Mehdi

@praneetdutta @GoogleDeepMind @HelloPomo go pomo!!

I quit @GoogleDeepMind to build a marketing team that never stops working. Today, we're launching @HelloPomo. Your marketing, from insight to execution, unified. Pomo watches everything happening across your market, tells you exactly what to do next (the budget shift to make, the trend to jump on, the buyer to answer), then prepares the assets for your approval. @JoeC7007 and I have raised a $4.5M Seed led by @KindredVentures (@stevejang ), with backing from @SevenStars_VC (@stevenl ), @svangel (@AndreaShuyuWang ), @databricks, Timeless (@gauravahuja, @eshan_shetty), @645Ventures, @scottbelsky, @mghissassi & @mascarom. Reply with your website. We'll run Pomo for 10 companies and show you the growth opportunities it finds and the work it creates.

@bneyshabur @mirendil @a16z @kleinerperkins Congrats 🎉

Today, I’m excited to formally announce @mirendil with my amazing co-founders Harsh Mehta, Shayan Salehian, and Tara Rezaei! We’re fortunate to work with @a16z and @kleinerperkins, who led our seed round of $200M, followed by a major investment from NVIDIA, among others. Mirendil exists to accelerate science and technology, and through them, to help solve humanity's most pressing problems. Self-accelerating AI R&D is the most direct path to delivering on AI's broader promise, which is why we believe the most important application of AI is AI itself. Get this loop right, and it compounds. It fundamentally changes the rate of progress itself across all domains. We believe this capability should be democratized. It should be used to power all scientific efforts trying to innovate at the frontier. There are far more important problems—and broader ones—than any single lab can take on, so more groups should be able to pursue them. This pulls concentration of power away from a few labs: businesses and science labs can own their AI and infrastructure, keep their margins, and control their own destiny instead of ceding it all to a single AI lab. We’re a small team with a singular focus. Our founding team consists of 20 researchers and engineers from frontier institutions including Anthropic, xAI, Google DeepMind, and OpenAI, united by a passion for science and a drive to build the technologies that move it faster. If you want to build the system that builds systems, join us! @HarshMeh1a, @shayan_, @tararezaeikh

@frydwia @Accel @viktor__com 🔥🚀

Today, we’re announcing Viktor’s $75M Series A, led by @Accel . @viktor__com was supposed to be a small experiment. It became the AI coworker 10x'ing real businesses. $15M in annualized revenue run rate. In 10 weeks. – Small companies saving millions of dollars – Sourcing hundreds of thousands in new revenue in their first 30 days – Whole teams getting half their week back – Companies running 40% leaner without cutting output Viktor is not another AI tool. It’s the first true AI employee. The vision that has been with us since 2023 when we started the company has finally been shipped. Back then, it was just the two of us, with a very small but dedicated team, iterating for years. Failing multiple times. Showing products that users didn't even want to test! But we never gave up. Our decisions were often wrong. Certainly more often than not! We kept trying. Now we’ve shipped something people love. Worth every sleepless night. Every sacrifice. The best employees don’t need to be told what to do. Neither does Viktor. Grateful to @Accel, our team, our earliest users, and everyone who believed this category could be bigger than chat.

@sarahookr @adaption_ai 🚀🎉

Most model trainings have failed outside of frontier labs. Even inside frontier labs, knowing how to train for very different capabilities is often a matter of taste. Today, we introduce AutoScientist by @adaption_ai which sets out to change that.

@sudip_r0y 🚀🎉

Less than 1,000 people know how to shape a frontier model. AutoScientist is our attempt to change that. Describe the outcome. It automates the rest.

@DJ_Mankowitz @a16z @generalcatalyst @xtxmarkets @MattEvantic @_CommonMagic 🎉🚀

Today Ethos announces our $22.75M Series A led by @a16z, with participation from @generalcatalyst, @xtxmarkets, @MattEvantic, and @_CommonMagic. Ethos turns what you know into recurring income - matching your expertise to expert calls, research, AI training, fractional work, and full-time roles. 35,000 people are joining Ethos every week. People are making $10,000 every month on Ethos. AI shouldn’t replace you. It should make you irreplaceable. Build your profile at askethos.com.

@sarahookr 🚀🚀

Today, I am very proud to share our $50M in funding to build AI systems that continually learn across languages, cultures and industries. Even more important proud to share why this is important to us here: adaptionlabs.ai

Beginnings are very special. Today is an important day for @adaptionlabs. Today a handful of one-size-fits-all-models are optimized for the average use case. Averages erase the exceptional. Everything intelligent adapts. So should AI.

@sudip_r0y @Adaption 🔥🔥

Today we’re sharing what we’re building at @Adaption, and announcing a $50M seed. Most AI today is powerful, but rigid. The world changes. Models don’t. We’re building AI systems that can adapt as the world does. 🧵

Introducing Odyssey-2 Pro—a frontier world model that generates long-running, interactive simulations in 720p! We're also launching the first world model API, to enable devs to build magical apps. We're now in the GPT-2 era of world models. Let the explosion of apps commence!

Gradium is out of stealth to solve voice. We raised $70M and after only 3 months we’re releasing our transcription and synthesis products to power the next generation of voice AI.

Introducing Odyssey-2: instant, interactive AI video! This is a general-purpose model that instantly imagines 20FPS video you can interact with in open-ended ways. It feels like true sci-fi. Type a few words, and AI instantly imagines a video that feels alive.

@interaction congrats team Poke!

Say hi to Poke.com! 👋🏼🌴

Today, we’re launching Parsed. We are incredibly lucky to live in a world where we stand on the shoulders of giants, first in science and now in AI. Our heroes have gotten us to this point, where we have brilliant general intelligence in our pocket. But this is a local minima. We now have an ecosystem of burgeoning tasks where each requires a different kind of intelligence, a different context, a whole host of implicit assumptions and latent knowledge and domain expertise that is very difficult to cram into a system prompt. The big labs want you renting their $50k/month amnesiac interns that forget everything between conversations. Generic behemoths that get quantised, versioned and deprecated behind the scenes, where the only element of control you have is your messy monolithic user prompt. We want people who need their own intelligence to be able to not only access it, but also control it. And whilst the big general models are unbelievably good chatbots and coding agents and purveyors of the world, specialisation of intelligence is required. Clinical scribes, marketing compliance agents, legal red-lining models, insurance policy recommenders, the list goes on. And so that’s what Parsed does: deploy your own frontier model that actually learns. We eval your specific task, build a custom evaluation harness, optimise a model just for you, and host it with continual learning. We bake all the context and knowledge of your task into the model itself, from your engineers to your domain experts to customer feedback, all in a tight SFT → RL loop, with useful interpretability made possible by the open-source ecosystem we build on top of. No more 2000-word prompts with seventeen "IMPORTANT: NEVER DO X" clauses. Your model gets better at YOUR job every single day; the amnesiac pseudo-gods have had their run. Your model, your data, your moat. Let's build 🫡

1/ Introducing ⚪️CircleGuardBench — a new benchmark for evaluating AI moderation models. Here’s why it’s cool: – Tests harm detection, jailbreak resistance, false positives, and latency – Covers 17 real-world harm categories – First benchmark designed for production-level evaluation 🤗 blog: huggingface.co/blog/whitecirc… 🏆 leaderboard: huggingface.co/spaces/whiteci…

@RonakGandhj @StructifyAI Congrats guys 🎉👏

Being the “data guy” used to suck. @StructifyAI gives them a break. We’ve deployed >1M of our AI agents to turn the web into custom, clean datasets for customers. Now, we’re making it free so you can too. Tag your team's data jockey to get you both a little something on us 👀

@bwhite5290 🚀🎉👏

Announcing Axiom: Eliminating drug toxicity, without using animals! Alex Beatson and I founded Axiom a little over over a year ago with the mission of eliminating drug toxicity by replacing traditional experiments, such as animal testing, with AI models. We are excited to announce that we've raised $15M in seed funding from Amplify Partners, Dimension Capital, and Zetta Ventures to get this done. It has been a wild ride since we got started. Within our first year, we've created the world's largest human toxicity dataset, encompassing data on over 100,000 molecules generated through proprietary lab methods combined with 1000s of clinical outcomes structured using LLMs. With this dataset, we trained an AI model which predicts drug-induced liver injury more accurately than traditional physical experiments, addressing a leading cause of clinical failure recently exemplified by Pfizer's discontinued obesity drug. We publicly launched this model at the Society of Toxicology conference in March and the response has been tremendous! We're actively conducting or finalizing pilot studies with diverse partners, including six of the twenty top pharmaceutical companies, a major agrochemicals firm, many biotechnology companies, hedge funds, and strategic partners. We're honored to partner with these insanely great scientists to rigorously assess our AI models and explore how to best integrate them into their drug discovery workflows. The expertise of these scientists is crucial for validating and thoroughly evaluating these new methods. We have multiple publications out already but we plan to share a lot more data from these early pilot studies in the coming months. At the same time, the FDA recently announced plans to phase out animal testing over the next few years, emphasizing that animal studies will become "the exception rather than the norm." The HHS Secretary RFK has said that "they have found AI to be much more precise in identifying the impacts of toxins" on the human body. With our recent progress, Axiom is uniquely positioned to support the FDA and scientific community in realizing this shift. We will eliminate clinical trial failures due to toxicity. And we don't need animals to do it. For more, you can read Andrew Dunn's Endpoints article on Axiom in the comments.

Baked-in Brilliance: Reranking Meets RL 🍞 Meet mxbai-rerank-v2, our second-gen rerankers built on Qwen2.5 (thanks, @Alibaba_Qwen) & refined with GRPO from @deepseek_ai. They outperform open & closed-source models while staying fully open. 👇

Agents are making their way to our browsers, automating research and transactional workflows. But what if agents could build and optimize software products to eventually run a whole company? We're about to find out! @airstreet has led a $3M day one round for @Fern_Labs 👀👇

I’m launching Topology, an early-stage venture firm dedicated to frontier technology. We've raised an oversubscribed $75M Fund 1. Topology backs founders who put everything on the line to bend the arc of human progress. Meet us at the edge. @topology_vc