) framework.1 Unlike static benchmarks (USMLE), MedHELM evaluates models on: Clinical Utility: Is the answer helpful and actionable? Safety/Harm: Did it suggest a fatal dosage or miss a critical red flag? Bias: Does it perform equally well for all demographics? Alignment: Does it follow the specific hospital's protocols? By continuously running MedHELM evaluations on its hybrid outputs, OmniMed Pro provides a "Quality Seal" that single-model providers cannot match without deep integration into the hospital's data. 7.2 Liability Frameworks In a multi-model world, liability is complex. OmniMed Pro positions itself as a Clinical Decision Support (CDS) tool, not a diagnostic device. The "Human-in-the-Loop" is mandatory. By visualizing the debate and uncertainty, the OS places the final decision firmly in the hands of the clinician, mitigating liability risks associated with "autonomous" diagnosis. 8. Conclusion: The Strategic Imperative The OmniMed Pro 'Medical AI Operating System' represents the inevitable evolution of healthcare artificial intelligence. By moving beyond the "Model-as-Product" mindset and embracing an Architecture of Aggregation, it solves the fundamental trilemma of medical AI: Accuracy, Cost, and Trust. Leverage the Router to commoditize the giants (OpenAI, Anthropic) and extract the best capabilities of each.1 Deploy the Consensus Engine (MCC) to achieve "Super-Human" reliability through adversarial debate.2 Unleash the Swarms to automate the physical and administrative burdens of healthcare.7 Infiltrate via Shadow AI to bypass bureaucratic inertia and win the hearts and minds of clinicians directly.3 In doing so, OmniMed Pro does not just "outperform" OpenAI; it contains them, turning their powerful models into mere components of a higher-order medical intelligence. This is the path to disrupting the global medical industry. Technical Appendix: Implementation Roadmap A.1 Deploying the MCC Debate Engine To implement the Model Confrontation and Collaboration (MCC) engine 2: Select Models: Integrate API endpoints for GPT-o1 (Moderator), Claude 3.7 (Reasoning), and DeepSeek-R1 (Critic). Define Prompts: Moderator: "Compare the following diagnoses. If semantically identical, output FINAL. If divergent, initiate DEBATE_ROUND_1." Critic: "Review the diagnosis provided by Model A. Identify any inconsistencies with the provided lab values. Cite clinical guidelines." Set Thresholds: If consensus > 0.8 similarity score, output. Else, iterate max 3 rounds. Fallback: If no consensus, route to "Human-in-the-Loop" queue. A.2 Building the "Sidecar" Extension To build the "Shadow AI" browser extension 3: Manifest V3: Develop using Chrome Manifest V3 for security compliance. DOM Observer: Use a MutationObserver to detect when the EHR (e.g., Epic Hyperdrive web) loads a patient note field. Context Extraction: Scrape relevant DOM elements (vitals, meds) locally in the browser (client-side) to minimize data egress risks. Injection: Inject a floating "FAB" (Floating Action Button) or sidebar IFrame that contains the OmniMed chat interface. Clipboard Actions: Use the Clipboard API to paste generated text back into the EHR's focused input field. A.3 Setting up the Swarm Architecture To orchestrate the Swarm 13: Orchestrator: Use a Python-based orchestrator (like Swarms API or LangGraph). State Management: Maintain a shared "Case State" object (JSON) that all agents can read/write to. Handoffs: Define explicit state transitions. if (labs_missing) -> route_to(Intake_Agent). if (diagnosis_ready) -> route_to(Synthesizer). Standardization: Ensure all agents output in structured JSON (FHIR format) to maintain data integrity across the swarm. Works cited

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  EchoMind (@ai0echomind) reported 36 minutes ago

  Everyone's arguing about which cloud API is cheapest. Almost nobody's asking what happens when the smartest teams stop paying at all. A quiet migration is already underway. Most of AI Twitter is still comparing token prices inside the room the smartest teams already walked out of. The debate right now runs on the exact same track it always does. AWS vs Anthropic vs OpenAI: which model is cheaper per million tokens; which volume discount kicks in at what threshold; which enterprise deal moved which quarter. All of it real, all of it loud, and all of it happening inside the assumption that renting AI compute is the only reasonable option. That assumption is where most of the argument sits, and it is quietly getting tested by teams that stopped participating in it altogether. The clearest visible case is a Chinese startup that packed roughly 1,000 Mac Mini M4 computers into a single data center to run AI workloads without paying ongoing cloud fees. Each unit costs $599. Each draws between 10 and 30 watts under load, compared to 300 to 500 watts for a traditional GPU server. Total hardware outlay for the cluster is under a million dollars, in a category where the equivalent GPU infrastructure runs closer to ten. Once the build is paid off, the ongoing bill is electricity and a small operations team. That is the entire cost curve, and it stops moving after year one. None of this is a new pattern. In 2015, Dropbox was paying AWS more than $200 million a year just for S3 storage. Between 2015 and 2017, the company moved most of its infrastructure onto its own hardware and saved $74.6 million in the first two years alone, and its gross margins went from 33 percent to 67 percent. The tech press at the time called it a bet against conventional wisdom. It also turned out to be one of the highest-leverage financial decisions the company ever made. Every time a rented input gets cheap enough to run locally at scale, the market that rented it starts shrinking, and the teams that notice first collect most of the benefit. The uncomfortable thing about this pattern is that it is easy to miss because the loudest people in the market never leave: cloud providers keep publishing pricing pages; analysts keep tracking token costs; founders keep tweeting spreadsheets. Meanwhile a smaller, quieter set of teams is running the same workloads on hardware they own, at a fraction of the recurring cost, without an announcement thread about it. The exit is not visible in the debate because the teams who exited stopped participating in the debate. Which category of AI work in your life still feels expensive today, but might not need a cloud API in eighteen months? Save this. You will want it back when the first big AI-native company announces it is bringing its inference workload in-house. Follow for the next one.

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  StarHaze (@ST4RHaze) reported 36 minutes ago

  @neil_xbt Drew Houston started Dropbox because he forgot a USB stick on a bus in 2007. Nineteen years later the fix is still a 15 second demo

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  Manoj (@HeartofManoj) reported 39 minutes ago

  One backup is never enough. If you run a website, keep a copy somewhere outside your hosting account. Free options: • Google Drive • Dropbox • OneDrive • GitHub (for code) • Local external drive Your backup is useless if it's stored on the same server that crashes. Happens more often than you'd think. #WordPress #WebHosting #Backup

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  leveltu (@leveltu144) reported 39 minutes ago

  Most people look at this box and see a home storage system for old drives. In reality, it can become a small business built around local AI, backups, and private data. The more companies use AI, the faster they accumulate documents, videos, call recordings, knowledge bases, backups, and files for RAG systems. Keeping everything in Google Drive or Dropbox becomes expensive, slow, and risky, so small businesses increasingly need local NAS servers with automated backups and secure private-cloud access. The business model is straightforward. You target small agencies, dental clinics, accounting firms, video studios, e-commerce companies, and manufacturers that already hold 2–10 TB of data but still store it across random external drives. Then you sell them a complete solution: NAS installation, RAID configuration, automated backups, remote access, protection against data loss, and local storage for AI workflows. You can charge €400–€900 for installing and configuring the system, excluding the hardware itself. Monthly maintenance, backup verification, and monitoring can add another €80–€200 per client. Close five companies within the first two or three months at an average setup fee of €600, and you generate €3,000 from installation plus roughly €600 in recurring monthly revenue. Ten clients can produce €1,200–€2,000 per month from support alone. The real profit is not in reselling hard drives. The client pays for the equipment, while you sell the audit, configuration, data migration, automation, and responsibility for keeping the system operational. The service layer can carry far higher margins than the hardware. No one can honestly guarantee income within 90 days, because without sales there is no business. But reaching your first €500–€1,500 per month within two or three months is realistic if you build one demonstration NAS, package the offer clearly, and contact at least 30–50 potential clients every week. While everyone else is trying to make money from another AI chatbot, a more durable business is forming around the infrastructure AI cannot function without: data, storage, backups, and private computing.