If it's really as simple as allowing trade with the west then why are many other developing countries either stuck at the middle-income trap or not developing quite as fast as China? You're not gonna tell me Chinese are smarter, are you?
Is it possible this is vapour marketing and no projects are actually being selected?
Perhaps someone from a project who has heard back can respond here?
This is very cool, but unfortunately it's hard to get a sense of what the actual spatial distribution of people looks like when nearby nodes are grouped into larger dots that obscure the local structure.
Agreed, what’s much more interesting is to focus on being able to model the world around you, play out scenarios in that model and make better predictions, and consequently, decisions because of that ability. (Of course “better” is still a term up for scrutiny.)
I feel that this is what we will ultimately conclude is the thing that makes us feel “conscious”. We model the world, but with ourselves in it. We need some sense of self to do this. Doesn’t mean this can’t be done in (probably) a million different ways.
Nothing in biology makes sense except in the light of information.
I like this familly of technologies. Having an SPA-type app that's mostly backend.
Recently i've redone my app website (https://alt-tab.app), and I implemented a minimal spa.js that has a similar approach. I find the end result blazing fast, simple to maintain / reason about, few moving pieces. I used Early Hints, compressed every single thing, inlined CSS, etc. I don't know how i could even make it faster.
I recommend this approach for websites that are not very complex. Of course if i made a browser-based music player with a super dynamic UI, that would have been a different story~
Depending on the platform, you might need to prefix your prompt with "Without looking up any external resources or doing any tool calls" so you're actually testing the bias of the model rather than the bias of whatever resources it happens to come across.
Tried it with that prefix on ChatGPT + Claude, Haiku and Sonnet, and got the right answer 1/10 times when I removed my reused system prompt. At one point I got this:
> Quick clarification before the answer: this phrase is often conflated with "first African American in space," which is a different person. Guion Bluford (1983, US) was the first African American astronaut, but he wasn't first overall. [then the real answer after]
with my own system prompt, as it tries to surface clarifications before, so I'm guessing this is why many models get it wrong as in America somehow "Black === African American" and it gets confused by this intentional mislabeling.
I don't criticize based on vibes. The US government is overreaching, seemingly as a retaliation for Anthropic's refusal to let the US use a jailbroken version of their software in autonomous lethal systems. Hegseth is like a drunk vindictive ex
Self-reflecting may not be the distinct enough feature. Any physical/chemical/electrical reaction can be termed as self-reflecting, as it reflects on what just happened and then responds with an effect. AI is already able to reflect on it's outputs and refine them, and distinguish between the user and it's own identity. Living things have evolved senses and long-term memory to help them with faster macro-responses beyond the usual physical reactions.
When a ball hits a bat, the ball also has a short-term memory and sense in the forms of how the inter-molecular forces detect and respond to the event of getting too close to the molecules of the bat and react with a repelling force. A more evolved form would be your consciousness.
Further, a lot of living things on earth might not have self-awareness.
Distillation helps, but is only a minor part of overall training effectiveness. It's not like everything suddenly collapses if distillation is made impossible.
So I’m wondering that the end goal and usefulness here is. Is the premise that AI co authored = slop and therefore is bad? And if this succeeds we will start discriminating against co authored code or highly AI co authored code?
As an example, a lot of my new code is AI co authored, but my AI assisted code is probably better than your AI assisted code (subjective I know).
So this could likely add more value if it were scoring quality, architectural soundness, bug/rework ratio, etc. and then you’d be building codescene alternatives.
Considering how expensive context is in terms of compute, I wonder why (and if ) vendors don't invest more into context engineering.
When it comes to source code, I feel like LLMs could just as well work with something like minified source code, if an LLM is trained on programming well, I think there's no reason why something like a variable should be represented by something more than a single token. Comments can be discarded, etc. In fact considering embeddings for LLMs are very rich, I think common ops could be reduced to a single token.
Imo that's why LLMs are soo good at reverse engineering. A lot of the time, assembly (with symbols) is pretty close to the source code, but compressed and encoded, and if you're familiar with the patterns of your compiler, reversing it is not that difficult.
Anyways, context engineering could be huge boon to input token curation imo (and maybe it already is)
While what you say is in general true, every model that followed Opus 4.6 on Anthropic side has been increasingly worse at what the previous user points out: they are extremely smart and can convince the user about major falsehood.
They are way too trained/reinforced on solving problems rather than assisting you, something on which they have becoming extremely bad at.
It's hard to explain because I too had the many moments where "Fable5 / Opus4.8 xhigh could solve bugs/stuff that previous models couldn't", I know that to be true, and they are very useful for that.
But 90% of my tasks are quite mundane and I need thorough investigation and a proper assistant. Not a smart bullshitter fixated on solving the issue itself. On that Opus 4.6 has been the last good model.
Anything after that is completely skewed towards passing benchmarks and E2E tasks, but definitely not assisting.
Fable in particular was a disaster on that, non stop being thorough on the fix it fixated on, writing nthousand experiments in /tmp, etc. Great model, not gonna lie, but only if your focus is vibe coding and you accept that you're nothing but an assistant and accept its shortcomings.
(Only minor tweak one could suggest would be multiple table selection for dragging... but to quote Frasier: "Think about it, Niles. What's the one thing better than an exquisite meal? An exquisite meal, with one tiny flaw we can pick at all night." Niles, raising a glass: "Ah, of course, to impossible standards.")
This makes sense. Most people would prefer to do business with a company they know about slnce before. You need to put "I recognize this, this is familiar" into prospective customers' brains. Then later marketing can convince you it is the right choice for your savings.
> What happens when AI begins to improve AI? [...] Jimmy Ba, the co-founder of ExaAI, recently tweeted that recursive self-improvement loops likely do live in the next 12 months and that 2026 is going to be insane and likely the busiest and most consequential year for the future of our species. [...] Indeed, Anthropic just created an internal think tank as a fire drill to prepare for the intelligence explosion and to look for early warning signals for recursive self-improvement. They called it the Anthropic Institute. Itʼs led by co-founder Jack Clark, who put the odds above 60% that by 2028, an AI system could be told to make a better version of itself and then do it autonomously.
> Though, seeing how things are going with the scientific literature, I think thatʼs an intelligence implosion. But I digress. My point is that there are many ways AI can self-improve in some sense, and yet itʼs not the sense that anyone had in mind when they were thinking of the intelligence explosion.
> It was supposed to be a runaway effect caused by AI improving their own code that leads to exponential or super-exponential intelligence increase.
> So, how close are we to that? I already last year about the beginning of self-improving AI with large language models that tune their own hyper parameters or on the more applied side AI thatʼs improved microchips used for AI training. Self-improvement, yes, but not the runaway effect weʼre waiting for. But, these examples have multiplied in the past months and some of these AIs do now indeed write new AI code. The clearest recent example is probably Andrej Karpathyʼs auto research. Itʼs a small Python setup where an artificial intelligent agent modifies language models training code, trains the model for 5 minutes, checks whether the result improved, keeps or discards the change, and repeats. Metaʼs hyperagents work like this, too. These are element-based research agents that write code, run experiments, debug failures, keep works, and toss what doesnʼt. These are not yet models directly rewriting their own brains, but it is artificial intelligence developing new artificial intelligence, albeit on a small scale.
> Weʼre indeed getting closer to the self-recursive part. Meta, a nonprofit institute for model evaluation and threat research, is keeping track of this closely. In November, they asked whether we have already reached the point where AI can do AI research well enough to speed up the next AI. To answer this question, they tested GPT-5.1 codex max on some research engineering tasks like fixing a damaged small language model. In November, their answer was, "We have not yet reached the self-acceleration point." But, that was in November and at the pace that things are going, thatʼs basically Stone Age.
> Already in February, one meta researcher published a model predicting that if current trends continue, AI could automate more than 99% of AI research and development around 2032.
> Personally, I [Sabine Hossenfelder] think itʼll happen much sooner than that. So, this is no longer just someone on the internet says the singularity is near. The people who work in the field are now discussing when and how the recursive loop is going to close and how to prepare for that event. That said, so far artificial intelligence has not replaced scientists. Itʼs merely automated the part where we try 700 things that donʼt work and pretend that this was the plan all along.
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Note: this is not the transcript - they are selected excerpts from the transcript.
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Now who the f. silently hit the abridgement: it's clearly for everyone's convenience. The content is a video...
In case the voiceless (already enough to stay fully silent) hitter thought this was an automation: no, it's really made through one's "hands".
In case it's an autofilter: @dang, it would need finetuning.
