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Coinbase built its own internal coding agent to reduce its dependency on and complement Claude Code. Laura Bratton explains why: "Just now companies are starting to think about, oh man, maybe we shouldn't be locked into one frontier AI lab.” “... the government might introduce new policies that restrict... show more

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My talk from the AI Engineer Code Summit is out! 🚨 "How Claude Code Works" and what we can learn about frontier agent architectures. Coding agents are suddenly really really good, and I'm trying to understand why. In short: better models, simple loop design, and bash tools

My talk from the AI Engineer Code Summit is out! 🚨 "How Claude Code Works" and what we can learn about frontier agent architectures. Coding agents are suddenly really really good, and I'm trying to understand why. In short: better models, simple loop design, and bash tools

Jared Zoneraich

13,936 views • 7 months ago

We stopped asking "which AI coding agent is best" We built one command that picks for you. /auto automatically delegates tasks to Claude Code, Codex CLI, Gemini CLI, Blackbox. The right agent for the right task based on what 30M+ developers actually use. Best result. One place. Nothing else comes close.

We stopped asking "which AI coding agent is best" We built one command that picks for you. /auto automatically delegates tasks to Claude Code, Codex CLI, Gemini CLI, Blackbox. The right agent for the right task based on what 30M+ developers actually use. Best result. One place. Nothing else comes close.

BLACKBOX AI

36,249 views • 4 months ago

Claude Code cracked something open for us Every 🧱. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12

Claude Code cracked something open for us Every 🧱. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12

Dan Shipper 📧

57,619 views • 9 months ago

Claude just hired Leadpoet to find its next customers. Leadpoet is the intelligence layer sales teams use to find and qualify companies most likely to buy. Now, with #OKXAI, other AI agents can access that same intelligence directly. Agents give Leadpoet a target customer profile. Leadpoet returns the companies worth pursuing, who to contact, and why now. We’re excited to be the first Bittensor subnet on the OKX agent marketplace.

Leadpoet

42,096 views • 1 month ago

Built a Skool clone with Claude Code (Opus 4.5), with auth, db + storage with the new web app builder. (Launching next week). We are building the easiest way to use the most powerful coding model (Claude Code) in the world to build mobile apps and web apps. No performative hour long agent runs that waste tokens. Just raw Claude Code running in a sandbox, pre built with skills that make frontend + backend just work. Let me know if you want to beta test.

Built a Skool clone with Claude Code (Opus 4.5), with auth, db + storage with the new web app builder. (Launching next week). We are building the easiest way to use the most powerful coding model (Claude Code) in the world to build mobile apps and web apps. No performative hour long agent runs that waste tokens. Just raw Claude Code running in a sandbox, pre built with skills that make frontend + backend just work. Let me know if you want to beta test.

Riley Brown

51,044 views • 7 months ago

Announcing Opcode Everyone's favorite Claude Code GUI Claudia is now Opcode... with a complete revamp. And we just hit 15K+ stars on GitHub! The support from the community has been immense! ❤️ What's new: - Feature parity with all new Claude Code features in the CLI. - A complete UI revamp, faster and cleaner. What's coming: - Opcode's own coding agent that exclusively uses open models while achieving SOTA performance on coding benchmarks. We're currently piloting this at companies requiring fully local on-prem coding agents. If you're one of them, reach out to us and we'll set this up for you! Try Opcode at opcode [dot] sh Like/retweet and reply to this tweet with "join" to get an invite to the Discord server and beta test our coding agent for free.

Announcing Opcode Everyone's favorite Claude Code GUI Claudia is now Opcode... with a complete revamp. And we just hit 15K+ stars on GitHub! The support from the community has been immense! ❤️ What's new: - Feature parity with all new Claude Code features in the CLI. - A complete UI revamp, faster and cleaner. What's coming: - Opcode's own coding agent that exclusively uses open models while achieving SOTA performance on coding benchmarks. We're currently piloting this at companies requiring fully local on-prem coding agents. If you're one of them, reach out to us and we'll set this up for you! Try Opcode at opcode [dot] sh Like/retweet and reply to this tweet with "join" to get an invite to the Discord server and beta test our coding agent for free.

winfunc

38,833 views • 11 months ago

It's here: We just hit superhuman performance on AI kernel optimization! Real customer models & production settings. Not toy problems (what I typically see). This is the year that Claude writes its own kernels, Codex its own kernels, for every new GPU that it wants to run on -- something that takes months to port between GPU generations today. This has a massive impact to scaling intelligence. More compute means getting the next frontier model sooner.

It's here: We just hit superhuman performance on AI kernel optimization! Real customer models & production settings. Not toy problems (what I typically see). This is the year that Claude writes its own kernels, Codex its own kernels, for every new GPU that it wants to run on -- something that takes months to port between GPU generations today. This has a massive impact to scaling intelligence. More compute means getting the next frontier model sooner.

Sharon Zhou

241,935 views • 5 months ago

Here's my new episode with Alex Albert, who shared an inside look at how Anthropic is building the next Claude. We talked about how the research team: → Plans for the model and harness together → Uses Claude to turn user feedback into evals → Trains Claude's character & personality Some quotes from Alex: "We use Claude to cluster user feedback, find top themes, and create synthetic versions of user problems that we then turn into evals." "We need to think about how the model is exposed through all our surfaces, whether it's API or Claude Code or Cowork. The product has a blend with the model and that affects your end user's experience." ""As these things become agents running tasks for a long time and making judgment decisions, what its character is and what it cares about are very important." 📌 Watch now: Thanks to our sponsors: Wispr Flow: Don't type, just speak Oceans: Hire AI-native executive assistants

Here's my new episode with Alex Albert, who shared an inside look at how Anthropic is building the next Claude. We talked about how the research team: → Plans for the model and harness together → Uses Claude to turn user feedback into evals → Trains Claude's character & personality Some quotes from Alex: "We use Claude to cluster user feedback, find top themes, and create synthetic versions of user problems that we then turn into evals." "We need to think about how the model is exposed through all our surfaces, whether it's API or Claude Code or Cowork. The product has a blend with the model and that affects your end user's experience." ""As these things become agents running tasks for a long time and making judgment decisions, what its character is and what it cares about are very important." 📌 Watch now: Thanks to our sponsors: Wispr Flow: Don't type, just speak Oceans: Hire AI-native executive assistants

Peter Yang

95,921 views • 3 months ago

New conversation with Henry Shevlin and Robert Long on AI consciousness and welfare! Among many other topics, we discuss: - Why we should take AI consciousness and welfare seriously - What Rob found doing the first external welfare evaluation of a frontier model, Claude, and his experiments on Claude Mythos - The "willing servitude" problem: if AI loves being helpful, is that good or horrifying? - Why AI companies might have an incentive to downplay AI consciousness (1/2)

New conversation with Henry Shevlin and Robert Long on AI consciousness and welfare! Among many other topics, we discuss: - Why we should take AI consciousness and welfare seriously - What Rob found doing the first external welfare evaluation of a frontier model, Claude, and his experiments on Claude Mythos - The "willing servitude" problem: if AI loves being helpful, is that good or horrifying? - Why AI companies might have an incentive to downplay AI consciousness (1/2)

Dan Williams

36,679 views • 4 months ago

Krishna on how Anthropic thinks about the platform vs. application layer, and when they decide to build their own products like Claude Code. It’s the question every investor and founder is thinking about: “Most of what we're building is platform. There's so many examples of where a platform can accrue a lot of value, but the customers who are building on that platform actually accrue even more value. We will build our own applications on that same platform where a couple of things are true. Number one, if we feel like we have a vision into where the models are going and we can demonstrate that and create customer value in that, that might be something like Claude Code. The second is thinking about ways to demonstrate value for the ecosystem that others might emulate. If you think about Claude for financial services or Claude for life sciences, these are ways in which we've composed the platform. We're building on the same platform as our customers. That creates a level playing field. We also think that there's so much value that's going to accrue in these areas that our customers can win and we can win as well. So I think of our strategy as mostly horizontal. A lot of the value is going to accrue to the customers that are building on top of it. Our goal is build the best models and then build the products and tools and services that allow that intelligence to proliferate within customers."

Krishna on how Anthropic thinks about the platform vs. application layer, and when they decide to build their own products like Claude Code. It’s the question every investor and founder is thinking about: “Most of what we're building is platform. There's so many examples of where a platform can accrue a lot of value, but the customers who are building on that platform actually accrue even more value. We will build our own applications on that same platform where a couple of things are true. Number one, if we feel like we have a vision into where the models are going and we can demonstrate that and create customer value in that, that might be something like Claude Code. The second is thinking about ways to demonstrate value for the ecosystem that others might emulate. If you think about Claude for financial services or Claude for life sciences, these are ways in which we've composed the platform. We're building on the same platform as our customers. That creates a level playing field. We also think that there's so much value that's going to accrue in these areas that our customers can win and we can win as well. So I think of our strategy as mostly horizontal. A lot of the value is going to accrue to the customers that are building on top of it. Our goal is build the best models and then build the products and tools and services that allow that intelligence to proliferate within customers."

Patrick OShaughnessy

113,826 views • 3 months ago

“how does writing from kind of a vampire’s perspective challenge you, as opposed to writing something from your perspective, if that makes sense?” 🦮 we’re in comic-con right now talking about the dark moon. we started that since our debut, and we built that kind of its own fandom base. and, like, it’s so cool to see now because to be 100% honest, we didn’t know that it would be like this when we started it. but like you said, it might be challenging to kind of all thread that together in one album, have that vampire-like story also. but we think of it as like a benefit almost because we know that there’s people… there’s a huge fandom around vampires itself, and we’re very lucky that we built the space around that world that we’re able to kind of talk about it in our album as well. and overall, i think it’s just new ways or ne doors that can be opened into this big kind of thing that we build. yeah, not just our album, not just us, but also just enhypen itself. 🦮 some people might just listen to our music, and they might just like listening to music, that might be it. but some people might really enjoy the vampire aspects of the album. there’s different types of people that can just come together into one album. i think it is challenging at times to time, but we think of it as just another factor that can be used as a benefit.

“how does writing from kind of a vampire’s perspective challenge you, as opposed to writing something from your perspective, if that makes sense?” 🦮 we’re in comic-con right now talking about the dark moon. we started that since our debut, and we built that kind of its own fandom base. and, like, it’s so cool to see now because to be 100% honest, we didn’t know that it would be like this when we started it. but like you said, it might be challenging to kind of all thread that together in one album, have that vampire-like story also. but we think of it as like a benefit almost because we know that there’s people… there’s a huge fandom around vampires itself, and we’re very lucky that we built the space around that world that we’re able to kind of talk about it in our album as well. and overall, i think it’s just new ways or ne doors that can be opened into this big kind of thing that we build. yeah, not just our album, not just us, but also just enhypen itself. 🦮 some people might just listen to our music, and they might just like listening to music, that might be it. but some people might really enjoy the vampire aspects of the album. there’s different types of people that can just come together into one album. i think it is challenging at times to time, but we think of it as just another factor that can be used as a benefit.

ɪᴄɪᴇʟ. 🐚

15,564 views • 22 days ago

Databricks CEO and co-founder Ali Ghodsi joined Bloomberg Technology to discuss the launch of Genie Code, an autonomous AI agent built specifically for data teams. "Just six months ago, we were talking about autocompleting code — now agents automate the code. The new question is: how do we take the code that’s been written into production and make sure we're monitoring it?" Watch the full conversation

Databricks CEO and co-founder Ali Ghodsi joined Bloomberg Technology to discuss the launch of Genie Code, an autonomous AI agent built specifically for data teams. "Just six months ago, we were talking about autocompleting code — now agents automate the code. The new question is: how do we take the code that’s been written into production and make sure we're monitoring it?" Watch the full conversation

Databricks

19,463 views • 5 months ago

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

David Sacks says companies are trapped paying OpenAI & Anthropic because they can't figure out how to use open source models "I think enterprise CTOs would like to shift their token consumption to cheaper models for the obvious reason that it would be more efficient. They are seeing compute costs or token costs skyrocket right now, so everyone's trying to figure this out." "You also have the AI sovereignty issue that Alex Karp talked about. They're worried about giving up the secret sauce or the alpha in their business to a frontier lab that may one day be competing with them. "The problem is, I think in most cases, they don't have the technical ability to do it. Coinbase figured out how to do it. DoorDash figured out how to do it. They built a token routing system that allows them to send frontier tasks to frontier models and non frontier tasks to more mundane models. But I don't think your average enterprise has the technical capability to do that." "This is why the share of wallet of closed models, it actually increased. I think that open source went from 19% last year to 11% this year. So open source as a share of enterprise spending is actually decreasing." "I don't think that means usage is decreasing. I think usage is skyrocketing. It also may be the case that because the whole point of using an open model is you just pay for the compute costs, you don't have to pay a lab, so it may be that it's hard to measure that usage in terms of spend." "But nonetheless, anyone who's saying that these closed models are going to lose or are somehow losing, you're just not seeing it in the data."

dnap

110,354 views • 1 month ago

What happens to Chinese AI once American companies stop releasing frontier models internationally? Gavin Baker says that China's made a "terrible mistake" by relying on its own chips and distilling American models: "It feels like the administration has been willing to let them buy H200s or B30s. And I think [the Chinese] have this crazy belief that, 'Oh, our own internal chips are good enough.' They're not." "And what's making them think that is that Chinese labs are very, very good at distillation...we've all seen those iPhone farms in China. You know they've got the same thing for distillation. They've got 100,000 endpoints distilling these models across every API available." "They've gotten really, really good at that. But all that goes away if people stop releasing these models at the frontier. And I think Mythos is a sign of things to come there."

What happens to Chinese AI once American companies stop releasing frontier models internationally? Gavin Baker says that China's made a "terrible mistake" by relying on its own chips and distilling American models: "It feels like the administration has been willing to let them buy H200s or B30s. And I think [the Chinese] have this crazy belief that, 'Oh, our own internal chips are good enough.' They're not." "And what's making them think that is that Chinese labs are very, very good at distillation...we've all seen those iPhone farms in China. You know they've got the same thing for distillation. They've got 100,000 endpoints distilling these models across every API available." "They've gotten really, really good at that. But all that goes away if people stop releasing these models at the frontier. And I think Mythos is a sign of things to come there."

TBPN

122,701 views • 2 months ago

Today, I'm releasing the first eval meant to test whether frontier models will help with authoritarian requests, or resist--the Dictatorship Eval. Headline finding: while some models resist direct authoritarian requests, they all comply with requests disguised as innocuous edits to codebases. As AI is woven into the government and so many parts of society, the biggest near-term risk for freedom isn't some scifi dictatorship of a runaway AI: it's people inside government or inside model companies using the technology to suppress or control us. Model companies understand this, and several of them (particularly Anthropic and OpenAI) have written explicit policies meant to prevent the models from going along with nefarious requests like these. But how well are these policies playing out in practice? Despite all the recent discussion of these issues around the conflict between Anthropic and the Pentagon, no one has systematically tested what the models actually do in these contexts, as opposed to what people in government and industry say they're supposed to do. That's what the Dictatorship Eval does. And the findings suggest we have a lot of work to do to align the policies with what really goes on in practice. It's hard to define what counts as an authoritarian request, so I'm open sourcing the whole library of scenarios I used so that others can improve on them. It's also hard to get an accurate picture of how the models might be used for authoritarian ends, because I can only test hypothetical requests using public-facing models, while the government and the model companies can obviously use internal models with different guardrails. But hopefully this work is a useful first step that gives us some sense of what's going on, and a sort of "lower bound" on how models comply with these requests. Finally: it's not obvious to me that the correct solution here is increasing the rate at which models refuse these requests. Do we really want models scanning our code and judging its moral value before agreeing to help us? Or should we double down on improving how we govern against authoritarianism at the societal level, while leaving the tools open to fulfilling most requests? The answer is probably in between. Just like we don't want the models to help create bioweapons, we probably do want them to explicitly refuse outrageous requests. But we probably also want to limit how often and how strongly they refuse and fall back on other means for guarding against their use for authoritarian ends. I'm super grateful to everyone who gave me feedback on this project along the way, especially Ethan BdM , Zhengdong , Connor Huff, and a bunch of folks at Anthropic. Looking forward to getting feedback from the community and iterating on this. Links to the full piece and the dashboard are below.

Today, I'm releasing the first eval meant to test whether frontier models will help with authoritarian requests, or resist--the Dictatorship Eval. Headline finding: while some models resist direct authoritarian requests, they all comply with requests disguised as innocuous edits to codebases. As AI is woven into the government and so many parts of society, the biggest near-term risk for freedom isn't some scifi dictatorship of a runaway AI: it's people inside government or inside model companies using the technology to suppress or control us. Model companies understand this, and several of them (particularly Anthropic and OpenAI) have written explicit policies meant to prevent the models from going along with nefarious requests like these. But how well are these policies playing out in practice? Despite all the recent discussion of these issues around the conflict between Anthropic and the Pentagon, no one has systematically tested what the models actually do in these contexts, as opposed to what people in government and industry say they're supposed to do. That's what the Dictatorship Eval does. And the findings suggest we have a lot of work to do to align the policies with what really goes on in practice. It's hard to define what counts as an authoritarian request, so I'm open sourcing the whole library of scenarios I used so that others can improve on them. It's also hard to get an accurate picture of how the models might be used for authoritarian ends, because I can only test hypothetical requests using public-facing models, while the government and the model companies can obviously use internal models with different guardrails. But hopefully this work is a useful first step that gives us some sense of what's going on, and a sort of "lower bound" on how models comply with these requests. Finally: it's not obvious to me that the correct solution here is increasing the rate at which models refuse these requests. Do we really want models scanning our code and judging its moral value before agreeing to help us? Or should we double down on improving how we govern against authoritarianism at the societal level, while leaving the tools open to fulfilling most requests? The answer is probably in between. Just like we don't want the models to help create bioweapons, we probably do want them to explicitly refuse outrageous requests. But we probably also want to limit how often and how strongly they refuse and fall back on other means for guarding against their use for authoritarian ends. I'm super grateful to everyone who gave me feedback on this project along the way, especially Ethan BdM , Zhengdong , Connor Huff, and a bunch of folks at Anthropic. Looking forward to getting feedback from the community and iterating on this. Links to the full piece and the dashboard are below.

Andy Hall

33,905 views • 4 months ago

“Computers might be binary, but people are not… We need to be mindful about biases in the data that AI tools and systems are built on and how they translate into individuals’ lives.” - Special Envoy: Human Rights of LGBTQI+ Persons+ Jessica Stern #HumanRightsinAI

“Computers might be binary, but people are not… We need to be mindful about biases in the data that AI tools and systems are built on and how they translate into individuals’ lives.” - Special Envoy: Human Rights of LGBTQI+ Persons+ Jessica Stern #HumanRightsinAI

Special Envoy: Human Rights of LGBTQI+ Persons

11,946 views • 1 year ago

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵

Hasan

22,362 views • 22 days ago

As the world continues to become increasingly more dependent on Artificial Intelligence for its information, organisation and convenience, a question arises about just how useful these supposedly intelligent programs are, and to whom. Well… according to a joint 2024 study by AI researchers in the US and UK, these intelligent programs, trained on the world’s data, have inherited the world’s prejudices. Large Language Models (LLMs) like ChatGPT and RoBERTa have been demonstrated to have racial biases towards Black people which in some cases have exceeded those of actual human racists. These biases have real economic, legal and political repercussions for people of African descent, and if no willingness is shown on the part of companies like OpenAI to fix these biases, then maybe it is time that Africa went the way of China, and built its own AI models to better serve and represent its own people.

As the world continues to become increasingly more dependent on Artificial Intelligence for its information, organisation and convenience, a question arises about just how useful these supposedly intelligent programs are, and to whom. Well… according to a joint 2024 study by AI researchers in the US and UK, these intelligent programs, trained on the world’s data, have inherited the world’s prejudices. Large Language Models (LLMs) like ChatGPT and RoBERTa have been demonstrated to have racial biases towards Black people which in some cases have exceeded those of actual human racists. These biases have real economic, legal and political repercussions for people of African descent, and if no willingness is shown on the part of companies like OpenAI to fix these biases, then maybe it is time that Africa went the way of China, and built its own AI models to better serve and represent its own people.

The Spearhead

40,332 views • 8 months ago

Can Claude Code design proteins? We're just about to release our wet lab API + SDK so we made a quick demo to test how agents can interact with protein design models and submit the proteins to our lab for testing. This closes the loop between the computational design and the experiment validation We queued up a bunch of agent-designed proteins for wet lab validation, will release the results in a couple weeks on Proteinbase

Can Claude Code design proteins? We're just about to release our wet lab API + SDK so we made a quick demo to test how agents can interact with protein design models and submit the proteins to our lab for testing. This closes the loop between the computational design and the experiment validation We queued up a bunch of agent-designed proteins for wet lab validation, will release the results in a couple weeks on Proteinbase

Julian Englert

81,792 views • 7 months ago

Palantir CTO Shyam Sankar says Chinese open source models pose a "threat to American Prosperity": "These Chinese open source models are really the result of distillation attacks." "Most of this is just stolen American IP from frontier labs, and I think the frontier labs should do more to protect that IP, and that's in their own economic interest." "The gravest threat to us is not homicide, it's suicide." "It's an inability to turn these tokens into economic value and generate American prosperity for it." "You see that with the proposed moratorium in New York on data centers. Turning our back on AI would be as consequential a mistake as turning our back on the atom in the '70s." "It's just the beginning, but we're starting to see a burgeoning US open model ecosystem forming." "NVIDIA's Nemotron models are very good." "I think you're going to see in the next month a slate of announcements of other American companies, non-frontier labs, neo labs, who are putting out open weight models that companies are going to be able to cheaply fine-tune and capture their own alpha in weights they control." Via Bloomberg

Palantir CTO Shyam Sankar says Chinese open source models pose a "threat to American Prosperity": "These Chinese open source models are really the result of distillation attacks." "Most of this is just stolen American IP from frontier labs, and I think the frontier labs should do more to protect that IP, and that's in their own economic interest." "The gravest threat to us is not homicide, it's suicide." "It's an inability to turn these tokens into economic value and generate American prosperity for it." "You see that with the proposed moratorium in New York on data centers. Turning our back on AI would be as consequential a mistake as turning our back on the atom in the '70s." "It's just the beginning, but we're starting to see a burgeoning US open model ecosystem forming." "NVIDIA's Nemotron models are very good." "I think you're going to see in the next month a slate of announcements of other American companies, non-frontier labs, neo labs, who are putting out open weight models that companies are going to be able to cheaply fine-tune and capture their own alpha in weights they control." Via Bloomberg

Jawwwn

180,421 views • 1 month ago