Podcasts about storing

The National Transport Authority has revealed it is spending nearly €20,000 a week on the storage of 98 electric buses that have still not been deployed to the national fleet due to a lack of charging infrastructure in Galway and Dublin depots. We get the details from Aisling Moloney Political Reporter, Irish Independent.

The National Transport Authority has revealed it is spending nearly €20,000 a week on the storage of 98 electric buses that have still not been deployed to the national fleet due to a lack of charging infrastructure in Galway and Dublin depots. We get the details from Aisling Moloney Political Reporter, Irish Independent.

The Mount // Week 8 // Misconceptions of Storing, Seeing & Serving Pastor Ashley Wilkerson Matthew 6:19-21 NIV  19 “Do not store up for yourselves treasures on earth, where moths and vermin destroy, and where thieves break in and steal. 20 But store up for yourselves treasures in heaven, where moths and vermin do not destroy, and where thieves do not break in and steal. 21 For where your treasure is, there your heart will be also. YOUR STORING - Recognize what you value. Matthew 6:21 NIV  21 For where your treasure is, there your heart will be also. YOUR STORING - Recognize what you value. YOUR SEEING - Recognize your worldview. Matthew 6:22-23 NIV  22 “The eye is the lamp of the body. If your eyes are healthy, your whole body will be full of light. 23 But if your eyes are unhealthy, your whole body will be full of darkness. If then the light within you is darkness, how great is that darkness! Ayin Tovah - Hebrew Idiom of “Good Eye”: Refers to the practice of seeing the world through a perspective of generosity and compassion; focusing on the good. Ayin Ra'ah - Hebrew Idiom of “Bad Eye”: Refers to the practice of seeing the world through scarcity, selfishness, greed, envy; focusing on the negative. Matthew 6:24 NIV  24 “No one can serve two masters. Either you will hate the one and love the other, or you will be devoted to the one and despise the other. You cannot serve both God and money. YOUR STORING - Recognize what you value. YOUR SEEING - Recognize your worldview. YOUR SERVING - Recognize your devotion. Deuteronomy 8:18a NIV  18 But remember the Lord your God, for it is he who gives you the ability to produce wealth… 1 Timothy 6: 2b-10 NIV 2b These are the things you are to teach and insist on. 3 If anyone teaches otherwise and does not agree to the sound instruction of our Lord Jesus Christ and to godly teaching, 4 they are conceited and understand nothing. They have an unhealthy interest in controversies and quarrels about words that result in envy, strife, malicious talk, evil suspicions 5 and constant friction between people of corrupt mind, who have been robbed of the truth and who think that godliness is a means to financial gain. 6 But godliness with contentment is great gain. 7 For we brought nothing into the world, and we can take nothing out of it. 8 But if we have food and clothing, we will be content with that. 9 Those who want to get rich fall into temptation and a trap and into many foolish and harmful desires that plunge people into ruin and destruction. 10 For the love of money is a root of all kinds of evil. Some people, eager for money, have wandered from the faith and pierced themselves with many griefs. Hebrews 13:5 NIV  5 Keep your lives free from the love of money and be content with what you have, because God has said, “Never will I leave you; never will I forsake you.” YOUR STORING - Recognize what you value. YOUR SEEING - Recognize your worldview. YOUR SERVING - Recognize your devotion.

As technology continues to evolve, so does the way our personal information is collected and stored. We look at the massive new data centres being built around the world and the concerns some experts are raising about privacy, security and the future of digital information. Just how much data is being kept on us and who has access to it? The answer might surprise you!See omnystudio.com/listener for privacy information.

This week marks the start of our summer Listener Mail series, where we answer your questions, share our opinions, and dive into some of the topics that matter most to home growers and cannabis enthusiasts. In this episode, we discuss drug driving tests, how they work, the challenges they present for cannabis consumers, and some practical considerations for staying on the right side of the law. We also share our thoughts on the current testing system and the issues many medical and recreational consumers have with it. We then move on to growing, answering questions about the best way to run a perpetual grow using autoflowers, including how to keep a steady supply without overcomplicating your setup. We also cover caring for worms in living soil systems, what they need to thrive, and how they contribute to a healthy soil ecosystem. Finally, we tackle nutrient storage, discussing how long different nutrient solutions can be kept after mixing, and the key differences between storing salt-based nutrients and organic feeds. As always, it's a relaxed and informative session packed with practical advice, grow talk, and a few laughs along the way. If you've got questions you'd like covered in future Listener Mail episodes, be sure to send them in!

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

Guest speaker Eddie VanDyke shares a powerful lesson from 1 Timothy 6 and Philippians 4 on true wealth, contentment, and pursuing the things of God during this 5th Sunday service.In a world focused on money, possessions, and status, Scripture reminds us that “godliness with contentment is great gain.” This lesson explores the dangers of the love of money, the importance of generosity, and how Christians can learn to be content in every circumstance through Christ.Topics covered include: 1 Timothy 6:3–21  Philippians 4:10–13  The pursuit of riches vs. the pursuit of godliness  Learning contentment through faith  Storing up treasure in heaven  Generosity and eternal perspective  “I can do all things through Christ who strengthens me” This encouraging message reminds us that true life is not found in material success, but in faithfully following Christ.

Dahlias are bold, flamboyant, sometimes slightly outrageous - and one of the great stars of the late summer garden.But they can also feel a little intimidating. When do you plant them? Which varieties are easiest to grow? Do you have to lift the tubers every winter? How do you support them, feed them, protect them from pests, and keep them flowering into autumn?In this episode, Rod Whiting is joined by Dahlia expert Kim O'Brien - Head Gardener at an RHS-funded garden in Cambridgeshire, flower grower, floral designer, BBC local radio gardening expert, regular Gardeners' World speaker, and columnist for Amateur Gardening magazine.Her passion for Dahlias really shines through, and the tips keep on coming.

Episode Summary In this episode of Prosperity Thinkers Podcast, hosts Spencer Shaw and Kim Butler break down one of the most misunderstood financial topics of 2026: where to safely store cash in an unpredictable economy. As markets fluctuate and interest rates remain uncertain, Kim explains why cash is more than an emergency reserve — it's a strategic tool for solving problems and capturing opportunities. The conversation explores why many families are underprepared financially, the importance of emergency and opportunity funds, and why whole life insurance policies from mutual insurance companies can function as a powerful long-term cash asset. The episode also dives into the "time value of money," borrowing against cash value instead of withdrawing savings, and why comparing loan interest rates incorrectly creates confusion in online financial conversations. Spencer and Kim challenge modern "bro finance" narratives and explain why wealthy individuals and institutions often maintain larger cash positions than most people realize. This episode is a practical discussion about liquidity, flexibility, leverage, and financial preparedness in uncertain times. Links & Resources For resources and additional information of this episode go to Empower Your Finances With Our Prosperity Podcast Empowering Parents, Nurturing Futures - Prosperity Parents Kim D. H. Butler Keywords Cash flow Whole life insurance Emergency fund Opportunity fund Financial freedom Cash value insurance Infinite banking Liquidity Time value of money Passive wealth strategy Wealth preservation Interest rates Financial preparedness Investment strategy Borrowing against assets Mutual insurance companies Compound interest Financial education Real estate investing Wealth building Episode Highlights 00:00–00:40 – Spencer introduces the episode by discussing the uncertainty of the 2026 market and interest rate environment. 00:00–01:05 – Kim explains why cash is essential for both emergencies and opportunities. 00:01–02:20 – Discussion on why most families lack properly funded emergency and opportunity funds. 00:02–03:00 – Kim shares why some investors should hold up to 40% of their assets in cash. 00:03–03:34 – Mutual life insurance companies are introduced as strategic cash storage vehicles. 00:03–04:27 – Spencer references Berkshire Hathaway's massive cash holdings to support the concept. 00:04–05:14 – Difference between inaccessible cash and usable cash value inside whole life insurance. 00:05–06:25 – Kim explains the "time value of money" and why withdrawing savings interrupts compounding growth. 00:06–07:04 – How borrowing against life insurance cash value works in practice. 00:07–08:03 – Real estate down payment example using policy loans while preserving asset growth. 00:08–09:01 – Warning against comparing the wrong interest rates in financial strategies. 00:09–10:21 – Kim breaks down the four financial "lanes" people confuse when evaluating cash value strategies. 00:11–12:00 – Discussion about why life insurance policy loans cannot suddenly be called due like traditional leverage. 00:12–12:41 – No approval process required for borrowing against life insurance cash value. 00:13–14:14 – Final takeaway: build a strong financial foundation instead of chasing temporary financial hacks.

A listener asked for a deep dive on dahlias and I'm delivering, from the ground up (pun intended). But we're not stopping there. In this episode, I'm covering the full world of spring-planted bulbs: dahlias, gladiolus, cannas, calla lilies, and tuberous begonias. You'll learn when and how to plant them, how to grow them for maximum blooms, how to cut them for the vase (because that's half the fun), and, critically, how to handle end-of-season care based on where you live. If you're in a warmer zone, some of these can stay in the ground. If you're in a colder zone like me, we're digging them up, curing them, storing them, and doing it all again in the spring. Let's dig in! Quick-Reference: Zone-Based Overwintering Guide Zone 9-10+: Leave everything in the ground. Mulch lightly after frost. Let plants rest and re-emerge in spring. Zone 8: Cannas and callas can stay with heavy mulch. Dahlias and glads: consider digging, especially in colder parts of the zone. Zone 7: Mulching is a gamble. Reliable: dig dahlias and glads. Cannas may survive with very heavy mulch in milder Zone 7. Zone 6 and colder: Dig everything. Cure properly. Store in cool, dry, dark, frost-free conditions. Replant after soil warms to 60°F in spring. Bloom Timing and Vase Life at a Glance Dahlias — Bloom: midsummer to first hard frost | Vase life: 5–8 days | Zone to leave in ground: 9+ Gladiolus — Bloom: ~70–90 days after planting | Vase life: 7–10 days | Zone to leave in ground: 8+ (with mulch) Cannas — Bloom: midsummer to frost | Vase life: 4–7 days | Zone to leave in ground: 8+ (with mulch) Calla Lilies — Bloom: summer | Vase life: 10–14 days | Zone to leave in ground: 8–9+ depending on type Tuberous Begonias — Bloom: summer all season | Vase life: 3–5 days | Zone to leave in ground: 9+ References University of Minnesota Extension — Dahlias: From Garden to Vase. extension.umn.edu University of Missouri Extension — Growing Dahlias (G6600). extension.missouri.edu NC State Extension Gardener Toolbox — Dahlia pinnata. plants.ces.ncsu.edu Colorado State University Extension — Dahlias, Fact Sheet 7.418. extension.colostate.edu Iowa State University Extension — Gladiolus for the Home Garden (PM 874). extension.iastate.edu University of Florida IFAS Extension — Gladiolus Production. edis.ifas.ufl.edu University of Illinois Extension — Canna Lily in the Garden. web.extension.illinois.edu Michigan State University Extension — Digging and Storing Tender Bulbs. canr.msu.edu University of Vermont Extension — Storing Tender Bulbs Over Winter. uvm.edu USDA Plant Hardiness Zone Map (2023). planthardiness.ars.usda.gov Just Grow Something: https://justgrowsomething.com Gardening Courses: https://justgrowsomething.com/courses Just Grow Something Merch and Downloads: https://justgrowsomething.com/shop Just Grow Something Gardening Friends Facebook Group: https://www.facebook.com/share/g/18YgHveF5P/ Check out how you can become a patron on Patreon: https://www.patreon.com/JustGrowSomething Feed my coffee habit: https://buymeacoffee.com/justgrowsomething Amazon storefront: https://www.amazon.com/shop/justgrowsomething Get 10% off and FREE shipping on my favorite raised planters at Planter Box Direct using code JUSTGROW10: https://planterboxdirect.com/?ref=593 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This is the abbreviated version of Checkup's call-in about the boxes, bins and forgotten belongings adult kids have left behind. This episode looks at how families negotiate inter-generational storage wars and how to decide what stays, what goes and who's responsible for it all.

Spring cleaning is stirring up a familiar family tension: the boxes, bins and forgotten belongings adult kids have left behind. This episode looks at how families negotiate these cross-generational storage wars and how to decide what stays, what goes and who's responsible for it all.

A 'climate battery' system helps plants thrive all winter – no fossil fuels needed. Learn more at https://www.yaleclimateconnections.org/ 

This Week In Startups is made possible by:Pilot -⁠ https://Pilot.com/TWIST⁠Grasshopper Bank -⁠ https://Grasshopper.bank/TWIST⁠Quo -⁠ https://Quo.com/TWiST⁠Plaud - ⁠https://Plaud.ai/twist⁠Anthropic just declared every unauthorized secondary sale of its stock "void" — naming Hiive, Forge, Sydecar, Upmarket, and others in a public hit list. Jason and Alex sit down with Jenny Fielding (Everywhere Ventures), Dave McClure (Practical VC), and Sam Lessin (Slow Ventures) to unpack what the AI lab's move to limit secondary trades means for SPV operators, brokers, and the founders trying to keep control of their cap tables. Plus: a real story of a founder who returned a $15M Series A six months after closing because Claude was going to eat his startup, SaaS moats, and just what does it mean to be rich?Timestamps:0:00 Guest introductions0:48 Anthropic voids unauthorized SPV trades8:41 Accredited investor reform & the SEC sophisticated investor test8:58 Quo (formerly OpenPhone) - Quo gives you a clean, modern way to handle every customer call, text, and thread all in one place. Try it free at https://quo.com/TWiST11:43 Naval's USVC closed-end fund as a workaround16:41 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at https://Plaud.ai/twist and use code TWIST for 10% off!17:48 Pro-rata rights battles: when Series A investors push seed investors out19:36 Grasshopper Bank: Time is money. Don't waste either. Go to https://grasshopper.bank/twist and get an exclusive $500 cash bonus just for opening an account.29:13 Pilot: Focus on your product, let Pilot handle your bookkeeping. Pilot provides the most reliable accounting, CFO, and tax services for startups and small businesses. Head to https://pilot.com/twist and get $1,200 off your first year.30:23 Storing wealth in stories vs. cash flows34:19 Cerebras and Fervo Energy IPOs — meaningful liquidity?37:54 Will SpaceX, Anthropic, OpenAI IPOs redistribute capital or compound it?45:58 The $15M Series A founder who returned the money because of Claude50:01 Should founders pivot or return capital when the world changes?56:43 OpenAI's $6.6B tender and Shruti Gandhi's viral SF cost-of-living tweet1:00:25 Intercom rebrands to Fin: the AI-first late-stage pivotSubscribe to the TWiST500 newsletter:⁠ https://ticker.thisweekinstartups.com⁠Check out the TWIST500:⁠ https://www.twist500.com⁠Subscribe to This Week in Startups on Apple:⁠ https://rb.gy/v19fcp⁠Follow Lon:X:⁠ https://x.com/lons⁠Follow Alex:X:⁠ https://x.com/alex⁠LinkedIn:⁠ ⁠https://www.linkedin.com/in/alexwilhelm⁠Follow Jason:X:⁠ https://twitter.com/Jason⁠LinkedIn:⁠ https://www.linkedin.com/in/jasoncalacanis⁠Check out all our partner offers:⁠ https://partners.launch.co/⁠Great TWIST interviews:⁠ Will Guidara,⁠⁠ Eoghan McCabe⁠,⁠ Steve Huffman⁠,⁠ Brian Chesky⁠,⁠ Bob Moesta,⁠⁠ Aaron Levie⁠,⁠ Sophia Amoruso⁠,⁠ Reid Hoffman⁠,⁠ Frank Slootman⁠,⁠ Billy McFarland⁠Check out Jason's suite of newsletters:⁠ https://substack.com/@calacanis⁠Follow TWiST:Twitter:⁠ https://twitter.com/TWiStartups⁠YouTube:⁠ https://www.youtube.com/thisweekin⁠Instagram:⁠ https://www.instagram.com/thisweekinstartups⁠TikTok:⁠ https://www.tiktok.com/@thisweekinstartups⁠Substack:⁠ https://twistartups.substack.com⁠

In this episode of For the Love of Weather, we're chatting to Sally Sattary and Tom Cox from ‘Decent Energy' about renewable energy, smart technology, and how the weather plays a huge role in powering our homes

Chronic stress belly fat is more than a feeling — it changes your hormones, metabolism, and cravings. Nurse Doza breaks down 5 specific mechanisms — cortisol, insulin resistance, leptin dysfunction, fatty liver, and late-night eating — explaining exactly why belly fat won't budge and what to do about it. FEATURED PRODUCT:  The Metabolic Pack from MSW Nutrition is the complete protocol for everything covered in this episode. Liver Boost combines turmeric (curcumin), green tea extract (EGCG), NAC, and alpha-lipoic acid to reduce inflammation, support liver detoxification, and combat insulin resistance at the cellular level. Mitochondriac delivers resveratrol and key mitochondrial support to activate AMPK, improve cellular energy, and lower inflammatory markers linked to chronic stress and weight gain. Zen contains an adaptogenic blend with Rhodiola, Ginseng, Eleuthero, Schisandra, and bovine adrenal glandular support — specifically designed to regulate cortisol and protect adrenal health. Together, these three supplements form a 60–90 day metabolic reset built to address the root causes of chronic stress belly fat.

Chronic stress belly fat is more than a feeling — it changes your hormones, metabolism, and cravings. Nurse Doza breaks down 5 specific mechanisms — cortisol, insulin resistance, leptin dysfunction, fatty liver, and late-night eating — explaining exactly why belly fat won't budge and what to do about it. FEATURED PRODUCT:  The Metabolic Pack from MSW Nutrition is the complete protocol for everything covered in this episode. Liver Boost combines turmeric (curcumin), green tea extract (EGCG), NAC, and alpha-lipoic acid to reduce inflammation, support liver detoxification, and combat insulin resistance at the cellular level. Mitochondriac delivers resveratrol and key mitochondrial support to activate AMPK, improve cellular energy, and lower inflammatory markers linked to chronic stress and weight gain. Zen contains an adaptogenic blend with Rhodiola, Ginseng, Eleuthero, Schisandra, and bovine adrenal glandular support — specifically designed to regulate cortisol and protect adrenal health. Together, these three supplements form a 60–90 day metabolic reset built to address the root causes of chronic stress belly fat.

You've probably seen the headlines about luxury investments outperforming the stock market… but is that actually true? And more importantly, is this a game only for millionaires, or is there a way for the rest of us to get in on it too? Today, Nicole is joined by Dana Auslander, former Blackstone executive and founder of Luxus, a luxury alternative asset manager with the first dedicated Hermès Birkin fund. In this conversation, Dana unpacks the viral headlines, why her investment thesis puts Hermès bags ahead of other luxury brands like Chanel and Louis Vuitton, and how to invest in a Birkin without buying a Birkin. Then, Nicole and Dana zoom out and explain what the luxury investment trends mean for retail investors, how the macroeconomy impacts luxury investments, and what the counterfeiting problem could mean for the whole market. Then, Dana goes beyond bags and rates watches, art, wine, and jewelry as alternative investments. Check out Nicole's financial literacy course The Money School Find a Financial Advisor or Financial Coach from Nicole's company Private Wealth Collective Watch video clips from the pod on Money Rehab's Instagram and Nicole Lapin's Instagram Follow Luxus and learn more about the Birkin Fund Here's what Nicole covers with Dana: 00:00 Are You Ready for Some Money Rehab?  01:27 Are Birkins Actually Better Than the S&P 500?  02:00 What Is a Veblen Good — and Why It Matters  04:06 How Much Is a Birkin, Really?  04:29 The Secret to Getting One From Hermès  05:21 Manufactured Scarcity: How Hermès Controls Demand  06:12 The Rise of the Secondary Market  07:35 Gross vs. Net Returns: What the Charts Don't Show You  09:24 Jane Birkin's Bag Sold for $10.8 Million — Dana Was There  13:00 Is Chanel Actually Investment-Grade?  14:00 Birkin vs. Stock Market: Where Should You Put Your Money?  16:38 How the Luxus Fund Works  21:00 How to Invest Without Buying a Birkin  23:36 Sourcing Bags Through Private Dealer Networks  27:15 Storing, Authenticating, and Selling the Bags  28:33 How to Become an Accredited Investor  30:07 Is Buying a Birkin a Proxy for Hermès Stock?  32:20 The K-Shaped Economy and Luxury Demand  35:10 The Counterfeit Problem Is Getting Scary  38:18 Luxury Investment Ratings: Watches, Art, Wine, Jewelry  43:05 Secure the Bag: Financial Literacy for Women All investing involves the risk of loss, including loss of principal. This podcast is for informational purposes only and does not constitute financial, investment, or legal advice. Always do your own research and consult a licensed financial advisor before making any financial decisions or investments.

At Times, Are You Unrepentant in Your Spiritual Life and, Therefore, “storing up wrath for yourself on the {Judgement} day of wrath”? MESSAGE SUMMARY: Penitence is not a once in a lifetime occurrence. Penitence is a daily and moment by moment occurrence. None of us is perfect, and not one of us always does the will of God. Paul, in Romans 2:4-5, is unequivocal in establishing the need for your continuing penitence and the consequences of your not repenting of your sins on a real-time basis: “Or do you presume on the riches of his kindness and forbearance and patience, not knowing that God's kindness is meant to lead you to repentance? But because of your hard and impenitent heart you are storing up wrath for yourself on the day of wrath when God's righteous judgment will be revealed.". Therefore, we all need to repent and return to the Lord. You can become cocky and arrogant in your spiritual life – “I am a Christian; God has done this for me.”. As the Psalmist tells us in Psalms 32:5 of his confession and his penitence: “I acknowledged my sin to you, and I did not cover my iniquity; I said, ‘I will confess my transgressions to the LORD,' and you forgave the iniquity of my sin.". You can rest on our perceived laurels; but these perceptions, of personal goodness and righteousness, can lead you quickly to a need for your penitence.     TODAY'S PRAYER: Most merciful God I confess that I have sinned against you in thought, word, and deed, by what I have done, and by what I have left undone. I have not loved you with my whole heart; I have not loved my neighbors as myself. I am truly sorry and I humbly repent. For the sake of your son Jesus Christ, have mercy on me and forgive me; that I might delight in your will, and walk in your ways, to the glory of Your Name. Amen.  TODAY'S AFFIRMATION: Today, I affirm that, because I am in Jesus Christ, I will rejoice in Him (Philippians 4:4). “I can do everything through Him who gives me strength.”. (Philippians 4:14). SCRIPTURE REFERENCE (ESV): Mathew 3:8-11; Romans 2:4-5; 2 Corinthians 7:10; Psalms 140:1-13. A WORD FROM THE LORD WEBSITE: www.AWFTL.org. THIS SUNDAY'S AUDIO SERMON: You can listen to Archbishop Beach's Current Sunday Sermon: “Marriage – A Current Assessment (Christ the King Anglican Church; Birmingham, AL)”, at our Website: https://awordfromthelord.org/listen/ DONATE TO AWFTL: https://mygiving.secure.force.com/GXDonateNow?id=a0Ui000000DglsqEAB

Fresh Pressed Olive Oil Club (exclusive buy-2-get-1-free deal): https://freshpressolive.com/3QxTG5P 

In this episode of the Guns Podcast US, hosts Brent Wheat and Roy Huntington dive deep into the highly debated topic of staging firearms for home defense. Is it better to carry a concealed weapon on your person at all times, or should you strategically hide firearms throughout your house for quick access? The duo explores the practicalities, tactical considerations, and common pitfalls of keeping staged guns in living rooms, bedrooms, and even vehicles. Drawing from their extensive law enforcement experience, Brent and Roy share real-world stories about the hidden dangers of complex lock boxes, the truth about keeping a gun in your car, and why building muscle memory through training is critical when seconds count. They also discuss clever staging methods, such as utilizing gun magnets and concealment furniture, while cautioning against unsafe practices like stashing firearms deep inside couch cushions. Whether you rely on a bedside lock box, a concealed garage gun, or even a non-lethal impact weapon like a baseball bat, this episode is packed with essential insights to help you build a layered and effective home defense strategy. Tune in to learn how to prepare your home and your mindset for the unexpected. Key Takeaways ·         If you are more than a second or two away from your firearm during a home invasion, it is likely too far away. ·         Carrying a firearm on your person while at home is often more effective and reliable than staging multiple guns around the house. ·         Always maintain standardization in how and where you stage your weapons to avoid mental freeze during a high-stress scenario. ·         Avoid stashing guns in couches or under pillows, as they tend to migrate out of position and collect function-inhibiting debris. ·         Mechanical lock boxes are generally more reliable than electronic ones, but both require extensive practice to open under extreme stress. ·         Storing guns in vehicles overnight or in plain view invites theft; always secure or remove firearms when parking your car outside. ·         Alternative defense tools, like baseball bats or heavy garage tools, can serve as excellent deterrents and secondary options when a firearm is not accessible. Gear Featured in This Episode Looking for the storage and safety solutions mentioned in the video? Check out the links below: •    SOFhold Magnets – Magnetic mounting solutions for quick-access storage. https://SOFhold.com •    Streamlight SpeedLocker – Secure, portable power-free storage. https://Streamlight.com •    Skinner Garment Bag – The ultimate low-profile transport system. https://SkinnersSights.com --- Have a topic idea or a guest you'd like to see in a future episode? Let us know in the comments or email editor@gunspodcast.us Never miss an episode! Subscribe to our YouTube channel or sign up for our newsletter to get the Guns Podcast delivered straight to your inbox each week. Buy our Merch! Visit Gunspodcast.us

In the fifth installation of our Canvas series, we talk about different ways to store thick threads such as perle #5 and Watercolours. A lot of our focus is on the excellent products produced by Jenny White of Lucky Jonquil at luckyjonquil.com. We hope our suggestions help and invite you to share how you store your thicker threads in the comments.—Beth, Cindy, and Gary Links: CyberPointers: https://www.cyberpointers.org/ American Needlepoint Guild: https://www.needlepoint.org/default.aspx Lucky Jonquil website: https://www.luckyjonquil.com EverTite stretcher bars, Frame Lock corner devices, and tacks: http://www.needlestack.com/WebStore/Accessories/StretcherBars.html Adjust-A-Frame stretcher bars: Download the price list.

I sit down with Amir, who's back on the pod, and we walk through the full stack of taking a business idea from zero to a validated, A/B-tested landing page in a single session. I use Idea Browser's new MCP integration with Claude Code to pull project context, generate a lead magnet concept, design a landing page in Paper, and then wire up analytics and live experiments through HumbleLytics — all without writing a single line of front-end code manually. We cover the tools, the workflow, and why this stack creates massive arbitrage for marketers and builders right now. Timestamps 00:00 – Intro and Episode Preview 02:30 – Building a Growth Strategy with Idea Browser 06:10 – Designing Landing Pages in Paper 08:38 – Refining Copy, Layout, and Components in Paper 20:06 – Deploying Landing Page and Adding HumbleLytics Analytics 28:38 – Running A/B Experiment on the Headline 32:44 – The Arbitrage Opportunity and Closing Thoughts Links Mentioned: Amir's Agentic Marketing Skill: https://startup-ideas-pod.link/amir_marketing_skill Key Points Idea Browser now connects to Claude Code as an MCP, letting you pull project context, growth strategies, and skills directly into the terminal for building and iterating on business ideas. Paper replaces the traditional Figma-to-developer handoff by letting you design, iterate, and refine landing pages visually — all connected to Claude Code so changes stay in sync. HumbleLytics enables no-code A/B experiments that dynamically update page content without deploying new code, so you can test headlines, CTAs, and layouts in real time. Storing performance context (A/B results, revenue data, growth metrics) back into Idea Browser compounds your results over time because every future decision is informed by past data. This full stack — Idea Browser, Paper, Claude Code, HumbleLytics — creates a significant arbitrage opportunity right now because almost nobody is using it at this level. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND AMIR ON SOCIAL Humblytics: https://humblytics.com/?via=community X/Twitter: https://x.com/amirmxt Youtube: https://www.youtube.com/@amirmxt

TV architect Dermot Bannon talks Brendan through what's hot and what's not in our homes right now. He talks about retrofitting, the benefits of insulation but also how smaller interventions, like a new countertop or a Velux window can make the biggest difference.

summary This episode explores the importance of maintaining a comprehensive contact list for home management, covering categories from service providers to legal and financial contacts. Learn practical tips to organize and centralize your home-related contacts for easier management and emergency preparedness.  key  topics Building a comprehensive contact list for home management Tracking service providers, contractors, and utility companies Storing important documents and account information Benefits of centralized contact management for homeowners Practical tips for organizing contacts and documents  sound bites "Track all your service providers and contractors" "Attach documents and contracts to your contacts" "Start small: gather five contacts in 30 days" Chapters 00:40 Building Your Trusted Contact List 03:47 Service Providers and Contractors 06:34 Utility and Service Providers 09:26 Property Protection Contacts 12:29 Organizing Your Contact Information 15:38 Practical Tips for Homeowners    

Eleanor Millman, Senior Staff Product Manager, and Mina Tawadrous, Associate Director of Platform Engineering at SiriusXM, join host Justin Reock to discuss how platform teams can scale prioritization without relying on revenue.They share how SiriusXM moved beyond RICE to build a custom framework for internal platforms, using weighted factors like developer speed, reliability, cost, and trust to guide decisions across teams.The episode also explores their concept of “assumptions as code,” in which teams store and reuse assumptions in a central repository to reduce misalignment and improve decision-making, with AI helping to surface and validate those assumptions.They close with how this system is shaping SiriusXM's 2026 prioritization approach and what it signals about a broader shift toward builder-driven product development.Where to find Eleanor Millman: • LinkedIn: https://www.linkedin.com/in/eleanor-millman-98b10350Where to find Mina Tawadrous: • LinkedIn: https://www.linkedin.com/in/mina-tawadrous Where to find Justin Reock:• LinkedIn: https://www.linkedin.com/in/justinreockIn this episode, we cover:(00:00) Intro(01:17) Mina's role and path into platform engineering(02:03) Eleanor's background and shift into product(03:15) Scaling prioritization across platform engineering teams(05:41) Aligning platform priorities with stakeholders(09:08) Evolving RICE into a platform-specific prioritization framework(11:33) Iterating on the prioritization framework over time(16:57) How the framework, data, and conversations drive alignment(19:06) Storing assumptions as code in a central repository(26:47) Resolving assumption conflicts with user interviews(30:47) How stored assumptions integrate with AI workflows(35:30) Standard mode and different user personas(37:20) The industry shift towards builders(41:04) The challenges of platform engineering(43:36) How SiriusXM is prioritizing in 2026Referenced:• Measuring AI code assistants and agents• SiriusXM • VMware• How SiriusXM revamped their platform and developer experience• RICE Scoring Model | Prioritization Method Overview• The evaporating cloud: A tool for resolving workplace conflict

Is your body keeping the score? Carrying fear in your neck, anxiety in your chest, or old wounds in your jaw? You're not imagining it, and Dr. Brian Paris has the science to prove it. Michael welcomes Dr. Brian Paris, neuro-chiropractor, pro triathlete, and embodiment coach, for a raw and revelatory conversation about what's really keeping you stuck. From the vagus nerve to the startle reflex, from flow states to being the hollow bone, Dr. Brian reveals why healing has never been about thinking your way out, it starts in the body. This isn't about fixing your mindset. This is about coming home to your biology so that spirit, intuition, and true connection finally have a place to land. Key Topics Why biology precedes psychology, and what happens when we try to think our way out of fear first.  The truth about the vagus nerve: why 80% of its signals travel from your body to your brain, and what that means for healing. What neuroception is, and how your nervous system is silently scanning for threat or safety beneath your awareness, every second of every day. Why issues are never in the tissues, and where the real source of chronic pain actually hides. The startle reflex: how unresolved trauma gets locked into your eyes, forehead, and upper neck, and the simple hands-on technique to begin releasing it right now. Why burnout is not a badge of honor, and how intentional recovery is just as sacred as any training. How sensitivity is your superpower, and what empaths must do to stop absorbing the world's inflammation as their own. The 4% rule for entering flow states, and why being bored or anxious are the signal you've missed the sweet spot. Being the hollow bone: the ancient shamanic principle that will transform how you hold space for others. The Practice That Changes Everything: Breath. Not as a concept, as a medicine. When you breathe into the belly, into the ribs, into the clavicles, and pour it all slowly back out, you are not just calming down. You are switching your entire nervous system from protection to connection. And from that place, everything becomes possible. Join the Inspire Nation Soul Family!

The guys talk about baseball, magicians, and donating kidneys. They also learn about storing trauma in your body and do a mock draft of flags.Follow the show on X/Twitter: @passthegravypod, @AlexJMiddleton, @NotPatDionne, and @RobertBarbosa03

This week on the Buck Junkies Podcast, we've brought Jacob and Dustin from Rolling Thunder to talk ALL THINGS turkey calls!... Timestamps: 00:00 - Intro 00:17 - Welcome Jacob and Dustin! 00:49 - Rollin' Thunder winning at the NWTF 05:09 - What calls were used to win at the NWTF? 07:12 - The BEST calls for a beginner 13:06 - Storing calls after the season 17:04 - Talkin' about the Rolling Thunder Trumpet call 21:00 - The GO TO calls when we're in the woods 25:08 - Coming up with the names for calls 32:09 - Rolling Thunder Charity Golf Scramble 37:49 - Turkey hunting with Rolling Thunder 39:22 - Turkey hunting in Florida 44:56 - What's on the horizon for Rolling Thunder? 48:31 - The most odd custom calls that have been requested  51:17 - Breaking into National Sales 53:09 - The HARDEST place to hunt turkeys 57:00 - Breakfast or Food after the hunt? 1:00:10 - Closing Notes

Ronan and Dave return to chat through the week of cycling geeking. From emerging trends in one-piece cockpits to the latest in tech news, this episode has plenty of information nuggets. As usual, members of Escape Collective have access to the member-only podcast feeds where they receive the full versions of our episodes. Every week, this includes the bonus section of Geek Warning – Ask a Wrench, where you'll hear pro mechanics answer technical questions from members. This week, the mechanics answer questions related to restoring an older rim brake bike and the reasons to do it; tubeless sealants and dehumidifiers; and a handful of questions related to new disc brake setup.  Time stamps:   4:20 -  Mixing and matching cockpits  18:00 - Do modern handlebar measurements measure up?  24:40 - Escape Explained  25:45 - Prologo joins the pressure mapping game  34:45 - Safety first: Pikio Labs' Si helmet  39:00 - New Canyon spotted  41:00 - A PSA for crummy pump heads 46:00 - Ask a Wrench (members only)  46:30 - Bringing an old CAAD9 back to life and whether to upgrade the drivetrain 53:30 - The Chisel dropbar conversion continues 58:30 - Storing bikes with tubeless  1:02:45 - A fussy and sticky SRAM hydraulic disc brake, plus some sneaky tips 

Geez, that old LNG terminal idea didn't last long, did it? Seven weeks - that's it. From the moment it was announced on February 9 to the first knife stuck in it today, seven weeks to the day. Now, before you come at me arguing that the LNG terminal hasn't been killed - yes, it has. It is dead. The Herald report this morning that multiple ministers are privately admitting they may have to kill the project did not happen by accident. This is Politics 101 when you're winding something down. You start slowly and by the time you actually kill it - say in two or three months - people have already got used to the idea. Then factor in that the Prime Minister was on Mike Hosking Breakfast this morning and didn't sound super enthusiastic. That, I think, backs up the suspicion. Now, if you wanted to pull me up on anything, it's that it's not 100 percent dead. There's always a chance something changes and it slips through. But today, I'd put the chances of death at 80 to 90 percent. And it's not for the reasons we're being told - namely that gas prices are now too high because of Operation Epic Fury. We already knew seven weeks ago that an LNG terminal would be at the mercy of international gas prices. That was one of the main warnings about the idea. At the moment, we pay domestic gas prices. The minute you start importing LNG, you're paying international prices. The real reason this is being cut is because it was never a good idea. And I think they had to run it out long enough to truly realise how many people thought it was a bad idea. Spending $1 billion on what is essentially a short-term fix is a hell of a lot of money - and that's assuming it comes in on budget. Given what we know about infrastructure projects in this country, it could easily cost a lot more than $1 billion. And it is a short-term fix. Unless we suddenly strike a big gas discovery in the next few months, this country is going to have to wean itself off gas. You're going to have to stop using gas and start using something else - probably electricity in homes and something different again for industrial and commercial users. Storing gas in a terminal was only ever about managing the transition while we moved away from gas. That's an enormous amount of money to spend on a transitional solution. On top of that, forcing people to pay for it through an LNG levy was political toxin - especially during a cost-of-living crisis. So now you've got double political toxin, in a cost-of-living crisis that's just been supercharged by a war in Iran. So consider this one already dead - because it was never a good idea in the first place. LISTEN ABOVESee omnystudio.com/listener for privacy information.

Welcome to Day 2826 of Wisdom-Trek, and thank you for joining me. This is Guthrie Chamberlain, Your Guide to Wisdom – Theology Thursday – The Law of Attraction and the Prosperity Gospel: A Biblical Response. Wisdom-Trek Podcast Script - Day 2826 Welcome to Wisdom-Trek with Gramps!   I am Guthrie Chamberlain, and we are on Day 2826 of our Trek.   The Purpose of Wisdom-Trek is to create a legacy of wisdom, to seek out discernment and insights, and to boldly grow where few have chosen to grow before. Our current series of Theology Thursday lessons is written by theologian and teacher John Daniels. I have found that his lessons are short, easy to understand, doctrinally sound, and applicable to all who desire to learn more of God's Word. John's lessons can be found on his website   theologyinfive.com.   Today's lesson is titled:  The Law of Attraction and the Prosperity Gospel: A Biblical Response. In recent years, two teachings have gained wide popularity both inside and outside the church: the Law of Attraction and the Prosperity Gospel. Promoted in self-help books, social media, and even some pulpits, they promise health, wealth, and success to those who follow their formulas. To many, these messages sound like hope in an uncertain world. Yet beneath their appealing surface, both rest on foundations far removed from biblical truth. The Law of Attraction suggests that the universe responds to human thoughts and desires, delivering blessings when individuals focus positively. The Prosperity Gospel teaches that financial abundance and physical well-being are signs of God's favor. Though they sound spiritual, both movements originate outside of Scripture and subtly reshape Christian faith into a pursuit of personal gain. The first segment is: Roots and Historical Background The Law of Attraction grew out of the 19th-century New Thought movement in America. Figures like Phineas Quimby and William Walker Atkinson blended mesmerism, Eastern ideas, and metaphysical speculation. They taught that sickness, poverty, and failure result from negative thinking, while success comes from visualizing the life one desires. These ideas found modern expression in books like The Secret and continue to influence popular culture. The Prosperity Gospel shares similar roots. In the early 20th century, E. W. Kenyon merged Christian language with New Thought ideas, teaching that believers could “speak” health and wealth into existence through faith-filled words. This laid the groundwork for the Word of Faith movement, further developed by preachers like Kenneth Hagin, Kenneth Copeland, and others. With the rise of televangelism and global media, the Prosperity Gospel spread rapidly. Both movements also reflect ancient patterns found in pagan religion, where prosperity was seen as proof of divine approval. Fertility cults promised abundance to those who performed rituals or gave offerings. The Prosperity Gospel repeats this logic, replacing ritual with faith declarations and calling it Christianity. The Second Segment is: Wealth in Scripture The Bible does not condemn wealth, and Jesus never taught that having riches is inherently wrong. What Scripture warns against is trusting in wealth or making it an idol. The love of money—not money itself—is the root of many kinds of evil. Wealth can distract, deceive, and distance people from depending on God. Scripture presents wealth as a test of stewardship. Believers are warned not to place their hope in riches but in God, who provides everything. The rich are called to be generous, to care for the poor, and to use their resources to advance God's kingdom. The accumulation of wealth is never condemned, but hoarding it selfishly or viewing it as a sign of spiritual superiority is. Jesus cautioned that riches can choke out spiritual growth and make it harder to enter the kingdom. Yet He also welcomed the wealthy and honored faithful givers. The issue is not how much one has but where one's treasure lies. Storing up treasure in heaven is the mark of a faithful heart. The third segment is: The True Source of Blessing Biblical blessing is not measured by outward success but by one's relationship with God. Paul declared himself content whether in poverty or abundance because his strength came from Christ. He saw hardship, not comfort, as the training ground of faith. God's promises center on salvation, sanctification, and eternal reward, not financial gain. Trials, sacrifice, and generosity are normal parts of the Christian life. The goal is not to manipulate spiritual laws for personal benefit but to seek first the kingdom of God and trust Him to provide what is truly needed. In Conclusion: The Law of Attraction and the Prosperity Gospel appeal to human desires but distort the message of Scripture. Their roots in paganism, New Thought, and self-focused religion expose them as counterfeits. They reduce God to a cosmic vending machine and faith to a technique for self-enrichment. The Bible offers a better way. Trust in God's providence. Seek His kingdom. Use whatever resources you have to serve others. Whether rich or poor, the true reward is Christ Himself. To further your study, consider these Discussion Questions How does the Prosperity Gospel distort the biblical view of wealth and blessing? In what ways does the Law of Attraction contradict the doctrine of God's sovereignty? What dangers arise when Christian faith is reduced to a tool for personal success? Why is it important to understand the historical and philosophical roots of these teachings? How can believers cultivate a biblical view of contentment and generosity? Join us next Theology Thursday to learn J.R.R. Tolkien's Theological Imagination: Rebellion, Redemption, and the Divine Pattern. If you found this podcast insightful, please subscribe and leave us a review, then encourage your friends and family to join us and come along tomorrow for another day of  ‘Wisdom-Trek,  Creating a Legacy.'                    Thank you so much for allowing me to be your guide, mentor, and, most importantly,   I am your friend as I serve you through this Wisdom-Trek podcast and journal. As we take this Trek of life together, let us always:                   Liv Abundantly.      Love Unconditionally.              Listen Intentionally.             Learn Continuously.               Lend to others Generously.                 Lead with Integrity.                 Leave a Living Legacy Each Day.                 I am Guthrie Chamberlain, reminding you to,   “Keep Moving Forward, Enjoy your journey, and create a great day, every day!  Join me next time for more daily wisdom!

Europe's industrial future will be defined not by ambition, but by execution.In this episode, Marta Sjögren (Co-Founder & Co-CEO, Paebbl) joins Carmel Rafaeli (Founding Partner, The Table) and Andreas Munk Holm to explore what it really takes to build and scale deep tech companies in Europe.Paebbl is turning captured CO₂ into permanent mineral form—replacing emissions-intensive materials like cement while removing carbon from the atmosphere. But as Marta explains, the real challenge isn't just scientific. It's aligning capital, timing, and conviction.They discuss:– Why deep tech companies fail (and it's rarely the tech)– Fundraising as a system of signals, not storytelling– How to evaluate investors beyond capital– Designing capital stacks for industrial scale– Why rounds stall—and how to build real momentum– The role of co-CEO leadership in complex companiesThe conversation also highlights the structural funding gap for women-led climate ventures—and how The Table is working to change it.This episode is part of Leaders Shaping a Resilient Planet, spotlighting founders building Europe's industrial future with discipline, depth, and long-term conviction.Listen now and follow for more.

Storing up wrath...

MMP Ep. 282: https://open.spotify.com/episode/0oiORKGMCNVUvNCuPgwPNi?si=qBtACk6MRKOFIeddQPGsigBook Your COMPLEMENTARY CONSULTATION and CALORIE CALCULATION Call: https://calendly.com/d/2p8-mxx-dgf/free-consultation-call-zoom⁠⁠⁠⁠⁠⁠If eating less and moving more hasn't worked lately, this episode is your next step. Today we walk through the practical shift that helps your body start responding again — not by forcing fat loss, but by changing the signals your body receives.You'll learn the order to focus on first, what most women accidentally do backwards, and how to move from a body that holds onto energy… to one that's willing to use it.Because progress in this season isn't created by pushing harder.It's created by setting the right conditions so results can finally happen. 

TRASH CULTURE•Patrick will never get the hang of the Manage Comics Reporting functions. •Dal's statues. •Ordering from 100% Soft. •The experience of using Content360 for social posts. •Lunar box depth: The pros and cons. •Storing next week's comics. •Spine creases on comics is TOTALLY NORMAL. ---------- Contest of Challengers #771 This episode is dedicated to Davey Bang and Auggie Matthews. Theme: Adam WarRock (with Mikal kHill) Intro: James VanOsdol (with Danhausen and Chris Jericho) Outro: James VanOsdol (with Danhausen) "Patrick" Voices: Richie Kotzen, Christopher Daniels, James Acaster, Sue Marasciulo (Trent's Mom), RJ City, Sebastian Bach, Arune Singh, James VanOsdol "Dal" Voices: James VanOsdol, RJ City, Dalton Castle, Sue Marasciulo (Trent's Mom), Kevin Conroy, Kris Statlander, Skye Blue, Bryce Remsberg, Arune Singh Dal and Patrick Artwork: Bella Spagnuolo https://bellaspagnuoloart.myportfolio.com/ This episode was digitally edited by Cleanvoice. ----------Challengers Comics + Conversation 1845 N Western Ave • Chicago, IL 60647 773.278.0155 • ChallengersComics.com

Federal records show Ottawa still has 21 million expired pandemic PPE items in storage, costing taxpayers millions each year.   Read the full article here: https://www.coastalfront.ca/read/ottawa-storing-21-million-expired-pandemic-ppe-items-costing-taxpayers-millions   PODCAST INFO:

Why does eating less and exercising more often stop working after 40? I've lived through the frustration of doing everything “right” and still watching fat accumulate around my midsection. In this episode, I explain why midlife fat gain isn't about discipline; it's about hormones shifting your body into storage mode. When you support your sleep, muscle, and hormone balance, fat loss stops being a fight and starts becoming a side effect of getting healthy. What you'll learn: (02:55) How declining estrogen increases cortisol and insulin, leading to belly fat storage. (04:21) Why thyroid function, testosterone, and muscle loss dramatically impact metabolism. (09:56) How extreme calorie restriction and excess cardio can slow thyroid function and reduce muscle mass. (11:16) How to assess visceral fat risk using simple waist-to-hip and waist-to-height measurements. (16:13) Why protein-first meals and strategic meal timing improve insulin sensitivity. (18:53) How poor sleep increases insulin resistance, cortisol, and obesity risk. (21:32) How cyclic caloric restriction can support fat loss without harming thyroid health. Love the podcast? Here's what to do: Subscribe to the podcast. Leave a review. Text a screenshot to me at 813-565-2627 and wait for a personal reply because your voice is so important to me. Want to listen to the show completely ad-free? Go to http://subscribetojj.com Click “TRY FREE” and start your ad-free journey today! When you're ready, enjoy the VIP experience for just $4.99 per month or $49.99 per year (save 17%!) Full show notes (including all links mentioned): https://jjvirgin.com/notcalories Learn more about your ad choices. Visit megaphone.fm/adchoices

Thank you for joining us today for worship! Pastor Bobby continues in Matthew 6 in our Sermon on the Mount series called Follow Me. In this message he discusses fasting and storing up treasure in Heaven.

Welcome back to the Grow From Your Heart Podcast with your host Rasta Jeff! Leave comments and tell me what you think of the show! Visit AC Infinity and use code IRIEARMY to save 10%. https://www.acinfinity.com/ref=RASTAJEFF&utm_campaign=affiliate_promotions&utm_medium=social&utm_source=affiliate Pre-Order your AC Infinity Spectron Cameras! https://acinfinity.com/spectron-7-ai-powered-grow-camera-4k-with-thermal-imaging-and-under-canopy-vision/?ref=RASTAJEFF&utm_campaign=affiliate_promotions&utm_medium=social&utm_source=affiliate Get your AC Infinity VPD Thermometer Here! https://acinfinity.com/vpd-thermometer-handheld-environmental-monitor/?ref=RASTAJEFF&search_query=vpd&searchid=3441165&utm_campaign=affiliate_promotions&utm_medium=social&utm_source=affiliate

Episode Summary In this episode of the Be a Smarter Homeowner podcast, Beth Dodson and John Bodrozic explore HomeZada's innovative Visual Design AI feature and how it transforms the way homeowners approach remodeling and home improvement projects. They discuss common homeowner pain points — including overwhelm, budgeting uncertainty, and communication challenges with contractors — and explain how visual planning tools help bring clarity and confidence to the process. Visual Design AI allows homeowners to experiment with design choices, generate realistic budgets, create material lists, and adjust project scope before ever hiring a contractor. The conversation highlights how preparation leads to smoother projects, fewer regrets, and better collaboration with professionals. Ultimately, this tool empowers homeowners to take a proactive, informed approach to managing their homes and renovations. Key Takeaways HomeZada is a digital home management platform designed for homeowners Visual Design AI helps homeowners see their renovation ideas before starting Many homeowners skip critical planning steps before contacting contractors Early design clarity leads to more accurate budgeting Homeowners can quickly generate material lists and cost estimates Project scope can be adjusted to align with budget constraints Clear design plans improve contractor communication and reduce conflict The platform helps reduce overwhelm and decision fatigue Homeowners can prioritize projects based on timing and finances Storing project information allows for future updates and long-term planning Contractors prefer working with well-prepared, informed homeowners Title Options Transforming Home Renovations with Visual Design AI Plan Smarter, Remodel Better: The Power of Visual Design From Overwhelmed to Organized: A Better Way to Plan Home Projects Sound Bites "Visual Design AI is so important." "You can start to adjust the scope." "Turns project overwhelm into ease." "Reduce conflict with contractors." "This is just a win-win in that capacity." Chapters 00:40 Introduction to HomeZada and Visual Design AI 01:40 Common Homeowner Pain Points 04:10 Why Design and Budgeting Go Hand in Hand 06:40 How Visual Design AI Builds Clarity and Confidence 11:10 Creating Projects, Material Lists, and Cost Estimates 16:40 Adjusting Scope to Match Your Budget 20:40 Communicating Effectively with Contractors 23:40 Long-Term Planning and Smarter Homeownership 25:05 Introduction to HomeZada and Project Management 27:10 The Importance of Visual Design in Remodeling 30:23 Gaining Confidence and Clarity in Home Projects 34:21 Selecting and Managing Contractors Effectively 37:58 Empowering Homeowners with HomeZada  

Storing items from loved ones can feel like an act of love—but when storage becomes unsustainable, guilt quietly builds. In this episode, Stephanie explores how to store inherited and sentimental items realistically, without shame, and how to recognize when care has turned into obligation.In This Episode We Talk AboutWhy inherited items feel heavier than our own belongings The real (often hidden) costs of storage How good intentions can still lead to damage What it means to store with love and honesty How to assess your actual capacity—financially and emotionallyMentioned in This EpisodeThe Year of the Storage Rooms Storage as postponed decision-making Sustainable caretaking vs. silent guilt Review full show notes and resources at https://theorganizedflamingo.com/podcast Hosted on Acast. See acast.com/privacy for more information.

It may be impossible to overemphasize the importance of storing up Scripture in our hearts. Today, Sinclair Ferguson describes the value of equipping ourselves to face future challenges by memorizing God's Word in the present. Read the transcript: https://ligonier.org/podcasts/things-unseen-with-sinclair-ferguson/storing-up-scripture-in-our-hearts/ A donor-supported outreach of Ligonier Ministries. Donate: https://donate.ligonier.org/ Explore all of our podcasts: https://www.ligonier.org/podcasts

(00:00) — Welcome and guest credentials: Dr. Gray introduces Dr. Christine Crispin and frames the workshop.(02:10) — Redefining “premed”: Shift from “I'm going to med school” to ongoing career exploration.(05:40) — First‑year success: Why freshman year should prioritize academics and campus adjustment.(08:45) — Dip, don't dive: A toe‑dip into service or shadowing without hurting grades.(12:00) — Do first‑years need advising?: One early meeting to avoid wrong turns and set expectations.(13:40) — Map your courses to MCAT: Align chem/bio/phys/biochem sequencing with your test timeline.(14:58) — Planning the first summer: Add clinical, service, research, or EMT/MA training.(18:05) — Getting certified as an MA: Capier mention and how CCMA can open clinical roles.(19:53) — Work hours that work: Balance school first; per diem and single weekly shifts count.(22:05) — Small hours, big totals: Why 2–4 weekly hours compound into strong experience.(23:40) — Non‑clinical options and impact: Alternatives when sites won't take volunteers and creating your own service.(26:10) — Research reality check: Useful skills, not the centerpiece unless MD‑PhD.(28:10) — Why clinical and shadowing matter: Test fit for patient care and physician responsibilities.(31:46) — What counts as clinical: Direct patient interaction vs adjacent roles that don't qualify.(32:43) — Shadowing continuity: Avoid one‑and‑done; keep modest, ongoing exposure.(34:50) — Sophomore advising focus: Decide timeline, identify gaps, and meet each semester.(36:34) — Recovering from GPA dips: Diagnose causes, seek help, and build an upward trend.(39:13) — Summer before junior year: MCAT study or rinse‑and‑repeat on experiences.(40:10) — The gap year decision: Experiences, GPA trajectory, goals, and bandwidth.(43:23) — Readiness check: Confirm hours, recency, MCAT timing, and letters before applying.(45:58) — MCAT score myths: Why you don't need a 520 and sane score ranges.(48:45) — Letters of rec strategy: Cultivate relationships early; ask for strong letters in spring.(52:01) — Committee letters cautions: Consider expectations but watch harmful timing delays.(53:38) — Storing and QA'ing letters: Using a letter service to reduce technical errors.(54:36) — When advising crosses lines: Schools pre‑screening letters and why that's problematic.(55:24) — Activities recap and risk: Consistency across core experiences and avoiding “late.”(56:48) — Rolling admissions timing: Complete files earlier to lower risk of being overlooked.(59:09) — Not day‑one or bust: Early enough beats first‑minute submission.(01:00:10) — Strong apps are reflective: Authentic, integrated stories over forced themes.What makes a “successful premed” isn't a checklist—it's an exploration mindset. Dr. Ryan Gray and Dr. Christine Crispin break down a realistic path from freshman year through application season. First year, be a college student: master study habits, time management, and campus life. Then add experiences gradually—a toe‑dip into service or shadowing—without sacrificing grades. Map your courses to the MCAT at your institution, and use advising sparingly but strategically to avoid wrong turns. Learn how small, consistent hours in clinical work, non‑clinical service, and shadowing compound over time and why research is valuable but not required unless you're MD‑PhD bound. They clarify what truly counts as clinical, how to choose non‑clinical service when options are limited, and why reflection and authenticity—not themes and checkboxes—elevate your application. You'll also hear how to decide on a gap year, the real risk of applying later in a rolling admissions process, and a practical plan for letters of recommendation, including committee letter pitfalls. This conversation replaces pressure with...

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Going Paperless: To Be or Not to Be? Episode 366 – Over the years, it seems that each of us—whether by choice or not– has been moving gradually from paper statements and checks to digital. Is it time to cut the cord completely? More SML Planning Minute Podcast Episodes Transcript of Podcast Episode 366 Hello, this is Bill Rainaldi, with another edition of Security Mutual's SML Planning Minute. In today's episode, is it time to go paperless? Like many people, I tend to save stuff: like credit card bills, bank statements, paper receipts, etc. I throw them into an empty file drawer until the end of the year. Then, on an annual basis, I'll sort through this giant pile of paper, organize everything and place it into a series of folders, which take up space in my filing cabinet. It all leads to one inevitable question: Why? What's the point of spending all this time organizing all this paperwork that, likely, I'm never going to look at again. Certainly, some items, such as cards and notes from family members, are worth saving. But what about the other 95 percent? For many of us, it's simply the force of habit. Going digital has its advantages. For one thing, you may find that once you're used to it, digital documents can be easier to organize and access, and you'll save time in the process. Not to mention the space you can save in your house, and the overall environmental impact. Has the time come for most of us to go fully paperless? If so, where do we even begin? The process often starts with a few small steps such as getting some of your statements by email or paying some of your bills using a direct transfer rather than a paper check. But there's still a lot of paper. What's the next phase if you want to get more organized? Here are a few steps you can take: Switch to online billing and statements. Using online tools with financial institutions and service providers, such as your cellular company, can make a big dent in your paper clutter. The truth is, if you need to look up one of your old statements, it'll probably take less time to find it online than if you had to dig through your paperwork. Pay bills online. You can schedule your online payments through your bank. They can make your payments automatically every month, or if you don't want to go that far, they can automatically remind you when a payment is due.   When was the last time you sent a check somewhere, only to have it lost in the mail? This is one way to avoid such a hassle. Plus, in most cases, by paying online you can decide exactly what day the other party receives the funds.   There are limits, of course. Your landlord may still want a paper check. Same thing with certain vendors, like your landscaper or cleaning service if you have one. So at least for now, no matter how far you want to take this, you're still going to be writing a few checks. Digital note-taking. If you take a lot of notes during meetings, whether for business or personal reasons, a digital note-taking platform can help. And not just with the process itself, but also with providing easy access later on. Some of the most well-known platforms are Evernote, Microsoft OneNote, and Notion.[1] Your to-do list. Most smartphones have a “to-do” app which can help organize your essential work and/or personal tasks. They make it very hard to forget your priority items. Taking advantage of digital signatures. Digital signature tools eliminate the need to print and physically sign important documents. It's a good way to save your time and resources. Among the most popular of these tools are Adobe Acrobat Sign and Docusign.[2] Storing your digital information. You'll need to select a place to keep your data safe and organized. Some of the most popular are Google Drive, Microsoft OneDrive and Dropbox.[3] One more tip: It might be best to start a project like this on a going-forward basis. That is, try not to think much about the big pile of paperwork you already have. There's no need to feel overwhelmed by that backlog. You'll get to it someday. And when you do, you might consider purchasing a quality paper shredder to help you through your pile. There are also shredding services you can contract that will pick up any documents you set aside for disposal. For now, it's more important just to get started with something. But also note that there are limits to how far you can go. Not many people ever truly achieve a 100 percent digital lifestyle. There are some items that you'll still need to keep a paper copy of, such as wills, birth certificates, title deeds and stock certificates. You might also want to keep a paper printout of your most important online account data, perhaps in a safe. It could save time and money for your family should something happen to you. But more than that, there are likely some paper items that you will never be able to replace. I received a birthday card from my grandmother in 1976 with a crisp new $5 bill in it. It still sits on my desk with the $5 intact. I wouldn't trade it for anything. [1] Erdem. “How to Go Paperless: A Step-by-Step Guide.” Clinked.com. https://www.clinked.com/blog/go-paperless (accessed December 31, 2025). [2] Id. [3] Duffy, Jill. “7 Easy Tips to Finally Go Paperless.” PCMag.com. https://www.pcmag.com/how-to/7-easy-tips-to-finally-go-paperless (accessed December 31, 2025). More SML Planning Minute Podcast Episodes This podcast is brought to you by Security Mutual Life Insurance Company of New York, The Company That Cares®. The content provided is intended for educational and informational purposes only. Information is provided in good faith. However, the Company makes no representation or warranty of any kind regarding the accuracy, reliability, or completeness of the information. The information presented is designed to provide general information regarding the subject matter covered. It is not to serve as legal, tax or other financial advice related to individual situations, because each individual's legal, tax and financial situation is different. Specific advice needs to be tailored to your situation. Therefore, please consult with your own attorney, tax professional and/or other advisors regarding your specific situation. To help reach your goals, you need a skilled professional by your side. Contact your local Security Mutual life insurance advisor today. As part of the planning process, he or she will coordinate with your other advisors as needed to help you achieve your financial goals and objectives. For more information, visit us at SMLNY.com/SMLPodcast. If you've enjoyed this podcast, tell your friends about it. And be sure to give us a five-star review. And check us out on LinkedIn, YouTube and Twitter. Thanks for listening, and we'll talk to you next time. Tax laws are complex and subject to change. The information presented is based on current interpretation of the laws. Neither Security Mutual nor its agents are permitted to provide tax or legal advice. The applicability of any strategy discussed is dependent upon the particular facts and circumstances. Results may vary, and products and services discussed may not be appropriate for all situations. Each person's needs, objectives and financial circumstances are different, and must be reviewed and analyzed independently. We encourage individuals to seek personalized advice from a qualified Security Mutual life insurance advisor regarding their personal needs, objectives, and financial circumstances. Insurance products are issued by Security Mutual Life Insurance Company of New York, Binghamton, New York. Product availability and features may vary by state.​ SubscribeApple PodcastsSpotifyAndroidPandoraBlubrryby EmailTuneInDeezerRSSMore Subscribe Options

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Visceral fat is not stubborn. It is protective fat stored when the body senses stress. In this episode, Ben explains why cortisol is the true fat-storage switch and walks you through a 30-day protocol to turn it off. You'll learn why calories and workouts are not the real problem, and how hormones like cortisol and insulin control belly fat, inflammation, sleep, and metabolic health. What You'll Learn: Why visceral fat is hormonally driven, not calorie-driven How cortisol and insulin work together to store belly fat The ideal intermittent fasting schedule to lower insulin and cortisol Why eating earlier in the day improves fat-burning sleep The benefits of a weekly 24-hour fast, including autophagy and growth hormone Which foods spike cortisol and must be removed for 30 days The best anti-inflammatory foods and fats to support fat loss How nitric oxide and blood flow unlock visceral fat Why walking is more effective than intense cardio for belly fat The sleep strategies that shut off cortisol at night How minerals help release stored fat and toxins Why your thoughts and gratitude directly impact fat loss hormones FREE GUIDE:  The World's Easiest Breakfast Diet - https://bit.ly/4jeLvFE 

In this episode, Ben Azadi shares the exact strategy that allows you to eat carbs like bread, rice, potatoes, and even pizza without spiking insulin or storing fat. If you're over 40 and feel like carbs work against you, this episode is a complete game-changer. You'll learn the four metabolic levers that instantly flatten glucose spikes:• Fiber first to slow glucose absorption• Protein + healthy fat before carbs to stabilize blood sugar• Apple cider vinegar or lemon water to improve insulin sensitivity• A 10-minute walk after meals to shuttle glucose into muscles Ben also reveals his Carb Timing Blueprint—when to eat carbs (morning, after workouts) and when to avoid them (late at night, stressed, sedentary). You'll get his 3-Step Smart Carb Formula: prep your body, eat carbs last, move afterward — plus answers to top listener questions about resistant starch, vinegar timing, carb choices, and insulin resistance. Whether you want better energy, flatter glucose curves, or freedom to enjoy your favorite foods again, this episode shows you how to use carbs strategically to support fat loss and metabolic health. FREE GUIDE: How To Lose 1 Pound Per Day- https://bit.ly/3Mj9siH