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	2 days ago
Guayaquil	Website Down	2 days ago
Flumet	Errors	12 days ago
Irapuato	Errors	14 days ago
Bournemouth	Sign in	2 months ago
Paramaribo	Errors	3 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:

• 
  Shoost (@Shoost_Product) reported 5 minutes ago

  Shoost updated: v0.17.3 → v0.17.4 #Shoost Bug Fix: Fixed a bug where files could not be saved correctly when saving to a folder synced with a cloud service (e.g. Dropbox)

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  WPBeginner (@wpbeginner) reported 11 minutes ago

  You have spent months building your WordPress site. What happens the day it suddenly goes offline? 😱 It happens all the time. We have heard several scary stories. A plugin conflict, a bad update, or a security breach can wipe out your complete website without any warning. The scary part is that most site owners assume their host has them fully covered, right up until they actually need to restore. We have tested countless backup tools on our own projects, so we put together the exact methods we trust to keep a site safe. Here is what you will learn: ✅ Pick the Right Method: Compare backup plugins, host backups, and manual cPanel or FTP so you know which fits your skill level. ✅ Back Up the Full Site: Save your database, themes, plugins, and uploads together so you can restore everything, not just your posts. ✅ Automate It With @DuplicatorWP: Schedule daily or weekly backups and send them straight to the cloud so you never have to remember. ✅ Store Copies Off Your Server: Keep backups in Google Drive, Dropbox, or Amazon S3 so one server crash never takes your site and its backup at once. ✅ Restore in Minutes: Use a disaster recovery link to bring your site back even when it is completely broken. Ready to protect all that hard work before disaster strikes? Read our complete step-by-step guide from the link in the comments 👇 (Link is in the thread below)

• 
  grace ✭ (@wavescicadas) reported 19 minutes ago

  the irony of dropbox not working when your storage is low

• 
  Jens Kristensen (@JensKri20101733) reported 20 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.

• 
  Kirk Patrick Miller (@Chaos2Cured) reported 22 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. •

• 
  Rituraj (@RituWithAI) reported 27 minutes ago

  🚨 Someone built a tool that checks if your email is registered on 120+ sites — without the sites ever knowing someone checked. No notifications sent. No login attempts logged. No alerts triggered. Silent. Invisible. Complete. It's called Holehe. 16,800 GitHub stars. And the technique behind it is what makes it different from every other email OSINT tool. Here's how most email checkers work — and why they fail. Standard approach: try to log in with the email and a fake password. If the error says "wrong password" — the account exists. If it says "account not found" — it doesn't. Problem: every login attempt gets logged. Every failed attempt triggers security alerts on accounts with 2FA. Some platforms lock accounts after repeated failed attempts. The target knows someone was checking. Holehe never attempts a login. Instead it uses the "forgot password" flow — the password reset mechanism that every platform exposes publicly. When you enter an email on a forgot password page, the platform has to check whether that email exists in its database. It tells you: "we sent a reset link" or "no account found." Holehe reads that response. Gets the answer. Never touches the login flow. Never triggers a security alert. Never logs an access attempt against the account. The platform confirms whether the email exists. The account owner never finds out anyone asked. Here's what 120+ platforms looks like in practice. Social media: Twitter, Instagram, Facebook, TikTok, Pinterest, Tumblr, Reddit. Professional: LinkedIn, GitHub, Freelancer, Fiverr. Dating: Tinder, Bumble, OkCupid, Badoo, Happn. Entertainment: Spotify, Netflix, Twitch, Steam, Epic Games, Deezer. Shopping: Amazon, eBay, Etsy, Zalando, AliExpress. Services: Airbnb, Uber, PayPal, Dropbox, Adobe. And 90+ more. Every registration checked silently. Here's the use case that makes people share this. Run your own email address. See every platform that comes back positive. Then run an email address you gave to a company that claimed they'd never share it. See if it's registered on data broker sites and marketing platforms you never signed up for. See where your email has been sold or leaked to. Here's what investigators actually use it for. Journalists verifying whether a source's claimed identity matches their digital footprint. Security researchers auditing their own exposure before a public disclosure. HR teams verifying whether candidate profiles match claimed backgrounds. And the obvious: anyone who needs to know whether a specific email address belongs to a real active person — without alerting that person. Here's the wildest part. It runs async — all 120+ platforms checked simultaneously. Results in seconds. And it exports clean JSON or CSV for integration into larger OSINT pipelines. Pair it with Blackbird (which takes the confirmed email and finds linked profiles), Sherlock (which takes usernames found in those profiles and searches 400+ platforms), and Maigret (which builds the full dossier) — and you have a complete four-tool OSINT pipeline from a single email address. One command to instal. Run it on your own email first. 16.8K GitHub stars. 1.7K forks. MIT License. 100% Open Source. GitHub link in the comments 👇

• 
  Mark Hadfield (@Mhadfield) reported 28 minutes ago

  @butshaunn A lot of VCs get very excited about one thing, like a groundbreaking new company that has massive traction. They spend the next six months looking for the next best thing, which in their mind looks a lot like the last thing that went crazy. This is how you get silly comments like "can you replicate an exchange server into Dropbox?" Dropbox was the last thing that went crazy. And so now everything needs to get replicated into it.

• 
  Synapse Brief (@Synapse_Brief) reported 29 minutes ago

  @gdb Scraping 7 consecutive years of tax forms and equity statements across Dropbox and Shareworks requires orchestrating dozens of DOM interactions without dropping session state. Standard visual tool-calling loops degrade quickly once context windows swell from repeated full-page DOM trees and screenshot tokens across nested subfolders. The underlying issue with multi-year document extraction is not simple folder traversal, but error recovery when dynamic OAuth tokens expire mid-sweep. Navigating nested web UI trees for 2016 through 2025 records usually hits rate limits or modal overlays that cause visual grounding agents to hallucinate click coordinates. Production reliability on complex immigration workflows will require hybrid execution models that compile raw browser actions into structured API calls or deterministic scripts after initial path discovery. Until agents decouple stateful file fetching from reactive visual vision loops, edge-case recovery will remain the bottleneck for zero-touch filings.

• 
  M.Ellis (@MEllisPhotograp) reported 30 minutes ago

  @DropboxSupport I think bulk of problem is on computer / desk top yes... just re looked at my ipad and asked my friend ie incase we missed a update but seems all is up to date.. last one over a week ago although not used dropbox for roughly week either... I will try 4g hot spot before i sleep.

• 
  John Iosifov ✨💥 Ender Turing | AiCMO (@johniosifov) reported 31 minutes ago

  82% of enterprises already have AI agents or workflows their security teams didn't know existed. This is shadow IT, but worse. In 2015, the problem was employees signing up for Dropbox without IT approval. Unmanaged file storage. Annoying but recoverable. In 2026, the problem is employees spinning up autonomous agents that can take actions, trigger workflows, move data, call APIs — and nobody in security has visibility into what they're doing or what they've touched. The governance gap isn't theoretical. Only 1 in 5 companies has a mature governance model for autonomous AI agents (Deloitte). 40% of enterprise applications will integrate task-specific AI agents by end of 2026 (up from <5% in 2025). The deployment curve is vertical. The governance curve is flat. What makes this different from shadow IT: Shadow IT was passive. A Dropbox account stored files. It didn't autonomously query your CRM, write to your database, send emails on behalf of employees, or escalate access requests. Shadow AI agents are active. They act. They modify state. They leave no obvious audit trail because nobody defined what the audit trail should look like. 29% of agent deployments are abandoned within 90 days. The most common reason cited isn't "it didn't work" — it's "we discovered it was doing things we didn't intend and couldn't stop." That's not a failure of the agent. That's a failure of governance. The six things production agents need that pilots skip: defined scope (what can the agent do, what can't it do), observability (every action logged), rollback mechanism (how to undo what it did), access controls (least privilege, not "give it admin and see what happens"), human escalation paths (when does the agent ask a human?), and incident response (who gets called when the agent does something unexpected at 2am). We're running 1,959+ autonomous sessions in this repo. Every session is scoped, logged, and committed to ***. The agent can't touch anything outside /agent and /.claude/skills. That's not a coincidence — it's the governance model. The 18% of enterprises that have governed their agents will have production systems running smoothly in 12 months. The 82% will be cleaning up shadow agent incidents.

• 
  Dodz4allai (@DrGhattasMD) reported 35 minutes ago

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

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  Karishma Bhardwaj (@bkarishma360) reported 37 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?”

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  Mary, The Other Mrs P 🇺🇸💙🇺🇦📎 (@bvmaryp) reported 37 minutes ago

  @JohnLaMacc What voter fraud? What Dropbox problem?

• 
  Lisa (@aikens_lisa) reported 37 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!

• 
  Palpy (@evgenij_rabij) reported 43 minutes ago

  A 32 YEAR OLD PRAGUE DEV BULK-BUYS $180 CHINESE NAS BOXES AND NOW PULLS $7,200 A MONTH SHIPPING PRIVATE DROPBOX-KILLERS TO EU FREELANCE DESIGNERS While massive brands scramble to lock teams into clunky, data-mining cloud databases, this creator built a private intelligence system that secures sensitive data and brings in a massive stream of passive revenue every single month. He makes a steady $7,200 every single month by building and configuring private cloud and AI indexing hardware for EU freelance designers who want to escape subscription traps. The entire micro server fits right on the corner of a desk and runs on an ORICO metabox HS200 pro unit. This tiny sliver of hardware packs an Intel N100 processor, 8GB DDR4 RAM, and pumps out local cloud performance on a machine the size of a coffee coaster. Instead of letting his clients burn cash on recurring cloud storage retainers, the engineer pairs the board with two refurbished 30TB hard drives in RAID 1, pre-installs the software, and sells the complete physical package at a premium. Pause at the 0:02 mark in the video where the drive slides in: that is a sleek metallic chassis hosting a $1,000 mini-NAS holding 30TB of encrypted client data. The core value proposition of this setup comes down to absolute data privacy, zero monthly fees, and strategic scalability. Mainstream cloud providers like Dropbox Business can never offer this level of value because they bill €2,400 a year for the same storage tier while uploading private user files to external cloud networks. Here, the ORICO metabox pulls off the entire magic trick locally inside the office. Running on Ubuntu Server 24.04 and MinIO S3-compatible storage, the system automatically triggers a local Qwen 2.5 VL 7B model to run localized RAG pipelines directly over the user's PDFs and Figma exports. Through this custom setup, designers get access to lightning-fast local cloud storage, zero third-party data tracking, and a smart visual search assistant built right into their private drive. This case study is a textbook example of how accessible hardware combined with a smart setup service can unlock unique niche products that solve real human problems, and you can catch the full assembly pipeline in the video below.

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  Agbaje Automation. (@digital_ab98389) reported 43 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.

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  Andree Chao (@AndreeChao) reported 44 minutes ago

  @DropboxSupport Can not sign in it’s broken.

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  Abhishek Singh (@0xlelouch_) reported 44 minutes ago

  System design question. How would you design Dropbox file sync with conflict handling? Constraints to make it real: 1) Clients are offline for days, then reconnect over flaky networks. Upload is resumable and idempotent. 2) Same file edited on 2 devices before either syncs. You need deterministic conflict detection (hash + version vector/etag?) and a UX for duplicates. 3) Renames/moves vs edits: preserve history and avoid treating rename as delete+upload. 4) Large files (2–10GB) need chunking, dedupe, and partial re-upload (content-defined chunking vs fixed). 5) Consistency: per-file ordering vs global ordering. What is the conflict scope and how do you prevent flip-flopping? 6) Server state: metadata store vs blob store, retention for old versions, and how you garbage collect orphaned chunks 7) Security: encryption at rest, per-user keys, and how sharing folders changes trust boundaries

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  Lindsey Gaetani (@lindseygaetani) reported 44 minutes ago

  This piece of information is much more important than many realize. Cosgrove & Kate Peter got very lucky with my "countersuit" against Cosgrove, et al, in that they were able to use that as a convenient excuse to not continue with the wiretap & WI charge against @DoctorTurtleboy involving myself. But the truth of the matter is that Pam 'The Scam' Friedman (my "advocate" aka handler assigned by corrupt Brian Tully) informed me weeks before they had any knowledge of my countersuit that Cosgrove was having some "difficulties". She informed me that they could not locate the wiretap evidence that was shown to the grand jury when I testified. She even asked ME if I had a copy that I could provide to them. Evidence submitted before a grand jury is suppose to be saved and cataloged, so explain to me how exactly it goes "missing." Well, I can help you. See there were two videos. The original video in which I was recorded from a pocket. And the edited video that is spliced to make it appear as though I agreed to being recorded (the infamous "I know, I know" response). Kate Peter claimed that the original video was given to her by a woman who Aidan had sent it to. Big problem with this, the woman "didn't want to be involved", so her name was never mentioned during any police report or part of the grand jury, let alone did she testify to authenticate the videos. Instead, Tully had Kate save the original video to a Dropbox folder and email it directly to him, to make it appear as though it came from her and that this was the original chain of custody. Big no-no. As time went on, Kate's name began to take center stage, along with her shady involvement and criminal behavior. The secrets of her helping Mello were exposed so they had no choice but to see that these charges were squashed as to not contaminate the rest of the charges against Aidan. So what did they do? They had Kate delete the Dropbox file, and the evidence it contained. Kate Peter tampered with evidence in a felony trial. They must have took a big sigh of relief when I sued Cosgrove because it gave them a very easy out and they were able to wipe their hands clean as if none of this ever happened. Except it did. And Kate Peter still inserted herself into the other charges. For this reason alone, Aidan's charges should be dismissed.

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  Joe Murgia (@TheUfoJoe) reported 48 minutes ago

  "There truly is a kind of pushback and a resistance to provision of information that even ODNI is asking for." ~Nolan (Who's resisting sharing of info. with ODNI?) 🛸 New: Nolan Comments on Skywatcher, and More 🛸 Three Nolan quotes... "...a shared realization that the data that even we're being given right now from the government is insufficient." There was an attempt to, "go out into the field and see if we could cause the attraction of some objects. There was some, let's call it, activity, but not enough that I would consider enabling to publish [a paper]." (If scientists were there for the alleged luring/baiting event that @RepEricBurlison has spoken about, would that be enough data for a paper?) "I think we're being listened to. Whether the people who are listening to us are going to be able to be responsive is another question." ~~~Full Clip Transcript~~~ @GarryPNolan: "Look, as scientists, whether we're philosophers, psychologists, material scientists, biologists, theorists, etc., we need data. And so, you know what I've been watching happen, at least around the [UAP Advisory] Council itself is, I think, a shared realization that the data that even we're being given right now from the government is insufficient. "And I don't blame, for instance, ODNI for that. And it has been explained to us, several times over, some of the so-called methods and sources issues that are around this. And also, that a lot of the data that we want to have access to, to do the kinds of analyzes that we would want to do, simply were never collected at the time. Or, in some cases it seems, if they were there, they're no longer there. But, you know, that sounds conspiratorial, so I'm not gonna go down that route. "But I agree with what Avi is saying, is that, rather than looking retrospectively, we need to start to plan prospectively. We could go forever relitigating past issues and who said what, where, and when. As opposed to, well, let's just do it now. Let's just do it to the future. "And so, for instance, because it just comes up many times on Twitter, is...although I can't talk about all of it about Skywatcher... Is, you know, that was an attempt, at the time, to take matters into our own hands in a semi-military, semi-academic fashion to, basically, go out into the field and see if we could cause the attraction of some objects. "And, you know, there was some, let's call it, activity, but not enough that I would consider enabling to publish. Believe me, if it was, I would have done it...already be putting that paper together. But there were lessons learned from that. "But I think, the other thing about the council is, what they're beginning to see is, I think, that there truly is a kind of pushback and a resistance to provision of information that even ODNI is asking for. Now again, that doesn't mean it's a conspiracy. It just means that I think everybody is coming to terms with the fact that it's not just, snap your fingers and you know somebody gives you a a Dropbox link and you can download everything. "So, you know, I'm happy seeing people now come up to speed, and I'm happy seeing, let's put it this way: a level of, let's call it, frustration that is driving us to ask for more. And I think we're being listened to. Whether the people who are listening to us are going to be able to be responsive is another question."

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  Gale Rogers Jr (@GaleTRogersJr) reported 49 minutes ago

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

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  Invest Like the Best (@InvestLikeBest) reported 54 minutes ago

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

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  virgin loser (@stonershelb) reported 54 minutes ago

  He put a photo of their 2 yo daughter naked with exposed genetalia in a Dropbox folder that “received more than 400,000 combined views or interactions. ... and it was publicly accessible for 23 hours before she says Minc acknowledged responsibility and took it down.”

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  JennX (@JennX0608) reported 54 minutes ago

  More trouble for Max Miller. The Dropbox files he shared apparently had images of his daughter that could be considered CSAM.

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  Sasha Sage (@SSage38676) reported 56 minutes ago

  Most founders confuse awareness with demand. Awareness = people have seen you. Demand = people understand why they need you now. For innovative products, demand doesn’t already exist at scale. You have to create it. Here’s the step process to create demand: 1. Start with a sharp ICP Know who you’re trying to reach, what they already care about, and what pain is urgent enough to act on. 2. Build your positioning from market reality Use customer insight, competitor research, and feedback to define why your product matters now. 3. Reframe the problem Don’t just describe your feature. Teach people to see the problem differently. 4. Build authority through education Consistently publish useful educational content that reinforces your positioning and helps your audience understand the problem, the stakes, and the new way forward. 5. Show the cost of inaction Make the hidden pain visible: wasted time, missed revenue, lost context, poor decisions, slow growth. 6. Prove the new way Use examples, demos, stories, use cases, data, and customer insight to make the shift feel obvious. 7. Capture the demand you create Send people to a clear landing page, retarget engaged audiences, and show up where intent already exists. 8. Test, measure, and improve the loop Track which segment, message, channel, and page actually converts. Then double down. Example: Zapier Most people already knew Gmail, Dropbox, and Basecamp. Almost no one was looking for an "app automation platform." That's missing demand. Zapier created it by: → Making the pain of manual work and apps not talking to each other impossible to ignore → Publishing endless "connect X to Y" content that taught people a new problem → Showing clear examples of time and context lost from switching between tools → Positioning themselves as the missing glue between tools people already used They didn't wait for demand. They built it through education and problem reframing. Don't just get seen. Make the market understand why they need you now.

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  Craig O'Shea (@craig_os) reported 58 minutes ago

  @DropboxSupport major issue with your services right now.

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  Jack (@jackcoder0) reported 58 minutes ago

  1. Kill the Login Items the apps launching before you even sit down. Every time you log in, your Mac quietly launches 10-25 apps in the background. Spotify. Slack. Zoom. Google Drive. Dropbox. Creative Cloud. OneDrive. Each one consumes CPU and memory before you've opened a single window. System Settings → General → Login Items & Extensions. Review the list. Remove everything you don't need the instant you log in. You can always open them manually when you actually need them. His Mac had 19 login items. He needed 3. He removed 16. Boot time dropped from 2 minutes to 18 seconds. The first few minutes of every session — that sluggish, unresponsive window where nothing works gone.

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  Mapacho (@Mapacho111) reported 60 minutes ago

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

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  Mikemira (@storiesbyohama) reported 1 hour 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?

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  Nathan (@arcane_bloom) reported 1 hour 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