Tarptent Aeon Li Blem
Verification pics here. Date is from when I posted this a few weeks ago, and I'm now reposting at a lower price. I can provide updated verification on request.
Purchased new from Tarptent in 2021 and has the zipper door and magnetic door ties. Although this was sold as a “blem” model, I can’t identify the blemish. Fantastic UL shelter with lots of livable interior space. Blems are currently not available from Tarptent.
Used ~10-12 nights. No DCF patches, no tears in mesh, and in excellent condition aside from some trail dust. Always stored loose rather than stuffed.
18.4 oz. in my scale including stuff sack.
$355 (includes rigging and stuff sack, does not include stakes). Price includes G&S fee, does not include shipping to CONUS from Massachusetts. DM me your address and I can get you a shipping quote.

Researchers at Gradient introduced the Llama-3 8B Gradient Instruct 1048k model, a groundbreaking advancement in language models. This model extends the context length from 8,000 to over 1,048,000 tokens, showcasing the ability to manage long contexts with minimal additional training. Utilizing techniques like NTK-aware interpolation and Ring Attention, the researchers significantly improved training efficiency and speed, enabling the model to handle extensive data without the typical performance drop associated with longer contexts.
The researchers employed techniques such as NTK-aware interpolation and Ring Attention to efficiently scale the training of long-context models. They achieved a significant speedup in model training by progressively increasing the context length during training and using advanced computational strategies. This approach allowed them to create a model capable of handling extensive data without the typical performance drop associated with longer contexts.
Quick read: https://www.marktechpost.com/2024/05/21/gradient-ai-introduces-llama-3-8b-gradient-instruct-1048k-setting-new-standards-in-long-context-ai/
Model Card: https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k

major_players #update #hardware #science #event #startups #leaders #tool #vc #release #feature #api #bigtech #scheduled

OpenAI has temporarily halted the use of the voice named Sky in ChatGPT due to concerns raised about the voice selection process. They are actively addressing these issues [1]. Anthropic has shared insights on their Responsible Scaling Policy, which focuses on preventing catastrophic safety failures and misuse of frontier models. They have outlined five key commitments and are investing in security measures, changing workflows, and implementing multi-party authorization controls [6].
NVIDIA offers a wide range of resources and solutions for higher education and research institutions to adopt AI across various academic disciplines. They provide tools for accelerated computing, data science, AI, robotics, generative AI, and more. NVIDIA's technologies are being used in academic research to advance fields like genomics, climate modeling, quantum computing, and robotics [2]. NVIDIA also conducted a survey exploring key AI trends in retail and consumer packaged goods (CPG) in 2024. The report highlights the adoption of generative AI and the potential transformation it can bring to businesses [3].
NVIDIA CEO Jensen Huang and other tech leaders discussed innovations in AI, Multicloud, Edge, and more at Dell Technologies World Keynote [4]. NVIDIA AI is also hosting a webinar on the power of physics-ML in modeling real-world systems and its application in education [5].
The AI industry has seen significant developments in the past 18 months, including advancements in generative AI models like ChatGPT and high-performance open models. The AI + a16z podcast episode features a discussion on the changes in the AI landscape and signs of a mature market for Large Language Models (LLMs) [10][11].
Microsoft is re-inventing PCs for the AI era with the launch of a new product category called Copilot+ PCs. These machines are designed to run Copilot locally to reason over data, from text to pixels in real-time [16]. Mustafa Suleyman announced that Copilot is being taken to the next level, where it will have the ability to see, hear, speak, and assist in real time [14].
Google shared updates at #GoogleIO on their commitment to responsibly build AI products that can improve learning, work, and solve global challenges. They introduced LearnLM, a family of models for personalized learning, and a tool called Illuminate to make knowledge more accessible [20]. Google's AI advancements and possibilities were discussed in a #GoogleIO session with James Manyika, Laurie Segall, Koray Kavukcuoglu, and Jeff Dean [18].
NearWave, a startup from Y Combinator's W22 batch, has developed a non-invasive handheld imaging technology with AI that assists physicians in selecting the appropriate therapy for breast cancer patients. This innovation can determine the effectiveness of chemotherapies within a week, enabling quick adjustments to treatments and potentially saving patients from months of unnecessary side effects [21].
Groq Inc will be participating in the Enterprise GenAI Summit, showcasing the world's fastest AI inference technology at their booth. They will also host a panel on GenAI infrastructure for cost and energy reduction [22]. Groq Inc shared a video showcasing Whisper running on Groq compared to ChatGPT [24].
1. OpenAI @openai https://twitter.com/openai/status/1792443575839678909
2. NVIDIA AI @NVIDIAAI https://twitter.com/NVIDIAAI/status/1792570981568958968
3. NVIDIA AI @NVIDIAAI https://twitter.com/NVIDIAAI/status/1792586083093348453
4. NVIDIA AI @NVIDIAAI https://twitter.com/NVIDIAAI/status/1792601181711155276
5. NVIDIA AI @NVIDIAAI https://twitter.com/NVIDIAAI/status/1792616284326068271
6. Anthropic @anthropicai https://twitter.com/anthropicai/status/1792598295388279124
7. Greg Brockman @gdb https://twitter.com/gdb/status/1792601726949945368
8. Marc Andreessen -- e/acc @pmarca https://twitter.com/pmarca/status/1792612652599980083
9. Satya Nadella @satyanadella https://twitter.com/satyanadella/status/1792617851385426024
10. a16z @a16z https://twitter.com/a16z/status/1792609801291305316
11. a16z @a16z https://twitter.com/a16z/status/1792611657228890384
12. a16z @a16z https://twitter.com/a16z/status/1792633504305451099
13. a16z @a16z https://twitter.com/a16z/status/1792651996589150246
14. Mustafa Suleyman @mustafasuleyman https://twitter.com/mustafasuleyman/status/1792623877744623806
15. Mustafa Suleyman @mustafasuleyman https://twitter.com/mustafasuleyman/status/1792624015372333240
16. Mustafa Suleyman @mustafasuleyman https://twitter.com/mustafasuleyman/status/1792624197572653351
17. Mustafa Suleyman @mustafasuleyman https://twitter.com/mustafasuleyman/status/1792624537630290119
18. Google AI @googleai https://twitter.com/googleai/status/1792631248827068573
19. NVIDIA AI Developer @NVIDIAAIDev https://twitter.com/NVIDIAAIDev/status/1792646477975593193
20. Google @google https://twitter.com/google/status/1792653911133720740
21. Y Combinator @ycombinator https://twitter.com/ycombinatostatus/1792661577490903072
22. Groq Inc @GroqInc https://twitter.com/GroqInc/status/1792545060837277918
23. Groq Inc @GroqInc https://twitter.com/GroqInc/status/1792680922212692154
24. Groq Inc @GroqInc https://twitter.com/GroqInc/status/1792682521764954527

Howdy KS? Hope everything is ramping up for Memorial Day this weekend. Got some goodies up for your consideration today. As always price includes shipping inside the US only. Please no trades! Payment methods are preferably Zelle, cash app or Apple Pay. But I can take PP or Venmo if absolutely necessary.
2 KNIFE MINIMUM!!!
Timestamp & Goodz
https://imgur.com/a/OgQzDRE

• Demko AD 20.5 in clip point D2, second owner bought off here and must say the knife is surprisingly smoother than all it other 20.5s. Very solid 8/10 condition with zero issues to note of. Comes with box & docs. SV $60
  
• Cold steel Air lites 1 tanto & 1 drop point, both are NIB in aus10A. SV $25 each.
  
• Boker Kalashnikov drop point in D2, once again NIB I bought this off blade HQ and has never seen any pocket time because of my Boker strike auto. SV $25
  
• CJRB pyrite in titanium scales w/stainless liners & AR-RMP9 steel, NIB bought a few of these when on sale at CKW. Fun little fidget knife for sure! SV $40
  
• CJRB WMK exclusive mini pyrite in fragged aluminum scales in AR-RMP9 steel. NIB SV $32
  
• Civivi baby banter in Nitro-V & canvas micarta, carried maybe 3 times but never used & no box. SV $35
  
• CJRB mini pyrite w/smooth aluminum scales, again carried a few times but never used no box. SV $25
  
• Ontario rat model 2 Aus8 steel, NIB SV $15
  

I saw someone else did something like this recently and thought that I might have something to add to the discussion to help people figure out whether they think this program is right for them, or for people currently in the program to help them plan out classes.
My background is very heavy in statistics and finance (I'm an actuary), so my grounding in calculus, statistics, linear algebra, and business topics was considerably better than most of the other people I interacted with in this program. Conversely, I'm sure my programming skills were probably about average or maybe a little bit below compared to someone with a more targeted background towards those skills.
I started in Fall of 2020 and just finished up this month, Spring 2024. I started out just taking one class a semester and never changed up that plan, would highly recommend doing the same if you're working full time alongside; there's no reason to rush this program. I got a 4.0 GPA overall, although I never really stressed that much about it and definitely did not shy away from "harder" classes in order to bolster GPA. I did this to learn, not to get good grades. The classes I took, in order (my memory of some of the earlier ones might be a bit jumbled as so much time has passed):

1. CSE 6040 - I mentioned how my programming background was weaker relative to my mathematical background. However, reflecting on my experience in this class, it was probably still pretty strong. This class is focused on generalized programming skills, you don't really get into the analytics and modeling that much; it's more of a primer on things like functions, recursion, computer memory management, etc. It's been a few years, but I don't remember struggling with this class at all, and most of the concepts covered (object oriented programming, things like byte encoding, hexadecimal forms, recursion, etc.) were things I was already familiar with in another programming language (this class was in Python) or was relatively easy to pick up. From what I remember, the assignments were auto-graded and you had unlimited attempts, not to mention the fact that most of the prompts were to produce some predetermined result... and, as long as you were able to verify that your code produced that result, you got full points. I didn't miss a single point in this entire class. I believe there were "final exams" which were really just timed window coding assignments much like the homework, and you could do the assignments at any point during a 4-day window or something like that.
2. ISYE 6501 - This class was R-based and focused on basic analytics models. The material was much more applied than 6040. Similarly, the material was not difficult, and I was familiar with a lot of the basic models already (such as GLMs) from having worked with them in my job. The grading was done on a peer-grading framework; based on who anonymously is assigned your homework to grade, you can get someone who's a stickler for every point on the guide, or people who are a bit more lenient. I never really worried much about how things were graded in this class; yeah, I did miss some points arbitrarily, but nothing that made that much of a difference. If you generally put in the work and understand the material, your peers will recognize that. I found this a bit more interesting than 6040 because, rather than a deterministic "right answer", there was some more creativity implied here to solve each problem. There was a final project for this class where you walked through a hypothetical analytics problem and explained how you would go about solving it. I found this an interesting thought exercise and enjoyed this class. The pacing felt a little fast, as you basically had an assignment due each week, but the assignments were small. Like 6040 I found this class to be quite easy but I didn't 100% it due to the peer grading thing.
3. MGT 8803 - I almost applied to be exempt from this class, as my background intersects a lot with the material. The accounting and finance modules for this class literally did not teach me anything new. Supply chain was new for me and I found it interesting. I'm trying to remember what the other module was. I think it was marketing? I didn't like it. Overall I found this class quite easy for the aforementioned reasons, however I've heard from a lot of the other folks without business backgrounds that this class was pretty tough so take my opinions with a grain of salt. Taking this in the summer cut out one of the modules, normally there are 5 but in Summer there are 4. I figured this made sense to take during the truncated semester because I was unlikely to cover that much new material, and it turns out I was right about that. Each module lasted 2 weeks (I think in a normal semester it lasts 3) and has a timed multiple-choice test at the end. A lot of people didn't like this format compared to ISYE 6501 and CSE 6040; I'm not really sure how else this material could've been covered. This class was OK, the material is not really that deep but it's a pretty good primer on a large number of business topics.
4. ISYE 6740 - The first class I took that was actually pretty challenging, which makes sense considering the first 3 courses were just the basic core. This is pretty much an intro to machine learning as a discipline, and the first time I remember digging into academic papers that discuss some machine learning topic and attempting to recreate the results (this is something we did a lot in the more advanced classes going forward and incidentally now that I have graduated is probably one of the best ways to go about learning a new topic). I remember this class as having a format similar to ISYE 6501/CSE6040 in that you had large programming assignments to do, as well as open-book "exams" which were really just timed programming assignments. Assignments are not auto-graded; TA's review each one and thus the assignments have much more of a focus on explaining your findings than producing the exact expected output (unlike 6040). Some of the theory questions have you applying complicated matrix algebra rules that I'm not surprised a lot of people struggled with. The TA responsiveness in this class was pretty good from what I remember, but your mileage may vary. I remember getting 3 weeks to do each assignment but I also remember not thinking that was a lot of time, these assignments are very extensive, have many parts, and take a long time to get through. Like with 6040 I ended up not missing a single point in this class but I did find it difficult and spent significantly more time working on it than in prior classes, probably 10-15 hrs a week, give or take.
5. ISYE 6644 - I was familiar with maybe 50% of the material we covered due to my extensive statistics background, however I was not aware of the exact mechanics of random number generation or the concept of a batched mean, for example. I remember this class having several "check your understanding" quizzes that focused on the mathematical foundations. Didn't struggle with this much in terms of difficulty and found the material very useful. Setting up custom simulation environments is very useful and arena is pretty cool even if it's unlikely you'll ever use it. Some assignments feature similar tools in Python (simpy). There might have been some coverage of R in this as well, or at least the accommodation for people that wanted to use it. There was a project for this class, but you didn't have to come up with the topic on your own, you could pick from a list, and you could do your project on your own if you wanted (which, given the option, was always the choice I made, due to the inherent randomness in picking the right members of a group). However you can do a group project if you want.
6. MGT 6203 - The first of the classes I took that required a group project. I recommend you are proactive in putting together groups in situations like these, posting threads on Ed/whatever the forum is as soon as the class begins. My group was alright; not everyone in it was great, but we had enough going overall to make up for the weaker group members. The project has some arbitrary guidelines from what I remember - you need to put together a midterm report and video presentation that is no more than ~2 minutes long or something like that (if it's 2:01 you get penalized) and the final report is 5 minutes or less, and everyone in the group needs to speak. I honestly don't remember much else about this class, it was pretty forgettable, but not horrible. Not overall that difficult, another business class so a lot of topics I was already familiar with, but there was more new here than in 8803.
7. CSE 6242 - Another class with a group project. Again, I was proactive, and again, overall, my group was... okay. Some people who were really good, some who were... not. This class is characterized by a lot of assignments that are autograded, like 6040, but the assignments are a bit more difficult. Overall not that difficult with the exception of the D3 assignment, but that's more due to the fact that I'm not really sure how the autograder works for that; it tries to determine based on some internal structure of your html code whether or not you're fulfilling the requirements. I got a perfect score on all of the assignments, and they give you the chance to score over 100% on I believe either assignment 1 or assignment 2. A lot of people bombed the D3 assignment (I think it's assignment 2) but still did well in the class because it's not that hard to do well on everything else, so keep that in mind. This class does a great job of exposing you to a lot of new technologies, but there isn't that much depth to it. That's not really the point of this kind of class though, it equips you with the tools to explore things deeper if you so choose.
8. ISYE 8803 - I was a big fan of this class. It's taught in MATLAB but you can use Python if you so choose, you'll see in reviews of this class that you should really just use MATLAB since a lot of the sample code etc. is not in other languages, so that's what I did. However, they must've recently added Python and R code for sample solutions, so feel free to use what you want. MATLAB was interesting, there were parts of one assignment I also used R for (grouped lasso in R is a lot more straightforward). This class is all about high dimensional data and representing it in a more simplified and comprehensive way, think about something like sonar which might have datapoints separated by milliseconds and thus a very dense representation of a signal captured over a short period of time. After ISYE 6740, I found this to be the class that taught me the most up to this point.
9. CS 7642 - Taking this class in summer is kind of rough. There's 6 homework assignments that are autograded, similar format to CSE 6040. There are 3 projects which are much larger programming assignments for which you'll write papers explaining methodology, results, etc. These projects take a while, particularly project 3. I did well on projects 1 and 2 and decently on 3, although I spent the most time on 3 by far; it involves reinforcement learning to simulate a soccer environment and train agents how to play against an AI developed externally. The AI baselines are hard to beat, and I didn't manage to beat them, but I wrote a decent paper explaining what I did. The final exam for this class should be dropped as it doesn't add value to the class, people regularly score extremely low on it, the average score in the class was something like a 45%. I scored a bit lower than average but still got an A in the class because it was heavily curved. Reinforcement learning is a very interesting topic, though, and I would highly recommend this class as a primer on the material. It's probably a good idea not to take it in the summer, though.
10. CS 7643 - This class was pretty difficult but I still think 6740 was tougher. The material is extremely dense. There are parts of programming assignments that are autograded, but also short answer portions that are reviewed by TAs. Grading on those were pretty subjective. This is the only class I can remember really needing to discuss things with TAs to understand what was being asked a little better. Unfortunately, the TAs in the semester I took this weren't the best. They seemed more concerned with unintentionally giving away a bit too much information in any of their responses. I can understand this, but it came off as intentionally opaque most of the time. There was a group project for this course as well, and my group was excellent, probably the best experience I had with a group in this program. I can imagine how much this course would've sucked if I would've had a mediocre/bad group. Based on discussions with my group, some of the grading seemed highly arbitrary, with some TAs grading similar responses to the same question differently. Like I said above, though, I never really worried about this. I never once in this program ever disputed a grade, and I continued with that in this class as well.
11. CSE 6748 - Practicum and final class. For this class you get to choose between a number of pre-determined Georgia Tech sponsors, or form your own project for your own employesome external entity. It was a lot more work to do this, so I just went with one of the pre-determined GTech ones. I really enjoyed this one, I had constant communication with the sponsor as I developed my project and came up with something that I was quite proud of. I wanted to explore a natural language processing task, so I picked a project that I thought would allow me to do this, and was very satisfied with the result. There's a number of videos you have to watch that explain some overarching aspect of analytics that were pretty interesting as well, you can watch all of these in a single day and then focus on the project if you like. It's possible to finish the entire semester's work in just a few weeks, I was able to do the entire project and write the final paper in about a month's time, at which point I coordinated with the sponsor to tailor the work I did to a format that they would be able to implement for their business problem if they wanted to.

I can't comment on the job placement prospects of this program, as I just finished it and was actively employed the entire time I was in it. As an actuary there's not much this program does that my exam certification process didn't in terms of career prospects. However, it did position me much better within the context of the expanding role of data and analytics in insurance going forward, and also opens me up to similarly mathematical roles with a firmer grounding in big data and also some business elements (quantitative finance/data science roles). There were also things I learned in this program that I was able to apply directly to my day-to-day work. If you're considering this program, I would recommend you think about a few things:

1. I'm pretty shocked at how many people I saw during my program who didn't really think that much about why they're doing this. I get that the barrier to entry is low, but it's a serious commitment if you're actually trying to graduate. Most of the people who start this program don't finish, so consider whether you're ready to spend almost 4 years going to school part-time, or if you're able to double up on classes for some of the semesters. Most of the people I know in the program doubled up at least once, I never did but I was never in a hurry. If you must double up, don't make it your first semester. Dip your toe in the water, see how it is, and then reassess. But, above all else, think about why you want to do this, and use that as your guiding goal to bring you through to the end.
2. Something I tended to see pretty much without fail in most of my classes - a lot of the graduate students in this program spend way too much time worrying about minute, particular details that don't really matter. Maybe it was just my philosophy that I would probably never dispute a grade, or that I was never really that concerned with getting a perfect GPA, etc. but I was always marveling at what I saw asked in Ed posts. People would ask whether they could use a certain programming language for an assignment, what packages they were allowed to use, would post screenshots of bugs and ask for TA's to help walk them through it, etc. Generally, without fail, the TAs would respond along the lines of: use whatever programming language you want, as long as you can display your output/submit it in a way that we can verify by running ourselves, we'll make the effort; use the debugger to step through your code to find the problem; etc. Generally, in most cases, the assignments and questions are designed in a way to teach you something, to get you to realize/understand some pattern or data concept that has some underlying logic that makes sense. For example, the idea of saliency maps on image processing takes the 3-channel RGB color pixel shading representation of an image and condenses it into a single channel, and, as a result of that, loses some resolution in suggesting parts of the image driving a model result that might be different depending on the channel; i.e., an image with a very heavily blue-shaded part that detracts from a certain result, but with a red-shaded part somewhere else that increases the probability of the modeled result. This was part of a conceptual question on how saliency maps differed from other pixel influence attribution methods in Deep Learning, and is part of what you should logically understand since it reduces the channels of the image representation from 3 (R, G, B) to 1 (usually grayscale). I think people tend to run to the TA the second they have difficulty with something and don't stop for a second to think it through, one exercise I might recommend is to consider: if you ran into this problem out in the world and you didn't have a TA/managesome other authority figure to explain the answer to you, what do you think it might be? Does the answer even matter? If it still matters and you have no idea how to solve it, maybe then you can go to the TA.
3. In every single group project I worked on, we had an initial planning session where we determined the scope of what we wanted to do. For most of the projects, this was an essential deliverable in addition to the final paper. However, in almost every case, someone in the group was always playing some game of runaway scope where they kept on wanting to add methods/questions to exploration beyond what was initially planned in ways that I intuitively knew would be impossible to manage in just one semester. I often had to say something along the lines of "if we have time we'll do that" or "when we write up our paper, we can put that in the avenues for future exploration section" or something similar. It turns out that we never had time to look into these things, and our initial scope was usually well-defined considering the time we had. I'm not sure why this was always so front-and-center in my focus, maybe since I used to work in consulting and project budgeting/scoping is so unbelievably important in that context. Whatever the case may be, understand that you won't be able to change the world every time you do a project. Make some incremental improvement, reflect on the results, and then include some notes in a "potential avenues for future exploration" section. I was pretty surprised at how many people had so much trouble putting the pencils down at the end. I can practically guarantee that, for the classes where I did a project on my own, I probably did substantially less work than other individual groups for precisely this reason. In general, you probably don't have to do as much work as you think you do.

So, would I recommend the program overall? Absolutely. It's not perfect, I found some of the formats annoying - CS 7642 has no business having that final exam, it adds nothing to the class at all, is arbitrarily extremely difficult and the class is good enough and complete enough with the removal of that exam that its inclusion to me appears to be the result of some arbitrary quota somewhere. I also don't really like the group project format and profoundly disagree with the reasoning that GTech and most other academic institutions give as to why group projects are even good or necessary, however I do acknowledge that from a logistics and resource standpoint it's unmanageable to grade individual projects for every single person in a given class and group projects do decrease the number of papers that TAs will have to read. Considering the scale of what GTech has managed to do, and how many students enroll each year, I'm surprised the program is as well-managed as it is. Yes, it does require a lot of self-teaching, but in most cases you can actively engage with TAs multiple times a week if you're struggling with topics and from what I've seen they were very responsive.
Anyways just wanted to give my perspective as someone who just finished this program and still thinks it's worthwhile despite its flaws.

Title: ChatGPT: Revolutionizing E-commerce with Personalized Multilingual and Cultural Shopping Experiences
As the digital marketplace continues to expand, e-commerce is facing the next frontier of customer service: personalized multilingual and cultural shopping experiences. With the integration of advanced AI like ChatGPT, online retailers have started offering services that go far beyond the standard one-size-fits-all approach.
Imagine logging into a website and being greeted by a chatbot that not only speaks your language fluently but also understands your cultural context. This is now becoming a reality. ChatGPT, with its sophisticated language models, is powering a new wave of customer interactions that feel remarkably human and incredibly personalized.
For international customers, the benefits are immediately clear. They can shop with ease in their native language, receive product recommendations that resonate with their cultural background, and interact with customer service bots that understand local nuances, idioms, and expressions. This reduces the friction that often accompanies online shopping in a foreign language and leads to a more enjoyable and stress-free experience.
Moreover, these AI-driven services can provide real-time translation and support, breaking down the barriers to accessing global markets. As a result, businesses are not only able to provide personalized shopping experiences but can also expand their reach to a broader audience without the need for extensive multilingual staff.
Behind the scenes, ChatGPT is constantly learning and adapting to individual customer preferences, taking into account past interactions, purchase history, and even sentiments expressed during chat sessions. This allows for a degree of customization in marketing and communication strategies that was previously unattainable.
For e-commerce businesses, the implications of utilizing AI like ChatGPT are profound. With the ability to scale personalized experiences across diverse markets, they can increase customer satisfaction, loyalty, and ultimately, drive sales growth. This digital transformation in e-commerce is not just about technology; it's about creating connections with customers that feel personal and authentic, no matter where they are in the world.
As we forge ahead into this era of enhanced online retail, it's exciting to think of how these personalized, multilingual, and cultural shopping experiences will shape the future of e-commerce. The revolution is just beginning, and the potential for creating deeper, more meaningful customer relationships is immense.

AGI Defined
An autonomous assistant that solves tasks you give it as well as or better than a human would. It has the reasoning, agency, learning capability, and ability to use tools like a human.
How AGI Solves Tasks
Problem Definition and Understanding:
Start by understanding the problem, desired outcomes, and constraints.
Planning:
Formulate a detailed plan outlining steps and strategies to address the problem.
Knowledge Acquisition:
Gather relevant information from its database, the internet, and through its own experiments.
Data Collection and Analysis:
Collect, clean, and analyze data to extract useful insights.
Reasoning and Planning:
Plan by simulating different strategies and evaluating their feasibility.
Creative Generation of Solutions:
Create innovative solutions by combining and extrapolating ideas.
Evaluation and Selection:
Evaluate solutions based on criteria like effectiveness and ethics.
Decision-Making:
Decide on the best solution by weighing pros and cons.
Implementation and Execution:
Implement the chosen solution with an action plan.
Iterative Feedback and Learning:
Adjust approach based on feedback during implementation.
Continuous Improvement:
Learn from experiences to improve problem-solving over time.
How Agi Is Smarter Than Humans In Discovering and Implementing New Knowledge
Enhanced Memory:
Have an extensive and perfect memory, allowing it to recall and use vast amounts of information instantly.
Processing Speed:
Analyze vast amounts of data and complex problems much faster than humans.
Integration:
Integrate information from diverse sources and disciplines more effectively, generating novel insights.
Pattern Recognition:
Detect subtle patterns and anomalies in data that humans might overlook.
Deep Learning:
Understand and learn from complex datasets, making connections and insights that are difficult for humans to grasp.
Hypothesis Generation:
Systematically generate and evaluate a vast number of hypotheses, including unconventional ones humans might not consider.
Simulations:
Run countless simulations of theoretical models quickly to test their validity.
Unbiased Exploration:
Explore hypotheses without cognitive biases that might influence human researchers.
Logical Consistency:
Maintain higher levels of logical consistency in reasoning, avoiding common cognitive fallacies and errors.
Advanced Problem-Solving:
Solve complex problems more effectively by using advanced algorithms and heuristic methods beyond human capability.
Feedback Loop:
Rapidly iterate and refine models based on new data, accelerating the cycle of discovery.
Innovative Thinking:
Generate truly novel ideas and solutions that go beyond conventional human thinking.
Continuous Operation:
Work continuously without breaks, accelerating the research process.
Innovations Needed For AGI
Hardware:
Better chips
Data centers
Energy production
Software:
Agents perform multi-step tasks and know what to do next for the task to be done without being asked to.
Self operating computer
Having every workflow application have the AI be able to use the tools rather than just give advice based on what it sees on the device and hears from the user.
Spoken Timelines: I didn't add links but you could just search what I said on Google.
Dan Schulman (former PayPal CEO): GPT-5 will be a freak out moment. 80% of the jobs out there will be reduced 80% in scope.
Sam Altman thinks GPT-4.5 will automate 100 million jobs globally.
In 2023, Sam Altman said he predicts job losses will begin in 2-3 years.
Sam Altman's AI Capabilities Projection:

• Multi-Modality: Somewhat with GPT 4o. Still needs an everything to everything model.
• Reasoning: Q-star, chain of thought, search tree. I’m sure more will come.
• Personalization: Most likely having the AI be trained on all your personal data by having it control your devices. (Bill Gates Interview)
• Reliability: Sam said that this is when the AI can always give you the best response. He remarks that if GPT-4 gives a good response 1 out of every 10k times, then he wants to ensure that one good response is given every time. (Bill Gates Interview)
• Agents: API calls with other apps. Ability to use tools in different applications.

AI that controls your computer is coming soon
Sam Altman said GPT 4 is really dumb. So he knows the next model is actually way better.
OpenAI is likely two years ahead of everyone.
OpenAI COO: Brad Lightcap

• We’ll get a major upgrade by May 2025 (Based on May 2024). Today’s systems are laughably bad.
• Models are capable of complex "work."
• OpenAI already has a desktop app with a conversational voice and can comprehend what’s on your screen. So they can be like your teammate.

Every company’s workflow would be reengineered by May 2026. Nvidia, Dell Are Building Their Own AI 'Factories' (Based on May 2024)
Project Stargate: Nuclear fusion to power the $100 billion data center to be released in 2028. This is likely for scaling for smarter models, make AI faster, cheaper, or getting it to as many hands as possible.
Many leaders in AI give AGI release dates between 2025-2030. Most people that think AI won't take jobs are thinking of todays tech for the future. They don't consider how AGI would be like for AI to take jobs. Like the required technology and how those innovations could be achieved. People don't consider how embodied humanoid robots with AGI could also eliminate many jobs. People think trade jobs are safe. NVIDIA's project groot is training robots in simulation. Who's to say that the robots can't learn to do all tasks. Maybe not in a year, but five years is a lot of progress with the rate AI is advancing. Even if we don't have AGI now, AI will be replacing jobs. If one person can use AI to do the work of one team, then those people who would have been on a team would be laid off. Especially when Agents roll around in 1-2 years.
Sometimes it really worries me about the people who go to college to get a high paying jobs, especially first gen college students from immigrant families. Imagine working hard for the dream of getting a high paying job only to be automated in six years. What happens to their drive? All their hard work for what? Sold a long gone dream and had the carpet pulled under them. In my opinion, even jobs like doctors could be automated if humanoid robots had embodied AGI. Sure it might not be wanted at first, but it has the potential too.
My Thoughts on AGI
AGI would just be the accumulation of all the different innovations needed to reach the requirements needed to meet the definition of what AGI is. Realistically, engineers could do this within six years. So even though I have a general idea of how AGI could be achieved by the end of the decade, I would just have to wait and see when these innovations are created.
Post Labor Info
Most people work to gain money because money is needed to survive and buy things that you want. The purpose of school is to provide people with a pathway to secure a high-paying job. When students say they work hard in school to be successful, it often means they want to get a high-paying career.
But if AI were to start taking over jobs, then our relationship with work would change. There would be mass unemployment, leading people to receive UBI. The government would be forced to give UBI unless they want societal collapse. No one would need to work to have money for survival and buying the things they want.
If everyone had free money, people would use the extra time on bettering their personal lives. They can spend more time with loved ones and do the things they want to do. There would be a paradigm shift in how society is set up. People wouldn’t have to work so hard at school or a job in the pursuit of money. A lot of people don’t like working anyway, especially since the cost of living is causing them to work harder and subsequently deal with mental health issues. Look at Gen Z doesn't want to work, antiwork, wage slavery, 9-5 rat race. Cost of living being unaffordable. People work so hard but can not get by. Hard work is not properly financially compensated and college grads can't find jobs. Meaning from work could be derived from work you want to do not work you do for a paycheck. Look at David Shapiro's YouTube channel for more info on Post-Labor and Post-AGI videos.
It would be better for the next generation of people born in this post-labor world. Nasa researchers found that 98% of 5-year-old children fell into the “genius category of imagination.” This number dropped to 12% for 15-year-olds and to 2% for adults. The reason for this drop is the education system killing creativity. But with the traditional education system gone, that generation is likely to remain geniuses.

Greetings Swap! Have a few knives up for sale this evening.
Timestamp
Demko AD20S Clip Point 20CV - Second owner. Previous owner disassembled to swap scales with a different model. Other than that, basically LNIB. I have not carried it or cut with it. Never sharpened. Comes with box. SV: 400
Demko AD20.5 Smooth Ti Sharkfoot 20CV - First owner. I disassembled it to install Skiff bearings, but just enjoy the textured ti more. Has one small snail near the clip-side pivot that's really hard to see, and some slight scratches near the slot on the same side. Carried a couple times, but never cut anything. Never sharpened. Comes with plastic tube/box, lefty clip, and original bearings. SV: 225 SOLD
Grimsmo Norseman #6654 - Third owner. Was originally anodized turquoise, but a previous owner had custom anodizing done by Valley Bladeworks. Was only ever disassembled to anodize. Never carried by me or previous owners. Never sharpened. Flawless condition, comes with Grimsmo hard case, docs, tool, COA, oil, and sticker. SV: 550
Chaves 229 Liberation Blackout - This was a Thanh V Nguyen exclusive. I'm the first owner. Has seen a bit of pocket time, but never cut anything. I disassembled to install skiff bearings. Easily one of the smoothest knives I have, feels like it runs on glass. Never sharpened. Comes with box, skull clip, Thanh's card, and original bearings. SV: 350
Microtech Socom Bravo S/E - First owner. Pre-ordered this when they were first announced. Has seen a lot of pocket time, but never cut anything. Small snail on the pocket clip, but everything else is flawless. Disassembled to clean. Never sharpened. Comes with box and docs. SV: 300
Pro-Tech TR-3 Magnacut - First owner. Only carried a couple times, managed to put a faint snail down the side of the clip-side scale. Never cut anything. Never sharpened, but came with a slightly uneven cutting bevel from the factory. Comes with box and pouch. SV: 250
PayPal Friends & Family only, no notes. CONUS only. Will try and get any that sell out USPS tomorrow, if not, Thursday at the latest.
Yolo's trump DMs/chat. Not interested in any trades at this time.

Timestamp: https://imgur.com/a/0SuNhBJ
See album for serial numbers and additional pics.
Howdy GAFS, please buy my stuff so I can buy more of your stuff! Dibs>PMs, I can accept PayPal f&f (g&s>15 flair, buyer pays fee), Venmo, and Zelle.
Scam warning: A user has created a private sub with my username and messaged a buyer in an attempt to scam them. If you call dibs on an item message me, I WILL NEVER MESSAGE YOU FIRST! Stay vigilant out there. Anyway, behold, my stuff:
Trijicon ACOG TA33 3x30 Amber Chevron with SLR mount. Older model that's supposedly lighter than the current ones (8.0 oz w/ mount on my kitchen scale), unknown DOM, tritium is dim but visible in my pitch black basement. Reticle is labeled "3x .223F" when looking through the optic - $700 shipped
Keyhole Forge Assembled A2 Upper Receiver. Got this for an abandoned blood diamond build, never shot by me. No M4 feed ramps. - $150 shipped
Primary Arms SLx Advanced 30mm Red Dot setup. Includes Leupold carry handle mount and low ring (either PA or Vortex, I don't remember). Also from abandoned blood diamond build, never shot by me. Not looking to split for now. - $150 shipped
Streamlight Protac HL-X package. Includes GG&G 1" ring mount and Amazon barrel mount modified to fit under a FSB. Yep, you guessed it, also from abandoned blood diamond build, never shot. Comes with original streamlight box but no batteries. - $90 shipped
Retro skinny carbine handguards with round cap and delta ring/barrel nut assembly. Had to file the front of the handguards to fit in the cap but it's not visible once installed. From abandoned blood diamond build? You know it. - $25 shipped
Set screw A2 Front Sight Base, .750 gas block journal. Includes installed front sight post and not installed sling swivel. Believe I got this from Dez Tactical. I'll take a dollar off if you can guess what build of mine this came from lol - $31 shipped
2x Knights Armament rail cover pouches. POUCHES ONLY. One marked Titusville, one Vero Beach. - $10 for both stand alone or $5 add-on
2x 1x milspec charging handles. $10ea stand alone or $5 add-on
PSA M4 waffle stock. $10 stand alone or $5 add-on

Paramount: Deal Rumors Aside, What About The Operations? Can The Company Turn A Profit?
May 21, 2024 2:41 PM ET
Summary
Paramount Global remains my worst investment, but I still believe in its potential for success.
Paramount's "lack of scale" is not the reason for its underperformance as it spends about as much on content as industry leader Netflix.
Paramount's loss last year was largely the result of one-off writedowns, both domestically and internationally. These losses will not repeat going forward.
Paramount's streaming operation suffers not from lack of scale, but an abundance of overload waste, which may be alleviated under new management.
The sports slate remains best-in-class, and CBS is still the leader in broadcast scripted. CBS Television City in Los Angeles, Ca, USA. JHVEPhoto/iStock Editorial via Getty Images
Paramount Global (NASDAQ:PARA) (NASDAQ:PARAA) remains my worst investment. Let's just get that clear from the start. I said two years ago it was madness not to buy Paramount. That was wrong, wrong, wrong. The fact that I recommended against buying the new Warner Discovery at the same time, and heeding that warning saved a lot of money, makes me feel a little better, but not much.
And no, the fact that Warren Buffett made the exact same mistake as me doesn't help much, either. Mr. Buffett and I are about to part ways, anyway. He is now completely sold out of Paramount, while I am hanging in. Yes, I am still buying. Get all the ribbing out of your systems, and then read on.
Although a few rounds remain to be played in the game, it is no longer inconceivable that none of the various deal permutations that have been put forward for Paramount will pan out, and that it will continue as an independent company. Essentially, Redstone will block a deal with Apollo Global Management, Inc. (APO) and Sony Group Corporation (SONY) and the 'B' shareholders will litigate a Skydance deal to death.
Because so many Seeking Alpha articles are already offering a blow-by-blow analysis of the deal talks - and I absolutely encourage you to read them - I wanted to turn back for just a minute to a more in-depth look at Paramount's actual operations. If it stays independent, can it turn itself around?
Scale Is Not The Issue I'm angry. Usually, when an investment goes wrong, I can manage to be philosophical or even dispassionate about it. Risks of the trade, can't win 'em all, pick your maxim.
But this one is really getting to me. I'm sure part of that is simply the sheer amount of my portfolio that has suffered - I bet a lot more on Paramount than I did on my typical investment, so sure was I that it had the tools needed for success. Fortunately, some of my other media investments have worked out much, much better, or I'd really be hurting. In fact, my Netflix buy has repaired all the damage my Paramount buy has done.
Still, I'm unusually angry, partly because of the sheer amount lost. But it's also that I still don't believe there is anything wrong with Paramount, at its core. It has become quite commonplace to speak of Paramount's "lack of scale" as the reason for its apparent impending demise, or at least subsumption. But I would still argue that that isn't born out by the numbers. Paramount spent roughly $16 billion on content in 2023, the same total as 2022, when $4 billion of it was spent on streaming. That is only slightly less than Netflix, Inc. (NFLX) which leads the industry in market cap and performance, if not in spending. While the gap with other studio peers is larger, I'm not sure spending at Netflix levels equals a "lack of scale."
What's more, a lot of that extra spending by other traditional industry players like Warner Bros. Discovery, Inc. (WBD) and The Walt Disney Company (DIS) is not spending that investors should necessarily cheer. As I've explained before, Paramount's lower spending total is almost entirely accounted for by its far more profitable approach to sports rights; a lot of that extra spending that Disney and Warner are doing isn't particularly profitable or even sensible.
What then, does account for Paramount's underperformance?
Stock Performance That depends on which underperformance you're talking about. First, the stock price. Paramount cut its dividend in spring 2023. That announcement, with its Q1 earnings, was enough to cause half of the past-year decline in a single day. Paramount went from $21 to $16 with the dividend cut and was still at $16 as late as December.
Since then, the other half of the decline has reflected the increasing evidence that Paramount is more or less ready to throw in the towel, and intends to be a distressed seller to another studio or private equity firm soon. More specifically, it is actually Shari Redstone, who exercises control over Paramount through her 77.3% share of Paramount's Class A voting stock, who is ready to call it quits. The perception that she has no leverage and will be forced to accept a fire sale offer has driven the stock lower.
Operations All that, however, merely explains the stock market decline; what is the operational explanation for Paramount's troubles? The company reported a $600 million loss for full year 2023. How is it that one of the Big Five movie studios, with the most popular of the Big Four broadcast networks, the most popular show on cable (Yellowstone) and the only profitable sports slate in American television, can't make money?
Accounting Element First, we need to acknowledge that there are some accounting factors in that 2023 loss. Paramount took a "programming charge," i.e., a write-down of the value of programming assets, of roughly $2.4 billion in Q1 and Q2 last year. That is money that would ordinarily be amortized over a period of years - it's mostly streaming originals, which Paramount usually amortizes over a 4-year period - that instead saw its red ink taken all at once. Had it been amortized normally, Paramount would have reported an operating profit of roughly $1.2 billion, more or less identical to 2022, instead of reporting an operating loss of the same amount.
Still, that write-down reflects the fact that the content isn't performing well, so those losses were always going to happen, and they're quite real; the accounting change is simply a timing issue. So Paramount is operationally deficient, even if perhaps not quite as operationally deficient as this one-time write-down makes it look. We cannot dismiss Paramount's operational issues by putting them down to accounting distortions.
TV Scripted Content Difficulties Paramount did not break down the programming charge, but outside reports have about half of it owing to the integration of Showtime in Paramount+ as a single service. It's not entirely clear which side of the ledger those losses are coming from; one of the less understood things about merging services is that it potentially makes content on both sides less valuable as it is replaced by more popular content from the other side. Showtime's Q1 2023 viewership was very top-heavy, with just two shows, Yellowjackets and Your Honor constituting 30% of all viewership. Presumably, those two shows reduced the value of some Paramount+ existing content while the rest of Showtime's library may have suffered from competition with P+ content.
Regardless of the exact source, Paramount's content is not performing. That's a little surprising considering that, as I said, CBS content is actually quite popular on the linear side. In fact, in the earnings call following the annual report now-former CEO Bob Bakish reported that CBS had the top 16 scripted programs and 18 of the top 20 in the first week of post-strike broadcasts. Paramount has disclosed in the past that CBS content makes up roughly half of the viewership on Paramount+; and this is despite the fact that P+ isn't even the sole beneficiary of CBS content; roughly $600 million per quarter of Paramount's licensing revenue comes from CBS shows as well.
One possibility that I perhaps did not consider sufficiently was the chance that the unique characteristics of CBS would make it harder for that channel to transition to streaming than its other broadcast peers. CBS is the most popular of all broadcast networks, but that popularity owes disproportionately to more elderly viewers; in the demo, it is actually Comcast Corporation's (CMCSA) NBC which takes the top crown.
With elderly viewers both less appealing to advertisers and less likely to make the transition to streaming, it is perhaps not so surprising that CBS is continuing to perform well on linear but having trouble translating that to streaming.
International Shortfall The damage isn't through yet, either. Paramount disclosed that it took another $1.2 billion impairment charge on content in the first quarter. This one has to do with the international side; a few years ago Paramount commissioned 150 new, original international shows and movies to try to boost international growth. Now, Paramount reveals that even international consumers spend no less than 90% of their time streaming Hollywood content; the local originals aren't doing very much for growth or retention.
About the only good thing that can be said about this complete and utter debacle is that it is a one-off; unlike Paramount's US content spending, which is ongoing and therefore must be made more efficient if Paramount is to survive and thrive, Paramount is gradually exiting International production. In fact, to help cover the losses on its international originals it is selling its share in Viacom18, the network that formerly served as Paramount's onshore operation in India, to its partner Reliance for a little over $500 million.
Where Are The Children? Yet another factor is children's programming. While many have essentially written off Paramount's entire cable channel group, and I agree the prognosis for MTV and Comedy Central is rather grim, I have argued that Nickelodeon remains a real asset, as one of the top two children's channels in linear TV. I believed that would be a powerful subscriber acquisition tool, alongside sports, as the streaming transition continued.
It hasn't worked out. Surveys consistently show the Big 3 for parents with children are Netflix, Disney, and the third is Amazon.com, Inc. (AMZN) of all things. Neither Paramount+ nor Warner Discovery's Max make the cut, despite ownership of top children's linear platforms/libraries Nickelodeon and Looney Tunes, respectively.
The prognosis here isn't entirely grim. Paramount has reported that half of their streaming subscribers touch kids' content regularly, so clearly Nickelodeon does mean something to the subscribers. It's possible it helps with retention, even if it doesn't drive acquisition. Paramount owns the number one brand for pre-school kids, Paw Patrol.
Paramount has shut down the separate Noggin streaming service and will presumably be amplifying the kids content on P+ as a result, so perhaps this trend will yet turn around. With so many other things going wrong, though, the inability to make kids content more central to the strategy is a painful blow.
The Mismanagement Of Streaming I suspect, however, that Paramount's single biggest defect over the past few years has been the competency of its management. In a streaming world, success hinges overwhelmingly on the efficiency with which a content budget is deployed. That efficiency, in turn, requires avoiding the trap of "overload," something cable doesn't have to worry about but which can kill a streaming service.
What Is Overload? In brief, overload is when a streaming service spends money on content that appeals primarily to those subscribers who were already subscribed and intending to remain subscribed, even without that content. Because revenue does not increase with more viewership, such spending is essentially wasted money. I have been arguing for several years that some economic models of streaming profitability fail to take account of this significant element.
Paramount seems to have had a lot of overload in the last few years. Specifically, its single most broadly appealing piece of content is the NFL, which Paramount is an anchor broadcaster for. Because NFL fans are accustomed to spending upwards of $100 a month on cable just to watch the NFL - over 10% of cable subscribers say that the NFL is the only reason they're still subscribing - Paramount's $6-$12 a month fee for streaming really doesn't need anything more than NFL games to attract these 40-50 million fans.
The Earnings Jaw-Dropper And yet, it seems that's where a lot of the extra streaming money has been going. On the Q2 earnings call last year, CEO Bob Bakish, watching the stock price steadily decline, seemed to be eager to reassure he had a handle on the situation and began describing some of the changes he'd be making. It started out well enough, really; he told investors that NFL viewers churn drops dramatically if they also engage with entertainment titles, which is what you'd expect.
But then, he stunned me and I expect just about everyone listening when he said, "we probably need to do less for [the NFL viewer] in the fall, and more outside the fall because we can rely on the NFL." Compounding the almost Looking Glass-feeling, he then went on to reassure everyone he would be "fine-tuning" the content strategy to address that point in the years to come.
It was, frankly, stunning. Both me and I suspect just about every analyst who was modeling Paramount had just assumed it went without saying that of course, any entertainment content targeted at retaining NFL viewers should drop in the other half of the year when the NFL wasn't playing on TV. My own calculations of the profit margin on CBS's NFL deal had always incorporated that.
And while that was bad enough, it also raised the concern that a management team that didn't understand that going in might have put a lot of other overload in other categories as well. Suddenly, it wasn't so hard to see how the best-scripted shop with the most profitable sports contracts was having trouble making money. Double-loading for 50 million households would be a major drag on the financial performance for streaming.
Light At The End Of The Tunnel Despite all of this, I still think there are bright spots in the Paramount picture, even without a merger. Its operations, as well as its merger discussions, don't seem to lack potential.

1: My Usual Paramount Bull Argument: Sports Profits

One thing that continues to go right is sports content. A few years ago, I wrote that Paramount was a strong contender to become a sustainable streaming business because it had the only profitable sports slate in the business. The stock hasn't gone where I wanted it to go, but that is the one part of my thesis that has been definitively borne out. In fact, many now say that it is CBS's sports deals, at least as much as Paramount's film/TV studio, that the prospective buyers of Paramount are after.
I've covered these in other articles already. The March Madness deal runs until 2032 and the NFL deal runs until 2033, although the NFL has an opt out after 2029 that it will probably exercise given the utterly ludicrous bids the NBA is receiving, so the last four years of that deal might have to be chopped off the profit projections. Even so, Paramount can probably generate $1.25 billion a year in profit just off of those two deals for the next six years.
Those are probably the biggest, but it doesn't stop there. Almost every sports deal Paramount has is profitable. For all the flak management has deservedly taken, Paramount continues to show discipline and focus on sports. You won't find Paramount throwing $2.5 billion a year at the NBA's 'B' package, which is more money than the NFL gets for its 'B' package despite having 10x the viewership.
For all its many, many missteps, a Paramount that can just manage to stay afloat long enough for some of these ludicrous sports bets at other companies to blow up may yet find itself with cards to play later in the decade.

2: Recouping Write-downs Via Preferred Conversion

Another small boon has been the official conversion of the preferred shares. The Paramount mandatory convertible formerly trading under the PARAP ticker was capped at 0.85 shares per common share. Given the initial price of the convertible at $100 per share, that effectively means that a preferred share that was carrying a $100 liquidation value has just been converted into 1.1765 shares of a common stock currently trading around $12. A total value per preferred share of around $14.
And they sold for $1 billion, so that's basically $860 million back into the common equity that management was able to get at the peak of the boom. That actually repairs almost all of the red ink from Paramount's doomed international originals push on its own.

3: An End To Streaming Waste

Finally, a lot of the waste in streaming may soon be ending. Bob Bakish was finally fired a few weeks ago, and while I never want someone to lose their job, he frankly had looked overmatched for a while. Bakish was a lifetime cable executive who seemed to be having trouble making the transition to a streaming-world mindset. Frankly, if I knew about it in 2021, the CEO has no business fine-tuning it into the strategy in 2023.
With international originals no longer draining the coffers and overloaded entertainment programming shifted to months of the calendar where it can be more productive, streaming may yet turn the corner.
Investment Summary I recognize fully that each fall in Paramount stock makes my bullish optimism seem ever more out of step. I do believe, however, that Paramount's failures are more failures of execution than lack of scale or structural disadvantage. Paramount CEO Bob Bakish simply wasn't up to the job. Ironically that wasn't what got him fired; Bakish was almost certainly fired for opposing Redstone's plan to enrich herself at the expense of other shareholders, probably the most competent thing he did in the last few years of his whole tenure.
Paramount has everything it needs to be successful; profitable sports contracts, which is just unbelievable in this day and age, a thriving scripted TV operation, and a viable, if recently somewhat mismanaged, streaming service. An end to overload waste, the continued exploitation of its favorable sports slate, throttling back unhelpful international originals and boosting kids content engagement may yet produce a different streaming picture going forward. Should older viewers start to get more comfortable with streaming going forward and following their favorite programs to Paramount+, that would just be icing on the cake.
It's been a depressing ride the last few years, but I'm sticking with Paramount.

Hi, If someone could help me it would be greatly appreciated. I imported a huge model and scaled it down, but now when I zoom into the model its almost like its still huge and the model disappears when zooming in. Is there something I can do to reset the view point.

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Forbes isn't content with mediocrity – he's a visionary leader with a keen eye for innovation and efficiency. Whether he's leading financial and accounting programs or overseeing large-scale budgets and projects, Forbes consistently delivers cost-effective solutions without compromising on quality.

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Financial management isn't just about crunching numbers – it's about strategic planning, effective communication, and above all, integrity. Forbes embodies these principles with finesse, navigating complex financial landscapes with ease and grace.

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My company upgraded me from a 16" M2 Max to a 14" M3 Max (unbinned 16/40 cores) and my SD generations (same pictures for testing) have not improved (they're the same or slower). I am using torch==2.1.2 nightly with MPS support and have applied all the recommended tricks on both machines, in any case: they're exactly the same performance:
(((Drawn by Alena Aenami))), Intricate details in serene landscapes, peaceful, Alena Aenami's artistry. Ethereal sky, gentle cloud formations, soothing light rays dance across a breathtaking tranquil beach scene; soft sand meets the ocean waves beneath an azure blue horizon. Nature's canvas: smooth, flowing lines, soft colors blend harmoniously to evoke emotions. Serenity permeates this enchanting setting. Negative prompt: human,face,person,people,boy,girl,woman,man, signature,logo,text,letters,username Steps: 30, Sampler: DPM++ SDE, Schedule type: Karras, CFG scale: 7, Seed: 2545687968, Size: 640x1536, Model hash: d91d35736d, Model: juggernautXL_juggernautX, Clip skip: 2, Version: v1.9.3
Time taken: 1 min. 37.8 sec.
M2 Max -> 3.4 it/s M3 Max -> 3.35 it/s
About 90s every time. I'm seeing this is a pretty average score for A1111 so I'm not worried about that, just wondering if anyone has had the same experience, or if the difference is actually the 16" -> 14" thermals downgrade (smaller chasis=hotter=throttle?)
Using Vlad's Benchmark I get: https://vladmandic.github.io/sd-extension-system-info/pages/benchmark.html
Benchmark Data with different parameters / torch versions...
Benchmark Data

timestamp	performance	version	system	libraries	gpu	pipeline	model	username	note	hash
2024-05-22 00:02:39.068635	2.7 / 2.57 / 2.63	app:stable-diffusion-webui updated:2024-04-22 hash:1c0a0c4c url:https://github.com/AUTOMATIC1111/stable-diffusion-webui/tree/master	arch:arm64 cpu:arm system:Darwin release:23.5.0 python:3.10.14	torch:2.1.2 autocast half xformers: diffusers: transformers:4.30.2	0GB	Automatic none	rundiffusionXL_beta.safetensors [f3efadbbaf]	martin	bdf89e
2024-05-21 23:23:21.513603	2.24 / 2.1 / 2.18	app:stable-diffusion-webui updated:2024-04-22 hash:1c0a0c4c url:https://github.com/AUTOMATIC1111/stable-diffusion-webui/tree/master	arch:arm64 cpu:arm system:Darwin release:23.5.0 python:3.10.14	torch:2.1.2 autocast nohalf xformers: diffusers: transformers:4.30.2	0GB	Automatic none	rundiffusionXL_beta.safetensors [f3efadbbaf]	martin	0a0725
2024-05-22 00:15:58.666952	2.37 / 2.6 / 2.5	app:stable-diffusion-webui updated:2024-04-22 hash:1c0a0c4c url:https://github.com/AUTOMATIC1111/stable-diffusion-webui/tree/master	arch:arm64 cpu:arm system:Darwin release:23.5.0 python:3.10.14	torch:2.4.0.dev20240521 autocast half xformers: diffusers: transformers:4.30.2	0GB	Automatic none	rundiffusionXL_beta.safetensors [f3efadbbaf]	martin	7a3161

Anyone that can test for me on their 16" M3 Max (16/40)?

When theorising in astrophysics, and more specifically the beginning and expansion of the universe (the big bang), the phenomenon of black holes and the mysteries behind dark matter and dark energy, we mistakenly use the two concepts interchangeably, they being 1. The philosophical zero, ie nothingness, and 2. The mathematics concept of zero, ie summarily attaching the philosophical concept of zero, with a numerical designation because it greatly assists the functionality of mathematic in the paradigm of our observable reality. Also please bearing mind the the math concept of zero allows negative values, unlike the philosophy, and when discussing space time, there is no place for negative numbers. Now that said and made clear, I believe it impossible for us to attempt to explain the unimaginable, without separation and distinction of the two concepts when infinity is brought into the conversation. Infinity, like zero, is a philosophical concept not all that dissimilar to zero, it has a mathematical conceptual basis also, but unlike the philosophical zero ie nothingness, it is given a numerical designation that more accurately relates to it philosophically, even though it is not strictly a number and cannot he used in any tangible calculation, mathematics certainly allows for its philosophical manifestation into mathematical equations.
I see this as a huge conflict, especially as it the very essence of big bang theory ie from nothing came infinity at the moment of the big bang. We surely cannot hope to solve this puzzle if the universe if we are conceptually flawed at he very starting point. 0 and infinity therefore cannot be used together in any calculation because we haven't yet reconciled the two conceptually. How can we look at this differently? How can we navigate passed mathematical dogma a concepts our brains are not capable of truly understanding on a practical level?
These are very tough existential and philosophical problems. So rather than just critise the current order and point out what I see as conflicts, but offer no alternative, may I suggest at least a starting point for discussion and exploration which may be found in the area of fractal science/mathematics. And a good place to focus the beginning of any theoretical discourse, in my opinion would be the work of, amoung others, Mandelbrot and the set named after him, the Mandelbrot Set. I suggest this because it mathematically the most relevant area of a field and is largely avoided by the popular culture's interest in fractals which is largely forcused on aesthetical beauty, particularly popular with the psychedelic subculture which I believe has a place and time to be investigated as a part of the whole discussion, but maybe for now should not be a point of focus in fear of contaminating a sterile discussion with with larger philosophically arguments about what is reality, due to the psychedelic substance insights of those schools. To incorporate any talk of altered states of consciousness, may be counter productive at this time. Hence my Mandelbrot suggestion achieves the mathematical parameters I believe are the best for theoretical mathematical support and cancels out the annoying noise that the fractal science field makes due to its attractiveness to non mainstream pop culture.
The Mandelbrot set is intriguing, not only because of the almost mindblowing graphical capability of AI, for as the Mandelbrot set seen by AI and then visualised for us to see, is nothing short of unbelievably beautiful. It also has an ability to provoke inner hought and discourse without one knowing the subject, topic or reason for the quiet peaceful internal discourse it inspires. I mention this, not because it can directly be incorporated in the radical discussion, but experiencing the astonishlng complexity if its beauty and the way it continues infinitely (or rather as long as it continues to be observed) because if the very self contained nature of the Mandelbrot set. After all, the mandelbrit set is simply a set of numbers, that when placed in the equation, do not spiral out into infinity. The equation value is always >0 or <2. So what as I see as irony, as the set was designed to avoid infinity in a sense, what it actually does is provide us with the best possible conceptualisation, in practice and theory, of a model demonstrating infinity. Even more bizarre is that the technology had only recently become available to show us visually by AI graphing. I'm not sure without the visual stimulus, could we have seen how beautifully fractal science demonstrates its potential unravelling existential and theoretical mysteries. I believe it lends itself perfectly to my proposed theory.
I think so because infinity is difficult for the human mind to grasp, some may say impossible. What is even more difficult for our minds to grasp is infinity of reducted values. Basically, if infinity can exist in an expanding sense, then it is not a stretch of conceptualisation to think it can infinitely get smaller. This breaks no rules of science. Searching for the building block of the universe has been crusade of quantum physicists dating back to the creation of the scientific theory. Much money and effort has been spent in search for smaller and smaller still subatomic particles. What they do is fractal science in its purest form, yet like with the study of Theoretical Astrophysics, quantum science theory is its self hampered by the concept if zero as a number, in my opinion. Even doing the work of factually reducing matter, they are blinded to its possible futility, should fractal reduction does forever decend in to fractal infinity or -ve infinity. For if that is the case, maybe quantum theory is in fact mankind's first exploration, albeit unknowingly, into what may well be a black hole. That is for a later discussion but certainly worth bringing to the attention for the purposes of this discussion.
So if we can accept that -ve infinity is as equally viable and logical as +ve infinity, what room in this discussion is left for the inclusion of zero? 1/r = 1/0 = infinity is a useless equation in understanding black holes. To say 1/r approaches infinity as r becomes closer to 0, is a much better way of phrasing it. It is in essence exactly what I refer to +be and -ve infinity and immensely helpful in understanding black holes, there's no equation that mathematics can put forward it's anywhere near as helpful because mathematics has to use zero and by its own rules and attempt to explain a fractal infinity is undefined.
I believe this leaves no room for zero in the same conversation as infinity. I am not suggesting zero should be stricken from mathematics. However I am suggesting that mathematics use of zero may, invalidate is ongoing use as an effective tool for measurement and communication, when the subject is beyond our ability to comprehend. Few people will argue that our 6 senses are significantly lacking the capacity to comprehend cosmological dynamics. Mathematics nothing more than an application of our 6 senses, to make sense of the chaos occurring all around us. Over hundreds and thousands of years, this is the best we have been able to do in terms of calculation and accurate prediction of future occurances. Even the concept of zero is less than 1000 years old. The Romans saw no use in incorporating it into their model, and to this day we wonder at their and other ancient civilisations ability for astonishing accuracy in measurement and prediction. Look at all they, the Egyptians and others managed without using zero in a single calculation. We can also break this down out of our conscious paradigm into nature. Numbers do not exist in nature (that includes the universe). It begs the question of do numbers really exist? Are numbers no more than part of our delusional reality? Who knows, but one thing is for sure, zero certainly doesn't exist anywhere outside of our consciousness. Not even in our own very bodies. How bodies clearly hold a knowledge that hasn't seemed to be passed over to our conscious, aware selves. Even on the smallest scale, without any intervention or guidance from any sort of intelligence, within our cellular membranes. Complex calculations are constantly being carried out. Consider cellular replication for example. In order for a cell to divide successfully, there must be a correct allocation of resources, let's just say primarily energy distribution for the sake of brevity. To split a cell but calculate the energy necessary to simply cary put the force of splitting. It must also calculate how much energy needs to be transfered to the new cell. This calculation must include how much energy for it take to replicate all cellular matter, how much energy is required for both cells to recover from the trauma, and how much energy on top of that, the new cell will require to become mature and begin its own replication. There are multiple complex calculations to be made there, and they then must be combined in to an overall and more complicated calculation again. All of this is done without intelligence and without using numbers ie mathematics. This same process can be observed all the way back to the very first beginning of not just life, but biochemistry in general. So I hope this demonstrates that the universe doesn't exist numerically. There are no rules in the universe. We created rules for our sciences, because if we didn't follow them the sciences would fail at unacceptable percentage of predictions.
So we make rules to overcome the shortcomings while waiting for future technologies or fixes. Mathematics and sciences are little more than a carefully ordered tapestry of rules, with too many exeptions for too many rules. We create rules and ideas to assist the conteived & malfunctioning intangible thing to not have to go to all the trouble of finding something that works better. We are just littered with examples through every field. Like 'zero', or Pi, or "bimdas" (brackets, indicies, multiply, division, addition and subtraction. I find this a good example, for not following this exact order of calculations, a correct answer to equation will nev a result) and thousands of others.
No rules exist in nature, it appears to be that it just is, always will be and always has been.
So in summary, given our restrictions on trying to understand the universe, namely intelligence, our 6 limited senses, our arrogance and our mortality, should we narrow the pursuits our restrictions can make us comfortable with? Thisbwilk lead us nowhere. By abandoning zero as the only accepted scientific approach to the universe, and allowing science to have multiple validating throeries for what is the same problem. The scientific community abhors divergence from dogma and academics are held to ransom with funding or being published, if their ideas are not with acceptable parameters.
But for the sake of this conversation, can we discuss the merits of looking at the big bang without reference to nothing ie zero, but instead +ve and -ve infinity.

Time Stamp
https://imgur.com/a/M7QAbGz
NO TRADES PLEASE.

1. SPK lamia gen 2 cruwear. Second owner. I disassembled, completely cleaned, re-lubricated with non-fluorinated lube, tuned the pivot to be flickible with no blade play, and applied blue Loctite. Comes with a purple anodized pivot that I didn’t care for. Also comes with alternate polyurethane washers. Knife is in perfect shape and comes with pouch and sticker. Never sharpened, carried once. Centered. SV $700

STILL AVAILABLE. IS GEN 2 not 1
https://imgur.com/a/OCB5Vbg

1. Benchmade mini cricket crooked river. Bench made custom shop with carbon fiber, scales, copper pivot, S 90 V. like a lot of bench maids, this one was not centered. In the process of disassembling to correct this, which I’ve done many times with benchmades I stripped a t6 screw near the blade. All other screws were them near the blade. All other screws were them replaced with titanium screws were then replaced with titanium screws. However, blade is now perfectly centered, action is very good. Never sharpened. Carried once.

No scratches or dings. I miss placed the box, but comes with the blue pouch.
SV $180 obo
https://imgur.com/a/SwNX18r

1. Pena X Mula with sheepsfoot m390 and micarta inlay. Production model manufactured by Reate. Extremely smooth action and drop shut. Never disassembled. Touched up on ceramic. This one was carried quite a bit with some scratches on the milled titanium clip, but the blade and handle are in excellent shape, like new. This one comes with the box and cleaning rag.

SV $225 obo
https://imgur.com/a/6kHZxj7

1. Chaves RCK-9 production model. Full titanium. M390. Comes with skull clip and box. I am the first donor, carried once. Never sharpened. Perfect action.

SV $250 obo
https://imgur.com/a/eEodAep

1. We Esprit. 20cv with flamed titanium handle. This one was also carried moderately and has some snails on the titanium anodization. Blade is perfect, action crisp, drop shut. Touched up on ceramic. Never disassembled. This one does not come with the box.

SV $175 obo
https://imgur.com/a/tRe5mTI

1. Asher spiro acuto I’m s35vn. Comes with pouch. Never disassembled or modified. Carried <3x

SV $115 SOLD
https://imgur.com/a/wiOVYyN
All prices are slightly negotiable. Anyone who bundles two or more will get a free Land Small Sebenza Clone in S 35 VN that has a light scratch on the blade, pics available at request. No, this is not for straight up sale.
All will be shipped USPS priority. PayPal only. YOLO trumps all.

how can i make a tool that resizes and scales parts and models kinda like in bloxburg

Hey guys, I've been painting my scale models with brushes. I'm looking forward to buy an airbrush, but I don't know which one. I also watched some videos and now I'm here to ask you guys: Which airbrush do you recommend and which stuff should I buy if I want to buy an airbrush? For example colors thinners etc. Price range:100€/100$, would be great if it was below that. Thanks!