I keep going back to the debate with myself. Do I give my now-young kids 500K in an ETF in 14 years or do I send them to college? And the debate is not about how they will spend it (I can hold on to it too), it’s about whether to spend the money or just pass on the wealth to them. People have given me tons of reasons why college is good for them - independence, etc etc. I can find a way to give them independence without spending 500k. I haven’t settled the debate but articles like this are not helping the college path.
I strongly believe college is oversold but mostly because kids are told they just need to get "any degree", which is a blatant lie. It can totally be worth it if you are objectively looking at what outcomes a specific degree will get you.
My father offered me a similar deal 25 years ago. I chose college, but itsnt clearly correct. 250k (my offer) would be 2 million today, which is more than my savings from 20 years of work. I would have needed a different career, but that could be better or worse. If I had that and was an electrician for example, I would be far better off
I'm childless, but I go through that with nieces and nephews. I decided to give them a lump sum as a high school graduation gift to do with as they please.
IMO you could learn a lot more, as a young person, starting a small business than you'll ever learn at college, and it'll cost less even if you fail. So I don't want to make it a "college fund" or anything like that.
Funny. He foreshadowed this in a recent interview. Saying that he may fall out of touch with evolving approaches and if any of the frontier labs would have him, he’d be interested.
The warm up rounds to filter out the fluffy includes asking what is a Matrix, do this calculation, what is a LLM. 2nd round include stuff like explain the binary search algorithm, write a double linked list in C, and a take home project.
Would have been great to hear that his inability to do the interview memorization bullshit as a senior was why he didn't get hired somewhere like OpenAI. lol.
Except the good companies probably dont make you do silly stupid outdated interview practices without the tools you can actually use on the job today, right?
He is good at fleecing the plebs which MUCH MORE important in modern day America. From top to the bottom, it's a scam aaaaaall the way down.
Although, to be fair, Amodei has kind of overtaken Altman in the art of being the best hype man/scammer. If they won't buy in, hell, promise to double their lifespan.
it was a brash example without thinking. I don't know his background that well, so my intent was just to say he would struggle with a major frontier company or startup to pass the basic technical interview theater of technical memorization that others are expected to do. And it was also a way to say he would get an exception, as unfair as it may be. And as I said it would be funny that this is true.
Good for him, his public work these last ~1-2 years has been influential for me, as I'm sure it has for others.
I even share his concern about struggling to keep pace with the rate of change lately, and agree that my working in a frontier lab or any other such environment would certainly help with that!
I have a weird background mix of analytic philosophy, linguistics/NLP, propaganda research, and long-term institutional data science/strategy work, which unfortunately does not make ATS systems especially low-friction as I try to jump industries.
So I keep busy the best I can: lately building tooling around runtime observability, intent legibility, and intervention in LLM systems.
> I have a weird background mix of analytic philosophy, linguistics/NLP, propaganda research, and long-term institutional data science/strategy work, which unfortunately does not make ATS systems especially low-friction as I try to jump industries.
There's a choice to be made between helpfully defeating someone's ATS and searching for more clueful employers. I'll probably be walking paper resumes into local offices next time around anyhow.
...except the operating system. And the silly notch. And the weird keyboard. And the hard palm-cutting corner. And the reflective screen. and the finger-print-magnet materials. And the small amount of RAM. and the small SSD. And the weight.
Other than that, it's perfect!
(On the blance,still better than any other laptop)
Does anyone know of any HR departments actually using LLMs for scoring, selection, extraction, classification or any real use cases? I'm curious to hear about it and how they are using it.
In Multimodal yes, but Opus is definitely edging out in Text/Reasoning and Agentic benchmarks.
I think the general skepticism is because they are late to race, and they are releasing a Opus-4.6-equivalent model now, when Anthropic is teasing Mythos.
How many proprietary use cases truly need pre-training or even fine-tuning as opposed to RAG approach? And at what point does it make sense to pre-train/fine tune? Curious.
You could take a model like the one referenced in the article, retool it with Forge for oh I don't know, compost, and use it to flag batches that contain too much paper for instance.
These kinds of applications would work across industries, basically anywhere where you have a documented process and can stand to have automated oversight.
You can fine tune small, very fast and cheap to run specialized models ie. to react to logs, tool use and domain knowledge, possibly removing network llm comms altogether etc.
rag basically gives the llm a bunch of documents to search thru for the answer.
What it doesn't do is make the algorithm any better. pre-training and fine-tunning improve the llm abaility to reason about your task.
For coding use cases you may want a way to search for symbols themselves or do a plain text exact match for the name of a symbol to find the relevant documents to include. There is more to searching than building a basic similarity search.
Sorry but who mentioned coding as a use-case? My comment was general and not specific to the coding use-case, and I don't understand where did you get the idea from that I am arguing that building a similarity search engine would be a substitute to the symbol-search engine or that symbol-search is inferior to the similarity-search? Please don't put words into my mouth. My question was genuine without making any presumptions.
Even with the coding use-case you would still likely want to build a similarity search engine because searching through plain symbols isn't enough to build a contextual understanding of higher-level concepts in the code.
I mentioned coding as a use case in my comment you replied to. You were asking for an example for when one wouldn't use vector search and I provided one. I did not say similarity search would be a substitute. I said that for the coding case you do not need it.
>you would still likely want to build a similarity search engine
In practice tools like Claude Code, Codex, Gemini, Kimi Code, etc are getting away with searching for code with grep / find and understanding code by loading a sufficient amount of code into the context window. It is sufficient to understand higher level concepts in the code. The extra complexity of maintaining vector database top of this is not free and requires extra complexity.
In your point you said "There is more to searching than building a basic similarity search." which assumed and implied all kinds of things and which was completely unnecessary.
> In practice tools like Claude Code, Codex, Gemini, Kimi Code, etc are getting away with searching for code with grep / find and understanding code by loading a sufficient amount of code into the context window
Getting away is the formulation I would use as well. "Sufficient amount" OTOH is arguable and subjective. What suffices in one usage example, it does not in another, so the perception of how sufficient it really is depends on the usage patterns, e.g. type and size of the codebases and actual queries asked.
The crux of the problem is what amount and what parts of the codebase do you want to load into the context while not blowing up the context and while still maintaining the capability of the model to be able to reason about the codebase correctly.
And I find it hard to argue that building the vector database would not help exactly in that problem.
And yet your blog says you think NFTs are alive. Curious.
But seriously, RAG/retrieval is thriving. It'll be part of the mix alongside long context, reranking, and tool-based context assembly for the forseeable future.
I don't think RAG is dead, and I don't think NFTs have any use and think that they are completely dead.
But the OP's blog is more about ZK than about NFTs, and crypto is the only place funding work on ZK. It's kind of a devil's bargain, but I've taken crypto money to work on privacy preserving tech before and would again.
The issue I had with RAG when I tried building our own internal chat/knowledge bot was pulling in the relevant knowledge before sending to the LLM. Domain questions like "What is Cat Block B?" are common and, for a human, provide all the context that is needed for someone to answer within our org. But vectorizing that and then finding matching knowledge produced so many false positives. I tried to circumvent that by adding custom weighting based on keywords, source (Confluence, Teams, Email), but it just seemed unreliable. This was probably a year ago and, admittedly, I was diving in head first without truly understanding RAG end to end.
Being able to just train a model on all of our domain knowledge would, I imagine, produce much better results.
I have no interest in anything crypto, but they are making a proposal about NFTs tied to AI (LLMs and verifiable machine learning) so they can make ownership decisions.
So it'd be alive in the making decisions sense, not in a "the technology is thriving" sense.
> Of course you would have to set a temperature of 0 to prevent abuse from the operator, and also assume that an operator has access to the pre-prompt
Doesn't the fact that LLM's are still non-deterministic with a 0 temperature render all of this moot? And why was I compelled to read a random blog post on the unsolved issue of validating natural language? It's a SQL injection except without a predetermined syntax to validate against, and thus a NP problem we've yet to solve.
Just after that extremely gentle poke about a grift that died many years ago, you'll be pleased to see that I address the very silly claim about RAG in a straightforward, ad rem way.
I still use AirPods for listening but if I’m ever taking a call, I always use EarPods (USB-C). The microphone quality is multiple times better and that’s important to me. Especially for work. It only took me a few times to hear other people will AirPods to be tainted. It just seems unprofessional now because of how bad it sounds.
Mostly same story. Tinkered for hours with Windows 3.1 floppy disks. Reinstalling OS’s all the time because I’d break stuff or I’d just want a fresh slate. I loved pushing the boundaries. In my 30’s I slowed down with the tinkering because of life (kids, work). I thought I lost the ability to tinker. But recently at 42, I bought a MacBook for the sole purpose of tinkering on the couch at the end of the day (basically after being on computer the whole day, I didn’t want to be in office anymore). And slowly, it’s coming back. I’m playing with new things, learning about Neural Networks, learning about Softare Defined Radio, installing tons of random libraries and tools to test that out. It’s coming back. Keep pushing on it and hopefully it returns for you too!
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