Dropbox status: access issues and outage reports

Problems in the last 24 hours

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

• Errors (75%)
• Website Down (25%)

Live Outage Map

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

City	Problem Type	Report Time
Nottingham	Errors	29 days ago
Guayaquil	Website Down	29 days ago
Flumet	Errors	1 month ago
Irapuato	Errors	1 month ago
Bournemouth	Sign in	3 months ago
Paramaribo	Errors	4 months 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.

Dropbox Issues Reports

Latest outage, problems and issue reports in social media:

• 
  The Dollar Bin Vulture (@BinVulture) reported 3 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.

• 
  Tibor Hudik (@htunlogic) reported 4 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.

• 
  More Gravy (@lotsmoregravy) reported 5 minutes ago

  @FFT1776 Deputize our military and send them to literally every last damn polling station and dropbox in the United States of America. Every last damn one. Give them the authority to handle **** on the spot. Problem solved.

• 
  Mapacho (@Mapacho111) reported 6 minutes ago

  @MajorianBTC But that’s the problem. Bitcoin in its current state is a decentralized Dropbox. Bitcoin failed and there’s no alternative.

• 
  Obsequious Deacon (@ObstinateDeacon) reported 7 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.

• 
  Agbaje Automation. (@digital_ab98389) reported 7 minutes ago

  Cut manual data entry time by 80% with one n8n workflow: trigger on new CSV in Dropbox, parse, map to Google Sheets, flag errors, alert Slack, log runs. Automate, reduce errors, save hours. DM me for demo.

• 
  Ed Giansante (@edugiansante) reported 7 minutes ago

  community is not a Slack channel. I've been building communities for 15 years across Zynga, Dropbox, Wix, Persona, and my own project Edublin. And the biggest misconception I still hear is: "we launched a Slack, so we have a community." You don't. Slack, Discord, forums, Circle... those are all tools. Community is what happens when people trust each other enough to be honest. I've seen companies spend six figures on community platforms and end up with a ghost town. I've also seen a group chat of 12 people generate more value than a 10,000 person Slack. The difference is the architecture: who's in the room, how they got there, what the norms are, and whether people feel safe enough to say what they actually think. At Dropbox, we had 400 million users. The "community" wasn't a platform, it was the trust between power users who helped each other solve problems the support docs couldn't. They needed to know they were talking to someone who understood their situation. At Wix, I built an 80K partner community. The platform was secondary. What mattered was that web designers felt seen by a company that historically marketed to DIY users. The community was the signal that Wix took professionals seriously. Edublin started as a blog answering questions for Brazilian expats moving to Ireland, with no dedicated platform or app. It became the largest community of its kind because the trust was real. People showed up because they knew they'd get an honest answer. Community is trust. Community is the reason someone comes back. Every time I evaluate whether a community is working, I ask one thing: would these people show up even if the tool disappeared? If yes, you have something real. If not, you have a group chat.

• 
  John B. Holbein (@JohnHolbein1) reported 8 minutes ago

  Replication has become much easier in the era of generative AI. I'm not the first person to say that. However, I've seen fewer people acknowledge a specific aspect of this lowered cost for replicating scientific work: Generative AI will very soon allow us to assess the robustness of individual scholars' full bodies of work. Soon, we will be to compute measures of which scholars do robust science, and which do not. What's wild is that we may be able to almost do that already. Let me show you what I mean. In June, I gave Claude a pretty basic prompt. It read: "I have a big task for you. I want you to start a folder. Call it Acemoglu Replications. Then, go find as many replication archives for Daron Acemoglu as you can. Keep a spreadsheet of the ones you can find and those you can't. Then, start a replication/reproduction effort on those articles. People have in the past criticized the research designs and general robustness of his individual papers. I want to know how strong his body of work is as a whole. Don't come in with any prior beliefs; be dispassionate." I let Claude run overnight while I slept. When I came back in the morning, 29 of Acemoglu's replication archives were fully loaded in my Dropbox. All the code reproducing the paper's results had run. And there was a first draft of a paper assessing the robustness of Acemoglu's full body of empirical work. I'll admit, the first draft of the paper wasn't great. But with 15 short follow up messages--which took me about an hour to write--I was able to prompt engineer a paper-length examination of Acemoglu's work. I've attached the screen shot of the abstract below. I think this reassessment of Acemoglu's work is certainly not done. I'm posting the abstract as a proof of concept, rather than a definitive answer. I'm not posting the full paper yet because I think it still needs more work. Ultimately, I paused this project for three reasons. 1.) Limited time/topical expertise: Most of Acemoglu's work is outside of my area of topical expertise. So, I have limited time to work on it. What this type of a project really needs is someone who has the time and the know-how to dig into each of the replication's individually to make sure they are doing the right things. I think the ideal approach combines the breadth that LLMs afford and the depth of attention/expertise that humans can give. 2.) Questions about the value of the "assess one scholar at a time" enterprise: I totally get that having a database of scholar-level robustness metrics would be very valuable in theory. But what I don't know is whether this approach is truly valuable. Moreover, doing so would come with distinct challenges. a.) Many journals have very restrictive space constraints. A body of work approach would, of necessity, be very long. b.) Collecting replication archives is harder for some types of scholars (those who post them all on their websites) than others (those who don't). c.) We'd have to think hard about questions like: what scholar-specific robustness metrics would be best? And: how would we deal with the fact that prolific authors' robustness metrics would be estimated much more precisely than less prolific scholars? Additionally, I'm just not sure that "taking on" one scholar at a time has enough scientific merit to pursue. If I measured how robust an individual scholars' work is, I'd ideally want to know where that metric stands vis-a-vis the rest of scholars in that field/area. To do that, we'd ideally want the population of these scholars or, at minimum, a random sample. Concretely, if Acemoglu has, say, 78% of published headline results reproducible under some standardized protocol, is that excellent, mediocre, or terrible? To answer that, you need a reference distribution. That makes a random or otherwise well-defined sample of scholars much more attractive than selecting prominent individuals one by one. (I'll acknowledge that I may just be wrong on #2. Arguing against myself, I do agree that human-driven reproduction/replication work rarely assesses full/representative slices of a field. Instead of assessing one scholar at a time, we assess one paper at a time. Field-wide detective work is becoming more common, but my sense is that it's still the exception rather than the rule.) 3.) Cost/benefit considerations and replication norms: we have very weakly formed norms around reproduction/replication generally speaking. We have basically no developed norms around replicating individual authors one at a time. What this means is that the people who would lead a scholar-by-scholar replication effort will, likely, bear a heavy cost and, potentially, reap limited benefits. On the costs side, focusing on scholars' total bodies of work risks making the replicators look petty, vindictive, and antisocial. Enough of the scientific field is hostile towards replications of individual papers. Imagine what will happen if/when a scholar submits a scholar-specific "take down" of a full body of work. My sense is that it's common enough for scholars having their work replicated to be asked to be a reviewer for those manuscripts. I've seen very hostile responses when one paper is at issue. Imagine what type of reviewer Acemoglu would be for a paper that took on his entire body of empirical work! Even if Acemoglu weren't a reviewer, prolific authors tend to have wide coauthor/friend networks. The rally-around-my-friend dynamic we often see would certainly work against this type of paper being published. Even a completely neutral analysis acquires an accusatory character simply because the sampling unit is a named person. And that creates an unfortunate problem of its own: readers may interpret the choice of scholar as evidence that the investigators expected to find something. On the benefits side, replicating individual scholars' total body of work may offer limited payoffs. What journals would accept this type of scholar-specific replication? I'm not sure the top ones would. Conclusion: Generative AI has enormous potential in assessing and, ultimately, enhancing the robustness of scientific research. Instead of asking questions like, “does this famous individual paper replicate?”, we can begin asking questions like: -“What proportion of published empirical findings in [field X] survive a common robustness protocol?” -“How much of the variation in replicability is attributable to papers, authors, journals, methods, or subfields?” -“Are scholars persistently more or less robust across their work?” -“Can we predict which findings will prove fragile?” I may just be wrong on what I think about a one-at-a-time full body examination of scientific research. If I am, please let me know! I am also happy to chat one-on-one with anyone who is curious to learn more about the early-stage Acemoglu-specific replication project.

• 
  JP Invests (@JP_Invests) reported 8 minutes ago

  $DBX - Dropbox added 96,000 paying users this quarter. I said this morning to watch that line after last quarter's roughly 14,000 sequential adds. They did seven times that, a third consecutive quarter of growth, to 18.19M. The stock is down 4%. Everything I said to watch on the growth side came in fine. Revenue $631.5M, above both the $624-627M guide and the $627M street. Non-GAAP EPS $0.75 against $0.74. Non-GAAP operating margin 39.7%, above the full-year range. ARPU $139.68, up from $138.32. What went the wrong way is the part I said would actually move it. Free cash flow fell to $235.2M from $258.5M a year ago, and the margin went from 41.3% to 37.2%. And the buyback decelerated: $330M this quarter against $410M in the same quarter last year, with first-half repurchases down 19%. Unlevered free cash flow rose to $283.5M, and the gap between the two numbers is interest. Cash paid for interest went to $48.3M from $17.9M. The buyback is debt-funded and the debt now costs something. Diluted share count is down 18% year over year to 226.8M, which is the one thing still working mechanically. Two things about the release itself. Guidance isn't in it — Dropbox moved the numbers to supplemental materials on its investor site this quarter, which breaks with how it has reported. And the entire release is quoted by a co-CEO who writes "stepping into this role." The 8-K contains no disclosure of a leadership change. Twenty-nine percent of the float is short. $DBX

• 
  ericleebristol (@iamericbristol) reported 8 minutes ago

  Email scams but they also use a press cloud server that can hold up to millions of emails and send out messages in a bulk but to hundreds of people complicated when it comes to taking down email scams cuz you have to find where they coming from answer in a cloud compressed server that can hold up to millions of emails frequently combine compressed attachments (like ZIP, RAR, 7Z, or TGZ files) with cloud storage or servers to deliver malware, phishing pages, or credential-stealing links while trying to bypass security filters. Common patterns 🚩Malware in compressed attachments: Attackers send emails with ZIP or similar archives containing executables, scripts (VBS/JS), or disguised files. Some use specially crafted or nested ZIPs, password-protected archives (password given in the email body), or less-common formats that email gateways may not fully unpack or scan. Opening/extracting the archive can install remote-access tools, stealers, or ransomware. Cloud storage phishing (“storage is full” or similar alerts): Emails impersonate Google Drive, OneDrive, iCloud, Dropbox, or generic “Cloud Storage” services. They claim storage is full, a payment failed, or files will be deleted, creating urgency. Links often point to real cloud infrastructure (e.g., Google Cloud Storage Azure Blob, or other legitimate buckets) that host redirect pages or fake login/upgrade forms. This makes the links look trustworthy and helps them pass filters🚩.

• 
  Hiten Shah (@hnshah) reported 12 minutes ago

  @varadh @NotionHQ Bunch of markdown. Google Docs handled it well, but some of my markdown files can get gnarly and even docs chokes a bit. I’m also a minimalist with tools and workflows, trying to get away with the least amount of tools which helps a lot with speed and efficiency. I should be a power user of Notion. Since we’re here, I have plenty of folks (like you) that I know who work there now who I’ve met and like. I’m always rooting for you folks, as a result. Here are some unsolicited thoughts from holding it in for too long. I’ve used every document and notes app under the sun and spent a lot of time in Dropbox Paper and Hackpad before it. Early user of writely which became Google Docs. There are core product principles around a product like Notion that Notion breaks or seemingly optimizes for Notion over the user’s experience. At @CrazyEgg, where I haven’t worked full-time for 17 years until about a year ago, the team loves Notion. So I’m forced to use it. My most common activity is to export things out of Notion into my a chatbot or agent of choice. That has little pricks in the process than paper cuts. But all ouchies count against you. For example, in chrome, when trying to print a page (i am weird and print web pages to convert them to pdf), I can’t trust that all the content will come through. And the issues are inconsistent. I understand this is likely an edge case of an edge case, but all ouchies count, prickle or stabbing. If you got this far, thanks for reading my rant. I one shotted it with the agent between my ears.

• 
  Dito (@morpiggg) reported 13 minutes ago

  @1password i miss the old dropbox with list of accounts to login instead of moving my mouse to to the top center of the page. how do i revert?

• 
  Abhi • AMZBoosted.com (@AbhiChauddhari) reported 19 minutes ago

  Everyone downloads Seller Central reports manually. Every. Single. Day. The problem isn’t downloading reports. It’s remembering to do it before you need the data. AMZBoosted schedules report downloads automatically and sends them wherever you want. Google Sheets. Dropbox. Telegram.

• 
  GHOST 🌙 (@ghosstty_) reported 21 minutes ago

  OPUS 5 + HIGGSFIELD CAN TURN A 6-QUESTION FORM INTO A $35K WEBSITE. IN ONE SESSION. FOR $4 IN TOKENS. no mockup. no wireframe. no mood board. six answers from the client. that's the input. a live website is the output. here's the form. here's what each question does. and here's why agencies charge $35K for what this produces in one session. → QUESTION 1: "WHO IS THIS SITE FOR?" not "describe your target audience in 500 words." one sentence. "CFOs at mid-size SaaS companies looking to switch billing providers." this one answer sets the tone, the copy angle, the visual weight, and the CTA hierarchy. an agency runs a two-hour discovery call to get this. I get it in a google form on monday. → QUESTION 2: "WHAT SHOULD A VISITOR DO?" book a demo. buy the product. join the waitlist. one action. this kills scope creep before it starts. no "maybe we should also add a blog and a careers page." one page. one goal. one conversion. → QUESTION 3: "SHARE 3 SITES YOU LIKE AND SAY WHY." not "what's your brand aesthetic?" nobody can answer that. "I like stripe because it's clean. I like linear because of the motion. I like notion because it feels simple." three links. three reasons. that's the design system seed. → QUESTION 4: "WHAT MAKES YOU DIFFERENT FROM COMPETITORS?" one paragraph. sometimes one sentence. this becomes the headline. the subhead. the entire above-the-fold story. a copywriter would interview the founder for an hour. this question does it in 30 seconds. → QUESTION 5: "SEND YOUR LOGO, BRAND COLOURS, AND ANY EXISTING ASSETS." a dropbox link. a google drive folder. sometimes just three hex codes and a png. this grounds the design in reality. no inventing a brand from scratch. no "let's explore some directions." → QUESTION 6: "WHEN DO YOU NEED IT LIVE?" not a timeline negotiation. a date. "next friday." done. that's when it ships. → WHAT HAPPENS NEXT friday night. six answers go into the pipeline. Opus 5 takes the answers and builds. design system. responsive layout. copy. CMS. all from the spec those six answers created. Higgsfield generates the hero media. product shots. clips. matched to the brand. saturday I review. adjust. polish. sunday morning - walkthrough link in the client's inbox. → WHY THIS WORKS because $35K was never the cost of building a website. it was the cost of figuring out what to build. discovery calls. alignment meetings. three directions. two rejected. scope changes. revision rounds. all of that is just a slow, expensive way of answering six questions. I ask the questions upfront. the client answers in 10 minutes. the machine builds from the answers. $4 in tokens. one session. same site. the full system - the form, the pipeline, the stack, and the pricing model - is in the article below. reply "FORM" and follow me - I'll send you the full playbook.

• 
  Mikemira (@storiesbyohama) reported 22 minutes ago

  Just imagine getting accepted into the most exclusive startup club on earth… Then being told you have two weeks to find a complete stranger to as your partner. That’s exactly what happened to Drew Houston in 2007. He had the idea for Dropbox. He had a rough demo. @ycombinator liked it. But @paulg was clear... Single founders rarely make it. You need a co-founder. Right now. Drew’s friends couldn’t join. Time was running out. What would you do? He put out the word. A mutual friend connected him to a quiet MIT student named Arash Ferdowsi. They had never met. They sat down in the student center. Talked for about two hours. About code. About the problem. About the future. At the end of that conversation Arash said yes. He dropped out of MIT the next week with only one semester left. Two weeks later they walked into the YC interview together. They got in. The rest is history: a company that became worth billions. It looked reckless. It felt like a shotgun wedding. Yet it worked because both were all-in from the first conversation. I’ve studied hundreds of startups that never made it past the idea stage. Most founders wait too long for the “perfect” partner. They overthink chemistry. They protect their equity. They miss the window. You can’t wait for certainty. Sometimes the right co-founder is the person willing to jump with you before the proof exists. The speed of that decision can be the difference between staying a solo dreamer and building something real. What would you risk in two weeks if the right person walked in?

• 
  Noise (@NoiseesoiN) reported 30 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).

• 
  Hago Community (@HAGOCommunity) reported 32 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.

• 
  Indragie Karunaratne (@indragie) reported 33 minutes ago

  I used the first Dropbox beta back in 2006 and was sold right away - I’m still a paying customer of the product today, 20 years later. But there hasn’t been much innovation in this space since then and we’re in the midst of a broad shift in how people interact with computers. I’m excited to back this great team and see what a modern take on this problem looks like!

• 
  abheet nigam (@MaginAbheet) reported 34 minutes ago

  Dropbox rejected billions of dollars of acquisition offers only to later realise down the line that they were building a feature not product. Which other companies show a similar pattern today?

• 
  kingofDEpin (@kingofdepin) reported 36 minutes ago

  @DropboxSupport @LIBSCRUSHER we can't login and link creation etc is not working. please fix

• 
  Lowkea (@lowkea713) reported 38 minutes ago

  Dropbox if you could please fix your self I have an uncomfortable amount of music in your app and now I can’t log in

• 
  Robert J Abalos (@robertjabalos) reported 39 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.

• 
  Ivan Kirigin (@ikirigin) reported 44 minutes ago

  @sethbannon I think I’d agree with the sentiment, and not the assessment. We’re not close to too many startups building weapons. The company with “drone” in the name that has the navy and other defense units as obvious potential customers doesn’t make a trend. If I had to put my finger on what has changed, Palantir and Anduril unlocked the latent interest in the space that matches the historically close connection between Silicon Valley and defense. It was just that the Overton window was locked down by people afraid to talk about what defense really means, and in many cases aren’t even Americans with the opinions. I say this as someone who worked at iRobot in defense before my time in YC, Facebook, Dropbox, and Lyft. Each had lots of people comfortable assuming others will think about and solve the ***** problems.

• 
  Cole Trickle (@46_ColeTrickle) reported 44 minutes ago

  @RudeOnion 2 of 2 Mobile games that save progress in the cloud Esports streaming and matchmaking iCloud Photos and iCloud Drive Google Photos and Google Drive OneDrive, Dropbox, Box Automatic phone backups “Find My” / device-location services Amazon Alexa / Echo Google Home / Nest Apple HomeKit Ring, Nest, Arlo cameras and doorbells Smart thermostats, lights, locks, and plugs Connected cars (Tesla app, GM, Ford, Hyundai remote start and maps) Fitness equipment that syncs workouts Smart TVs and streaming sticks ChatGPT, Grok, Gemini, Copilot Voice assistants (Siri, Alexa, Google Assistant) Photo and video filters, auto-captions, and recommendations Spam filters and fraud detection Autocomplete and predictive text Advertising networks that decide which ads you see Recommendation engines (“you might also like”) Content-delivery networks that make videos start instantly DNS (the phone book of the internet) Certificate and login systems that keep accounts secure Backup and disaster-recovery copies of everything above

• 
  Nathan (@arcane_bloom) reported 46 minutes ago

  He was OpenAI's first business hire in 2018. This week, after eight years, he walked out the door. > Brad Lightcap > studies economics and history at Duke, starts as a JP Morgan investment banking analyst > moves into strategic finance at Dropbox, then joins Y Combinator's Continuity Fund > meets Sam Altman through YC, gets pulled into a tiny nonprofit called OpenAI in 2018 as its first business hire > becomes CFO, then rises to COO, helps run the company through the ChatGPT launch and its climb to the most valuable startup on earth > moved off the COO title in April 2026 into a vague "special projects" role > in August, posts on X that he's leaving after eight years to "start something new" > his exit lands one month after product chief Fidji Simo also stepped down > walks away right as OpenAI preps a monster IPO on an $852 billion valuation

• 
  Phillip Shepard (@Phillip_shepard) reported 49 minutes ago

  @RobertJBye One thing I do on a daily basis - I have a skill that is called the “video analyzer skill” and I record a screen record with my iPhone and microphone one - I talk about all the issues I need fixed while showing it in video - send it via Claude mobile app - it runs the skill - transcribes and makes its self a html doc with the video frames that the issues exist in - then fixes the issues - builds a test flight and I update it - very useful… if the video is too big I send it via Dropbox which syncs to my Mac

• 
  Lisa (@aikens_lisa) reported 49 minutes ago

  @TaiyoDevil I printed out fics before I had an e-reader called Dropbox. I was there when Tumblr fell. I had to scrape fan sites and the half-good alternatives to get my fix! AO3 is the best thing to happen to fandom. And you can put pictures on them!

• 
  Y (@ys_tachikake) reported 51 minutes ago

  @DropboxSupport @LIBSCRUSHER Can't login..

• 
  Karishma Bhardwaj (@bkarishma360) reported 52 minutes ago

  @shahzamannn_ Your SaaS idea doesn’t need to be complicated. Stripe moves money. Postman sends API requests. Notion organizes information. Dropbox syncs files. The lesson? Simple problem + huge market + great execution = massive company. Stop asking, “Is my idea too simple?” Start asking, “How many people have this problem?”

• 
  Lalit Dhalia (@lalit_dhalia) reported 55 minutes ago

  @GrokInsider U are acting exactly like that hackernews comment who was shitting on Dropbox launch, "u can setup your own ftp server". Same energy guy. If u have to ask this, u are NOT the audience bro. Come on.