This video is pretty great. “The joke is this is not a joke” comment in there… how many of us understood everything that was said and then felt like maybe we need a different hobby…
Given the sheer volume of cheap stuff that had been coming straight from China, and the end of de minimus, my first guess would be the majority of this is Chinese and other foreign goods that are now being imported in bulk to minimize duties and costs of handling paperwork, then distributed state-side. Lots of new business (and resulting extra costs to consumers) in logistics, without as much of an increase on the manufacturing side.
I mean, it’s not like US clothes manufacturers, for example, can compete with East Asia even with 100% tariffs (on the wholesale price). Not even close. Ditto electronics, most toys, et c. Lots and lots of really high-volume stuff that was getting drop shipped through e.g. Amazon sellers, not to mention lots of traditional US brands that were shipping straight from overseas warehouses.
MCP is fine if your tool definition is small. If it's something like a sub-agent harness which is used very often, then in fact it's probably more context efficient because the tools are already loaded in context and the model doesn't have to spend a few turns deciding to load the skill, thinking about it and then invoking another tool/script to invoke the subagent.
Is that true? I had tool use working with GPT-4 in 2023, before function calling or structured outputs were even a thing. My tool instructions were only half a page though. Maybe the long prompts are causing problems?
They're talking about "skills" which are not the same thing as tools. Most models haven't been trained on the open SKILL spec, and therefore aren't tuned to invoke them reliable when the need occurs.
> value would be in analyzing those rich traces with another agent to extract (failure) patterns and learnings
Claude Code supports hooks. This allows me to run an agent skill at the end of every agent execution to automatically determine if there were any lessons worth learning from the last session. If there were. new agent skills are automatically created or existing ones automatically updated as apporpriate.
Yes, I've done the same. But the issue is that the agent tends to learn too many lessons, or to overfit those lessons to that single session. I think the benefit of a tool like this is that you can give that agent a wider view when formulating recommendations.
Another anecdote. Nobody in my extended family has more than 3 kids. My grandmothers from both sides had more. But the trend is pretty clear. Fewer kids for the modern generation. Regardless of the level of education and income. In fact, the lower education/income ones in my extended family have fewer kids.
> an amazing future of perfect code from agentic whatevers will come to fruition...
Nobody credible is promising you a perfect future. But, a better future, yes! If you do not see it, then know this. You have your head firmly planted in the sand and are intentionally refusing to see what is coming. You may not like it. You may not want it. But it is coming and you will either have to adapt or become irrelevant.
Does Copilot spit out useless PR comments. 100% yes! Are there tools that are better than Copilot? 100% yes! These tools are not perfect. But even with their imperfections, they are very useful. You have to learn to harness them for their strengths and build processes to address their weaknesses. And yes, all of this requires learning and experimentation. Without that, you will not get good results and you will complain about these tools not being good.
> I am still waiting to see where it increases productivity...
If you are a software engineer, and you are not using using AI to help with software development, then you are missing out. Like many other technologies, using AI agents for software dev work takes time to learn and master. You are not likely to get good results if you try it half-heartedly as a skeptic.
And no, nobody can teach you these skills in a comment in an online forum. This requires trial and error on your part. If well known devs like Linus Torvalds are saying there is value here, and you are not seeing it, then then the issue is not with the tool.
“Of all the points the other side makes, this one seems the most incoherent. Code is deterministic, AI isn’t. We don’t have to look at assembly, because a compiler produces the same result every time.”
This is a valid argument. However, if you create test harnesses using multiple LLMs validating each other’s work, you can get very close to compiler-like deterministic behavior today. And this process will improve over time.
It helps, but it doesn't make it deterministic. LLMs could all be misled together. A different story would be if we had deterministic models, where the exact same input always results in the exact same output. I'm not sure why we don't try this tbh.
Are you sure that it’s T=0. My comment’s first draft said “it can’t just be setting temp to zero can it?” But I felt like T is not enough. Try running the same prompt in new sessions with T=0, like “write a poem”. Will it produce the same poem each time? (I’m not where I can try it currently).
That’s the opposite of clean-room. The whole point of clean-room design is that you have your software written by people who have not looked into the competing, existing implementation, to prevent any claim of plagiarism.
“Typically, a clean-room design is done by having someone examine the system to be reimplemented and having this person write a specification. This specification is then reviewed by a lawyer to ensure that no copyrighted material is included. The specification is then implemented by a team with no connection to the original examiners.”
No they don't. One team meticulously documents and specs out what the original code does, and then a completely independent team, who has never seen the original source code, implements it.
What they don't do is read the product they're clean-rooming. That's kinda disqualifying. Impossible to know if the GCC source is in 4.6's training set but it would be kinda weird if it wasn't.
True, but the human isn't allowed to bring 1TB of compressed data pertaining to what they are "redesigning from scratch/memory" into the clean room.
In fact the idea of a "clean room" implementation is that all you have to go on is the interface spec of what you are trying to build a clean (non-copyright violating) version of - e.g. IBM PC BIOS API interface.
You can't have previously read the IBM PC BIOS source code, then claim to have created a "clean room" clone!
If that's what clean room means to you, I do know AI can definitely replace you.
As even ChatGPT is better than that.
(prompt: what does a clean room implementation mean?)
From ChatGPT without login BTW!
> A clean room implementation is a way of building something (usually software) without copying or being influenced by the original implementation, so you avoid copyright or IP issues.
> The core idea is separation.
> Here’s how it usually works:
> The basic setup
> Two teams (or two roles):
> Specification team (the “dirty room”)
> Looks at the original product, code, or behavior
> Documents what it does, not how it does it
> Produces specs, interfaces, test cases, and behavior descriptions
> Implementation team (the “clean room”)
> Never sees the original code
> Only reads the specs
> Writes a brand-new implementation from scratch
> Because the clean team never touches the original code, their work is considered independently created, even if the behavior matches.
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