holtkam2

This guy's head will explode when he realizes that AI has been better at coding than humans for years and yet software engineering employment % has barely twitched...

If you want to fully understand / contribute to / fully leverage the most powerful technology humanity has ever devised (AI), you must learn to read and write code. That’s the only reason anyone should need.

If you used a time machine to go back to 2021 and showed someone the best open source LLMs from 2026 they would surely say “yeah that’s AGI”

This is the way. But also - keep your head up - your mindset matters more than you think. If you consider yourself a victim of your circumstances with no agency, this will be harder for you. So get your mentality right. Something along the lines of 'this is an exciting new era and I'm well prepared to master this new domain like I have many times before'.

The way I think of it has evolved a lot over the last 5 years. At this point I think human brains probably do something analogous to next token prediction when we think. For all the hype, I think LLMs are actually more, not less, intelligent than that average person realizes. I think it’s legit, actual intelligence, not just “artificial” intelligence. That may be a hot take but it’s just my perception.

at a certain point you're gonna need to change your benchmark because this will end up in the model's training set

I wish I could upvote this twice. We (devs) really REALLY need to consider on-device compute before going to the cloud for LLM inference.

IDK if the author's 'metacognition' needs to be a feature of the LLM itself.

I could imagine a harness that 1) reads LLM output 2) uses a research sub-agent to attempt to verify any factual claims 2) rephrase the main agent's output such that it conveys uncertainty if the factual claim cannot be independently verified

No, I'm not considering a career change. But my career will change.

I started off as a javacript developer. Then I was a full stack SWE. Then I was an Applied AI Engineer. I think enterprises will have a need for folks with technical expertise to deliver value - often new software - for a long time.

Until an enterprise like capital one can operate without anyone in the organization knowing how any of their technical infrastructure works (never?) I expect I'll be able to find work.

Just use the codex plugin for vscode ?

Someone tell me if my idea is dumb:

You know how adding ".ai" to your company name / domain name increases your valuation an order of magnitude or whatever?

Well has anyone tried chaining .ai's to their company name...???

For example could x.ai increase their valuation another 5-10x by renaming themselves x.ai.ai?

What about perplexity.ai.ai.ai?

Seems like a low-hanging fruit for increasing valuation / attention on your company, but let me know if I'm missing something.

This article forgot the strongest argument for hand writing your code. When the process is done, you understand it at a depth far beyond any vibe coder who created the same thing

Moving from SF to Chicago this week. This made me feel a little more excited. TY for the share!

To answer your question directly: yes I am bored of talking about AI. I think it’s funny how the folks who are screaming most loudly about their AI expertise typically have not built anything of value with it. They are so focused on the tool they have forgotten that the output is what mattered all along

Level 9: agent managers running agent teams Level 10: agent CEOs overseeing agent managers Level 11: agent board of directors overseeing the agent CEO

Level 12: agent superintelligence - single entity doing everything

Level 13: agent superagent, agenting agency agentically, in a loop, recursively, mega agent, agentic agent agent agency super AGI agent

Level 14: A G E N T

Are you telling me juniors aren’t facing hard problems anymore? I doubt that’s the case. They’re probably banging their heads against the wall trying to understand why this or that doesn’t work… just as they always had before. AI isn’t a magical wand that makes everything just “work”. Probably never will be.

Never - data centers will always offer more power if you only care about raw inference speed. HOWEVER I think that we'll reach the 'good enough' bar super soon. In 2-3 years I expect apple macs to be able to run a model as 'good' as Claude 4.6 sonnet at 90% of the inference speed we're used to from a cloud API.

Yes, I'm sure by then there will be better models on offer via cloud providers, but idk if I'll even care. I'm not doing science / research or complex mathematical proofs, I just want a model good enough to vibe code personal projects for fun. So I think at that point I'll stop being a OpenAI / Anthropic customer.

At the end of the day you need humans who understand the business critical (or safety critical) systems that underpin the enterprise.

Someone needs to be held accountable when things go wrong. Someone needs to be able to explain to the CEO why this or that is impossible.

If you want to have AI generate all the code for your business critical software, fine, but you better make sure you understand it well. Sometimes the fastest path to deep understanding is just coding things out yourself - so be it.

This is why the truly critical software doesn’t get developed much faster when AI tools are introduced. The bottleneck isn’t how fast the code can be created, it’s how fast humans can construct their understanding before they put their careers on the line by deploying it.

Ofc… this doesn’t apply to prototypes, hackathons, POCs, etc. for those “low stakes” projects, vibe code away, if you wish.

Idk, anyone who wants to understand and apply the most powerful and transformative technology in human history…?

Has anyone noticed Amazon or AWS shipping features faster than their pre-GenAI baseline? I haven't

No matter how fast and accurately your AI apps can spit out code (or PowerPoints, or excel spreadsheets, or business plans, etc) you will still need humans to understand how stuff works. If it’s truly business critical software, you can’t get around the fact that humans need to deeply understand how and why it works, in case something goes wrong and they need to explain to the CEO what happened.

Even in a world where the software is 100% written by AI in 1 millisecond by a country of geniuses in a data center, humans still need to have their hands firmly on the wheel if they won’t want to risk their businesses well being. That means taking the time to understand what the AI put together. That will be the bottleneck regardless of how fast and smart AI is. Because unless the CEO wants to be held accountable for what the AI builds and deploys, humans will need to be there to take the responsibility for its output.

What is so enjoyable about this is that it'll be all musk's fault when we look back and realize Tesla just died off over time. Once other companies started actually competing with him, he wasn't smart enough to hold the lead. Lucid, byd, even hyundai, all made better cars within a few years, so Elon just basically gave up. I love that

That’s insane. He’s the David Goggins of coyotes. Also, that water is cold as hell this time of year. I couldn’t do that. Give him a metal and enter him in the Escape from Alcatraz triathlon (I assume he can ride a bicycle?)

Also, if you find yourself saying “Trump is doing just what I would do in this situation!” that is not a good sign. Unless you have tons of experience and expertise in geopolitics and international relations, you probably wouldn’t make the smartest moves in this scenario if you were president.

Well if you did, I’d say congrats, the arctic is now more secure, but your once loyal allies now dislike you. Was that worth the cost? In a hot war with a near peer you would want them on your side. The odds they would be willing to do so are now far lower.

I’ll buy a cybertruck if and only if the board replaces Elon. I’m serious

I agree and disagree. In my day job as an AI engineer I rarely if ever need to use any “classic” deep learning to get things done. However, I’m a firm believer that understanding the internals of a LLM can set you apart as an gen AI engineer, if you’re interested in becoming the top 1% in your field. There can and will be situations where your intuition about the constraints of your model is superior compared to peers who consider the LLM a black box. I had this advice given directly to me years ago, in person, by Clem Delangue of Hugging Face - I took it seriously and really doubled down on understanding the guts of LLMs. I think it’s served me well.

I’d give similar advice to any coding bootcamp grad: yes you can get far by just knowing python and React, but to reach the absolute peak of your potential and join the ranks of the very best in the world in your field, you’ll eventually want to dive deep into computer architecture and lower level languages. Knowing these deeply will help you apply your higher level code more effectively than your coding bootcamp classmates over the course of a career.

You mean its ability to use powershell, or something else?

I’d go further: it’s not enough to be able to prove that your code works. It’s required that you also understand why it works.

Otherwise you’ll end up in situations where it passes all test cases yet fails for something unexpected in the real world, and you don’t know why, because you don’t even know what’s going on under the hood.

If Tesla fires Musk I'll buy one of their cars that day