bwfan123
This part is lost on many. The value of data is in the theories it confirms or more importantly disconfirms, and defining the trading edge is not easy after accounting for costs. I am wary of black-boxes that produce an edge - not only because I dont know how it works, but also because regimes shift unpredictably, and what works today may stop working tomorrow. That being said, AI can be useful to help automate many routine processes just like any other software.
I felt this too recently and I linked it to excessive passive information grazing exacerbated by AI. Symptoms of this are being in a daze or mental fog, and not being in touch with the feeling of curiosity. Reading doesnt help since for me, reading is passive process of meme gathering. What helps in my view is effortful creation in the form of writing code, hand writing essays, creating music etc.
I am told AGI has been achieved. If so, shouldnt these systems be out and about on their own ? Looking at 1st proof submissions in batch 2 it is clear that fully autonomous AI systems have a long way to go.
AI harnessing human labor with the incentive of 2M in free tokens is the way my skeptic eye sees it, or humans being duped as reverse-centaurs.
Try putting an LLM agent in a deterministic workflow without humans in the loop. My experience with this is not encouraging. Getting it to work requires sprinkling some context magic and hoping and praying the LLM does the right thing. More astrology or religion and less science. Great for use cases with humans-in-the-loop, but less than impressive when you need determinism and reliable operation.
This is one of the problems young engineers are going to face who rely excessively on AI to generate code. Their intuitions on what constitutes good code will not sharpen, since they are not exercising the tool that sharpens it which is writing code. Intuitions start fuzzy, and incorrect, and gradually sharpen with precise communication of said intuition in the form of writing code or proofs. Reading alone may delude one into a sense of false mastery where intuitions are actually fuzzy, but one thinks otherwise.
For most of us, writing code is the way to carve out intuition into an artifact. But, I have noticed some people are able to read deeply - and by that I mean, reverse the code to understand the intuition that brought it to life. This is a rare skill and I dont have it, but some do. Not just for code, but also for any book - fiction or non-fiction - some are able to deconstruct the scenarios much better than others, and in that sense understand what they read.
This core problem remains unsolved. The solution presented in the article with Human In The Loop and some skill-magic such as "Write principles, not rules etc." is unsatisfactory because it offers no guarantees whatsoever. I find it difficult to harness agents into deterministic workflows which need to produce reliable outcomes.
Where is it a proof that there are many unnamed things ? I could only see it as an argument that there are no "unnameable" things.
Why the angst ? I suspect this announcement punctured a lot of people's bubbles, and many are in disbelief and denial and hence the emotional reaction seen here. That a company which never designed chips could suddenly leapfrog the best in the industry. What many forget is that openAI and anthropic are in a unique position to own the end-user experience, and that provides them a distinct advantage. But, making announcements and actually delivering are two different things, and it remains to be seen if these are actually viable. In any case, it gives openai leverage over their vendors.
You have to recognize that a vast majority of enterprises fall in the category of "Dumb IT". To them, IT is a cost-center and overhead as their main line of business is something else. And, they look up to the likes of McKinsey for wisdom.
You can have a long conversation with AI about pain and suffering, but you have felt it and AI hasnt. The difference is that, for us humans, words are grounded by something other than other-words, words are grounded by experience. Meaning for us is operational in the real world in the form of perception, feeling and emotions, and movement, and words symbolize said meaning. Meaning for LLMs is something in the concept-space that is detached from any worlds. I suspect there should be a test which can tease apart the ungroundedness of the LLMs just from having a conversation with it.
Mojo looks nice but with coding agents the landscape has shifted at least in my view. There is no need for DSLs and abstractions that reduce human cognitive load. So, I still struggle with the usecase for Mojo. If you want close to metal performance, code in something that touches metal (c,c++), and doesnt abstract you away from it or require unsafe primitives. As a programmer who cares for performance, you want the least separation between your tools and the hw in the sense that you can visualize precisely what the hw is doing with no abstractions to hide it.
Deepseek v4 and Kimi k3 have tightened the screws on the frontier models. With open-weights, anyone can host these for cost of compute. So, there is zero leverage left for the frontier labs.
What implodes it in my view is that enterprise adoption will stall. Enterprises are struggling to actually use these things in real workflows outside of coding and support.
right. Abstractions taken to the max. When tech solves problems that only 0.001% care about. NFT smelt similar.
When you dont know the cause, you dont have a fix. Thats the biggest issue i have with all of AI is that we dont know how it works, and yet we think it will be great ! This is more like a religious belief than a scientific one. There is no causal model of how it works, there is no theory. And the temerity to call it intelligence is annoying.
> Try as you might, it's nearly impossible to get the entirety of the context, intentions, business implications, budget constraints, and what-have-you written down and accessible to a coding agent.
Flashback to Dijkstra that english does not have a machine model [1]. Precision and Determinism is what we pay for. Riffing on a quote: Precision walks, bullshit talks.
[1] https://www.cs.utexas.edu/~EWD/transcriptions/EWD06xx/EWD667...
I was scratching my head reading it. The cognitive load was high with cryptic sentences like the above. AI-slop or not, there is no excuse for dense prose particularly if it is intended for a human reader.
> Still not sure what that means, but some analytic number theorists seem excited
ie, I prompted AI and it put out a giant pile of tokens. I dont know what it means, but I hope someone gets excited. Mathematicians are now the priests and shamans chanting incantations and taking the holy blessings from the AI gods.
They are the marketing wing of the AI ecosystem, and in bed with the SV AI-elite. Their recent article on how SpaceX would drive 500B in data-center revenue was ludicrous-mode. Lets revisit this in a few years.
If there is anything to learn from the history of science, it is that breakthroughs happen via better or new theory and not by brute-force compute [1].
Glad to see the extra engineering effort that went into creating this local model and making it run well on a consumer device. I use qwen3.5-coder, and am waiting to kick the tires on this one. I hate to say this, but kudos to Meta ! I hope apple and others follow suit and create similar local models for other use cases like audio, images and video that can run on a laptop.