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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...

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Anton Livaja

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484,752 просмотров • 8 дней назад •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.

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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.
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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.
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"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."
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"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."

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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.

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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...

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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:
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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:

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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."
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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 просмотров • 2 месяцев назад

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."
1:12

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 просмотров • 17 дней назад

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.
1:33

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 просмотров • 1 год назад

"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."
1:48

"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

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