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We finally have a product that helps with frontier AI research. Frontier LLMs are a) nerfed at AI research b) lack immediate GPU availability and c) lack access to the right skills and research material. "Teach me how vision models see" Goodfire Silico takes a vision transformer (ViT), loads... show more

Deedy

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74,579 views • 2 months ago •via X (Twitter)

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Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Base Labs

243,123 views • 1 month ago

"Our approach is to build products and conduct research that are in service of accelerated AI deployments. Our platform team builds tools and context primitives that enable faster deployment. Our research team builds frontier systems, including a state-of-the-art RL stack. We then take that research and product and forward-deploy with our customers to help deliver real value." Thanks Founders You Should Know for having us. Open roles at:

"Our approach is to build products and conduct research that are in service of accelerated AI deployments. Our platform team builds tools and context primitives that enable faster deployment. Our research team builds frontier systems, including a state-of-the-art RL stack. We then take that research and product and forward-deploy with our customers to help deliver real value." Thanks Founders You Should Know for having us. Open roles at:

Applied Compute

45,197 views • 6 months ago

The verdict is in! Frontier models can pass the bar, yet they struggle on comprehensive legal research Today we're releasing Legal Research Bench, a benchmark that measures models’ ability to solve realistic legal research tasks across eight areas of U.S. law Instead of awarding partial credit, Legal Research Bench measures whether a model can conduct exhaustive legal analysis. We grade against a strict, all-pass rubric written by practicing lawyers. A model only receives full credit if every required legal element is correct Claude Opus 4.8 leads with 43.8% all-pass accuracy, followed by GPT 5.5 (40.4%) and Claude Sonnet 4.6 (38.5%). While top models score around 80% with partial credit, none exceed 44% when every required legal element must be correct The gap between partial and all-pass accuracy shows how difficult it remains for AI to produce complete, reliable legal research. We hope that Legal Research Bench helps better measure, and ultimately close that gap Lots of exciting work happening in Legal AI from Harvey and Crosby. Excited for the legal research benchmarks ahead!

The verdict is in! Frontier models can pass the bar, yet they struggle on comprehensive legal research Today we're releasing Legal Research Bench, a benchmark that measures models’ ability to solve realistic legal research tasks across eight areas of U.S. law Instead of awarding partial credit, Legal Research Bench measures whether a model can conduct exhaustive legal analysis. We grade against a strict, all-pass rubric written by practicing lawyers. A model only receives full credit if every required legal element is correct Claude Opus 4.8 leads with 43.8% all-pass accuracy, followed by GPT 5.5 (40.4%) and Claude Sonnet 4.6 (38.5%). While top models score around 80% with partial credit, none exceed 44% when every required legal element must be correct The gap between partial and all-pass accuracy shows how difficult it remains for AI to produce complete, reliable legal research. We hope that Legal Research Bench helps better measure, and ultimately close that gap Lots of exciting work happening in Legal AI from Harvey and Crosby. Excited for the legal research benchmarks ahead!

Vals AI

17,501 views • 3 months ago

one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.

one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.

Max

10,537 views • 8 months ago

Manus, the new AI product that everyone's talking about, is worth the hype. This is the AI agent we were promised. Deep Research+Operator+Computer Use+Lovable+memory. Asked it to "Do a professional analysis of Tesla stock " and it did ~2wks of professional-level work in ~1hr!

Manus, the new AI product that everyone's talking about, is worth the hype. This is the AI agent we were promised. Deep Research+Operator+Computer Use+Lovable+memory. Asked it to "Do a professional analysis of Tesla stock " and it did ~2wks of professional-level work in ~1hr!

Deedy

1,523,876 views • 1 year ago

Can LLMs invent better ways to train LLMs? At Sakana AI, we’re pioneering AI-driven methods to automate AI research and discovery. We’re excited to release DiscoPOP: a new SOTA preference optimization algorithm that was discovered and written by an LLM! Our method leverages LLMs to propose and implement new preference optimization algorithms. We then train models with those algorithms and evaluate their performance, providing feedback to the LLM. By repeating this process for multiple generations in an evolutionary loop, the LLM discovers many highly-performant and novel preference optimization objectives! Paper: GitHub: Model: We proudly collaborated with the University of Oxford (Foerster Lab for AI Research (now part of BOLD)) and Cambridge University (Mihaela van der Schaar) on this groundbreaking project. Looking ahead, we envision a future where AI-driven research reduces the need for extensive human intervention and computational resources. This will accelerate scientific discoveries and innovation, pushing the boundaries of what AI can achieve.

Can LLMs invent better ways to train LLMs? At Sakana AI, we’re pioneering AI-driven methods to automate AI research and discovery. We’re excited to release DiscoPOP: a new SOTA preference optimization algorithm that was discovered and written by an LLM! Our method leverages LLMs to propose and implement new preference optimization algorithms. We then train models with those algorithms and evaluate their performance, providing feedback to the LLM. By repeating this process for multiple generations in an evolutionary loop, the LLM discovers many highly-performant and novel preference optimization objectives! Paper: GitHub: Model: We proudly collaborated with the University of Oxford (Foerster Lab for AI Research (now part of BOLD)) and Cambridge University (Mihaela van der Schaar) on this groundbreaking project. Looking ahead, we envision a future where AI-driven research reduces the need for extensive human intervention and computational resources. This will accelerate scientific discoveries and innovation, pushing the boundaries of what AI can achieve.

Sakana AI

556,037 views • 2 years ago

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Roberta Raileanu

105,157 views • 1 year ago

Ex-OpenAI Psyho says much of AI research is probably glorified hyperparameter optimization, with surprisingly few frontier lab researchers trying something genuinely new: "In competitive programming, we optimize for challenge and fun, and AI research is like the sad reality. In competitive programming, a good problem is one where when someone reads it, they have no clue how to solve it, optimized for creativity." "In AI research, it depends what part you're working in. A lot of people are probably doing a glorified hyperparameter optimization. There are people trying to come up with something completely new." "Agents reduce mundane work in AI research. You no longer have to thoroughly read papers. But you have 50 times more papers that are complete slop and completely useless. Maybe not 50, but it's a big problem today." "Every person in a lab would have completely different responsibility. I really wonder how many people in frontier labs, like at OpenAI or Anthropic, how many of them are trying something genuinely new. I would imagine that number is actually pretty low."

Ex-OpenAI Psyho says much of AI research is probably glorified hyperparameter optimization, with surprisingly few frontier lab researchers trying something genuinely new: "In competitive programming, we optimize for challenge and fun, and AI research is like the sad reality. In competitive programming, a good problem is one where when someone reads it, they have no clue how to solve it, optimized for creativity." "In AI research, it depends what part you're working in. A lot of people are probably doing a glorified hyperparameter optimization. There are people trying to come up with something completely new." "Agents reduce mundane work in AI research. You no longer have to thoroughly read papers. But you have 50 times more papers that are complete slop and completely useless. Maybe not 50, but it's a big problem today." "Every person in a lab would have completely different responsibility. I really wonder how many people in frontier labs, like at OpenAI or Anthropic, how many of them are trying something genuinely new. I would imagine that number is actually pretty low."

MTS

14,432 views • 1 month ago

Vlad Feinberg (Vlad Feinberg) is Google DeepMind’s pre-training area lead and I asked him all about how to land a job at a frontier lab like Google DeepMind, Anthropic or OpenAI. In this episode: • Skills frontier labs need • Differences between software engineering and AI research • Domains that matter for frontier research • Concrete steps engineers can take to get closer to research • Jeff Dean spot bonus story Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:33 - Skills frontier labs need 08:45 - The difference between AI research and engineering 21:41 - Domains that matter for the frontier 30:50 - Marketing yourself to frontier labs 35:13 - Concrete steps engineers can take 38:29 - Overview of pre-training areas 47:23 - Jeff Dean spot bonus story 50:14 - Favorite Gemini war story 58:59 - Advice for his younger self 01:03:07 - Outro

Vlad Feinberg (Vlad Feinberg) is Google DeepMind’s pre-training area lead and I asked him all about how to land a job at a frontier lab like Google DeepMind, Anthropic or OpenAI. In this episode: • Skills frontier labs need • Differences between software engineering and AI research • Domains that matter for frontier research • Concrete steps engineers can take to get closer to research • Jeff Dean spot bonus story Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:33 - Skills frontier labs need 08:45 - The difference between AI research and engineering 21:41 - Domains that matter for the frontier 30:50 - Marketing yourself to frontier labs 35:13 - Concrete steps engineers can take 38:29 - Overview of pre-training areas 47:23 - Jeff Dean spot bonus story 50:14 - Favorite Gemini war story 58:59 - Advice for his younger self 01:03:07 - Outro

Ryan Peterman

191,659 views • 3 months ago

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing

Latent.Space

62,796 views • 3 months ago

Today, we’re launching Prodigy Research (YC S26), the frontier AI trading research lab. We’re training the world’s best foundation model for quantitative finance. Our AI quant outperforms a top 10% trader at Jane Street, and we've already achieved more than 100% returns in live trading over our YC batch, while major indices were flat or down. Prodigy’s model beats Claude Fable and GPT-5.6 Sol at autonomous quant research. Our founders are brothers who have built strategies generating billions in PnL on a Jane Street trading desk, trained SoTA foundation models at Google DeepMind, and shipped cutting-edge AI to billions of users at Apple. A new era has arrived in the world of quantitative finance. To learn more, contact founders@prodigy.markets. Prodigy Research (YC S26)

Today, we’re launching Prodigy Research (YC S26), the frontier AI trading research lab. We’re training the world’s best foundation model for quantitative finance. Our AI quant outperforms a top 10% trader at Jane Street, and we've already achieved more than 100% returns in live trading over our YC batch, while major indices were flat or down. Prodigy’s model beats Claude Fable and GPT-5.6 Sol at autonomous quant research. Our founders are brothers who have built strategies generating billions in PnL on a Jane Street trading desk, trained SoTA foundation models at Google DeepMind, and shipped cutting-edge AI to billions of users at Apple. A new era has arrived in the world of quantitative finance. To learn more, contact [email protected]. Prodigy Research (YC S26)

Michael Wang

1,941,743 views • 1 month ago

Prodigy Research (YC S26) is the frontier AI trading research lab. We’re training the world’s best foundation model for quantitative finance. We've already achieved 140% returns in live trading over our YC batch, while major indices were flat or down. Prodigy’s model beats Claude Fable and GPT-5.6 Sol at autonomous quant research. Our founders are brothers who have worked at Jane Street, trained SoTA foundation models at Google DeepMind and shipped cutting-edge AI to billions of users at Apple. A new era has arrived in the world of quantitative finance. To learn more, contact founders@prodigy.markets

Prodigy Research (YC S26) is the frontier AI trading research lab. We’re training the world’s best foundation model for quantitative finance. We've already achieved 140% returns in live trading over our YC batch, while major indices were flat or down. Prodigy’s model beats Claude Fable and GPT-5.6 Sol at autonomous quant research. Our founders are brothers who have worked at Jane Street, trained SoTA foundation models at Google DeepMind and shipped cutting-edge AI to billions of users at Apple. A new era has arrived in the world of quantitative finance. To learn more, contact [email protected]

Prodigy Research (YC S26)

24,473 views • 1 month ago

Two big steps towards our vision for @NotebookLM as the ultimate research platform: • Integrating Deep Research, with a set of only-at-Notebook features that let you explore the retrieved sources • Launching a series of Featured Notebooks curated by Google Research These developments are designed to enhance the full life cycle of research and scholarship: using the power of AI to assemble the knowledge base you need to advance your understanding, and then making your work accessible and intelligible to a wider audience using all the explanatory tools that Notebook offers. If you've used DeepResearch in the Gemini app, you already know that it's a pioneering advance in assembling complex, grounded information on any topic imaginable—collecting an entire trove of material for you and writing a nuanced research report that summarizes the findings. But because NotebookLM is designed to manage and explore potentially hundreds of sources, the Deep Research report is only the beginning of your journey. In our integration, Deep Research gives you an overview all of the sources it found during its research phase, with annotated commentary explaining how each source related to your original query. You can then choose to import some or all of the sources to the notebook, along with the report itself, which you can then explore or transform using the full suite of tools that Notebook offers: grounded chat with citations, Mind Maps, Audio/Video overviews, and much more. And it's that suite of tools that make the Google Research Featured Notebooks so compelling as well. Each notebook contains a curated collection of articles on a specific topic, published by the Google Research team. Think of them as a kind of knowledge base of Google's best thinking on a series of compelling research questions: How do scientists link genetics to health? How will quantum computing be useful? If you're a specialist in these fields, you can read the original papers or ask nuanced questions in chat and advance your understanding of the latest developments. But these notebooks can also make the complex but important topics understandable to non-specialists or students. Each notebook comes with pre-generated audio and video overviews, flashcards, and other Studio artifacts designed to make the scientific and technological concepts accessible and interesting. And you can always explore the material with our new "Learning Guide" chat mode that effectively gives you a personal tutor to enhance your understanding. There's much more to come on this front, but you can see in these two announcements how we see Notebook as both a workbench for conducting research and a publishing platform for sharing the results of that research once you're ready to make it public. Deep Research is rolling out this week to all users. The first two Google Research notebooks are live now, both of them deep dives into our most recent discoveries involving genetics and health. (Links in the following tweets.) We'll be publishing new notebooks in the series every other week or so for the next few months.

Two big steps towards our vision for @NotebookLM as the ultimate research platform: • Integrating Deep Research, with a set of only-at-Notebook features that let you explore the retrieved sources • Launching a series of Featured Notebooks curated by Google Research These developments are designed to enhance the full life cycle of research and scholarship: using the power of AI to assemble the knowledge base you need to advance your understanding, and then making your work accessible and intelligible to a wider audience using all the explanatory tools that Notebook offers. If you've used DeepResearch in the Gemini app, you already know that it's a pioneering advance in assembling complex, grounded information on any topic imaginable—collecting an entire trove of material for you and writing a nuanced research report that summarizes the findings. But because NotebookLM is designed to manage and explore potentially hundreds of sources, the Deep Research report is only the beginning of your journey. In our integration, Deep Research gives you an overview all of the sources it found during its research phase, with annotated commentary explaining how each source related to your original query. You can then choose to import some or all of the sources to the notebook, along with the report itself, which you can then explore or transform using the full suite of tools that Notebook offers: grounded chat with citations, Mind Maps, Audio/Video overviews, and much more. And it's that suite of tools that make the Google Research Featured Notebooks so compelling as well. Each notebook contains a curated collection of articles on a specific topic, published by the Google Research team. Think of them as a kind of knowledge base of Google's best thinking on a series of compelling research questions: How do scientists link genetics to health? How will quantum computing be useful? If you're a specialist in these fields, you can read the original papers or ask nuanced questions in chat and advance your understanding of the latest developments. But these notebooks can also make the complex but important topics understandable to non-specialists or students. Each notebook comes with pre-generated audio and video overviews, flashcards, and other Studio artifacts designed to make the scientific and technological concepts accessible and interesting. And you can always explore the material with our new "Learning Guide" chat mode that effectively gives you a personal tutor to enhance your understanding. There's much more to come on this front, but you can see in these two announcements how we see Notebook as both a workbench for conducting research and a publishing platform for sharing the results of that research once you're ready to make it public. Deep Research is rolling out this week to all users. The first two Google Research notebooks are live now, both of them deep dives into our most recent discoveries involving genetics and health. (Links in the following tweets.) We'll be publishing new notebooks in the series every other week or so for the next few months.

Steven Johnson

104,878 views • 10 months ago

You do not have to read a research paper from start to end for the first pass. This is a huge academic misconception. There are key parts of a paper and a specific order that students should be taught. ▶️ Here's how to read a research paper quickly using AI:

You do not have to read a research paper from start to end for the first pass. This is a huge academic misconception. There are key parts of a paper and a specific order that students should be taught. ▶️ Here's how to read a research paper quickly using AI:

Amina Yonis, PhD

40,764 views • 3 years ago

Cyber defense is entering the superintelligence era. ⚡ Introducing the CrowdStrike Cyber Superintelligence Lab, the first frontier AI research organization built for cyberdefense and AI safety. The Lab delivers CrowdStrike SafeMind, a family of purpose-built security models and harnesses. SafeMind runs as one system designed to deliver AI safety: an offensive model that finds the attack path, a defensive model that closes it, and the harnesses that operate both in the same loop. Built using NVIDIA Nemotron open models and using CoreWeave’s AI Cloud for training and inference, SafeMind goes beyond AI that simply identifies threats, it defeats them. Learn more:

Cyber defense is entering the superintelligence era. ⚡ Introducing the CrowdStrike Cyber Superintelligence Lab, the first frontier AI research organization built for cyberdefense and AI safety. The Lab delivers CrowdStrike SafeMind, a family of purpose-built security models and harnesses. SafeMind runs as one system designed to deliver AI safety: an offensive model that finds the attack path, a defensive model that closes it, and the harnesses that operate both in the same loop. Built using NVIDIA Nemotron open models and using CoreWeave’s AI Cloud for training and inference, SafeMind goes beyond AI that simply identifies threats, it defeats them. Learn more:

CrowdStrike

58,094 views • 1 month ago

One of the most common challenges of indie hacker is product demand positioning and marketing. On March 8, I launched a Skill collection website in just three days, and I’ve been maintaining it ever since. Along the way, I’ve done a lot of work: optimizing features, updating content, adjusting positioning... but something always seems missing. Recently, I came across a very impressive AI research product that helps validate product feasibility and avoids falling into the trap of self-indulgence. It’s called atypica.AI. Let me break down why this product is actually impressive How it works？ It's very simple. After you give it a business problem, it first clarifies your needs and plans the research approach like a research team. It will help you step into the perspective of your target users and validate whether a product is worth building — before you commit.

One of the most common challenges of indie hacker is product demand positioning and marketing. On March 8, I launched a Skill collection website in just three days, and I’ve been maintaining it ever since. Along the way, I’ve done a lot of work: optimizing features, updating content, adjusting positioning... but something always seems missing. Recently, I came across a very impressive AI research product that helps validate product feasibility and avoids falling into the trap of self-indulgence. It’s called atypica.AI. Let me break down why this product is actually impressive How it works？ It's very simple. After you give it a business problem, it first clarifies your needs and plans the research approach like a research team. It will help you step into the perspective of your target users and validate whether a product is worth building — before you commit.

Jason Zhu

84,745 views • 4 months ago

Head of Product at Claude on how Anthropic's sales team is integrating external data into their AI to create custom sales decks and land potential customers People are still missing the fact that the internal teams inside these LLM companies have access to frontier models and dont have the same token constraints that rest of us do That within itself is a competitive advantage, and will create a loop where foundational AI labs grow their business at a much faster and efficient rate than everyone else From Cat Wu's conversation with Lenny

Head of Product at Claude on how Anthropic's sales team is integrating external data into their AI to create custom sales decks and land potential customers People are still missing the fact that the internal teams inside these LLM companies have access to frontier models and dont have the same token constraints that rest of us do That within itself is a competitive advantage, and will create a loop where foundational AI labs grow their business at a much faster and efficient rate than everyone else From Cat Wu's conversation with Lenny

Boring_Business

75,704 views • 5 months ago