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Today we’re launching the newest version of Paradigm When we started Paradigm, the goal was never to tack AI onto existing spreadsheets. It was to build a new type of interface that does the work for you. Now we’re pushing that vision much further. Workflows turn Paradigm into a... show more

Anna Monaco

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243,954 views • 4 months ago •via X (Twitter)

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New work: Visual brand refresh and website design for Paradigm Paradigm is an investment firm that focuses on research at the frontier. The refreshed identity ties together rich visual tapestries of impossible shapes, a node system that represents the inkling of a new idea, and a typeface system rooted in deep research and exploration. ▷

adam ho

33,272 views • 1 year ago

The DEPRESSING reality of AI adoption curves Advanced AI just broke into it's third major paradigm since launching. Paradigm One was the simple autocomplete engine. GPT-2 and original GPT-3 were glorified autocomplete tools, just "next token predictors." Paradigm 1.5 was when we added "instruct-aligned" GPT-3, which set the stage for Paradigm Two. Paradigm Two was the chatbot era, with ChatGPT being the front-runner. Paradigm 2.5 was when we started adding reasoning, tool use, and RAG, which set the stage for agentic abilities (Paradigm 3). OpenClaw just blew the lid off Paradigm 3, and we're just at the beginning of this new ramp-up in capabilities. The thing is... each of these paradigm shifts creates fundamentally different UX and technical affordances. While most Fortune 500 companies are still struggling to figure out the cyber security, legal, and financial risks of chatbots, the industry is going whole hog into autonomous agents. And many people will try to graft their understanding of chatbots onto agents. But that's like trying to compare the electric lightbulb to the electric motor.... ...yes they both ran on electricity, but their uses, affordances, limitations, and risks were fundamentally different. Yes, autocomplete, chatbots, and agents all run on "next token prediction" but that's like saying "it all runs on electrons." My goal today is to help give you a better intuition as to why adoption is so slow and to give you a new reference frame to understand that agents are a phase change from chatbots. But also... the state and big companies are going to move depressingly slow on all of this...

The DEPRESSING reality of AI adoption curves Advanced AI just broke into it's third major paradigm since launching. Paradigm One was the simple autocomplete engine. GPT-2 and original GPT-3 were glorified autocomplete tools, just "next token predictors." Paradigm 1.5 was when we added "instruct-aligned" GPT-3, which set the stage for Paradigm Two. Paradigm Two was the chatbot era, with ChatGPT being the front-runner. Paradigm 2.5 was when we started adding reasoning, tool use, and RAG, which set the stage for agentic abilities (Paradigm 3). OpenClaw just blew the lid off Paradigm 3, and we're just at the beginning of this new ramp-up in capabilities. The thing is... each of these paradigm shifts creates fundamentally different UX and technical affordances. While most Fortune 500 companies are still struggling to figure out the cyber security, legal, and financial risks of chatbots, the industry is going whole hog into autonomous agents. And many people will try to graft their understanding of chatbots onto agents. But that's like trying to compare the electric lightbulb to the electric motor.... ...yes they both ran on electricity, but their uses, affordances, limitations, and risks were fundamentally different. Yes, autocomplete, chatbots, and agents all run on "next token prediction" but that's like saying "it all runs on electrons." My goal today is to help give you a better intuition as to why adoption is so slow and to give you a new reference frame to understand that agents are a phase change from chatbots. But also... the state and big companies are going to move depressingly slow on all of this...

David Shapiro (L/0)

17,266 views • 5 months ago

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹

Yacine Mahdid

12,091 views • 2 months ago

1/ Today we’re releasing a preview of ChatGPT Pulse to Pro users on mobile. Pulse is a new interaction paradigm: instead of waiting for your questions, ChatGPT proactively does stuff and brings you useful, personalized updates each day.

1/ Today we’re releasing a preview of ChatGPT Pulse to Pro users on mobile. Pulse is a new interaction paradigm: instead of waiting for your questions, ChatGPT proactively does stuff and brings you useful, personalized updates each day.

Nick Turley

80,455 views • 10 months ago

PhD Students – How to identify a research gap in seconds? Use this FREE tool to identify research gap in seconds. 1. Go to 2. Click on 𝑇𝑜𝑝𝑖𝑐 𝐸𝑥𝑝𝑙𝑜𝑟𝑒𝑟 ---> 𝐶𝑟𝑒𝑎𝑡𝑖𝑣𝑒 𝑇𝑜𝑜𝑙 3. Enter your topic e.g., deep learning for healthcare 4. Now click on 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒 𝑖𝑑𝑒𝑎𝑠 5. ResearchCollab will give you the following 7 gaps ➝ Research that combines multiple existing research ➝ Research that adapts a new approach/technology ➝ Research that modifies existing variables in research ➝ Research that incorporates new variables ➝ Research that eliminates variables from research ➝ Research that re-arrange existing variables Pick the gap you think has the most potential. Then click on 𝑡𝑜𝑝𝑖𝑐 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐂𝐨𝐥𝐥𝐚𝐛 will dive deeper into the selected topic It gives you different angles of the chosen research gap. It is very helpful and at the same time very easy to use Try 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐂𝐨𝐥𝐥𝐚𝐛 today:

PhD Students – How to identify a research gap in seconds? Use this FREE tool to identify research gap in seconds. 1. Go to 2. Click on 𝑇𝑜𝑝𝑖𝑐 𝐸𝑥𝑝𝑙𝑜𝑟𝑒𝑟 ---> 𝐶𝑟𝑒𝑎𝑡𝑖𝑣𝑒 𝑇𝑜𝑜𝑙 3. Enter your topic e.g., deep learning for healthcare 4. Now click on 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒 𝑖𝑑𝑒𝑎𝑠 5. ResearchCollab will give you the following 7 gaps ➝ Research that combines multiple existing research ➝ Research that adapts a new approach/technology ➝ Research that modifies existing variables in research ➝ Research that incorporates new variables ➝ Research that eliminates variables from research ➝ Research that re-arrange existing variables Pick the gap you think has the most potential. Then click on 𝑡𝑜𝑝𝑖𝑐 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐂𝐨𝐥𝐥𝐚𝐛 will dive deeper into the selected topic It gives you different angles of the chosen research gap. It is very helpful and at the same time very easy to use Try 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐂𝐨𝐥𝐥𝐚𝐛 today:

Faheem Ullah

106,224 views • 4 months ago

Proactive AI Agents Today’s AI products are reactive. You give the model a prompt, it responds with an answer. These are useful, but I’m excited about products that take this further and shift the paradigm from “ask → answer” to “observe → act." These agents will continuously monitor context in the background across all of your connected tools and data, predict what matters, and take action before being asked to do so – much like a human does. So instead of you prompting the model, the model will start prompting you. Examples here could include agents that remind you about tasks you forgot to complete, resolve customer issues before support tickets are filed, or debug and ship code fixes automatically. This shift represents a new paradigm where AI products behave more like humans and less like tools. We’re already starting to see this dynamic with products like OpenClaw, Poke, and more - and we’ve only scratched the surface of capabilities here. We’re accepting applications for the next cohort of a16z a16z speedrun 🧊 – If you’re building the next generation of AI products, apply online.

Proactive AI Agents Today’s AI products are reactive. You give the model a prompt, it responds with an answer. These are useful, but I’m excited about products that take this further and shift the paradigm from “ask → answer” to “observe → act." These agents will continuously monitor context in the background across all of your connected tools and data, predict what matters, and take action before being asked to do so – much like a human does. So instead of you prompting the model, the model will start prompting you. Examples here could include agents that remind you about tasks you forgot to complete, resolve customer issues before support tickets are filed, or debug and ship code fixes automatically. This shift represents a new paradigm where AI products behave more like humans and less like tools. We’re already starting to see this dynamic with products like OpenClaw, Poke, and more - and we’ve only scratched the surface of capabilities here. We’re accepting applications for the next cohort of a16z a16z speedrun 🧊 – If you’re building the next generation of AI products, apply online.

Kenan Saleh

77,493 views • 2 months ago

Google isn’t betting on a single AI architecture. Sundar Pichai, CEO of Google: “We’re going to push the diffusion paradigm as hard as possible.” “All of today’s mainline Gemini models are autoregressive. Diffusion is a different paradigm.” “For the same capability, diffusion can be much faster.” “It’s behind the mainline models today, but there will be areas where it’s the right tool.” “We’re pushing multiple directions in parallel, and bringing them together where it makes sense.”

Google isn’t betting on a single AI architecture. Sundar Pichai, CEO of Google: “We’re going to push the diffusion paradigm as hard as possible.” “All of today’s mainline Gemini models are autoregressive. Diffusion is a different paradigm.” “For the same capability, diffusion can be much faster.” “It’s behind the mainline models today, but there will be areas where it’s the right tool.” “We’re pushing multiple directions in parallel, and bringing them together where it makes sense.”

Forward Future

152,919 views • 7 months ago

A commenter recently asked me if the the possibility of the death of our current system giving birth to a new cultural, economic and social paradigm is a cause for hope. The reality is human progress is a myth —societies often go backwards. I fear that we are now ushering in a new Dark Age of fascist authoritarianism.

A commenter recently asked me if the the possibility of the death of our current system giving birth to a new cultural, economic and social paradigm is a cause for hope. The reality is human progress is a myth —societies often go backwards. I fear that we are now ushering in a new Dark Age of fascist authoritarianism.

Chris Hedges

48,846 views • 5 months ago

Today, we’re releasing our research preview ( to let you look inside your AI. We've created a desktop interface that helps you understand and control Llama 3's behavior. You can 1) see Llama 3's internal features (the internal building blocks of its responses) and 2) precisely adjust these features to create new Llama variants. Try it out and share your findings with #GoodfireAI

Today, we’re releasing our research preview ( to let you look inside your AI. We've created a desktop interface that helps you understand and control Llama 3's behavior. You can 1) see Llama 3's internal features (the internal building blocks of its responses) and 2) precisely adjust these features to create new Llama variants. Try it out and share your findings with #GoodfireAI

Goodfire

67,161 views • 1 year ago

Turn any repetitive task into an API. We build an agent that reverse-engineers the network requests to create APIs/tools for your tasks. This is a new paradigm for browser agents, making them reliable, fast, and cheap.

Turn any repetitive task into an API. We build an agent that reverse-engineers the network requests to create APIs/tools for your tasks. This is a new paradigm for browser agents, making them reliable, fast, and cheap.

Magnus Müller

34,404 views • 8 months ago

The Toolshed is the newest feature inside Paradigm SHIFT that turns data to insights the moment you upload it. Built for pitch design, outing recaps, MLB comps, and more. The toolshed has everything a coaching staff needs to make faster, data-backed decisions.

The Toolshed is the newest feature inside Paradigm SHIFT that turns data to insights the moment you upload it. Built for pitch design, outing recaps, MLB comps, and more. The toolshed has everything a coaching staff needs to make faster, data-backed decisions.

Paradigm Player Development

21,788 views • 2 months ago

Amjad Masad and Adam D'Angelo debate whether the current LLM paradigm will get us to AGI: Amjad: “I don't think we're going to get to the next level of human civilization until we crack the true nature of intelligence.” Adam: “Nothing seems fundamentally so hard that it couldn't be solved by the smartest people in the world working incredibly hard for the next five years on it.” Amjad: “But basic research is different... Thomas Kuhn, this philosopher of science, talks a lot about how these research programs end up becoming like a bubble and sucking all the attention and ideas.” Adam: “ This is maybe our disagreement. I think the current paradigm is pretty good. And I think we're nowhere near the sort of diminishing returns of continuing to push on it... I would just bet that you can keep doing different innovations within the paradigm to get there.” Amjad Masad Adam D'Angelo

Amjad Masad and Adam D'Angelo debate whether the current LLM paradigm will get us to AGI: Amjad: “I don't think we're going to get to the next level of human civilization until we crack the true nature of intelligence.” Adam: “Nothing seems fundamentally so hard that it couldn't be solved by the smartest people in the world working incredibly hard for the next five years on it.” Amjad: “But basic research is different... Thomas Kuhn, this philosopher of science, talks a lot about how these research programs end up becoming like a bubble and sucking all the attention and ideas.” Adam: “ This is maybe our disagreement. I think the current paradigm is pretty good. And I think we're nowhere near the sort of diminishing returns of continuing to push on it... I would just bet that you can keep doing different innovations within the paradigm to get there.” Amjad Masad Adam D'Angelo

a16z

96,129 views • 8 months ago

What if AI could finally reveal your Social Capital and put it to work? Gigi v2 is live. We spent the last decades optimizing for Content. The AI era will be about Context. For the first time, AI can surface social data that was invisible. It can map the value of your network, measure your credibility, and read you reputation, which are the foundations of something we’ve never been able to quantify until now: Social Capital. The only currency that drives access, influence, and opportunities in life. Because in a world where AI does the work, the only edge left is who you are, and how people see you. New paradigm. New protocol. New status game. Let’s play. 👉 Join the beta (link in comments) 💬 Follow me and Comment to skip the line 🔄 Repost for super priority access

What if AI could finally reveal your Social Capital and put it to work? Gigi v2 is live. We spent the last decades optimizing for Content. The AI era will be about Context. For the first time, AI can surface social data that was invisible. It can map the value of your network, measure your credibility, and read you reputation, which are the foundations of something we’ve never been able to quantify until now: Social Capital. The only currency that drives access, influence, and opportunities in life. Because in a world where AI does the work, the only edge left is who you are, and how people see you. New paradigm. New protocol. New status game. Let’s play. 👉 Join the beta (link in comments) 💬 Follow me and Comment to skip the line 🔄 Repost for super priority access

Clara Gold

98,831 views • 1 year ago

Wrapping up the year and coinciding with #NeurIPS2024, today at Meta FAIR we’re releasing a collection of nine new open source AI research artifacts across our work in developing agents, robustness & safety and new architectures. More in the video from Joelle Pineau. All of this work is part of FAIR’s continued work towards the goal of achieving advanced machine intelligence A few highlights from what we’re releasing today: • Meta Motivo: A first-of-its-kind behavioral foundation model that controls the movements of a virtual embodied humanoid agent to perform complex tasks. • Meta Video Seal: a state-of-the art comprehensive framework for neural video watermarking. • Meta Explore Theory-of-Mind: A program-guided adversarial data generation for theory of mind reasoning. • Meta Large Concept Models: A fundamentally different training paradigm for language modeling that decouples reasoning from language representation. And much more! We’re excited to share this work with the research community and look forward to seeing how it inspires new innovation across the field. Details and access to everything released by FAIR today ➡️

Wrapping up the year and coinciding with #NeurIPS2024, today at Meta FAIR we’re releasing a collection of nine new open source AI research artifacts across our work in developing agents, robustness & safety and new architectures. More in the video from Joelle Pineau. All of this work is part of FAIR’s continued work towards the goal of achieving advanced machine intelligence A few highlights from what we’re releasing today: • Meta Motivo: A first-of-its-kind behavioral foundation model that controls the movements of a virtual embodied humanoid agent to perform complex tasks. • Meta Video Seal: a state-of-the art comprehensive framework for neural video watermarking. • Meta Explore Theory-of-Mind: A program-guided adversarial data generation for theory of mind reasoning. • Meta Large Concept Models: A fundamentally different training paradigm for language modeling that decouples reasoning from language representation. And much more! We’re excited to share this work with the research community and look forward to seeing how it inspires new innovation across the field. Details and access to everything released by FAIR today ➡️

AI at Meta

156,123 views • 1 year ago

Paradigm co-founder Matt Huang says allocating capital to Bitcoin and Ethereum in 2018 is a lot like investing in the AI sector today: "When we launched Paradigm in 2018, a lot of our thesis was that we knew crypto would expand massively. And then there was this open question — should you invest in the core protocols like Bitcoin and Ethereum, or invest in startups?" "Obviously in hindsight, the answer was both." "I think it's a lot like the AI landscape now, with this question of — do you own the model companies, or do you own the application layer on top? In 2018, Bitcoin and Ethereum were like OpenAI and Anthropic, and those are the things to own."

Paradigm co-founder Matt Huang says allocating capital to Bitcoin and Ethereum in 2018 is a lot like investing in the AI sector today: "When we launched Paradigm in 2018, a lot of our thesis was that we knew crypto would expand massively. And then there was this open question — should you invest in the core protocols like Bitcoin and Ethereum, or invest in startups?" "Obviously in hindsight, the answer was both." "I think it's a lot like the AI landscape now, with this question of — do you own the model companies, or do you own the application layer on top? In 2018, Bitcoin and Ethereum were like OpenAI and Anthropic, and those are the things to own."

TBPN

58,410 views • 4 months ago

Introducing Workflows, a new way to build your own tools inside of Runway. Now you can create your own custom node-based workflows chaining together multiple models, modalities and intermediary steps for even more control of your generations. Build the Workflows that work for you. Available now for our Creative Partners and Enterprise customers. Coming soon to all plans.

Introducing Workflows, a new way to build your own tools inside of Runway. Now you can create your own custom node-based workflows chaining together multiple models, modalities and intermediary steps for even more control of your generations. Build the Workflows that work for you. Available now for our Creative Partners and Enterprise customers. Coming soon to all plans.

Runway

226,900 views • 9 months ago

We’re launching Volrix. An MCP server that lets you backtest and research trading ideas directly from Claude and Chatgpt. Connect your AI agent, describe a strategy, and run backtests across index and commodity derivatives. Link in the comments, Try it for free!

We’re launching Volrix. An MCP server that lets you backtest and research trading ideas directly from Claude and Chatgpt. Connect your AI agent, describe a strategy, and run backtests across index and commodity derivatives. Link in the comments, Try it for free!

Niraj Munot

148,505 views • 1 month ago

🔥 RFK Jr. says his goal is to give medical freedom and choice back to the people. “We’re fighting not to put ourselves in a position to tell you what to do.” “That was the old way of doing things at HHS.” “Right now, our philosophy is we’re going to provide the best information to the American public, but people need to do their own research and people need to be the CEOs of their own health and their children’s health.” “What we know after COVID is that governments are corruptible, that people in authority lie, that there’s all kinds of incentives that lead them to tell noble lies for your own good, which turn out not to be for your good.” “We have to do our own research, protect our family, and protect our own health.” “The same way that you research an automobile before you buy it or a baby carriage or a car seat … you look on consumer reports, you do your own research, you make up your own mind about what’s good for you and your family.” Secretary Kennedy HHS Isabel Brown

🔥 RFK Jr. says his goal is to give medical freedom and choice back to the people. “We’re fighting not to put ourselves in a position to tell you what to do.” “That was the old way of doing things at HHS.” “Right now, our philosophy is we’re going to provide the best information to the American public, but people need to do their own research and people need to be the CEOs of their own health and their children’s health.” “What we know after COVID is that governments are corruptible, that people in authority lie, that there’s all kinds of incentives that lead them to tell noble lies for your own good, which turn out not to be for your good.” “We have to do our own research, protect our family, and protect our own health.” “The same way that you research an automobile before you buy it or a baby carriage or a car seat … you look on consumer reports, you do your own research, you make up your own mind about what’s good for you and your family.” Secretary Kennedy HHS Isabel Brown

Children’s Health Defense

12,859 views • 6 months ago

“There are more companies than ideas by quite a bit. Compute is large enough such that it's not obvious that you need that much more compute to prove some idea. AlexNet was built on 2 GPUs. The transformer was built on 8 to 64 GPUs. Which would be, what, 2 GPUs of today? You could argue that o1 reasoning was not the most compute heavy thing in the world. For research, you definitely need some amount of compute, but it's far from obvious that you need the absolutely largest amount of compute. If everyone is within the same paradigm, then compute becomes one of the big differentiators.” Ilya Sutskever

“There are more companies than ideas by quite a bit. Compute is large enough such that it's not obvious that you need that much more compute to prove some idea. AlexNet was built on 2 GPUs. The transformer was built on 8 to 64 GPUs. Which would be, what, 2 GPUs of today? You could argue that o1 reasoning was not the most compute heavy thing in the world. For research, you definitely need some amount of compute, but it's far from obvious that you need the absolutely largest amount of compute. If everyone is within the same paradigm, then compute becomes one of the big differentiators.” Ilya Sutskever

Dwarkesh Patel

203,605 views • 8 months ago