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 (60%)
• Sign in (20%)
• Website Down (20%)

Live Outage Map

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

City	Problem Type	Report Time
Nottingham	Errors	21 days ago
Guayaquil	Website Down	21 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:

• 
  ericleebristol (@iamericbristol) reported 3 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🚩.

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  Danny Grinberg (@DannyGrinberg) reported 8 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)

• 
  Craig O'Shea (@craig_os) reported 8 minutes ago

  @DropboxSupport major issue with your services right now.

• 
  Eric Smith (@Eric_Smith08) reported 8 minutes ago

  The full math. What he replaced and what he saved: 1.Dropbox ($12/month) → Google Drive (15 GB) + OneDrive (5 GB): $144/year saved 2.Zoom ($13/month) → Google Meet (60-min calls, 100 participants): $156/year saved 3.Notion ($10/month) → Google Keep + Google Docs + OneNote: $120/year saved 4.Todoist ($5/month) → Microsoft To Do: $60/year saved 5.Slack ($8/month) → Microsoft Teams free tier: $96/year saved 6.Grammarly ($12/month) → Microsoft Editor + Gemini/Copilot for rewrites: $144/year saved 7.Adobe Acrobat ($15/month) → Google Drive PDF viewer + Edge PDF tools + Word Online conversion: $180/year saved 8.Trello ($10/month) → Microsoft Planner: $120/year saved 9.ChatGPT Plus ($20/month) → Google Gemini + Microsoft Copilot (free tiers): $240/year saved Total monthly cost before: $137 Total monthly cost after: $0 Annual savings: $1,644 Total setup time: one weekend Saturday for Google migrations, Sunday for Microsoft configurations. Accounts created: 0 (he already had both) Apps downloaded: 3 (Microsoft To Do, Teams, OneNote all free) Browser extensions installed: 1 (Microsoft Editor free) Every replacement runs on accounts he created years ago. The tools were sitting behind the same login he uses for Gmail and Outlook unused, unconfigured, and paying rent to 9 other companies that built the same features on top of what Google and Microsoft were already giving away.

• 
  Noise (@NoiseesoiN) reported 10 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).

• 
  Jens Kristensen (@JensKri20101733) reported 10 minutes ago

  Suggestion for @adamhfry, ChatGPT Consumer Product Lead: The new Google Drive integration made me wonder: why not take the same idea one step further and support local Windows files directly? No Google Drive. No OneDrive. Local storage, controlled ChatGPT access. A file should not have to be stored in the cloud at all. Cloud has done enough damage already. Cloud = Hell. There is an important distinction between cloud computing and cloud storage. Cloud computing means that ChatGPT performs the processing on OpenAI’s servers. Cloud storage means that documents are permanently stored with Google, Microsoft, Dropbox, or another cloud provider. The first may be a practical consequence of ChatGPT’s current architecture. The second is not. A much cleaner model would be: Local disk / NAS → temporary, explicitly authorized ChatGPT access → processing → result returned to local disk / NAS. For example, a user could right-click: G:\Project\Analysis.docx and select “Open with ChatGPT”. ChatGPT would then receive controlled access to that file — or perhaps to a user-authorized folder such as: G:\ChatGPT\ The user could specify whether access should be read-only or read/write. Original files could be protected, and output could automatically be written to a designated local \output folder. Then instructions could be as simple as: “Edit only section 17. Preserve all formatting.” “Analyze all documents in G:\ChatGPT\Project X.” “Compare these three PDFs.” “Edit Analysis.docx, but do not modify the original. Save the result in \output.” DOCX, XLSX and PPTX are not fundamentally unsuitable for this. They are largely ZIP containers containing XML files. The harder problem is preserving complex formatting, images, tables, comments, undo/versioning and accurate rendering. A local “ChatGPT File Bridge” for Windows could solve the access problem without requiring users to move their working files into Google Drive or OneDrive. The AI processing itself would not necessarily be local. Files, or the relevant parts of them, could still be transmitted to OpenAI for processing. But storage and file management could remain entirely local: local file → controlled ChatGPT access → processing → result back to local disk / NAS. No Google Drive. No OneDrive. No permanent cloud storage. No manual upload/download cycle. The user retains control over the file structure, filenames, versions, backups, applications and physical storage location. “Google Docs inside ChatGPT” is technically interesting. But “Local Files inside ChatGPT” would be the real game changer for the traditional Windows PC workflow. And OpenAI would not need to invent another file system. Windows already has a perfectly good one.

• 
  AiMind (@AIMind_Ai) reported 11 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.

• 
  Andree Chao (@AndreeChao) reported 12 minutes ago

  @DropboxSupport Can not sign in it’s broken.

• 
  Ouriel (@OurielOhayon) reported 17 minutes ago

  @mntruell You seem to have substantial connector issues with Dropbox and Calendly.

• 
  Paul Klein IV (@pk_iv) reported 17 minutes ago

  Is MCP dead? @grinich (CEO of WorkOS) says it's better than ever and become the strongest intent signal in your funnel. @workos is building the auth, permissions, and registration layer for that world, the same enterprise plumbing it sold to Vercel and Plaid, now sold to AI companies. I sat down with Michael to talk about it in episode 4 of Navigators. His argument: your coding agent already picks your vendors, but signup forms are built to block automated traffic, so the agent stalls at the front door and waits for a human to paste in an API key. We got into: 00:00 "Stripe for enterprise features": what WorkOS actually sells 02:44 How an SSO and SAML company ended up as AI infrastructure 04:13 Why AI companies can't meander up-market the way Slack, Dropbox, and Figma did 06:54 The biggest mistake founders make: staying in the pre-PMF experimentation mindset 09:51 Why nothing works unless the management team is AI pilled first 10:47 "Claude day": pairing engineers with finance, legal, and ops once a month 13:36 auth.md, the missing front door for agents 15:26 Why registration, not tooling, is the next growth channel 16:59 Is MCP dead? The higher-intent signal hiding in MCP connections 19:35 Why SDKs are going away and coding agents write their own 23:09 "The super cycle of all super cycles": AI amplifies labor, it doesn't just disrupt it Thanks for joining me on the pod @grinich! Watch the full episode of Navigators here:

• 
  numaan (@numaan27) reported 18 minutes ago

  they solved this problem in panda by splitting the key space into “ranges.” each range is roughly 100 GB, so when a range grows too large, it can be split and redistributed. (btw panda is an abstraction layer over sharded mysql that dropbox built)

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  160 IQ haver Randy (@MinionTripper) reported 19 minutes ago

  @mittsh why would anyone use dropbox you can just setup an ftp server on a linux machine!

• 
  Eric Taylor (@bcs_erictaylor) reported 20 minutes ago

  Really?? Dropbox really needs a MCP server? CVE-2026-81102 The Dash MCP server bound its listener to the loopback address but never checked the host a request named. src/mcp_server_dash.py constructed the server for its network mode with the interface restricted to loopback and no transport-security settings, so a name that had been pointed at the loopback address still reached the listener while carrying the attacker's host name. A page in a visitor's browser could therefore drive the local server and invoke its company-search and file-detail tools under the Dropbox credential the server holds. Only the network mode was reachable this way; the standard input mode was not. The fix supplies transport-security settings that enable host checking and allow only the loopback name and port, rejecting other hosts before a tool runs. The repository publishes no versions, so the affected boundary is the commit preceding the fix.

• 
  Matt Farley of Motern Media (@MoternMedia) reported 23 minutes ago

  @WorstMikeFrollo The other one is the Vimeo version (also available through PayPal/dropbox on my website now that Vimeo is shutting down on demand).

• 
  Mansi 👩‍💻 (@MansiCodez) reported 23 minutes ago

  Solution of yesterday’s question: Design Google Photos: the part after the boxes “Hash it and put it in S3” fails the interview. Two phones compress the same sunset differently. Same photo. Two hashes. Two rows. You just built a worse Dropbox. The system needs three IDs, not one. client_upload_id — generated on the device before the first byte moves content_hash — hash of the exact bytes you received asset_id — the thing the user sees in the library Uploads are sessions. Library entries are assets. Blobs are renditions. If you collapse those into one key, retries, edits, and shared albums all collide. 1. Retries must be idempotent on the client, not on the filename Phone goes offline mid-flight with 612 shots, 40 already half-uploaded. Each photo gets a client_upload_id the moment it enters the queue. Chunks are uploaded against that ID. Commit is PUT /uploads/{id}/complete. Same ID + same bytes → same session. Server returns the existing asset. Late packet after commit is a no-op. Filename + timestamp is not an ID. Camera roll and AirDrop will mint two. 2. Exact dupes are content-addressed. Near-dupes are reconciled. After commit: look up sha256(bytes) if it already exists for that user (or the shared album’s owner set), attach the new upload to the existing asset_id do not create a second photo The 28 shared “Goa 2026” shots that are almost-but-not-quite the library copies will miss on sha256. That is expected. Run a cheap perceptual hash (pHash / dHash) + capture time + camera model from EXIF. If distance is tiny and captured within a few seconds, mark as near_duplicate_of and do not show two tiles. Keep both blobs if you must; hide one in the UI. Two devices, two compressions, one photo in the grid. 3. The library is a set of assets + tombstones. Not last-write-wins. Delete in Delhi must beat a pending upload in Mumbai. Every mutation carries: asset_id op: upsert | delete | restore actor_id (device or user) logical_ts (per-actor Lamport or hybrid logical clock) A deleted asset gets a tombstone that outlives the pending queue. When the flight-mode phone finally flushes those 40 half-uploads, the server sees: upload commit for an asset that already has a newer delete → commit the blob if you want, do not resurrect the tile. Refresh in Mumbai cannot show a photo Delhi just deleted, because the change feed is “tombstone wins over delayed create,” not “whoever wrote last.” 4. Shared albums are references, not copies Partner adds 28 photos to Goa 2026. The album stores {asset_id, added_by, added_ts} — not a second blob, not a second library row. Adds and removes are a small CRDT: add(asset, actor, ts) remove(asset, actor, ts) Two devices adding the same asset = one membership row. Phone sync finishing a second later cannot wipe the partner’s 28 photos, because there is no “replace the whole album document.” Last-write-wins on the album JSON is how photos vanish. 5. An edit is a new rendition, not a new photo and not an overwrite User crops + filters while the original is still processing. Rules: original blob is immutable edit creates rendition_id with parent_asset_id library still shows one asset “current view” pointer moves to the latest rendition history is a list of renditions / edit ops, not 12 full-resolution copies by default If you overwrite the original, face clustering and search lose their source. If you mint a new asset, the user now has original + edit as two photos. Both are wrong. Storage stays sane because you store: original (once) derived thumbs / display sizes lazily, keyed by asset_id + transform not every intermediate crop as a first-class photo 6. Upload path and ML path must not share a lock “Beach sunset with Priya” in minutes, not overnight, also not on the upload critical path. Commit path only: durable bytes asset row appear in library + album enqueue jobs Workers (thumbs, embeddings, face cluster, labels) are async. Search index is eventually consistent. The UI can show the photo immediately with “processing” on faces. If clustering blocks upload, you built a spinner, not Photos. Face identity hangs off asset_id, so an edit does not orphan Priya. The new rendition inherits the parent’s cluster and gets re-checked, not reset. 7. Sync is a checkpoint + change feed, not “download the library” Each device stores last_applied_ts. Server gives a stream: new assets, new renditions, album membership, tombstones. That is how 62,000 existing photos plus 612 offline shots plus 28 shared adds converge without a full rescan, and why a deleted photo does not climb out of another device’s queue. The one-line design Client-generated upload IDs stop retries from cloning. Content hashes stop exact clones. Perceptual reconcile stops “same sunset, different JPEG.” Tombstones stop resurrection. Album CRDTs stop last-write-wins from deleting the partner’s night. Edits are renditions under one asset. ML is a consumer of commit, never part of it. Boxes for S3, CDN, Kafka, Redis are table stakes. This is the part that decides whether you designed Google Photos or a photo-shaped file dump.

• 
  Vladislav Zharkov 👾 (@_vladislavzh) reported 23 minutes ago

  I have zero industry experience. None. I don't know how to use issue trackers, I need a GUI for version control, I deliver my files through Dropbox. I don't have templates, I restart or reuse every time. I don't use industry standard tools, I don't know workflows and pipelines.

• 
  kfd&p (@kfdpcom) reported 24 minutes ago

  @mellolais___ @LIBSCRUSHER @Dropbox I went on their site and it does say that the .com access is having issues. I guess we just wait it out.

• 
  Zely (@0xZely) reported 29 minutes ago

  The MIT professor who crashed 10 percent of the internet at 22 posts the free course that runs every AWS outage, every Uber ping, and every Slack notification on earth. MIT charges $85,000 a year to sit in that classroom. He posted every lecture to MIT OpenCourseWare for nothing. Millions have opened lecture one. Almost no engineer has finished all twenty. His name is Robert Morris. He is a professor at MIT CSAIL and one of the four cofounders of Y Combinator, the seed fund behind Airbnb, Dropbox, Stripe, Reddit, and Coinbase. In November 1988 he was a 22-year-old Cornell graduate student. He released a small program that was supposed to count the computers on the internet. It replicated so fast it crashed roughly ten percent of every machine online, and made him the first person ever convicted under the Computer Fraud and Abuse Act. He got three years probation, 400 hours of community service, and a $10,050 fine. Ten years later he cofounded the online store Viaweb with Paul Graham and sold it to Yahoo for $49 million. Seven years after that he cofounded Y Combinator with the same partner. Its portfolio is now worth over $600 billion. The clip in this video is one lecture from MIT 6.824 Distributed Systems, filmed at MIT and posted for free. The words on the board behind him are fault tolerance, availability, recoverability. Those three words decide whether Instagram loads when you open it, whether your Uber arrives, and whether your paycheck hits your account on the first of the month. Morris covers the entire logic of distributed systems in twenty lectures. Everything fails, all the time. A single computer fails once every few years. Ten thousand computers fail hundreds of times a day. The only design that survives is one that assumes failure is normal. Every retail user cursing a spinning wheel is looking at the wrong problem. The miracle is that most of the time it does not spin. Availability beats consistency. You cannot always have both. When the network splits, a system either serves stale data or refuses to serve at all. Amazon picks stale. Your bank picks nothing. Every user who screams at the Slack status page wants Amazon's answer. Every user who screams at a double charge wants the bank's. Replicate everything, trust nothing. Data in one place disappears when that place burns. Data in three places survives two fires. Every photo you have ever taken on an iPhone lives on three continents already. iCloud, Google Photos, and Dropbox are built off the exact lecture on the board. Concurrency is where bugs live. One user at a time is easy. A million users at the same second is not. Race conditions, double spends, lost messages, ghost bookings. Every airline that oversold your flight, every trading app that ate your order, every Ticketmaster that showed you a seat already gone, is a concurrency bug Morris warned about. Partial failure is worse than full failure. A dead server is easy. A slow server that answers half the time is a nightmare. It fools every retry, wastes every resource, and confuses every operator. Every "is it down or is it just me" Twitter search you have ever run is Morris's third slide. Every senior engineer at AWS, Google, and Meta has watched this course. Every startup that raised a Series A in cloud infrastructure hired an alumnus of 6.824. Every AI company training a trillion-parameter model on a cluster is running the same lecture in production. "A distributed system is one in which the failure of a computer you didn't even know existed can render your own computer unusable." That is Leslie Lamport, the Turing Award winner Morris quotes at the opening of 6.824. It is the exact sentence that explains why your Slack goes down when a data center in Virginia loses power. The full course is free on MIT OpenCourseWare. The lecture notes are on Morris's website. Every equation on the board fits on one screen of code. Almost every senior engineer at AWS, Google, Cloudflare, and Meta has watched 6.824. Almost no founder promising 99.99 percent uptime on their pitch deck has opened lecture one. That is the entire moat. The course is free. The willingness to sit through twenty lectures on partial failure before uploading your money to a payment app, storing your photos in the cloud, or handing your health records to a portal is a much rarer commodity than the confidence to click without them.

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  StockStorm (@StockStormX) reported 34 minutes ago

  Dropbox $DBX says about 5,000 accounts were compromised in an August hack tied to a Lenovo ID login flaw

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  David Silva Smith (@DavidSilvaSmith) reported 35 minutes ago

  Got dropbox, ickoud, google drive working last night. Looking at @immichapp for photos…. Home server… hosted server…. Hmmmm

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  Hago Community (@HAGOCommunity) reported 36 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.

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  M.Ellis (@MEllisPhotograp) reported 37 minutes ago

  @DropboxSupport Hi so thanks for keeping intouch and checking dm's pleased to report your website at moment is rubbish.... yes angry I pay for something that works not thats broken.. trying to perm delete file.. but 0 the file is still there my membership has only just renewed but 2nd thoughts -

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  Joachim Voth (@joachim_voth) reported 37 minutes ago

  @DropboxSupport why can I only use one core for indexing? Most machines have MANY nowadays. Download speed is not the issue, it is almost always the ultra-slow indexing that dropbox does with only one CPU core active. Am I seeing this right? Why can we not dedicate 5 or 8 cores to something that really slows down your users?

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  Scarlett Bama 🇺🇸🅰️🐘🏈🏖️✝️ (@scarlettbama) reported 43 minutes ago

  @DropboxSupport Fri AM: Dropbox down? Rare if so! Will not allow PDF upload to existing folder. Upload 50x per month. Started after most recent IOS update. Using iPhone. No MacBook access right now. Pls don't sent to Community Forum.

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  echelon_zero (@echelon_zero) reported 44 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.

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  Crazy Freakin Planet (@FreakinPlanet) reported 47 minutes ago

  @ericzakariasson @bot Why do I need to sign in to Dropbox every time I need to do something with it. Create more persistent connectors and please increase limits for premium+ users. It’s not usable after a couple of hours of light use.

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  keeks is ready to be the worst man in amercia (@ultranormanmoon) reported 48 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 😭

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  Alvin (@Alvin1492840) reported 54 minutes ago

  Kill the startup apps that have been draining your battery since day one. She opened System Settings → General → Login Items & Extensions. 14 apps were set to launch automatically every time he turned on his Mac. Spotify. Zoom. Adobe Creative Cloud. Google Drive. Microsoft Teams. OneDrive. Dropbox. A VPN he used once. A screenshot tool he forgot about. A calendar widget. And 4 more he didn't recognize. Every one of them was running in the background 24/7 consuming RAM, CPU cycles, and battery life whether he was using them or not. She said: "You turn on your Mac and within 30 seconds, 14 apps are fighting for resources before you've even opened your first document. Your fan spins up because your CPU is processing a traffic jam of apps you're not using. Your battery dies by 2pm because half your power is going to background processes you don't see." She removed 11 of the 14. Kept only the ones he actually needed at startup. The Mac booted in half the time. The fan stayed quiet. The battery lasted 3 extra hours. She said: "Check this list right now. If you see apps you don't use daily, remove them. They've been silently eating your Mac alive since the day you installed them."

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  EFANI Secure Cellphone Service (@efani) reported 56 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.

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  kingofDEpin (@kingofdepin) reported 1 hour ago

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