www.maginative.com
Bing (multimodal) image input is free!
I couldn't find this using the search.
I wanted to start a discussion about the use of AI-generated solutions on Programming.dev. Personally, I've found that AI-powered tools have been incredibly helpful in solving programming questions. I won't name any specific commercial software, but I use one that combines GPT-4 and web search to get more factual information. I write some answers I think I might revisit to the [ShareGPT](https://lemmy.fmhy.ml/c/sharegpt) community, but I would prefer posting programming solutions to this instance. However, I'm not sure if AI-generated solutions are welcomed on programming.dev. I'd love to hear your thoughts on this. If AI-generated responses are accepted, how should we format the answers, should we just copy paste without quoting, should we quote the model, just mention that it's AI-generated,...?
InternetPirate 1 year ago • 100%
Just change lemmy.post.create to lemmy.post.createe to trigger an AttributeError. That way you can debug the code without creating any posts. You can also use many print statements all around the code, I would use two for each line to make sure the computer isn't fooling you. Lastly, you can spin up your own Lemmy instance to not have to worry about the generated posts.
I'm wondering if it's possible to see the local feed of another instance from the one I'm using. I'm interested in exploring content from other instances without having to visit every single community, but I'm not sure how to do it. I've tried searching for a way to do this on the documentation and using the Lemmy search, but I haven't found any clear instructions. Does anyone know how to see the local feed of another instance? Any help or guidance would be greatly appreciated!
InternetPirate 1 year ago • 100%
Testing.
https://join-lemmy.org/docs/users/03-votes-and-ranking.html
Edit: I was wrong the ranking that works like forums is New Comments and yes it seems to take into account the OP comments.
In Lemmy, the active filter view is designed to prioritize posts with the latest activity, similar to how forums work. However, it remains unclear whether commenting on your own post in Lemmy will bump it on the active filter view. Some forum platforms, such as Discourse, allow a practice known as the "ghost bump," where users can make a post and delete it to draw attention to their post without adding new content[^1]. While it is uncertain if this is possible on Lemmy, it's worth noting that even if it were, it would result in an unnecessary comment that cannot be completely removed. The comment would still be visible, indicating that it was deleted by the post's creator. If you have any experience with Lemmy's active filter view or know whether commenting on your own post bumps it, please share your thoughts in the comments below. [^1]: [What is "Bumping Topics"](https://meta.discourse.org/t/what-is-bumping-topics/125790/10)
As an enthusiastic supporter of Lemmy, I am eager to contribute to the project. However, I hold strong reservations about writing a single line of code for a project hosted on a Micro$oft server. While I have created a few issues on GitHub, I firmly believe that my contributions could be significantly amplified if there were a mirror of Lemmy that utilized Forgejo hosting outside the United States. I would be absolutely delighted to have the opportunity to contribute more actively to this incredible project if such an alternative hosting option were available.
Singularity | Artificial Intelligence (ai), Technology & Futurology
Will Superintelligent AI End the World? | Eliezer Yudkowsky | TED (video 11.07.2023)
InternetPirate 1 year ago • 60%
threadreaderapp.com
GPT-4's details are leaked. It is over. Everything is here: https://archive.is/2RQ8X **Parameters count:** GPT-4 is more than 10x the size of GPT-3. We believe it has a total of ~1.8 trillion parameters across 120 layers. **Mixture Of Experts - Confirmed.** OpenAI was able to keep costs reasonable by utilizing a mixture of experts (MoE) model. They utilizes 16 experts within their model, each is about ~111B parameters for MLP. 2 of these experts are routed to per forward pass. **MoE Routing:** While the literature talks a lot about advanced routing algorithms for choosing which experts to route each token to, OpenAI’s is allegedly quite simple, for the current GPT-4 model. There roughly ~55B shared parameters for attention. **Inference:** Each forward pass inference (generation of 1 token) only utilizes ~280B parameters and ~560 TFLOPs. This contrasts with the ~1.8 trillion parameters and ~3,700 TFLOP that would be required per forward pass of a purely dense model. **Dataset:** GPT-4 is trained on ~13T tokens. These are not unique tokens, they count the epochs as more tokens as well. Epoch number: 2 epochs for text-based data and 4 for code-based data. There is millions of rows of instruction fine-tuning data from ScaleAI & internally. **GPT-4 32K** There was an 8k context length (seqlen) for the pre-training phase. The 32k seqlen version of GPT-4 is based on fine-tuning of the 8k after the pre-training. **Batch Size:** The batch size was gradually ramped up over a number of days on the cluster, but by the end, OpenAI was using a batch size of 60 million! This, of course, is “only” a batch size of 7.5 million tokens per expert due to not every expert seeing all tokens. **For the real batch size:** Divide this number by the seq len to get the real batch size. just stop with this misleading numbers already. **Parallelism Strategies** To parallelize across all their A100s GPUs They utilized 8-way tensor parallelism as that is the limit for NVLink. Beyond that, they are using 15-way pipeline parallelism. (likely used ZeRo Stage 1. It is possible they used block-level FSDP) **Training Cost** OpenAI’s training FLOPS for GPT-4 is ~2.15e25, on ~25,000 A100s for 90 to 100 days at about 32% to 36% MFU. Part of this extremely low utilization is due to an absurd number of failures requiring checkpoints that needed to be restarted from. If their cost in the cloud was about $1 per A100 hour, the training costs for this run alone would be about $63 million. (Today, the pre-training could be done with ~8,192 H100 in ~55 days for $21.5 million at $2 per H100 hour.) **Mixture of Expert Tradeoffs** There are multiple MoE tradeoffs taken: For example, MoE is incredibly difficult to deal with on inference because not every part of the model is utilized on every token generation. This means parts may sit dormant when other parts are being used. When serving users, this really hurts utilization rates. Researchers have shown that using 64 to 128 experts achieves better loss than 16 experts, but that’s purely research. There are multiple reasons to go with fewer experts. One reason for OpenAI choosing 16 experts is because more experts are difficult to generalize at many tasks. More experts can also be more difficult to achieve convergence with. With such a large training run, OpenAI instead chose to be more conservative on the number of experts. **GPT-4 Inference Cost** GPT-4 costs 3x that of the 175B parameter Davincci. This is largely due to the larger clusters required for GPT-4 and much lower utilization achieved. AN estimate of it's costs is $0.0049 cents per 1k tokens for 128 A100s to inference GPT-4 8k seqlen and $0.0021 cents per 1k tokens for 128 H100’s to inference GPT-4 8k seqlen. It should be noted, we assume decent high utilization, and keeping batch sizes high. **Multi-Query Attention** OpenAI are using MQA just like everybody else. Because of that only 1 head is needed and memory capacity can be significantly reduced for the KV cache. Even then, the 32k seqlen GPT-4 definitely cannot run on 40GB A100s, and the 8k is capped on max bsz. **Continuous batching** OpenAI implements both variable batch sizes and continuous batching. This is so as to allow some level of maximum latency as well optimizing the inference costs. **Vision Multi-Modal** It is a separate vision encoder from the text encoder, with cross-attention. The architecture is similar to Flamingo. This adds more parameters on top of the 1.8T of GPT-4. It is fine-tuned with another ~2 trillion tokens, after the text only pre-training. On the vision model, OpenAI wanted to train it from scratch, but it wasn’t mature enough, so they wanted to derisk it by starting with text. One of the primary purposes of this vision capability is for autonomous agents able to read web pages and transcribe what’s in images and video. Some of the data they train on is joint data (rendered LaTeX/text), screen shots of web page, youtube videos: sampling frames, and run Whisper around it to get transcript. [Dont want to say "I told you so" but..] **Speculative Decoding** OpenAI might be using speculative decoding on GPT-4's inference. (not sure 100%) The idea is to use a smaller faster model to decode several tokens in advance, and then feeds them into a large oracle model as a single batch. If the small model was right about its predictions – the larger model agrees and we can decode several tokens in a single batch. But if the larger model rejects the tokens predicted by the draft model then the rest of the batch is discarded. And we continue with the larger model. The conspiracy theory that the new GPT-4 quality had been deteriorated might be simply because they are letting the oracle model accept lower probability sequences from the speculative decoding model. **Inference Architecture** The inference runs on a cluster of 128 GPUs. There are multiple of these clusters in multiple datacenters in different locations. It is done in 8-way tensor parallelism and 16-way pipeline parallelism. Each node of 8 GPUs has only ~130B parameters, or… twitter.com/i/web/status/1… The model has 120, so it fits in 15 different nodes. [Possibly the there are less layers on the first node since it needs to also compute the embeddings] According to these numbers: OpenAI should have trained on 2x the tokens if they were trying to go by chinchilla's optimal. [let alone surpass it like we do] This goes to show that they are struggling to get high quality data. Why no FSDP? A possible reason for this could be that some of the hardware infra they secured is of an older generation. This is pretty common at local compute clusters as the organisation usually upgrade the infra in several "waves" to avoid a complete pause of operation.… twitter.com/i/web/status/1… **Dataset Mixture** They trained on 13T tokens. CommonCrawl & RefinedWeb are both 5T. Remove the duplication of tokens from multiple epochs and we get to a much reasonable number of "unaccounted for" tokens: The "secret" data. Which by this point we already get rumors that parts of it came from twitter, reddit & youtube. [Rumors that start to become lawsuits] Some speculations are: - LibGen (4M+ books) - Sci-Hub (80M+ papers) - All of GitHub **My own opinion:** The missing dataset it a custom dataset of college textbooks collected by hand for as much courses as possible. This is very easy to convert to txt file and than with self-instruct into instruction form. This creates the "illusion" that GPT-4 "is smart" no matter who use it. Computer scientist? sure! it can help you with your questions about P!=NP Philosophy major? It can totally talk to you about epistemology. Don't you see? It was trained on the textbooks. It is so obvious. There are also papers that try to extract by force memorized parts of books from GPT-4 to understand what it trained on. There are some books it knows so well that it had seen them for sure. Moreover, If i remember correctly: It even know the unique ids of project Euler exes.
Singularity | Artificial Intelligence (ai), Technology & Futurology
Focused Transformer: Contrastive Training for Context Scaling - 256k context length AI
InternetPirate 1 year ago • 100%
The paper actually demonstrates a 16-million context window with 92% accuracy. Most models can be retrained to have a 100k context window with over 92% accuracy, but the accuracy drops to 74% at 256k. The code has already been released on GitHub as well. I'm excited to see the development of 100k models using this method soon!
Summary: > Focused Transformer: A new technique for long-context language modeling. The paper introduces Focused Transformer (FOT), a method that uses contrastive learning and external memory to improve the structure of the (key, value) space and extend the context length of transformer models. FOT can fine-tune existing large models without changing their architecture and achieve better performance on tasks that require long context. > > LONGLLAMA: Extending LLaMA’s context length with FOT. The paper demonstrates the application of FOT to fine-tune OpenLLaMA models, which are large language models with memory augmentation. The resulting models, called LONGLLAMAs, can handle a context length of up to 256k tokens and show improvements on few-shot learning tasks such as TREC and WebQS. > > Distraction issue: A key challenge for scaling context length. The paper identifies the distraction issue as a major obstacle for using large memory databases in multi-document scenarios. The distraction issue occurs when keys from irrelevant documents overlap with keys from relevant ones, making them hard to distinguish. FOT alleviates this issue by exposing the memory attention layer to both positive and negative examples during training. ELI5 > Sure! Imagine you have a toy box with lots of toys inside. You want to find your favorite toy, but there are so many toys that it's hard to find it. The Focused Transformer is like a special helper that can look inside the toy box and find your favorite toy quickly, even if there are lots of other toys in the way. It does this by remembering which toys are important and which ones are not, so it can find the right toy faster. Does that make sense? Implications > The Focused Transformer (FOT) technique has the potential to improve the performance of language models by extending their context length. This means that the models can better understand and incorporate new information, even when it is spread across a large number of documents. The resulting LONGLLAMA models show significant improvements on tasks that require long-context modeling, such as retrieving information from large databases. This research could have implications for natural language processing, code generation, quantitative reasoning, and theorem proving, among other areas. It could also make it easier to fine-tune existing large-scale models to lengthen their effective context. Is there anything else you would like to know?
No Stupid Questions
Where can I seek medical advice online?
InternetPirate 1 year ago • 33%
You don't have any idea of how GPT works. Read about it and then we can talk.
No Stupid Questions
Where can I seek medical advice online?
InternetPirate 1 year ago • 40%
Comparing current LLMs with autocomplete is stupid. An autocomplete can't pass law or biology exams in the 90th percentile like GTP-4 can.
Recently, I found myself questioning the accuracy of a diagnosis provided by a doctor I visited. Surprisingly, an AI seemed to offer a more insightful assessment. However, I understand the importance of not solely relying on AI-generated information. With that in mind, I'm eager to discover a reputable online platform where I can seek medical advice. Ideally, I hope to find a community where I can obtain multiple opinions to make a more informed decision about my health. If anyone could recommend such a site, I would greatly appreciate it.
www.youtube.com
French courts have been imposing disproportionately severe sentences for minor offenses, including 10 months in prison for stealing a can of Red Bull and one year for a homeless boy with schizophrenia caught looting a luxury store. The overwhelmed courts rush cases, provide minimal time for defendants, and prioritize punishment under the instruction of the Justice Minister. Furthermore, the French government is censoring social media and justifying it by claiming to protect public order, but it infringes upon free speech and mirrors tactics used by authoritarian regimes. The justice system exhibits a double standard, favoring the privileged, and creates a class divide, leading to unrest. Ironically, the government compares itself to oppressive nations while undermining democratic principles.
www.youtube.com
French courts have been imposing disproportionately severe sentences for minor offenses, including 10 months in prison for stealing a can of Red Bull and one year for a homeless boy with schizophrenia caught looting a luxury store. The overwhelmed courts rush cases, provide minimal time for defendants, and prioritize punishment under the instruction of the Justice Minister. Furthermore, the French government is censoring social media and justifying it by claiming to protect public order, but it infringes upon free speech and mirrors tactics used by authoritarian regimes. The justice system exhibits a double standard, favoring the privileged, and creates a class divide, leading to unrest. Ironically, the government compares itself to oppressive nations while undermining democratic principles.
www.youtube.com
French courts have been imposing disproportionately severe sentences for minor offenses, including 10 months in prison for stealing a can of Red Bull and one year for a homeless boy with schizophrenia caught looting a luxury store. The overwhelmed courts rush cases, provide minimal time for defendants, and prioritize punishment under the instruction of the Justice Minister. Furthermore, the French government is censoring social media and justifying it by claiming to protect public order, but it infringes upon free speech and mirrors tactics used by authoritarian regimes. The justice system exhibits a double standard, favoring the privileged, and creates a class divide, leading to unrest. Ironically, the government compares itself to oppressive nations while undermining democratic principles.
InternetPirate 1 year ago • 100%
Hopefully there are some people more positive than that, willing to change society so AGI doesn't make most humans starve to death or be imprisoned.
finance.yahoo.com
[YANDHI - WAR WITH THE MATRIX (KANYE AI X BIG BABY GANDHI)](https://youtube.com/watch?v=CGyPqImBOjY
youtube.com
FediverseInternetPirate 1 year ago • 96%
I feel like this is what happened when you’d see posts with hundreds / thousands of upvotes but had only 20-ish comments.
Nah it's the same here in Lemmy. It's because the algorithm only accounts for votes and not for user engagement.
You Should Know
YSK: Sorting by 'Hot' instead of 'Active' will show more diverse content on Lemmy
InternetPirate 1 year ago • 33%
You can't claim it's different either, so? I'll still claim whatever the fuck I want.
You Should Know
YSK: Sorting by 'Hot' instead of 'Active' will show more diverse content on Lemmy
InternetPirate 1 year ago • 100%
You can't have a source to Reddit's proprietary algorithms lol. Ask u/spez.
Singularity | Artificial Intelligence (ai), Technology & Futurology
Using ChatGPT to turn almost anyone into a replica digital assistant (26.06.2023)
InternetPirate 1 year ago • 100%
I personally prefer it colorful as it is, or even more colorful like Matt Wolfe's Midjourney generated thumbnails. It's a nice change of pace from the usual thumbnails.
You Should Know
YSK: Sorting by 'Hot' instead of 'Active' will show more diverse content on Lemmy
InternetPirate 1 year ago • 100%
Top Hour is the same as Reddit's rising option.
InternetPirate 1 year ago • 100%
I was thinking about this a few days ago. GANs and the Simulation Hypothesis: An AI Perspective
InternetPirate 1 year ago • 100%
Locked in a room with an internet connection? A lot. But without any contact with the outside world? Not nearly as much. It could have other people running experiments for it with an internet connection, but not without one.
Anyway, whether or not the AGI can interact with the real world undermines the purpose of my explicit statement in the question. I specifically mentioned that it only operates as a human on a computer. I didn't mention it could acquire a physical body, so let's just assume it can't and can't use other people to do physical labor either.
InternetPirate 1 year ago • 100%
I heard disruptive science is slowing down which I think means pretty much everything possible has already been thought of. So talking about things that exist, do you mean a cheaper solar panel or wind/water turbine? Or are we talking about science fiction like an Arc Reactor?
InternetPirate 1 year ago • 100%
This sounds like science fiction. Even if the AGI were capable of creating plans for a fusion reactor, for example, you would still need to execute those plans. So, what's the point of everyone having access to the plans if the same electrical companies will likely be responsible for constructing the reactor?
InternetPirate 1 year ago • 100%
I honestly think that with an interesting personality, most people would drastically reduce their Internet usage in favor of interacting with the AGI. It would be cool if you could set the percentage of humor and other traits, similar to the way it's done with TAR in the movie Interstellar.
InternetPirate 1 year ago • 40%
I wouldn't be surprised if corporations just asked the AI to make as much money as possible at the expense of everything else. But people like living in capitalist countries anyways, while complaining about the lack of safety nets. Otherwise they would move to countries like China, North Korea or Cuba.
Singularity | Artificial Intelligence (ai), Technology & Futurology
Singularity Predictions Mid-2023
Singularity | Artificial Intelligence (ai), Technology & Futurology
Singularity Predictions Mid-2023
InternetPirate 1 year ago • 100%
18/Jul/2025 - Date predicted by GPT-4 based on the table
Roadmap: AI’s next big steps in the world – Dr Alan D. Thompson
Singularity | Artificial Intelligence (ai), Technology & Futurology
Singularity Predictions Mid-2023
Singularity | Artificial Intelligence (ai), Technology & Futurology
Singularity Predictions Mid-2023
InternetPirate 1 year ago • 100%
The kind that uses gas? I honestly wouldn't have thought someone would be interested in open-sourcing this. I would prefer if it designed an open-source Roomba or, while we're at it, a robot body so that it could perform more tasks. But you would still have to build it yourself.
Imagine an AGI (Artificial General Intelligence) that could perform any task a human can do on a computer, but at a much faster pace. This AGI could create an operating system, produce a movie better than anything you've ever seen, and much more, all while being limited to SFW (Safe For Work) content. What are the first things you would ask it to do?
InternetPirate 1 year ago • 100%
Europe
Time off and a healthy lifestyle are driving some young Americans to Europe: 'You are a person first and a worker 2nd'
InternetPirate 1 year ago • 94%
America becoming a third world country.
Showerthoughts
Lemmy could have a filter so that people don't have to read content they don't want
InternetPirate 1 year ago • 100%
A browser extension.
Showerthoughts
Lemmy could have a filter so that people don't have to read content they don't want
InternetPirate 1 year ago • 100%
I always see german posts in english because of the the web translation extension. The only reason I know it's german is because of the language tag.
Showerthoughts
Lemmy could have a filter so that people don't have to read content they don't want
InternetPirate 1 year ago • 100%
You are right. I moved it to the ui.
github.com
I thought it was a great idea when I read it in [this comment](https://lemdro.id/comment/108908). That way, if you didn't want to hear about Reddit, you wouldn't have to.
Open Source
[SOLVED] Firefox takes too long to open lately, what lightweight browser would you recommend for Linux?
InternetPirate 1 year ago • 100%
That was it thanks.
InternetPirate 1 year ago • 66%
dupe
Maybe I've installed too many plugins? I have close to 20. Edit: It was just an issue on Arch Linux. > No, I am 40 plugins all active and they have not changed my browser load time almost at all. > > Make sure there are no other issues. If you are on Arch Linux, there is a problem with xdg-desktop-portal-gnome that if installed will slowdown loading of many programs.
Both platforms offer unique features, but also come with limitations. While Lemmy offers diverse content, it lacks robust tag metadata for organizing and searching images. On the other hand, boorus excel at categorizing images with tags but lack the discussion and the diverse content from Lemmy. Why haven't we seen a platform that combines the best of both worlds? How do you envision it would be like?
Singularity | Artificial Intelligence (ai), Technology & Futurology
Microsoft LongNet: One BILLION Tokens LLM — David Shapiro ~ AI (06.07.2023)
InternetPirate 1 year ago • 100%
Perplexity has pretty much solved that since it searches the internet and uses the information it finds. But I don't know about any advances to solve it directly in LLMs.
Last month, I developed a script because lemmy.ml had become too slow. Unfortunately, I have the same problem again, but this time there are too many instances to evaluate, causing the script to take an excessively long time to complete. I'm seeking advice on how to enhance the script to simultaneously ping multiple instances. Are there any alternative scripts available that might provide a more efficient solution? ``` git clone https://github.com/LemmyNet/lemmy-stats-crawler cd lemmy-stats-crawler cargo run -- --json > stats.json ``` ```python #!/usr/bin/env python3 import json import time import requests import requests.exceptions from typing import List, Dict TIME_BETWEEN_REQUESTS = 5 # 10 * 60 = 10 minutes TIME_TOTAL = 60 # 8 * 60 * 60 = 8 hours def get_latency(domain): try: start = time.time() if not domain.startswith(("http://", "https://")): domain = "https://" + domain requests.get(domain, timeout=3) end = time.time() return end - start except requests.exceptions.Timeout: return float("inf") def measure_latencies(domains, duration): latencies = {} start_time = time.time() end_time = start_time + duration while time.time() < end_time: latencies = measure_latencies_for_domains(domains, latencies) time.sleep(TIME_BETWEEN_REQUESTS) return latencies def measure_latencies_for_domains(domains, latencies): for domain in domains: latency = get_latency(domain) latencies = add_latency_to_domain(domain, latency, latencies) return latencies def add_latency_to_domain(domain, latency, latencies): if domain not in latencies: latencies[domain] = [] latencies[domain].append(latency) return latencies def average_latencies(latencies): averages = [] for domain, latency_list in latencies.items(): avg_latency = sum(latency_list) / len(latency_list) averages.append((domain, avg_latency)) return averages def sort_latencies(averages): return sorted(averages, key=lambda x: x[1]) def get_latency_report(domains, duration): latencies = measure_latencies(domains, duration) averages = average_latencies(latencies) return sort_latencies(averages) def get_instances(data: Dict) -> List[Dict]: instances = [] for instance_details in data["instance_details"]: instances.append(instance_details) return instances def get_domains(instances: List[Dict]) -> List[str]: return [instance["domain"] for instance in instances] def load_json_data(filepath: str) -> Dict: with open(filepath) as json_data: return json.load(json_data) def main(): data = load_json_data('stats.json') instances = get_instances(data) domains = get_domains(instances) report = get_latency_report(domains, TIME_TOTAL) for domain, avg_latency in report: print(f"{domain}: {avg_latency:.2f} seconds") if __name__ == "__main__": main() ```
We could have AI models in a couple years that hold the entire internet in their context window.
youtube.com
Singularity | Artificial Intelligence (ai), Technology & Futurology
Assuming we achieve superintelligence within the next 7 years, what would surprise you if humanity hasn't accomplished by then?
InternetPirate 1 year ago • 100%
VR game that plays exactly like the Yu-Gi-Oh! TV show. Seeing the creatures brought to life.
Singularity | Artificial Intelligence (ai), Technology & Futurology
Assuming we achieve superintelligence within the next 7 years, what would surprise you if humanity hasn't accomplished by then?
InternetPirate 1 year ago • 100%
Asteroid mining.
Piracy: ꜱᴀɪʟ ᴛʜᴇ ʜɪɢʜ ꜱᴇᴀꜱ
I totally would
InternetPirate 1 year ago • 20%
.
Piracy: ꜱᴀɪʟ ᴛʜᴇ ʜɪɢʜ ꜱᴇᴀꜱ
Piracy is not immoral (sometimes)
InternetPirate 1 year ago • 100%
There should be a simple way to give money to workers for those of us who wouldn't give a single cent to faceless corporations.
Piracy: ꜱᴀɪʟ ᴛʜᴇ ʜɪɢʜ ꜱᴇᴀꜱ
Piracy is not immoral (sometimes)
InternetPirate 1 year ago • 50%
I still like to know why people pirate.
No Stupid Questions
What are the incentives for hosting a Lemmy instance?
InternetPirate 1 year ago • 100%
To me the only benefit in defederation is in blocking instances with illegal content. I would never use StackOverflow if there was a chance of not seeing the top answer to the questions I search.
The primary incentive that comes to mind is improved availability. Often, instances can become slow, so I use another. By hosting a local instance I could always have a smooth experience. Scores are federated, resulting in a consistent global feed across instances and a lack of uniqueness for each instance. I wish hosting an instance provided a more customized experience like [this](https://github.com/LemmyNet/lemmy/issues/3477). It would be a great incentive.
Technology
Introducing the Orange Pi 5B: A Powerful and Affordable Single-Board Computer
InternetPirate 1 year ago • 100%
Yeah, better software availability.
Technology
Introducing the Orange Pi 5B: A Powerful and Affordable Single-Board Computer
InternetPirate 1 year ago • 100%
I had planned to use it as a replacement for my desktop, but I encountered a few issues while installing some of the programs I usually use. As a result, I decided to utilize it for self-hosting certain programs instead.
cross-posted from: https://lemmy.fmhy.ml/post/616834 > cross-posted from: https://lemmy.fmhy.ml/post/616828 > > > The Orange Pi 5B is a versatile single-board computer that offers impressive performance at an affordable price. With its Rockchip RK3588S 8-core 64-bit processor, it delivers a powerful computing experience, making it an excellent alternative to the Raspberry Pi 4[^2^][^3^]. > > > > ### Key Features and Specifications > > > > - Rockchip RK3588S 8-core 64-bit processor (quad-core A76 + quad-core A55) > > - Main frequency up to 2.4GHz > > - 4GB/8GB/16GB/32GB LPDDR4/4x memory options > > - Support for 8K video codec > > - Wi-Fi 6 and Bluetooth 5.0 with BLE support > > - 32GB/64GB/128GB/256GB eMMC storage options > > - USB 2.0/3.0, HDMI 2.1, Gigabit LAN port, TF card slot, and Type-C power supply > > > > The Orange Pi 5B provides a wide range of interfaces, including HDMI output, GPIO interface, M.2 PCIe2.0, Type-C, Gigabit LAN port, 2x USB 2.0, and 1x USB 3.0[^4^]. It supports various operating systems, such as Orange Pi OS, Android 12, Debian 11, and Ubuntu 22.04[^1^]. > > > > ### Performance and Benchmarks > > > > In the Geekbench 5 benchmark, the Orange Pi 5B scored 1016 for single-core and 2869 for multi-core, significantly outperforming the Orange Pi 4. Its power consumption is higher than other single-board computers, consuming 3.3 watts at idle and 7.3 watts at full load[^8^]. > > > > ### Conclusion > > In conclusion, the Orange Pi 5B is a powerful and affordable single-board computer that offers a wide range of features and impressive performance. With its versatile interfaces and support for various operating systems, it is an excellent choice for a variety of applications, from edge computing and artificial intelligence to smart home solutions and more[^4^]. > > > > Citations: > > > > [^1^]: http://www.orangepi.org/html/hardWare/computerAndMicrocontrollers/details/Orange-Pi-5-plus.html > > > > [^2^]: https://www.androidpimp.com/embedded/orange-pi-5-5b-case/ > > > > [^3^]: https://www.phoronix.com/review/orange-pi-5 > > > > [^4^]: http://www.orangepi.org/html/hardWare/computerAndMicrocontrollers/details/Orange-Pi-5B.html
cross-posted from: https://lemmy.fmhy.ml/post/616828 > The Orange Pi 5B is a versatile single-board computer that offers impressive performance at an affordable price. With its Rockchip RK3588S 8-core 64-bit processor, it delivers a powerful computing experience, making it an excellent alternative to the Raspberry Pi 4[^2^][^3^]. > > ### Key Features and Specifications > > - Rockchip RK3588S 8-core 64-bit processor (quad-core A76 + quad-core A55) > - Main frequency up to 2.4GHz > - 4GB/8GB/16GB/32GB LPDDR4/4x memory options > - Support for 8K video codec > - Wi-Fi 6 and Bluetooth 5.0 with BLE support > - 32GB/64GB/128GB/256GB eMMC storage options > - USB 2.0/3.0, HDMI 2.1, Gigabit LAN port, TF card slot, and Type-C power supply > > The Orange Pi 5B provides a wide range of interfaces, including HDMI output, GPIO interface, M.2 PCIe2.0, Type-C, Gigabit LAN port, 2x USB 2.0, and 1x USB 3.0[^4^]. It supports various operating systems, such as Orange Pi OS, Android 12, Debian 11, and Ubuntu 22.04[^1^]. > > ### Performance and Benchmarks > > In the Geekbench 5 benchmark, the Orange Pi 5B scored 1016 for single-core and 2869 for multi-core, significantly outperforming the Orange Pi 4. Its power consumption is higher than other single-board computers, consuming 3.3 watts at idle and 7.3 watts at full load[^8^]. > > ### Conclusion > In conclusion, the Orange Pi 5B is a powerful and affordable single-board computer that offers a wide range of features and impressive performance. With its versatile interfaces and support for various operating systems, it is an excellent choice for a variety of applications, from edge computing and artificial intelligence to smart home solutions and more[^4^]. > > Citations: > > [^1^]: http://www.orangepi.org/html/hardWare/computerAndMicrocontrollers/details/Orange-Pi-5-plus.html > > [^2^]: https://www.androidpimp.com/embedded/orange-pi-5-5b-case/ > > [^3^]: https://www.phoronix.com/review/orange-pi-5 > > [^4^]: http://www.orangepi.org/html/hardWare/computerAndMicrocontrollers/details/Orange-Pi-5B.html
The Orange Pi 5B is a versatile single-board computer that offers impressive performance at an affordable price. With its Rockchip RK3588S 8-core 64-bit processor, it delivers a powerful computing experience, making it an excellent alternative to the Raspberry Pi 4[^2^][^3^]. ### Key Features and Specifications - Rockchip RK3588S 8-core 64-bit processor (quad-core A76 + quad-core A55) - Main frequency up to 2.4GHz - 4GB/8GB/16GB/32GB LPDDR4/4x memory options - Support for 8K video codec - Wi-Fi 6 and Bluetooth 5.0 with BLE support - 32GB/64GB/128GB/256GB eMMC storage options - USB 2.0/3.0, HDMI 2.1, Gigabit LAN port, TF card slot, and Type-C power supply The Orange Pi 5B provides a wide range of interfaces, including HDMI output, GPIO interface, M.2 PCIe2.0, Type-C, Gigabit LAN port, 2x USB 2.0, and 1x USB 3.0[^4^]. It supports various operating systems, such as Orange Pi OS, Android 12, Debian 11, and Ubuntu 22.04[^1^]. ### Performance and Benchmarks In the Geekbench 5 benchmark, the Orange Pi 5B scored 1016 for single-core and 2869 for multi-core, significantly outperforming the Orange Pi 4. Its power consumption is higher than other single-board computers, consuming 3.3 watts at idle and 7.3 watts at full load[^8^]. ### Conclusion In conclusion, the Orange Pi 5B is a powerful and affordable single-board computer that offers a wide range of features and impressive performance. With its versatile interfaces and support for various operating systems, it is an excellent choice for a variety of applications, from edge computing and artificial intelligence to smart home solutions and more[^4^]. Citations: [^1^]: http://www.orangepi.org/html/hardWare/computerAndMicrocontrollers/details/Orange-Pi-5-plus.html [^2^]: https://www.androidpimp.com/embedded/orange-pi-5-5b-case/ [^3^]: https://www.phoronix.com/review/orange-pi-5 [^4^]: http://www.orangepi.org/html/hardWare/computerAndMicrocontrollers/details/Orange-Pi-5B.html
InternetPirate 1 year ago • 100%
dupe
[ELI5 Dead Internet Theory](https://lemmy.fmhy.ml/post/609101)
So we can clearly see the most popular distros and the reasons why people use them, please follow this format: - Write the name of the Linux distro as a first-level comment. - Reply to that comment with each reason you like the distro as a separate answer. For example: - Distro (first-level comment) - Reason (one answer) - Other reason (a different answer) Please avoid duplicating options. This will help us better understand the most popular distros and the reasons why people use them.
Hey everyone! I noticed that the discussions around the recently released self-hosted alternative to Steam and Origin, have been focused on its controversial name. Let's shift the conversation and talk about the features you would or wouldn't like to see in this software?
To complement this post: [[Poll] All useful tools to download and organize media.](https://lemmy.fmhy.ml/post/584073)
Please write a single answer per comment to clearly see the most popular tools. Vote on the ones you like. There were too many options in [this post](https://lemmy.fmhy.ml/post/489632) and I want to see what are the really interesting ones. To complement this post: [What file-sharing and media organizing software do you wish that existed?](https://lemmy.fmhy.ml/post/584842)
youtube.com
TLDR: A discussion about a new game that resembles WoW but with a more casual and seasonal model. The game seems to lack a clear identity, borrowing elements from different games without excelling at any of them. However, it offers an alternative for players looking for a less grind-intensive experience with enjoyable PvP. The conversation touches on nostalgia, lore, and the convenience of playing on mobile. While the game has potential, there is a desire for something revolutionary in the MMO genre that offers a fresh and universally appealing experience.
Hello fellow Lemmy users and enthusiasts! Today, we want to dive into the topic of balancing scores on Lemmy and discuss some of the different options that have been proposed. We'll cover the suggestions mentioned in the official GitHub repository[^1026], as well as some additional ideas that could contribute to a fair and relevant scoring system. 1. Affinity to Admin: One of the proposed options is to increase the weight of votes based on the user's affinity to the admin[^1026]. This means that the content of the instance would be most relevant to the admin, incentivizing self-hosting Lemmy. This approach aims to prioritize the preferences of the admin, potentially resulting in a more tailored and focused community for that particular instance. 1. Score Posts based on Community Size: Another suggestion put forward in the GitHub repository is to score posts based on the community size at the time of voting[^2794]. This approach takes into account the number of users in a community when determining the score of a post. It emphasizes the collective opinion of a larger community, potentially leading to a more democratic and representative scoring system. 1. Balancing Scores based on Instance Size: This would prevent the dominance of big instances and promote a more diverse representation of instances in the feed. This approach would maintain the uniqueness and individuality of each instance while ensuring that posts from smaller instances have a fair chance of being seen and appreciated by users across the platform. 1. Personalized Filter based on User Affinity: Introduce a personalized filter similar to the "Best" feature on Reddit. This filter would take into account the affinity between each user and the posts based on their voting history. By keeping a score of the upvotes and downvotes given by a user[^2370], Lemmy could analyze the user's preferences and provide a more customized feed that aligns with their interests. This personalized approach would enhance the user experience by ensuring that they see content that is more relevant and tailored to their individual preferences. 1. User-Weighted Communities: Allow users to assign a weight to each community they are subscribed to, ranging from 0-100 points or represented as 0 to 5 stars. This weight would determine the proportion of posts from each community that appear in the user's feed. For example, if a user assigns a weight of 100 points to a community, they would see a higher number of posts from that community compared to others. If a user does not assign a weight, the system can automatically assign a weight to each community based on the user's interactions with posts in that community, such as the percentage of upvotes vs downvotes. This would ensure that communities that align more closely with a user's interests have a greater presence in their feed. 1. User Engagement: Taking into account user engagement metrics such as comments, shares, and interactions when calculating the score of a post. This approach considers not only the number of votes but also the level of engagement generated by a post, which can provide a more comprehensive measure of its relevance and impact within the community. 1. Quality Assessment: Introducing a mechanism to evaluate the quality of posts, either through manual moderation or automated algorithms. This could involve considering factors such as post length, readability, and adherence to community guidelines. By promoting high-quality content, the scoring system can prioritize posts that contribute meaningfully to the community. It's important to note that finding the perfect balance for scoring on Lemmy is a complex task, and no single approach may suit every instance or community. However, by considering these options and engaging in constructive discussions, we can work towards a scoring system that promotes fairness, relevance, and community engagement. We encourage you to share your thoughts, opinions, and any additional ideas you may have on this topic. Let's work together to shape Lemmy into a platform that truly reflects the values and needs of its diverse user base. Thank you for being a part of the Lemmy community! Sources: [^3241]: [Voting Affinity and Engagement Analysis](https://github.com/LemmyNet/lemmy/issues/3241) [^1026]: [The rank of a post in the aggregated feed should be inversely proportional to the size of the community #1026](https://github.com/LemmyNet/lemmy/issues/1026) [^2794]: [Score posts based on community size at the time of voting #2794](https://github.com/LemmyNet/lemmy/issues/2794) [^2370]: [Keep a score of the upvotes and downvotes given to user. #2370](https://github.com/LemmyNet/lemmy/issues/2370)
To repost all your YouTube subscription videos with above-average popularity on Lemmy using Python, you'll need to follow these steps: 1. Get a YouTube API key[1]. 2. Use the YouTube API to fetch your subscription videos[2]. 3. Determine the popularity threshold (e.g., average views, likes, or comments). 4. Filter the videos based on the popularity threshold. 5. Use Pythorhead to interact with Lemmy and post the filtered videos[3]. Here's a sample Python script to achieve this: ```python import requests from pythorhead import Lemmy # Replace with your YouTube API key and Lemmy credentials YOUTUBE_API_KEY = 'your_youtube_api_key' LEMMY_USERNAME = 'your_lemmy_username' LEMMY_PASSWORD = 'your_lemmy_password' # Fetch your YouTube subscription videos def get_youtube_subscriptions(api_key): # Replace with your YouTube channel ID channel_id = 'your_youtube_channel_id' url = f'https://www.googleapis.com/youtube/v3/subscriptions?part=snippet&channelId={channel_id}&maxResults=50&key={api_key}' response = requests.get(url) data = response.json() return data['items'] # Determine the popularity threshold def get_popularity_threshold(videos): # Calculate the average views, likes, or comments of the videos # Replace this with your preferred popularity metric pass # Filter videos based on the popularity threshold def filter_videos(videos, threshold): # Filter the videos based on the popularity threshold # Replace this with your preferred popularity metric pass # Post filtered videos on Lemmy using Pythorhead def post_videos_on_lemmy(videos): lemmy = Lemmy("https://lemmy.dbzer0.com") lemmy.log_in(LEMMY_USERNAME, LEMMY_PASSWORD) community_id = lemmy.discover_community("your_lemmy_community") for video in videos: title = video['snippet']['title'] url = f'https://www.youtube.com/watch?v={video["id"]}' lemmy.post.create(community_id, title, url) # Main script if __name__ == '__main__': videos = get_youtube_subscriptions(YOUTUBE_API_KEY) threshold = get_popularity_threshold(videos) filtered_videos = filter_videos(videos, threshold) post_videos_on_lemmy(filtered_videos) ``` Replace the placeholders with your YouTube API key, Lemmy credentials, and YouTube channel ID. You'll also need to implement the `get_popularity_threshold` and `filter_videos` functions based on your preferred popularity metric (e.g., views, likes, or comments). Please note that this script is just a starting point, and you might need to modify it according to your specific requirements. Citations: [1] https://blog.hubspot.com/website/how-to-get-youtube-api-key [2] https://gist.github.com/Yiannis128/4a9c016236edf41493176a59bb0a1be0 [3] https://github.com/db0/pythorhead