☆ Yσɠƚԋσʂ ☆
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Joined 6Y ago
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Cake day: Jan 18, 2020

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basically, large capitalists will buy up the assets from failed companies and make bank









The way that normally works is that the US will isolate a country that can’t fight back and then commit war crimes there for decades because it’s a good profit vehicle for the military industry. They control the level of escalation, and they’re able to turn heat up when they want or to cool things down when they need to.

The difference with Iran is that it’s not isolated, and it’s able to fight back in a meaningful way. The US does not have escalatory dominance in this conflict. And that’s a whole new development for the burger reich.


not to mention that Iran is systematically destroying US radars across the region as we speak



Indeed, a lot of the US capabilities and limitations are being revealed now. I’d also be shocked if China wasn’t quietly testing stuff like radars in Iran to see if they can detect US stealth jets for example.


You’d think the US would’ve already been learning these lessons in Ukraine, but evidently they were not. And this is the first time the US got itself into a war where it does not control escalation. I don’t see why Iran would settle for anything less than pushing the US out of the region entirely at this point.


The crazy part here is that NATO evidently hasn’t learned anything at all over past 4 years.


I’m really excited to play it. I’m going through Planet of Lana 2 right now, and this one’s next on my list. Good to hear it’s a lot like DE in a fantasy setting.



The core thesis of this paper is that the AI community needs to stop treating autonomous agents as just another text generation problem and start building comprehensive infrastructure to support closed loop learning. The authors argue that achieving reliable agentic behavior requires a full stack ecosystem that unifies data synthesis with sandboxed execution and specialized reinforcement learning. To prove this point they introduce the Agentic Learning Ecosystem which consists of an RL framework called ROLL alongside a sandbox manager named ROCK and an agent interface known as iFlow CLI. They believe that isolating models in static training environments is a dead end for solving complex real world workflows. The team developed an open source model named ROME using a tightly integrated training pipeline with reproducible execution environments which allowed a relatively small 30 billion parameter model to rival or beat massive proprietary models exceeding 100 billion parameters on difficult software engineering benchmarks. A big part of their argument rests on the idea that credit assignment in reinforcement learning needs to change. They propose a novel algorithm called Interaction Perceptive Agentic Policy Optimization which shifts the reward focus from individual text tokens to broader semantic interaction chunks. This chunk level optimization stabilizes the training process over long horizons and prevents the policy collapse often seen in complex tool use scenarios. We're increasingly seeing a shift of priorities away from raw data scale and focus on the systematic infrastructure as the actual bedrock of next generation models.
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Yeah, I just randomly ran across it and was like this looks amazing.








Exactly, when you dig into all the complaints people have about this tech, they’re ultimately just symptoms of the underlying capitalist relations.













Instant LLM Updates with Doc-to-LoRA and Text-to-LoRA
Regular LoRA training is basically a standard gradient descent optimization loop where you have to curate a dataset, run backpropagation, and slowly update the low-rank matrices over many steps. It is computationally expensive and tedious every single time you want to teach the model a new trick or feed it a new document. What Sakana AI built with Doc-to-LoRA completely bypasses that repetitive training loop at deployment time by introducing a hypernetwork. They shifted the massive computational burden upfront through a meta-training phase where a separate neural network actually learns how to predict the correct LoRA weights directly from an input document or task description. Once that hypernetwork is trained, generating a new LoRA adapter only takes a single sub-second forward pass instead of a full fine-tuning run. You just feed a document into the frozen base model to get its token activations, and the hypernetwork instantly spits out the custom LoRA weights. This is incredibly effective for solving the long-term memory bottleneck in large language models. Instead of shoving a massive document into the context window for every single query, which completely eats up your VRAM and spikes latency, you permanently internalize that knowledge into a tiny adapter footprint of under fifty megabytes. They also designed a clever chunking mechanism that processes the document in small segments and concatenates the resulting adapters. This allows the model to perfectly recall information from documents that are tens of thousands of tokens longer than its actual native context limit. It essentially turns a slow and expensive engineering pipeline into a cheap and instant forward pass. source code https://github.com/SakanaAI/Doc-to-LoRA
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Even if this ends up being a narrow domain speedup, it’s still massive, and coding tasks happen to be one of the big practical applications for LLMs. I can also hybrid approaches going forward, where specialized models end up being invoked based on the task at hand.



Machine learning community has been stuck on the autoregressive bottleneck for years, but a new paper shows that it's possible to use diffusion models to work on discrete at scale. The researchers trained two coding focused models named Mercury Coder Mini and Small that completely shatter the current speed and quality tradeoff. Independent evaluations had the Mini model hitting an absurd throughput of 1109 tokens per second on H100 GPUs while the Small model reaches 737 tokens per second. They are essentially outperforming existing speed optimized frontier models by up to ten times in throughput without sacrificing coding capabilities. On practical benchmarks and human evaluations like Copilot Arena the Mini tied for second place in quality against huge models like GPT-4o while maintaining an average latency of just 25 ms. Their model matched the performance of established speed optimized models like Claude 3.5 Haiku and Gemini 2.0 Flash Lite across multiple programming languages while decoding exponentially faster. The advantage of diffusion relative to classical autoregressive models stems from its ability to perform parallel generation which greatly improves speed. Standard language models are chained to a sequential decoding process where they must generate an answer exactly one token at a time. Mercury abandons this sequential bottleneck entirely by training a Transformer model to predict multiple tokens in parallel. The model starts with a sequence of pure random noise and applies a denoising process that iteratively refines all tokens simultaneously in a coarse to fine manner until the final text emerges. Because the generation happens in parallel rather than sequentially the algorithm achieves a significantly higher arithmetic intensity that fully saturates modern GPU architectures. The team paired this parallel decoding capability with a custom inference engine featuring dynamic batching and specialized kernels to squeeze out maximum hardware utilization.
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Right, the real issue is that there needs to be a layer between the app and the LLM which handles authorization and decides whether the data is confidential before it’s ever sent to a remote server. It’s not even an LLM issue, it’s just bad architecture in general.




What the article is saying is that people were using Outlook on their company computers, and Outlook exposed the data to Copilot by sending it outside the company.




That’s a good point, I didn’t really consider Xiaomi being Android based. If Google did go through with this, they could do the same thing Huawei did too, and just fork. I really wish I could get Chinese phones in Canada, maybe now that the relations with the US are souring, there might be a possibility.

I do think everything comes down to the ecosystem in the end, and that’s what ends up being the lock in mechanism. If you live in a country using a western software stack, and you need to communicate with other people, it becomes really difficult using any platform that doesn’t support popular apps everybody uses.

One of the worst developments in the tech space was proprietary protocols becoming the norm with stuff like Slack WhatsApp, and Discord. Now huge chunks of the public are locked into that because all their friends are on those platforms.



It’s amazing how you think that your trolling is so original that I have to come up with a fresh response to it from scratch


I did read it. Maybe you should work on that reading comprehension of yours. Might even learn what the difference between communism and fascism is. 🤣


The term authoritarianism is utterly meaningless because all governments rely on coercion to maintain their authority. The state is fundamentally an instrument that’s used by the ruling class to maintain its dominance. The whole notion that political systems can be neatly categorized into authoritarian or democratic binaries is deeply infantile.

The reality is that every government derives its authority from its monopoly on legal violence. The ability to enforce laws, suppress dissent, and maintain order is derived from control over police, military, and judicial systems. Whether a government is labelled authoritarian or democratic, the fundamental basis of its power lies here. Therefore, the only meaningful questions to ask are which class interests it represents, and to what extent can it be held accountable to them.

What ultimately matters is which class controls the institutions of state violence. In capitalist democracies, the government represent the interests of the economic elites who fund political campaigns, own media outlets, and control key industries. Western public lacks the mechanisms necessary to hold the government to account, and the ruling class is disconnected from the broader population. That’s precisely what’s driving political discontent all across western sphere today. Meanwhile, in so-called authoritarian regimes, the ruling party serves the working class as seen in countries like China, Cuba, or Vietnam. Hence why there is widespread public trust in these government and they enjoy broad support from the masses.

The fact that you have political understanding of a small child really explains a lot about this whole discussion.


People earning enough money to be able to buy housing for their children isn’t the own you think it is. Certainly doesn’t happen in capitalist fash regimes like the one you live in. 🤣


How to say you’re an ignoramus without saying it. China is a socialist state led by a communist party. Socialism is the transitional stage when the working class holds power, but the established relations have not yet abolished. Anybody with even a minimally functioning brain would understand that you can’t just flip a switch and go from one type of system to another, that there would necessarily be some sort of a transition period.




I don’t support a capitalist fash regime, but evidently you’re so ignorant that you can’t tell the difference between communism and fascism.



Sure buddy, China’s just like the USA. These are definitely things that happen in capitalist countries. 🤣

90% of families in the country own their home giving China one of the highest home ownership rates in the world. What’s more is that 80% of these homes are owned outright, without mortgages or any other leans. https://www.forbes.com/sites/wadeshepard/2016/03/30/how-people-in-china-afford-their-outrageously-expensive-homes

The real (inflation-adjusted) incomes of the poorest half of the Chinese population increased by more than four hundred percent from 1978 to 2015, while real incomes of the poorest half of the US population actually declined during the same time period. https://www.nber.org/system/files/working_papers/w23119/w23119.pdf

Real wage (i.e. the wage adjusted for the prices you pay) has gone up 4x in the past 25 years, more than any other country. This is staggering considering it’s the most populous country on the planet. https://www.youtube.com/watch?v=Cw8SvK0E5dI

From 1978 to 2000, the number of people in China living on under $1/day fell by 300 million, reversing a global trend of rising poverty that had lasted half a century (i.e. if China were excluded, the world’s total poverty population would have risen) https://www.semanticscholar.org/paper/China’s-Economic-Growth-and-Poverty-Reduction-Angang-Linlin/c883fc7496aa1b920b05dc2546b880f54b9c77a4

From 2010 to 2019 (the most recent period for which uninterrupted data is available), the income of the poorest 20% in China increased even as a share of total income. https://data.worldbank.org/indicator/SI.DST.FRST.20?end=2019&amp%3Blocations=CN&amp%3Bstart=2008

Chinese household savings hit another record high in 2024 https://www.wsj.com/livecoverage/stock-market-today-dow-jones-bank-earnings-01-12-2024/card/chinese-household-savings-hit-another-record-high-xqyky00IsIe357rtJb4j

Student debt in China is virtually non-existent because education is not run for profit. https://www.forbes.com/sites/jlim/2016/08/29/why-china-doesnt-have-a-student-debt-problem/

The typical Chinese adult is now richer than the typical European adult https://www.businessinsider.com/typical-chinese-adult-now-richer-than-europeans-wealth-report-finds-2022-9


when one lacks the intellect to follow through the implications of what they’re saying, hilarity ensues


People who support actually existing socialist state are fascist actually. Peak liberal intellect on display here.


Confidential generally means data that is internal to a particular organization and is not meant to be publicly shared.




Unfortunately, I really can’t see a critical mass of devs organizing to do that. What I’d really like to see is for somebody to start selling hardware with something like GrapheneOS installed out of the box. Having to get an older phone, and then side load the OS is just too high a bar for even most technical people to jump over. But if you could just buy a phone that works out of the box, that could drive adoption. And then the ecosystem would develop around it completely outside Google control.



The sheer amount of intellectual dishonesty you’ve displayed here is deplorable. The other person tried to engage with you in good faith, explaining how you’re twisting the facts, but you just double down instead of having the integrity to acknowledge you were wrong. It’s frankly pathetic behavior.


China does have unemployment, but it doesn’t have an unemployment problem. Generally, people who are unemployed, are doing it by choice. Some people decide delaying joining the workforce because they want to get higher education, and they can be more picky about choosing jobs. Other people are between jobs. And the point you make regarding housing and access to basic necessities is the key part I think. This is basically what people advocate for in the west when they talk about basic income. It’s not the ideal lifestyle, but you’re not living on the street, and you have your needs met even if you aren’t working.