Reddit Outage Map

Community Discussion

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Reddit Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Rashi Umapathi (@rashiumapathi) reported 6 minutes ago

  @victor_bigfield Exactly. A good Reddit reply meets the buyer inside the problem instead of making them walk through your funnel.

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  Raph Guilhem (@raph_guilhem) reported 9 minutes ago

  📁 35 ad formats that print money (steal these) ├─ 📁 Social proof │ ├─ Trustpilot reviews │ ├─ Text message │ ├─ Customer testimonial │ ├─ Email screenshot │ ├─ Reddit post │ └─ IG Story │ ├─ 📁 Offers & urgency │ ├─ Bundle deal │ ├─ Transformation │ ├─ “We’re sorry” │ ├─ Meme offer │ └─ Low-stock alert │ ├─ 📁 Raw & native │ ├─ AI podcast │ ├─ Green screen │ ├─ Text on skin │ ├─ Whiteboard │ ├─ Claymation │ ├─ Doodle │ └─ Native content │ ├─ 📁 Problem → solution │ ├─ “You can avoid…” │ ├─ Emergency scenario │ ├─ Crossed-out problems │ ├─ Side effects │ └─ “Don’t buy this” │ ├─ 📁 Comparisons │ ├─ Tier list │ ├─ Venn diagram │ ├─ Old vs. new │ └─ Us vs. them │ └─ 📁 Hooks ├─ Warning ├─ Hack 101 ├─ Myth vs. fact ├─ Stat headline ├─ “X reasons why” └─ Breaking news

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  Naqui (@naqui_s) reported 9 minutes ago

  Everyone is talking about ChatGPT’s Reddit citations dropping. But the headline misses the more interesting part. We analyzed 50 days of Reddit citations across ChatGPT, Perplexity and Gemini, from July 1 to August 19. The dataset contains ~44 million AI response citations linking to Reddit. And there are some interesting signals in it. First, Reddit is still heavily used as a source in AI Search. Across the period: - Perplexity: 31.64M citations - ChatGPT: 10.03M citations - Gemini: 2.82M citations Perplexity alone accounts for 71.1% of all Reddit citations in our dataset. That is a huge difference. But the more interesting finding is what happens over time. ChatGPT's Reddit citation behavior has changed significantly. The chart shows a clear spike around the end of July and beginning of August, followed by a substantial decline in the second half of August. Meanwhile, Perplexity continues to generate a much larger volume of Reddit citations. So I don't think the right conclusion is: "Reddit is becoming less important for AI Search." The better conclusion is: Different AI engines are developing very different source preferences. And that has a big implication for brands. If you're investing in Reddit to improve your visibility in AI Search, you shouldn't simply track: "Are we posting on Reddit?" You should be tracking: 1. Are AI engines citing Reddit for our category? If citation volume is falling, simply publishing more Reddit content may not solve the problem. 2. Which AI engines are actually using Reddit? Our data shows a massive difference between Perplexity, ChatGPT and Gemini. Your Reddit strategy should reflect where your audience's AI discovery is actually happening. 3. Which discussions are getting cited? Not every Reddit thread has equal value. I'd prioritize discussions that are already being surfaced by AI engines, particularly threads that demonstrate genuine experience, comparisons, recommendations or problem-solving. 4. What happens to your competitors when you're not there? This is the metric I think brands should pay much more attention to. If ChatGPT is answering a category question using Reddit and your competitor is repeatedly being mentioned in the cited discussions, that's a much more actionable insight than knowing your Reddit account has 500 karma. The big shift We're moving from "build a Reddit presence" to: "Understand how AI engines consume Reddit, then build for that behavior." That's a very different discipline. And it's why we're collecting this data at @Pierviewai Our goal isn't to tell brands to create more content. It's to help them understand what AI engines are actually citing, what is changing, and where the gaps are. The AI Search landscape is changing too quickly to rely on generic GEO playbooks. Measure first. Then optimize.

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  Alyssa (@Alykkat) reported 9 minutes ago

  Ever reflect on paths you didn't end up going down & realizing how incredibly lucky you are that you didn't? A two years ago, I made it to the final round for a role at Reddit to be the community manager for reddit mods. Cause can you imagine what my life would be like today if I got that job? 🙈

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  Naqui (@naqui_s) reported 10 minutes ago

  Everyone is talking about ChatGPT’s Reddit citations dropping. But the headline misses the more interesting part. We analyzed 50 days of Reddit citations across ChatGPT, Perplexity and Gemini, from July 1 to August 19. The dataset contains ~44 million AI response citations linking to Reddit. And there are some interesting signals in it. First, Reddit is still heavily used as a source in AI Search. Across the period: - Perplexity: 31.64M citations - ChatGPT: 10.03M citations - Gemini: 2.82M citations Perplexity alone accounts for 71.1% of all Reddit citations in our dataset. That is a huge difference. But the more interesting finding is what happens over time. ChatGPT's Reddit citation behavior has changed significantly. The chart shows a clear spike around the end of July and beginning of August, followed by a substantial decline in the second half of August. Meanwhile, Perplexity continues to generate a much larger volume of Reddit citations. So I don't think the right conclusion is: "Reddit is becoming less important for AI Search." The better conclusion is: Different AI engines are developing very different source preferences. And that has a big implication for brands. If you're investing in Reddit to improve your visibility in AI Search, you shouldn't simply track: "Are we posting on Reddit?" You should be tracking: 1. Are AI engines citing Reddit for our category? If citation volume is falling, simply publishing more Reddit content may not solve the problem. 2. Which AI engines are actually using Reddit? Our data shows a massive difference between Perplexity, ChatGPT and Gemini. Your Reddit strategy should reflect where your audience's AI discovery is actually happening. 3. Which discussions are getting cited? Not every Reddit thread has equal value. I'd prioritize discussions that are already being surfaced by AI engines, particularly threads that demonstrate genuine experience, comparisons, recommendations or problem-solving. 4. What happens to your competitors when you're not there? This is the metric I think brands should pay much more attention to. If ChatGPT is answering a category question using Reddit and your competitor is repeatedly being mentioned in the cited discussions, that's a much more actionable insight than knowing your Reddit account has 500 karma. The big shift We're moving from "build a Reddit presence" to: "Understand how AI engines consume Reddit, then build for that behavior." That's a very different discipline. And it's why we're collecting this data at @Pierviewai Our goal isn't to tell brands to create more content. It's to help them understand what AI engines are actually citing, what is changing, and where the gaps are. The AI Search landscape is changing too quickly to rely on generic GEO playbooks. Measure first. Then optimize.

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  Moneim Rahma (@moneimrahma) reported 13 minutes ago

  I’m 18 and just made my first $2K selling software. 3 lessons I learned: 1. Research first and try to understand the product, customer, and problem. 2. Build quickly without having to sacrifice quality. 3. Don’t start from zero what helped me the most was learning from GitHub repos, Reddit threads, and designs on X. Next Goal: $10k

• 
  Brady Madden (@bmadden12) reported 15 minutes ago

  ChatGPT citations of Reddit just dropped 86% If Reddit comments were your AI search strategy... RIP 🪦 New data from Promptwatch shows Reddit went from roughly 4% of all ChatGPT Search citations [one of the largest shares of ANY domain] to about half a percent. In a single day. Color me shocked. People have been spamming Reddit for years. It got worse the moment word spread that Reddit mentions could land you in ChatGPT answers. Well, the platforms noticed. They always do. Reddit comments should NEVER have been anyone's whole strategy. A small piece at most. Any tactic built entirely on a loophole always gets discovered. All of this said, HELPFUL Reddit comments still have real value: 🌲 They genuinely help the person asking [novel concept, I know] 🌲 They show off actual expertise where your buyers hang out 🌲 Reddit threads still rank in high-value Google SERPs for searches potential customers make 🌲 They build brand recognition the slow, durable way The difference is intent: Comment to help, and you'll typically get upvotes and increase your chances of more visibility. Comment to manipulate, and get banned or called out by Reddit users. Worth noting: the drop could partly reflect how the data was collected, and citation patterns shift constantly. Which kind of proves the point... chasing any single citation source is chasing a moving target. The playbook that survives every shake-up like this: Be genuinely useful everywhere your potential customers are. Own your visibility through your website, your content, and your brand. If your visibility can vanish in one day, it was never yours. Build on ground you own. 🌲🌲🌲 #SEO #AISearch

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  VITARTA🇺🇦🏆ProArtGaming (@VITARTA1) reported 24 minutes ago

  @tanosiie After 20 years of trying "best mouses" I feel - market can't give me what I want. 6 years ago I write on reddit how important if shell will be lightweight. But was down voted to 0 and hated in comments. Now we see what is what

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  IronBearBT (@IronBearBT) reported 26 minutes ago

  @alpassione7 @sntvizzy Is this screenshot trying to justify MoMs plot to explain why the plot is a repeat? Just want to check. Please clarify also please fix the grammer if you're able to. It looks like it was typed on Reddit during a heated debate.

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  Akshay Sharma (@Akshay_Growth9) reported 26 minutes ago

  Your Customers Might Be Your Best Copywriters The more I learn marketing, the more I think customers are often better copywriters than marketers. We write: “Improve operational efficiency.” A customer says: “I’m tired of wasting two hours on this every Friday.” Same problem. Completely different impact. One sounds like marketing. The other sounds like something a real person would actually say. I’m starting to think good copy is less about sounding clever and more about listening closely enough to borrow the customer’s own language. Curious where have you found your best copy ideas: customer calls, reviews, Reddit or somewhere else?

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  AI DeepExistence (@ruben_cora27112) reported 28 minutes ago

  Inside: 6 named, copy-paste prompt techniques — from a viral Reddit self-audit trick to a research-backed diversity fix called SSOT. These aren't tips you'll forget; they're named methods that rewire how any AI responds to you. 📌 Save this before you need it, and follow for more

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  Yume_X (@yume_arasaki) reported 28 minutes ago

  You've been hearing about DFlash2 on Qwen 3.8 27B all week. 134 tok/s on a 3090, 70 on a MacBook, 236 on an H200. All true, all a bit hard to understand... I ran it on my box today to find out what it is and whether it makes sense on yours. It's the biggest clean speed win in local inference right now, with a cost most posts skip: it eats your VRAM headroom, and headroom is context. Map below, run instructions with caveats under it. WHAT IT IS Qwen3.8-27B ships with a speed tool already inside: the native MTP head, trained into the checkpoint. It predicts 7 tokens ahead, the model verifies in one pass. No download, no VRAM, one flag. If you're not using it, turn it on today. DFlash2 is the other kind: a separate 2B draft model (3.85GB, 1.14GB quantized) from z-lab / Inco AI. It drafts an 8-token block in one parallel pass, a selector walks the best path through top-16 candidates per position, your 27B verifies. Greedy output is unchanged, lossless by construction, quality parity on matched suites. One extra accepted token per verify step for ~1% latency. The DFlash family passed 3.5M downloads, SGLang made it the official cookbook recipe Aug 21, vLLM merged it (PR #52816), llama.cpp is PR-only, MLX runs it natively. The difference that matters is where they live: - MTP is inside the model. Free. - DFlash2 is outside. It takes VRAM, draft state, and prefills your prompts too. On a limited-VRAM card, DFlash2 doesn't just add speed. It removes headroom, and headroom is context. A 24GB card at big context: your usable window shrinks. That's the trade nobody's speed posts show. THE MAP Every number attributed. Two are ours. DGX Spark (mine) 👇 Matched suite, same image and checkpoints: MTP 25.62 tok/s single-stream, DFlash2 28.38. The external draft beats the factory head by 11% at identical GSM8K / HumanEval / IFEval scores. At c8 it flips: 92 vs 124 for MTP, the factory head wins under load. 128GB unified: no context squeeze. My run today: 30.4 tok/s steady on code generation (2048-token module, temp 0), accept length ~3.2. Our fleet build-day on this recipe: 45 tok/s on JSON verdicts, 141 aggregate at c8. Content-dependent, see below. RTX 3090, 24GB 👇 Frontier zone. iamMess (Reddit): 134 tok/s claimed, no recipe. TeksEdge: same rig 82 vanilla → 114 MTP → 138 DFlash2. syv-ai tuned stack: ~114 single, ~1,000 aggregate at 64 streams. Receipts thin. Headroom math: Q4 target ~16-17GB + ~1.2GB draft + KV. At 110k context you're at the ceiling. Big context and DFlash2 don't coexist on 24GB. RTX 4090 (mine), 24GB. 👇 The measured row. analogalok: 60 on MTP → 83-87 with DFlash2 (headline said 90, his table said 83-87, plan on the range). 23.96GB of 24 at 110k context. That is the headroom story in one number. Prefill takes a hit too, the draft prefills your prompt. RTX 5090, 32GB. 👇 ~148 tok/s with speculation in roundup data, no clean DFlash2 row published. Headroom is not an issue here with 32GB of vram. Another super win for the 5090 (on top of Minimax H3) Strix Halo, 128GB unified. 👇 MTP builds 24-36 tok/s, real chat 11-24. No DFlash2 row yet. Bandwidth-poor, memory-rich: no VRAM tax, but the bandwidth cost might bite. Watch this lane. Mac, M4 Pro class (mine too). 👇 Paired benchmarks: 4-bit 14.7 → 33.8 tok/s (2.30x, acceptance 5.14), 8-bit 8.4 → 30.5 (3.63x). Best decode on this hardware today. Mac, M5 Max class. 👇 The 70 tok/s claim is credible: the M4 Pro number scaled by ~2x bandwidth. Same machine on llama.cpp GGUF: 16-35. Backend matters as much as silicon. On Mac, MLX is the path (z-lab dflash package or oMLX), block_size 5 or less on quantized targets. llama.cpp mainline doesn't have it yet. 16GB. No. The model alone at Q4 is ~16-17GB. Run the 9B class, different post. 8GB and below. Not this model. Small dense or small-active MoE, or API. THE WORKLOAD COLUMN Same box, same drafter, same day: prose 17-23 tok/s, code 32-40, math 41-44, JSON 45+. Speculation accelerates predictable tokens; your content sets your acceptance rate. A 134 claim and a 20 claim can both be real. A DFlash2 number without a workload attached is not usable. One number is easy to post, that's why that tends to be the content. The way I do it, is I just accept the real speed is somewhere "inbetween" most operations are partly prose, partly structured. WHEN IT MAKES SENSE - Solo, single-stream, code/math/JSON, mid context. One flag, one 4GB download. - 24GB at context under ~32k: fits with room. - Apple Silicon MLX: 2.3-3.6x. - Spark-class memory: no squeeze. WHEN IT DOESN'T - Long context. The killer: one 4x3090 sweep had acceptance 45.7% → 1.1% at 256k, decode 68.9 → 2.6 tok/s with the draft ON. Off is 26x faster. Long agent sessions: bare or MTP-only. - Shared box: MTP wins by c8 (124 vs 92). - Tight VRAM: 23.96/24GB at 110k on a 4090. - Prefill-heavy: one Spark case halved prefill (73 → 38 tok/s). - llama.cpp mainline: not merged. Its n-gram speculation needs no draft model and beat draft-based methods on repetitive code in one community test, DFlash2 isn't the only lever How to Run and Sources in Reply 👇

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  Diyanshu Patel (@diyanshu0360) reported 31 minutes ago

  In 12 months, "are we cited in ChatGPT?" will be a normal line in B2B marketing reports. Most teams will scramble to fix it then. The ones who win it are quietly building their Reddit presence right now. Boring today. Unfair advantage tomorrow.

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  Zack Chao (@ZackSeeker) reported 32 minutes ago

  Saw a Reddit post today from someone looking for a simple browser-based spending tracker. They didn’t want bank sync. They were fine entering purchases manually. They just wanted something simpler than Excel. That’s almost exactly the tradeoff I’m building Matrix Ledger around. Manual expense tracking isn’t necessarily the problem. Spreadsheet-style entry is. Matrix Ledger keeps the intentional act of logging your own spending, but lets you describe purchases naturally and turns them into structured entries automatically. Reduce logging friction, not spending awareness.

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  Alan (@AlanNguyen2197) reported 33 minutes ago

  @seebiscut yeah, Reddit algorithm is simple: Hard selling = Speedrun to get banned. Being a helpful = Slow climb to real conversion.