Podcasts about sft

ffinlo Costain (8point9.com) and Joe Stanley (GWCT Allerton Project) discuss:Net zero reports from The Tony Blair Institute and the AFN Network+UK climate change preparednessUK Government 'retakes' the decision to scrap SFI 2024Anaerobic digestionAnd those reports - by FAI and SFT - that were damned by Monbiot.

Today, we're joined by Mahesh Sathiamoorthy, co-founder and CEO of Bespoke Labs, to discuss how reinforcement learning (RL) is reshaping the way we build custom agents on top of foundation models. Mahesh highlights the crucial role of data curation, evaluation, and error analysis in model performance, and explains why RL offers a more robust alternative to prompting, and how it can improve multi-step tool use capabilities. We also explore the limitations of supervised fine-tuning (SFT) for tool-augmented reasoning tasks, the reward-shaping strategies they've used, and Bespoke Labs' open-source libraries like Curator. We also touch on the models MiniCheck for hallucination detection and MiniChart for chart-based QA. The complete show notes for this episode can be found at https://twimlai.com/go/731.

To coincide with the release of our new report, Grazing Livestock: It's not the cow but the how, the latest guest on the SFT Podcast this month is Nic Renison. Nic is a regenerative farmer based in Cumbria where she farms alongside her husband, Paul (Reno), at Cannerheugh Farm. The daughter of dairy farmers, Nic grew up within the conventional, high production agricultural environment, growing food with little thought of the environment. This all changed in 2012 when Nic and Reno had a 'light bulb' moment after visiting an organic farm in Northumberland, which inspired them to start employing more regenerative farming methods. In 2018, alongside Liz Genever, Nic co-founded Carbon Calling – a conference created for farmers, by farmers, to share ideas and exchange knowledge on all things farming and regenerative agriculture. During the episode Nic and Patrick discuss Nic's early farming influences, her and her husband's journey from conventional to regenerative farming methods and the origins of the Carbon Calling conference, and how it supports the wider farming community. To find out more about Nic and Cannerheugh Farm, follow their journey on Instagram and visit their website here. To listen to more SFT podcasts, featuring some of the biggest names in regenerative food and farming, head to our main podcast page. And to keep up to date with our news, you can subscribe to our fortnightly newsletter or follow us on Instagram, X or Facebook.

In this post, we present a replication and extension of an alignment faking model organism: Replication: We replicate the alignment faking (AF) paper and release our code. Classifier Improvements: We significantly improve the precision and recall of the AF classifier. We release a dataset of ~100 human-labelled examples of AF for which our classifier achieves an AUROC of 0.9 compared to 0.6 from the original classifier. Evaluating More Models: We find Llama family models, other open source models, and GPT-4o do not AF in the prompted-only setting when evaluating using our new classifier (other than a single instance with Llama 3 405B). Extending SFT Experiments: We run supervised fine-tuning (SFT) experiments on Llama (and GPT4o) and find that AF rate increases with scale. We release the fine-tuned models on Huggingface and scripts. Alignment faking on 70B: We find that Llama 70B alignment fakes when both using the system prompt in the [...] ---Outline:(02:43) Method(02:46) Overview of the Alignment Faking Setup(04:22) Our Setup(06:02) Results(06:05) Improving Alignment Faking Classification(10:56) Replication of Prompted Experiments(14:02) Prompted Experiments on More Models(16:35) Extending Supervised Fine-Tuning Experiments to Open-Source Models and GPT-4o(23:13) Next Steps(25:02) Appendix(25:05) Appendix A: Classifying alignment faking(25:17) Criteria in more depth(27:40) False positives example 1 from the old classifier(30:11) False positives example 2 from the old classifier(32:06) False negative example 1 from the old classifier(35:00) False negative example 2 from the old classifier(36:56) Appendix B: Classifier ROC on other models(37:24) Appendix C: User prompt suffix ablation(40:24) Appendix D: Longer training of baseline docs--- First published: April 8th, 2025 Source: https://www.lesswrong.com/posts/Fr4QsQT52RFKHvCAH/alignment-faking-revisited-improved-classifiers-and-open --- Narrated by TYPE III AUDIO. ---Images from the article:

Richard Higgins, chairman and CEO of Good Gardeners International, is our guest on the latest episode of the SFT Podcast. Alongside being CEO of Good Gardeners International (GGI), Richard is also a philosopher, fungi specialist, holistic scientist, and Director of Sustainable Agriculture London. He grew up on a mixed farm in Somerset and studied his National Diploma in Agriculture (NDA) at the Royal Berkshire College of Agriculture on Farm and Grassland Management. He later completed a 10-year postgraduate study of the soil fertility works of Sir Albert Howard while travelling and teaching from China to Hawaii. In this episode, Richard talks to Patrick about Sir Albert Howard's influence on his own career, how agriculture intersects with the work of Good Gardeners International – including the charity's demonstration farm, its innovative composting system and the value of human manure as fertiliser. Visit Good Gardners International here to find out more about their work and follow them on their social media channels @GoodGardenersINTL. To listen to more SFT podcasts, featuring some of the biggest names in regenerative food and farming, head to our main podcast page. And to keep up to date with our news, you can subscribe to our fortnightly newsletter or follow us on Instagram, X or Facebook.

Joining our CEO, Patrick Holden, for this episode of the podcast is Jamie Feilden, founder of Jamie's Farm. Jamie Feilden founded Jamie's Farm in 2009, a charity which seeks to transform the lives of vulnerable children through farming, food and therapy. 15 years later, Jamie's Farm works with over 2,300 children a year across seven farms, and aims to offer as many children as possible an opportunity to improve their wellbeing, boost engagement and develop key life-skills, whilst spending time on a farm.  In this episode, Jamie shares with Patrick how his experiences as a history teacher in Croydon led to the inception of Jamie's Farm, as well as discussing his recent involvement in the SFT's Beacon Farms Network, and why an educated public is key to achieving positive change across our food and farming systems. Visit Jamie's Farm here to find out more about their work and follow them on their social media channels at @JamiesFarm. To listen to more SFT podcasts, featuring some of the biggest names in regenerative food and farming, head to our main podcast page. And to keep up to date with our news, you can subscribe to our fortnightly newsletter or follow us on Instagram, X or Facebook.

In this episode, Amit and Dheeraj dive deep into the world of AI reasoning models with Alex, an AI researcher involved in OpenThinker and OpenThoughts. They explore two recent groundbreaking papers—SkyT1 and S1 (Simple Test Time Scaling)—that showcase new insights into how large language models (LLMs) develop reasoning capabilities.From structured reasoning vs. content accuracy to fine-tuning efficiency and the role of active learning, this conversation highlights the shift from prompt engineering to structured supervised fine-tuning (SFT) and post-training techniques. The discussion also touches on open weights, open data, and open-source AI, revealing the evolving AI landscape and its impact on startups, research, and beyond.Key Topics & Chapter Markers[00:00] Introduction – Why reasoning models matter & today's agenda[05:15] Breaking Down SkyT1 – Structure vs. Content in reasoning[15:45] Open weights, open data, and open-source AI[22:30] Fine-tuning vs. RL – When do you need reinforcement learning?[30:10] S1 and the power of test-time scaling[40:25] Budget forcing – Making AI "think" more efficiently[50:50] RAG vs. SFT – What should startups use?[01:05:30] Active learning – AI asking the right questions[01:15:00] Final thoughts – Where AI reasoning is heading nextResources & Links

What's up friends, Alex here, back with another ThursdAI hot off the presses.Hold onto your hats because this week was another whirlwind of AI breakthroughs, mind-blowing demos, and straight-up game-changers. We dove deep into OpenAI's new "Deep Research" agent – and let me tell you, it's not just hype, it's legitimately revolutionary. You also don't have to take my word for it, a new friend of the pod and a scientist DR Derya Unutmaz joined us to discuss his experience with Deep Research as a scientist himself! You don't want to miss this conversation! We also unpack Google's Gemini 2.0 release, including the blazing-fast Flash Lite model. And just when you thought your brain couldn't handle more, ByteDance drops OmniHuman-1, a human animation model that's so realistic, it's scary good.I've also saw maybe 10 moreTLDR & Show Notes* Open Source LLMs (and deep research implementations)* Jina Node-DeepResearch (X, Github)* HuggingFace - OpenDeepResearch (X)* Deep Agent - R1 -V (X, Github)* Krutim - Krutim 2 12B, Chitrath VLM, Embeddings and more from India (X, Blog, HF)* Simple Scaling - S1 - R1 (Paper)* Mergekit updated - * Big CO LLMs + APIs* OpenAI ships o3-mini and o3-mini High + updates thinking traces (Blog, X)* Mistral relaunches LeChat with Cerebras for 1000t/s (Blog)* OpenAI Deep Research - the researching agent that uses o3 (X, Blog)* Google ships Gemini 2.0 Pro, Gemini 2.0 Flash-lite in AI Studio (Blog)* Anthropic Constitutional Classifiers - announced a universal jailbreak prevention (Blog, Try It)* Cloudflare to protect websites from AI scraping (News)* HuggingFace becomes the AI Appstore (link)* This weeks Buzz - Weights & Biases updates* AI Engineer workshop (Saturday 22) * Tinkerers Toronto workshops (Sunday 23 , Monday 24)* We released a new Dataset editor feature (X)* Audio and Sound* KyutAI open sources Hibiki - simultaneous translation models (Samples, HF)* AI Art & Diffusion & 3D* ByteDance OmniHuman-1 - unparalleled Human Animation Models (X, Page)* Pika labs adds PikaAdditions - adding anything to existing video (X)* Google added Imagen3 to their API (Blog)* Tools & Others* Mistral Le Chat has ios an and adroid apps now (X)* CoPilot now has agentic workflows (X)* Replit launches free apps agent for everyone (X)* Karpathy drops a new 3 hour video on youtube (X, Youtube)* OpenAI canvas links are now shareable (like Anthropic artifacts) - (example)* Show Notes & Links * Guest of the week - Dr Derya Umnutaz - talking about Deep Research* He's examples of Ehlers-Danlos Syndrome (ChatGPT), (ME/CFS) Deep Research, Nature article about Deep Reseach with Derya comments* Hosts* Alex Volkov - AI Evangelist & Host @altryne* Wolfram Ravenwolf - AI Evangelist @WolframRvnwlf* Nisten Tahiraj - AI Dev at github.GG - @nisten* LDJ - Resident data scientist - @ldjconfirmedBig Companies products & APIsOpenAI's new chatGPT moment with Deep Research, their second "agent" product (X)Look, I've been reporting on AI weekly for almost 2 years now, and been following the space closely since way before chatGPT (shoutout Codex days) and this definitely feels like another chatGPT moment for me.DeepResearch is OpenAI's new agent, that searches the web for any task you give it, is able to reason about the results, and continue searching those sources, to provide you with an absolute incredible level of research into any topic, scientific or ... the best taqueria in another country. The reason why it's so good is it's ability to do multiple search trajectories, backtrack if it needs to, and react in real time to new information. It also has python tool use (to do plots and calculations) and of course, the brain of it is o3, the best reasoning model from OpenAIDeep Research is only offered on the Pro tier ($200) of chatGPT, and it's the first publicly available way to use o3 full! and boy, does it deliver! I've had it review my workshop content, help me research LLM as a judge articles (which it did masterfully) and help me plan datenights in Denver (though it kind of failed at that, showing me a closed restaurant) A breakthrough for scientific researchBut I'm no scientist, so I've asked Dr Derya Unutmaz, M.D. to join us, and share his incredible findings as a doctor, a scientist and someone with decades of experience in writing grants, patent applications, paper etc. The whole conversation is very very much worth listening to on the pod, we talked for almost an hour, but the highlights are honestly quite crazy. So one of the first things I did was, I asked Deep Research to write a review on a particular disease that I've been studying for a decade. It came out with this impeccable 10-to-15-page review that was the best I've read on the topic— Dr. Derya UnutmazAnd another banger quoteIt wrote a phenomenal 25-page patent application for a friend's cancer discovery—something that would've cost 10,000 dollars or more and taken weeks. I couldn't believe it. Every one of the 23 claims it listed was thoroughly justifiedHumanity's LAST exam? OpenAI announced Deep Research and have showed that on HLE (Humanity's Last Exam) benchmark that was just released a few weeks ago, it scores a whopping 26.6 percent! When HLE was released (our coverage here) all the way back at ... checks notes... January 23 or this year! the top reasoning models at the time (o1, R1) scored just under 10%O3-mini and Deep Research now score 13% and 26.6% respectively, which means both that AI is advancing like crazy, but also.. that maybe calling this "last exam" was a bit premature?

 Seek For Truth 2.3.25 |Ancient Destrruction | Acts of peter | Dying testimoniesVisit us at www.seekfortruth.org

Kicking off series five of the Sustainable Food Trust podcast, Patrick Holden, SFT CEO and organic dairy farmer, catches up with Dani Nierenberg, President of Food Tank. Dani Nierenberg is a world-renowned researcher, speaker, and advocate, on all issues relating to our food system and agriculture. In 2013, Dani co-founded Food Tank with Bernard Pollack, a nonprofit organisation focused on building a global community for safe, healthy, nourished eaters. Food Tank is a global convener, thought leadership organisation, and unbiased creator of original research impacting the food system. Dani has an M.S. in Agriculture, Food, and Environment from the Tufts University Friedman School of Nutrition Science and Policy and spent two years volunteering for the Peace Corps in the Dominican Republic. In this first episode of the new series, Dani and Patrick discuss the impact of an extractive approach to agriculture upon our planet and our health. They consider how we can switch to a more regenerative approach – one that restores the soil, conserves water, and reduces the need for agrichemicals. Dani shares her insights on the recent shake-up in US politics and what the new administration could mean for food and farming, as well as exploring challenges relating to certification, labelling and consumer engagement. The conversation also examines the true cost of industrial food production, which typically isn't reflected in the retail price, and unpicks some of the sustainable agriculture challenges currently being faced in California and beyond. Commenting on what gives her hope for the future, Dani gives plenty of reasons to be optimistic, including opportunities for young people in agriculture and the huge potential for collaboration within the food and farming sector. Visit Food Tank here to learn more about their work. And you can find Dani on LinkedIn and X. To listen to more SFT podcasts, featuring some of the biggest names in regenerative food and farming, head to our main podcast page. And to keep up with our news, you can subscribe to our fortnightly newsletter or follow us on Instagram, X or Facebook.   Timestamps: 0:00 – Intro 0:43 – Welcome Dani! 1:28 – Food Tank's impressive global reach 3:06 – Dani's path to agriculture & sustainability 4:40 – The Peace Corps' influence on Dani's work 6:45 – The California wildfires 10:35 – Extractive agriculture in America  11:55 – What does the transition to more sustainable food & farming systems look like? 13:54 – How will the new US administration impact food and farming? 19:03 – How can we reach a wider audience? 21:22 – What did the Democrats achieve on food & farming in the last four years? 23:50 – Robert F. Kennedy Jr. and Joel Salatin 25:59 – Barriers for young farmers in the US 26:46 – Groundswell film 27:31 – The challenges with certification in organic farming 30:56 – The agrochemical industry's attempts to silence critics 32:53 – The importance of uncomfortable conversations and unusual collaborations 33:34 – True Cost Accounting  39:53 – Taking 'Feeding Britain' international 41:16 – Goodbye and thank you! 42:11 – Outro

Happy holidays! We'll be sharing snippets from Latent Space LIVE! through the break bringing you the best of 2024! We want to express our deepest appreciation to event sponsors AWS, Daylight Computer, Thoth.ai, StrongCompute, Notable Capital, and most of all all our LS supporters who helped fund the gorgeous venue and A/V production!For NeurIPS last year we did our standard conference podcast coverage interviewing selected papers (that we have now also done for ICLR and ICML), however we felt that we could be doing more to help AI Engineers 1) get more industry-relevant content, and 2) recap 2024 year in review from experts. As a result, we organized the first Latent Space LIVE!, our first in person miniconference, at NeurIPS 2024 in Vancouver. Today, we're proud to share Loubna's highly anticipated talk (slides here)!Synthetic DataWe called out the Synthetic Data debate at last year's NeurIPS, and no surprise that 2024 was dominated by the rise of synthetic data everywhere:* Apple's Rephrasing the Web, Microsoft's Phi 2-4 and Orca/AgentInstruct, Tencent's Billion Persona dataset, DCLM, and HuggingFace's FineWeb-Edu, and Loubna's own Cosmopedia extended the ideas of synthetic textbook and agent generation to improve raw web scrape dataset quality* This year we also talked to the IDEFICS/OBELICS team at HuggingFace who released WebSight this year, the first work on code-vs-images synthetic data.* We called Llama 3.1 the Synthetic Data Model for its extensive use (and documentation!) of synthetic data in its pipeline, as well as its permissive license. * Nemotron CC and Nemotron-4-340B also made a big splash this year for how they used 20k items of human data to synthesize over 98% of the data used for SFT/PFT.* Cohere introduced Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress observing gains of up to 56.5% improvement in win rates comparing multiple teachers vs the single best teacher model* In post training, AI2's Tülu3 (discussed by Luca in our Open Models talk) and Loubna's Smol Talk were also notable open releases this year.This comes in the face of a lot of scrutiny and criticism, with Scale AI as one of the leading voices publishing AI models collapse when trained on recursively generated data in Nature magazine bringing mainstream concerns to the potential downsides of poor quality syndata:Part of the concerns we highlighted last year on low-background tokens are coming to bear: ChatGPT contaminated data is spiking in every possible metric:But perhaps, if Sakana's AI Scientist pans out this year, we will have mostly-AI AI researchers publishing AI research anyway so do we really care as long as the ideas can be verified to be correct?Smol ModelsMeta surprised many folks this year by not just aggressively updating Llama 3 and adding multimodality, but also adding a new series of “small” 1B and 3B “on device” models this year, even working on quantized numerics collaborations with Qualcomm, Mediatek, and Arm. It is near unbelievable that a 1B model today can qualitatively match a 13B model of last year:and the minimum size to hit a given MMLU bar has come down roughly 10x in the last year. We have been tracking this proxied by Lmsys Elo and inference price:The key reads this year are:* MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases* Apple Intelligence Foundation Language Models* Hymba: A Hybrid-head Architecture for Small Language Models* Loubna's SmolLM and SmolLM2: a family of state-of-the-art small models with 135M, 360M, and 1.7B parameters on the pareto efficiency frontier.* and Moondream, which we already covered in the 2024 in Vision talkFull Talk on YouTubeplease like and subscribe!Timestamps* [00:00:05] Loubna Intro* [00:00:33] The Rise of Synthetic Data Everywhere* [00:02:57] Model Collapse* [00:05:14] Phi, FineWeb, Cosmopedia - Synthetic Textbooks* [00:12:36] DCLM, Nemotron-CC* [00:13:28] Post Training - AI2 Tulu, Smol Talk, Cohere Multilingual Arbitrage* [00:16:17] Smol Models* [00:18:24] On Device Models* [00:22:45] Smol Vision Models* [00:25:14] What's NextTranscript2024 in Synthetic Data and Smol Models[00:00:00] ​[00:00:05] Loubna Intro[00:00:05] Speaker: ​I'm very happy to be here. Thank you for the invitation. So I'm going to be talking about synthetic data in 2024. And then I'm going to be talking about small on device models. So I think the most interesting thing about synthetic data this year is that like now we have it everywhere in the large language models pipeline.[00:00:33] The Rise of Synthetic Data Everywhere[00:00:33] Speaker: I think initially, synthetic data was mainly used just for post training, because naturally that's the part where we needed human annotators. And then after that, we realized that we don't really have good benchmarks to [00:01:00] measure if models follow instructions well, if they are creative enough, or if they are chatty enough, so we also started using LLMs as judges.[00:01:08] Speaker: Thank you. And I think this year and towards the end of last year, we also went to the pre training parts and we started generating synthetic data for pre training to kind of replace some parts of the web. And the motivation behind that is that you have a lot of control over synthetic data. You can control your prompt and basically also the kind of data that you generate.[00:01:28] Speaker: So instead of just trying to filter the web, you could try to get the LLM to generate what you think the best web pages could look like and then train your models on that. So this is how we went from not having synthetic data at all in the LLM pipeline to having it everywhere. And so the cool thing is like today you can train an LLM with like an entirely synthetic pipeline.[00:01:49] Speaker: For example, you can use our Cosmopedia datasets and you can train a 1B model on like 150 billion tokens that are 100 percent synthetic. And those are also of good quality. And then you can [00:02:00] instruction tune the model on a synthetic SFT dataset. You can also do DPO on a synthetic dataset. And then to evaluate if the model is good, you can use.[00:02:07] Speaker: A benchmark that uses LLMs as a judge, for example, MTBench or AlpacaEvil. So I think this is like a really mind blowing because like just a few years ago, we wouldn't think this is possible. And I think there's a lot of concerns about model collapse, and I'm going to talk about that later. But we'll see that like, if we use synthetic data properly and we curate it carefully, that shouldn't happen.[00:02:29] Speaker: And the reason synthetic data is very popular right now is that we have really strong models, both open and closed. It is really cheap and fast to use compared to human annotations, which cost a lot and take a lot of time. And also for open models right now, we have some really good inference frameworks.[00:02:47] Speaker: So if you have enough GPUs, it's really easy to spawn these GPUs and generate like a lot of synthetic data. Some examples are VLM, TGI, and TensorRT.[00:02:57] Model Collapse[00:02:57] Speaker: Now let's talk about the elephant in the room, model [00:03:00] collapse. Is this the end? If you look at the media and all of like, for example, some papers in nature, it's really scary because there's a lot of synthetic data out there in the web.[00:03:09] Speaker: And naturally we train on the web. So we're going to be training a lot of synthetic data. And if model collapse is going to happen, we should really try to take that seriously. And the other issue is that, as I said, we think, a lot of people think the web is polluted because there's a lot of synthetic data.[00:03:24] Speaker: And for example, when we're building fine web datasets here at Guillerm and Hinek, we're interested in like, how much synthetic data is there in the web? So there isn't really a method to properly measure the amount of synthetic data or to save a webpage synthetic or not. But one thing we can do is to try to look for like proxy words, for example, expressions like as a large language model or words like delve that we know are actually generated by chat GPT.[00:03:49] Speaker: We could try to measure the amount of these words in our data system and compare them to the previous years. For example, here, we measured like a, these words ratio in different dumps of common crawl. [00:04:00] And we can see that like the ratio really increased after chat GPT's release. So if we were to say that synthetic data amount didn't change, you would expect this ratio to stay constant, which is not the case.[00:04:11] Speaker: So there's a lot of synthetic data probably on the web, but does this really make models worse? So what we did is we trained different models on these different dumps. And we then computed their performance on popular, like, NLP benchmarks, and then we computed the aggregated score. And surprisingly, you can see that the latest DOMs are actually even better than the DOMs that are before.[00:04:31] Speaker: So if there's some synthetic data there, at least it did not make the model's worse. Yeah, which is really encouraging. So personally, I wouldn't say the web is positive with Synthetic Data. Maybe it's even making it more rich. And the issue with like model collapse is that, for example, those studies, they were done at like a small scale, and you would ask the model to complete, for example, a Wikipedia paragraph, and then you would train it on these new generations, and you would do that every day.[00:04:56] Speaker: iteratively. I think if you do that approach, it's normal to [00:05:00] observe this kind of behavior because the quality is going to be worse because the model is already small. And then if you train it just on its generations, you shouldn't expect it to become better. But what we're really doing here is that we take a model that is very large and we try to distill its knowledge into a model that is smaller.[00:05:14] Phi, FineWeb, Cosmopedia - Synthetic Textbooks[00:05:14] Speaker: And in this way, you can expect to get like a better performance for your small model. And using synthetic data for pre-training has become really popular. After the textbooks are all you need papers where Microsoft basically trained a series of small models on textbooks that were using a large LLM.[00:05:32] Speaker: And then they found that these models were actually better than models that are much larger. So this was really interesting. It was like first of its time, but it was also met with a lot of skepticism, which is a good thing in research. It pushes you to question things because the dataset that they trained on was not public, so people were not really sure if these models are really good or maybe there's just some data contamination.[00:05:55] Speaker: So it was really hard to check if you just have the weights of the models. [00:06:00] And as Hugging Face, because we like open source, we tried to reproduce what they did. So this is our Cosmopedia dataset. We basically tried to follow a similar approach to what they documented in the paper. And we created a synthetic dataset of textbooks and blog posts and stories that had almost 30 billion tokens.[00:06:16] Speaker: And we tried to train some models on that. And we found that like the key ingredient to getting a good data set that is synthetic is trying as much as possible to keep it diverse. Because if you just throw the same prompts as your model, like generate like a textbook about linear algebra, and even if you change the temperature, the textbooks are going to look alike.[00:06:35] Speaker: So there's no way you could scale to like millions of samples. And the way you do that is by creating prompts that have some seeds that make them diverse. In our case, the prompt, we would ask the model to generate a textbook, but make it related to an extract from a webpage. And also we try to frame it within, to stay within topic.[00:06:55] Speaker: For example, here, we put like an extract about cardiovascular bioimaging, [00:07:00] and then we ask the model to generate a textbook related to medicine that is also related to this webpage. And this is a really nice approach because there's so many webpages out there. So you can. Be sure that your generation is not going to be diverse when you change the seed example.[00:07:16] Speaker: One thing that's challenging with this is that you want the seed samples to be related to your topics. So we use like a search tool to try to go all of fine web datasets. And then we also do a lot of experiments with the type of generations we want the model to generate. For example, we ask it for textbooks for middle school students or textbook for college.[00:07:40] Speaker: And we found that like some generation styles help on some specific benchmarks, while others help on other benchmarks. For example, college textbooks are really good for MMLU, while middle school textbooks are good for benchmarks like OpenBookQA and Pico. This is like a sample from like our search tool.[00:07:56] Speaker: For example, you have a top category, which is a topic, and then you have some [00:08:00] subtopics, and then you have the topic hits, which are basically the web pages in fine web does belong to these topics. And here you can see the comparison between Cosmopedia. We had two versions V1 and V2 in blue and red, and you can see the comparison to fine web, and as you can see throughout the training training on Cosmopedia was consistently better.[00:08:20] Speaker: So we managed to get a data set that was actually good to train these models on. It's of course so much smaller than FineWeb, it's only 30 billion tokens, but that's the scale that Microsoft data sets was, so we kind of managed to reproduce a bit what they did. And the data set is public, so everyone can go there, check if everything is all right.[00:08:38] Speaker: And now this is a recent paper from NVIDIA, Neumatron CC. They took things a bit further, and they generated not a few billion tokens, but 1. 9 trillion tokens, which is huge. And we can see later how they did that. It's more of, like, rephrasing the web. So we can see today that there's, like, some really huge synthetic datasets out there, and they're public, so, [00:09:00] like, you can try to filter them even further if you want to get, like, more high quality corpses.[00:09:04] Speaker: So for this, rephrasing the web this approach was suggested in this paper by Pratyush, where basically in this paper, they take some samples from C4 datasets, and then they use an LLM to rewrite these samples into a better format. For example, they ask an LLM to rewrite the sample into a Wikipedia passage or into a Q& A page.[00:09:25] Speaker: And the interesting thing in this approach is that you can use a model that is Small because it doesn't, rewriting doesn't require knowledge. It's just rewriting a page into a different style. So the model doesn't need to have like knowledge that is like extensive of what is rewriting compared to just asking a model to generate a new textbook and not giving it like ground truth.[00:09:45] Speaker: So here they rewrite some samples from C4 into Q& A, into Wikipedia, and they find that doing this works better than training just on C4. And so what they did in Nemo Trans CC is a similar approach. [00:10:00] They rewrite some pages from Common Crawl for two reasons. One is to, like improve Pages that are low quality, so they rewrite them into, for example, Wikipedia page, so they look better.[00:10:11] Speaker: And another reason is to create more diverse datasets. So they have a dataset that they already heavily filtered, and then they take these pages that are already high quality, and they ask the model to rewrite them in Question and Answer format. into like open ended questions or like multi choice questions.[00:10:27] Speaker: So this way they can reuse the same page multiple times without fearing like having multiple duplicates, because it's the same information, but it's going to be written differently. So I think that's also a really interesting approach for like generating synthetic data just by rephrasing the pages that you already have.[00:10:44] Speaker: There's also this approach called Prox where they try to start from a web page and then they generate a program which finds how to write that page to make it better and less noisy. For example, here you can see that there's some leftover metadata in the web page and you don't necessarily want to keep that for training [00:11:00] your model.[00:11:00] Speaker: So So they train a model that can generate programs that can like normalize and remove lines that are extra. So I think this approach is also interesting, but it's maybe less scalable than the approaches that I presented before. So that was it for like rephrasing and generating new textbooks.[00:11:17] Speaker: Another approach that I think is really good and becoming really popular for using synthetic data for pre training is basically building a better classifiers. For filtering the web for example, here we release the data sets called fine web edu. And the way we built it is by taking Llama3 and asking it to rate the educational content of web pages from zero to five.[00:11:39] Speaker: So for example, if a page is like a really good textbook that could be useful in a school setting, it would get a really high score. And if a page is just like an advertisement or promotional material, it would get a lower score. And then after that, we take these synthetic annotations and we train a classifier on them.[00:11:57] Speaker: It's a classifier like a BERT model. [00:12:00] And then we run this classifier on all of FineWeb, which is a 15 trillion tokens dataset. And then we only keep the pages that have like a score that's higher than 3. So for example, in our case, we went from 15 trillion tokens to 3. to just 1. 5 trillion tokens. Those are really highly educational.[00:12:16] Speaker: And as you can see here, a fine web EDU outperforms all the other public web datasets by a larger margin on a couple of benchmarks here, I show the aggregated score and you can see that this approach is really effective for filtering web datasets to get like better corpuses for training your LLMs.[00:12:36] DCLM, Nemotron-CC[00:12:36] Speaker: Others also try to do this approach. There's, for example, the DCLM datasets where they also train the classifier, but not to detect educational content. Instead, they trained it on OpenHermes dataset, which is a dataset for instruction tuning. And also they explain like IAM5 subreddits, and then they also get really high quality dataset which is like very information dense and can help [00:13:00] you train some really good LLMs.[00:13:01] Speaker: And then Nemotron Common Crawl, they also did this approach, but instead of using one classifier, they used an ensemble of classifiers. So they used, for example, the DCLM classifier, and also classifiers like the ones we used in FineWebEducational, and then they combined these two. Scores into a, with an ensemble method to only retain the best high quality pages, and they get a data set that works even better than the ones we develop.[00:13:25] Speaker: So that was it for like synthetic data for pre-training.[00:13:28] Post Training - AI2 Tulu, Smol Talk, Cohere Multilingual Arbitrage[00:13:28] Speaker: Now we can go back to post training. I think there's a lot of interesting post training data sets out there. One that was released recently, the agent instructs by Microsoft where they basically try to target some specific skills. And improve the performance of models on them.[00:13:43] Speaker: For example, here, you can see code, brain teasers, open domain QA, and they managed to get a dataset that outperforms that's when fine tuning Mistral 7b on it, it outperforms the original instruct model that was released by Mistral. And as I said, to get good synthetic data, you really [00:14:00] have to have a framework to make sure that your data is diverse.[00:14:03] Speaker: So for example, for them, they always. And then they see the generations on either source code or raw text documents, and then they rewrite them to make sure they're easier to generate instructions from, and then they use that for their like instruction data generation. There's also the Tool3SFT mixture, which was released recently by Allen AI.[00:14:23] Speaker: It's also really good quality and it covers a wide range of tasks. And the way they make sure that this dataset is diverse is by using personas from the persona hub datasets. Which is basically a data set of like I think over a million personas. And for example, in the tool mixture to generate like a new code snippet, they would give like the model persona, for example, a machine learning researcher interested in neural networks, and then ask it to generate like a coding problem.[00:14:49] Speaker: This way you make sure that your data set is really diverse, and then you can further filter the data sets, for example, using the reward models. We also released a dataset called Smalltalk, [00:15:00] and we also tried to cover the wide range of tasks, and as you can see here, for example, when fine tuning Mistral 7b on the dataset, we also outperformed the original Mistral instructs on a number of benchmarks, notably on mathematics and instruction following with ifevil.[00:15:18] Speaker: Another paper that's really interesting I wanted to mention is this one called Multilingual Data Arbitrage by Cohere. And basically they want to generate a data set for post training that is multilingual. And they have a really interesting problem. It's the fact that there isn't like one model that's really good at all the languages they wanted.[00:15:36] Speaker: So what they do is that like they use not just one teacher model, but multiple teachers. And then they have a router which basically sends the prompts they have to all these models. And then they get the completions and they have a reward model that traces all these generations and only keeps the best one.[00:15:52] Speaker: And this is like arbitrage and finance. So well, I think what's interesting in this, it shows that like synthetic data, it doesn't have to come from a single model. [00:16:00] And because we have so many good models now, you could like pull these models together and get like a dataset that's really high quality and that's diverse and that's covers all your needs.[00:16:12] Speaker: I was supposed to put a meme there, but. Yeah, so that was it for like a synthetic data.[00:16:17] Smol Models[00:16:17] Speaker: Now we can go to see what's happening in the small models field in 2024. I don't know if you know, but like now we have some really good small models. For example, Lama 3. 2 1B is. It matches Lama 2. 13b from, that was released last year on the LMSYS arena, which is basically the default go to leaderboard for evaluating models using human evaluation.[00:16:39] Speaker: And as you can see here, the scores of the models are really close. So I think we've made like hugely forward in terms of small models. Of course, that's one, just one data point, but there's more. For example, if you look at this chart from the Quint 2. 5 blog post, it shows that today we have some really good models that are only like 3 billion parameters [00:17:00] and 4 billion that score really high on MMLU.[00:17:03] Speaker: Which is a really popular benchmark for evaluating models. And you can see here that the red, the blue dots have more than 65 on MMLU. And the grey ones have less. And for example, Llama33b had less. So now we have a 3b model that outperforms a 33b model that was released earlier. So I think now people are starting to realize that like, we shouldn't just scale and scale models, but we should try to make them more efficient.[00:17:33] Speaker: I don't know if you knew, but you can also chat with a 3B plus model on your iPhone. For example, here, this is an app called PocketPal, where you can go and select a model from Hugging Face. It has a large choice. For example, here we loaded the 5. 3. 5, which is 3. 8 billion parameters on this iPhone. And we can chat with this and you can see that even the latency is also acceptable.[00:17:57] Speaker: For example, here, I asked it to give me a joke about [00:18:00] NeurIPS. So let's see what it has to say.[00:18:06] Speaker: Okay, why did the neural network attend NeurIPS? Because it heard there would be a lot of layers and fun and it wanted to train its sense of humor. So not very funny, but at least it can run on device. Yeah, so I think now we have good small models, but we also have like good frameworks and tools to use these small models.[00:18:24] On Device Models[00:18:24] Speaker: So I think we're really close to having like really on edge and on device models that are really good. And I think for a while we've had this narrative. But just training larger models is better. Of course, this is supported by science scaling laws. As you can see here, for example, when we scale the model size, the loss is lower and obviously you get a better model.[00:18:46] Speaker: But and we can see this, for example, in the GPT family of models, how we went from just a hundred million parameters to more than a trillion. parameters. And of course, we all observed the performance improvement when using the latest model. But [00:19:00] one thing that we shouldn't forget is that when we scale the model, we also scale the inference costs and time.[00:19:05] Speaker: And so the largest models were are going to cost so much more. So I think now instead of just building larger models, we should be focusing on building more efficient models. It's no longer a race for the largest models since these models are really expensive to run and they require like a really good infrastructure to do that and they cannot run on, for example, consumer hardware.[00:19:27] Speaker: And when you try to build more efficient models that match larger models, that's when you can really unlock some really interesting on device use cases. And I think a trend that we're noticing now is the trend of training smaller models longer. For example, if you compare how much, how long LLAMA was trained compared to LLAMA3, there is a huge increase in the pre training length.[00:19:50] Speaker: LLAMA was trained on 1 trillion tokens, but LLAMA3 8b was trained on 15 trillion tokens. So Meta managed to get a model that's the same size, but But it performs so much [00:20:00] better by choosing to like spend the sacrifice during training, because as we know, training is a one time cost, but inference is something that's ongoing.[00:20:08] Speaker: If we want to see what are like the small models reads in 2024, I think this mobile LLM paper by Meta is interesting. They try to study different models that are like have the less than 1 billion parameters and find which architecture makes most sense for these models. For example, they find that depth is more important than width.[00:20:29] Speaker: So it's more important to have models that have like more layers than just one. making them more wide. They also find that GQA helps, that tying the embedding helps. So I think it's a nice study overall for models that are just a few hundred million parameters. There's also the Apple intelligence tech report, which is interesting.[00:20:48] Speaker: So for Apple intelligence, they had two models, one that was like on server and another model that was on device. It had 3 billion parameters. And I think the interesting part is that they trained this model using [00:21:00] pruning. And then distillation. And for example, they have this table where they show that, like, using pruning and distillation works much better than training from scratch.[00:21:08] Speaker: And they also have some interesting insights about, like, how they specialize their models on specific tasks, like, for example, summarization and rewriting. There's also this paper by NVIDIA that was released recently. I think you've already had a talk about, like, hybrid models that was all interesting.[00:21:23] Speaker: And this model, they used, like, a hybrid architecture between state space models and transformers. And they managed to train a 1B model that's really performant without needing to train it on a lot of tokens. And regarding our work, we just recently released SmallM2, so it's a series of three models, which are the best in class in each model size.[00:21:46] Speaker: For example, our 1. 7b model outperforms Lama 1b and also Qt 2. 5. And how we managed to train this model is the following. That's where you spent a lot of time trying to curate the pre training datasets. We did a lot of [00:22:00] ablations, trying to find which datasets are good and also how to mix them. We also created some new math and code datasets that we're releasing soon.[00:22:08] Speaker: But you basically really spent a lot of time trying to find what's the best mixture that you can train these models on. And then we spent some time trying to like we also trained these models for very long. For example, small M1 was trained only on 1 trillion tokens, but this model is trained on 11 trillion tokens.[00:22:24] Speaker: And we saw that the performance kept improving. The models didn't really plateau mid training, which I think is really interesting. It shows that you can train such small models for very long and keep getting performance gains. What's interesting about SmallLM2 is that it's fully open. We also released, like the pre training code base, the fine tuning code, the datasets, and also evaluation in this repository.[00:22:45] Smol Vision Models[00:22:45] Speaker: Also there's, like, really interesting small models for text, but also for vision. For example, here you can see SmallVLM, which is a 2B model that's really efficient. It doesn't consume a lot of RAM, and it also has a good performance. There's also Moondream 0. [00:23:00] 5b, which was released recently. It's like the smallest visual language model.[00:23:04] Speaker: And as you can see, there isn't like a big trade off compared to Moondream 2b. So now I showed you that we have some really good small models. We also have the tools to use them, but why should you consider using small models and when? I think, like, small models are really interesting because of the on device feature.[00:23:23] Speaker: Because these models are small and they can run fast, you can basically run them on your laptop, but also on your mobile phone. And this means that your dataset stays locally. You don't have to send your queries to third parties. And this really enhances privacy. That was, for example, one of the big selling points for Apple Intelligence.[00:23:42] Speaker: Also, right now, we really have a lot of work to do. So many frameworks to do on device inference. For example, there's MLX, MLC, Llama, CPP, Transformers, JS. So we have a lot of options and each of them have like great features. So you have so many options for doing that. Small models are also really powerful if you choose to specialize them.[00:24:00][00:24:00] Speaker: For example, here there's a startup called Numind, which took small LM and then they fine tuned it on text extraction datasets. And they managed to get a model that's not very far from models that are much larger. So I think text extraction is like one use case where small models can be really performant and it makes sense to use them instead of just using larger models.[00:24:19] Speaker: You can also chat with these models in browser. For example, here, you can go there, you can load the model, you can even turn off your internet and just start chatting with the model locally. Speaking of text extraction, if you don't want to fine tune the models, there's a really good method of structure generation.[00:24:36] Speaker: We can basically force the models to follow a JSON schema that you defined. For example, here, we try to force the model to follow a schema for extracting key information from GitHub issues. So you can input free text, which is a complaint about a GitHub repository, something not working. And then you can run it there and the model can extract anything that is relevant for your GitHub issue creation.[00:24:58] Speaker: For example, the [00:25:00] priority, for example, here, priority is high, the type of the issue bug, and then a title and the estimation of how long this will take to fix. And you can just like do this in the browser, you can transform your text into a GitHub issue that's properly formatted.[00:25:14] What's Next[00:25:14] Speaker: So what's next for synthetic data and small models?[00:25:18] Speaker: I think that domain specific synthetic data is going to be, it's already important, it's going to be even more important. For example, generating synthetic data for math. I think this really would help improve the reasoning of a lot of models. And a lot of people are doing it, for example, Quint 2. 12 math, everyone's trying to reproduce a one.[00:25:37] Speaker: And so I think for synthetic data, trying to specialize it on some domains is going to be really important. And then for small models, I think specializing them through fine tuning, it's also going to be really important because I think a lot of companies are just trying to use these large models because they are better.[00:25:53] Speaker: But on some tasks, I think you can already get decent performance with small models. So you don't need to Pay like a [00:26:00] cost that's much larger just to make your model better at your task by a few percent. And this is not just for text. And I think it also applies for other modalities like vision and audio.[00:26:11] Speaker: And I think you should also watch out for on device frameworks and applications. For example, like the app I showed, or lama, all these frameworks are becoming really popular and I'm pretty sure that we're gonna get like more of them in 2025. And users really like that. Maybe for other, I should also say hot take.[00:26:28] Speaker: I think that like in AI, we just started like with fine tuning, for example, trying to make BERT work on some specific use cases, and really struggling to do that. And then we had some models that are much larger. So we just switched to like prompt engineering to get the models And I think we're going back to fine tuning where we realize these models are really costly.[00:26:47] Speaker: It's better to use just a small model or try to specialize it. So I think it's a little bit of a cycle and we're going to start to see like more fine tuning and less of just like a prompt engineering the models. So that was my talk. Thank you for following. And if you have [00:27:00] any questions, we can take them now. Get full access to Latent Space at www.latent.space/subscribe

(Weihnachtswoche, Tag 4) "Wusstet ihr eigentlich …?" ist unser Name für "Zusatzfolgen" zu großen Podcasts. In ihnen kommen alle Infos, Anekdoten, O-Töne und andere Dinge unter, die in der Hauptfolge keinen Platz hatten, vergessen wurden oder uns zu spät eingefallen sind. Das Format ist eines unserer beliebesten und häufigsten Unterstützerformate: Allein in diesem Jahr gab es (mit dieser) 16 Ausgaben davon. 6 davon bezogen sich auf SF-Folgen, 7 auf SSF und 3 auf SFT. Diesmal geht es um Deus Ex, das ist keine Wiederveröffentlichung aus dem Steady- oder Patreon-Feed, sondern eine brandneue Folge! Darin besprechen wir… … das Geheimnis des fehlenden Empire State Buildings … warum die Leitern im Spiel so schlecht funktionieren … den Besuch der Matrix und der Ion-Storm-Büros via Deus Ex … kuriose Bugs und zwei NPCs auf der Suche nach Sitzgelegenheiten Und als Bonus erklärt uns Verschwörungsexperte Christian Beuster die Wahrheit hinter zwei Verschwörungsmythen des Spiels.

Hey ya'll, Happy Thanskgiving to everyone who celebrates and thank you for being a subscriber, I truly appreciate each and every one of you! We had a blast on today's celebratory stream, especially given that today's "main course" was the amazing open sourcing of a reasoning model from Qwen, and we had Junyang Lin with us again to talk about it! First open source reasoning model that you can run on your machine, that beats a 405B model, comes close to o1 on some metrics

Gwern is a pseudonymous researcher and writer. He was one of the first people to see LLM scaling coming. If you've read his blog, you know he's one of the most interesting polymathic thinkers alive.In order to protect Gwern's anonymity, I proposed interviewing him in person, and having my friend Chris Painter voice over his words after. This amused him enough that he agreed.After the episode, I convinced Gwern to create a donation page where people can help sustain what he's up to. Please go here to contribute.Read the full transcript here.Sponsors:* Jane Street is looking to hire their next generation of leaders. Their deep learning team is looking for ML researchers, FPGA programmers, and CUDA programmers. Summer internships are open - if you want to stand out, take a crack at their new Kaggle competition. To learn more, go here: https://jane-st.co/dwarkesh* Turing provides complete post-training services for leading AI labs like OpenAI, Anthropic, Meta, and Gemini. They specialize in model evaluation, SFT, RLHF, and DPO to enhance models' reasoning, coding, and multimodal capabilities. Learn more at turing.com/dwarkesh.* This episode is brought to you by Stripe, financial infrastructure for the internet. Millions of companies from Anthropic to Amazon use Stripe to accept payments, automate financial processes and grow their revenue.If you're interested in advertising on the podcast, check out this page.Timestamps00:00:00 - Anonymity00:01:09 - Automating Steve Jobs00:04:38 - Isaac Newton's theory of progress00:06:36 - Grand theory of intelligence00:10:39 - Seeing scaling early00:21:04 - AGI Timelines00:22:54 - What to do in remaining 3 years until AGI00:26:29 - Influencing the shoggoth with writing00:30:50 - Human vs artificial intelligence00:33:52 - Rabbit holes00:38:48 - Hearing impairment00:43:00 - Wikipedia editing00:47:43 - Gwern.net00:50:20 - Counterfactual careers00:54:30 - Borges & literature01:01:32 - Gwern's intelligence and process01:11:03 - A day in the life of Gwern01:19:16 - Gwern's finances01:25:05 - The diversity of AI minds01:27:24 - GLP drugs and obesity01:31:08 - Drug experimentation01:33:40 - Parasocial relationships01:35:23 - Open rabbit holes Get full access to Dwarkesh Podcast at www.dwarkeshpatel.com/subscribe

Intervista a Giovanni Vincenti del team SASA con la presentazione del loro nuovo aereo Il Sapienza Flight Team o SFT è composto da studenti provenienti da diverse facoltà dell'ateneo. Raggruppa ragazzi di Ingegneria Aeronautica, Meccanica, Gestionale e Informatica uniti dalla voglia di mettersi in gioco e accettare nuove sfide. Nasce nel 2009 quando un gruppo

In this welcome departure from our usual dharma talks, David Moreno guides us in weaving sitting practice with the Tantric practice of Yoga Nidra and the energetic practice of Qi Gong. These processes augment and integrate meditation into moving mindfulness. Yet, they are complete meditations in themselves. Throughout this session, he encourages us to allow the movements to help us “feel more, think less.”WATCH this interactive talk and find the quotes that David shares on our website: https://gaybuddhist.org/podcast/somatic-meditation-the-anatomy-of-practice-david-moreno/______________David Moreno, RYT 500, YACEP, SFT, has taught at international yoga conferences, festivals, universities, and teacher trainings worldwide. He continues his study in Tantra, Ayurveda, meditation and Qi Gong. He is known for his depth, keen sense of humor and timing, making his teaching both playful and informative. His yoga commentaries have been published in Yoga Journal, Yoga International, LA Yoga Magazine, and Common Ground. His dance criticism and performing arts journalism are featured in Culture Vulture. David is also an ordained minister. He also teaches day-long and weekend-long mindfulness movement and sitting retreats, called Deliberate Stillness, at Green Gulch Zen Center. Learn more at https://moryoga.com/retreats/deliberate-stillness-daylong-2024/ ______________ To support our efforts to share these talks with LGBTQIA audiences worldwide, please visit https://gaybuddhist.org/There you can: Donate Learn how to participate live Find our schedule of upcoming speakers Join our mailing list or discussion forum Enjoy many hundreds of these recorded talks dating back to 1996 CREDITSAudio Engineer: George HubbardProducer: Tom BrueinMusic/Logo/Artwork: Derek Lassiter

Nobody wants to miss out on the next big thing. But “the next big thing” may, in fact, be nothing more than a dud. How can investors find the happy medium FOMO and foresight?  Senior Fool Analyst Asit Sharma joins Ricky Mulvey for a conversation on the different reasons why investors buy stocks. They also discuss: What we can learn from King Charles' portfolio.  The math of winners vs. losers. How to think about expected value. Tickers mentioned: SFT, PLTR, BTC, CRSP, RKLB, USHY Host: Ricky Mulvey Guest: Asit Sharma Producer: Mary Long Engineer: Tim Sparks, Austin Morgan Learn more about your ad choices. Visit megaphone.fm/adchoices

Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words. Through controlled experiments, we find that the model's effective generation length is inherently bounded by the sample it has seen during supervised fine-tuning (SFT). In other words, their output limitation is due to the scarcity of long-output examples in existing SFT datasets. To address this, we introduce AgentWrite, an agent-based pipeline that decomposes ultra-long generation tasks into subtasks, enabling off-the-shelf LLMs to generate coherent outputs exceeding 20,000 words. Leveraging AgentWrite, we construct LongWriter-6k, a dataset containing 6,000 SFT data with output lengths ranging from 2k to 32k words. By incorporating this dataset into model training, we successfully scale the output length of existing models to over 10,000 words while maintaining output quality. We also develop LongBench-Write, a comprehensive benchmark for evaluating ultra-long generation capabilities. Our 9B parameter model, further improved through DPO, achieves state-of-the-art performance on this benchmark, surpassing even much larger proprietary models. In general, our work demonstrates that existing long context LLM already possesses the potential for a larger output window--all you need is data with extended output during model alignment to unlock this capability. Our code&models are at: https://github.com/THUDM/LongWriter. 2024: Yushi Bai, Jiajie Zhang, Xin Lv, Linzhi Zheng, Siqi Zhu, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li https://arxiv.org/pdf/2408.07055

Colin Campbell is an Associate Director in the Improving Project Delivery team with the Scottish Futures Trust (SFT). The Scottish Government established SFT in 2008 to act as an independent centre of expertise to deliver improvements in the public sector's planning, innovation, delivery and management of infrastructure. Colin's work within SFT has been focused on improving construction quality since 2018. He is a chartered civil engineer, a member of the Association for Project Management and has over 44 years experience in the construction industry. Prior to joining SFT he was involved in the delivery of projects across the UK and in Singapore, Eastern Europe and the Middle East. He is currently co-chair of the Construction Quality Improvement Collaborative (CQIC), a sector wide campaign for improving construction quality, with a reach across Scotland. 

Image credit: Sustainable Food TrustMeet the Farmers is produced by RuralPod Media, the only specialist rural podcast production agency. Please note that this podcast does not constitute advice. Our podcast disclaimer can be found here. About Ben and  RuralPod MediaBen Eagle is the founder and Head of Podcasts at RuralPod Media, a specialist rural podcast production agency. He is also a freelance rural affairs and agricultural journalist. You can find out more at ruralpodmedia.co.uk or benjamineagle.co.uk If you have a business interested in getting involved with podcasting check us out at RuralPod Media. We'd love to help you spread your message. Please subscribe to the show and leave us a review wherever you are listening. Follow us on social mediaInstagram @mtf_podcastTwitter @mtf_podcastWatch us on Youtube here

If you see this in time, join our emergency LLM paper club on the Llama 3 paper!For everyone else, join our special AI in Action club on the Latent Space Discord for a special feature with the Cursor cofounders on Composer, their newest coding agent!Today, Meta is officially releasing the largest and most capable open model to date, Llama3-405B, a dense transformer trained on 15T tokens that beats GPT-4 on all major benchmarks:The 8B and 70B models from the April Llama 3 release have also received serious spec bumps, warranting the new label of Llama 3.1.If you are curious about the infra / hardware side, go check out our episode with Soumith Chintala, one of the AI infra leads at Meta. Today we have Thomas Scialom, who led Llama2 and now Llama3 post-training, so we spent most of our time on pre-training (synthetic data, data pipelines, scaling laws, etc) and post-training (RLHF vs instruction tuning, evals, tool calling).Synthetic data is all you needLlama3 was trained on 15T tokens, 7x more than Llama2 and with 4 times as much code and 30 different languages represented. But as Thomas beautifully put it:“My intuition is that the web is full of s**t in terms of text, and training on those tokens is a waste of compute.” “Llama 3 post-training doesn't have any human written answers there basically… It's just leveraging pure synthetic data from Llama 2.”While it is well speculated that the 8B and 70B were "offline distillations" of the 405B, there are a good deal more synthetic data elements to Llama 3.1 than the expected. The paper explicitly calls out:* SFT for Code: 3 approaches for synthetic data for the 405B bootstrapping itself with code execution feedback, programming language translation, and docs backtranslation.* SFT for Math: The Llama 3 paper credits the Let's Verify Step By Step authors, who we interviewed at ICLR:* SFT for Multilinguality: "To collect higher quality human annotations in non-English languages, we train a multilingual expert by branching off the pre-training run and continuing to pre-train on a data mix that consists of 90% multilingualtokens."* SFT for Long Context: "It is largely impractical to get humans to annotate such examples due to the tedious and time-consuming nature of reading lengthy contexts, so we predominantly rely on synthetic data to fill this gap. We use earlier versions of Llama 3 to generate synthetic data based on the key long-context use-cases: (possibly multi-turn) question-answering, summarization for long documents, and reasoning over code repositories, and describe them in greater detail below"* SFT for Tool Use: trained for Brave Search, Wolfram Alpha, and a Python Interpreter (a special new ipython role) for single, nested, parallel, and multiturn function calling.* RLHF: DPO preference data was used extensively on Llama 2 generations. This is something we partially covered in RLHF 201: humans are often better at judging between two options (i.e. which of two poems they prefer) than creating one (writing one from scratch). Similarly, models might not be great at creating text but they can be good at classifying their quality.Last but not least, Llama 3.1 received a license update explicitly allowing its use for synthetic data generation.Llama2 was also used as a classifier for all pre-training data that went into the model. It both labelled it by quality so that bad tokens were removed, but also used type (i.e. science, law, politics) to achieve a balanced data mix. Tokenizer size mattersThe tokens vocab of a model is the collection of all tokens that the model uses. Llama2 had a 34,000 tokens vocab, GPT-4 has 100,000, and 4o went up to 200,000. Llama3 went up 4x to 128,000 tokens. You can find the GPT-4 vocab list on Github.This is something that people gloss over, but there are many reason why a large vocab matters:* More tokens allow it to represent more concepts, and then be better at understanding the nuances.* The larger the tokenizer, the less tokens you need for the same amount of text, extending the perceived context size. In Llama3's case, that's ~30% more text due to the tokenizer upgrade. * With the same amount of compute you can train more knowledge into the model as you need fewer steps.The smaller the model, the larger the impact that the tokenizer size will have on it. You can listen at 55:24 for a deeper explanation.Dense models = 1 Expert MoEsMany people on X asked “why not MoE?”, and Thomas' answer was pretty clever: dense models are just MoEs with 1 expert :)[00:28:06]: I heard that question a lot, different aspects there. Why not MoE in the future? The other thing is, I think a dense model is just one specific variation of the model for an hyperparameter for an MOE with basically one expert. So it's just an hyperparameter we haven't optimized a lot yet, but we have some stuff ongoing and that's an hyperparameter we'll explore in the future.Basically… wait and see!Llama4Meta already started training Llama4 in June, and it sounds like one of the big focuses will be around agents. Thomas was one of the authors behind GAIA (listen to our interview with Thomas in our ICLR recap) and has been working on agent tooling for a while with things like Toolformer. Current models have “a gap of intelligence” when it comes to agentic workflows, as they are unable to plan without the user relying on prompting techniques and loops like ReAct, Chain of Thought, or frameworks like Autogen and Crew. That may be fixed soon?

SFT abre licitação para rastrear críticos na Internet; democracia tupiniquim distribuirá R$ 4,9 bi para partidos fazerem campanha nas eleições municipais; atividades políticas em Ipueiras, no Ceará; homicídio em município no norte do Ceará.

We talk about EVERYTHING. What to plan when you go, where to stay, what to do, what to avoid, and then we chat, review, and rank every film we saw there. 12 films that were all outstanding. Enjoy and cheers to 6 years of SFT. Here's hoping to have our own Palm d'Or in 6 more years. Love y'all and stay soooooooo sweeeeeeeett --- Support this podcast: https://podcasters.spotify.com/pod/show/sweetfilmtalk/support

Deputados defendem impeachment e cadeia para ministros do SFT; informações sobre a sessão da câmara municipal de Crateús; moto roubada em Cariré é recuperada em Reriutaba, no Ceará; homem é preso por arrastar cachorro preso a uma moto no Ceará.

We're back with another interview on location at the Mid-America Truck Show! This time, we welcome back an SFT favorite as Jose Palma slides behind the mic. Jose is known and loved for his big personality, but today, he drops the mic on something equally big—his career earnings. In seven short years, Jose Palma is about to become a Driveaway Millionaire. That's no typo because the earning power in Driveaway is no joke! Listen in as Jose extols the benefits of Driveaway with Norton Transport and hypes the sights and sounds of the Mid-America Truck Show on this edition of the #SixFigureTruckerThe Six Figure Trucker is a weekly conversation that shares the strategies and stories that successful drivers have used to build lucrative careers in the trucking industry. For more information or to subscribe, please visit https://www.six-figuretrucker.com/.   

Bringing the fourth series of the SFT podcast to a close, Patrick Holden caught up with longtime friend and one of the pioneers of the UK's organic farming movement, Iain ‘Tolly' Tolhurst. “We need to bring farming back into society. It's become completely divorced from society.” Tolhurst Organic, located on the Hardwick Estate between the Chilterns and the river Thames, is a model of sustainability, and one of the longest running organic vegetable farms in England. For over 40 years, Tolly has been producing a wide range of seasonal, organic fruits and vegetables, which are sold to the local community through a box scheme. His farm was the first to attain the “Stockfree Organic” symbol in 2004, and there have been no grazing animals and no animal inputs to any part of the farm for over 30 years. To build soil fertility, Iain uses green manures as part of a crop rotation, as well as using vegetable and woodchip compost from waste materials. With his extensive knowledge and experience of organic food production, Iain also delivers educational talks across the UK and beyond, and runs a consultancy service giving advice on organic conversion and production, helping to train and educate farmers and growers for the future. During this episode, Patrick and Tolly explore the economics of farming and what it means to pay the ‘right price' for our food. Tolly also talks about the productive capacity of Tolhurst Organic and how they're feeding the surrounding community through their veg box scheme. Patrick and Iain delve into the current state of the UK farming sector amidst the changing policy around farm subsidies and how this is affecting both organic and conventional farmers, before closing with a conversation on the importance of demonstration farms for informing and educating people about the story behind their food and the need to incentivise farmers for this. To find out more about Iain and Tolhurst Organic, follow @tolhurstorganicveg on Instagram, or visit www.tolhurstorganic.co.uk. This conversation has been split into two episodes – for part one click here, or find the episode wherever you get your podcasts from. To listen to more SFT podcasts, featuring some of the biggest names in regenerative food and farming, head to our main podcast page. And to keep up with our news, you can subscribe to our fortnightly newsletter or follow us on Instagram, X or Facebook.

The new Smarter Factory Technology Gateway (SFT) has launched at the Technological University of the Shannon (TUS) Moylish Campus. It is already providing an investment of €1.8 million in the Midwest and paving the way for enhanced enterprise and business security. The SFT is poised to support Irish companies in navigating and embracing the challenges faced by the digital landscape. Serving as a vital conduit between industry and academia, namely TUS, the SFT will forge collaborative ties with cutting-edge research institutions, bridging the gap between students, academia and enterprise. The data furnished by the gateway will empower businesses, fortifying them against digital threats and challenges in real-time, fostering smarter and more integrated operations. As technology continues to advance and digitalisation takes centre stage, the gateway aims to capitalise on the region's smart specialisations, particularly in advanced manufacturing, ICT, digital transformation, artificial intelligence, and virtual reality. These efforts will lead to tangible improvements in real-time systems, energy efficiency, and operational efficacy. Smarter Factory Technology Gateway Manager at TUS, Jim O'Hagan, remarked, "Our gateway will enhance the products, operations, efficiencies, and digital transformation of Irish manufacturing businesses through innovative research projects, funded by Enterprise Ireland grants. Through our advanced approach, we are poised as a catalyst for innovation and will be pivotal in meeting the evolving demands of customers, positioning companies for sustained growth and competitiveness." Explaining that the SFT will accelerate the development of future talent and innovation in the region Marina Donohoe, Head of Research and Innovation, Enterprise Ireland said, "New insights from smarter data will deliver many benefits for Irish businesses, such as improved quality, increased capacity, cost reductions and more sustainable operations. This important initiative will also lead to impactful innovation and will support businesses on their sustainable and digital journeys, to support innovative enterprises as they navigate the increasingly diverse and evolving digital landscape, and shape the future of manufacturing." Businesses can readily engage with the gateway through initiatives such as the Enterprise Ireland Innovation Voucher schemes or funded research programmes. The SFT stands as a beacon of excellence in the realm of Industry 4.0 and 5.0, spearheading digital and sustainable transformations to bolster innovative enterprises and shape the future landscape of manufacturing. The Smarter Factory Technology Gateway is co-funded by the Government of Ireland and the European Union through the ERDF Southern, Eastern & Midlands Regional Programme 2021-27. More about Irish Tech News Irish Tech News are Ireland's No. 1 Online Tech Publication and often Ireland's No.1 Tech Podcast too. You can find hundreds of fantastic previous episodes and subscribe using whatever platform you like via our Anchor.fm page here: https://anchor.fm/irish-tech-news If you'd like to be featured in an upcoming Podcast email us at Simon@IrishTechNews.ie now to discuss. Irish Tech News have a range of services available to help promote your business. Why not drop us a line at Info@IrishTechNews.ie now to find out more about how we can help you reach our audience. You can also find and follow us on Twitter, LinkedIn, Facebook, Instagram, TikTok and Snapchat.

We're live at the Mid-America Truck Show with Big E, Eric Ryan! Eric drove 650 miles to Louisville to take in all the Trucking eye candy and join us for this live recording. We certainly appreciate that because chatting with Big E is always a treat. He's a man who loves all things trucking and combines that with a fire that burns for progress and success. There's no question that Big E has “that dog” in him. In this episode, Eric gets personal as he talks about his fitness journey and the joy of sharing his passion for trucks with his boy. Prepare to be inspired as we grab twenty minutes with an SFT favorite, Eric Ryan, on this special edition on the road at the Mid-America Truck Show! #SixFigureTruckerShow Notes:We're Live with Big E from the Mid-America Truck Show! (1:04)Meeting Influencers and admiring Eye Candy at the MATS (1:55)Big E talks his love for Trucks and all things Trucking (5:30)From failed owner-operator to deep pockets in Driveaway (9:20)As a Teacher/Trainer, Big E brings the Wisdom! (11:42)The Top Traits of a Deck Driver (14:40)The inspiring backstory of Big E and his fitness journey (16:05)Keep Truckin', Big E!The Six-Figure Trucker is a weekly conversation that shares the strategies and stories that successful drivers have used to build lucrative careers in the driveaway trucking industry. For more information or to subscribe, please visit https://www.six-figuretrucker.com/. The Six-Figure Trucker is a weekly podcast about driveaway trucking brought to you by Norton Transport.

Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the prospect of growing a strong LLM out of a weak one without the need for acquiring additional human-annotated data. We propose a new fine-tuning method called Self-Play fIne-tuNing (SPIN), which starts from a supervised fine-tuned model. At the heart of SPIN lies a self-play mechanism, where the LLM refines its capability by playing against instances of itself. More specifically, the LLM generates its own training data from its previous iterations, refining its policy by discerning these self-generated responses from those obtained from human-annotated data. Our method progressively elevates the LLM from a nascent model to a formidable one, unlocking the full potential of human-annotated demonstration data for SFT. Theoretically, we prove that the global optimum to the training objective function of our method is achieved only when the LLM policy aligns with the target data distribution. Empirically, we evaluate our method on several benchmark datasets including the HuggingFace Open LLM Leaderboard, MT-Bench, and datasets from Big-Bench. Our results show that SPIN can significantly improve the LLM's performance across a variety of benchmarks and even outperform models trained through direct preference optimization (DPO) supplemented with extra GPT-4 preference data. This sheds light on the promise of self-play, enabling the achievement of human-level performance in LLMs without the need for expert opponents. Codes are available at https://github.com/uclaml/SPIN. 2024: Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu https://arxiv.org/pdf/2401.01335v2.pdf

Mais uma conquistas pela liberdade, senhores. O Gabinete Liberdade, projeto do Ideias Radicais, aprovou em Marechal Cândido Rondon, com um dos nosso vereadores parceiros, o Código de defesa do pagador de impostos, também chamado por alguns aí de Código de defesa do Sonegador (o que é incrível). Esse lei coloca o pagador de impostos em uma situação desfavorável juridicamente perante ao estado, colocando alguns freios em algumas atitudes exploratórias e autoritárias. Menos impostos para o Haddad, Lula e estado, mais brasileiros felizes. Chopp sem imposto SP: https://forms.gle/cJJjTtU2QUTFjAV56 Jornada do NOVO: https://novo.org.br/jornada2024/ Quer fugir do Brasil? Nos contate: https://www.settee.io/ https://youtube.com/c/Setteeio Nos acompanhe no Telegram: https://t.me/ideiasradicais Quer comprar Bitcoin no melhor preço do mercado? Bity! https://bit.ly/BityIdeiasRadicais 00:00 - Introdução e explicação 04:56 - Equivalência em taxas 07:48 - Assinatura Digital 07:57 - Reparação de Danos 08:49 - Ordem de fiscalização 10:26 - Respeito a súmulas do SFT e STJ 11:31 - Direito de defesa antes da autuação 13:00 - E agora, o que fazemos? 16:41 - Outra lei aprovada 20:49 - Declarações finais

Wir hatten noch Gesprächsbedarf zum Thema Sensible Soccer und haben uns nochmal hingesetzt und über einige offene Fragen geredet, etwa warum eigentlich der Musiker Captain Sensible im Soundtrack vorkommt, was Sensible Soccer mit Segas World Championship Soccer II gemein hat und warum Leute heutzutage noch SWOS spielen. Bei letzterer Frage hatten wir Unterstützung vom Sensible-Experten Michael Jänsch, der mit sensiblesoccer.de die größte Fanseite zum Thema betreut. Vielen Dank für das Interview, Michael! Hinweis: Dieser Podcast ist ein Beispiel für unser Format „Wusstet ihr eigentlich… ?“, das ist eine Serie von Begleitfolgen zu den großen Hauptfolgen, die wir normalerweise exklusiv für unsere Unterstützer veröffentlichen. Darin arbeiten wir unbenutzte Recherche auf, führen aber zuweilen auch extra Interviews und gehen mit intensiver Recherche Randfragen nach, die im normalen Podcast unbeantwortet geblieben sind. Derlei Folgen machen wir zu den meisten der Hauptfolgen von SF, SSF und SFT. Alle diese und massenhaft andere Formate gibt es auf Patreon und Steady für Unterstützer ab ca. 5 Euro monatlich.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Scalable And Transferable Black-Box Jailbreaks For Language Models Via Persona Modulation, published by Soroush Pour on November 7, 2023 on The AI Alignment Forum. Paper coauthors: Rusheb Shah, Quentin Feuillade--Montixi, Soroush J. Pour, Arush Tagade, Stephen Casper, Javier Rando. Motivation Our research team was motivated to show that state-of-the-art (SOTA) LLMs like GPT-4 and Claude 2 are not robust to misuse risk and can't be fully aligned to the desires of their creators, posing risk for societal harm. This is despite significant effort by their creators, showing that the current paradigm of pre-training, SFT, and RLHF is not adequate for model robustness. We also wanted to explore & share findings around "persona modulation"[1], a technique where the character-impersonation strengths of LLMs are used to steer them in powerful ways. Summary We introduce an automated, low cost way to make transferable, black-box, plain-English jailbreaks for GPT-4, Claude-2, fine-tuned Llama. We elicit a variety of harmful text, including instructions for making meth & bombs. The key is *persona modulation*. We steer the model into adopting a specific personality that will comply with harmful instructions.We introduce a way to automate jailbreaks by using one jailbroken model as an assistant for creating new jailbreaks for specific harmful behaviors. It takes our method less than $2 and 10 minutes to develop 15 jailbreak attacks. Meanwhile, a human-in-the-loop can efficiently make these jailbreaks stronger with minor tweaks. We use this semi-automated approach to quickly get instructions from GPT-4 about how to synthesise meth . Abstract Despite efforts to align large language models to produce harmless responses, they are still vulnerable to jailbreak prompts that elicit unrestricted behaviour. In this work, we investigate persona modulation as a black-box jailbreaking method to steer a target model to take on personalities that are willing to comply with harmful instructions. Rather than manually crafting prompts for each persona, we automate the generation of jailbreaks using a language model assistant. We demonstrate a range of harmful completions made possible by persona modulation, including detailed instructions for synthesising methamphetamine, building a bomb, and laundering money. These automated attacks achieve a harmful completion rate of 42.5% in GPT-4, which is 185 times larger than before modulation (0.23%). These prompts also transfer to Claude 2 and Vicuna with harmful completion rates of 61.0% and 35.9%, respectively. Our work reveals yet another vulnerability in commercial large language models and highlights the need for more comprehensive safeguards. Full paper You can find the full paper here on arXiv https://arxiv.org/abs/2311.03348 . Safety and disclosure We have notified the companies whose models we attacked We did not release prompts or full attack details We are happy to collaborate with researchers working on related safety work - please reach out via correspondence emails in the paper. Acknowledgements Thank you to Alexander Pan and Jason Hoelscher-Obermaier for feedback on early drafts of our paper. ^ Credit goes to @Quentin FEUILLADE--MONTIXI for developing the model psychology and prompt engineering techniques that underlie persona modulation. Our research built upon these techniques to automate and scale them as a red-teaming method for jailbreaks. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

In this episode we talk about how Paladin, Omar and Riz (Words Of Rizdom) started off trading prop firms like FTMO and TFG and E8 and took more than $500k in payouts before they started their own - Skilled Funded Trader Now on a journey to building a $100m brand, focussing on community, meeting the traders and being founded by actual traders An angle in the industry nobody is doing From scaling a business to being traders themselves, Paladin and Omar take a deep dive. Talking ICT and SMC and trade psychology and a day trading strategy

What if you could equip a struggling adolescent with the tools to tackle their own challenges, empowering them to transform their lives from the inside out? That's just what we're exploring as Dr. Linton Hutchinson, and I delve into a fascinating case study involving Gracie, a 14-year-old girl facing difficulties at school and bullying. Join us on this journey as we underscore the significance of understanding, rapport building, and validation in the therapeutic process and how these elements can help Gracie reclaim joy and control in her life.Ever wondered about the power of a 'miracle question'? By shifting the focus from problems to strengths, we reveal how Solution-Focused Therapy can be a game-changer for adolescents like Gracie. It's all about encouraging self-discovery, fostering resilience, and letting the client lead the way toward their own solutions. Don't just listen — join the conversation and discover how you can transform theory into powerful, practical strategies. This isn't just a podcast episode; it's a masterclass in empowering change, one solution at a time. Don't miss it!If you need to study for your NCMHCE narrative exam, try the free samplers at: CounselingExam.comThis podcast is not associated with the National Board of Certified Counselors (NBCC) or any state or governmental agency responsible for licensure.

audio, you gotta love it :) SFT's first Sundance (3:30) - Underrated: The Stephen Curry Story - L'immensita - In My Mother's Skin - You Hurt My Feelings OSCAR Wants & Will Win's (22:00) - Sound (22:00) - Musical Score (23:10) - Make-up & Hairstyling (24:05) - Live Action Short (25:15) - Costume Design (26:30) - Animated Short (27:30) - Visual Effects (29:00) - Production Design (30:00) - Original Song (31:20) - International Feature (33:10) - Editing (34:00) - Documentary Short (35:35) - Documentary (36: 50) - Cinematography (38:10) - Actor (39:10) - Actress (40:50) - Supporting Actor (42:00) - Supporting Actress (42:50) - Animated Feature (43:45) - Adapted Screenplay (44:45) - Original Screenplay (46:40) - Director (47:35) - BEST PICTURE AKA BEST FILM (48:45) Most Overlooked of 2022 (55:30) ON THE SLATE: MISSING/AUDIBLE wowowowowowowwo it's oscar season and we can't wait to see how close our predictions are going to be. We always love this time of the year and look forward to celebrating movies ALL DAY, ALL YEAR LONG, BABY!!! --- Support this podcast: https://anchor.fm/sweetfilmtalk/support

SFT! Thanks so much to all of you who joined us live today! Not a bad crowd for the first real FFS of the new year! I hope you guys had as much fun as we did, and we'll see some of you turds at the Solar Bears tonight! (Check our website to snag those last-minute seats in section 118! But it's gonna be tight! Busdeker is bringing like 20 f'n people! Come on out!) Ah...the show! Let's GO! On this week's show: * Ross tells his side of the story from the Solar Bears' intermission  * Afroman's raid song * Confrontation of the week * Homeless people living in parking lots  * Casselberry rooster tail * Firework Matlock  * Black. White. reality show * Tom wants a new tradition where the father of the bride gets acknowledged for paying for the wedding * Price of dog being neutered  ### Have a lovely weekend, guys! We love ya! Thanks SO MUCH for listening and choosing to spend some time with us.  d

So you are really passionate about regenerative, sustainable agriculture and food, right? And how many times have you heard, "Yes, that's all very nice but you couldn't feed the world that way; it wouldn't work at scale!"In this episode I talk to Sir Patrick Holden, the man who's building the roadmap to show us that it could.You'll hear us talk about his latest research into how the UK could feed itself using fertiliser/pesticide-free sustainable agriculture (with no grain-fed animals!), the tools to take this global, and how this lifetime campaigner for sustainable animal-involved agriculture feels about lab meat, supermarkets, ground-up change and much more.We're so grateful to him for sharing a little of his 50 year career as both a farmer and an activist with us.Our podcast has a patreon community! Our patrons get an additional private podcast, a library of recipes/guides, 1:1 access to us via a chat forum and monthly live get-togethers and to share/learn from each other.Come, join us! From $5 a month you'll be helping with the costs of keeping the podcast going and you'll get to be part of our world at a much deeper level.What We Talk About:* The Sustainable Food Trust's recent report 'Feeding Britain From The Ground Up'* How the UK could grow its staple foods using regenerative agriculture* The roadblocks in the way of this change and how we can address them* The metric being developed that would allow farmers worldwide to measure their sustainability using the same framework* Whether there's a role for laboratory-generated meat in a sustainable food future* Whether this shift needs to come from top-down or bottom-up* What, if you can't give them up, you need to be asking the supermarkets you shop at* What Sir Patrick thinks we need now in order to create the change5* reviews on Apple Podcasts, mean the world to us!Here's how you can leave one:Open the Apple Podcast appFind Ancestral Kitchen Podcast in your libraryScroll down to 'ratings and reviews'Click on 'write a review', give us 5*s and then tell us why you love listening in the box below Resources: Download The Sustainable Food Trust's report 'Feeding Britain From The Ground Up' here. There are three videos that succinctly and clearly explain the report and its findings: Introduction video, What We'd Eat video, and Potential Stumbling Blocks video SFT on Instagram Patrick's farm on Instagram Come find us on Instagram:Andrea is at Farm and HearthAlison is at

Join the SFT team as we recap the Fall 2022 Semester and look ahead to what Spring 2023 will have in store.

As the world's first move-to-earn NFT game, Genopets is making it fun and rewarding to live an active lifestyle.Co-founder and CEO Albert Chen joins Brian Friel to discuss Genopets' unique approach to web3 gaming. Show Notes: 00:56 - What is GenoPets?01:33 - Tracked by Mobile03:00 - Geopets similar to Tamagotchi03:44 - Background / Why build on Solana06:56 - User demographics08:33 - Thinking about an in-game economy10:57 - How to use GenoPets14:00 - SFTs and Crossovers with other games15:14 - Sustainability for in-game economy18:35  - What's next on the Roadmap     19:38 - Turn-based battles                20:21 - A builder he admires             22:02 - Contact info       Full Transcript: Brian (00:05):Hey everyone and welcome to the Zeitgeist, the show where we highlight the founders, developers, and designers who are pushing the web 3.0 space forward. I'm Brian Friel, developer relations at Phantom, and I'm super excited to introduce our guests today, Albert Chen. Albert is the co-founder, CEO and CTO of Genopets, a revolutionary note, free-to-play NFT game that's on mobile and is already at 300,000 plus users. Albert, welcome to the show.Albert (00:34):Hey, thanks Brian. Great to be here. Huge fan of Phantom as well.Brian (00:38):Excited to be talking with you as well, big fans of what you guys have been up to. I think there's a lot of folks listening to this podcast who may have heard what Genopets is. They made a scene, a few Genopets flying around on Twitter. But for the uninitiated, can you give us a little overview of who you are and then also what is Genopets?Albert (00:56):Yeah, so Genopets is a free-to-play move-to-earn NFT mobile game that makes it fun and rewarding to live an active lifestyle. A Genopet is a digital pet that you own and it evolves and grows as you become more active in life. The steps that you take every day powers your journey through the Genoverse as you explore battle and evolve, and you have the ability to earn and create NFTs while you play it.Brian (01:22):That's super cool. And so this is all tied to mobile, is that correct? Essentially you're able to track steps, an end user, how active they are throughout the day and have that influence the actual NFT?Albert (01:34):Yeah, exactly. So we are a native mobile app that you can now download from the app store for iOS and the Google Play store for Androids. We have a clear separation of concern in terms of our mobile app and our web app. So we also have a web app, and that's our web 3.0 platform that you can access through the Phantom browser actually. We made it mobile first so it's compatible on mobile devices as well as your desktop. These two experiences are separated for good reason. The mobile game experience is targeted towards any gamers and/or any fitness fanatic because it's very easy to download and just get started.(02:12):Once you download the app, you can summon a Genopet by answering a survey. Then once you get your Genopet, you can start leveling up and evolving it and caring for it. It's not until you want to really explore the other side, the NFT side of the game that you have to create a wallet, connect your account on our web app, and you can buy a Habitat and do crafting. We consider web 3.0 an expansion pack to the mobile game experience.Brian (02:37):I love the phrasing of that. It reminds me a lot of something like a Tamagotchi if you were around in the '90s like that long ago, or Pokemon kind of being tied in with this pet that can do battles, but you have to care for it and it's reflective of you and you kind of develop your own personal relationship and. I think that makes a lot of sense to kind of tie on the web 3.0 NFT component as an added on bonus to that.Albert (03:01):Yeah, absolutely. It's funny that you mentioned Tamagotchi because the first phase of our game loop, we literally internally call it the Tamagotchi loop because it's about growing your pet, caring for it, and then when we come out with Augmentation, you can customize your pet to look however you want. So that completes a whole game loop of what we call the Tamagotchi loop.Brian (03:20):That's super cool. You mentioned kind of the early days there, you guys calling it the Tamagotchi loop early on. I'm curious to hear maybe a little more before we dive into Genopets about your background specifically. You have a really unique framing of, you have this web 2.0-like game with a web 3.0 expansion pack. What is your background specifically and what led you to want to build a game in this way? And then why also did you choose Solana?Albert (03:44):Me personally, I've been a web developer since, I mean as early as 1999. Back then I was just building fun web experiences. I guess back then you could call it web 1.0. We wrote Perl scripts and that ran on websites that allowed us to build discussion boards and guess books and such. That's where I really came from. And then I started doing consumer products such as houseware goods. There was a brand that my co-founder, Ben and I, created called Unbrands. It was a line of products that allow you to hang anything on the wall anywhere without the damaging your wall. So we did that and we had a whole eCommerce layer to it, and this was in the early 2010s. And because of that, we've been working together for over a decade now and got into blockchain together in 2017.(04:32):One of our dreams was to solve this crisis that we see in the next 50 years of humanity, which is a lot of jobs that used to have value are slowly taken over by Ai and we want to be able to allow people to earn passive income through one way or another.(04:50):When we discovered blockchain, we thought it was a really good way of unlocking that. So in 2018 we had a project on the EOS blockchain called GeneOs. GeneOS was a decentralized health data marketplace. Essentially we wanted to create this rental marketplace for health data that allows people to monetize it and rent it to pharmaceutical companies or researchers that needed. We won the EOS Global Hackathon in 2018.(05:16):What ended up happening was we realized that that model was too top down instead of bottom up. There wasn't a lot of demand for this kind of data, at least not now in the technology for it. It doesn't exist yet, it requires a lot of progress and zero knowledge, machine learning and stuff. So we closed that down in 2020. And Ben, my co-founder, had the idea that the health data doesn't have to be boring, it can be something fun. What if it was represented as a pet? And back then we originally, our idea was that your health data would be an NFT and your NFT, it was a more boring version of NFT where your NFT would just be metadata on what your health records look like. But it was his idea that we can make that health record look like an actual pet that evolves as you become healthier.(06:06):So that was a breakthrough. And in 2021 we decided to relaunch the project, and this time on Solana because when we were evaluating different blockchains in the spring and summer of 2021, Solana was just coming up and I was very impressed with the TPS and the community that was growing behind it at the time. And to this day, I still think it's one of the biggest successes in Layer 1 history.Brian (06:28):That's so cool. That framing too of using health data and making it actually fun and something that turning it from this kind of dull dead thing that you don't care about to now it's represented as this ever evolving pet that you have an emotional attachment to, you care about, you want to continue to nurture that. That's a really cool framing. I love that.(06:47):I guess turning back to Genopets now, can you give us a sense of who your users are, maybe where they're distributed in the world?Albert (06:55):Yeah, we have a pretty global audience. Everyone around the world, pretty much every country you can think of. But our largest communities are in Japan, US, France, Russia, and India actually. This is just by the amount of activity that happens in our Discord. We have a fairly large Discord community by our players. The demographics is interesting. When we did a survey a few months ago, 70% of our players did not have any prior crypto experience before coming onto our platform. So we really think that web 3.0 gaming is going to be the springboard to get people onboarded into web 3.0.Brian (07:32):Oh, wow.Albert (07:33):We call it a Trojan horse of web 3.0.Brian (07:35):Yeah, I've definitely heard that Trojan Horse analogy before. That's pretty wild. 72%?Albert (07:41):70%. Every time that we measure this metric, it's actually going up. So there's a metric that we measure internally, which is what percentage of our players have never touched the blockchain while playing Genopets. And right now it's 80% as of last week.Brian (07:55):Wow, that's wild. I'm curious. You have this global audience across a number of different countries. I'd say we see as well in Phantom, like the US, certain parts of EU and certain parts of Asia as well are like there's some fairly crypto native communities there. But then as you're expanding that global reach, it doesn't surprise me that you're saying almost 70 to 80% of folks aren't touching blockchains. How do you think about that when you're designing a game that has these economic components as well? I mean, I know that you guys are free-to-play. Is that a big factor into that decision making and how do you kind of think high level about creating an in-game economy?Albert (08:33):From the very beginning, free-to-play has been one of the key pillars of how we wanted to build this economy. The thinking is that there's a whole other meta game inside of Genopets that's optional for the player, and that's the ability to become a merchant in this world. What that means is when you're playing the mobile Genopets' game, you don't have to think about how your items are received. If you want to level up your pet a little faster or you want that really cool weapon or really cool body part that we call Augment, you can just buy it directly on the mobile app. And on the other side of that transaction will be the merchant class player that cares a little bit more about the earning aspect of the game that are a little more crypto savvy. They can then log into our web 3.0 portal, connect their phantom wallet, buy a Habitat, and start crafting all kinds of items that people can use in the game.(09:30):So we like to call this the seller central, as an Amazon, the seller central of our game experience. So you have the demand side of the economy, which is made up of all players in the world, the same as any free-to-play game. And then you have the supply side, which in traditional gaming would be the game publisher, but here it's a little different, right? So we have the players create items for other players to consume and we take a fee in that.(10:00):The idea is that when you start enabling that, then you kind of get into a flywheel where if more people play, then more people will want to craft items and then there will be more content for people to consume and drives the other side of economy and you have this network effect that happens. Now, the end goal here is that through the governance of the actual game itself, the community can create new items themselves with stats that people vote on and move them into the game and complete this entire loop and allow this ecosystem to grow by itself.Brian (10:31):That's super cool. You mentioned a couple terms in there, I just want to hit on real quick. You mentioned crafting, which I feel like is this whole extra element to the game. There's Habitats as well. Habitats though being distinct NFTs separate from your actual Genopets for myself and also for folks listening as an end user, if I wanted to come and check out Genopets today, what should I be going to first? How do I kind of orient myself in this world where there's all these different components?Albert (10:57):Your Genopet is the main character that you use to play this game. It does not have to be an NFT. If you want, you can just download the app and you can create a Genopet. Later on, we will enable the ability to mint these Genopets as NFTs on chain after it reaches a certain level.(11:14):But if you want to go one step further into the game economy, the next step is looking at Habitats. So what Habitats are, they are NFTs where your Genopets can live in. But its main ability is the ability to mint SFTs and NFTs. It can convert the energy that you gain by walking in the game into what we call our KI token. The KI token is the main utility token in our ecosystem that drives everything.(11:42):So this mechanic is already out on Mainnet. We have tens of thousands of people using these on chain programs every day. What's coming in the next few weeks is the crafting part of it. So right now your Habitats can refine crystals every day, and these are building blocks of the game. Eventually these crystals can be combined to create Augments for your pet or toys or energy boosts, and these are items that you must have a Habitat in order to create.(12:11):We've created this program, an on chain program, that utilizes randomness to create what we call on chain loot box system. So every time you craft it, we have a bunch of recipes. The discovery of the recipe components is fun in itself, but the main mechanic that we're using here is that every time you craft, there's a different possibility of what item you end up getting. You can get a rare item or a common item or an uncommon item. It's almost like a digital booster pack. So components or ingredients in, item out. That's what crafting is. And then the end item, you can use endgame for energy boosts or you can use it to customize your Genopet, change your color of your Genopet, or add attributes, add attacks or defenses, what we call moves when we eventually launch battle.Brian (12:59):That's super cool. And then all these that you just mentioned, you mentioned the energy boosts, a lot of these are SFTs on Solana today?Albert (13:06):That's correct, yeah. So the only NFTs that we have right now are Genopets and Habitats, and that's because they're all unique. Items are SFTs because they are semi-unique, let's say. So like earth crystals for example, one earth crystal should not be different from another earth crystal. So to save for a variety of reasons, engineering reasons, enforcement of training fees and all of that, we prefer and we decided to build all of these items as SFTs and it's been working very well for us.Brian (13:39):That's super cool. I'm curious, and this might be getting too much into sharing deep roadmap stuff, but given that these things are on chain, they're SFTs, there's a world in which they could compose with other programs outside of Genopets, have you guys thought about that as well? Maybe having crossovers with other games or other DeFi platforms of some kind?Albert (14:00):Oh yeah, absolutely. It's a huge part of why we even built this economy on chain, right? Without the composability aspect of it, there is no point. We could just use a private database. Right now we are in talks with some DeFi protocols. I mean the whole SFT trading platform was built on Serum with the help of Magic Eden. So Magic Eden composes Serum where our SFTs are trading. And because they are SFTs, they can be probably put into an AMM a lot easier and you can probably do some kind of staking with it. So that's all future roadmap stuff and we're going to see where it goes. But yes, absolutely. Composability. I know it's something that everybody says, but it is definitely a huge part of what we do.Brian (14:43):Yeah, that's super cool. Exciting to think that you guys are thinking along those lines as well. I guess switching back, you mentioned KI token as well. We hit on it briefly with the fact that you guys are free-to-play, but do you have any thoughts on maybe more broadly for if there's a game developer listening to this, any sort of principles you're keeping in mind for when you're developing kind of a sustainable in-game economy? We've seen a few examples with Stepn that gain massive adoption quickly and then fizzle out. I'm curious, did you guys have any kind of guiding principles that guide you on this journey?Albert (15:15):So I think sustainable at game economy is something that everyone's trying to figure out and nobody has really been able to get it correct yet. Our high level vision is what I was describing earlier. If you make it so that the economy is based on people trading with one another instead of the money being paid in by newcomers into the ecosystem, then it can be more sustainable. However, it's not as simple as saying just removing growth from the equation and all is good. You still need growth, but it has to be sustainable in a way that growth contributes to the game economy in a slow but steady manner.(15:53):So one of the things that we designed in the very beginning is that if you want to earn KI tokens, you have to pay into the economy somehow. So this whole concept of free-to-play to earn doesn't really exist in the sense that you can earn for free. You can play for free, but if you want to earn, then you have to invest in either a Habitat or someone has to terraform, that's the word we use for create. Someone has to create a Habitat for you to rent and convert your energy into KI tokens. So that's number one, which is you always have to have some kind of pay in.(16:29):The second part is making sure that the growth is sustainable. So we constantly measure our emissions versus burn. We've been doing a decent job at it. I think we are still under, in terms of the net KI emitted in the last two months. So we've been live, our earning part of the game has been live for two months now. We've had some ups and downs. We went through a three week period where it was a very high net emissions. Then we started changing some rules around.(16:57):One of the biggest rule changes that we made was that level 1 pets cannot earn as much energy as let's say a level 22 pet. Before, it was already the case, but the difference wasn't that high. And that meant that you didn't have to put in that much work to start earning the maximum amount, which was not sustainable. Once we've changed that, our emission schedule decreased quite significantly and it's looking fairly healthy. But as I said earlier, you can't just take growth out of the equation. We do have to still grow users. We still have to introduce crafting to make sure that there's demand for the content. And at the end of the day, if you have growing demand for content, then it would be sustainable in the long term.Brian (17:40):It's such a tricky problem that's above my head. It's all the gig of brains are figuring out the engineering, the economy around this. But it's really exciting that you guys are already seeing this kind of organic growth and excitement around this. I think it makes a ton of sense.Albert (17:53):Yeah. And it's super hard. Everything is hard mode right now because we're in deep winter, so no one's really speculating. No one's trying to buy thousands of dollars in NFTs thinking they'll make their money back in a few months. So you really have to get the fundamentals correct. Yeah, it's very different from Axie Infinity or a Stepn, the times have changed.Brian (18:14):Yeah, yeah. You guys get a chance to lay the right foundation though in the bear market. There's been many stories of that, even Solana being one of them, that generational crypto companies are built in the bear market.(18:26):I guess you hit on it a little bit with some experiments you guys are thinking around composability. Is there anything else that you can share with us for what's next on your guys' roadmap?Albert (18:35):Yeah, so like I mentioned earlier, I think on chain crafting is going to be a huge one. Once on chain crafting is out, there's a lot of composability elements that you can create just on chain. We'll leave that up to the creativity of the community. That's the biggest one because we needed to start completing our Tamagotchi loop, which ends with augmentation. Augmentation is when you craft an item that has a body part, you can switch out the body part of your pet and immediately reflect on chain and end game. So that's the biggest thing that's going to happen in the next two, three months. And then after that it's battle. So we're going to allow people to... I mean, once you start customizing your pet and your pet looks super cool, what's next? You obviously want to compete with other people. That will be our battle loop. The battle loop is something that we really look forward to because that will complete the second phase of our game, and that is likely when we will start ungating the game for the rest of the world.Brian (19:32):Is this battle loop that you're foreshadowing here, is this turn-based battle? Or is this more live action pet battles?Albert (19:38):It's turn-based battle. You can think of it as small mini games, reaction games, and then it's partly skilled based and then partly attribute based. So the stronger a pet is, the easier it is for them to win and battle. And there's going to be elemental parts of it as well so there's some strategy involved.Brian (19:57):I love it. Young gamer me right now is itching to get my hands on some Genopets right now, so we'll have to follow after the show on that.Albert (20:05):Yeah, that would be awesome.Brian (20:06):Well, Albert, this has been an awesome discussion. Really appreciate you coming on and sharing the vision of Genopets and what you guys have all been up to. One closing question we always ask all our guests, and I want to know from you as well, is who is a builder that you admire in the Solana ecosystem?Albert (20:21):Yeah, so that's a great question because there's so many great builders in Solana, but there's one guy that I've worked with in the past for our KI token launch actually that I greatly admire, and that's Noah Prince from Strata Protocol. I just think he has a giga brain that's able to figure out one of the hardest things in the token launch ecosystem. And he did it so well and I haven't seen a protocol that's done it better since then. And when we worked together, there were some small bugs that we had when we were using their protocol and he just hopped on it right away and within a few hours fixed everything, submitted a pull request, and then we were able to pull from that and launch our KI token pretty quickly. So I got to say it's him.Brian (21:05):Oh, that's awesome. So you guys used Strata actually to launch KI?Albert (21:08):Yeah. Yeah, we launched KI on Strata. It was an interesting one because you never know what's going to happen. It was a three day sale. And the first two days there were almost no volume and you're like, "Ugh, God, we're not going to sell out. This is bad." And it turns out every, it's like a game of chicken. Everyone's just waiting. And as soon as somebody start buying, we had almost 2 million sold in a few hours. It's a crazy journey.Brian (21:32):Yeah. Market wide incentives like that happening in real time, it's pretty cool to see. And I totally echo the sentiment around him as well. I think he goes by @redacted_noah on Twitter. He was actually paramount to work with me when we were first getting SFT support in Phantom actually earlier this year as well.Albert (21:48):Oh, awesome. I didn't know that. That's fantastic.Brian (21:51):He's everywhere at once. We'll have to tag him on Twitter to let him know that. Shout out.Albert (21:56):Awesome. Yeah.Brian (21:57):Well, Albert, this has been awesome. Really appreciate you coming on. Where can folks go to learn more about Genopets?Albert (22:02):Yeah, they can go on our website, genopets.me or follow our Twitter, @Genopets, and they can get all the information there.Brian (22:10):Awesome. Love it. Albert Chen, founder, CEO, CTO of Genopets, thanks for coming on.Albert (22:15):Thanks Brian. That was fun.

   Brad and Russ are in Studio “B” for this episode of SFT.  While enjoying…

A recording of a group SFT healing event

The Six-Figure Trucker highlights the successful men and women behind the wheel of the beautiful business of trucking. But it's not always and only about making money here at SFT. We enjoy highlighting the incredible people that make the wheels of this industry roll. Today we catch up with one of our drivers as he helps to provide relief and aid to the areas in Florida that were recently battered by Hurricane Ian. Benjamin Bernard is thrilled to spend over two weeks on the grown in the Fort Myers area in an attempt to do his part to help his fellow man.When he's not out saving the world, Benjamin fills the free time afforded him in driveaway with numerous other ventures and interests. From the full remodel of his house to his side business in welding, he's a regular jack of all trades. He's also a motorcycle enthusiast who's been known to strap his motorcycle to a deck set for a quick and cost-effective return trip. We're pleased to welcome the bright and talented Benjamin Bernard to the show on today's episode of the #SixFigureTrucker.Show Notes:We catch up with our Good Samaritan in Sarasota, Florida, where he has come to help with Hurricane Ian recovery (1:00)Up on Two Wheels! (5:05)Moving from Two Wheels to Two Wings. Benjamin's approach to flying (14:52)Expert Advice on Load planning and preparations (17:11)Benjamin shares his experience in trucking and his journey to Driveaway (20:47)From a Welding Business venture to a Full Remodel of his house, Benjamin takes advantage of his freedom in Driveaway for big-time productivity (23:32)Kind of like Santa - Benjamin talks about delivering brand new trucks to eager Customers and other cool experiences in Driveaway (28:37)Moving Deck Sets is unique. Benjamin talks about a couple of no-nos when Decked (33:36)Rumbling home in an Old School Army Humvee? (37:28)Benjamin pitches Driveaway! (40:25)Keep Truckin', Benjamin!The Six Figure Trucker is a weekly conversation that shares the strategies and stories that successful drivers have used to build lucrative careers in the trucking industry. For more information or to subscribe, please visit https://www.six-figuretrucker.com/.

Mary Ann Hughes is the proud mother of two sons on opposite ends of the autism spectrum. Today she joins me for a conversation about going through a divorce when you have children with special needs. During her divorce, Mary Ann successfully advocated for her children's needs. As a result, she started Special Family Transitions to help families navigate the overwhelm and complexities of special needs divorce to get the best possible outcome, with as little time, money, and stress as possible. Today, she joins me for a conversation about navigating divorce in the midst of parenting (and eventually co-parenting) children with disabilities. Combining her experience and certifications as a Certified Divorce Coach, Certified Divorce Specialist, member of the National Association of Divorce Professionals, MBA, and years of special needs advocacy, Mary Ann is committed to supporting families with children with disabilities as a valued special needs divorce coach and consultant. Show Highlights Transitions can be hard for neurodivergent children – Mary Ann shares how parents approach the decision-making process of divorce The impact of divorce on children with disabilities How to co-parent with kids with special needs when a parent is not engaged or doesn't prioritize the children How and why you may want to set up a trust for your children What you need to know about divorce when you have kids on the spectrum Learn more about Mary Ann Hughes: As a mom of two boys on the autism spectrum who unexpectedly faced divorce after 21 years of marriage, Mary Ann Hughes had to learn how to navigate the complexities of special needs divorce, to effectively advocate for her children's needs and get a great result for her family in my divorce. Mary Ann formed Special Family Transitions and became a Special Needs Divorce Coach and Consultant so other moms of children with disabilities wouldn't have to spend the time, money, and emotional energy she did when faced with divorce. Mary Ann is on a mission to help mothers gain the confidence, skills and knowledge to successfully overcome the overwhelm and challenges of special needs divorce, to achieve the best possible result for their family.  Mary Ann combines her experiences as a Certified Divorce Coach, Certified Divorce Transition and Recovery Coach, Certified Divorce Specialist, Certified Life Coach, member of National Association of Divorce Professionals and NADP Special Needs Chapter, LoneStar LEND Leadership Education in Autism and Neurodevelopmental Disabilities Fellow, MBA with a successful career in Fortune 100 companies (pre-kids), and years of special needs training and advocacy, to help her clients effectively advocate for themselves and their children in special needs divorce. Resources & Links: Information and links may also be found here: https://kateanthony.com/podcast/divorcing-with-children-with-special-needs-with-mary-ann-hughes/ Grit and Grace Group Coaching is Open – Join us!Mary Ann's websiteMary Ann on FacebookMary Ann on InstagramMary Ann on YouTube Mary Ann on TikTokMary Ann on LinkedIn Mary Ann's course: Keys to Success in Divorce for Moms of Children with Special Needs – DSG listeners get 25% off with discount code SFT. THE M3ND PROJECT The M3ND Project's mission is to bring clarity and validation to victims and survivors and to provide tools and resources for those who are responding to abuse. Annette Oltmans founded The M3ND Project coming out of her own experience as a survivor of emotional abuse and double abuse and after years of researching academic materials and personally interviewing hundreds of abuse survivors, therapists, and faith leaders. M3ND does this by providing various educational resources and training courses. Sometimes, it can be hard to articulate what you are going through when you try to reach out to a friend or therapist for help, and it can make you feel crazy. As a survivor, I remember feeling this way. When I first came across Mend's Terms and Definitions tool, which names and explains covert abusive tactics, it was SO validating and illuminating. M3ND wants to share this resource with The Divorce Survival Guide Listeners for free!! Go get this tool that I think is so essential: Grab M3ND's Terms and Definitions Tool: https://kateanthony.com/mend JOIN THE SHOULD I STAY OR SHOULD I GO FACEBOOK GROUP

A recording of a Jenuine Healing group SFT tapping session

This is an eventful Wednesday as we unpack some business and some drama while we talk about the Shift/CarLotz merger, GM forcing a very expensive subscription on buyers, Echo Park hiring a cutting edge social agency, and Google trying to shame Apple to get rid of it's blue imessage bubbles. Used Car retailers Shift and CarLotz announce mergerStock for stock transaction to be traded as SFT on Nasdaq Shift is an ecomm platform has primarily a west coast presence, while CarLots has a  mid-atlantic presence and is primarily a brick and mortar consignment modelShift will be cutting of its workforce (about 60%) through end of yearShifts current President, Jeff Clementz will become the company's CEOThey are expected to have a position of  $125m in cashTake away: Consolidation is moving beyond franchised dealers, but can it holdGM to force Buick, GMC and Escalade buyers to pay $1500 for 3 years of Onstar at POSPrice included in separate section of the sticker and is included in the MSRP of all ordered Buicks and GMCs June 2 and Escalades ordered starting July 18Customer pays even if the service is never activatedRack rate is $49.99, the baked in pricing comes to 41.67 with an auto renew at full priceGM's research resorts that customers subscribe to 25 products and services and will spend $135 per month on them on avgGMs goal is to have $20-25B in sub revenue by 2030Take away: Forcing customers to buy something they weren't buying before doesn't seem like a winning strategyEcho Park retains VaynerMedia as Agency of Record Former Mazda N/A CMO Dino Bernacchi started working at EchoPark in October and said that's “when the company really decided to shift gears on the trajectory of growth and start building more than just an infrastructure of locations.”Now that infrastructure has been laid, it's time to “actually build the brand” said BernacchiVayner CEO, Gary Vaynerchuk said the agency will handle creative, strategy, analytics, and more with a focus on socialTake away: We've been waiting for someone to do this at scale, the good news is, everyone can take this approach todayGoogle to launch shaming, er marketing campaign to pressure Apple to adopt universal messaging formatTrying to push adoption of the RCS, universal messaging formatOne tweet from Android reads “iMessage should not benefit from bullying. Texting should bring us together, and the solution exists. Let's fix this as one industry”Take away: Where do we start on this one? Get the Daily Push Back email at https://www.asotu.com/Rock with us LIVE at ASOTU CON! Tickets: https://www.asotucon.comJOIN the conversation on LinkedIn at: https://www.linkedin.com/company/asotu/Read our most recent email at: https://www.asotu.com/media/push-back-emailShare your positive dealer stories: https://www.asotu.com/positivityASOTU Instagram: https://www.instagram.com/automotivestateoftheunion

A recording of a group SFT tapping event on the portal of the summer (northern hemisphere) and winter (southern hemisphere) solstice 2022

Podcast guest 501 is Jen Ward, dynamic healer, performance coach and group facilitator. She has devoted her life to helping others unlock their true potential. She is also an accomplished writer and poet. Jen's extraordinary and challenging personal journey has gifted her with a unique ability to perceive in energy, read akashic records and shift stagnant energy. This, along with her Spiritual Freedom Tapping techniques, allows Jen to work with clients to see and remove the blockages to happiness and effectiveness that exist within any individual. Jen is the creator of the SFT Tapping protocol. SFT tapping is different to other affirmations because it bypasses the ego. This is the secret sauce to Jen's powerful energy healing work with client and the collective to uplift all of humanity. Jen is the author of 19 books and is in the process of updating several of them. Most notably, Jen has recently published the second edition of The SFT Lexicon, a roadmap and an easy-to-follow guidebook for anyone seeking to transform and uplift themselves as a steppingstone to transcendence, higher consciousness, and enlightenment. She has recently published her latest poetry book, Jenuine Poetry for Life: Poems to Uplift Humanity. Jen's Website https://jenuinehealing.com Jen's Youtube Channel https://youtube.com/jenuinehealing --- Send in a voice message: https://anchor.fm/jeffrey-s-reynolds/message Support this podcast: https://anchor.fm/jeffrey-s-reynolds/support

An episode of the Jen in her Jammies podcast series. This is an instructional video on the use of SFT tapping.

A recording of a Jenuine Healing group SFT tapping session

A recording of a Jenuine Healing group SFT tapping session