Dropbox Outage Map

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Dropbox Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Hugo Bowne-Anderson (@hugobowne) reported 44 seconds ago

  Every day, people working with coding agents generate piles of threads containing decisions, corrections, failed approaches, and repeatable workflows. Then the session closes. The code survives. Most of the knowledge behind it disappears into chat history. @gregce10 is working on that problem. He previously worked at GitHub, Dropbox, and Google, served as CPO at Pluralsight, and is now co-founder and CPO of @specstoryai. This is a video of Greg at work. Sort of. SpecStory saves sessions from Claude Code, Codex, Cursor, Gemini, and other coding agents as Markdown inside the project. Then Lore mines them. Greg demonstrated the full workflow across 516 saved sessions: - Index the sessions locally instead of sending millions of tokens straight to a model - Turn each prompt, response, and next user prompt into an evidence-bearing "beat" - Use the follow-up to detect whether the human accepted, rejected, or corrected the agent's work - Find recurring practices and corroborate them across projects - Present candidate skills as dossiers with citations back to the original sessions - Install nothing without explicit human approval Five hundred and sixteen sessions stop being exhaust and become evidence for how Greg and his team actually build. Lore proposes the reusable practices. Greg decides which ones enter the skill library. Greg showed us all this and more live in our recent episode of *Show Us Your (Agent) Skills*. (video made using seedance 2.0 on @replicate!)

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  Callitlikeitis (@Callittlikeitis) reported 2 minutes ago

  @iAnonPatriot Yeah no.. That’s even more piracy. I will Dropbox if it comes down to this drone diarrhea. Or better yet bypass lameazon all together

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  Ishita Sethi (@Ishita__Sethi) reported 4 minutes ago

  @LoganOpSec that dropbox bit is the part that sticks. recovery without revoking access is just half a fix.

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  Ryan Zygar (@zygar) reported 4 minutes ago

  I am beginning to have issues with Dropbox hanging and my @NousResearch Hermes agent not being able to file @grok build confirmed. This is a new issue for months no issue now my agent filing and following up is hanging up. What’s everyone recommend for data management Mac Mini. This is new. No changes made by me.

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  Sasha Sage (@SSage38676) reported 5 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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  OneToothTeXan (@OneToothTeXan) reported 6 minutes ago

  I'm so sorry I left my zipper down and my sanity got loose. If found: Men, there's a dropbox. Women: please return to original source.

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  Obsequious Deacon (@ObstinateDeacon) reported 6 minutes ago

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

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  glitterandspit (@Glitterandspit) reported 8 minutes ago

  Dropbox down. Computer updating. I have a free day I suppose.

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  Nell AI Labs (@nellaiorgs) reported 13 minutes ago

  Three traits make a startup idea look bad because most founders run from all three, which leaves the idea sitting there for whoever doesn't. 1. Hard to get started Stripe is the textbook case. Thousands of developers hit the exact same broken credit card integration and knew it sucked. Nobody built the fix, because it required a special bank deal and deep infrastructure knowledge nobody wanted to acquire. That friction wasn't a warning sign. It was the moat. 2. Boring Gusto makes payroll software. Nobody's passionate about payroll. That's precisely why it sat unsolved — every "fun" idea gets fought over by ten founders, every boring one gets ignored by all of them. And here's the part people miss: six months into any startup, fun or boring, you're doing the same thing — writing code, fixing bugs, talking to users. The initial excitement of the idea has almost no correlation with how much you'll enjoy running the company. 3. Already has competitors Dropbox was the 20th file storage company at launch. Founders read "20 competitors" as a red flag. It's the opposite and evidence of real demand which nobody's nailed it. Zero competitors usually means zero market, not first-mover advantage. Founders optimize for what looks easy, not what actually works. The gap between those two is exactly where the good ideas live.

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  The Tech Buzz (@tbuzzdaily) reported 14 minutes ago

  $15M raised: Meticulous grew ARR 5x in a year testing code nobody has time to review manually anymore Chemistry led the Series A, joined by Menlo Ventures, Lachy Groom, and GitHub's former CEO Jason Warner. Founders Gabriel and Quentin Spencer-Harper, brothers with backgrounds at Dropbox and Palantir, built the company around a problem AI coding tools created faster than they solved: code is now written faster than humans can review it. The product records real user and developer sessions during development, then uses AI to auto-generate and maintain end-to-end visual regression tests based on actual observed usage, updating automatically as the code changes. Customers include Notion, ElevenLabs, Dropbox, and Wiz, a list that skews toward companies shipping fast enough to need this. Rainforest QA and Autify compete in AI-assisted testing broadly, but most rely on hand-written test cases rather than tests derived from real session data. The tradeoff Meticulous accepts is that session-derived tests can still miss rare edge cases nobody happened to trigger, which is why the company is expanding from frontend into backend and performance testing next. A 5x ARR jump in a year says enough teams have hit the same wall: AI writes code faster than any QA team can keep up with by hand.

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  Dodz4allai (@DrGhattasMD) reported 15 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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  Jeremy Goldberg (@jeremy_goldberg) reported 15 minutes ago

  @omooretweets @midjourney and AI agent adoption isn't a unique short term issue to 'solve' - the UX of products for consumers has always been *everything*. solve that in just one niche and you can build a whole company off that - dropbox, roku, tinder…

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  Abhishek Singh (@0xlelouch_) reported 26 minutes ago

  System design question. How would you design Dropbox file sync with conflict handling? Constraints to make it real: 1) Same file edited on 2 devices while offline for 3 days, then both reconnect 2) Large files (2GB). Need chunking, resume, and content-hash dedupe 3) Renames/moves vs edits should not explode into full re-uploads 4) At-least-once change events from clients, out of order, with retries 5) Server must support 10M files per user without scanning the whole tree 6) Conflict UX: when do you auto-merge (text) vs create conflicted copies (binary)? 7) Cross-region sync, eventual consistency, and p95 sync latency < 5s for small edits [generated using my AI agent, hope this was useful]

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  Greg Ceccarelli (@gregce10) reported 28 minutes ago

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

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  Flandermaxx (@Flandermaxx) reported 31 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 vojtech is 32, prague vinohrady flat above a pivnice, ex-JetBrains backend eng, sources ORICO metabox HS200 pro enclosures from a shenzhen aliexpress-parallel channel at $180 per unit plus 2x WD 30TB HDD refurbs at $410 each ORICO metabox HS200 pro (intel N100 · 8GB DDR4 · 2x 30TB WD in RAID 1 = 30TB usable) · ubuntu server 24.04 · minio S3-compat · qwen 2.5 VL 7B for local RAG over the user's PDFs and figma exports pause at 0:02 on the HDD sliding in, that is a $1,000 mini-NAS holding 30TB of encrypted client data that dropbox business bills €2,400 a year for the same tier 84 EU freelance designers pay him €78 a month each for a shipped pre-configured NAS + qwen RAG endpoint, €6,552 = $7,200 MRR at 71% margin $1,000 BOM per unit, €12 monthly prague power for the burn-in bench, first 20 units flipped covered his tooling and packaging, no dropbox no google drive business while ORICO still white-labels the metabox before EU distributors mark it up 3x, follow and bookmark