Stability Audio

Stability Audio AI

Stability Audio AI

Stability Audio AI

Stability Audio AI

Stability Audio AI

Stability Audio AI

Stability Audio AI

Stability Audio AI

Stability Audio AI

Stability Audio AI

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

9 months, 60+ cells — what I observed building with AI

I've been building a modular personal operating system on top of Claude Code for 9 months. ~60 isolated folders ("cells"), each owning one concern — text-to-speech, clipboard management, dictation, radial menu, keyboard cleaner, screenshot, GIF recording, activity tracking, and more. I run 6-8 agents daily, 8-10 hours. These are patterns I noticed over 9 months. Not rules — observations. Your mileage will vary. Heads-up: this isn't a starter guide. I'm assuming you've already been building with Claude Code (or similar) for a while. If you're just starting out, some of this may feel overwhelming — skim the headers and come back when a section clicks. For context — here's me building with a broken arm, one-handed, in Turkish: https://www.youtube.com/watch?v=Akh2RHCzab0&t=628s — not a narration of this post, just a session where some of these patterns show up in use (custom menus, voice, conv tool, invariants). The #1 thing I noticed: my input > my prompt I noticed AI doesn't follow my prompts the way I expect. What seems to happen is — AI follows ME. My brain, my real-time corrections, my navigation. I write a system prompt. My brain is in that context. I intuitively correct AI when it drifts. When I step away from that context — the prompt alone seems to fail within a few turns. I noticed this clearly when I was tired. After 8-10 hours, same system prompt, same hooks, same architecture — things started breaking. The navigation was off, the input was off. It felt like the controller was my brain, not my text. **Priority stack — what I observed matters most:** rank what what I noticed ──── ─────────────────────── ────────────────────────────────────── 1 my input brain context seemed to matter most 2 project context fractals, folder structure, existing code 3 system prompt + hooks helps, but felt less impactful than 1 and 2 4 manifest registry YAML front-matter — guessable felt better than strict 5 truth tables layer + gate — AI processes one layer at a time Fractals: AI seems to copy the nearest cell This reminded me of company culture — people sometimes copy the person next to them more than the rules document. I noticed AI doing something similar. I have ~60 folders with the same structure: Cells/{name}/ ├── MANIFEST.md ← YAML front-matter: name, platform, commands, hooks ├── product/ │ ├── engine/ ← immutable logic (switch/dispatch) │ └── runtime/ ← mutable data (seed/config/UI) └── fossil/ ← quick-access snapshots for me (git is too many hops when I need speed) When AI needs to create a new cell, I noticed it looks at the nearest existing cell and copies the pattern. No instruction needed. The convention seemed to become the instruction. (I learned later this kind of structure has a name — apparently it's called swarm architecture. I didn't set out to build one; the cell-shape just kept paying off until the system was already operating that way.) cell-browser My cell browser. 60+ folders, each with a colored icon. (1) The grid shows every cell — database, dictation, elevenlabs, speech, etc. (2) Tabs at top: Context, Logs, Commands, Transforms — for controlling the system. (3) While talking, I pick a cell and copy its context to AI. (4) Bottom tabs give different views: File Paths, Source Content, Symbols, Manifest. The MANIFEST.md registers each cell into parent cells (telegram, mac, claude) via front-matter. AI reads structured metadata instead of scanning all source code. clipboard-panel Clipboard panel. Left: searchable list of everything I copied, with timestamps. Right: rendered MANIFEST.md preview — elevenlabs cell YAML front-matter visible (type, pain, capabilities, consumer cells). This is what AI reads instead of scanning source files. What I've come to believe: **guessable + predictable felt better than strict + verbose** — for my case. Switch cases: I noticed the compiler catches more than instructions I use Swift exhaustive enums. Each state = explicit case. The compiler catches missing ones. public enum RunContext: String, CaseIterable, Sendable { case claudeCodeSession // auto-view default case claudeCodeNoSession // browse default case standalone // no Claude Code env case piped // raw output case fzfCallback // internal mechanism } conv-tool Terminal: `conv 4f7bf66f...` extracted a session — 16 turns, ~17.2k content, ~186.2k context. Token breakdown: User 1.8k (4%), Thinking 24.7k (68%), Response 5.4k (15%), Tools 2.4k (6%), Agents 1.6k (4%). Each category is a case in a Swift enum. I noticed tables seem to work better than if/else chains for me. If AI needs to handle a new case, the compiler forces it. No silent miss. I tell AI: make every state transformation obvious. When I click the record button, idle → recording. When I click stop, recording → processing. When I click cancel, recording → discarded. Every transition = explicit switch case. If I forget the context, AI can see the code and think correctly. Truth tables: every decision is a

Reasoning comparison. Audio to voice, voice to voice and text to text.

A while back (December 2025), OpenAI advised that they are moving to a voice first future. However, I haven't seen much refinement in voice to voice. Does anyone have any suggestions to improve their interactions? My text to text and audio to text is perfectly fine. Here are the issues I am seeing: - Assistant reverts to generic over friendly. I assume this is prioritising safety guidelines and such which isn't a problem but the safety overrides reasoning and is incredibly fragile around nuanced cognitive tasks. Example: I was unpacking machinery that I had to setup and have experience with that I have in my profile/about me. Text to text explained the setup checks and documentation as well as gotchas. Voice to voice: Explained how to carefully open a box. Including handling tape and box cutter and box placement. - Unable to handle slang or localised language. Text to text knows the AU common words. Example: Arvo = afternoon in Australia Text to text: Understands and acts accordingly. Voice to voice: the text indicates Arvo was read but the response was avocado related. Over all, I've run a few tests and by measuring consistency, behaviour stability, security posture and interaction comparisons. At a loss of what to do or where to go. Is there further development on this that I may have missed or a product roadmap anyone knows of? submitted by /u/ValehartProject [link] [comments]