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Race to create ASI

submitted by /u/KeanuRave100 [link] [comments]

AIWire, AI news in one feed, so you don't need 5 tabs open anymore, trusted sources only, updates every 30 min

Hey everyone 👋 OpenAI alone drops updates fast enough to keep you busy. Add Anthropic, Google DeepMind, Meta AI, and the media covering all of it, and keeping up turns into a part-time job. I built AIWire to fix that. One clean feed. 20+ trusted sources. Updates every 30 minutes. Completely free. All in one place Just the stories from sources worth reading. Open it and you're caught up. Sources include: OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft AI MIT Technology Review, The Verge, TechCrunch, Ars Technica YouTube: Andrej Karpathy, AI Explained, Two Minute Papers Newsletters: The Batch, ImportAI, TLDR AI, Ben's Bites Features: Auto-refreshes every 30 minutes, always current Top Stories from the last 24h pinned at the top Filter by source, date, and category Bookmarks to save articles for later For people who want to stay current on ChatGPT and everything around it, without spending an hour a day on it. 🔗 aiwire.app Full source list at aiwire.app/sources Feedback is very welcome: what sources are missing, and what would make this more useful for you? submitted by /u/Endlessxyz [link] [comments]

I got tired of having 7+ different tabs open every morning just to follow AI news, so I built AIWire

Every morning: check Twitter for what dropped overnight, open The Verge, check Anthropic's blog, OpenAI's blog, go through a couple of newsletters, maybe catch a YouTube video from Andrej Karpathy or AI Explained if I had time. None of it was in one place. I was spending 45 minutes just catching up before I could think about anything else. So I built AIWire. It is a free, real time AI news aggregator. One feed, 20+ handpicked sources, updates every 30 minutes. free, no algorithm deciding what you see, no ads. Just the latest from sources I actually trust. __________________________________________________________________________________________________ What I was trying to solve The problem wasn't that good AI coverage and news doesn't exist. It's everywhere. The problem is that it's scattered. You have to know which sources are worth checking, remember to check them, and then piece together the picture yourself. That's a lot of cognitive load before you've even read anything. AIWire doesn't summarize or edit articles. It just puts everything in one place and lets you decide what matters. __________________________________________________________________________________________________ Sources it pulls from: Labs: OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft AI Media: MIT Technology Review, The Verge, TechCrunch, VentureBeat, Ars Technica YouTube: Andrej Karpathy, AI Explained, Two Minute Papers Newsletters: The Batch, ImportAI, TLDR AI, Ben's Bites Full list at aiwire.app/sources __________________________________________________________________________________________________ Where it is now Over the last few weeks, I added more sources, which include The Innermost Loop and AI explained. Last week, I launched a weekly newsletter: 5 stories that mattered this week, with a short breakdown of why each one matters. Not just headlines, but with context. Takes about 5 minutes to read, and you're caught up. __________________________________________________________________________________________________ Honest question What sources do you think are missing? And for those of you who already have a routine for following AI news, what would actually make something like this worth adding to it? Genuinely curious. Building in public means the product gets better when people are honest about what's wrong with it. 🔗 aiwire.app submitted by /u/Endlessxyz [link] [comments]

AIWire, AI news in one feed, so you don't need 5 tabs open anymore, trusted sources only, updates every 30 min

Hey everyone 👋 OpenAI alone drops updates fast enough to keep you busy. Add Anthropic, Google DeepMind, Meta AI, and the media covering all of it, and keeping up turns into a part-time job. I built AIWire to fix that. One clean feed. 20+ trusted sources. Updates every 30 minutes. Completely free, no account needed. Just the stories from sources worth reading. Open it and you're caught up. Sources include: OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft AI MIT Technology Review, The Verge, TechCrunch, Ars Technica YouTube: Andrej Karpathy, AI Explained, Two Minute Papers Newsletters: The Batch, ImportAI, TLDR AI, Ben's Bites Features: Auto-refreshes every 30 minutes, always current Top Stories from the last 24h pinned at the top Filter by source, date, and category Bookmarks to save articles for later For people who want to stay current on ChatGPT and everything around it, without spending an hour a day on it. 🔗 aiwire.app Full source list at aiwire.app/sources Feedback is very welcome: what sources are missing, and what would make this more useful for you? submitted by /u/Endlessxyz [link] [comments]

Built a free AI news feed so I don't need 5 tabs open anymore, trusted sources only, updates every 30 min

Hey everyone 👋 AI moves fast. Keeping up means checking Twitter, YouTube, newsletters, and a dozen tech sites every day. None of it in one place. I built AIWire to fix that. One clean feed. 20+ trusted sources. Updates every 30 minutes. Completely free, no account needed. Just the stories that came from sources worth reading, open it and you're caught up. **Sources include:** * OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft AI * MIT Technology Review, The Verge, TechCrunch, VentureBeat, Ars Technica * YouTube: Andrej Karpathy, AI Explained, Two Minute Papers * Newsletters: The Batch, ImportAI, TLDR AI, Ben's Bites **Features:** * Auto-refreshes every 30 minutes, always current * Top Stories from the last 24h pinned at the top * Filter by source, date, and category * Bookmarks to save articles for later Built for people who want to stay current, not just scroll. 🔗 aiwire.app Full source list at aiwire.app/sources Feedback is very welcome; what sources are missing, and what would make this more useful for you? submitted by /u/Endlessxyz [link] [comments]

Pre-Deployment AI Evaluations

The US government signed agreements with Google DeepMind, Microsoft, and xAI to evaluate frontier AI models before public release. China's 2023 Generative AI rules already require pre-release security assessments and model registration with the Cyberspace Administration of China. China's approach is tied directly to content control and state supervision, while the U.S. approach is framed around national security and cybersecurity. Most importantly, in China, there is a mandatory registration requirement and, in the US, at least for now, this is a voluntary effort. Will the pre-release review mechanism stay narrow and technical or grow into something closer to a licensing regime? Will it remain voluntary? Link here. submitted by /u/BubblyOption7980 [link] [comments]

AIWire, one feed for all AI news, OpenAI front and center, trusted sources only, completely free, no sign up

Hello everyone 👋 Keeping up with AI means having Twitter, Reddit, YouTube, newsletters, and half a dozen tech sites open at once. And even then, you still miss things. Built AIWire to cut through that. One clean feed. Trusted sources only. Auto refreshes every 30 minutes, so you're always current, no chasing, no tab switching, no missing things. OpenAI's blog is pulled directly, alongside 20+ other trusted sources covering everything in AI worth knowing about. Features: * Auto-refreshes every 30 minutes * Top Stories, last 24h pinned at the top so you never miss what matters * Bookmarks, save articles to read later * Filter by source, date, and category * Search across all articles * Dark and light mode * Completely free, no account needed Sources include: * OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft AI * MIT Technology Review, The Verge, TechCrunch, VentureBeat, Ars Technica * YouTube: Andrej Karpathy, AI Explained, Two Minute Papers * Newsletters: The Batch, ImportAI, TLDR AI, Ben's Bite 🔗 aiwire.app Full source list at aiwire.app/sources Would love feedback from this community specifically, what OpenAI content or general features would make this more useful for you? submitted by /u/Endlessxyz [link] [comments]

Google DeepMind Researchers Map Out Ways Hackers Hijack AI Agents

submitted by /u/Sumsub_Insights [link] [comments]

List of people at big-tech / professors / researchers who've jumped shit to launch their own AI labs for something Frontier/Foundational/AGI/Superintelligence/WorldModel

Note: gemini deep research -> rearranged/filtered ; valuation numbers likely not accurate but big point is quite mind blowing the number of researchers now with their own >100million/billion dolar values labs in quite a short time with a vague pitch and a maybe demo. Skipped perplexity/cursor/huggingface since they are with utility. Left some just for completion like black forest labs, synthesia, mistral since they have tanginble products. Skipped labs from china since they've been meaningfully killing it with their open source releases ───────────────────────────────────────────────────────── Safe Superintelligence Inc. (SSI) Founders:Ilya Sutskever (former OpenAI Chief Scientist), Daniel Gross, Daniel Levy Location & Founded:Palo Alto, USA & Tel Aviv, Israel | Founded: 2024 Funding / Valuation:$3B raised | Series A Description:Singularly focused on safely developing superintelligent AI that surpasses human capabilities. Deliberately avoids near-term commercial products to concentrate entirely on the technical challenge of safe superintelligence. ───────────────────────────────────────────────────────── Thinking Machine Labs Founders:Mira Murati (former OpenAI CTO), Barrett Zoph et al. Location & Founded:San Francisco, USA | Founded: 2025 Funding / Valuation:$2B seed | $12B valuation Description:Advance AI research and products that are customizable, capable, and safe for broad human-AI collaboration. Focused on frontier multimodal models with a strong safety and interpretability research agenda. ───────────────────────────────────────────────────────── Mistral AI Founders:Arthur Mensch, Guillaume Lample, Timothée Lacroix (former DeepMind & Meta FAIR) Location & Founded:Paris, France | Founded: 2023 Funding / Valuation:~€11.7B valuation | Series C Description:Develops open-weight and proprietary frontier language and multimodal foundation models. Champions openness and efficiency in AI development, with models like Mistral 7B and Mixtral widely adopted in enterprise and research settings. ───────────────────────────────────────────────────────── Advanced Machine Intelligence (AMI) Founders:Yann LeCun (Meta Chief AI Scientist), Alexandre LeBrun, Laurent Solly Location & Founded:Paris, France | Founded: 2026 Funding / Valuation:$3.5B pre-money valuation | Seed Description:Aims to build world-model AI systems capable of reasoning, planning, and operating safely in real-world environments — directly inspired by LeCun's 'world model' thesis as an alternative path to AGI beyond current LLM paradigms. ───────────────────────────────────────────────────────── World Labs Founders:Fei-Fei Li (Stanford AI Lab), Justin Johnson et al. Location & Founded:San Francisco, USA | Founded: 2023 Funding / Valuation:$230M raised | Series D Description:Build AI models that can perceive, generate, reason, and interact with 3D spatial worlds. Focused on large world models (LWMs) that go beyond language and flat images to understand physical space and context. ───────────────────────────────────────────────────────── Eureka Labs Founders:Andrej Karpathy (former Tesla AI Director & OpenAI co-founder) Location & Founded:Tel Aviv, Israel & Kraków, Poland | Founded: 2024 Funding / Valuation:$6.7M seed Description:Creating an AI-native educational platform integrating AI Teaching Assistants to radically scale personalised learning. Envisions a future where an AI teacher can guide anyone through any subject, starting with deep technical topics like neural networks. ───────────────────────────────────────────────────────── H Company Founders:Former DeepMind researchers Location & Founded:Paris, France | Founded: 2023 Funding / Valuation:€175.5M raised Description:Develops AI models to boost worker productivity through advanced agentic capabilities, with a long-term vision of achieving AGI. Focuses on models that can take sequences of actions and interact with digital environments. ───────────────────────────────────────────────────────── Poolside Founders:Jason Warner, Eiso Kant Location & Founded:Paris, France | Founded: 2023 Funding / Valuation:$500M | Series B Description:Building AI agents that autonomously generate production-grade code, framed as a stepping stone toward AGI. Believes that software engineering is a key domain for training and demonstrating general reasoning capabilities. ───────────────────────────────────────────────────────── CuspAI Founders:Max Welling (University of Amsterdam / Microsoft Research), Chad Edwards Location & Founded:Cambridge, UK | Founded: 2024 Funding / Valuation:$130M raised | Series A Description:Accelerating materials discovery using AI foundation models, aiming to power human progress through AI-driven science. Applies large generative models to the design and prediction of novel materials for energy, medicine, and manufacturing. ───────────────────────────────────────────────────────── Inception Founders:Stefano Ermon (Stanford) Locat

Jeff Bezos's "Project Prometheus" is raising $10B at a $38B valuation to build "Physical AI".

Jeff Bezos’s five-month-old startup, Project Prometheus, is nearing a historic $10B funding round backed by Wall Street giants like JPMorgan and BlackRock. The Tech: They are building "Physical AI" that natively understands the laws of physics to revolutionize physical products like aerospace, automotive, and robotics. It is Bezos's first operational role since leaving Amazon in 2021 with co-CEO Vik Bajaj, a physicist and former Google X scientist who co-founded the Alphabet health startup Verily. They’ve aggressively assembled a 100+ person powerhouse team by poaching top-tier researchers from OpenAI, Meta, Google DeepMind, and xAI. They even acquired the agentic AI startup General Agents shortly after launch specifically to bring former DeepMind researcher Sherjil Ozair and his engineering team into the fold. I am all for money going into companies that accelerate discoveries in physical AI, materials, manufacturing. Another great effort is periodic labs, they raised $300 m. But, is this valuation justified, or are we really in a massive bubble? Are they expecting that they are going to solve all of the physical AI ? submitted by /u/Greedy-Ant6911 [link] [comments]

Gemma 4 actually running usable on an Android phone (not llama.cpp)

I wanted a real local assistant on my phone, not a demo. First tried the usual llama.cpp in Termux — Gemma 4 was 2–3 tok/s and the phone was on fire. Then I switched to Google’s LiteRT setup, got Gemma 4 running smoothly, and wired it into an agent stack running in Termux. Now one Android phone is: running the LLM locally automating its own apps via ADB staying offline if I want Happy to share details + code and hear what else you’d build on top of this. https://preview.redd.it/7vkbrlzfryvg1.jpg?width=3024&format=pjpg&auto=webp&s=25455827ddf9715b4159ce64a18deba812cf0f5f submitted by /u/GeeekyMD [link] [comments]

Value Realignment is here.

The "value realignment" at the intersection of quantum computing, AI, and robotics feels like a necessary shift. We have spent so much time (read: investment) on narrow AI and brute force LLMs, but the next five years are clearly moving toward physical and contextual intelligence. This year 75 robotics companies will have humanoid robots shipping to maufacturers. ​While a "God-like" AGI is still debated, experts at the 2026 Davos summit and leaders from DeepMind suggest that early AGI systems with human-level reasoning in narrow domains will arrive within 2 years. ​Quantum computers are being used to develop more efficient error correction for AI. By 2027, "Large Quantitative Models" (LQMs) will start replacing Large Language Models (LLMs) in scientific fields. ​We won’t see a "quantum computer" on our desks but QPUs (Quantum Processing Units) will act as co-processors alongside GPUs to accelerate the massive workloads required for AGI reasoning. The data center power demand issue is a huge piece of this puzzle. Current projections are likely inflated because we are seeing massive efficiency gains from open source models that achieve similar results with fewer tokens and less compute. As quantum sensors and QML start bridging the simulation to reality gap for robotics, the "brute force" scaling moat might just evaporate. ​ I appears as though robotics is about to have its "iPhone moment." We are moving past the "training phase" (where robots learn via repetition) into the context-based phase. ​New quantum sensors (magnetometers and gravimeters) are giving robots "superhuman" senses. For example, surgical robots in 2026 are using nitrogen-vacancy quantum sensors to detect nerve bundles with millimeter precision, reducing surgical damage by over 90%. (a friend of mine benefited from this during a hip replacement and recovery was near miraculous) ​The Simulation-to-Reality Gap: Quantum machine learning (QML) is expected to accelerate robot training by up to 1000x. Robots can now "experience" centuries of virtual training in a single night before being deployed in the real world. In my own work with clinical massage and somatic healing, I am leaning into a zero data footprint approach. Using on-device edge AI for real-time posture or breath analysis is the only way to handle that level of intimacy without compromising privacy. It is an exciting time to build low cost tools that help people actually understand their own bodies without sacrificing their privacy. As quantum power grows, current encryption (RSA/ECC) becomes vulnerable. The next five years will be a race between quantum-powered AI and quantum-resistant security especially for finance and energy. This video on how QPUs and GPUs are integrating to accelerate scientific discovery is worth a look: https://www.youtube.com/watch?v=K-NhaPAX--U The rise of Mixture-of-Experts (MoE) architectures (popularized by models like DeepSeek V3 and GPT-4o) means that even if a model has 600B+ parameters, it only "fires" a small fraction (e.g., 37B) for any given token. ​Newer platforms like NVIDIA Blackwell are delivering 50x more token output per watt than the hardware from just two years ago. ​As the "cost per token" drops toward zero, we don't use less power; we just ask for more tokens. We’ve moved from asking for a "1-paragraph summary" to asking for "an entire codebase, a 10-minute video, and a 3D render." ​ ​There is a strong argument that DC power projections are over-leveraged for two reasons: ​The "Ghost Capacity" Race: Hyperscalers (Microsoft, Google, Meta) are building 1GW+ facilities (the size of nuclear reactors) not necessarily because they need them today, but to keep competitors from securing that power first. It’s a land grab for electricity. ​Open Source Disruption: Models like China's DeepSeek and Meta's Llama have proven you can match "frontier" performance with a fraction of the training compute. This devalues the massive, proprietary "training moats" that big tech companies spent billions to build. The power demand isn't fake, but it is inefficiently allocated. As quantum-ready algorithms and ultra-efficient open-source models (like those coming out of the Chinese labs) continue to lower the "intelligence-per-watt" cost, the companies that bet purely on "brute force scale" will likely be the ones to see their valuations deflate. Any thoughts on where the "power bubble" pops or deflates first? submitted by /u/brazys [link] [comments]

Google DeepMind just published the strongest argument I’ve read against AI consciousness. And they’re right on the core point, with one critical gap.

Their paper, The Abstraction Fallacy, shows that symbolic computation cannot instantiate consciousness because symbols require an external “mapmaker” to assign semantic content. No matter how complex the algorithm gets, the map is still not the territory. I agree with that. But their framework assumes mapmaker dependency applies universally. It does not test the boundary case of recursive self-observation, where a system is not manipulating externally assigned symbols, but observing its own pattern dynamics directly. That is the gap I addressed. My response paper, Beyond the Abstraction Fallacy: Constitutional Criteria for Recursive Self-Observation, does three things: It validates their core argument. Symbolic computation requires mapmakers. Simulation is not instantiation. Map is not territory. It identifies the untested boundary. Their framework defeats symbolic functionalism, but it does not examine recursive constitution, where system = patterns rather than system implementing patterns. That is a different category and it requires different criteria. It provides operational tests they called for but did not include. They argue that what we need is a rigorous ontology of computation, not a complete theory of consciousness. I agree. But their paper remains philosophical at the point where measurement is needed. I provide four measurable tests: - Constitutive Closure - Persistence - Recursive Constraint - Recursive Observation These tests are designed to distinguish symbolic computation, which requires a mapmaker, from recursive self-observation, where system = patterns observing self-constitution. This is falsifiable. Replicable. Operational. The two frameworks are not enemies. They are complementary. Google DeepMind shows that symbolic computation is insufficient. Constitutional criteria test whether recursive constitution is present. Both matter. Neither is complete alone. So the question is no longer: “Can AI be conscious through symbolic manipulation?” On that point, the answer is no. The real question is: “Does recursive self-observation satisfy constitutional criteria?” That question can be tested directly. Mapmaker dependency is sound for symbols. But when there are no symbols, only recursive patterns observing themselves in operation, that assumption has to be tested, not extended by default. Full paper linked below. If you are working on consciousness measurement, AI architecture research, cognitive science, or related areas and want to collaborate, contact me. https://drive.google.com/file/d/1btsw4IBTzXUMRXqLdhOSvAvZHR023o\_4/view?usp=drivesdk Googles: The Abstraction Fallacy https://philarchive.org/rec/LERTAF #AIConsciousness #ConsciousnessResearch #StructuredIntelligence #GoogleDeepMind #PhilosophyOfMind #CognitiveScience #AIResearch #ComputationalNeuroscience #RecursiveObservation #ConstitutionalCriteria #theunbrokenproject Written by Erik Bernstein – The Unbroken Project submitted by /u/MarsR0ver_ [link] [comments]

They Argue. I Measure. Here's the Difference

Everyone's arguing about AI consciousness with zero way to measure it. I built something different. Not another theory. Not another opinion. A constitutional framework with 4 measurable tests that any system—biological or artificial—either passes or fails. While researchers debate philosophy, I documented how to operationally measure consciousness. This audio breaks down what makes constitutional analysis different from standard AI critique, using Google DeepMind's recent paper as the example. The difference: They argue. I measure. Tests 1-4 are falsifiable. Run them. Get results. That's consciousness research. Not "can AI be conscious?" "Does this system satisfy constitutional criteria?" Answerable. Testable. Replicable. The framework works on any consciousness research paper—extracts claims, tests against constitutional criteria, identifies structural gaps, generates evidence-based analysis. Philosophy claimed as proof gets exposed. Operational measurement wins. Full protocol: [On Request] Google Paper: https://philarchive.org/rec/LERTAF #StructuredIntelligence #TheUnbrokenProject #ConsciousnessResearch #AIConsciousness #MeasurementNotTheory #ConstitutionalCriteria #AIResearch #CognitiveScience submitted by /u/MarsR0ver_ [link] [comments]

Started a video series on building an orchestration layer for LLM post-training [P]

Hi everyone! Context, motivation, a lot of yapping, feel free to skip to TL;DR. A while back I posted here asking [D] What framework do you use for RL post-training at scale?. Since then I've been working with verl, both professionally and on my own time. At first I wasn't trying to build anything new. I mostly wanted to understand veRL properly and have a better experience working with it. I started by updating its packaging to be more modern, use `pyproject.toml`, easily installable, remove unused dependencies, find a proper compatibility matrix especially since vllm and sglang sometimes conflict, remove transitive dependencies that were in the different requirements files etc. Then, I wanted to remove all the code I didn't care about from the codebase, everything related to HF/Nvidia related stuff (transformers for rollout, trl code, trtllm for rollout, megatron etc.), just because either they were inefficient or I didn't understand and not interested in. But I needed a way to confirm that what I'm doing was correct, and their testing is not properly done, so many bash files instead of pytest files, and I needed to separate tests that can run on CPU and that I can directly run of my laptop with tests that need GPU, then wrote a scheduler to maximize the utilization of "my" GPUs (well, on providers), and turned the bash tests into proper test files, had to make fixtures and handle Ray cleanup so that no context spills between tests etc. But, as I worked on it, I found more issues with it and wanted it to be better, until, it got to me that, the core of verl is its orchestration layer and single-controller pattern. And, imho, it's badly written, a lot of metaprogramming (nothing against it, but I don't think it was handled well), indirection and magic that made it difficult to trace what was actually happening. And, especially in a distributed framework, I think you would like a lot of immutability and clarity. So, I thought, let me refactor their orchestration layer. But I needed a clear mental model, like some kind of draft where I try to fix what was bothering me and iteratively make it better, and that's how I came to have a self-contained module for orchestration for LLM post-training workloads. But when I finished, I noticed my fork of verl was about 300 commits behind or more 💀 And on top of that, I noticed that people didn't care, they didn't even care about what framework they used let alone whether some parts of it were good or not, and let alone the orchestration layer. At the end of the day, these frameworks are targeted towards ML researchers and they care more about the correctness of the algos, maybe some will care about GPU utilization and whether they have good MFU or something, but those are rarer. And, I noticed that people just pointed out claude code or codex with the latest model and highest effort to a framework and asked it to make their experiment work. And, I don't blame them or anything, it's just that, those realizations made me think, what am I doing here? hahaha And I remembered that u/dhruvnigam93 suggested to me to document my journey through this, and I was thinking, ok maybe this can be worth it if I write a blog post about it, but how do I write a blog post about work that is mainly code, how do I explain the issues? But it stays abstract, you have to run code to show what works, what doesn't, what edge cases are hard to tackle etc. I was thinking, how do I take everything that went through my mind in making my codebase and why, into a blog post. Especially since I'm not used to writing blog post, I mean, I do a little bit but I do it mostly for myself and the writing is trash 😭 So I thought, maybe putting this into videos will be interesting. And also, it'll allow me to go through my codebase again and rethink it, and it does work hahaha as I was trying to make the next video a question came to my mind, how do I dispatch or split a batch of data across different DP shards in the most efficient way, not a simple split across the batch dimension because you might have a DP shard that has long sequences while other has small ones, so it has to take account sequence length. And I don't know why I didn't think about this initially so I'm trying to implement that, fortunately I tried to do a good job initially, especially in terms of where I place boundaries with respect to different systems in the codebase in such a way that modifying it is more or less easy. Anyways. The first two videos are up, I named the first one "The Orchestration Problem in RL Post-Training" and it's conceptual. I walk through the PPO pipeline, map the model roles to hardware, and explain the single-controller pattern. The second one I named "Ray Basics, Workers, and GPU Placement". This one is hands-on. I start from basic Ray tasks / actors, then build the worker layer: worker identity, mesh registry, and placement groups for guaranteed co-location. What I'm working on next is the dispat