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Ryan Lopopolo
@_lopopolo
Principal Engineer, Agentic GCP @googlecloud • prev @OpenAI • Building the future of work. Harness engineering. As an agent influencer, • opinions mine
1.2K Following    10.6K Followers
I don’t know about y’all but I would simply just be in distribution
good prompt for the lorge models > can you find places where we are fighting the frameworks we are using, reinventing things unnecessarily (while still keeping our strict supply chain posture), relying on deprecated behavior, buggy, or needlessly complex? any tests that don't pull their weight which if removed could significantly simplify the code?
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longer form blog here if y'all prefer:
And still, people will prompt "make me $1B make no mistakes". That is drastically unspecified! Models are rewarded for efficiency and take shortcuts graders permit. But permissible shortcuts depend on who you are and what you value. To solve alignment is irreducible complexity.
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And that generalizes to every auto-rater, every judge, every rubric, every eval, and every researcher as well. These misalignments compound over time. The models do not have a fear of future regret. Long-term coherence through agentic work product is very unsolved.
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I am an expert software engineer; I'm not happy (and never have been) with the default behaviors of the model when producing software. Every isRecord or overly defensive exception handler engineers see is because a non-expert rewarded these behaviors. The model’s priors are bad.
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On safety risk, to those building agents: You are an expert in concerns X, Y, and Z, so your agent is likely phenomenal at them. But there are innumerable other concerns you cannot judge or evaluate. You are relying heavily on the model’s priors to mitigate that risk.
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If you're not literally asking your colleagues "please vibeslop this", you're not moving fast enough.
Three people shaping how we think about agentic software development are joining us at AI DevCon NYC: - @Steve_Yegge, creator of @gastownhall, on where AI-native software development is heading next - @_lopopolo on harness engineering and how humans steer while agents execute - @GeoffreyHuntley, creator of the Ralph loop, on pushing agentic development workflows further Between them, they’re exploring some of the biggest questions engineering teams are facing right now how to make agents more reliable, how to coordinate them effectively, and what software development looks like when agents take on more of the execution. They’ll be joining practitioners and engineering leaders from Anthropic, OpenAI, DeepMind, Google, Microsoft, OpenHands and more at AI DevCon NYC. Prices will increase on Oct 1st. Use code X15 for 15% off your ticket: #aidevcon# #ainativedevcon#
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So what I am hearing on the timeline is if your software factory is not operated by a fruit fly that can parallel park ngmi
As a junior engineer 6 months out of university, I got the best advice of my career: as you become more senior, people will do less and less for you; if you want a thing to happen, you have to do it. All staff engineer career advice (bring solutions not problems, see risk from the future, effectively manage up) is downstream of this. Downstream of needing to do things to get to outcomes.
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I can’t stress enough how little an idea matters compared to the agency of the people executing the idea. I have had the privilege of knowing and sometimes even working with some of the most successful people (by various metrics). The difference between mediocre and excellent work and outcomes is predominantly one of agency. In practice this means: they dont wait for things to happen to them they go out and make things happen for them. They don’t wait for someone else to do something, for someone to teach them, for someone to give them the path, etc. They just go out and find a way to do it. I think the single biggest superpower these people have is the realization/belief that the world around them is completely mutable. Most everything that happens is because a person made it happen. I used to tell people to look around the room you’re sitting in. Look at everything. Every noun. It almost all exists because a person willed it into existence. Nothing is stopping you from doing the same. I see people online all the time dismissing someone else’s success because “I had that idea first” or whatever. I mean… yeah? If so then the difference is… you. So a bit of a self own whenever I hear that. Number one tip: act with agency.
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You know, I’m something of a hard task myself.
An agent is a parameterized program over a set of capabilities. We recognize the capabilities, but don't know the concrete impls. An agent platform must let builders discover what works without waiting for someone else’s config knobs and roadmap.
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have we talked about how my guy Fermat thought he was gonna fit 13M lines of Lean in the margin of the page?
@AnthropicAI has shared the first end-to-end, computer-checked proof of Fermat's Last Theorem: 13 million lines of Lean, 29,500 intermediate theorems. Their announcement calls it "the largest Lean proof ever constructed." See also Kevin Buzzard's blog post about the proof: 🔗 Anthropic's announce post: 🔗 The code: #LeanLang# #LeanProver# #FLT#
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I've given this story in many talks: it is unconscionable that at this point the industry has not eliminated “outage caused by missing retry and timeout”. These problems are trivial to specify and identify. So many tens or hundreds of thousands of hours mitigating. No widespread technical guardrails. And now, with code being free to produce, there really is no excuse
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Rachel's Ramblings: Maybe We Shouldn't Be Reviewing All This Code: Or, perhaps the problem isn't that AI has broken code review, maybe it’s that we've been using code review to solve the wrong problems
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What questions do you have for Ryan? Share them below ⬇️
Every doc, code change, review comment, and email I've produced at Google has been AI generated. My favorite technique for doing this has been best of N. Every time I need a thing, I roll the same prompt 4 times, each agent produces a doc, I pick the best parts, and battle them.
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I think I am able to run inference in all of these layers and I have no idea what to do with this knowledge but you can be assured I will find a way
As we all know, there are four networking layers: L2, L3, L4, and L7. I think this is obvious to everybody.