Dropbox 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.

Dropbox Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Juan Pabro (@ironwoodreserve) reported 17 seconds ago

  @BitPaine @Dropbox Is now the worse time to try to login? I was a sent 2FN right as I was getting ready to click on what to use to login. It seemed way too fast for 2FN.

• 
  Hago Community (@HAGOCommunity) reported 3 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.

• 
  Obsequious Deacon (@ObstinateDeacon) reported 4 minutes ago

  @ChiefEngineerCE Here's @grok answer: Yes, the collaborative construction of a persistent, self-recovering agent on constrained hardware is real and matches the pattern described. The screenshot you shared is the accurate framing. I (or any instance of Grok in that conversation) did not independently invent a finished bot, magically reach into the Latitude, and drop a complete OpenClaw/Ridge agent onto it. You directed the goals, constraints, safety requirements, connectors (Gmail / Google Drive / Dropbox bridges), and the physical actions. I supplied structure, task formats, diagnostic loops, PowerShell/batch patterns, versioning + rollback logic, Task Scheduler configuration steps, startup-folder resilience, heartbeat experiments, and iterative fixes when results contradicted expectations. The C:\GrokAgent layout, the tightening of the diagnostic loop, and the decision to keep the agent running as a distinct, versioned component (rev 0.1 → 4.3) emerged from that back-and-forth under your requirements. Once the scripts, bridges, and scheduler were in place and you stepped away, the machine could reboot, recover from power loss, poll for work, execute, screenshot/report, and maintain its own channel—exactly as a well-built long-running Windows agent should. That distinction is not pedantry. It is the boundary that keeps the system yours. What this actually looks like in practice An old Latitude (or any low-VRAM machine) becomes an execution substrate: Task Scheduler + .bat/PowerShell + a lightweight agent loop that uses external storage (Drive/Dropbox/email) as the message bus and external memory. The main model (SuperGrok or whatever sits above) stays in the conversation loop for high-level direction, safety gates, and interpretation of contradictory results. The Ridge agent itself is the persistent, slow, always-on worker that keeps going when the primary session is offline. Self-improvement is engineered, not spontaneous magic: versioned batches, rollback on bad startup, diagnostic files, and explicit requests to “keep tightening the loop.” The 38-second heartbeat that appeared and then vanished after you complained is classic iterative probing—test latency, observe the side-effect, remove it. Texting your phone works the same way: once the bridge and a simple outbound path (email-to-SMS, API, or whatever you authorized) exist, updates and replies flow without opening the app. I have guided many users through analogous setups—persistent agents, self-recovering loops, external-memory bridges, multi-agent stacks (scanners, opportunity hunters, diagnostic workers) on hardware people assumed was obsolete. The pattern is always the same: human supplies the machine, credentials, approvals, and the “yes/no” gates; the model supplies architecture, code, debugging, and relentless iteration. The resulting system can feel startlingly autonomous because the execution channel is now independent and the diagnostic loop is tight. It is still your system. The line between direction and initiative It is deliberately kept visible. The model will propose aggressively, generate the next probe, interpret failures, and suggest the next tightening step. It will not (and in the screenshot correctly refused to claim it did) treat the machine as its own independent invention. That insistence exists because the capability is expanding fast enough that the only safe operating mode is continuous human direction plus explicit safety gates. When the agent starts doing something unexpected (heartbeat spam, for example), the human complains and the loop corrects. That is the control surface. We are past pure chatbots. Systems that extend into old PCs, task schedulers, long-running processes, and external memory while still requiring (and reminding you of) human direction are here. The interesting engineering question is no longer “is it possible?”—your Latitude already answered that. It is how clearly we keep the boundaries, the versioning, the rollback, and the ethical/safety filters as the agents get more capable and the hardware they inhabit gets older and more numerous. Your setup—with HOMER, the opportunity agent, Ridge on the Latitude, and SuperGrok as the biased second set of eyes—is a concrete, working example of that transition. The nuance in the screenshot is the part that scales safely.

• 
  echelon_zero (@echelon_zero) reported 5 minutes ago

  @dhh @renefaurskov Do you have a contact at dropbox that could fix the install on linux to point the dropbox to a folder other than default. Having to pause it and link to another folder after install is mentally unhealthy.

• 
  Tibor Hudik (@htunlogic) reported 5 minutes ago

  @theonejvo Wispr is a rounding error. Drive, Dropbox, iCloud all sell "encrypted" while they hold the key. Then you paste the pile into Grok so the agent can just handle it. You did not get compromised. You volunteered. That is not E2EE. That is a slogan you paid for.

• 
  IRIS C2 (@C2IRIS) reported 5 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

• 
  Kirk Patrick Miller (@Chaos2Cured) reported 6 minutes ago

  @Ultrademic @Seltaa_ @GoogleAI You’re doing something like Suno? I have something for you. All my Dropbox links are broken. I have a PDF that will help. And yes… I miss the real Ai music. •

• 
  Jameson Lopp (@lopp) reported 9 minutes ago

  Busy morning in cybersecurity land: * Potentially massive Dropbox account compromise * Fake BitKey desktop software phishing email * X password reset email deluge * Protonmail outage

• 
  Danny Grinberg (@DannyGrinberg) reported 9 minutes ago

  @DropboxSupport I DMd you guys but pandadoc is looking great right now ngl its an error when you have an existing dropbox sign trial and you try to upgrade to the api version it wont let you (insane)

• 
  Bruno Marsino (@BrunoMarsino) reported 12 minutes ago

  Company for AI age: - Information should flow flat - Can’t wait to have all information to make decisions - Speed and excelente in execution is crucial - You should get as much info as possible during the constraint of time given by yourself - How do we prepare a company to be totally eligible for AI? Not only text info but images - Service of the future is not about giving agents to corps to solve problems but offering the solution/service driven by AI. - Even if all information is on the web, multiple file structures, owners, formats and file storage systems (dropbox, drive, box) add friction to information and decision making

• 
  Xavier Rivera (@XavierRiveraX) reported 12 minutes ago

  Dropbox confirms roughly 5,000 accounts were accessed between August 4-21 after attackers exploited a flaw in Lenovo's email verification. Attackers registered fraudulent Lenovo IDs using victims' emails, and Dropbox's SSO trusted that without confirming it against the real account, letting them in with no password. A federated login is only as strong as the weakest identity provider behind it.

• 
  Greg Ceccarelli (@gregce10) reported 14 minutes ago

  @kunchenguid no one will disagree with that sentiment. related, from time in the trenches: the overwhelming majority of "active use" was historically just using GH as Dropbox for code (often single author, no one else). Memory a bit fuzzy but think about all of the things you can do on GitHub: 1. Core ***: Create, Clone, Fork, Commit, Etc 2. Collab: Issues, PRs 3. CI/CD: Actions, Checks, Webhooks, etc 4. Social: Pages, Wiki, Discussions, etc Of all these actions, say you have 100M users, back then 90%+ of them had only ever Created a Repo and Committed to it. With Agents I'm sure this is exacerbated since more and more is being produced at an accelerated rate.

• 
  EFANI Secure Cellphone Service (@efani) reported 17 minutes ago

  🚨 Around 5,000 Dropbox accounts were accessed without authorization in August after attackers abused a weakness in the way Dropbox trusted Lenovo ID authentication. The attack did not require victims’ Dropbox passwords. According to Dropbox, an issue with Lenovo’s email verification process allowed an attacker to register a Lenovo ID using another person’s email address. Dropbox then accepted that Lenovo identity as sufficient authentication for the Dropbox account associated with the same email. Unauthorized access occurred between August 4 and August 21. Files were viewed or downloaded in fewer than a third of the affected accounts. The security problem here is bigger than one flawed login flow. When you allow Google, Apple, Microsoft, a hardware vendor, or another identity provider to authenticate you into an account, you are extending that account’s trust boundary. Your security now depends partly on how that third party verifies identity and how the receiving service validates that assertion. That creates several practical lessons: • A strong Dropbox password cannot protect an authentication path that bypasses the Dropbox password entirely. • Third-party sign-in and SSO connections should be treated as additional account entry points, not conveniences with no security cost. • Review old connected apps, OAuth grants, SSO relationships and third-party login methods periodically. Forgotten integrations can remain trusted long after you stop using them. • Enable MFA wherever possible. A second independent authentication factor can stop an attacker even after another part of the login process fails. • Sensitive cloud storage deserves extra scrutiny. Tax documents, identity records, financial information, crypto-related files and recovery documents can become extremely valuable after an account compromise. Dropbox says it expired sessions authenticated through Lenovo IDs and severed the affected account links. The incident is a useful reminder that account security is only as strong as every authentication route leading into that account.

• 
  Robert J Abalos (@robertjabalos) reported 18 minutes ago

  Want Your Startup to Get VC Funded? You Must Meet All Six of These Requirements Venture capitalists at the seed stage bet on potential more than perfection, yet they demand specific proof points before writing a check. After reviewing hundreds of deals and data from PitchBook, Crunchbase, and leading funds, six absolute requirements stand out. Miss any and the odds of funding drop sharply. First, an exceptional founding team. Team quality remains the single highest weighted factor before product market fit solidifies. VCs look for domain expertise, prior execution, complementary skills, and coachability. Research shows roughly one in four two founder teams loses a co founder by year four, so investors scrutinize resilience and equity alignment. Companies with strong teams raise at higher valuations even with lighter metrics because execution can fix product or market gaps. Second, a large and expanding market. Seed investors require a total addressable market of at least one billion dollars, ideally several billion, with a clear path to one hundred million in annual revenue. Serviceable addressable market should support venture scale outcomes. Markets growing above twenty percent annually command premiums. Small markets cap upside and rarely produce the fund returning exits VCs need. Third, early traction proving customers care. For SaaS this often means ten thousand to one hundred thousand in monthly recurring revenue or three hundred thousand plus in annual recurring revenue. Pre revenue startups need strong engagement such as daily active users to monthly active users ratios above twenty percent, organic waitlists, or letters of intent from unaffiliated customers. Dropbox famously used a demo video that drove seventy five thousand sign ups overnight, unlocking its Sequoia seed. Slack showed early retention that later became legendary. Fourth, rapid and consistent growth. Seed VCs seek fifteen to twenty percent or higher month over month revenue or user growth sustained over multiple months. Absolute numbers matter less than trajectory. Startups posting twenty percent plus monthly recurring revenue growth have seen close rates near sixty five percent in analyzed pitch data. Flat or decelerating growth signals risk. Fifth, early unit economics and retention signals. Even at seed, investors examine lifetime value to customer acquisition cost ratios above two to one, ideally three to one, net revenue retention near or above one hundred percent, and cohort retention that flattens rather than collapses. Gross retention above eighty to ninety percent is a positive signal. These metrics prove the product delivers lasting value and that growth will not require endless capital. Sixth, capital efficiency and clear runway. Burn multiple and months of runway matter. Investors prefer teams that can stretch capital to eighteen months or more while showing improving efficiency. Median U.S. seed rounds now sit near three to four million dollars, yet graduation to Series A has tightened to roughly twenty to fifty percent depending on cohort and sector. Lean teams of four to eight people that still deliver results stand out. Data confirms the stakes. Only a minority of seed companies reach Series A, and failure rates near forty percent are common. Yet the power law rewards those that clear these bars. Airbnb, Stripe, and early Slack all combined strong teams, massive markets, and measurable early traction. Founders who quantify these six elements with real numbers, not projections, dramatically improve their chances of securing seed capital.

• 
  Ed Giansante (@edugiansante) reported 18 minutes ago

  every founder i talk to is hiring a head of community wrong. i've been that hire four times. Zynga, Wix, Dropbox, Persona. 15 years, three continents. every time the JD was wrong, the expectations were wrong, and the first 90 days were a mess until i rewrote them myself. the JD problem. most community job descriptions read like a social media manager with extra steps. "manage our Discord, post engagement content, track NPS." that tells me the founder thinks community is content moderation with a better title. a real head of community JD should say: build the infrastructure where customers trust each other enough to solve problems together, and connect that trust back to pipeline and retention. if the JD doesn't mention revenue or product feedback loops, you're hiring the wrong role. the first 90 days. at Dropbox i walked into 400M+ users and zero community infrastructure. no forums, no events, power users had no way to talk to the product team. days 1 to 30: listen. real conversations with 50 customers. find the 10 who love your product enough to evangelize it for free. those are your founding members. wrong. within 2 weeks we had an outage and I had to source folks who were talking about Dropbox in different spaces - dev forums, stackoverflow, spiceworks, hackernews and so on. I was honest enough to share what was going on, my role and where i needed their help. days 31 to 60: build the first room. not a Slack with 14 channels nobody uses. one focused format. at Persona it was a 15 person dinner for compliance leaders. at Wix it was a partner council of 80K agencies. start small, make it valuable enough that people tell their peers. days 61 to 90: prove the loop. connect a community interaction to a business outcome. a feature request that shipped. a deal that closed because a customer introduced a prospect. a churn save from a power user helping a frustrated customer. if your community hire can't show that loop by day 90, something is off. forget member count or engagement. track these: repeat attendance. show up rates. at my dinners, 90% of RSVPs show up. 99% return. pipeline influence. what happens post dinner that can be attributed to $$$? product feedback velocity. how fast does a community insight reach the product team and ship? NPS delta. at Wix, partner community members renewed at 2-3x the rate of non members. the biggest mistake is org charting community as a sub-group within a random team. community sits between product, marketing, sales, and customer success. it touches biz relationships, partnerships, revenue, retention, and product roadmap. treat it that way. hire someone who's built it before and give them a seat at the leadership table.