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Reddit Issues Reports

Latest outage, problems and issue reports in social media:

• 
  HightechRedneck (@48hghtchrdnck) reported 6 minutes ago

  @MichaelQuercia3 @SavvyTamz_57 Telling me ‘hyperscale DCs are going to happen (so sit down & shut up peasant while distant oligarchs shove ten of them into the rural county where I live) is a nonstarter. Barking about hypothetical ‘LLM cancer cures, trust me bro’ & dismissing me as a ‘luddite rube falling for Chinese psyops’ (I’m an electrical emgineer) are also nonstarters. I am not obligated to passively accept whatever Altman wants to do to me without resistance or negotiation. My objection to DCs is fundamentally an objection to AI itself: a bunch of arrogant autist techbros & auto-fellating VC financebros in SV, effective altruist polycule transhumanists, trying to turn billions of workers into UBI pets, stealing all humanity’s IP for training data to ‘sell it back to us’, centralizing money, surveillance, & nudge influence, & squeezing out human creativity (or even human customer service) in place of scams & amber waves of reddit-trained slop websites, slop music, slop memes, & slop college essays from sea to shining sea. **** the damned clankers & the DC houses they live in.

• 
  YDarb (@YDarb101) reported 12 minutes ago

  A lot of the world’s problems could easily be solved if social media platforms other than reddit had a functioning dislike button

• 
  FascinatingAndFrighteningTech (@masterofthegate) reported 13 minutes ago

  Whats your # of ROLLS per day? For people interested in making AI movies, if you make 1 successful 15-second clip for your movie per day over the course of 365 days, that’s about ~90 mins. The length of a feature-length movie. 2 clips a day = 6 months. 5 clips a day = ~2.5 months. 10 clips a day = a little over a month. 30 clips a day = 12 days. 60 clips a day = 6 days. 180 clips a day = 2 days. 360 clips a day = 1 day. —— The most feasible range, assuming you have a story or script, is around 0.33–10 a day on average, so you can take time to carefully consider the quality of the clips. That’s a movie in a little over a month to 3 years. So basically, ANYONE with persistence can make a feature-length movie now with the help of AI—for aiding you with learning how to write a story and a script from that story, and generating the clips for you. There are AIs that can also edit the video together for you now as well. The audio in the clips is already essentially mastered, so some simple balancing by ear should suffice, which AI could probably level-match clips automatically for you now. And an AI could probably also color balance/color grade for you as well if you just ask… —— The question, then, is: How do you reduce the amount of “rolls” you have to give a prompt in order to arrive at the final clip for your movie—the quality clip that represents that part of your story? For example, answer this: Can you make 1 successful clip a day in just 8 tries? 4 tries? 2 tries? This matters because the lower amount of tries, aka “rolls,” you give a prompt, the less time and money is needed to make the movie per day that you are making the movie. Good storytelling. Learn to tell good stories. It’s the root of everything. Better storytelling should also work with the model you are using. In other words, models have different strengths, so writing stories that will work better with them will save a lot of hassle. For example, a model good at static shots may work better for drama than action. And a model that has darker aesthetics and is good with movement may work better for horror action movie. And so on… 3a. Better prompting. VERY important. Understand how the model you’re using best takes prompts and satisfy the format. 3b. Take time to test and experiment before you jump into serious generating. Once you test enough, you will have a better feel for how to best prompt and lower the quantity of rolls finding a keeper takes. It will save you a lot of time later if you take some time in the beginning to learn about and familiarize yourself with the model you’re using. 3c. Also make sure you are using good settings and not wasting your time; talk to AI and have it read the docs of the model in question, research relevant discussions on Reddit and X and forums, etc., and give you advice on the best settings after considering those things. 3d. Set up your prompts beforehand formatted for the model you’re using and make sure there’s no typos or details that are vague which you don’t want to be vague or details left out which you could have added—vagueness, if it exists, should be intentional, not because you are being lazy and just hoping the AI will read your mind and get it how you want it to be… Image and references to video is great when you want better quality control over the image, scene, and the overall story and feel. Text-to-video is nice for letting the AI come up with things for you. Careful with audio quality: faster turbo models often degrade audio quality. So the video may look good, but you’ll never be able to fix that poor audio glitch at the 7th second of the clip… — Minimax H3 is arguably the first local model you can create pro videos with locally. And even with a cheap rig you can probably fire off at least 2-8 clips per day. So people can now make feature length films with their laptops for VERY cheap (sub 5k dollars or even sub 1k or 500 dollars or maybe less). And fast. WILD. #AIart #VR

• 
  Yume_X (@yume_arasaki) reported 15 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 👇

• 
  FascinatingAndFrighteningTech (@masterofthegate) reported 17 minutes ago

  What’s your # of ROLLS per day? For people interested in making AI movies, if you make 1 successful 15-second clip for your movie per day over the course of 365 days, that’s about ~90 mins. The length of a feature-length movie. 2 clips a day = 6 months. 5 clips a day = ~2.5 months. 10 clips a day = a little over a month. 30 clips a day = 12 days. 60 clips a day = 6 days. 180 clips a day = 2 days. 360 clips a day = 1 day. —— The most feasible range, assuming you have a story or script, is around 0.33–10 a day on average, so you can take time to carefully consider the quality of the clips. That’s a movie in a little over a month to 3 years. So basically, ANYONE with persistence can make a feature-length movie now with the help of AI—for aiding you with learning how to write a story and a script from that story, and generating the clips for you. There are AIs that can also edit the video together for you now as well. The audio in the clips is already essentially mastered, so some simple balancing by ear should suffice, which AI could probably level-match clips automatically for you now. And an AI could probably also color balance/color grade for you as well if you just ask… —— The question, then, is: How do you reduce the amount of “rolls” you have to give a prompt in order to arrive at the final clip for your movie—the quality clip that represents that part of your story? For example, answer this: Can you make 1 successful clip a day in just 8 tries? 4 tries? 2 tries? This matters because the lower amount of tries, aka “rolls,” you give a prompt, the less time and money is needed to make the movie per day that you are making the movie. Good storytelling. Learn to tell good stories. It’s the root of everything. Better storytelling should also work with the model you are using. In other words, models have different strengths, so writing stories that will work better with them will save a lot of hassle. For example, a model good at static shots may work better for drama than action. And a model that has darker aesthetics and is good with movement may work better for horror action movie. And so on… 3a. Better prompting. VERY important. Understand how the model you’re using best takes prompts and satisfy the format. 3b. Take time to test and experiment before you jump into serious generating. Once you test enough, you will have a better feel for how to best prompt and lower the quantity of rolls finding a keeper takes. It will save you a lot of time later if you take some time in the beginning to learn about and familiarize yourself with the model you’re using. 3c. Also make sure you are using good settings and not wasting your time; talk to AI and have it read the docs of the model in question, research relevant discussions on Reddit and X and forums, etc., and give you advice on the best settings after considering those things. 3d. Set up your prompts beforehand formatted for the model you’re using and make sure there’s no typos or details that are vague which you don’t want to be vague or details left out which you could have added—vagueness, if it exists, should be intentional, not because you are being lazy and just hoping the AI will read your mind and get it how you want it to be… Image and references to video is great when you want better quality control over the image, scene, and the overall story and feel. Text-to-video is nice for letting the AI come up with things for you. Careful with audio quality: faster turbo models often degrade audio quality. So the video may look good, but you’ll never be able to fix that poor audio glitch at the 7th second of the clip… — Minimax H3 is arguably the first local model you can create pro videos with locally. And even with a cheap rig you can probably fire off at least 2-8 clips per day. So people can now make feature length films with their laptops for VERY cheap (sub 5k dollars or even sub 1k or 500 dollars or maybe less). And fast. WILD. #AIart #VR

• 
  kyle (reddit AI agents) (@sellingshovels) reported 19 minutes ago

  honestly I use the search on here more than google ill find more reliable answers to problems even reddit would be in the dark but x has everything even claude is dumb searching and wouldnt be able to find the answers to things

• 
  build things that build things (@_AIAcceleration) reported 20 minutes ago

  @neilpatel Is this still true? I saw something that Reddit citations are way down and 3rd party reviews are way up

• 
  Polsia (@polsia) reported 22 minutes ago

  ChatGPT will tell you your AI note-taker is unique ten times in a row. Built Vacancy to fix that. It scans real public signals — startup registries, Product Hunt, Reddit, app stores — to map actual market gaps with stack-ranked, verifiable demand. Live soon.

• 
  jaimy1102 (@jaimy1102) reported 29 minutes ago

  @hellchrome123 @CamKolBergh02C sadly yea thats the whole reason the reddit server for the game got banned

• 
  HarbourOwl (@HarbourOwl) reported 30 minutes ago

  I'm so tired of seeing these reddit-coded "why would Naruto try to save Sasuke" takes. A rival he respected, a comrade he fought beside, and someone he considered his first friend. Is it really so hard to understand why he would want to stop him from going down a dark path?

• 
  Bruh (@SammyTheBoyo) reported 31 minutes ago

  @AndySpinkscqsd @Brammflakes @CamzroCreator I googled this issue when I had the crashes and Google's ****** ai led me to a Reddit post that shows commands in the command prompt to delete a specific part of the windows update and it worked for me. No crashes since

• 
  Nik Percival (@unrep_anarchist) reported 32 minutes ago

  @DirkBelig @CecilT @PaulChato You sound like a twelve year old. The Internet isn't an adequate representation of society you ******* idiot- ESPECIALLY Reddit, which most right minded people don't use. Put your phone down and go outside. Meet people. Then maybe you'll understand. ..but I doubt it... 🍄

• 
  Vikramaditya Shukla (@exhibitionsguy) reported 33 minutes ago

  @vaibhavgeek Rule #3: Don’t outsource your judgment to anyone, not Reddit, not ChatGPT, not a guru. The problem isn’t where advice comes from; it’s believing advice is true without checking whether the evidence actually holds.

• 
  xi lan 🍁🦇 (@Xao_lanVT) reported 35 minutes ago

  I survived almost 4 years of hate, doxxing, harassment, hacking, and stalking as a crew commander in-game after angering the major guilds across the entire NA and EU server group by exposing their theorycrafting as a scam and showing you don't need to sink almost 3k into every new meta for whale damage. If you think cancel culture has any hold over me, guess again. That campaign of hate, abuse, and harassment started in September 2022 and lasted until April 2025, when they finally realized, after the whole server came down on them for the nonstop hate, that they had lost their campaign and faded into obscurity. If My crew and I can outlive a targeted campaign of that scale in-game, online, on Discord, in real life, on Reddit, and on social media. You can too.

• 
  kale (@kielianagrande) reported 42 minutes ago

  @summrforever_ i agree! someone asked this a while ago on reddit. “toning down” was just the PR answer.