Adept

Adept AI

Adept AI

Adept AI

Adept AI

Adept AI

Adept AI

Adept AI

Adept AI

Adept AI

Adept 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

Got access to Claude Code through my employer. I’m not in IT, I manage a sales unit. How do I make the best use of it?

I see a lot of folks here that are clearly more technically adept than I in coding. I’m looking for ideas I can use Claude Code to automate tasks like pulling/organizing/ visualizing data. Any other ideas on how I ca make the most of Claude code? submitted by /u/picabuser [link] [comments]

How to perform a trivial type of refactor?

I am frustrated with attempting simple refactors. For example I have 18kLOC across 2 rust source files and I need to get them broken down to regain some sanity in the project. I want to do a simple refactor, e.g. a large series of "move function X into file Y" and a round of fiddling with imports at the end. Once you reach a certain level of scale the operation might still be doable by the model by raw-dogging the code edit... But it's clearly inefficient. In the past i was pretty excited about code actions in editors being able to largely handle this sort of thing with traditional code, but it turns out these capabilities did not become ubiquitous. For example vs code has had a nice code action for typescript and javascript called "Move function" which would allow you to enter a specified file to target for this action. That is an example of a beautiful tool call capability to give to an agent to be able to make. But I don't see anything out there that can do this. I only see having to pony up like 50% of my 5h limit to do this trivial refactor i would be able to do in 2 minutes by hand, but i know our tech would be capable of doing amazing things that are a lot more intricate if we only tried to support them. Instead we are fully committed to rawdogging all the damn code into the black box no matter how simple a transformation we intend to make to them. So I could maybe open up the editor and manually do this particular refactor in 20 minutes, but I don't code by hand anymore... It looks like a pretty serious gap. But I've been researching today and I have found essentially nothing. LSP and AST/treesitter based MCPs abound, but they are largely about giving your agent the ability to quickly search codebases. That's fine but not what I need (modern models and harnesses are adept at just using bash wizardry to crush that task without added AST or LSP based help, and besides LSP is never worth spinning up and using over bare grep search because LSPs are almost always insanely inefficient. Overall maybe LSP code actions are the correct abstraction to build off of for this kind of stuff, i do worry that engaging LSPs could be impractical due to performance issues but I think LLMs could potentially excel at autonomously working through trickiness in their config during startup and I guess when an operation is done, the LSP could get unloaded. submitted by /u/michaelsoft__binbows [link] [comments]

Bit of a strange question?

i am looking for what would be the best Al for a project. for reference I am not at all adept at using AI. I like simulating MMA fights using the game EA SPORTS UFC 5. I have kept track and multiple google documents the events of 10 tournaments and 3 side show events, detailing the record of fighters, summary of each match and method of victory. I would love an Al tool that can manage all the information in a database of sorts, so if i ask something like has X fighter ever thought Y fighter etc it could tell me. It would be really useful for matchmaking and getting me hyped for the fights. submitted by /u/WhoMattB [link] [comments]