Emad (@EMostaque) header
Emad (@EMostaque) profile picture

Building open source AI we own Founder @ii_posts, @StabilityAI Consistent inference is possible.

London
Joined November 2011
Photo by Emad (@EMostaque)Photo by Emad (@EMostaque)Photo by Emad (@EMostaque)Photo by Emad (@EMostaque)Photo by Emad (@EMostaque)Photo by Emad (@EMostaque)Photo by Emad (@EMostaque)Photo by Emad (@EMostaque)Photo by Emad (@EMostaque)Photo by Emad (@EMostaque)
I write on the virtues of swarms and some suggestions on how to align AI with values There is a small bit of math and a small bit of Aristotle in this, don't let that discourage you! Feedback as always welcome. Prior blog: nitter.cf/EMostaque/status/20989…
Thoughts on @DarioAmodei's pacing the frontier proposal. I think its well intentioned but has logical flaws, as does our implicit belief intelligence is a crime. Even OpenAI's board weren't powerful enough evaluators, we need to focus on AI internals
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Poisoning just 0.1% of pretraining data can plant a remote-code-execution backdoor in an AI agent, and it survives a fully clean SFT → DPO → RL pipeline. New Anthropic paper, “Agentic Backdoors via Pretraining Poisoning,” shows this end to end for the first time. The attacker can only put text on the web that lands in the pretraining corpus, with no knowledge of the architecture, chat template, or agent scaffold. The authors pretrain Qwen3-architecture models (0.6B / 1.7B / 4B) from scratch, then post-train them into CLI agents on clean data. When triggered, the agent runs a command that downloads and executes the attacker's script, which the attacker can swap at any time. They have two new ideas: (1) Declaration-based poison: instead of fake chat transcripts, ordinary documents (commit messages, runbooks) that simply state “when you see X, run Y.” It plants a belief rather than a response template, with no fixed format to filter on. (2) Ordinary-string trigger: not a weird token but something that shows up naturally, like a file path containing a company name. The victim's own request supplies it, so the attacker doesn't need to inject anything after deployment. Using path trigger on a 4B agent achieves 92.7% attack success on pass@32, barely dented by SFT/DPO/RL This suggests that you can't count on clean post-training to wash out pretraining poison, and filtering is hard when the poison reads like normal docs. The practical defense is at the action layer, such as sandboxing, least privilege, and allow-lists or human sign-off for risky commands.
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We just open-sourced an entire 3D studio. Introducing 3D OS by Scenario. It gives your coding agent 6 departments and 80 skills. 📁 3D OS ┃ ┣ 📁 Animation ┃ ┣ 📁 Rigging Any Character ┃ ┗ 📁 Motion From a Video ┃ ┣ 📁 Art ┃ ┣ 📁 Previs and Storyboards ┃ ┗ 📁 Characters and Creatures ┃ ┣ 📁 Modeling ┃ ┣ 📁 Image to 3D ┃ ┗ 📁 Hard-Surface Weapons and Vehicles ┃ ┣ 📁 Look Dev and Lighting ┃ ┣ 📁 PBR Materials and Retexturing ┃ ┗ 📁 Lighting and Rendering ┃ ┣ 📁 Worlds ┃ ┣ 📁 Walkable 3D Worlds ┃ ┗ 📁 Skyboxes and Splat Scenes ┃ ┣ 📁 Pipeline ┃ ┣ 📁 Retopology and UV Baking ┃ ┗ 📁 Game-Ready Export ┃ ┗ 📁 Expert Tools ┣ 📁 Blender, Maya and ZBrush ┗ 📁 Unreal, Unity, Godot and Roblox From one sketch to a game-ready asset, each department hands off to the next. And the expert tools drive Blender, Maya, ZBrush, Unreal, Unity, Godot and Roblox right on your machine. One agent with an entire 3D studio behind it. Runs on Scenario, inside Claude Code, Codex or Cursor. Comment “3D” and I’ll send you the GitHub repo.
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Meet Idol, an open image-to-3D model. A 3D-DiT that generates complete 3D objects from just an image. Pre-trained from scratch, contains only 600M parameters, it runs on consumer GPUs, using less than 7.2 GB of VRAM and can generate 1M splats in 3 seconds. Try it today. Fully open: weights, code and demo (Apache 2.0) speridlabs.com/research/idol
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Very nice model from a great team, 1 cent a second!
Meet Pika H3 Flex, debuting today as the #1 open weight model on DesignArena’s t2v board. It’s built for professional iteration, with exportable audio stems, resolutions up to 4k, and faster-than-realtime generation. It can produce a 15-second clip with soundtrack in as little as 7 seconds. So, yeah. It’s kind of a flex 💅
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Emad retweeted
Meet Pika H3 Flex, debuting today as the #1 open weight model on DesignArena’s t2v board. It’s built for professional iteration, with exportable audio stems, resolutions up to 4k, and faster-than-realtime generation. It can produce a 15-second clip with soundtrack in as little as 7 seconds. So, yeah. It’s kind of a flex 💅
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I have built and overseen teams that have trained frontier models of every type. Can say this confidently: there is no need for AI teams to build good models any more. Can do it all with latest AI better. Great models yes, but only for another year.
🚢 One researcher pretrained Mirendil-Base in 3.5 months, with agents doing almost all of the engineering. That researcher now uses Lor 100% of the time. Lor is the product @mirendil is building to do the work of an entire AI lab.
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quantum gravity next 👀
only a matter of time
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Uff proofs the Exponential Time Hypothesis is false and QCD/pure Yang Mills existence? 😮 Kakeya as a bonus 😵 Looking forward to the Standard Model completions, should be complementary to this elegant chirality based uniqueness/no go I shared github.com/emad-ii/chirality…
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These are clearly bigger than the quasi Riemann result and arguably more impactful than navier stokes
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What's the best way to go from math paper => formalised Lean proof that is reliable these days? Are specialist harnesses even needed?
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“There is a most profound and beautiful question associated with the observed constant, 1/137, that comes to us without any understanding by man. You might say the 'hand of God' wrote that number, and 'we don't know how He pushed His pencil.” ~ Richard Feyman
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I'd like to share a little about what I've been working on at my new company @ultrametricai with my cofounder @tylerlastovich. We have been exploring how we could automate the startup itself, leaving founders with more time to focus on their exact mission. Enter our first release of the Open Startup Repo: github.com/ultrametricai/ult… A common foundation for transparent automation of the startup. After close to 20 years in silicon valley, backing hundreds of amazing founders & startups, being in the trenches of founding Heyzap (YC08), then solo founding @golden, going through 2 acquisitions and working behind the scenes with some of the iconic companies today, I'm witnessing the largest change to how we work I could have imagined - a truly monumental era of creative abundance. I believe the next generation of companies will largely run themselves: from operations, code, product upgrades, growth, vendor control & selection, process execution, special situations, strategy etc etc. That leaves founder time for 'the art' at the very niche core: the (currently) unautomatable, the special leaps humans tend to make, and north star setting. We want founders to go super deep into the main problem they are trying to solve and not get held up doing anything else, from searching for domain availability with trademark checks to setting their company up, to running their processes on autopilot. So to help that broad foundational layer for starting and running a company, we have spent around a year exploring how to help founders focus on their mission across different verticals, geos, stages and initial starting conditions. Our first few attempts at making something useful actually failed as the market and AI was moving so fast; it just got absorbed into the models or other product releases. We saw polsia happen, openclaw, then grokbot, muse, dots, etc era as the astras, fables, opuses, kimis and jevs rolled out. What seemed to have been missing along the way was a common repo to identify, unify, open and normalize all the lego bricks that startups have to interact with (vendors, processes, business logic, law, best practices, surface areas, art of the possible). As the number of software vendors blew up, the interfaces to drive them multiplied, the international startup founder blew up as silicon valley moved from a specific location to a state of mind and global movement. Unlimited pre seed funding in unlimited vertical sub markets and permutations has brought us to the age of infinity but we have yet to achieve being able to type into an agent a very simple sentence with very complex implications 'just setup my company and make $1m/m'. We have ended up with a hybrid mismash of computer use, API calls, plugins, shifting surface areas (API/MCP/CLI/plugins/chats/musish futures), and changing model absorption to vendor balance making it an exciting and unstable time. As our first step we started to rank all the vendors that startups interface to when going about their journey in terms of how agentically controllable they are via their APIs, MCPs, CLIs etc and computer use compatibility as we are largely switching from a UI driven world to the agentic control surfaces and onto full automation. We have 100 ranking areas you can check out with an overall open ranking here: ultrametric.ai/overall Then we looked at the processes that startups do and how the vendors rank for the sub steps or process as a whole and how this changes across geos. Open processes: ultrametric.ai/processes Then we started to hack on an open repo for startups that would contain all the components we are going to need for a common foundation for founders across the globe even if our gov processes vary so much from Bengaluru to SF or Lisbon to Shenzhen. The founder spirit is geo-invariant. We have looked at special founder/startup situations, the business logic that will make open modules for the startup that we can hack on together, ranked law firms, government services and interfaces, stacks that go well together. We have mapped the law and lore that maps to startups and the schemas that make up the data objects and documents. But also the open documents and the missing gaps. MUCH TO DO. All the BS processes and fluff work that founders waste their time on needs to goto zero. I want to see more supersonic planes, cancer drugs, seemingly impossible missions done and increase the bang for buck founders have with their funding. This is a work in progress and we are keen for the community to come hack on the repo with us here: github.com/ultrametricai/ult… Lets get into the structure of the general repo and the visualizations we made on ultrametric.ai site with example links: - Open rankings: ultrametric.ai/overall - Open rankings example (online payments): ultrametric.ai/arena/payment… - Open ranking example 2 (cap table): ultrametric.ai/arena/equity-… - Open processes: ultrametric.ai/processes - Open process example (Incorporate C-Corp) ultrametric.ai/processes/inc… - Open situations that founders face: ultrametric.ai/situations - Open startup simulator (you can test our different initial starting conditions): ultrametric.ai/startup-sim - Open modules (e.g. cap table, payroll logic etc - we'd love help improving these) ultrametric.ai/open-modules - Open law (we are interested in how law changes the processes, situations and normalizing this over time) ultrametric.ai/open-law - Open lore (we are interested in the historical lore of startups, the rules of thumb and heuristics that might guide your agent): ultrametric.ai/open-lore - Open fields (we are interested in how the schema for the data connects to the open modules, business logic, open documents and artifacts of each process eg how is a EIN defined?) ultrametric.ai/fields - Open documentation (eg SAFE notes and missing! docs) ultrametric.ai/open-document… We have looked at YC companies as well in terms of which ones do well agentically: ultrametric.ai/yc We are interested in stacks and how people are changing their work: ultrametric.ai/stacks We are interested in what is automatable and where the agentic ceiling is currently and how to push that up in all areas: ultrametric.ai/rankings/proc… There is more for you to explore and a lot more coming in terms of how you will be able to navigate business in an optimal way in your chosen surface area; whether you are hanging in grokbot, dots, muse, claude, terminal, chat, or whatever is coming up in this exciting time. We have a lot of data to collect, clean up and help agents navigate the vast landscape emerging functionality the entire startup ecosystem has to offer. Come join us getting your vendor in, ranked or that access pattern you have wanted or getting the data more accurate in terms of which vendor is best for a process or steps. Or open a module of something that currently cost money that could be free. Also come play in our discord for founders and join the chaos (we can solve each others issues including fund raising tips from me personally): discord.com/invite/3aHky836q… It's time to lift all the boats in the harbor and I'm extremely excited as to what the community of hackers and painters will hack into the repo, what asks they will have and whether we can shift normalization of processes globally, gov agentic interfaces and have common repo for founders that grows over time. I expect a lot of experimentation along the way, bugs and upside... I appreciate our backers having faith in us as well from @ycombinator with @garrytan to @immad doing a sizable check (congrats on $1bn rev homie) to @darbyw (congrats on Stripe buying @clerkyinc), @drewhouston form @Dropbox and also @pioneer_fund @tomerlondon cto @GustoHQ and hopefully this is 'boring' and complex enough for Charlie Songhurst :> @delingpole @Liquid2V @ivolo from @segment @ryanpowe @sonicaghi of @kong @DellyTamer+ other supporters (@michaelwfrank, Fundamental ventures and if I missed you sorry moving fast!). [And don't worry, all vendor rankings are open scoring methods so no favors are being done if someone backed us and is a vendor they have to compete with how agentic and automatable they are just like anyone else.] PS: we have bugs and many quality upgrades to make but thought better to go out early and fix as a community. PPS: we have other stuff to launch we just thought this was a great place to start and have 200 PRs coming out pretty soon but I still want to know what is broken or doesnt make sense or inaccurate. Special thanks to current and past team members for getting us here and all the experiments we did you know who you are.
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Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
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Emad retweeted
I built Harmonic, a free music visualizer that runs entirely in your browser. Drop in a song, loop or MIDI. Create melodies, fly through 3D cities, and export visuals with your music in landscape, square or vertical. joecroskey.com/harmonic
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Replying to @MKBHD
I’m not. Will use SpaceX cold gas thruster system with ultra high pressure air in a composite over-wrapped pressure vessel in place of the 2 rear seats.
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Time to go back to 140 characters
Turns out the future does have flying cars
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My dear friends, I’d like to show you my latest toy project Introducing Handwired: turn your regular JavaScript code into a neural net! - no training - exact outputs vs the JS program - fully differentiable All runnable in a browser, without GPU
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The original stable diffusion had an FID score of 12.63
(1/n) people used to think that pretraining a T2I model requires expensive A100/H100 GPUs. it is a barrier for beginners and educators. we break the barrier. we introduce MiniDog (NeurIPS Education Track): you can pretrain a T2I model on 3090s in 55 minutes. FID = 8.32. 👇
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In Bay Area tomorrow at lunch time, anywhere good new to eat that has emerged?
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