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muse spark 1.1 in AdaL Engineer beats Opus4.8 in Claude Code with 20% of the cost loop engineering, when done right, is beyond just running longer, its delivering better results when contexts are managed well and when workers are better prompted to stay honest. how GANs had changed image... show more

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Less than two weeks before he left Google to co-found Discovery Loop, Jeff Dean described the automated research loop the new company is named for, giving no hint he was about to leave to go do it. "There's this sort of the foundation of the scientific method of you propose an experiment, you implement what you need to run the experiment, and you evaluate the experiment, and then you get results from that." "And I think there are more and more problems that are now possible to implement where that whole loop of running not just a few experiments, but running many, many experiments because you're able to automate that loop and make the latency of that loop extremely low is going to be really, really important." "It's going to enable us to tackle lots of different problem domains in science and engineering and machine learning, model design itself, and also in engineering tasks like designing chips."

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$Boris Cherny, head of Claude Code at Anthropic: "I don't prompt Claude anymore. I have loops that are running; they are the ones prompting Claude and figuring out what to do. My job is to write loops." He's right. Karpathy and Steinberger arrived at the same conclusion from different angles. The skill has a new name. Loop engineering. Cherny's token bill is zero. He works at Anthropic. Steinberger burned $1.3 million in OpenAI credits in 30 days. He works at OpenAI. Loops are the right architecture. But the people evangelizing them aren't the ones paying. I wrote a breakdown of five plays that land most of what a loop gives you at a fraction of the cost, plus the one case where a loop is worth paying for.$

Boris Cherny, head of Claude Code at Anthropic: "I don't prompt Claude anymore. I have loops that are running; they are the ones prompting Claude and figuring out what to do. My job is to write loops." He's right. Karpathy and Steinberger arrived at the same conclusion from different angles. The skill has a new name. Loop engineering. Cherny's token bill is zero. He works at Anthropic. Steinberger burned $1.3 million in OpenAI credits in 30 days. He works at OpenAI. Loops are the right architecture. But the people evangelizing them aren't the ones paying. I wrote a breakdown of five plays that land most of what a loop gives you at a fraction of the cost, plus the one case where a loop is worth paying for.

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Happy to properly launch Anna, the proactive AI agent for parents! Uncovering a bit of the technology behind the scenes! Building Anna is where I learned: 💾 Memory as plain text sucks. You need structured memory. Like a full-blown PostgreSQL DB that stores your tasks and calendar in a structured manner. Most harnesses are good at coding-related stuff. Let it do the query. Don't let it vibe-search the memory. Let it vibe your SQL query 💭 Dreaming is a useful concept for enhancing memory to feed the LLM context. But DO NOT vibe your dream. Asking your agent to "hey, just dream and keep the relevant memory around" is a recipe for deleting a bunch of important information and keeping trash around. Your dream needs to have some Taxonomy (or better, Ontology). What information is important? For who? With what object? What can they do? And again, these are impossible to describe and act well without a proper schema 🔄 Loop Engineering is important for smoothing out rough edges in the system we build. But even expensive loop engineering with a state-of-the-art model can't out-engineer bad system design. The highest leverage an AI Engineer can do is actually building the right system design, and having an eye on both product delight and engineering scalability There are several more insights that I plan to cover in a dedicated video about Agentic AI Engineering. But it's actually a huge relief that the future of software engineering... is still software engineering

Happy to properly launch Anna, the proactive AI agent for parents! Uncovering a bit of the technology behind the scenes! Building Anna is where I learned: 💾 Memory as plain text sucks. You need structured memory. Like a full-blown PostgreSQL DB that stores your tasks and calendar in a structured manner. Most harnesses are good at coding-related stuff. Let it do the query. Don't let it vibe-search the memory. Let it vibe your SQL query 💭 Dreaming is a useful concept for enhancing memory to feed the LLM context. But DO NOT vibe your dream. Asking your agent to "hey, just dream and keep the relevant memory around" is a recipe for deleting a bunch of important information and keeping trash around. Your dream needs to have some Taxonomy (or better, Ontology). What information is important? For who? With what object? What can they do? And again, these are impossible to describe and act well without a proper schema 🔄 Loop Engineering is important for smoothing out rough edges in the system we build. But even expensive loop engineering with a state-of-the-art model can't out-engineer bad system design. The highest leverage an AI Engineer can do is actually building the right system design, and having an eye on both product delight and engineering scalability There are several more insights that I plan to cover in a dedicated video about Agentic AI Engineering. But it's actually a huge relief that the future of software engineering... is still software engineering

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This is absolutely the best explanation of engineering I have ever seen. When it comes to any endeavor in life, most people are working hard to be physicists. Engineers are just trying to get the the thing done to it's intended result.

This is absolutely the best explanation of engineering I have ever seen. When it comes to any endeavor in life, most people are working hard to be physicists. Engineers are just trying to get the the thing done to it's intended result.

Jim Pfaff

125,909 просмотров • 2 лет назад