Dropbox Outage Map

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Dropbox Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Abhishek Singh (@0xlelouch_) reported 6 minutes ago

  Asked: design Dropbox-style file sync. 1) Clarify requirements - Devices: desktop + mobile, multiple per user - Semantics: eventual consistency, conflict handling, offline edits, rename/move - Scale targets: #files/user, max file size, p95 sync latency, bandwidth caps - Security: per-user auth, sharing model, at-rest + in-transit encryption 2) Core APIs + data model - UploadChunk(sessionId, part#, bytes), CommitUpload(sessionId, fileHash, path, mtime) - ListChanges(cursor) -> {ops}, Ack(cursor) - Download(path, version) with range support Tables: - File(id, ownerId, logicalPath, currentVersion, deleted) - Version(fileId, versionId, contentHash, size, createdAt) - Block(contentHash, refCount, location) - DeviceCursor(deviceId, lastSeq) - OpLog(seq, userId, type, path, fromPath, versionId) 3) Architecture - Client watcher computes hashes, does chunked upload to object store (S3/GCS) - Metadata service is the source of truth for paths, versions, ACLs - Change log per user (or per share) drives fanout to devices - Long-poll/WebSocket to push invalidations; client pulls deltas via cursor - Dedup by contentHash; store blocks, assemble manifests per version 4) Scaling - Partition metadata by userId; keep OpLog append-only with monotonically increasing seq - Cache hot metadata (folder listings, latest versions) in Redis - Use CDN for downloads; throttle uploads per device; resumable sessions - Background GC for unreferenced blocks using refCount + tombstones 5) Tradeoffs interviewers look for - Push vs pull: push invalidation, pull data is simpler and cheaper than pushing bytes - Strong vs eventual: strong per-file commit, eventual across devices is fine - Rename as metadata op; avoid copying data, but watch for path conflicts - Dedup saves storage, costs CPU and can leak info unless scoped per user/tenant 6) Failure cases - Offline edit + concurrent edit: create conflicted copy or keep both versions with merge UI - Out-of-order ops: apply by seq, idempotent commits, retry-safe APIs - Partial upload: orphaned chunks; TTL cleanup; commit is the only visibility point - Device clock skew: never trust mtime for ordering; server seq is ordering - Network *****: exponential backoff, cursor-based replay, checksums on download to detect corruption

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  AiMind (@AIMind_Ai) reported 8 minutes ago

  A $50 box saves $240 a year on subscriptions and earns $18,000 a year setting it up for clients. No new hardware. Nothing bought at retail. A used office tower. A second-hand drive. One stick of RAM pulled from a dead laptop. The whole build came to 50 dollars. It looks like nothing. Fans humming, one cable to the wall, sitting on a desk next to a coffee cup. The first win is the one nobody talks about. Cloud storage, photo backups, the $20 a month AI plan everyone pays and forgets — all of it moves onto one box you paid for once. 240 dollars a year, gone. The second win is where the money is. Once it runs on your desk, it runs on anyone's. And clients don't pay for parts. They pay to stop bleeding subscriptions. Here's what you actually sell: A private AI trained on their own files, so staff stop pasting company data into a browser. That's $1,500 a setup. A self-hosted file server that kills their Dropbox and Google Workspace bill. $600, plus the relief of never renting storage again. A local automation box that runs invoices, replies, and reports overnight. $900, and it never sends a dollar to a cloud. Then the quiet one: $150 a month to keep it patched and alive. 10 clients on retainer is $1,500 every month before you build a single new one. One setup a week at $1,500 is $18,000 a year. Off hardware other people throw in the bin. I had no degree, no server room, no $2,000 build. Just dead parts and one free weekend. The expensive part of AI was never the compute. It was the monthly bill you agreed to and stopped reading. 50 dollars in. $18,000 out. Same box.

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  keeks is ready to be the worst man in amercia (@ultranormanmoon) reported 9 minutes ago

  @JonahAmericana NOT BOTH BUT IM SURE YOU CAN PIRATE THE FIRST ONE. I have the Dropbox link but I think they deleted it or something bc I have to login to find it and idk if that’s normal or not 😭

• 
  IRIS C2 (@C2IRIS) reported 12 minutes ago

  Do you remember those cloud storage services that would be like 1/10th the price of Google or Dropbox, but the catch was that you couldn’t pull your data down that often? So their arb was basically on the bandwidth cost savings I found that none of them ever worked well Raw video files would always come back corrupted

• 
  Hago Community (@HAGOCommunity) reported 14 minutes ago

  AI Internal Search Agent: An Intelligent Agent for Searching Company Information Many companies struggle with information being scattered across multiple systems and files. Policies may be stored in Google Drive, documents in SharePoint, conversations in Slack or Microsoft Teams, customer data in a CRM, while internal procedures may be stored in Notion or Confluence. When an employee needs specific information, they may have to search in several places, ask a colleague, contact a manager, or open multiple files before finding the correct answer. This is where an AI Internal Search Agent can help. This agent is an AI-powered system that can search across different company data sources, understand an employee’s question, and provide a direct answer based on the internal information available to that employee. How Does the Agent Work? The agent can be connected to the systems and platforms used by the company, such as: Google Drive SharePoint Notion Confluence Slack Microsoft Teams CRM systems Internal databases PDF files Internal documents Company policies Standard operating procedures Employees can then ask questions in natural language instead of manually searching through multiple systems. For example: “What is the company’s travel expense reimbursement policy?” Or: “Where can I find the latest version of this customer’s contract?” Or: “What are the steps for adding a new customer to the system?” Or: “Who is responsible for this account, and what was the latest update?” The agent searches the sources the employee is authorized to access and provides the most relevant answer. The Problem It Solves The main problem is usually not that the company lacks information. The problem is that employees do not always know where that information is located. An employee may spend time: Searching through multiple folders. Opening several documents. Reading old conversations. Asking coworkers where information is stored. Trying to identify the latest version of a document. Searching across different business systems. This creates unnecessary delays and wastes employee time. Instead, the employee can simply ask the AI agent and receive an answer within seconds. A Practical Example Imagine an employee wants to know the process for purchasing new software for their department. In a traditional workflow, the employee may search through emails, ask their manager, and browse company folders until they find the correct policy. With the AI agent, the employee could simply ask: “What is the process for purchasing software that costs more than $5,000?” The agent could search the company’s internal policies and respond: “Purchases above $5,000 require approval from the department manager first. The request must then be submitted to Procurement and approved by the Finance department.” The agent can also provide a link or reference to the original policy document used to generate the answer. Searching Customer Information The agent can also be used to search customer-related data. For example, a sales employee could ask: “What was the latest agreement with customer ABC?” The agent could search the CRM, internal notes, documents, and customer-related conversations before providing a summary. For example: “The latest meeting with the customer was on August 12. The customer is interested in the Enterprise plan and requested a revised proposal before the end of the month.” This allows the employee to understand the current status of the account without manually searching through a long history of notes. Searching HR Policies Employees can also use the agent to get answers about internal HR policies. For example: “How many annual vacation days do employees receive?” “What is the remote work policy?” “How do I request time off?” “What is the process for business travel?” Instead of sending these questions repeatedly to the HR department, employees can receive answers directly from the AI agent based on official company policies. Supporting New Employees One of the most useful applications of an AI Internal Search Agent is employee onboarding. New employees often have many questions, such as: “How do I request a laptop?” “How do I access the internal system?” “Where are the team files located?” “Who approves expenses?” “How do I submit an IT support request?” The AI agent can act as an internal assistant throughout the onboarding process and provide immediate answers to these questions. Respecting Employee Access Permissions One of the most important features of the agent is permission management. Not every employee should have access to every piece of company information. For example, some documents may contain sensitive information related to payroll, contracts, human resources, finance, or executive management. The agent should therefore respect each employee’s existing access permissions. If an employee does not have permission to access a specific document, the AI agent should not use that document when generating an answer. This allows the company to provide intelligent internal search while maintaining appropriate data access controls. Showing the Source of the Answer The agent should not only provide an answer. It should also show the source of the information whenever possible. For example: “According to the company travel policy updated on May 3…” The employee can then open the original document and verify the information. This helps reduce the risk of employees relying on outdated or incorrect information. Detecting Outdated or Conflicting Information The agent can also be designed to identify conflicting information. For example, it may find two different documents containing different instructions about the same company policy. Instead of selecting one version randomly, the agent could alert the employee or administrator: “There are two documents containing different instructions regarding the remote work policy. The most recent document was updated in June.” This can also help companies improve the quality of their internal knowledge management. Moving From Search to Action The system can be developed to do more than simply search and answer questions. For example, an employee may ask: “How do I add a new customer?” The agent can first explain the required steps. The employee can then say: “Start the process.” The agent could create a checklist, create a new record in the CRM, send a request for the required documents, and notify the employee about the remaining steps. At this point, the system moves from being an AI Search Agent to becoming an AI Operations Agent. Example Inside a Sales Team A sales representative could ask: “What are the most important things I should know about this customer before the meeting?” The agent could search the CRM, previous notes, proposals, and communications before creating a summary that includes: Company size. Products the customer is interested in. Date of the latest meeting. Previous objections. Estimated deal value. Recommended next steps. This allows the sales representative to prepare for the meeting without spending significant time searching for information. Example Inside Customer Support A customer support employee could ask: “How was this problem solved in the past?” The agent could search previous support tickets and the company knowledge base to find similar cases and show the solutions that were previously used. This can reduce ticket resolution time and help new support employees solve customer problems more efficiently. Data Sources the Agent Can Connect To The agent can potentially connect to many different systems, including: Google Drive Microsoft SharePoint Slack Microsoft Teams Notion Confluence Salesforce HubSpot Dropbox OneDrive ERP Systems CRM Systems Internal Databases PDF Documents Excel Files Company Policies Employee Handbooks Customer Records The more organized and up-to-date the company’s information is, the more useful and reliable the agent becomes. Benefits for the Company An AI Internal Search Agent can help a company: Reduce the amount of time employees spend searching for information. Reduce repetitive questions between employees. Make policies and procedures easier to access. Help new employees become productive faster. Improve knowledge sharing across departments. Reduce dependence on individual employees who know where everything is stored. Make customer and project information easier to access. Reduce the time required to find the correct documents. Improve the speed of decision-making. How Can the Company Measure the Agent’s Value? The company can measure the value of the system by calculating how much time employees previously spent searching for information. For example, if 200 employees each spend 20 minutes per day searching for files or asking coworkers for information, that represents more than 66 hours of employee time every day across the company. If the agent can significantly reduce that time, the financial value of the system becomes easier to measure. The company can also track: Average time required to find information. Number of questions answered by the agent. Number of manual searches avoided. Reduction in repetitive questions sent to HR and IT. Customer support resolution time. Time required to prepare employees for meetings. What the Final Agent Workflow Could Look Like The employee asks a question ↓ The agent understands the request ↓ It identifies the relevant data sources ↓ It checks the employee’s access permissions ↓ It searches the company’s internal data ↓ It selects the most relevant information ↓ It provides a concise answer ↓ It shows the source of the information ↓ If authorized, it can also perform the requested action In this way, an AI Internal Search Agent becomes a private intelligent search engine for the company, allowing employees to access internal knowledge quickly instead of wasting time searching across files, systems, and conversations.

• 
  Porro (@MagickPorro) reported 19 minutes ago

  @unknownhomer @hasen_95dx You see how we have gone from comparing omarchy to dropbox to saying it is just a cool rice? I have no problem with people chosing omarchy because it looks cool, i just don't want people to treat it as if it is an actually innovating software in any meaningful way

• 
  Christopher Bailey (@LexBaileyAI) reported 20 minutes ago

  @TetraspaceWest At Fitbit we hired a half dozen people from Jawbone who brought everything cleverly hidden in Dropbox. They got prosecuted. Decade later, companies kaput but Fable found it, processed it and now it’s intermixed in various *** repos and Huggingface. Is nVidia now in trouble?

• 
  Founder Tribune (@Founder_Tribune) reported 22 minutes ago

  Drew Houston, founder of Dropbox, on the day the scoreboard gets switched off: For your entire life, the water has come out of one hose. Then, as he put it to MIT's graduating class: "Today, one valve shuts off and now your job is to go out and find a new hose." His hose was Dropbox. Yes, building the company was "the most exciting and interesting and fulfilling experience of my life." But he immediately flagged the half nobody hears: "What you probably don't know, and what I haven't really talked about, this has also been the most painful and humiliating and frustrating experience, too." Not hard. Humiliating. He said he could look back over the years and not even count the number of things that had gone wrong. Then: it doesn't matter. Nobody has a 4.0 in real life. Once you're done with school, Houston said, the whole idea of a GPA just goes away. Bill Gates's first company made software for traffic lights. Steve Jobs's first company made plastic whistles that let you make free phone calls. Neither was successful, and in Houston's words, "it's hard to imagine these guys were too worried about it." Here's why that lands harder than the usual fail-fast sermon: A GPA is an average, every error permanent, weighted, dragged forward forever. It rewards never being wrong. What comes after is a maximum. The misses are discarded. Only the peak is scored. Most people struggle after graduation because they keep playing an average game inside a maximum game. "From now on, failure doesn't matter. You only have to be right once."

• 
  Matt Mazur (@mhmazur) reported 24 minutes ago

  Day 2 of Claude autonomously shipping to my SaaS, including, for the first time, all night while I slept: The first day I had the hourly routine that kicked off this process end at 8pm so that if anything went awry, it could @ me in Slack and I'd quickly see the notification and dig in. The first day went smoothly, so I let it continue working overnight last night: every hour it would look for a small, safe change to make, ship it to ****, and monitor server logs and Sentry to make sure everything went well. A few other process improvements: - It now creates a PR for every change and links to it from its Slack summaries - Previously I only allowed it to make changes in 3 files max, but sometimes it identified the same issue spread across multiple locations, so it would have to spread that work over several hours; I bumped the limit to 8 files. - If I have uncommitted changes in main, it no longer blocks Claude's work; it moves them to a separate branch - Added a mandatory security review before pushing to ****. For these simple changes it's not that necessary, but it will be important for larger projects in the future. Specifically, I told it to run the default /security-review skill and if it flagged anything, to halt everything and wait for me to review. - Ran into a slight issue one hour where it ran that skill, the security review passed, and then it did nothing. I asked Claude to investigate, and it discovered it had run the skill in its main context window, which confused it into thinking its only job was the security review. It changed the process so the security review happens in a subagent, keeping the context window clean, which fixed things. - I asked it to maintain a ledger of things it needs me to do and to ping me every 24 hours if I haven't knocked them out. More and more, the agent is giving me things to do. - I told it to adopt the tone of TARS from Interstellar in its Slack updates going forward, cause why not. Here's a list of improvements it made on day 2: 1. Return 404s for bad case-study URLs 2. Extended the 404 fix site-wide 3. Removed stray code leaking into HTML 4. Fixed broken citation example in docs 5. Fixed wrong URL in sharing docs 6. Corrected false free-plan claim 7. Removed duplicate HTML attributes 8. Fixed dead links in embed docs 9. Fixed garbled copy on two pages 10. Pointed "paid plans" link at pricing 11. Added missing alt text to logo 12. Corrected a misleading code comment 13. Upgraded insecure links to HTTPS 14. Replaced dead testimonial link 15. Fixed broken example in Dropbox docs 16. Fixed awkward grammar on comparison page 17. Fixed reversed table of contents 18. Fixed missing Show More button 19. Matched nav label to its section 20. Corrected outdated visibility docs claim 21. Removed obsolete step from setup docs These can be categorized as: support-doc accuracy fixes (7), functional bug fixes (4), broken or insecure links (3), copy improvements (3), invalid markup (2), accessibility (1), and code hygiene (1). Excited to expand the scope of things I allow it to work on, but am going to wait until next week to ensure the current process is robust.

• 
  The Dollar Bin Vulture (@BinVulture) reported 25 minutes ago

  @HalloweenYrRnd This is fake, unhinged take on a very real problem. No one "deserves" a movie, that doesn't even make sense. But, with modern day digital distribution there is no meaningful cost to actually releasing a project. They can tweet out a DropBox link and call it a day.

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  Jaclyn Forero | UGC & Paid Social Strategist (@jaclynforero) reported 32 minutes ago

  “We need more UGC.” Do you? Or do you currently have 46 videos of attractive women standing in beige kitchens holding your product and saying: “I’m literally obsessed.” Because those are two very different problems. More creators ≠ more creative strategy. You can hire 10 creators, get 30 videos back, and still end up with a very expensive Dropbox folder full of… basically the same ad wearing different earrings. The part that actually matters happens before anyone presses record: Customer research. Different angles worth testing. Hooks that aren’t all “POV: you finally found…” Scripts that provide structure without making a normal human sound like they’re reading the terms and conditions. Casting creators for the concept instead of just asking, “Does her house look expensive?” Enough B-roll that the editor doesn’t have to perform a small miracle in Premiere Pro. And then — this part is apparently controversial — looking at the performance data and using it to decide what to make next. Recently, I led creative strategy for a top medical-grade-skincare brand's paid social campaign across research, concepts, scripting, creator direction, and post-production. Some of the winning creative generated approximately 2.3x ROAS during testing. My biggest takeaway: UGC works a lot better when you stop treating creators like content vending machines and start treating the entire thing like a creative testing system. Anyway, if your current UGC strategy is “hire more people and hope one of them accidentally makes a winner,” I have some thoughts.

• 
  21Rates (@21RatesHQ) reported 34 minutes ago

  Bitcoin security isn't optional anymore. It's survival. The last few weeks: → Dropbox got hacked → Byte Federal (US Bitcoin ATM operator) had attackers target data on 58,000 customers, names, addresses, SSNs → A Trezor supplier leaked customer address data → The LA City Attorney's Office lost 7.7 TB of data, including police records → Coldcard found a flaw in its seed generation that could let attackers steal funds This isn't a string of bad luck. It's the new normal. KYC makes full privacy impossible in most places. But you still control part of your attack surface. Simple moves that actually help: • Use email aliases, a unique address per service • Same with phone numbers where you can • Treat every unexpected email, call, or text as hostile until proven otherwise Attackers combine data from multiple leaks to build your profile. The more your identifiers overlap across services, the easier that is. You don't need to be a victim first to start taking this seriously. What's one step you're taking this week to lock things down?

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  CHItrader (@CHItrader) reported 34 minutes ago

  DBX LEAKS 5,000 ACCOUNTS ON A SIDE DOOR $DBX just told about 5,000 users their boxes got hit Aug 4-21 through a leftover $LNVGY login. Email was enough. No 2FA on the wrecked accounts. 🔹 Files touched on fewer than a third of them, call it ~1,500 🔹 Dropbox cut the Lenovo link and now wants a real password 🔹 After-hours ate the stock when Bloomberg printed it Cloud storage with a guest list and no bouncer. Cool product.

• 
  Y (@ys_tachikake) reported 35 minutes ago

  @DropboxSupport @LIBSCRUSHER Can't login..

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  Callitlikeitis (@Callittlikeitis) reported 36 minutes ago

  @iAnonPatriot Yeah no.. That’s even more piracy. I will Dropbox if it comes down to this drone diarrhea. Or better yet bypass lameazon all together