#Power of LLMs

[enterprise-cloud-services]

Explore the inner workings of LlamaIndex, enhancing LLMs for streamlined natural language processing, boosting performance and efficiency.

[rubylogan15]

Explore the inner workings of LlamaIndex, enhancing LLMs for streamlined natural language processing, boosting performance and efficiency.

[10001gecs]

one 100 word email written with ai costs roughly one bottle of water to produce. the discussion of whether or not using ai for work is lazy becomes a non issue when you understand there is no ethical way to use it regardless of your intentions or your personal capabilities for the task at hand

with all due respect, this isnt true. *training* generative ai takes a ton of power, but actually using it takes about as much energy as a google search (with image generation being slightly more expensive). we can talk about resource costs when averaged over the amount of work that any model does, but its unhelpful to put a smokescreen over that fact. when you approach it like an issue of scale (i.e. "training ai is bad for the environment, we should think better about where we deploy it/boycott it/otherwise organize abt this) it has power as a movement. but otherwise it becomes a personal choice, moralizing "you personally are harming the environment by using chatgpt" which is not really effective messaging. and that in turn drives the sort of "you are stupid/evil for using ai" rhetoric that i hate. my point is not whether or not using ai is immoral (i mean, i dont think it is, but beyond that). its that the most common arguments against it from ostensible progressives end up just being reactionary

i like this quote a little more- its perfectly fine to have reservations about the current state of gen ai, but its not just going to go away.

[snarp]

This scan of the Ganguli translation of the Mahabharata can't load in Archive.org's in-browser viewer. It's more than 8GB.

[terabyteunion]

man. we can dislike AI artwork without also believing in some divine human will or rehashing renaissance arguments about what's "True And Godly Art" and what's "Horrific And Poor Art"

[atcuality1]

Boost Your Brand’s Voice with Our AI Content Generator Solution

Atcuality offers a next-gen content automation platform that helps your brand speak with clarity, consistency, and creativity. Whether you’re a startup or an enterprise, content is key to growth, and managing it shouldn’t be overwhelming. At the center of our offering is a smart AI content generator that creates high-quality text tailored to your audience, industry, and goals. From social captions and ad creatives to long-form blog posts, our platform adapts to your needs and helps reduce time-to-publish dramatically. We also provide collaboration tools, workflow automation, and data insights to refine your strategy over time. Elevate your brand’s voice and reduce content fatigue with the intelligence and reliability only Atcuality can offer.

[flaskoflethe]

While on leave at the moment, we're not that far from returning to work. Given that we work in data science and development, and that part of what pushed our burnout was the literal abominations we were being asked to make using llm's, it seems prudent for us to start getting back into the swing of things technically. What better way to do this than starting to look through what's easily available about DeepSeek? It's, unfortunately, exactly what you'd expect.

We're not joking. The literal first sentence of the paper from the company explicitly states that recent LLM progression has been closing the gap toward AGI, which is utterly, categorically, epistemically untrue. We don't, scientifically, have a sufficient understanding of consciousness to be able to create an artifical mind. Anyone claiming otherwise has the burden of proof; while we remain open to reviewing any evidence supporting such claims, to date literally nothing has been remotely close to applicable much less sufficient. So we're off to a very, very bad start. Which is a real shame, because the benefits and improvements made to LLM design they claim in their abstract are impressive! A better model design, with a well curated dataset, is the correct way to get improvements in model performance! Their reductions in hardware hours for training are impressive, and we're looking forward to analyzing their methods - a smooth training process, with no rollbacks or irrecoverable decreases in model performance over the course of training are good signs. Bold claims, to be clear, but that's what we're here to evaluate. And the insistence that LLM's are bringing AGI closer to existence mean we have sufficient bias from the team that we cannot assume good faith on behalf of the team. While we assume many, or even most, of the actual researchers and technologists are aware of the underlying realities and limitations of modern "AI" in general and LLM's in particular, the coloring done by the literal first sentence is seriously harmful to their purpose.

While we'd love to continue our analysis, to be honest it doesn't support it? Looking at their code it's an, admittedly very sophisticated, neural network. It doesn't remotely pose a revolution in design, the major advances they cite are all integrating other earlier improvements. While their results are impressive, they are another step in the long path of constructing marginal improvements on an understood mechanism. Most of the introduction is laying out their claims for DeepSeek-v3. We haven't followed the developments with reduced precision training, nor the more exact hardware mapped implementations they reference closely enough to offer much insight into their claims; they are reasonable enough on face, but mainly relate to managing the memory and performance loads for efficient training. These are good, genuinely exciting things to be seeing - we'll be following up manu of their citations for further reading, and digesting the rest of their paper and code.

But this is, in no way shape or form, a bubble ender or a sudden surge forward in progress. These are predictable results, ones that scientists have been pursuing and working towards quietly. While fascinating, DSv3 isn't special because it is a revolution; it simply shows the methodology used by the commercial models, sinking hundreds of billions of dollars a year and commiting multiple ongoing atrocities to fuel the illusion of growth, isn't the best solution. It will not understand, it CAN not. It can not create, it can not know. And people who treat it as anything except the admittedly improved tool it is, are still techbros pushing us with endless glee towards their goal of devaluing labor.

[infinites-chaser]

pls... I've finally found it. The only AI writing op-ed worth reading:

AI embodies hypotheticals I can just imagine for myself: If only I could write all day and night. If only I were smarter and more talented. If only I had endless knowledge. If only I could read whole libraries. What could I create if I had no needs? What might this development mean for writing?

Considering limitlessness has led me to believe that the impediments of human writers are what lead us to create meaningful art. And they are various: limits of our body, limits of our perspectives, limits of our skills. But the constraints of an artist’s process are, in the language of software, a feature, not a bug.

Writing is a blood-and-guts business, literally as well as figuratively. As I type with my hands, my lungs oxygenate the blood that my heart pumps; my brain sends and receives signals. Each of these functions results in the words on this page.

...

In reducing my entire self to my cognition alone, akin to a computer, I’d forgotten the truth that I am inseparable from my imperfect body, with its afflictions and ailments. My books emerge from this body.

...

Compared with AI, we might seem like pitiful creatures. Our lives will end; our memory is faulty; we can’t absorb 191,000 books; our frames of reference are circumscribed. One day, I will die. I foreclose on certain opportunities by pursuing others. Typing this now means I cannot fold my laundry or have lunch with a friend. Yet I believe writing is worth doing, and this sacrifice of time makes it consequential. When we write, we are picking and choosing—consciously or otherwise—what is most substantial to us. Behind human writing is a human being calling for attention and saying, Here is what is important to me. I’m able to move through only my one life, from my narrow point of view; this outlook creates and yet constrains my work. Good writing is born of mortality: the limits of our body and perspectives—the limits of our very lives.

I can imagine a future in which ChatGPT works more convincingly than it does now. Would I exchange the hours that I spent working on each of my two books for finished documents spat out by ChatGPT? That would have saved me years of attempts and failures. But all of that frustration, difficult as it was in the moment, changed me. It wasn’t a job I clocked in and out of, contained within a tidy sum of hours. I carried the story with me while I showered, drove—even dreamed. My mind was changed by the writing, and the writing changed by my mind.

Working on a novel, I strain against my limits as a bounded, single body by imagining characters outside of myself. I test the limits of my skill when I wonder, Can I pull this off? And though it feels grandiose to say, writing is an attempt to use my short supply of hours to create a work that outlasts me. These exertions in the face of my constraints strike me as moving, and worthy, and beautiful.

Writing itself is a technology, and it will shift with the introduction of new tools, as it always has. I’m not worried that AI novelists will replace human novelists. But I am afraid that we’ll lose sight of what makes human writing worthwhile: its efforts, its inquiries, its bids for connection—all bounded and shaped by its imperfections—and its attempts to say, This is what it’s like for me. Is it like this for you? If we forget what makes our human work valuable, we might forget what makes our human lives valuable too. Novels are one of the best means we have for really seeing one another, because behind each effort is a mortal person, expressing and transmuting their realities to the best of their ability. Reading and writing are vital means by which we bridge our separate consciousnesses. In understanding these limits, we can understand one another’s lives. At least, we can try.

[yellowstonewolves-main]

This is really just obvious nonsense. I’m skeptical that a writers room full of human beings even takes less energy than Generative AI when you account for the energy costs of commuting, cleaning, and heat/aircon never mind taking so much less energy that pay+benefits+overhead for a human employee is cheaper. Is it not enough to say “we want to keep telling stories ourselves plus ai sucks pretty bad at writing right now?” Do we have to spread misinformation about energy costs?

I’m just going to leave this here

[unforth]

Y'all I know that when so-called AI generates ridiculous results it's hilarious and I find it as funny as the next guy but I NEED y'all to remember that every single time an AI answer is generated it uses 5x as much energy as a conventional websearch and burns through 10 ml of water. FOR EVERY ANSWER. Each big llm is equal to 300,000 kiligrams of carbon dioxide emissions.

LLMs are killing the environment, and when we generate answers for the lolz we're still contributing to it.

Stop using it. Stop using it for a.n.y.t.h.i.n.g. We need to kill it.

Sources:

[sonshinegreene]

AI-Powered Feedback for Your Novel: Free Prompt for Deep Dives

Hey y’all, Sumo Sized Ginger coming at you today with a free tool. After crafting my own prompt to assess my story, I decided I wanted to use it to share with the community. Like many of you, I’m constantly thinking about how to make my stories better. We pour everything into our writing, especially when we’re tackling challenging themes, using specific stylistic approaches, or aiming for a…

[digitalpreeyam]

Generative AI is revolutionizing the digital world by enabling machines to create human-like content, including text, images, music, and even code. Unlike traditional AI, which follows predefined rules, Generative AI uses deep learning models like GPT and DALL·E to generate new and unique outputs based on patterns in existing data.

This technology is transforming industries, from automating content creation and enhancing customer support to generating realistic artwork and simulations. Businesses leverage it for personalized marketing, while creatives use it for inspiration and design.

One key feature of Generative AI is its ability to produce high-quality, original content, mimicking human creativity. However, ethical concerns such as misinformation and bias require careful management. As AI advances, its role in shaping the future of digital content creation continues to grow, offering endless possibilities while demanding responsible use.

[digitalpreeyam-seoexpert]

Generative AI is revolutionizing the digital world by enabling machines to create human-like content, including text, images, music, and even code. Unlike traditional AI, which follows predefined rules, Generative AI uses deep learning models like GPT and DALL·E to generate new and unique outputs based on patterns in existing data.

This technology is transforming industries, from automating content creation and enhancing customer support to generating realistic artwork and simulations. Businesses leverage it for personalized marketing, while creatives use it for inspiration and design. One key feature of Generative AI is its ability to produce high-quality, original content, mimicking human creativity. However, ethical concerns such as misinformation and bias require careful management. As AI advances, its role in shaping the future of digital content creation continues to grow, offering endless possibilities while demanding responsible use.

[atcuality1]

Simplify Transactions and Boost Efficiency with Our Cash Collection Application

Manual cash collection can lead to inefficiencies and increased risks for businesses. Our cash collection application provides a streamlined solution, tailored to support all business sizes in managing cash effortlessly. Key features include automated invoicing, multi-channel payment options, and comprehensive analytics, all of which simplify the payment process and enhance transparency. The application is designed with a focus on usability and security, ensuring that every transaction is traceable and error-free. With real-time insights and customizable settings, you can adapt the application to align with your business needs. Its robust reporting functions give you a bird’s eye view of financial performance, helping you make data-driven decisions. Move beyond traditional, error-prone cash handling methods and step into the future with a digital approach. With our cash collection application, optimize cash flow and enjoy better financial control at every level of your organization.

[almochtarak]

Unlocking the Power of LLMs in 2025