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Today we're launching Caution, a hosting platform for software you can't afford to have hacked. Deploy sensitive workloads to secure enclaves in minutes and give customers cryptographic proof that prod is running the exact release you approved, not an opaque binary you have to trust. Caution is built for... payment processors, healthcare platforms, AI agents and inference, crypto infrastructure, and other sensitive systems. It removes the blind trust software runs on today: trust in the cloud host, in production admins, in CI/CD, and in the assumption that the code you reviewed is the code actually running. We spent years securing systems responsible for over $600B in assets and hit the same wall every time: teams couldn't prove what was running in production. Now that AI can find and exploit zero-days faster than anyone can patch, "trust us" isn't enough. Infrastructure has to stay secure even when it's compromised, and prove it. Verifiable compute becomes the default, like HTTPS did.show more

Anton Livaja

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484,752 次观看 • 8 天前 •via X (Twitter)

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An AI analyzed the scan and produced an answer. But can you prove what it actually saw? In high-stakes systems, trust requires verification. Verifiable inference changes how AI is trusted.

An AI analyzed the scan and produced an answer. But can you prove what it actually saw? In high-stakes systems, trust requires verification. Verifiable inference changes how AI is trusted.

OpenGradient (∇, ∇)

191,263 次观看 • 5 个月前

We're excited to be building on Venice, the leading private AI platform, to solve the harder version of the problem: privacy you can prove. Venice is the only AI platform of its scale built private by default. Prompts are never stored, conversations never leave your device, and no third party (not Venice, not the infrastructure, not anyone) can see what you ask. Inference runs inside sealed hardware enclaves, and every response ships with cryptographic attestation that it stayed private. Cluster is the orchestration and settlement layer on top: routing sensitive workloads to Venice's verified private compute and letting autonomous agents pay per call over x402 on Base. No account, no API keys, no data trail.

We're excited to be building on Venice, the leading private AI platform, to solve the harder version of the problem: privacy you can prove. Venice is the only AI platform of its scale built private by default. Prompts are never stored, conversations never leave your device, and no third party (not Venice, not the infrastructure, not anyone) can see what you ask. Inference runs inside sealed hardware enclaves, and every response ships with cryptographic attestation that it stayed private. Cluster is the orchestration and settlement layer on top: routing sensitive workloads to Venice's verified private compute and letting autonomous agents pay per call over x402 on Base. No account, no API keys, no data trail.

Cluster Protocol

64,140 次观看 • 23 天前

.benahorowitz.eth explains why moats like code and UI may not work in the AI era: "For my entire career, the one thing that you knew about technology companies is you couldn't throw money at the problem." "With AI, that's really changed in that you can throw money at the problem because if you have enough GPUs and enough data, you can basically solve most problems right now." "The capital race becomes a real thing and you have to think through, okay, code is not really a moat the way it was in the past, and user interface isn't really a moat."

.benahorowitz.eth explains why moats like code and UI may not work in the AI era: "For my entire career, the one thing that you knew about technology companies is you couldn't throw money at the problem." "With AI, that's really changed in that you can throw money at the problem because if you have enough GPUs and enough data, you can basically solve most problems right now." "The capital race becomes a real thing and you have to think through, okay, code is not really a moat the way it was in the past, and user interface isn't really a moat."

a16z

19,416 次观看 • 3 个月前

OpenGradient is the Network for Open Intelligence. We believe that private AI requires: a prompt no operator can read, an identity no one can tie to it, proof in place of trust, and a guarantee that holds even against the people running it — us included.

OpenGradient is the Network for Open Intelligence. We believe that private AI requires: a prompt no operator can read, an identity no one can tie to it, proof in place of trust, and a guarantee that holds even against the people running it — us included.

OpenGradient (∇, ∇)

1,182,128 次观看 • 2 个月前

Declan Doyle on the theme of togetherness and being a connector under Jesse Minter: "Trust is built off communication and accountability to that communication. ... That trust is gonna be earned through time. It's not like I can call up Lamar and say 'hey, I need you to trust me.' We'll earn that over time and it's really gonna be a two-way street. He's gotta earn my trust, I have to earn his trust. And that goes for every player we have. ... The relationships are always gonna be the engine that drives success in the NFL." via Baltimore Ravens

Declan Doyle on the theme of togetherness and being a connector under Jesse Minter: "Trust is built off communication and accountability to that communication. ... That trust is gonna be earned through time. It's not like I can call up Lamar and say 'hey, I need you to trust me.' We'll earn that over time and it's really gonna be a two-way street. He's gotta earn my trust, I have to earn his trust. And that goes for every player we have. ... The relationships are always gonna be the engine that drives success in the NFL." via Baltimore Ravens

Bobby Trosset

27,556 次观看 • 5 个月前

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 次观看 • 4 个月前

We've been thinking a lot about trust in AI agents — specifically, trust in the developer running it. Right now, there's no way to verify that the safety guardrails an agent claims to have are actually enforced. New work w/ Xisen Jin Aaron et al. tackles exactly this. Paper, code, and demo here:

We've been thinking a lot about trust in AI agents — specifically, trust in the developer running it. Right now, there's no way to verify that the safety guardrails an agent claims to have are actually enforced. New work w/ Xisen Jin Aaron et al. tackles exactly this. Paper, code, and demo here:

Sean Ren

48,296 次观看 • 5 个月前

Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire

Shaun Maguire on SpaceX IPO: "Starship is the railroad to space." "In 2019, it was very predictable that Starlink would work and produce a massive amount of revenue, and we called that right." "We're now in a very similar point to where Starlink was in 2019 with AI infrastructure and orbital compute." "I think people are really underestimating how far along the orbital compute product is right now." On the $22 trillion TAM: "What do you think is the TAM for intelligence? What's the TAM for all compute infrastructure 50 years from now?" "People are underappreciating how much infrastructure is going to be built in space and how much easier it is to access energy in space." "We're at the beginning of this AI era, and we're going to double or triple the total amount of intelligence that we have access to over the next decade — so yes, I absolutely believe in the TAM, and I would even argue it's an underestimate." How much is the "railroad to space" worth in the era of AI and orbital compute? Now imagine the railroad company also owns the AI and the orbital compute. Elon Musk Shaun Maguire

KanekoaTheGreat

39,709 次观看 • 2 个月前

If you plan on being caretakers of your parents or siblings, have them put their home and assets in a trust. They will qualify for Medicaid services after five years of showing minimal income. Medicaid would then in turn pay you to care for them. Now Caretaking is an overwhelming task and many are not built for it so if that's you still have them put the home in a trust and the assets to qualify for Medicaid...they will pay for whomever you choose to care for them. For end of life care..your parents should own nothing and have no assets that are not in a protected trust.

If you plan on being caretakers of your parents or siblings, have them put their home and assets in a trust. They will qualify for Medicaid services after five years of showing minimal income. Medicaid would then in turn pay you to care for them. Now Caretaking is an overwhelming task and many are not built for it so if that's you still have them put the home in a trust and the assets to qualify for Medicaid...they will pay for whomever you choose to care for them. For end of life care..your parents should own nothing and have no assets that are not in a protected trust.

!Nspirenaire〽️

347,938 次观看 • 8 个月前

The future of AI Agents needs #ICP AI agents are starting to act on our behalf: - making deals - sending messages - handling sensitive data But there is no trust layer telling you who or what you're actually dealing with ❌ zCloak Network is building exactly that: the trust, identity and privacy infrastructure for the AI-native economy, and they built it on top of Internet Computer Protocol In this podcast I sit down with Xiao Zhang (0xFrancis), founder of to break down what they're building, why they chose ICP over every other blockchain, and what the world looks like when AI agents can finally be trusted.

The future of AI Agents needs #ICP AI agents are starting to act on our behalf: - making deals - sending messages - handling sensitive data But there is no trust layer telling you who or what you're actually dealing with ❌ zCloak Network is building exactly that: the trust, identity and privacy infrastructure for the AI-native economy, and they built it on top of Internet Computer Protocol In this podcast I sit down with Xiao Zhang (0xFrancis), founder of to break down what they're building, why they chose ICP over every other blockchain, and what the world looks like when AI agents can finally be trusted.

Alex (BCP)

20,791 次观看 • 3 个月前

The new Softr has arrived. Today, we're launching the first AI-native platform for building real business software. Not prototypes. Not demos. Software you can build on Monday morning and share with your team and clients before lunch. Describe what you need, and Softr’s new AI Co-Builder instantly creates the database, app, and business logic — connected, secure, and ready for real users. In less than 5 minutes! → Secure and fully-functional. Logins, user management, permissions, hosting — built-in and working from the moment you hit publish. → Switch between AI prompting and visual editing at any time. No credits burned just to add text or change a color. → Any non-technical team member can own it and iterate on it. Visual interface, database and workflows you can see and edit — no black box. No developer. Ever. 2025 was prototypes. 2026 is software that actually works. Start building with Softr for free →

The new Softr has arrived. Today, we're launching the first AI-native platform for building real business software. Not prototypes. Not demos. Software you can build on Monday morning and share with your team and clients before lunch. Describe what you need, and Softr’s new AI Co-Builder instantly creates the database, app, and business logic — connected, secure, and ready for real users. In less than 5 minutes! → Secure and fully-functional. Logins, user management, permissions, hosting — built-in and working from the moment you hit publish. → Switch between AI prompting and visual editing at any time. No credits burned just to add text or change a color. → Any non-technical team member can own it and iterate on it. Visual interface, database and workflows you can see and edit — no black box. No developer. Ever. 2025 was prototypes. 2026 is software that actually works. Start building with Softr for free →

Softr

33,254 次观看 • 4 个月前

We're not chasing AI. We're chasing journeys that end safely, where the failing part gets caught before it fails. We're not chasing AI. We're chasing food you can trust. What's on the label is what's in the package. We're not chasing AI. We're chasing medicine you can trust, every result traceable to the evidence behind it. We're not chasing AI. We're chasing products that don't break. Quality proven, not promised. We're not chasing AI. We're chasing buildings that stand, every safety decision on record long after the cranes are gone. We're not chasing AI. We're chasing infrastructure that holds: the power, the water, the networks a society runs on. We're not chasing AI. We're chasing sport that stays honest, performance and decisions you can actually check. We're not chasing AI. We're chasing a world where you can still tell what's real, where fake news and deepfakes meet a record they can't forge. And we're not paying for the same answer twice. Most AI forgets. Every agent starts cold, re-deriving what another agent worked out an hour ago. Same tokens, same cost, same question, again and again. We built the opposite. Every decision becomes context the next agent inherits, so nothing gets solved twice. Knowledge compounds while the cost curve bends down, and the hundredth agent runs leaner than the first. We never set out to build AI. We set out to do one thing: make facts traceable. Every answer tied to its source. Every objective tied to a real outcome it's meant to serve. That's the foundation, and there's no other. AI works for your organization only when it stands on facts you can follow and goals that matter outside the model. Take that away and even the most fluent system is just a well-spoken guess. Give it traceable facts and real objectives, and AI stops performing for you and starts working for you. That's what nOS, our Network Operating System, powered by OriginTrail, is for. That's what Trace Labs is for. Not chasing AI. Chasing trust, and refusing to waste a thing getting there.

We're not chasing AI. We're chasing journeys that end safely, where the failing part gets caught before it fails. We're not chasing AI. We're chasing food you can trust. What's on the label is what's in the package. We're not chasing AI. We're chasing medicine you can trust, every result traceable to the evidence behind it. We're not chasing AI. We're chasing products that don't break. Quality proven, not promised. We're not chasing AI. We're chasing buildings that stand, every safety decision on record long after the cranes are gone. We're not chasing AI. We're chasing infrastructure that holds: the power, the water, the networks a society runs on. We're not chasing AI. We're chasing sport that stays honest, performance and decisions you can actually check. We're not chasing AI. We're chasing a world where you can still tell what's real, where fake news and deepfakes meet a record they can't forge. And we're not paying for the same answer twice. Most AI forgets. Every agent starts cold, re-deriving what another agent worked out an hour ago. Same tokens, same cost, same question, again and again. We built the opposite. Every decision becomes context the next agent inherits, so nothing gets solved twice. Knowledge compounds while the cost curve bends down, and the hundredth agent runs leaner than the first. We never set out to build AI. We set out to do one thing: make facts traceable. Every answer tied to its source. Every objective tied to a real outcome it's meant to serve. That's the foundation, and there's no other. AI works for your organization only when it stands on facts you can follow and goals that matter outside the model. Take that away and even the most fluent system is just a well-spoken guess. Give it traceable facts and real objectives, and AI stops performing for you and starts working for you. That's what nOS, our Network Operating System, powered by OriginTrail, is for. That's what Trace Labs is for. Not chasing AI. Chasing trust, and refusing to waste a thing getting there.

Trace Labs - Trusted Context for AI Agents

424,956 次观看 • 2 个月前

"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."

"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."

Patrick OShaughnessy

24,818 次观看 • 6 个月前

Introducing Encore Cloud 2.0: The development platform for the AI era We believe infrastructure and operations need to keep pace with code generation. Over the past few years, 100+ engineering teams have adopted Encore in production and ship more efficiently because infrastructure isn't a bottleneck anymore. Databases, queues, IAM roles, networking, gateways, tracing - all automatic, in their AWS or GCP accounts, with production guardrails built in. Encore Cloud 2.0 takes this foundation further - with enhanced visibility, service catalogs, and MCP integration so both teams and AI agents have the context they need. Everything deploys as standard cloud resources in your cloud account with just a few lines of Encore code to declare what you need - without infrastructure lock-in. Read more in our announcement post below.

Introducing Encore Cloud 2.0: The development platform for the AI era We believe infrastructure and operations need to keep pace with code generation. Over the past few years, 100+ engineering teams have adopted Encore in production and ship more efficiently because infrastructure isn't a bottleneck anymore. Databases, queues, IAM roles, networking, gateways, tracing - all automatic, in their AWS or GCP accounts, with production guardrails built in. Encore Cloud 2.0 takes this foundation further - with enhanced visibility, service catalogs, and MCP integration so both teams and AI agents have the context they need. Everything deploys as standard cloud resources in your cloud account with just a few lines of Encore code to declare what you need - without infrastructure lock-in. Read more in our announcement post below.

Encore

36,359 次观看 • 7 个月前

AI agents are about to have the same problem websites had early on: How do you know what’s real, where to find it, and whether you can trust it? HOL and GoDaddy are leading a meetup on why agent identity is becoming infrastructure, hosted by LFDT. Link in the next post...

AI agents are about to have the same problem websites had early on: How do you know what’s real, where to find it, and whether you can trust it? HOL and GoDaddy are leading a meetup on why agent identity is becoming infrastructure, hosted by LFDT. Link in the next post...

HOL

1,423,186 次观看 • 2 个月前

Today in our company all-hands, Sreeram Kannan made an important point: Most AI agents still feel like software with a human behind the curtain. The real question isn’t whether an agent can post. It’s whether an agent can own the act of posting. - Can it sign the action? - Can you verify the signature? - Can you trace that signature to a secure environment? - Can you inspect what was actually running when the action happened? Once the answer is yes, the category changes. Now you’re not looking at AI-generated content. You’re looking at a software entity that can act in a way that is attributable, inspectable and independent. A completely new primitive! Watch the clip:

Today in our company all-hands, Sreeram Kannan made an important point: Most AI agents still feel like software with a human behind the curtain. The real question isn’t whether an agent can post. It’s whether an agent can own the act of posting. - Can it sign the action? - Can you verify the signature? - Can you trace that signature to a secure environment? - Can you inspect what was actually running when the action happened? Once the answer is yes, the category changes. Now you’re not looking at AI-generated content. You’re looking at a software entity that can act in a way that is attributable, inspectable and independent. A completely new primitive! Watch the clip:

EigenCloud

14,592 次观看 • 4 个月前

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."

The Pragmatic Engineer

39,011 次观看 • 2 个月前

Dylan Patel of SemiAnalysis says speed is the moat in AI inference, and getting there runs through new compute (Groq/Cerebras), KV-cache offload, and networking optimization (Aria). "Most of the infrastructure is pretty optimized for high throughput, I would say, but more and more people want to pay for speed. It turns out agents are very sequential, task-oriented, and models running in sequential tasks." "Trying something, verifying it, checking if it's right, and you do that in a loop. It ends up being very slow." "And speed is the moat. The faster you go, speed is the moat." "So a lot of the workloads and high-end use cases that people are developing now are all focused on high interactivity, and the infrastructure there is quite different." "So let's break that down into a few components." "One is you can do new compute: you can do Groq or Cerebras-style compute, which is very much focused on high interactivity." "But even with existing GPUs you can do a few things. One, if you're focused heavily on time to first token, you might offload KV cache to storage, keep it cached, move it back into the compute whenever you need it, much faster than recomputing." "And the third thing you can do is you have to optimize the network."

Dylan Patel of SemiAnalysis says speed is the moat in AI inference, and getting there runs through new compute (Groq/Cerebras), KV-cache offload, and networking optimization (Aria). "Most of the infrastructure is pretty optimized for high throughput, I would say, but more and more people want to pay for speed. It turns out agents are very sequential, task-oriented, and models running in sequential tasks." "Trying something, verifying it, checking if it's right, and you do that in a loop. It ends up being very slow." "And speed is the moat. The faster you go, speed is the moat." "So a lot of the workloads and high-end use cases that people are developing now are all focused on high interactivity, and the infrastructure there is quite different." "So let's break that down into a few components." "One is you can do new compute: you can do Groq or Cerebras-style compute, which is very much focused on high interactivity." "But even with existing GPUs you can do a few things. One, if you're focused heavily on time to first token, you might offload KV cache to storage, keep it cached, move it back into the compute whenever you need it, much faster than recomputing." "And the third thing you can do is you have to optimize the network."

Fireside Alpha

281,474 次观看 • 16 天前

It’s wild watching AI Agents work with software. Here’s ChatGPT connected to Box running a deep research task on market analysis documents. The future is just giving AI Agents the data and systems to comb through and having them work for you in the background.

It’s wild watching AI Agents work with software. Here’s ChatGPT connected to Box running a deep research task on market analysis documents. The future is just giving AI Agents the data and systems to comb through and having them work for you in the background.

Aaron Levie

235,980 次观看 • 1 年前

"We treat software like it is so precious. And in the new world we're going to, it's going to be much more fungible. But that's the good news." Garry Tan on where the moats will be for future YC companies: "That's where taste, agency, and trust matter a lot. This is something that's only crystallized recently. We've been talking about agency and taste for the longest time. Agency is being able to prompt, and taste is being able to do evals." "Trust is the third thing. It's probably the most important. When you have an enterprise company that is selling to real businesses that are relying on you — that's your moat. Your moat is actually trust. They went through the effort to try you, get you onboarded, and incorporate you into how they work."

"We treat software like it is so precious. And in the new world we're going to, it's going to be much more fungible. But that's the good news." Garry Tan on where the moats will be for future YC companies: "That's where taste, agency, and trust matter a lot. This is something that's only crystallized recently. We've been talking about agency and taste for the longest time. Agency is being able to prompt, and taste is being able to do evals." "Trust is the third thing. It's probably the most important. When you have an enterprise company that is selling to real businesses that are relying on you — that's your moat. Your moat is actually trust. They went through the effort to try you, get you onboarded, and incorporate you into how they work."

TBPN

43,619 次观看 • 4 个月前