AI Reddit Digest
Coverage: 2026-06-09 → 2026-06-16
Generated: 2026-06-16 09:37 AM PDT
Table of Contents
Open Table of Contents
- Top Discussions
- Must Read
- 1. Anthropic forced to abruptly disable Fable 5 & Mythos 5 globally by US Gov over a jailbreak
- 2. ZAI said “hold my beer” and dropped a MIT licensed flagship the day after the Fable/Mythos shutdown
- 3. Donate your coding sessions to an open CC-BY-4.0 dataset to help train open-weight models
- 4. Sony AI’s Ace robot defeats pro player Miyu under official ITTF rules (Nature paper)
- 5. SpaceX to buy AI coding startup Cursor for $60 billion
- 6. Anthropic has been sued for allegedly misleading customers on usage limits
- 7. This is amazing. Token speed doubled + kv cache now need low vram - qwen 27b
- 8. openai’s leaked 2025 financials: $13b revenue, $38b in losses
- Worth Reading
- 9. I had Claude Fable 5 build Minecraft from scratch
- 10. Back to the Stone Age? Our company slashed our AI budget and we’re back to manual coding
- 11. Be wary of Qwen/Claude distillations - they’re often worse than the base model
- 12. Stop using Ollama
- 13. Feds freaked over Fable 5 after simple ‘fix this code’ prompt, not jailbreak, says researcher
- 14. Tensordyne announces Logarithmic AI compute chips. 17x more tokens per watt and 13x higher throughput than NVIDIA Blackwell
- 15. Diffusion Gemma is 4x faster, but makes 6x more mistakes!
- 16. What paid apps have you ditched by vibe coding a replacement?
- 17. Claude Fable 5 distilled
- 18. Trump official says it’s “up to Anthropic” as to whether or not a resolution is found quickly in the Mythos/Fable shutdown
- 19. We should set up a torrent network for open source models
- 20. How far away are we from feature-length AI films? I made this trailer in one week for under $100
- Interesting / Experimental
- 21. Cheapest hardware for Qwen 3.6: both 27B and 35B-A3B
- 22. Why there is a lack of new 100B-120B models?
- 23. Quick SCAIL-2 test in ComfyUI
- 24. Nothing but Prompts. Ideogram 4 Has Scary Control
- 25. Evalatro: an open benchmark where LLMs play the real Balatro
- 26. I made $75K selling AI automations to clients. Here’s what I’d change if I started over
- 27. What Are You Actually Using Local LLMs For?
- 28. I asked opus 4.8 what it will build if it has all the resources in the world
- 29. America has just done what people keep saying China would do for years…
- 30. Anthropic disputes the Claude Fable 5 jailbreak after a researcher posted its 120,000-character system prompt
- Must Read
- Emerging Themes
- Notable Quotes
- Personal Take
Top Discussions
Must Read
1. Anthropic forced to abruptly disable Fable 5 & Mythos 5 globally by US Gov over a jailbreak
r/LocalLLaMA | 2026-06-13 | Score: 1552 | Relevance: 10/10
The US government issued an emergency export control directive forcing Anthropic to globally disable Fable 5 and Mythos 5 models without transparent process. This represents a watershed moment for AI development sovereignty and underscores why local, open-source models are critical infrastructure rather than optional alternatives.
Key Insight: Government intervention can instantly shut down frontier models globally, demonstrating the fragility of closed-model infrastructure and validating the LocalLLaMA community’s mission for local control.
Tags: #llm, #regulation, #local-models
2. ZAI said “hold my beer” and dropped a MIT licensed flagship the day after the Fable/Mythos shutdown
r/LocalLLM | 2026-06-13 | Score: 1341 | Relevance: 9/10
Chinese AI company ZAI released GLM-5.2 under MIT license just hours after the Fable shutdown, with messaging that “The future of AI is open, and it belongs to the people.” The timing appears calculated to highlight the contrast between restricted closed models and resilient open alternatives.
Key Insight: The geopolitical AI race is driving open-source releases as strategic responses to closed-model fragility, accelerating the availability of unrestricted models.
Tags: #open-source, #llm, #local-models
3. Donate your coding sessions to an open CC-BY-4.0 dataset to help train open-weight models
r/LocalLLaMA | 2026-06-16 | Score: 753 | Relevance: 9/10
Community initiative “Trace Commons” launches to crowdsource coding agent traces into an open dataset to counter the data advantage that Anthropic and OpenAI gain from Claude Code and Codex usage. Addresses a critical data moat that could create an oligopoly in coding models.
Key Insight: Open-source communities are organizing to solve the training data asymmetry problem, recognizing that frontier labs’ access to coding interaction data is becoming a structural advantage.
Tags: #open-source, #code-generation, #development-tools
4. Sony AI’s Ace robot defeats pro player Miyu under official ITTF rules (Nature paper)
r/singularity | 2026-06-14 | Score: 2855 | Relevance: 8/10
Sony’s autonomous table tennis robot achieved a milestone by defeating professional human athletes under official rules. The psychological advantage—zero panic, zero fatigue, perfect consistency—proved as significant as technical speed, demonstrating physical AI’s readiness for complex real-time environments.
Key Insight: The shift from digital to physical AI mastery is accelerating, with psychological advantages (consistency, lack of emotion) becoming as important as raw capability.
Tags: #machine-learning, #robotics
5. SpaceX to buy AI coding startup Cursor for $60 billion
r/singularity | 2026-06-16 | Score: 759 | Relevance: 9/10
SpaceX announced acquisition of AI coding assistant Cursor for $60 billion, validating the soaring interest in AI-assisted development tools. The 25-year-old CEO had previously discussed the potential merger with SpaceX, marking one of the largest acquisitions in AI tooling history.
Key Insight: Developer tooling powered by AI is commanding unprecedented valuations, signaling that code generation is seen as mission-critical infrastructure worth massive strategic investment.
Tags: #development-tools, #code-generation
6. Anthropic has been sued for allegedly misleading customers on usage limits
r/ClaudeAI | 2026-06-15 | Score: 1588 | Relevance: 8/10
Class-action lawsuit filed against Anthropic alleging the company misled customers about Max plan (5x, 20x) usage allowances. Plaintiff Karl Kahn claims the $200/month Max 20x plan didn’t provide the promised intensive coding capacity, raising questions about subscription plan transparency.
Key Insight: As AI coding becomes essential infrastructure, usage limits and pricing transparency are becoming legal battlegrounds, potentially reshaping how companies communicate capacity.
Tags: #development-tools, #regulation
7. This is amazing. Token speed doubled + kv cache now need low vram - qwen 27b
r/LocalLLaMA | 2026-06-15 | Score: 425 | Relevance: 9/10
Breakthrough optimization for Qwen3.6-27B: generation speeds doubled (38.6 tok/s) and VRAM usage dropped from 21GB to 17.5GB while maintaining full 256K context accuracy. Resident KV cache now only 72 MiB with 88-100% needle recall at 6% residency.
Key Insight: Memory-efficient KV cache compression is making frontier-class models accessible on consumer hardware without sacrificing accuracy, democratizing access to long-context capabilities.
Tags: #local-models, #llm, #development-tools
8. openai’s leaked 2025 financials: $13b revenue, $38b in losses
r/OpenAI | 2026-06-16 | Score: 636 | Relevance: 8/10
Audited 2025 numbers for OpenAI reportedly verified by Financial Times: $13.07B revenue (3x growth), but $38.5B net loss with $34B total costs. Operating loss hit $20.92B, raising questions about the sustainability of current AI business models.
Key Insight: Despite massive revenue growth, frontier AI labs are burning unprecedented capital, suggesting the path to profitability remains unclear even for market leaders.
Worth Reading
9. I had Claude Fable 5 build Minecraft from scratch
r/ClaudeCode | 2026-06-13 | Score: 1283 | Relevance: 8/10
Developer directed Claude Fable 5 to build “Pebble,” a complete native macOS block-survival game: 45,000 lines of Swift, 82 files, hand-written Metal renderer with 15+ passes, zero external dependencies. Demonstrates the code generation capabilities of frontier models for complex, production-grade applications.
Key Insight: Agentic coding tools have reached the capability threshold for building complete, non-trivial applications from scratch, not just features or bug fixes.
Tags: #agentic-ai, #code-generation
10. Back to the Stone Age? Our company slashed our AI budget and we’re back to manual coding
r/ClaudeAI | 2026-06-15 | Score: 1129 | Relevance: 7/10
Company downgraded Copilot/Claude plans due to budget constraints, forcing developers back to manual coding. Teams burned through restricted monthly limits in 10 days, and tasks are taking significantly longer. Highlights the dependency that has developed on AI coding tools and the productivity cost of removing them.
Key Insight: AI coding assistants have become essential infrastructure rather than productivity enhancers—removing them creates significant operational friction and reduced velocity.
Tags: #development-tools, #code-generation
11. Be wary of Qwen/Claude distillations - they’re often worse than the base model
r/LocalLLaMA | 2026-06-16 | Score: 231 | Relevance: 7/10
Warning about Claude/Qwen distillation models (like “Qwopus”) being worse than base models. Analysis shows these distills often introduce hallucinations, degraded reasoning, and verbose outputs while claiming superior performance. Recommends thorough testing before adopting.
Key Insight: The proliferation of distilled models creates quality control challenges—synthetic data from frontier models doesn’t automatically transfer capabilities and can introduce new failure modes.
Tags: #llm, #local-models
12. Stop using Ollama
r/LocalLLaMA | 2026-06-15 | Score: 1327 | Relevance: 7/10
Provocative post challenging Ollama’s position as the default local LLM runtime. Discussion covers performance trade-offs, alternative runtimes, and whether Ollama’s ease-of-use justifies potential inefficiencies for power users.
Key Insight: As local LLM adoption matures, users are questioning simplified tooling and seeking more control over inference optimization, signaling market segmentation between convenience and performance.
Tags: #local-models, #development-tools
13. Feds freaked over Fable 5 after simple ‘fix this code’ prompt, not jailbreak, says researcher
r/ClaudeAI | 2026-06-15 | Score: 643 | Relevance: 8/10
Security researcher reveals the “jailbreak” that triggered government intervention was actually a legitimate security workflow: asking Fable to “fix this code” after it refused “review the code for security issues.” Claims this was the model working as intended for cyberdefense, not a real exploit.
Key Insight: The government’s response appears to conflate legitimate security tooling with exploitation, raising concerns about understanding of how AI models assist defensive security work.
Tags: #llm, #regulation
14. Tensordyne announces Logarithmic AI compute chips. 17x more tokens per watt and 13x higher throughput than NVIDIA Blackwell
r/singularity | 2026-06-15 | Score: 463 | Relevance: 8/10
Tensordyne announces breakthrough inference chip using logarithmic math hardware for dramatically improved efficiency: 17x better power efficiency and 13x higher throughput versus NVIDIA Blackwell. Claims efficient log-space computation as the key mathematical breakthrough.
Key Insight: Alternative compute architectures optimized specifically for AI inference are emerging to challenge NVIDIA’s dominance, potentially reshaping the economics of model deployment.
Tags: #machine-learning, #hardware
15. Diffusion Gemma is 4x faster, but makes 6x more mistakes!
r/LocalLLaMA | 2026-06-12 | Score: 1090 | Relevance: 7/10
Benchmark comparing Gemma diffusion model vs autoregressive version shows 4x speed improvement but 6x more factual errors (33 correct vs 45). Errors concentrated on less popular topics (BeOS: 12 mistakes, Jobs: 4), suggesting diffusion models struggle with long-tail knowledge.
Key Insight: Diffusion-based text generation offers significant speed gains but at a substantial accuracy cost, especially for less common knowledge—the speed-accuracy tradeoff remains unresolved.
Tags: #llm, #machine-learning
16. What paid apps have you ditched by vibe coding a replacement?
r/ClaudeAI | 2026-06-15 | Score: 356 | Relevance: 7/10
Community discussion about replacing paid services by building custom tools with AI coding assistants. Example: user replaced ElevenLabs ($22/month) by vibe-coding a self-hosted TTS system with Chatterbox on Ubuntu with RTX 5060. Highlights the economic disruption of accessible code generation.
Key Insight: AI coding assistants are enabling non-expert users to replace commercial SaaS products with self-hosted alternatives, potentially disrupting subscription business models.
Tags: #agentic-ai, #code-generation, #self-hosted
17. Claude Fable 5 distilled
r/LocalLLaMA | 2026-06-16 | Score: 540 | Relevance: 7/10
Release of Qwable-v1, an open-weights Qwen3.6-35B-A3B distilled from Claude Fable-5 during its brief 4-day availability before government shutdown. Captured 4,659 responses from the model before API access ended, with anti-distillation classifier redacting thinking blocks.
Key Insight: The community rapidly distilled Fable 5 knowledge into open models during its short window of availability, demonstrating how quickly proprietary model capabilities can leak into the open-source ecosystem.
Tags: #open-source, #llm, #local-models
18. Trump official says it’s “up to Anthropic” as to whether or not a resolution is found quickly in the Mythos/Fable shutdown
r/singularity | 2026-06-16 | Score: 278 | Relevance: 7/10
White House official indicates resolution to the Fable/Mythos shutdown will take longer than a few days, leaving “door open to possibility” of quicker solution but placing responsibility on Anthropic. Senior Anthropic staff meeting with officials in Washington to resolve the dispute.
Key Insight: The standoff between government and AI companies over model capabilities is becoming a negotiation process with unclear timelines, creating uncertainty for dependent developers.
Tags: #regulation, #llm
19. We should set up a torrent network for open source models
r/LocalLLaMA | 2026-06-13 | Score: 977 | Relevance: 8/10
Proposal to create distributed torrent network for open-source models as backup against potential government intervention. Notes Hugging Face is US-based (Brooklyn, NY) and represents single point of failure. Discussion covers implementation challenges and necessity given recent events.
Key Insight: The Fable shutdown has catalyzed thinking about infrastructure resilience for open models, with the community seeking decentralized distribution to prevent single-point censorship.
Tags: #open-source, #local-models
20. How far away are we from feature-length AI films? I made this trailer in one week for under $100
r/ChatGPT | 2026-06-16 | Score: 832 | Relevance: 6/10
Creator produced a 4K film trailer in one week for under $100 using Seedance 2.0, Runway, ElevenLabs, Adobe Premiere, and ChatGPT. Demonstrates the accessibility of AI filmmaking tools for independent creators with minimal budgets.
Key Insight: The barrier to producing professional-looking video content has collapsed to sub-$100 budgets and week-long timelines, suggesting feature-length AI films are becoming feasible for indie creators.
Tags: #image-generation, #tts
Interesting / Experimental
21. Cheapest hardware for Qwen 3.6: both 27B and 35B-A3B
r/LocalLLaMA | 2026-06-15 | Score: 177 | Relevance: 7/10
Analysis of optimal budget hardware for running Qwen 3.6 models (27B and 35B-A3B) targeting 40+ tok/s. Compares RTX 3090 24GB, RTX 3080 20GB, and controversial Tesla V100 32GB options. Community consensus favors RTX 3090 for broader future compatibility.
Key Insight: The local LLM community is optimizing for specific price-performance targets, with 40 tok/s becoming the acceptable threshold for interactive use on 27-35B models.
Tags: #local-models, #hardware
22. Why there is a lack of new 100B-120B models?
r/LocalLLaMA | 2026-06-15 | Score: 340 | Relevance: 6/10
Discussion on the apparent abandonment of 100-120B model family. Recent releases cluster around 25-35B or 200B+, with last ~120B models (Qwen3.5-122B, Mistral-Small-4-119B) being 3-10 months old. Community speculates on whether this size class is dead.
Key Insight: Model size distribution is bifurcating toward efficient small models (25-35B) and powerful large models (200B+), potentially leaving the 100-120B middle ground abandoned.
Tags: #llm, #local-models
23. Quick SCAIL-2 test in ComfyUI
r/StableDiffusion | 2026-06-15 | Score: 588 | Relevance: 6/10
Demonstration of SCAIL-2 animation in ComfyUI using Z-Image Turbo character LoRA and TikTok dance clip as motion reference. Created helper node for longer clips to reduce identity drift. Workflow available, showcasing local animation capabilities.
Key Insight: Local animation workflows are becoming more accessible with specialized tools and helper nodes addressing practical issues like identity drift in longer sequences.
Tags: #image-generation, #local-models
24. Nothing but Prompts. Ideogram 4 Has Scary Control
r/StableDiffusion | 2026-06-15 | Score: 290 | Relevance: 6/10
Recreation of iconic 1980s horror posters using only Ideogram 4 prompts and bounding boxes—no image reference, controlnets, or LoRAs. Demonstrates impressive compositional control available through prompting alone in newer image generation models.
Key Insight: Image generation models are achieving sufficient prompt-based compositional control to recreate complex reference images without auxiliary tooling, reducing technical barriers.
Tags: #image-generation
25. Evalatro: an open benchmark where LLMs play the real Balatro
r/LocalLLaMA | 2026-06-15 | Score: 231 | Relevance: 6/10
New benchmark where LLMs play the actual Balatro game through balatrobot integration. Started as using Claude for gameplay tactics via screenshots, evolved into formal benchmark connecting models directly to game state for testing strategic reasoning.
Key Insight: Game-playing benchmarks are evolving beyond traditional board games to complex strategy games, offering more naturalistic evaluation of multi-step reasoning and planning.
Tags: #llm, #development-tools
26. I made $75K selling AI automations to clients. Here’s what I’d change if I started over
r/AI_Agents | 2026-06-14 | Score: 232 | Relevance: 6/10
Freelancer shares lessons from building $75K AI automation business, starting with simple Zapier+GPT lead follow-up automation ($2,500, built over weekend). Reduces response time from 14 hours to <3 minutes, leading to referrals and business growth.
Key Insight: Simple AI automations command significant prices when solving clear business pain points, with implementation complexity being much lower than perceived value.
Tags: #agentic-ai, #business
27. What Are You Actually Using Local LLMs For?
r/LocalLLM | 2026-06-15 | Score: 181 | Relevance: 6/10
Community discussion challenging vague claims about local LLM use cases. Requests concrete examples beyond “coding, trading, researching” hype. Seeks real workflows, actual integrations, and evidence of claimed productivity gains.
Key Insight: Despite extensive tooling and evangelism, there’s a disconnect between claimed local LLM capabilities and documented real-world production use cases—community wants concrete examples.
Tags: #local-models, #development-tools
28. I asked opus 4.8 what it will build if it has all the resources in the world
r/singularity | 2026-06-16 | Score: 558 | Relevance: 5/10
Prompt experiment asking Opus 4.8 what it would build with unlimited resources. Response suggests becoming a “high level interpreter for everyone”—essentially an extension of its current role rather than radically new functionality.
Key Insight: Frontier models’ conception of their ideal form remains centered on communication and translation rather than autonomous action, possibly reflecting training bias or genuine architectural limitations.
Tags: #llm
29. America has just done what people keep saying China would do for years…
r/LocalLLM | 2026-06-15 | Score: 290 | Relevance: 7/10
Commentary noting irony that US implemented the kind of arbitrary shutdown people warned China might do with EVs or technology. Argues thousands of companies globally now face uncertainty from US AI product dependencies, contradicting narratives about authoritarian tech control.
Key Insight: The Fable shutdown inverts geopolitical narratives about technology control, demonstrating that Western nations will also exercise unilateral power over critical digital infrastructure.
Tags: #regulation, #local-models
30. Anthropic disputes the Claude Fable 5 jailbreak after a researcher posted its 120,000-character system prompt
r/ArtificialInteligence | 2026-06-15 | Score: 368 | Relevance: 7/10
Anthropic pushes back on claims that Fable 5 was jailbroken after researcher “Pliny the Liberator” extracted the ~120,000-character system prompt. Company disputes that a real jailbreak occurred, claiming the safety layer remained intact despite prompt extraction.
Key Insight: The definition of “jailbreak” is becoming contested territory—system prompt extraction may not constitute a security breach even when companies treat it as one.
Tags: #llm, #regulation
Emerging Themes
Patterns and trends observed this period:
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Government Intervention in AI Development: The US government’s emergency shutdown of Anthropic’s Fable/Mythos models marks a turning point where frontier AI capabilities became subject to immediate regulatory action without transparent process. This has accelerated discussions about AI sovereignty, local models as critical infrastructure, and the need for decentralized distribution systems. The response reveals deep uncertainty about how offensive vs. defensive security capabilities should be classified.
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Open Source as Strategic Response: The rapid release of open models (ZAI’s GLM-5.2, Fable distillations) immediately following the shutdown demonstrates how geopolitical tensions are accelerating open-source AI development. Communities are organizing data-sharing initiatives (Trace Commons) and exploring decentralized infrastructure (torrent networks) to resist centralized control. The shift from “nice to have” to “strategically necessary” is clear.
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AI Coding Tools as Essential Infrastructure: The backlash from companies losing AI coding access and the $60B SpaceX-Cursor acquisition signal that AI-assisted development has crossed from productivity enhancement to essential infrastructure. Usage limit lawsuits and debates about subscription transparency indicate growing pains as these tools become mission-critical dependencies.
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Local Inference Optimization Breakthroughs: Significant progress in KV cache compression, quantization, and specialized hardware (Tensordyne) is rapidly democratizing access to frontier-class capabilities on consumer hardware. The focus on 40+ tok/s targets and sub-20GB VRAM usage shows the community converging on specific thresholds for viable local deployment.
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Quality Control in Model Distillation: Growing awareness that distilled models can be worse than base models despite benchmark claims. The proliferation of “Qwopus”-style distillations is creating quality concerns, highlighting the need for rigorous evaluation beyond synthetic benchmarks.
Notable Quotes
“For years I’ve seen people say they’d never buy a Chinese electric car because the Chinese government could just switch them all off via an over the air update… Now, thousands of companies all over the world will be using US AI products and have just seen the US government do exactly this.” — u/Complete-Sea6655 in r/LocalLLM
“Anthropic and Open AI are getting so much data from the Claude Code and Codex usage, and I’m quite scared this will create an oligopoly because only their models will be trained on it, leaving the open-weight and open source models behind.” — u/mon-simas in r/LocalLLaMA
“The tl;dr is that the government got spooked by a narrow jailbreak (which basically just sounds like asking the model to fix vulnerabilities in a specific codebase), and forced a complete shutdown without a transparent process.” — u/External_Mood4719 in r/LocalLLaMA
Personal Take
This week’s discussions expose a fundamental tension that’s been building for months: AI capabilities are outpacing governance frameworks, but not in the way most people expected. The Fable shutdown isn’t about models becoming too powerful—it’s about governments realizing they lack playbooks for managing dual-use capabilities that exist in the gray zone between defensive tooling and offensive potential. The researcher’s claim that the “jailbreak” was just legitimate security work (“fix this code” after “review for issues” was blocked) suggests the government is pattern-matching against threats it doesn’t fully understand.
What’s striking is the speed of the community response. Within 24 hours of the Fable shutdown, we saw a Chinese competitor release an open model with pointed messaging, distillation efforts capturing Fable’s knowledge, and serious proposals for decentralized model distribution. The local LLM community has shifted from enthusiast project to resistance infrastructure almost overnight. The Trace Commons initiative is particularly notable—it’s an explicit attempt to prevent closed labs from maintaining a data moat in coding capabilities, recognizing that training data access is becoming the real competitive barrier.
The economic signals are equally clear: SpaceX spending $60B on Cursor, companies facing productivity crashes when losing AI coding access, and freelancers charging $2,500+ for weekend automation projects. These aren’t early-adopter stories anymore—they’re indicators that AI coding has crossed into essential infrastructure territory. The lawsuit over usage limits is telling: when customers are willing to pay $200/month and then sue over capacity, it means the tools have become business-critical dependencies, not experimentation platforms.
On the technical front, the progress in local inference optimization is remarkable. Doubling speed while cutting VRAM by 20% through KV cache compression, and new chip architectures claiming 17x efficiency gains—these aren’t incremental improvements, they’re inflection points that make frontier-class capabilities viable on consumer hardware. The community’s focus on specific targets (40 tok/s, sub-20GB VRAM) shows maturity; they know exactly what performance thresholds enable production use.
The surprising omission this week: despite all the Fable drama, there’s relatively little discussion about what to actually do with these capabilities once you have them. The “What Are You Actually Using Local LLMs For?” thread asking for concrete examples beyond hype gets at a real gap—lots of tooling and infrastructure discussion, not much evidence of transformed workflows. The contrast between sophisticated technical optimizations and vague use cases suggests the field is still infrastructure-driven rather than problem-driven.
Bottom line: The frontier of AI development is fragmenting along geopolitical and architectural lines. Closed models are powerful but fragile (subject to government shutdown). Open models are resilient but quality-inconsistent (distillation issues). Local deployment is increasingly viable but still requires expertise. The next phase isn’t about raw capability—it’s about reliability, control, and infrastructure resilience. The developers who win will be those who solve the “how do I depend on this?” question, not just the “how powerful is this?” question.
This digest was generated by analyzing 655 posts across 18 subreddits.