Reddit status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of Reddit 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 Reddit users through our website.

• Website Down (56%)
• Errors (23%)
• Sign in (21%)

Live Outage Map

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

City	Problem Type	Report Time
Melbourne	Website Down	4 days ago
Stuttgart	Errors	4 days ago
Bengaluru	Sign in	6 days ago
Paris	Errors	7 days ago
Gustavo Adolfo Madero	Website Down	9 days ago
Nagpur	Errors	12 days 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.

Reddit Issues Reports

Latest outage, problems and issue reports in social media:

• 
  ⟢ nightmare kitten ⟣ 𓃠 (@Walter_Su11ivan) reported 2 seconds ago

  @Hanakookie1 @Giovann35084111 Yup. I'm keenly aware that they have an Indian insider corruption problem. I remember reading horror stories on Reddit going way back right before the election. People getting their accounts drained & some saying they didn't everything right. 2FA, etc.

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  Zuki (@zukiweb3) reported 4 minutes ago

  Seen over my feed for the past 2 days; multiple accounts posting it. The dog looks like a cyclops with one eye. I tracked down the original Reddit post from 2 years ago, wild it's only getting popular now. The dog's name is $floof dex at 15k

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  🎀Victoria Hayes🎀 (@_Vicky0_0_) reported 6 minutes ago

  Technical debt warning: Many SaaS tools hide latency, memory leaks, and broken integrations behind polished marketing. Check G2/Reddit reviews before buying. The best tech decisions come from understanding failure modes, not launch videos.

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  lord baal (@lordbaal1) reported 6 minutes ago

  @Reddit Typical mods. Don't agree with the OP. So they just perma ban you, and perma mute you. Because you have "broken" some invisible rule. But yet can tell you which rule you broke.

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  Will Ballentine (@EveryDayFSDev) reported 9 minutes ago

  Another account was randomly shadow-banned on Reddit. Making something to fix this. founders should have a place to promote and discuss without risk of ban. stay tuned.

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  ShashanK🤺 (@shacrw_) reported 11 minutes ago

  @AndyMasley a lot of people still believe that LLMs aren't a good search engine which is just stupid. they probably have that mental model coz most of the uses they see are very simple, maybe don't use web search and so to them "ai search" means ai generating a list of things which might be hallucinated. biggest advantage of LLM+web search is that they can do query gen instead of the user thinking and typing a bunch of search queries. really helpful for long tail research. i remember seeing perplexity do this ~2 years back and it was amazing. i gave it some search params to search reddit then asked it to generate 10 different search queries + aggregate the results and it did. quite similar to the slop/pangram problem where people who don't use ai can't spot slop or don't believe in pangram and so you get those lit contests where ai entries win and even after pushback the judges declare no foul play. in both cases, misunderstanding is due to lack of usage and based on something which was true 2-3 yrs ago: hallucinations, made-up links and ai detectors which were all bad. anyways, since he's a science communicator I hope he educates his audience about this. the idiots on reddit are saying **** like "he has the resources...why doesn't he pay people to do research instead of using AI" which is just stupid on multiple levels.

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  GameCryptid (@GameCryptidVG) reported 16 minutes ago

  @DrHairyChic0 @Juliansiles12 @axewxoxo Just saying man. As lame as Reddit is, they cracked down on all that stuff years ago. Twitter is possibly the absolute worst mainstream social media for prohibiting that kind of content.

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  Jason (@JasonJh1319) reported 21 minutes ago

  $RDDT Based on the overwhelming sentiments, Reddit is either going to have a V-shape recovery or continue to stump investors by going down another 20-30% from here. Fun ride either way.

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  Zuki (@zukiweb3) reported 21 minutes ago

  Seen over my feed for the past 2 days; multiple accounts posting it. The dog looks like a cyclops with one eye. I tracked down the original Reddit post from 2 years ago, wild it's only getting popular now. The dog's name is $floof dex at 15k

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  Peter Quadrel (@Peter_Quadrel) reported 22 minutes ago

  The 9 Truths on Meta Most Media Buyers Hate to Admit 1. Spend is the MOST important metric. Spend = Meta's confidence in your asset. It's the output of all their AI and data. Most scaling mistakes happen because people ignore where Meta actually puts their money. 2. Run your own retargeting for $300+ products. Don't let Meta handle it automatically. With proper setup, you get actual incremental value, not just pulled-forward sales. 3. 7-day click OR incremental attribution wins on average. Tested across dozens of accounts. Consistently outperforms other setups for most brands. 4. 90% of metrics are useless. Hook rates, CTR, cost per click not even correlated with revenue. Plot twist: Higher CPMs actually correlate with BETTER performance. 5. Your scaling problem is promotional content. Your ads only appeal to people who already know your product. Top-funnel creative should blend into feeds organically. Test: If your ads would work as organic posts, they're probably good ads. 6. Media buying = budget control. CBO, ABO, cost controls are just different ways to control dollar flow per asset. 7. You don't need fancy tools until mid-eight figures. CAPI + Meta attribution = enough data. No Triplewhale or Northbeam 8. Success is 80% product-market fit + offer + angle. Everything else is optimization theatrics. 9. Most bad performance = you don't understand your market. Not about design or production quality. You don't know what customers actually care about. Talk to real people more and read more reddit/comments.

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  Richard Doom (@R1CH4RD00M) reported 22 minutes ago

  @zeronisART If you get off twitter and Reddit you will realise that it's a small vocal minority in the west that actually care. Unless it's deepfake most normie have no problem with AI.

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  Omar Choudhury (@OmarChoudhury99) reported 22 minutes ago

  A YouTuber walked away from creating videos because of malicious Reddit threads Her manager came to us -> All gone in 5 days Here's what happened... Her legal team said there was nothing they could do... So she got depressed and lost motivation for months, thinking it was over for her But her manager, who lives in Miami, happens to be a friend of mine He's a G He's worked with some of the biggest streamers and content creators in the space He kept insisting they should come to us for removals... But the rest of the team didn't want to do it But later on, when he found out she had completely stopped filming and streaming That's when he finally reached out to us on Monday So we stepped in And this morning... I sent him the update he'd been waiting for My legal team successfully took down both malicious Reddit threads, including the comments No traces left on mirror sites... No copies floating around... Everything was wiped That's how we operate If malicious Reddit threads, defamatory articles, or fake reviews are hurting your business, your brand, or your peace of mind... Don't wait for them to spread even further Come to the team that's trusted to handle the cases everyone else gives up on And if you know someone who needs our help... We've got a few thousand dollars in referral commissions waiting for qualified introductions We're the best in the game for a reason DMs are open 📲

• 
  Ciprian (@ciprian__b) reported 23 minutes ago

  It's officially been one week since I launched my latest product Over the past 3 weeks (2 in waitlist, 1 in launch) I made 6 Reddit posts which got in total: - 70k views - 500 upvotes - ~500 people landing on my page - 20 checkout sessions started - and 15 customers During this week I tried making sure people have what they need, fixed issues, added more content they requested to the app and so on Which meant marketing was more of an afterthought But this app is built around a community. The more people that contribute with content the more it will grow So the goals for the next week are: - Make it as easy as possible for people to contribute with their own stuff - and I guess more marketing? widen the top of the funnel? Will see how this goes. But now it feels like for the first time I make something people care about, so that's pretty cool

• 
  stackzz (@stackzz) reported 25 minutes ago

  🧠 Coinbase Drawings Vanished. Build One Backup. A Reddit trader said hours of Coinbase Advanced chart drawings disappeared after a 20-minute break. They rebuilt one chart. Gone again 20 minutes later. A third rebuild this time locked vanished too. Coinbase Support said drawings should persist, but duplicate tabs or interrupted saved-chart data can reset a chart. Their fix: one Advanced tab, hard refresh, rebuild, save; escalate if it repeats. That’s more than annoying UI. Your chart markup is working memory: levels, scenarios, evidence. If a venue can reset it, keep an external TradingView copy or screenshots before the session gets busy. Rent the execution venue. Don’t rent your memory.

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  hannah (@HockeywthHannah) reported 25 minutes ago

  @liloloveyou024 and yet people are acting like court had a problem in today’s reddit stories lol

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  A Lionlike Mane (@alionlikemane) reported 30 minutes ago

  @gingasvr Reddit people are the worst, don't let em get you down.

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  Brendan Playford (@BrendanPlayford) reported 32 minutes ago

  @yca_software Directories usually just give a temporary spike, so I get the most actual signups by finding active conversations on Reddit or X where people are already trying to solve the problem my SaaS fixes.

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  Maynard Baral (@hmmmtb) reported 32 minutes ago

  Brand 6 of 50 (or more): Beltwell — LymfoLotion Barrier-repair lotion for people whose legs itch so bad they scratch in their sleep. I read through 158 verbatims from Reddit, HealthUnlocked, diabetes forums, Quora. And here's what became clear... The pain is deep. So I wrote 5 video concepts. 5 statics. Each one pulled straight from the research. Doc's on the reply. Still down to get roasted. 60 ads down. 440 (or more) to go.

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  Ashni (@ashnichrist) reported 34 minutes ago

  Twitch will use your streams to train AI. @zachbussey leaked this info. He's a great resource for streaming news. My role is to help you understand what news actually matters, and what it means for you / the industry. So here's what you need to know: Your stream data is incredibly valuable. Livestream data is some of the most valuable for training AI. - It's multimodal (video, audio, text chat) which is very in-demand in the data market - It's social data. Real people interacting with each other teaches models to engage socially and have personalities (ai is very bad at this rn) - It's unscripted. The same reason Reddit data is so valuable: it's real - There is a lot of it. Millions of streamers, billions of hours of data Here's what Twitch will (probably) use this data for. None of this is confirmed, but we can make assumptions. 1. Generic foundation model training. This is boring & low stakes, these already exist and don't really hurt you 2. Livestream-specific products like auto-highlights / clips, moderation, streamer co-pilot tools, etc. They want to keep streamers & not make y'all use other tools in this category 3. But the worst one... they could use this to create AI streamers. You're probably thinking "they can't do this! I own my content." The unfortunate reality is.... Twitch vs. Kick vs. YouTube ToS & Your Rights You do not own the rights to your content on Twitch. They can sell your data to whoever they want and you agree to this by using the platform. But there is a silver lining: their rights are non-exclusive and you keep copyright. YouTube has the best platform rights for creators. You retain all ownership, their license is limited to just running their platform, AND they already have AI as *opt-in*. Kick is the worst... specifically the Kick Partner Program. Kick has a permanent, exclusive, sub-licensable buyout. You lose the right to license elsewhere the moment you monetize. So what's the actual problem for streamers? When platforms own the ability to sub-license your data, they can sell it to other companies and use it however they want. Twitch and Kick both have this ability. YouTube does not. If you stream on Kick or YouTube, you could end up as unpaid Research & Development for an AI that replaces you. There is precedent... The content rights argument is playing out across many industries. - SAG-AFTRA went on strike in 2023 and won - Reddit makes ~$130M /yr from Google & OpenAI. Redditors receive none of it - Federal legistlation battles are happening, like the No Fakes Act And many more. Why Twitch Might Grant Opt-In If you don't like AI, you probably would love to know that Twitch has potential to grant an opt-in feature similar to YouTube's Amazon has a large team of very talented lawyers. Litigation is very expensive, especially in a completely new category. Having opt-in consent could prevent a lot of headaches for their (expensive af) legal team. Also, YouTube already has this feature. If Twitch rolls out a forced default while its biggest competitor lets creators choose, Twitch looks anti-creator by direct comparison & loses users How to Make Money From Your Own Data You want to be paid for your data, not these big companies. That's why it's crucial to maintain the ability to sub-license your content (which KPP does NOT allow, Twitch & YouTube are safe) In order to make money from your data, you need to join an aggregator platform. These platforms pool users together to sell and monetize their data. It's going to happen anyway. You might as well be the beneficiary of it. Lmk if you want platform recommendations. What You Should Do Now 1. Demand an opt-in toggle like YouTube. Twitch has caved to pressure before 2. Keep copies of all your VODS in case you want to license them someday. This will be a new revenue line for streamers over the next few yrs 3. If you really hate this, stream on YouTube 4. If you are a Kick Partner, consider leaving the platform or renegotiating your contract The simple conclusion is... The move isn't to rage against AI or roll over for platforms Your move should be to force Twitch to say what they're building and demand the right to choose Protect your rights and force consent, then you decide whether to license your data for money or withhold it entirely Good luck everyone.

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  Alexander Trefonas (@a3voices) reported 35 minutes ago

  @VadimStrizheus the way that worked for me is make the app, then find old reddit comments on the topic via Google and talk about it there. Slow but consistent trickle of organic leads for years

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  Lo the B movie enthusiast 🧜🏾‍♀️ (@nerdymermaid101) reported 35 minutes ago

  In all seriousness apparently this is a common problem but…I can’t take it anymore. Never posting on Reddit though

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  The Boring Developer (@boringdev77) reported 37 minutes ago

  someone on Reddit has a tool getting thousands of visits a month wants to add hundreds of AI-generated free tool pages one problem: scared it'll get the whole domain penalised once Google notices the pattern that fear is the real cost of AI content now not the writing time. the trust budget.

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  zack (@whotfiszackk) reported 41 minutes ago

  while everyone is chasing algorithms i built something that feeds them automatically. and the difference between those two activities is the difference between a business that resets every month and one that compounds every week without requiring a new idea to trigger it. here's what i mean by that and why it matters more than anything else being talked about in this space right now: chasing the algorithm looks like this. studying what posted well last week and trying to replicate it this week. watching the metrics obsessively after every post looking for the signal that says this one is going to spread. changing the format because someone with a large following said carousels are dead now. changing the posting time because a thread said 7am outperforms 9am by 23%. changing the hook because the last three posts underperformed and something has to be the reason. the person chasing the algorithm is always one step behind it. because the algorithm isn't a static thing to be decoded. it's a signal-reading machine that responds to behavior it observes in real time. and the behavior it rewards most consistently is not the behavior most people are optimizing for. it's not the best hook. it's not the most aesthetic visual. it's not the most controversial take or the most contrarian angle or the most perfectly timed post. it's the account that consistently produces content that the right people find valuable enough to engage with in the ways that signal genuine value. saves. shares. replies that extend the conversation. profile visits that convert to follows. those signals tell the algorithm that the account produces something worth showing to more people. and the algorithm responds by showing it to more people. which produces more signals. which produces more distribution. which produces more signals. that's the flywheel. and most people are trying to hack into it from the outside instead of building the behavior that feeds it from the inside. feeding the algorithm automatically looks completely different from chasing it. it doesn't start with the algorithm at all. it starts with the specific person. the specific person with the specific problem at the specific moment the problem is loud enough to make them stop scrolling and actually read something. when the content is built for that person at that moment — not for the algorithm, not for the metrics, not for the platform — something happens that chasing never produces. the right people find it and engage with it in the exact ways the algorithm interprets as high quality signal. they save it because it solved something they needed solved. they share it because someone in their network has the same problem. they reply because the content opened a thought they wanted to continue. they follow because the profile clearly produces more of what they just found useful. every one of those behaviors is an algorithm feed. not manufactured. not gamed. not engineered through optimization. produced naturally by content that was built for the right person at the right moment. the account that consistently produces that content doesn't chase the algorithm. it feeds it automatically. because the algorithm is just a system that looks for evidence that an account is producing value for a specific audience. and an account built for a specific audience with a specific problem produces that evidence on every post without trying to. here's what the system that feeds the algorithm automatically actually looks like when it's running. it's not one thing. it's five things connected. the community presence that puts the content in front of the exact audience at the exact moment of acute pain. the saves from people who needed the answer badly enough to want to find it again. the follows from people who found the answer valuable enough to want more of it. the profile visits that signal the algorithm that the content is producing curiosity in the people who encounter it. all of those are algorithm feeds produced by being in the right community at the right moment with the right content. the email list that keeps the relationship warm enough that every send produces the kind of engagement that no cold audience ever matches. high open rates. high click rates. replies that extend the conversation. all of those tell the algorithm that the content is being actively sought by people who want it. not passively consumed by people who happened to scroll past it. the search content that answers the question the right person is typing at the moment they're most likely to act on the answer. the person who finds that content through search and engages with it deeply — reading the full article, clicking through to the product page, sharing it with someone who has the same problem — produces search signals that rank the content higher which produces more of the right traffic which produces more signals. the referral network that delivers people who arrive already warm because someone they trust told them to come. warm arrivals engage differently than cold traffic. they spend more time on the content. they click more deeply into the site. they convert at higher rates. they share more readily because they arrived predisposed to find the content valuable. all of those behaviors produce algorithm signals that cold traffic never generates at the same rate. and the content compounding system that takes every piece of content that performed well and redistributes it across every channel where the right audience lives. the same piece of content producing algorithm signals on twitter and linkedin and reddit and pinterest simultaneously is not four times the work. it's one afternoon of reformatting producing four times the distribution surface area. five systems. all of them producing algorithm signals automatically. all of them compounding on each other. all of them feeding the algorithm with exactly the behavior it rewards most. without requiring the operator to study the algorithm. without requiring a new optimization after every platform update. without requiring anything except the system continuing to run the way it was built to run. the operators who understand this have stopped talking about the algorithm entirely. not because the algorithm doesn't matter. because when you build for the right person the algorithm takes care of itself. the algorithm is not the game. the person is the game. the operator who spends their time understanding the specific person more deeply produces content that feeds the algorithm more effectively than the operator who spends their time studying the algorithm directly. because the algorithm is downstream of human behavior. and human behavior is downstream of whether the content actually solves something real for a real person at a real moment. solve the real thing for the real person at the real moment. the algorithm notices. and it responds the way it always responds to content that produces genuine signal from a specific audience. it distributes it further. to more of the right people. who produce more signal. who feed the algorithm more. who produce more distribution. not a spike. a flywheel. that runs automatically. after the system is built to feed it. the thing most people won't do because it doesn't feel like optimizing the algorithm. spend less time studying platform mechanics and more time studying the specific person. what they search at 11pm when the problem is loudest. what language they use in community threads when they're frustrated. what they save and why. what makes them share something with a colleague. what makes them follow an account they just discovered. all of those are data points about a person. and all of those data points produce content that feeds the algorithm better than any optimization ever discovered by studying the algorithm directly. the person is the algorithm's input. study the input. the output takes care of itself. that's the system. that's what feeds the algorithm automatically while everyone else is still chasing it if you wanna: • build offers that make you 6-figures a year with no prior required experience • expand your potential network to maximize profit • own distribution channels like pablo escobar • eat your competitors for breakfast while remaining faceless DM “DISTRO” to get access to my private program

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  matt knox (@mattknox) reported 42 minutes ago

  @lessin I gotta find a Reddit-level etymology nerd to see if it was an error at transfer between languages.

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  B (@CausalAgent) reported 43 minutes ago

  @QualiaQuanta it's extremely hard to pin down, reddit genetics

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  Senior Fancybottoms 🇺🇸 🇯🇵 (@Zachary02480085) reported 46 minutes ago

  @ssaymssiknacuoy @ozconsoul Reddit is not a source. Give me one example of Sony shutting down the license to an offline, complete on disk game.

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  Dodz4allai (@DrGhattasMD) reported 49 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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  Rish | ProductMinds (@rishxtweet) reported 51 minutes ago

  i built a system that scrapes reddit for conversations worth replying to for free. 5 subreddits in 15 seconds. scores threads by reply potential. drafts replies that don't sound like ai. 3 layers: ▸ opencli browser scraper ▸ hermes agent for scoring ▸ voice skill that evolves daily full playbook linked below. copy-pasteable code included. this will probably get taken down.

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  John (@hello_code_) reported 56 minutes ago

  @danielkleach Subreddit Signals is one I'd put in that bucket, finds customers already talking about your problem on Reddit but almost nobody knows it exists yet. Distribution is hard when you're heads down building. Also building AI Peekaboo, tracks why competitors show up in ChatGPT and Gemini answers when you don't, same problem different layer.

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  Gurnoor Singh (@gurnoor__) reported 57 minutes ago

  Brand owners under 1M/mo = don’t do this Brand owners over 1M/mo = do this religiously I’m talking about product research. Most brand owners immediately open Reddit Read customer reviews Analyse competitor ads And start collecting every pain point they can find. And then they end up with 50 pages of research and still have no idea what ad to write. Here’s why: Customer research can show you everything the market wants, but it cannot tell you which of those desires your product can actually satisfy. You need to understand the product first. And I don’t mean reading the product page and writing down a list of features. You need to understand: > What every feature actually does > What changes when someone uses the product > What problems it can genuinely solve > What outcomes it can help create > Which advantages competitors cannot credibly claim > Which types of customers would value each benefit most This creates the lens through which you conduct customer research. Without that lens, you’re just collecting random customer language and hoping an angle magically appears. Let’s say you sell a collagen supplement. If you begin with customer research, you could find hundreds of problems: > Fine lines > Dry-looking skin > Weak nails > Joint discomfort > Stiffness after training > Confusion about collagen types > Powders that taste horrible or clump But you still don’t know which problems your collagen can credibly connect to or how to explain that connection. Product research gives you that bridge. You might discover that your product contains peptides, provides a relevant amount per serving, dissolves easily and has evidence to provide specific outcome. Now your customer research has direction. You can investigate women concerned about changes in skin elasticity, active people dealing with joint discomfort, customers who gave up on collagen because of the taste, or people confused about whether they are taking enough. You know which problems to investigate because you already know what your specific formula, dosage and delivery experience can support. THIS is where your strongest angles come from. You connect a real capability inside the product to a powerful desire already present inside a specific customer. The product gives you the possible directions. The customer tells you which direction has the most sales power. This is why starting with customer research creates so much noise. You find emotionally powerful problems your product cannot solve, desires it cannot fulfil and sub-avatars it has no credible reason to serve. Then you force the connection in the ad, and the entire argument feels weak. The correct order is this: > Understand exactly what the product can do > Distinguish between features and benefits > Identify the problems and outcomes connected to those capabilities > Find the customers who care most about those outcomes > Study how those customers describe the problem in their own language > Build sub avatars and angles where product capability and customer desire meet And test it. Product research gives customer research a target. Without it, you’re collecting information. With it, you’re searching for the exact desire your product is best positioned to satisfy. To your Ascensionn, Gurnoor