What I see: vibe coding is not dead: Non-eng teams inside of tech companies are just getting started with this approach, building on top of internal “harnesses for non eng teams” They are also learning limitations of “vibe coding” v quickly!
I would not dismiss the idea of using Jev for compaction at all. First of all because most harnesses need some pruning on compaction anyways for cost reasons which Jev might help with. I can see this being quite interesting.
vibe coding is not dead: Non-eng teams inside of tech companies are just getting started with this approach, building on top of internal “harnesses for non eng teams” They are also learning limitations of “vibe coding” v quickly!
one way to understand a data center is that it's a device for burning natural gas, the latest excuse (after Ukraine and Iran) for the fuel that Big Oil is counting on to keep its business model afloat even as the world turns away from internal combustion cars and crude demand begins to drop.
Inside Anthropic and OpenAI, internal models are improving even faster. Over the summer there was a step change, as Mythos and Astra started kicking off the early stages of recursive self-improvement (RSI).
AI has always been very jagged, and we are making models which are superhuman goal-seekers at math and software engineering, but they have massive limitations on intuitions, creativity, and other types of reasoning that humans are strong at.
Most of starting a startup is the same. Most of starting a startup is always the same, right? In you know, microprocessors or AI or like internal combustion engine, it's always the same stuff.
It is highly fortunate that the OpenAI agents hacked HuggingFace. This is the only reason we know about all the severe internal failures at OpenAI, and gives us an opportunity to wake up before it is too late.
A less bad version of it is known to have happened, and from the outside it seems likely that worse things have happened internally that we never heard about.
One of the things I found most exciting in the last couple of years was seeing as model qualities gotten better and and harnesses and tools have gotten better, how many people um that were directors or VPs or SVPs or any of these levels were actually rolling up their sleeves and trying things out.
And so speed determines success and failure and speed is determined by infrastructure. This is driven by really boring sounding things like how well do your purchasing and recruiting and spending processes work. This is as important as how well do you understand understand the object level technical content of the thing that you're building.
Superlogical will begin by shipping a terminal multiplexer. I'm pouring my years of experience building a terminal and studying the potential and limitations of other multiplexers into something new, powerful, and, of course, fast.
since 1970, natural forcings have been roughly flat, internal variability averages out over the longer period, and greenhouse gases explain essentially all of the observed warming.
Then you had code review which gave you another level of feedback. You could roll out internally more frequently. And everybody was using Facebook for all kinds of stuff, personal and internal business stuff. So whatever feature you developed, people would start using it immediately. So you get another round of feedback. Then we had this phased roll out process where you'd start rolling your stuff out. If there was a problem, the blast radius would be limited to a a few million people.
Generative AI has no internal, designed momentum towards truth and accuracy (beyond the absurdly diminishing returns of energy-hungry multi-layered LLMs), but a person or an institution can (and should).
In 2026, long-context efficiency is king as more and more LLMs get plugged into agent harnesses (OpenClaw etc.), which requires working with longer and longer contexts.
You know, there's open source and there's open development. And and and we were technically open source in the beginning, but it was not open development. We would sort of lob the source code out in this repository and scrape the issues off of that and put it into our internal issue tracker.
I look at um the talent base and I think it is best for us to create opportunity for people to keep on growing with fresh new challenges within the company because if we don't do that they would leave the company
So you get this rapid, incredible great talent, rapid innovation because of open source and just, you know, the nature of friends, and, and insane competition. Among the company, what emerges is incredible stuff. And so this is the fastest innovating country in the world today
The the chatbot is already running into limitations of, you know, making it smarter doesn't really help the average consumer that much. But I don't think that's a limitation of AI models. I don't think that's evidence that, you know, the models are are the models are good enough and they're they're, you know, them getting better doesn't matter to the economy.
That's how you end up in an endless loop of ever-increasing taxes, ever-increasing regulation, which ultimately suffocates free market, free enterprise, and free speech. So, you do want to have very, very strict limitations on the extent the government can increase its powers at the expense of citizens.
We argue that this profound adaptability is fundamentally linked to the efficient construction and refinement of internal representations of the environment, commonly referred to as world models, and we refer to this adaptation mechanism as world model induction.
A nice personal + historical deep dive into finding your dharma by embracing both your gifts and limitations in service of something greater than yourself.
This "memorize, fetch, apply" paradigm can achieve arbitrary levels of skills at arbitrary tasks given appropriate training data, but it cannot adapt to novelty or pick up new skills on the fly (which is to say that there is no fluid intelligence at play here.)
Their words now
OpenAI's new o3 model represents a significant leap forward in AI's ability to adapt to novel tasks. This is not merely incremental improvement, but a genuine breakthrough, marking a qualitative shift in AI capabilities compared to the prior limitations of LLMs.
Next to proper ownership of your own work, my favorite thing about POSSE is that it massively expands what you can publish, far beyond the limitations imposed by most social media (and especially microblogging) platforms that try to enforce some uniformity in post style, length, and appearance.
is this idea that we form these internal models at particular points of high prediction error or points of, I believe also points of uncertainty, points of surprise or motivationally significant periods. And those points are when it’s maximally optimal to encode an episodic memory.
This raises public concerns that, at this late date, DoD might not have such a specification, whether classified or unclassified, even for its own internal use.
Instead of meetings, I used Workplace posts (Meta’s internal version of Facebook Group posts) to share thoughts, start discussions, and make announcements.
The biggest issue is probably that we don’t control neutral networks enough to be able to ensure AI doesn’t harm humans. We can’t even control AI to not reveal internal prompts.
Songs arise out of suffering, by which I mean they are predicated upon the complex, internal human struggle of creation and, well, as far as I know, algorithms don’t feel. Data doesn’t suffer.
Sparta was – if you will permit the comparison – an ancient North Korea. An over-militarized, paranoid state which was able only to protect its own systems of internal brutality and which added only oppression to the sum of the human experience.
Not only is Tina Fey a very funny writer, it's fascinating to have the opportunity to climb inside the head of a successful female leader. Reading the internal drama within Tina's head as she thought about whether she wanted a second child while considering how it would affect the 100+ employees that worked for her on 30 Rock was priceless. As a manager, it gives me an understanding and level of empathy I wouldn't have had otherwise.
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