Linkedin data is very valuable. Tons of businesses are built on the back of scraping LinkedIn. Kknd of disappointing they won this case tbh, because it basically just gave corpos extereme privledge to ruin people's lives. They've been trying to get this outcome for years.
Lighting my codebase on fire at the speed of light. Like microwaving the spaghetti.
I genuinly only see these speeds being useful for customer service/transactional workflows. Of which much smaller models can do the job (but those dont make tons of money for companies like Cerebras that need to pay off massive amounts of debt).
Nobody needs to code at 600 words per second. Using a 100tps model for an hour or so will leave you with 4-8hrs of code review and revision work.
Human code review? What is this, 2025? The modality today is write with one LLM, review by a different one, (important: two different model families will catch errors one series won't) then deploy right to production.
I would be very curious to see how you explain it to your customers.
Is it going to sound similar to this?
> You see, our well-meaning AI-generated code has caused all your data to be permanently deleted. In case you are confused as to who to blame, we would like to clarify that we did not write, nor review the code. So we cannot possibly bear any responsibility for its mistakes. The responsibility lies with the LLMs, not us. We have already fired the LLM which did the coding and the review. And we are already using their main competitors. Hopefully that settles your concern with the quality of our service and we are looking to have you on board of our next products.
This seems to work well enough for people who deploy cloud instances without redundancy to us-east-1 then blame AWS when there's an outage. I say this somewhat unironically because if one pushes the "move fast and break things" slider all the way to the right then they're necessarily assuming that type of risk. Of course some people will try to have their cake and eat it too [1] as regards velocity and quality but that's a separate discussion.
[1] I've never understood this idiom because if one isn't in possession of their cake before eating it then they're eating stolen cake which is a decidedly anti-social activity and orthogonal to the point of the saying. It should be "eat their cake and keep it too" or something.
Yeah, idk. I work on serious things. Thats not how I do things. Not everything is webdev hobby projects. There's basically no instance where offloading your code review to an llm is acceptable behavior, except maybe for a one off tool you need personally.
And if you tell the reviewer the author is a competitors model it becomes extra snarky and vigilant. Then give the review results to the author and tell it it's from the competition and it will also become slightly outraged.
No you dont need to code at 600 w/s BUT at those speeds, you can start doing things like asking multiple different agents the same question and picking the best solution each time without noticing the lag.
I prefer a fast model too, but you cannot get more done just because its faster. You just get to the human parts a bit faster. Code review, revision ect.
Self improvement during training, and AI self training are already happening. Easily/quickly are seemingly a factor of how much power/hardware you want to use at once.
With the level of compute they have they aren't stuck with frozen models like you are.
The infrastructure provisioning alone to train is heavily dependent on humans, as is dealing with failures (training runs fail a ton). Its not as simple as adding another ec2 on your dashboard. < 1k people in the world know how to do this, there will not be "recursive" or looped continual training for a long long long time. There are so many delicate inputs and controls. Not to mention the chains of businesses and the people required to operate them just to obtain the data needed, clean it and hand it to the llms.
The llms are supervising rlhf and creating synthetic data (to an extent) but they're nowhere close to being able to operate the full training stack end to end. This is a fantasy being sold to investors to create fomo.
Remember they're also limited by an effective memory of like 500k words a turn. Memory systems are lossy, so are swarm/sub agent mechanism. Im not worried about llms becoming self powered super entities anytime soon.
1. you can have luna clean up after itself and improve code 2. you might be doing something like video-editing, cad modeling, artistic direction, pcb routing, etc. that need to run a long time to "converge"
Has there ever been an instance in history when this strategy worked?
Plato argued that writing things down will make your memory worse, and less skilled as a debater (kind of true!)
How are the Luddites doing at textiles?
I remember the arguments that using 'high level languages' like C and Pascal will make you not understand machine specific details (kind of true!)
I respect that you want to learn how things are done, that is a great trait. But once you learn how its done, you should use the tools to free up cognitive load for more difficult tasks.
For me, long-running tasks are not about generating a lot of code. I’m very picky about what my code looks like. But I’ll happily run for long periods of time debugging problems and/or doing testing and validations. Depending on the problem space, this could mean hours of work for each iteration while it attempts to find a working solution
LLMs should not touch these technologies. Keep your slop factories in webdev. We have missles, planes and pacemakers to make still. If you ever feel like using your brain again, theres plenty of work to be done that llms cannot touch.
Surely those industries are not relying on humans writing "good code" to make sure faults are not introduced, right? There are static analysis and automated testing and rigorous QA processes, surely?
Why would you brag abput this. Most places make perf products do not use llms to write all their code. This isnt rare. You people dont know much outside of webdev. There isnt a single defense outfit doing it lol.
And why are the people calling people Luddite some of the least intelligent people I meet, and seem to be complete sheep fighting some psychological war on behalf of their billionaire lords who own the machinery.
Not wanting to hand off all your labor to a machine does not make you a luddite, nor should it be acceptable to call people that because you dont know how to have a real conversation.
It seems like "luddite" is a reasonable description for a strict "NO-AI" policy. You softened it to "not wanting to hand off all your labor" in this comment, but the word "any" would be a more apt way to describe the quote in the comment you replied to.
I already know how to code proficiently in more than 20 languages, that's the result of 40 years working in the field and being a 100X programmer. The summarizer is just a tool for myself, I build tools every day to simplify my life, not for sale, not for monetization, like tracking gym schedules or pickleball rallies with my friends, like kanban boards for my wife, they solve problems I have, not you, so that's enough for me and rewarding in itself
And AI does it all for me while I sip coffee and play mahjong (built by AI of course)
I mean this sincerely; the arrogance of calling yourself a 100X programmer is astounding. I don't know how you managed a 40 year career with that kind of attitude.
I havent met a single honest person who doesn't admit that their use or LLMs degrades their ability to do the same things without them. You're being dishonest.
What was edgy about asking someone to read a Thomas Pynchon essay on the word Luddite? Id argue their weird comment about people who don't like AI being luddites is more annoying and discriminatory.
AI maxxers know they're doing harm to themselves by overusing llms. The way they react so defensively when you point it out to them lets you know its real. Everyone wants to pretending like they have superpowers and are evolving into "100x" engineers, but in reality they're devolving.
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