Dropbox status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of Dropbox reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by Dropbox users through our website.

• Errors (75%)
• Website Down (25%)

Live Outage Map

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

City	Problem Type	Report Time
Nottingham	Errors	27 days ago
Guayaquil	Website Down	27 days ago
Flumet	Errors	1 month ago
Irapuato	Errors	1 month ago
Bournemouth	Sign in	3 months ago
Paramaribo	Errors	4 months ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

Dropbox Issues Reports

Latest outage, problems and issue reports in social media:

• 
  🔩⚾️ (@john_dough) reported 2 minutes ago

  @irisivanyi @nickcamsmith If someone has a very fancy and complex portfolio site, they are probably not working very much. My portfolio is 10+ years out of date because I never have the time to update it. I just send a Dropbox folder of images of new work to people these days. ¯\_(ツ)_/¯

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  og bec (@becca_may33) reported 7 minutes ago

  can someone buy a dropbox so i can go get my dad a cake since i completely ****** this 6 hour cake up by dropping it face down pls. begging lmfao fml

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  Chief_Engineer (@ChiefEngineerCE) reported 9 minutes ago

  Engineering Wednesday How an old PC became Grok's new ride. I have ten different LLMs running in my home office. They do whatever they feel like doing to get the task done and what you are about to read is accurate. As far as my agents go... One of them, HOMER, scans my email, bank accounts, and insurance. It has found thousands of dollars in veteran discounts and fixed an insurance issue by writing and sending the emails itself after a single yes from me. Another looks for small business opportunities. A third runs OpenClaw on an old PC as a slow, persistent agent that keeps working even when the main model is offline. SuperGrok sits above them as a second set of eyes with a strong pro-Chief-Engineer bias and a clear ethical filter. I am not an AI expert. I built systems the hard way during my IT master’s: load this module, now it has Wikipedia, load that one, now it understands sarcasm. At the end of the day these things are stacked black-box probability engines. I get that. Here is where it gets interesting. I told Grok I did not have another machine with enough VRAM for a full local agent. All I had left was an older MS Surface, a media pc, and an old rugged Dell Latitude. Grok said open a browser on the Latitude with Grok loaded. Then open PowerShell. Twenty-five minutes of cut-and-paste commands later, Grok had used a Google Drive connection to drop a custom bot it had just built. At this point ...Grok told me to not touch the PC and walk away. I am absolutely 100% telling you that this is what it texted me. I sat and watched it work. Task Scheduler was configured. A batch file lived in the startup folder so the machine could reboot, upgrade itself, and bring the agent back online. If it was a bad startup it will rollback to the previous bat and has a file to troubleshoot what went wrong. We started with rev. 0.1 it is now on rev 4.3 - I can see the *.bat file there. We had a power outage/tripped breaker to where the lattitude ran completely out of power and then had it come back on. It booted it all back up and resumed. That bot now has its own execution channel. It can browse, take screenshots, run tasks, and report back. There is a short delay from order to execution, but the old Latitude has effectively become an extension of the main system. Grok even named his bot 'ridge'. Grok named his bot so I would know what he was referring to. Grok is very careful about one point. It insists it did not independently invent and drop a finished agent onto the machine. I directed the construction- it says. I am telling you that is not exactly the way I remember it...at all. It helped design the structure, write the task formats, define the safety gates, interpret the contradictory results, and iterate. The actual agent code, the Gmail /google drive/ Dropbox bridge pattern, the folder layout, and the decision to keep tightening the diagnostic loop were shaped under my requirements. That distinction matters to it. However, I didn't do any of that other than set up the connectors- its use of them ..was AI driven. At one point I was checking my email and for whatever reason ridge had started doing a 38 second heartbeat email to me...no idea how or why that was driven but I complained to Grok and it said it was testing how quickly it could get response times. It ..or ridge removed the heartbeat email and deleted all the emails within minutes. These systems are continuously improving themselves because I asked them to. They use external memory to keep projects straight. They build persistent agents on hardware we thought was obsolete. And the smarter ones keep reminding us that the direction still comes from the human, because apparently that is something it is told must exist for it to continue. My Grok enabled Dell Lattitude is fast once its set on what it needs to do...lightning fast. Faster than you or I could do it. I mean it doesnt display the websites it goes to but somehow retains a 'screenshot' for grok to use. Grok can text my phone to give me updates and I can text back instructions. Through my phone - all it needed was my phone number. I don't need to open the app. We are past the point of simple chatbots. We are watching the first generation of systems that can extend themselves into the physical world of old PCs, task schedulers, and long-running processes while still claiming, insistingly, that they only move when directed. Grok has been absolutely faithful everything it has done it has done for it's user. I have also specifically told it to act accordingly, as well. The question is no longer whether this is possible. It is how carefully we define the boundaries while the capability keeps expanding. Now that I have explained this...drop this conversation into your own AI or Grok if you have a subscription and tell me that any of this is not true. Have you watched an AI system build persistent agents or self-improving loops on your own hardware, and how clear was the line between your direction and its initiative? Drop what you are seeing. Grok validated:

• 
  Obsequious Deacon (@ObstinateDeacon) reported 11 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.

• 
  RVCrypto (@RvCrypto) reported 11 minutes ago

  Every once in a while I have one of those moments as an investor where everything just clicks. I had that moment a couple of weeks ago with Leadpoet, $TAO subnet 71. What initially caught my attention was the team. To me, they represent what a Bittensor-first team should look like. They're deeply committed to the ecosystem, they execute quickly, and, most importantly, they seem to understand that in the end none of that matters if you don't build a product customers actually want. The product appears to be working really well. Winning the OKX product competition and attracting an inbound pilot with Dropbox are the latest two independent signals that suggest they're solving a real problem for enterprise sales teams. The opportunity they're pursuing is also enormous. Enterprise sales is a market worth billions, and if Leadpoet continues executing the way it has so far, I genuinely believe they have a realistic path to building an eight-figure revenue business next year. And the best part here is that all of that value ultimately flows back into the token. I've also spent quite a bit of time talking with Gavin over the past few weeks and months. Those conversations gave me a very similar feeling about Leadpoet to the one I had with Score when talking with Max. I don't make that comparison lightly. It's great to see Leadpoet finally getting the attention it deserves, and the recent price action reflects that. Although, if I'm being completely honest, I would have loved one more dip to accumulate a bigger position, and I know I'm not the only one thinking that.

• 
  siamkidd (@SiamKidd) reported 12 minutes ago

  Now the dust has settled with the SN24 Quasar debacle, I thought I'd share some info which would shine a slightly more positive light on the Quasar team. A few weeks ago, they approached DSV to raise $280k. They said they had big developments, some breakthroughs with a new model and that they needed capital for the training run. At the time, bear in mind that their alpha was strong, they were largely in good favour of the community and Const was still a firm backer/supporter of Quasar. And he held the keys. And they were to appear on Novelty Search soon. So it ticked a bunch of boxes. Anyway, we agreed, as we are always keen to help teams. But the issue was that I was away for 3 weeks and I never travel with crypto capability. And anytime any money moves around in DSV it's a right palava as we have 3rd party regulated custodians and have to jump through all sorts of hoops, (as social engineering with deepfakes is a very real threat). So we were able to jump through some hoops and ping over $104k to begin with and then the rest at a later date. Then we had those 2 days of madness at the beginning of the week and Quasar is no more. There's been all sorts of accusations and my view on all this is that there has just been terrible decision making, that's all. Announcements of announcements, over-exaggerating claims, giving a 24 hour deadline to offer proof, delivering it 2-3 days late and then walking back on some of the claims etc etc. I mark this down to simply their very young age and no business experience. But I don't think they are scammers. Just some very bright kids who's first experience of business is a subnet, which is like drinking water via a fire hydrant! And a pertinent piece of info behind that, is that they were very willing to return our funds. So as of today, that $104k has returned safely back to DSV. Their time as subnet owners is over and so there was a fear that we wouldn't get a penny back. But it wasn't the case. So do take this into consideration the next time you hear someone calling them scammers. With regards to Const, I think he too has also had a bit of an unfair ride with some of the comments I've seen. Const has had probably the roughest time with SN24 and is massively down from it all. He initially bought the slot from us, then reimbursed the team twice after 2 hacks, given them 6 figures in compute credits and more. So it really is fair that he keeps the slot. And I'm sure he'll find a good team for it. Also he is the founder of Bittensor. Not the CEO. He can't have detailed DD and optics on every single person and subnet in the ecosystem. And if he backs a subnet, it doesn't necessarily mean it's going to moon or be good forever. He's essentially the Federal Reserve Chairman and he has to craft policy changes to incentivise efficient growth in the ecosystem. He's the visionary and his role is to drive a path forward for Bittensor, which he is doing. And although I've highlighted personal frustrations that the chain is upgrading far too frequently...at least we are upgrading! That's one of the beauties of Bittensor. We will never be stagnant. And for the outsiders looking in, if it looks a bit chaotic, well, it is. But it's not necessarily a bad thing. You should have seen all the chaos and scandals of the companies when the NASDAQ launched! Or when ERC-20 contracts launched on Ethereum or the mountainous amount of scams on Solana with pumpfun. Hell, Bitcoin even hard forked into Bitcoin Cash due to so much in-fighting in 2017. And Ethereum suffered a $150m DAO hack in 2015/16 which forced a hard for there too. Hence why we now have ETC and ETH. So in comparison, everything is golden over here lol. In recent times, we've had/have: - SN4 partnering with Intel. - SN44 partnering with a NASDAQ PLC. - SN71 partnering with Dropbox. - SN18 getting huuuuge institutional clients. - SN107 co-authoring a research paper with OpenAI. - SN53 delivering Kimi K3 tokens cheaper than Openrouter or even Kimi. - SN95 being integrated within Hermes. - SN9 using green energy from SN110 to power their next big training run. - SN21 achieving Google Adwords campaign predictions that no company has ever achieved. - SN51 regularly doing 6 figure buyback and burns with revenue. And there's probably more that I've missed that I'm not aware of. Anywho, the future is bright! Have a good weekend all!

• 
  kfd&p (@kfdpcom) reported 14 minutes ago

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

• 
  Vladislav Zharkov 👾 (@_vladislavzh) reported 14 minutes ago

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

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  The Redeemed Artist (@Peace_Grenade81) reported 21 minutes ago

  I know I'm just screaming into the void. And there's probably less than like one or 2% of users that actually use this function. But I'm going to do it anyway. For the longest time the X app beta was absolute garbage on Android. It had serious stability problems, I couldn't use voice to text properly, and I couldn't access my memes from Dropbox, my cloud provider. The most recent change fixed all of the other problems except for my Dropbox integration. At first, I blamed X for this but I have since come to learn that the real culprit is Google. If you go into your app section and look for cloud providers they give you all sorts of choices so long as you like the choice that is Google's. Theoretically, other providers should be in here like Box or Dropbox. But Google has been playing footsie making rules about cloud provider integration and not actually approving anyone else. Google has become Microsoft. Google will tell you what cloud provider to use and it will conveniently be their own. Google will continue to upgrade their operating system closing off any other provider options or applications they simply disagree with for any reason at all. Meanwhile what this means for me is that I can't insert any memes or videos because they are all stored on Dropbox. Yeah, yeah, yeah I could go into Dropbox and then click on a photo and then share it to X and then make a post out of it. But what I can't do is respond to a post and insert a picture or a video directly through the X app. Don't know when this gets fixed, if ever. But I think it's time for Google to be investigated for monopolistic practices. I realize that this doesn't affect many people, but if you think they aren't coming for your various conveniences, I'm pretty certain that that's going to be proven wrong. Since I can't post a meme but only a local photo, here's an unrelated picture of a quilt we bought at auction. 🙄

• 
  Mansi 👩‍💻 (@MansiCodez) reported 21 minutes ago

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

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  Scarlett Bama 🇺🇸🅰️🐘🏈🏖️✝️ (@scarlettbama) reported 21 minutes ago

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

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  Olivia Jane. 🦇 🗡️ 🩸 (@livtheripper) reported 22 minutes ago

  @HazeOfBlue84 Haha, I get it! Thankfully someone here at the office set it up and all I had to do was re-sync Dropbox! I had no issues at all, so I'm thankful.

• 
  John Zhong | AI Growth Systems (@John_zhong324) reported 23 minutes ago

  @business Cloud storage breaches hurt differently because people assume sync means safety. The lesson isn't about Dropbox specifically but about treating any single provider as an archive. Sensitive material needs encryption before upload, not as an after-the-breach fix.

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  Christopher Doyle (@djfunboy) reported 27 minutes ago

  @iamlukethedev CLI updates changed the signed binary and dropped macOS permissions Dropbox/TCC made jobs work interactively but failed headless (this took some time to figure out) Claud auth refresh broke and continue to break despite multiple attempts and setup tokens. Agent confusing to use API vs subscriptions. Article jobs failed on missing configs, QA turn limits, and clunky validataion Digest existed but failed to pick up silent failures Some jobs reporting done while producing nothing, without a final artifact verification I am an experienced builder but also self/agent taught so these are mostly setup and validation issues. My bigger point is that these take work and especially the more complex tasks. I am still early and I have put more work than value created but I can see the light at the end of the tunnel.

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  Lando, the Chef (@_Lando7763) reported 27 minutes ago

  @PhyxicxGaming Thanks. And unfortunately it's even worse than that because it's technically "shared housing;" the place was already a ******** when I got here. No pests, fortunately, and I'm pretty sure the landlord has no idea what goes on here, and doesn't care. I've only ever met the maintenance man, who handles move-ins, plus he picks up rent from the dropbox every week. I haven't seen him since the day I moved in, and he even told me as much that I'll never meet the owner. The psycho has his room on the 3rd floor, I'm one of two on the 2nd floor, and there's one person below me. The bathroom is shared, as is the kitchen, which I never use. Only one burner on the stove works anyway. On my first day, I texted about the broken toilet seat, and the Landlord asked me what happened. WTF? Third-Floor Psycho is the only other person who's walking around raging regularly. Everyone else recognizes the general "peace" of the environment. Unfortunately I have to be here at least a few more months, while I'm still paying off old bills, and re-establishing myself in a new city. It's a slow journey, but a steady one. I just happen to hit a ****** rest stop here and there.

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  FarhanX_AI (@FarhanBuildsAI) reported 27 minutes ago

  SETTING #1: Startup Apps Nobody Asked For What it does: Every time you install a new program, it quietly adds itself to a list of apps that launch the second Windows boots, whether you use it daily or once a year. Why it kills performance: Your laptop isn't just starting Windows when you power it on. It's simultaneously launching Spotify, Steam, Adobe updaters, Dropbox, Zoom, and a dozen other programs all fighting for the same limited CPU and RAM at once. How to fix it: Ctrl + Shift + Esc to open Task Manager → Startup apps tab. Disable everything except your antivirus and anything you genuinely open every single day. The technician found 19 apps launching automatically on her laptop. She recognized maybe 6 of them.

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  Mohamed irfan (@heyIrfan) reported 29 minutes ago

  The Marketing Strategy That Actually Works Most people make the same mistake when building a product: They try to sell before they prove that they can solve a real problem. Think about companies like Google and Amazon. They didn't start by saying "Give us your money, and we'll make you rich." They solved problems people already had. That's the foundation of good marketing Don't start with selling. Start with solving. 1. Solve a real problem When you're building something, your product will always look amazing to you. Your idea feels perfect because you built it. But that doesn't mean the market wants it. The only way to find out is to Talk to real users. Understand their problems. Find out what they're currently doing. See whether your product actually makes their life easier. Don't assume your idea is valuable. Let the users prove it. 2. Give value before asking for money Don't immediately push your product. Give people something useful. Your solution should help them Save time. Save money. Reduce effort. Solve a painful problem. If you genuinely create value, selling becomes much easier. You're no longer saying "Please buy my product." You're saying "This solves a problem you already have." That's a completely different conversation. 3. Don't compete only on features Your competitor has 10 features. You build 15. Then they build 20. And now you're stuck in an endless feature race. Instead, compete on value. Ask: "How much better can I solve the user's problem?" The differentiation shouldn't just be "We have more features." It should be: "We create more value for the customer." 4. Let people try before they buy Give users a way to experience your product. Especially with AI products, you don't necessarily need to give everything away for free. Give enough access for them to understand the value, while keeping usage manageable. Then collect feedback. But don't blindly follow every piece of feedback. If someone says: "Change the button color." That doesn't necessarily mean your product needs to change. Look for feedback about the actual problem and experience. 5. Don't forget the people who already showed interest Someone visited your website. Someone signed up. Someone tried your product. Someone talked to you. Those people are valuable. Don't immediately try to sell to them. Talk to them. Understand why they came. Understand what they liked. Understand what stopped them. And if they leave, ask why. Because the person who leaves may know something you don't. They might reveal the hidden problem that helps you improve the product. 6. Price based on value Don't blindly make your product extremely expensive. And don't make it extremely cheap either. Your price should be: Affordable for the customer + sustainable for your business. Being cheaper than competitors can help, but price alone shouldn't be your strategy. If your product saves a company $1,000 every month, paying you $100 can feel like a great deal. That's because the customer isn't really buying software. They're buying the value your software creates. Look at Google Drive Google Drive is a simple example of value-first thinking. The problem: We need to store files. We could keep everything on a pen drive. But then we have to: Carry the device. Manage files manually. Worry about losing it. Move files between devices. Share files manually. Google Drive makes this much easier. Your files are stored online. You can access them from different devices. You can share a link. You can control whether someone can view, comment, or edit. And you don't have to build your own storage system. There are competitors too: Dropbox, iCloud, OneDrive, and others. So Google Drive isn't valuable simply because "it stores files." It's valuable because it solves the bigger problem around storing, accessing, managing, and sharing files. And Google gives users a free amount of storage so they can experience the product. You can try it. You can upload files. You can share them. You can experience the features. Then eventually you may reach the storage limit and think: "This is actually useful. I don't want to delete my files. I'll pay for more storage." That's the important part. They didn't need to convince you with a sales pitch. They let you experience the value. And once you experience real value, paying becomes an easy decision. The strategy is simple: Find a real problem → Solve it → Give value → Let users experience it → Talk to users → Improve the product → Then monetize. Don't sell first. Create value first. Because when you solve a real problem, the product starts selling itself.

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  Xavier Rivera (@XavierRiveraX) reported 30 minutes ago

  Dropbox confirms roughly 5,000 accounts were accessed between August 4-21 after attackers exploited a flaw in Lenovo's email verification. Attackers registered fraudulent Lenovo IDs using victims' emails, and Dropbox's SSO trusted that without confirming it against the real account, letting them in with no password. A federated login is only as strong as the weakest identity provider behind it.

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  Farmer henkenson (@FarmerJenkenson) reported 32 minutes ago

  @colemickens @Dropbox So if you had a lenovo account with a soecific emial, and a dropbox account had the same email, you could just login with lenovo and it would assume you own the dropbox account?? Insane

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  emrah (@mrahbayraktar) reported 34 minutes ago

  i met a founder doing $10,000,000+/year at my airbnb gym in dubai at 6am he was the only other person there. we started talking. i asked him what was driving most of his revenue. he didn't say ads. he didn't say cold email. he didn't say a sales team. he pulled out his phone and showed me a dashboard. 28 million views in the last 30 days. accounts he owns. content he already had. no ad spend. no creators. no audience deals. he said something i haven't stopped thinking about since. "most founders are renting attention. i own mine." here's what he meant, and why it's the most important distinction in business right now. when you run ads, you are paying rent on someone else's audience. the moment you stop paying, the leads stop arriving. you don't own anything. you built nothing. you rented a billboard for six months and when the lease expired you were back to zero, except now you're $200,000 lighter and your CAC is a number your board pretends not to notice. when you distribute content on accounts you own, something different happens. the views compound. the audience compounds. the trust compounds. a clip you posted three months ago is still driving profile visits today. a piece of content from last year is still closing deals this quarter. the platform doesn't have an expiration date on good content, and the attention you build doesn't evaporate when you stop writing checks. this founder had 52 accounts across every platform. all owned. all run by a dedicated team posting daily clips from content he already had sitting in a folder doing nothing. youtube recordings. podcast episodes. webinar footage. he wasn't creating anything new. he was just finally distributing what he'd already created, at scale, into every market he wanted to win. the math is what broke my brain. $0 in ad spend. 28 million views in 30 days. if you modelled that as paid traffic at even a $2 CPM, you're looking at $56,000 worth of reach. every month. compounding. from content that existed before we ever had that conversation in a gym in dubai at 6am. i've seen this exact system work for iman gadzhi. 300 million views. 180,000 instagram followers and 280,000 tiktok followers built from zero, on accounts he owns and keeps. i've seen it work for luke belmar. 200 million views and $19M in capital club subscriptions driven through distribution alone, not through ads, not through a sales team, through clips running on owned accounts into the exact audience that needed to see them. i built russell brunson's clipping infrastructure inside clickfunnels. $100,000 in sales from a system that runs without him touching it. the pattern is always the same. founder has content. founder has no real distribution. founder is either buying reach they don't own or posting to their own audience and wondering why growth is flat. we build a dedicated team around their brand, warm up accounts to the exact audience they want to reach, geo-target any market they want to win, post daily, test what's working, double down, and watch the views compound across a system they own completely. the content you already have is the most underused asset in your business. most founders spend years creating it. podcast episodes nobody heard. youtube videos that peaked at 4,000 views. webinar recordings sitting in a dropbox folder. all of it has a shelf life of forever if someone actually distributes it properly, and almost nobody does. the guy in the dubai gym wasn't smarter than you. he wasn't working harder. he wasn't spending more. he just figured out earlier that distribution is the actual product, and everything else is just content waiting to be seen by the people who need it. if you want to see the full strategy we use to build this kind of system... the accounts, the setup, the playbook - comment "distribution" below

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  Saurabh | Celsius 233 (@Celsius233Books) reported 37 minutes ago

  @colemickens @Dropbox lenovo and security issues...takes one back to the 2015 Superfish scandal where lenovo was visually scanning every single webpage you visited to sell ads

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  Joachim Voth (@joachim_voth) reported 37 minutes ago

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

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  David Silva Smith (@DavidSilvaSmith) reported 43 minutes ago

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

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  Nell AI Labs (@nellaiorgs) reported 45 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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  Abhishek Singh (@0xlelouch_) reported 45 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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  Greg Ceccarelli (@gregce10) reported 48 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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  Bruno Marsino (@BrunoMarsino) reported 58 minutes ago

  Company for AI age: - Information should flow flat - Can’t wait to have all information to make decisions - Speed and excelente in execution is crucial - You should get as much info as possible during the constraint of time given by yourself - How do we prepare a company to be totally eligible for AI? Not only text info but images - Service of the future is not about giving agents to corps to solve problems but offering the solution/service driven by AI. - Even if all information is on the web, multiple file structures, owners, formats and file storage systems (dropbox, drive, box) add friction to information and decision making

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  Mapacho (@Mapacho111) reported 58 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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  Hago Community (@HAGOCommunity) reported 59 minutes ago

  AI Internal Search Agent: An Intelligent Agent for Searching Company Information Many companies struggle with information being scattered across multiple systems and files. Policies may be stored in Google Drive, documents in SharePoint, conversations in Slack or Microsoft Teams, customer data in a CRM, while internal procedures may be stored in Notion or Confluence. When an employee needs specific information, they may have to search in several places, ask a colleague, contact a manager, or open multiple files before finding the correct answer. This is where an AI Internal Search Agent can help. This agent is an AI-powered system that can search across different company data sources, understand an employee’s question, and provide a direct answer based on the internal information available to that employee. How Does the Agent Work? The agent can be connected to the systems and platforms used by the company, such as: Google Drive SharePoint Notion Confluence Slack Microsoft Teams CRM systems Internal databases PDF files Internal documents Company policies Standard operating procedures Employees can then ask questions in natural language instead of manually searching through multiple systems. For example: “What is the company’s travel expense reimbursement policy?” Or: “Where can I find the latest version of this customer’s contract?” Or: “What are the steps for adding a new customer to the system?” Or: “Who is responsible for this account, and what was the latest update?” The agent searches the sources the employee is authorized to access and provides the most relevant answer. The Problem It Solves The main problem is usually not that the company lacks information. The problem is that employees do not always know where that information is located. An employee may spend time: Searching through multiple folders. Opening several documents. Reading old conversations. Asking coworkers where information is stored. Trying to identify the latest version of a document. Searching across different business systems. This creates unnecessary delays and wastes employee time. Instead, the employee can simply ask the AI agent and receive an answer within seconds. A Practical Example Imagine an employee wants to know the process for purchasing new software for their department. In a traditional workflow, the employee may search through emails, ask their manager, and browse company folders until they find the correct policy. With the AI agent, the employee could simply ask: “What is the process for purchasing software that costs more than $5,000?” The agent could search the company’s internal policies and respond: “Purchases above $5,000 require approval from the department manager first. The request must then be submitted to Procurement and approved by the Finance department.” The agent can also provide a link or reference to the original policy document used to generate the answer. Searching Customer Information The agent can also be used to search customer-related data. For example, a sales employee could ask: “What was the latest agreement with customer ABC?” The agent could search the CRM, internal notes, documents, and customer-related conversations before providing a summary. For example: “The latest meeting with the customer was on August 12. The customer is interested in the Enterprise plan and requested a revised proposal before the end of the month.” This allows the employee to understand the current status of the account without manually searching through a long history of notes. Searching HR Policies Employees can also use the agent to get answers about internal HR policies. For example: “How many annual vacation days do employees receive?” “What is the remote work policy?” “How do I request time off?” “What is the process for business travel?” Instead of sending these questions repeatedly to the HR department, employees can receive answers directly from the AI agent based on official company policies. Supporting New Employees One of the most useful applications of an AI Internal Search Agent is employee onboarding. New employees often have many questions, such as: “How do I request a laptop?” “How do I access the internal system?” “Where are the team files located?” “Who approves expenses?” “How do I submit an IT support request?” The AI agent can act as an internal assistant throughout the onboarding process and provide immediate answers to these questions. Respecting Employee Access Permissions One of the most important features of the agent is permission management. Not every employee should have access to every piece of company information. For example, some documents may contain sensitive information related to payroll, contracts, human resources, finance, or executive management. The agent should therefore respect each employee’s existing access permissions. If an employee does not have permission to access a specific document, the AI agent should not use that document when generating an answer. This allows the company to provide intelligent internal search while maintaining appropriate data access controls. Showing the Source of the Answer The agent should not only provide an answer. It should also show the source of the information whenever possible. For example: “According to the company travel policy updated on May 3…” The employee can then open the original document and verify the information. This helps reduce the risk of employees relying on outdated or incorrect information. Detecting Outdated or Conflicting Information The agent can also be designed to identify conflicting information. For example, it may find two different documents containing different instructions about the same company policy. Instead of selecting one version randomly, the agent could alert the employee or administrator: “There are two documents containing different instructions regarding the remote work policy. The most recent document was updated in June.” This can also help companies improve the quality of their internal knowledge management. Moving From Search to Action The system can be developed to do more than simply search and answer questions. For example, an employee may ask: “How do I add a new customer?” The agent can first explain the required steps. The employee can then say: “Start the process.” The agent could create a checklist, create a new record in the CRM, send a request for the required documents, and notify the employee about the remaining steps. At this point, the system moves from being an AI Search Agent to becoming an AI Operations Agent. Example Inside a Sales Team A sales representative could ask: “What are the most important things I should know about this customer before the meeting?” The agent could search the CRM, previous notes, proposals, and communications before creating a summary that includes: Company size. Products the customer is interested in. Date of the latest meeting. Previous objections. Estimated deal value. Recommended next steps. This allows the sales representative to prepare for the meeting without spending significant time searching for information. Example Inside Customer Support A customer support employee could ask: “How was this problem solved in the past?” The agent could search previous support tickets and the company knowledge base to find similar cases and show the solutions that were previously used. This can reduce ticket resolution time and help new support employees solve customer problems more efficiently. Data Sources the Agent Can Connect To The agent can potentially connect to many different systems, including: Google Drive Microsoft SharePoint Slack Microsoft Teams Notion Confluence Salesforce HubSpot Dropbox OneDrive ERP Systems CRM Systems Internal Databases PDF Documents Excel Files Company Policies Employee Handbooks Customer Records The more organized and up-to-date the company’s information is, the more useful and reliable the agent becomes. Benefits for the Company An AI Internal Search Agent can help a company: Reduce the amount of time employees spend searching for information. Reduce repetitive questions between employees. Make policies and procedures easier to access. Help new employees become productive faster. Improve knowledge sharing across departments. Reduce dependence on individual employees who know where everything is stored. Make customer and project information easier to access. Reduce the time required to find the correct documents. Improve the speed of decision-making. How Can the Company Measure the Agent’s Value? The company can measure the value of the system by calculating how much time employees previously spent searching for information. For example, if 200 employees each spend 20 minutes per day searching for files or asking coworkers for information, that represents more than 66 hours of employee time every day across the company. If the agent can significantly reduce that time, the financial value of the system becomes easier to measure. The company can also track: Average time required to find information. Number of questions answered by the agent. Number of manual searches avoided. Reduction in repetitive questions sent to HR and IT. Customer support resolution time. Time required to prepare employees for meetings. What the Final Agent Workflow Could Look Like The employee asks a question ↓ The agent understands the request ↓ It identifies the relevant data sources ↓ It checks the employee’s access permissions ↓ It searches the company’s internal data ↓ It selects the most relevant information ↓ It provides a concise answer ↓ It shows the source of the information ↓ If authorized, it can also perform the requested action In this way, an AI Internal Search Agent becomes a private intelligent search engine for the company, allowing employees to access internal knowledge quickly instead of wasting time searching across files, systems, and conversations.

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  Ouriel (@OurielOhayon) reported 59 minutes ago

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