It’s not so much an op’s issue as an architecture and code quality issue. If you have ever dug into the GitHub enterprise self hosted product you get an idea of the mess.
I get pretty good results with Claude code, Codex, and to a lesser extend Jules. It can navigate a large codebase and get me started on a feature in a part of the code I'm not familiar with, and do a pretty good job of summarizing complex modules. With very specific prompts it can write simple features well.
The nice part is I can spend an hour or so writing specs, start 3 or 4 tasks, and come back later to review the result. It's hard to be totally objective about how much time it saves me, but generally feels worth the 200/month.
One thing I'm not impressed by is the ability to review code changes, that's been mostly a waste of time, regardless of how good the prompt is.
Company expectations are higher too. Many companies expect 10x output now due to AI, but the technology has been growing so quick that there are a lot of people/companies who haven't realized that we're in the middle of a paradigm shift.
If you're not using AI for 60-70 percent of your code, you are behind. And yes 200 per month for AI is required.
You'll have to define modern workstation for me, because I was under the impression that unless you've purpose-built your machine to run LLMs, this size model is impossible.
You can run a 4 bit quantized 120B model on a 96GB workstation card, the Blackwell Pro workstation, which are $7500. Considering the 5090 is bought by gamers for $3300 it’s definitely attainable, even though it’s obviously expensive.
I’m running a gaming rig and could swap one in right now without having to change anything compared to my 5090, so no $5000 Threadripper or a $1000 HEDT motherboard with a ton of RAM slots, just a 1000 watt PSU and a dream.
When people say "modern workstation" in context of LLM, they usually mean its consumer(pro-sumer?) grade hardware on a single machine. As opposed to racks of GPUs that you can even buy as a mere mortal (min order size)
It doesn't mean you can grab your work laptop from 5 years ago and run it there.
Get a Mac Studio with however much memory you need, and ideally an Ultra chip (for max memory bandwidth), and there's your workstation. I regularly run quantized 100b+ models on my M1 Ultra with 128Gb RAM.
You might be missing that exactly the executives are the ones who can and will eliminate people. Why should they eliminate themselves? They'd rather use the same AI to invent a reason for them to stay.
Today’s llms are fancy autocomplete but lack test time self learning or persistent drive.
By contrast, an AGI would require:
– A goal-generation mechanism (G) that can propose objectives without external prompts
– A utility function (U) and policy π(a│s) enabling action selection and hierarchy formation over extended horizons
– Stateful memory (M) + feedback integration to evaluate outcomes, revise plans, and execute real-world interventions autonomously
Without G, U, π, and M operating llms remain reactive statistical predictors, not human level intelligence.
Looking at the human side, it takes a while to actually learn something. If you've recently read something it remains in your "context window". You need to dream about it, to think about, to revisit and repeat until you actually learn it and "update your internal model". We need a mechanism for continuous weight updating.
Goal-generation is pretty much covered by your body constantly drip-feeding your brain various hormones "ongoing input prompts".
You just train it on the goal. Then it has that goal.
Alternately, you can train it on following a goal and then you have a system where you can specify a goal.
At sufficient scale, a model will already contain goal-following algorithms because those help predict the next token when the model is basetrained on goal-following entities, ie. humans. Goal-driven RL then brings those algorithms to prominence.
Random goal use is showing to be more important than training. Although, last year someone trained on the fly during the competition, which is pretty awesome when you think about it.
Layman warning! But "at sufficient scale", like with learning-to-learn, I'd expect it to pick up largely meta-patterns along with (if not rather than) behavioral habits, especially if the goal is left open, because strategies generalize across goals and thus get reinforcement from every instance of goal pursuit during base training.
But also my intuition is that humans are "trained on goals" and then reverse-engineer an explicit goal structure using self-observation and prosaic reasoning. If it works for us, why not the LLMs?
edit: Example: https://arxiv.org/abs/2501.11120 "Tell me about yourself: LLMs are aware of their learned behaviors". When you train a LLM on an exclusively implicit goal, the LLM explicitly realizes that it has been trained on this goal, indicating (IMO) that the implicit training hit explicit strategies.
I'm not sure. In my experience humans without explicit goal generation training tend to under perform at generating goals. In other words, our out-of-distribution performance for goal generation is poor.
Noticing this, frameworks like SMART[1], provide explicit generation rules. The existence of explicit frameworks is evidence that humans tend to perform worse than expected at extracting implicit structure from goals they've observed.
1. Independent of the effectiveness of such frameworks
In fact, there is no technical threshold anymore. As long as the theory is in place, you can see such AGI at most half a year. It will even be more energy efficient than the current dense models.
After twenty years building out products in Silicon Valley I have come to the point where I have lost the plot. None of the projects at my last company seemed interesting, none of the projects I see other companies seem interesting. All AI, no substance.
So I’ll just sit at home and build robots till something interesting does pop up or my robots gain sentience and decide I’m the problem.
In my current job hunt, 80% of startups I talked to were AI for X and I fully agree - nalmost all of them were uninteresting or could even articulate why AI would make a difference.
Solves everything for everyone or solves everything for those who have economic means to adopt the solutions. One is a dystopia, the other is a utopia.
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