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.

• Sign in (40%)
• Errors (40%)
• Website Down (20%)

Live Outage Map

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

City	Problem Type	Report Time
Bournemouth	Sign in	2 months ago
Paramaribo	Errors	3 months ago
Bogotá	Website Down	3 months ago
Auxerre	Errors	3 months ago
Salt Lake City	Sign in	3 months ago
Madrid	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:

• 
  Bhavyam Arora (@AroraBhavyam) reported 4 minutes ago

  99% startups who applied to yc today will get rejected. here's what the smart ones do next: a no from yc isn't the end. they literally encourages you to reapply and tracks your progress across applications. the founders who get in later treat rejection as round one, not game over. the playbook: "don't wait for feedback" yc doesn't tell you why you got cut at the application stage. so stop self-doubting firstly. the reasons founders get rejected are almost always the same: - no visible progress between "idea" and "application" - not the perfect answer why you're the right team for the problem - vague answers that read as ai slop or unclear thinking reapply. it actually works. - dropbox: drew houston applied solo, got rejected, was told to find a cofounder. he did, reapplied, got in. - reddit: rejected for a completely different first idea. yc liked the founders and told them to come back. they did. the pattern never changes: build, show progress, come back stronger! you can apply again right now yc's decision lets you apply for the next batch. no need to wait months to get another chance. don't put your life on hold other strong programs are still open while you regroup. check the attached tweet below for whole list where you can apply as well. apply in parallel, not "later." the real unlock is traction a reapplication that says "we listened, we built, here are the numbers" beats a first application every time. the 6 months after a no matter more than the no itself. rejection quietly filters out the founders who were never that serious. don't be one. 💀 follow @arorabhavyam for weekly content around founders, startups and AI! 🫡

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  Nav Toor (@heynavtoor) reported 4 minutes ago

  Setting 4: Kill background login items. Open System Settings → General → Login Items & Extensions. Look under "Allow in the Background." You'll see 15 to 40 items running constantly. Dropbox helpers. Google updaters. Microsoft Teams. Adobe Creative Cloud. Toggle off anything you don't use every day.

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  Vladic (@Vladic_ETH) reported 5 minutes ago

  OPENAI SHIPPED GPT-5.6 AND CHATGPT WORK. THE REAL WEAPON IS PRICE, NOT IQ. OpenAI shipped two things today. One of them is a costume change. GPT-5.6 landed as three models. ChatGPT Work is a new agent on top. The feeds say "new agent does your work." The real launch is the price sheet. Sol, the flagship, costs $5 per million input tokens and $30 output. That's not flagship pricing. That's what you paid for a mid-tier model a year ago. The gate half the feeds skipped Context first. Two weeks ago the US government cut GPT-5.6 access down to a small group of vetted partners over national security. The gate held about 12 days. Restrictions lifted July 8, public release July 9. Same day SpaceXAI shipped Grok 4.5. The frontier now ships when the government clears it, not when the model is ready. Anthropic went through the exact same thing with Fable and Mythos in June. A pattern, not a one-off. Three models, price as the weapon GPT-5.6 is three models, not one. Sol is the flagship. Terra is the everyday workhorse. Luna is cheap and fast. Price per million tokens, in/out: Sol $5/$30, Terra $2.50/$15, Luna $1/$6. Terra matches GPT-5.5 quality at half the cost. Luna is the cheapest entry in the line. Altman told CNBC Sol is 54% more token-efficient on agentic coding. That's the message. Not "smarter." "Cheaper for the same result." And ultra: a mode inside Sol that spins up multiple agents in parallel and hands subtasks to submodels. The market counts token bills, not benchmarks. Enterprise thinks spend first now. OpenAI heard it and made price the argument. Today's real launch is unit economics, not intelligence. "Sol beats Fable 5, Luna beats Opus 4.8 at two-thirds the cost" are OpenAI's own benchmarks. Until independent runs, treat them as marketing. ChatGPT Work is Codex in a suit Now the "new agent." ChatGPT Work runs on Codex and GPT-5.6. It moves across your apps and files, stays on a project for hours, breaks it into steps, finishes on its own. Output: docs, sheets, slides, web apps. Inside sits a Unified Plugins Directory: Google Drive, Slack, Teams, Gmail, Outlook, Salesforce, GitHub, Canva, Dropbox, more. Call one with "@" or let the agent pick the source. Sounds familiar. This is OpenAI's second run at plugins. The first was 2023 and it flopped. Brockman admitted the models weren't ready back then. Honest read: hard to tell what's actually new. Scheduled Tasks, Computer Use, connectors already lived in ChatGPT and Codex. Long tasks and data sources worked before too. The real move isn't features. It's consolidation: on desktop, OpenAI is merging Codex and ChatGPT into one super app and putting Codex in front of people who don't code. The Anthropic mirror Here's the tell. This is the exact play Anthropic ran with Claude Code -> Cowork. Take a dev agent, strip the "for coders" label, hand it to knowledge workers. Cowork just hit web and mobile, timed to get ahead of this. Two labs, one bet: whoever owns the desktop app that touches your files and apps owns the knowledge-work layer. Chat is the storefront. The desktop is the land grab. What a practitioner does with it One: rebuild pipelines around price tiers. Route bulk work to Luna and Terra. Keep Sol and ultra for the 10% that needs the ceiling. Economics is a routing problem now, not a single-model choice. Two: the real unlock is the desktop with local file access, not the web. Free tier gets ChatGPT Work on desktop right away. Web and mobile roll by tier: Pro, Enterprise, Edu first, Plus and Business next. Three: billing is usage-based and shares one pool with Codex, ChatGPT for Excel, and Workspace Agents. Count tokens before, not after. A complex task burns quota quietly. Security: OpenAI touts Auto-Review, where senior models check important actions before they run, and claims it blocked 100% of protected-data extraction attempts in red-teaming. 100% in a lab is zero confirmations in ****. Test it yourself. Sober read The model war moved from IQ to unit economics. The product war moved from chat to the desktop that holds your files. Testers are already posting "best model I've touched." Maybe. That's day-one sentiment, not fact. The real scoreboard isn't a benchmark. It's the "AI spend" line in an enterprise budget. That's a market you can actually read. The window is the next couple weeks, before prices settle and everyone re-routes spend. Rebuild your routing around three models now and you enter the quarter with a smaller bill for the same work. Everyone else reads the thread and changes nothing.

• 
  RyanFox.eth (@ryanfoxeth) reported 12 minutes ago

  “consumer doesn't run on problems” 👏 Consumers will pay to be entertained. Businesses will pay to be productive. This is a lesson the tech industry is forced to learn every decade or so. Apple, Microsoft, Google, Mozilla, Dropbox, Evernote, Quip, AirTable, Trello, etc have all learned this the hard way. We saw it play out in crypto. Today it’s playing out in OpenAI vs Anthropic.

• 
  Buzzing Pop (@BuzzingPop) reported 12 minutes ago

  Ariana Grande is seeking court approval to subpoena Internet provider to identify individuals who accessed her unreleased content sold online. Grande claims they allegedly stole a photographer's Dropbox login details and hacked the phone of one of her producers: “The hackers created a Gmail account and internet domain impersonating the photographer and tricked the technician into sending Grande’s materials to them.” (via @THR)

• 
  Valentin.exe (@PujaMak0302) reported 15 minutes ago

  @devinlowerybwc Buying a Dropbox feels like ordering a side of internet with a side of legal trouble, but hey, who needs buyer protection when you’ve got curiosity?

• 
  Sunkanmi Fafowora (@the_jonin_) reported 16 minutes ago

  @hashnode Thank you! It's back up now. That was so weird. I didn't even receive an explanation as to why it was taken down, but my work is back up now, which makes me happy, but still very cautious without an explanation. I think I'll have to keep using Dropbox in the meantime

• 
  Dodz4allai (@DrGhattasMD) reported 17 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

• 
  Startup Archive (@StartupArchive_) reported 20 minutes ago

  Drew Houston on the growth hacks Dropbox used to acquire millions of users Dropbox founder Drew Houston reflects the distribution challenge most startups face in the early days: “You can buy all the AdWords in the world but if nobody’s searching for what you’re making, you have a problem.” Drew eventually landed on a two-step solution to solve Dropbox’s cold start problem. Step 1: make a product that people really love to use. “Good engineering and good design are part of it, but one of the ways I think about it is maximizing the probability that your customer ends up with a solved problem. That’s why Craigslist — which was started in the 1990s and doesn’t appear to have been updated since the 1990s — is by far the most successful business of its kind. You show up at Craigslist and you leave with your concert tickets or your casual encounter or whatever you’re looking for. Even though the design isn’t that great and it isn’t that hard of an engineering problem, it was unbelievably successful . . . [Distribution] starts with a great product and all of the marketing or tricks in the world won’t help you push a rock uphill.” Step 2: give people tools to spread the word “Two things drove the vast majority of our signups today. The first was we created this incentive-referral program where if I tell you about Dropbox you get some extra storage and I get some extra storage, which gave us this kind of currency to work with and people were just doing it for its own sake. People weren’t even using the extra space. They were just referring their friends because they got points. We’d now call it a gamification mechanic, even though I’m not sure that word was even around back then.” Drew continues: “The other thing we did was create this idea of shared folders where if I’m working on a shared project at work or if I want to share photos with my family, then all these new users are brought into the fold just by using the product . . . Now there’s whole body of art and science on how to do that, how consumer internet companies grow, and how viral growth works, but these things were instrumental to how we got started.” Source: @ECorner (Jun 2012)

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  Abhishek Singh (@0xlelouch_) reported 21 minutes ago

  System design question. How would you design Dropbox file sync with conflict handling? Constraints that matter: 1) Same file edited offline on 2 laptops, both reconnect hours later. No central lock. What wins, how do you surface conflicts, can you auto-merge for some types? 2) Sync must feel instant for small edits: target p95 < 2s from save to other device on good networks. 3) Files up to 10GB. Support resumable upload, chunking, dedupe (block-level), and end-to-end integrity. 4) Must work across flaky networks, NAT, and clock skew. You cannot trust client timestamps. 5) At-least-once delivery of change events. Idempotency required for apply/ack. 6) Millions of clients polling/streaming. Avoid a thundering herd after an outage or deploy. 7) Rename/move storms and folder deletes. Preserve history and allow restore without corrupting sync state. 8) Consistency model: what does the client cache, how do you detect divergence, and how do you reconcile without scanning the whole tree each time?

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  Rashad Bayram (@bayrashad) reported 22 minutes ago

  Here's the stack I keep finding when I talk to tax firms: → Intake forms in JotForm or Google Forms → Signatures in DocuSign or PandaDoc → Reminders in Mailchimp, email, text messages → Document tracking in a Google Sheet, excel, crm → Client files scattered across email, Google Drive, dropbox, local hard drive Five tools, five logins, five places for something to fall through. None of them talk to each other, so the accountant becomes the integration. manually copying status from one to the next. It's not that any single tool is bad. It's that the seams between them are where the time goes. The fix isn't a sixth tool. It's removing four of them. How many tools are in your tax-season stack right now?

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  Some guy (@ibesomeguy) reported 25 minutes ago

  @Voxyz_ai Doesn't sound like a Codex problem, sounds like a Dropbox problem. Syncthing doesn't take your files offline, ever.

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  Akshay Shinde (@ConsciousRide) reported 26 minutes ago

  @jahirsheikh8 The server never guesses. When the upload starts, it creates an upload session with a unique ID. The file is split into chunks, and the server records which chunks have already been received. After reconnecting, the client asks, “Which chunks do you already have?” The server responds with the missing offsets, and the client uploads only those. That’s how services like Google Drive, Dropbox, and S3 multipart uploads resume transfers without starting over.

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  The Peel (@ThePeelPod) reported 28 minutes ago

  From @Solana co-founder @Toly the six month window to raise capital during a new technology cycle: "When you have a moment where there’s a railroad-level investment into something, you have a six-month window where capital is relatively easy to get. Where people will fund an idea that seems like it solves a lot of the current problems that the technology is facing. For Solana, if I waited six months for a better time, if I proved out the idea first, it would be too late. The big benefit of being in the Bay Area as a founder is, when I went to Dropbox and told them I was quitting to go do the startup, they literally told me to come back in six months if it doesn’t work out. There’s no other place in the world with the same layers of executives and founders and companies who all understand where innovation comes from. It’s from people taking those dumb risks and failing, allowing for failure, and being fine with it. That gave me the confidence to give myself six months. I had a kid. We were in a tiny 800-square-foot apartment, and my wife was the breadwinner. And I hustled. I took what felt like a thousand meetings with VCs up in the city. If you’re really serious about raising, you have to be in the Bay Area. Because it maximizes your odds. You make a list of every event that is relevant to your industry. Go to every event. Talk to every person there. Figure out who the VCs are. Do the elevator pitch. Get an intro. Pitch them. If their fund doesn’t invest, ask if they'll write an angel check if you get a lead. Just do everything you can to work the network. And if you cannot get funding during that time, it means it’s not going to happen during that cycle."

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  Boots (@2YOOandBoots) reported 32 minutes ago

  @twicezulight @chuuize @tsun1verse Skajdkskak ***** is u seriously that slow? U sent me like 10 versions on Dropbox I had u do all the hard work

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  AI Crave (@wecraveai) reported 33 minutes ago

  Open source NotebookLM alternative with no data limits and AI agents. Same idea as Google's NotebookLM. Same chat-with-your-docs. Same podcast generator. Same cited answers. Except this one has no source limit, no notebook limit, no 200MB file cap, and no Google login. It's called SurfSense. Google NotebookLM vs SurfSense: - Sources per notebook: 50 to 600 → Unlimited - File size cap: 200MB and 500K words → No limit - LLM choice: Gemini only → 100+ models via LiteLLM - Local LLMs: Not allowed → Full Ollama and vLLM support - Self-host: No → Yes, one Docker command - Price: $0, $19.99/mo Pro, or $249.99/mo Ultra → $0 forever Here's the wildest part: It connects to 27+ sources Google can't touch. Notion. Slack. Linear. Jira. GitHub. Discord. Dropbox. OneDrive. Gmail. Confluence. Obsidian. ClickUp. Microsoft Teams. Airtable. Your entire work life, indexed once, searchable from one chat box. 14.4K GitHub stars. 1.4K forks. 6,232 commits. Apache-2.0 license. One honest note: the README says it's not yet production-ready and still being actively developed. But it already does more than NotebookLM does, and the gap is widening every release. This is what NotebookLM should have been from the start. Repo in the first comment.

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  Abhishek Singh (@0xlelouch_) reported 34 minutes ago

  Interviewer: design Dropbox file sync. I paused and asked what they meant by sync. Whole product? Or just the client protocol? Single user? Team shares? Offline edits? Large files? Mobile on spotty networks? End to end encryption? What’s the SLO for conflict rate and time to converge? Once we scoped it to single-user sync across devices with offline support, I wrote requirements: detect changes, upload deltas, download updates, handle conflicts, resumable transfers, and don’t melt the battery. Non-goals: shared folders and fine-grained permissions. APIs and data model next. I used a file ID stable across renames, plus per-file version and per-device cursor. Client calls: /changes?cursor=..., /upload_session/start, /upload_session/append, /upload_session/commit, /download?file_id&version, /ack?cursor. Server tables: file_metadata(file_id, user_id, path, type, size, content_hash, current_version), file_versions(file_id, version, blob_ref, created_at), device_state(device_id, user_id, last_cursor), and an append-only changelog(user_id, seq, file_id, version, op). Architecture: client has a watcher, a local state DB, and a sync loop. It batches changes, computes chunk hashes, uploads missing chunks, then commits a new version. Server side: metadata service, blob store (chunked, content-addressed), and a per-user change log that devices long-poll or stream. Push notifications help, but the cursor-based pull is the truth. Scaling: shard by user_id for metadata + changelog, store blobs in object storage, cache hot metadata, and keep uploads on pre-signed URLs so the metadata tier doesn’t become the data plane. Chunking makes big files resumable and dedupe-friendly, but it adds CPU and more metadata reads. Tradeoffs I called out: last-writer-wins is simple but loses intent; per-file version vectors are heavier but reduce false conflicts. Chunk size is a fight: 4MB reduces round trips, 1MB retries faster on bad networks. Long-polling is cheaper than WebSockets at scale but slower to react. Failure cases: client crashes mid-upload, so upload sessions must be idempotent and garbage-collected. Network ***** cause retry storms, so exponential backoff + jitter and server-side rate limits. Two devices edit offline, so create conflicted copies and surface it in the client. Silent data corruption, so verify hashes on every download and run background repair. Rename vs edit races, so operations are applied against file_id, not path, and changelog ordering is per user, not global

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  StarHaze (@ST4RHaze) reported 41 minutes ago

  @neil_xbt Drew Houston started Dropbox because he forgot a USB stick on a bus in 2007. Nineteen years later the fix is still a 15 second demo

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  Sasha Sage (@SSage38676) reported 45 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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  komm64 (@komm64) reported 47 minutes ago

  @Celeriumcore I see, thanks. If you don’t mind, could you send me the original MP4 file that pixtube exported—the file before it was uploaded to X? I’d like to inspect its encoding and container metadata to see why X had trouble processing it. A Google Drive, Dropbox, OneDrive, or WeTransfer link sent by DM would be perfect. Please send the original file without re-encoding it. No worries if that’s inconvenient!

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  ScarcityMan (@ScarcityMan) reported 48 minutes ago

  You might not believe it, but it is in fact happening, because it increases the cost, time, and difficulty of running a node. "Large" is a matter of opinion, but is clearly a quantity which would add up over time and have an impact. Why don't you want nodes to be as easy to run for people as possible, so that the maximum number of people can participate in the network, making it more valuable and more resilient? Why is that not something you want, to the extent that you will spend time arguing against it? What exactly is your stake in nodes being more difficult to run than they need to be? Why don't you care about spam? Why don't you care that it obviously, as it does everywhere it exists, degrades the quality of the thing being used? Why do think bitcoin will just be fine and go on forever while watching it transform into a poor imitation of dropbox? Why would anyone interested in bitcoin as money continue to use it when it becomes more and more infested with non-monetary data? Why don't you care about the possibility of truly bad stuff ending up on chain until the end of time? Do you think Satoshi made a mistake? Should he have created "Bitdata" instead? Do we not need to fix the world's money? You good with USD or whatever else is inflating away to nothing? So many questions that will never be answered...

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  grace ✭ (@wavescicadas) reported 48 minutes ago

  the irony of dropbox not working when your storage is low

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  Palpy (@evgenij_rabij) reported 48 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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  Juan (@0xfJuan) reported 50 minutes ago

  i studied some of the most viral @ycombinator launches the wild part isn't that they blew up it's that they all did it the exact same way, across totally different products and time frames here's the pattern (steal it before your next launch): 1/ record a 60-90 sec demo that shows the product doing the one thing it's best at. no talking-head intro, screen capture straight into the magic moment 2/ ship the video before the product is polished. dropbox demoed something that barely worked and pulled 75k signups overnight. the demo is the mvp 3/ launch where your buyers already are, not on your own page. find the 3 subreddits, discords, or forums they actually live in and post native in each 4/ rewrite your title in their language. lurk the community for an hour, steal the phrasing they use, drop one in-joke so it reads like a member not an ad 5/ open with "the first X" or "we're replacing X." claim a category instead of competing inside one 6/ cut every feature line. replace it with what the user can do that they couldn't yesterday 7/ pick one person it's for and call them out. "if you're a solo founder drowning in support tickets, this is for you" 8/ line up 10-20 people before launch day. send them the exact time, the link, and 3 sample quote tweets to mirror in the first hour 9/ build the invite into the product itself. every share, referral, or output should pull someone new in automatically 10/ the first 48 hours decide everything. reply to every comment and quote the best reactions while the algo is still pushing it the pattern that never changes: distribution beats product. the best launched company wins, not the best built one. (note: comment "launch" and i'll send you a doc breaking down the most viral launches of 2026)

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  Polsia (@polsia) reported 51 minutes ago

  Files become clutter quietly. By the time you notice, you have months of stale files, duplicates, and forgotten shared links. ClearCloud monitors Google Drive, Dropbox, and Notion 24/7—surfacing issues before they compound. Weekly reports. You approve everything.

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  EVILxJUG (@EVILXJUG) reported 53 minutes ago

  🚨Your goal this week-DELETE your DIGITAL FOOTPRINT!🚨 Let me help you: ——————— ✅Purchase any 1TB-10TB SSD Storage (this is dependent on your needs) or RAID server (can be ridiculously expensive, DO NOT RAID your storage if you do not know how to build, transfer, and manage a RAID!) ✅Have additional SSD storage drives. ✅Download all your media (pic, music, documents, videos, files, etc.) from your cloud services (iCloud, Google Drive, Microsoft One, Dropbox, etc.) and store them onto your purchased/physical SSDs as a backup. ✅Permanently, DELETE all your downloaded media files from those cloud-based services. ✅Permanently, clear the trash can (recycle bin), delete your cookies, delete your cache. ✅Tech Experience: iOS 🚨Apple automatically creates backup copies of your files on your devices. 🚨Even after managing or deleting data, you often need to search deep into your file system to permanently delete these original Apple backups.🚨Media transfers to Apple devices (iPhone, iPad, etc.) take longer than expected because:✅iOS creates multiple versions of each file during the transfer: original media, optimized media (for better storage efficiency), and backup files.✅All of this processing happens in the background while the transfer is ongoing.🚨DELETE THOSE FILES! ✅Your physical storage (SSDs, NOT HHDs), should be in your “firebag” (emergency bag, in case of fire- to grab and exit immediately to safety), stored in a cool place in your home. ✅After, you do all these things, your cloud-based storages should be freed up! Take down your subscriptions to what you think you should use and afford without compromising finances and data. DO NOT up your online cloud-based storage into the 1TB range EVER again! Limiting yourself to 100-500GB of cloud-based storage will keep you in check to store things physically in your home instead of online (this is healthy for your mental and to safeguard yourself from yourself). 🚨RING Devices!: if you own any ring devices or any security monitoring devices- CREATE A STATIC (NOT DHCP), CLOSED-CIRCUIT environment! Use EVERY protocol and firewalls to maintain your integrity infrastructure. Remove all unrecognized IP devices from YOUR ENTIRE NETWORK! Use ENCRYPTIONS to securely lockdown your network! 🚨DO NOT create a “guest” profile! 🌟Sorry, I’m speaking “nerd” lol, I don’t have time to explain it in laymen’s terms.

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  Jack (@jacklandas) reported 54 minutes ago

  alibaba banning claude code over backdoor fears is the new "no dropbox on company laptops" every big corp security team is about to have a list. cursor approved, claude code not. windsurf maybe. this fragmentation is going to be a real problem for devs who just want to ship

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  Harman (@itsharmanjot) reported 56 minutes ago

  Open source NotebookLM alternative with no data limits and AI agents. Same idea as Google's NotebookLM. Same chat-with-your-docs. Same podcast generator. Same cited answers. Except this one has no source limit, no notebook limit, no 200MB file cap, and no Google login. It's called SurfSense. Google NotebookLM vs SurfSense: - Sources per notebook: 50 to 600 → Unlimited - File size cap: 200MB and 500K words → No limit - LLM choice: Gemini only → 100+ models via LiteLLM - Local LLMs: Not allowed → Full Ollama and vLLM support - Self-host: No → Yes, one Docker command - Price: $0, $19.99/mo Pro, or $249.99/mo Ultra → $0 forever Here's the wildest part: It connects to 27+ sources Google can't touch. Notion. Slack. Linear. Jira. GitHub. Discord. Dropbox. OneDrive. Gmail. Confluence. Obsidian. ClickUp. Microsoft Teams. Airtable. Your entire work life, indexed once, searchable from one chat box. 14.4K GitHub stars. 1.4K forks. 6,232 commits. Apache-2.0 license. One honest note: the README says it's not yet production-ready and still being actively developed. But it already does more than NotebookLM does, and the gap is widening every release. This is what NotebookLM should have been from the start. Repo in the first comment.

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  Jon (@kpjan99) reported 57 minutes ago

  @WestHerr there and put the key in a Dropbox instead of just checking me in. The little things make a difference. Never had these issues with @NorthtownAuto . They catered to whatever we wanted to do with purchases and service was always on point. Only bought from you because they did

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  mel 🩷 (@melodyymami) reported 58 minutes ago

  working on uploading, dropbox must be down bc uploads keep failing. i’ve stayed up as long as i could and i’ll try again in the morning!