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schadenball .com is back for #UCLFantasy 25/26! 👀 Adjustable Mins Points model 📺Improved data viz 📈 Enhanced xPoints model 💰 DFS xPoints (FanTeam, etc.) 🆓 Team-level eG, CS odds, team strength, captaincy, market odds Thanks to all the supporters - absolute legends!❤️ #UCL

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FPL Schaden

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60,183 次观看 • 1 年前 •via X (Twitter)

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A 96th-minute winner to secure promotion: Bradford City AFC are back in League One. But can this fairytale final day spark a resurgence for the ex-Premier League team? Branded “too young, too cheap, out of his depth”, Ryan Sparks took over the club as CEO at one of the lowest points in its history. But now they’re on the up. 🏆 The transformation behind Bradford’s long-overdue promotion 📈 Turning a £4.2M failure into a £9M business 🏟️ 16K season ticket holders & record crowds fuelled by a community-first model 🛠️ Building culture from scratch: hiring, data, and trusting young talent 💰 Competing with parachute-payment giants All this on Business of Sport 🔥
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A 96th-minute winner to secure promotion: Bradford City AFC are back in League One. But can this fairytale final day spark a resurgence for the ex-Premier League team? Branded “too young, too cheap, out of his depth”, Ryan Sparks took over the club as CEO at one of the lowest points in its history. But now they’re on the up. 🏆 The transformation behind Bradford’s long-overdue promotion 📈 Turning a £4.2M failure into a £9M business 🏟️ 16K season ticket holders & record crowds fuelled by a community-first model 🛠️ Building culture from scratch: hiring, data, and trusting young talent 💰 Competing with parachute-payment giants All this on Business of Sport 🔥

Business of Sport

43,545 次观看 • 1 年前

Using Claude Fable 5, I built a model that predicts the entire 2026 FIFA world cup.. every single game, not just the final.. so let me break the whole thing down. what it does, how it works, and exactly how i built it.. #1 First what it does: it predicts all 104 games of the tournament. not just who lifts the trophy, but every group match, every knockout, the full path from the round of 32 to the final.. everything lands in one dashboard: > group stage, every match with each team's win % and the chance of a draw > standings, how all 12 groups are projected to finish > bracket, the full knockout tree with each team's odds of advancing > champion odds, who's most likely to actually win it all and it doesn't freeze after one prediction. the moment a real game is played, it locks that result in and re-runs everything around it. so the odds move live as the tournament goes, week by week you watch favorites rise and contenders collapse. #2. How it works: the core idea is simple. the model only ever predicts one thing, a single match. the real trick is the repetition. it learns from decades of match history, then plays the whole tournament out from the first game to the final, tens of thousands of times. each run it records who advanced and who won. do that enough and you stop getting one guess and start getting real odds, one team lifts the trophy in maybe 14% of the runs, another in 9%, and so on. #3. So, how i built it ? i didn't hand-write most of the code. i broke the project into 4 pieces, described each one to fable, and let it build while i focused on getting the football logic exactly right. - The data every international match going back over a century, around 50,000 games, plus each team's elo rating, which is the truest measure of strength, and the official 2026 schedule. garbage data means garbage predictions, so this part mattered most. - The features i turned that raw history into signals the model can learn from, the elo gap between the two teams, recent form, goals scored and conceded, and a home boost for the hosts, usa, canada and mexico. - The model for each match it predicts the expected goals for both sides, then turns that into win, draw and loss probabilities plus a likely scoreline. that's what feeds the simulation. - The tournament engine this was the hard part. the 2026 world cup is brand new, 48 teams, 12 groups, a round of 32 that's never existed before, and 8 "best third-placed" teams that slot into the bracket by a fixed fifa table. even the group tiebreakers changed this year, head to head now counts before goal difference. get any of it wrong and the whole bracket falls apart, so i built it carefully and tested the format until it was exact, then wrapped it in a simulation loop that plays the tournament out tens of thousands of times. and the last piece, the live part. as real results come in, they get locked, and only the unplayed games get re-simulated. that's what makes it a living model instead of a one-time prediction. all of it outputs to a clean dashboard you can actually read and screenshot.. right now, before kickoff, it already has a clear favorite to lift the trophy.. 👀 btw who's your pick to win the 2026 world cup?
0:20

Using Claude Fable 5, I built a model that predicts the entire 2026 FIFA world cup.. every single game, not just the final.. so let me break the whole thing down. what it does, how it works, and exactly how i built it.. #1 First what it does: it predicts all 104 games of the tournament. not just who lifts the trophy, but every group match, every knockout, the full path from the round of 32 to the final.. everything lands in one dashboard: > group stage, every match with each team's win % and the chance of a draw > standings, how all 12 groups are projected to finish > bracket, the full knockout tree with each team's odds of advancing > champion odds, who's most likely to actually win it all and it doesn't freeze after one prediction. the moment a real game is played, it locks that result in and re-runs everything around it. so the odds move live as the tournament goes, week by week you watch favorites rise and contenders collapse. #2. How it works: the core idea is simple. the model only ever predicts one thing, a single match. the real trick is the repetition. it learns from decades of match history, then plays the whole tournament out from the first game to the final, tens of thousands of times. each run it records who advanced and who won. do that enough and you stop getting one guess and start getting real odds, one team lifts the trophy in maybe 14% of the runs, another in 9%, and so on. #3. So, how i built it ? i didn't hand-write most of the code. i broke the project into 4 pieces, described each one to fable, and let it build while i focused on getting the football logic exactly right. - The data every international match going back over a century, around 50,000 games, plus each team's elo rating, which is the truest measure of strength, and the official 2026 schedule. garbage data means garbage predictions, so this part mattered most. - The features i turned that raw history into signals the model can learn from, the elo gap between the two teams, recent form, goals scored and conceded, and a home boost for the hosts, usa, canada and mexico. - The model for each match it predicts the expected goals for both sides, then turns that into win, draw and loss probabilities plus a likely scoreline. that's what feeds the simulation. - The tournament engine this was the hard part. the 2026 world cup is brand new, 48 teams, 12 groups, a round of 32 that's never existed before, and 8 "best third-placed" teams that slot into the bracket by a fixed fifa table. even the group tiebreakers changed this year, head to head now counts before goal difference. get any of it wrong and the whole bracket falls apart, so i built it carefully and tested the format until it was exact, then wrapped it in a simulation loop that plays the tournament out tens of thousands of times. and the last piece, the live part. as real results come in, they get locked, and only the unplayed games get re-simulated. that's what makes it a living model instead of a one-time prediction. all of it outputs to a clean dashboard you can actually read and screenshot.. right now, before kickoff, it already has a clear favorite to lift the trophy.. 👀 btw who's your pick to win the 2026 world cup?

Axel Bitblaze 🪓

63,796 次观看 • 3 个月前

Yogi Govt taking School 🏫 Education ✍️ in UP to another level 🔥 Walked into some remote rural schools in Prayagraj and what I saw there blew me off. I learnt that from last 4 years all the 1.5 lac schools with 2 crore children in Basic Education in UP are taught through strict minimum learning levels framework for maths 🧮 and languages 📝 (Mission Nipun) to enable children 👦 👧 to become grade competent. Data 📊 of every school, teacher and child is part of the online system. Using the app 📱 the monitoring team retrieves the school data 📈 using UDISE ID and access child wise data and takes a learning assessment on the app. Once the school, block or the whole district feels that 80% of their children are grade competent, external testing agency test them and declare them as Nipun (Grade level learning competency) All the chapters in the new curriculum have QR Codes , which takes the student/teachers to the video of that topic. Children get workbooks along with books to do extensive practice. Every teacher is encouraged to attend more and more trainings modules on the app and earn points and names of top achievers are called out at the highest level. I was amazed how every teacher was showing of their training scores with pride. Teachers apply for leaves on the app and their superiors (BSAs) approve them on the app (earlier it was a travel to BSA office for every remote teacher). Routine teachers transfers and placements is also done through the online system which was a big transfer-posting racket in Akhilesh Yadav’s time. Under Mission Kayakalp - 19 physical/ infrastructure parameters (electricity, tube-lights, fans, toilet, board, wall painting etc) of all the schools are monitored closely and marked on the app, where each school is geo-tagged. One more amazing reform is - Instead of government centrally procuring school bag 💼, school dress 👖 👔 and shoes 👟 for 2 crore students in 75 districts, now the money is cash 💰transferred in the accounts of the parents and many local village level entrepreneurs sell school dress/bag packets. Multiple surveys shows that parents are not misusing this amount and 97%+ parents are using it for the said purpose. Various government and CSR funds are being smarty tapped to equip the schools with more facilities and equipments. This all will surely show a considerable learning level jump 🔝 in school level chidlren in Uttar Pradesh, in any upcoming national survey. How Yogi Govt is massively impacting the life of 2 Crore chidlren in Uttar Pradesh is a treat for any educationist in the world 🌎. Hat’s Off to 🙌 to Department Of Basic Education Uttar Pradesh
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Yogi Govt taking School 🏫 Education ✍️ in UP to another level 🔥 Walked into some remote rural schools in Prayagraj and what I saw there blew me off. I learnt that from last 4 years all the 1.5 lac schools with 2 crore children in Basic Education in UP are taught through strict minimum learning levels framework for maths 🧮 and languages 📝 (Mission Nipun) to enable children 👦 👧 to become grade competent. Data 📊 of every school, teacher and child is part of the online system. Using the app 📱 the monitoring team retrieves the school data 📈 using UDISE ID and access child wise data and takes a learning assessment on the app. Once the school, block or the whole district feels that 80% of their children are grade competent, external testing agency test them and declare them as Nipun (Grade level learning competency) All the chapters in the new curriculum have QR Codes , which takes the student/teachers to the video of that topic. Children get workbooks along with books to do extensive practice. Every teacher is encouraged to attend more and more trainings modules on the app and earn points and names of top achievers are called out at the highest level. I was amazed how every teacher was showing of their training scores with pride. Teachers apply for leaves on the app and their superiors (BSAs) approve them on the app (earlier it was a travel to BSA office for every remote teacher). Routine teachers transfers and placements is also done through the online system which was a big transfer-posting racket in Akhilesh Yadav’s time. Under Mission Kayakalp - 19 physical/ infrastructure parameters (electricity, tube-lights, fans, toilet, board, wall painting etc) of all the schools are monitored closely and marked on the app, where each school is geo-tagged. One more amazing reform is - Instead of government centrally procuring school bag 💼, school dress 👖 👔 and shoes 👟 for 2 crore students in 75 districts, now the money is cash 💰transferred in the accounts of the parents and many local village level entrepreneurs sell school dress/bag packets. Multiple surveys shows that parents are not misusing this amount and 97%+ parents are using it for the said purpose. Various government and CSR funds are being smarty tapped to equip the schools with more facilities and equipments. This all will surely show a considerable learning level jump 🔝 in school level chidlren in Uttar Pradesh, in any upcoming national survey. How Yogi Govt is massively impacting the life of 2 Crore chidlren in Uttar Pradesh is a treat for any educationist in the world 🌎. Hat’s Off to 🙌 to Department Of Basic Education Uttar Pradesh

Shantanu Gupta

288,341 次观看 • 2 年前

Q&A Questions 👇👇 Question #1 1:10 “Do you spend more time on Edge than what you used to with Oddsjam? And is there something you miss or something you had with Oddsjam?” Question #2 3:06 “What makes EdgeZone different than Oddsjam? Are you showing how to develop models on our own or just showing us yours? What's the latest ROI and W-L using EdgeZone? Any discounts or free trials for Black Friday?” Question #3 11:03 “How do you determine / plan your diversity of bets between arbs, EV, middles, sharp money, etc” Question #4 14:10 “Which books to hammer pregame vs which to stay away? When to bet player props vs main lines, etc. how to keep accounts from being limited, they all will at some point but how to make them last” Question #5 17:07 “When did you decide to move on from strictly arbitrage to move EV and model based bets, and how has the move been?” Question #6 21:12 “What’s the best advice you have for people who play DFS and struggle with the ups and downs of variance? My followers would appreciate this.🤝” Question #7 23:56 “Detecting first mover books vs insanely good EV spot? Even if you don’t get to it man, I know I’m still going to learn a ton from the video you drop. Thanks x as always!” Question #8 27:21 “Definitive answer (or reason for each) on which bet to place first. Have heard very reputable people say both sides.” Question #9 30:23 “Is onyx odds still good for a fresh account?” Question #10 30:55 “P2s and beyond ins and out.what will get u busted and what won't. How much can u milk out of each account and what landmines to avoid.” Question #11 32:33 “With the gambling deduction changes in the Big Beautiful Bill coming in 2026, are Edge Zone, Odds Jam, etc. still worthwhile?” Question #12 33:56 “(1) Thoughts on prediction market arbitrage/positive EV? 2nd question if you have time ⬇️ (2) Less EV/Arb opportunities next year with less sharps betting with the new gambling tax law? Thanks brotha!” Question #13 36:46 “Assuming you have P2's, how do you decide on the arrangement with the P2's? Do you do a % of profits, flat fee? And what's a normal amount of compensation? I'm at the crossroads of needing a new P2 as my current P2 is pretty limited and trying to figure out what to offer”
39:43

Q&A Questions 👇👇 Question #1 1:10 “Do you spend more time on Edge than what you used to with Oddsjam? And is there something you miss or something you had with Oddsjam?” Question #2 3:06 “What makes EdgeZone different than Oddsjam? Are you showing how to develop models on our own or just showing us yours? What's the latest ROI and W-L using EdgeZone? Any discounts or free trials for Black Friday?” Question #3 11:03 “How do you determine / plan your diversity of bets between arbs, EV, middles, sharp money, etc” Question #4 14:10 “Which books to hammer pregame vs which to stay away? When to bet player props vs main lines, etc. how to keep accounts from being limited, they all will at some point but how to make them last” Question #5 17:07 “When did you decide to move on from strictly arbitrage to move EV and model based bets, and how has the move been?” Question #6 21:12 “What’s the best advice you have for people who play DFS and struggle with the ups and downs of variance? My followers would appreciate this.🤝” Question #7 23:56 “Detecting first mover books vs insanely good EV spot? Even if you don’t get to it man, I know I’m still going to learn a ton from the video you drop. Thanks x as always!” Question #8 27:21 “Definitive answer (or reason for each) on which bet to place first. Have heard very reputable people say both sides.” Question #9 30:23 “Is onyx odds still good for a fresh account?” Question #10 30:55 “P2s and beyond ins and out.what will get u busted and what won't. How much can u milk out of each account and what landmines to avoid.” Question #11 32:33 “With the gambling deduction changes in the Big Beautiful Bill coming in 2026, are Edge Zone, Odds Jam, etc. still worthwhile?” Question #12 33:56 “(1) Thoughts on prediction market arbitrage/positive EV? 2nd question if you have time ⬇️ (2) Less EV/Arb opportunities next year with less sharps betting with the new gambling tax law? Thanks brotha!” Question #13 36:46 “Assuming you have P2's, how do you decide on the arrangement with the P2's? Do you do a % of profits, flat fee? And what's a normal amount of compensation? I'm at the crossroads of needing a new P2 as my current P2 is pretty limited and trying to figure out what to offer”

THEARBFATHER

47,673 次观看 • 10 个月前

Chamath just delivered the clearest diagnosis of what is happening to enterprise software and the OpenAI Deployment Company is the most damning piece of evidence he could have picked. "The low end of the market is basically finished. There is no safe space." 90% of public SaaS stocks are down 30-80% from their 52 week highs, the median software stock is now negative over the last 3-6 months. Goldman Sachs reported that software forward P/E multiples fell from 35x to 20x, the lowest absolute level since 2014 and the smallest premium to the S&P 500 since 2010. The low end died first and fastest, because AI replaced it most directly. The small business tools, the lightweight project managers, the single function SaaS products that charged $49 a month per seat, those are being replaced by AI agents that do the same work as a workflow, not a product. You do not buy an AI powered tool, you describe what you need and it builds it and the seat based model that created the SaaS industry simply does not apply to that transaction. But Chamath's more interesting argument is about the high end and the tell he points to is perfect. OpenAI just raised $4 billion from 19 investors including TPG, Brookfield, Bain, and McKinsey to launch a consulting company and guaranteed those investors a 17.5% annual return to do it. On $4 billion in committed capital, that is roughly $700 million per year in guaranteed payouts, owed by a company that is projected to lose $14 billion in 2026. The goal of this venture is to compete directly with Deloitte, PwC, Ernst & Young, Andersen, and Cognizant. Think about what that structure reveals. OpenAI lost half of its enterprise LLM API market share from 50% to 25% between late 2023 and mid-2025, with Anthropic now leading at 32%. Its response was not to build a better model but rather to raise $4 billion, offer guaranteed PE-tier returns and hire embedded engineers to physically sit inside client organizations and make AI actually work in production. The reason, as Chamath identified, is that the high end of the market is not easy. "It's not like boop boop boop, put in a prompt and beep bap boop, it all works," he said and the data confirms exactly that. 88% of organizations running AI agents reported a security incident in the past year, 42% of C-suite executives say AI adoption is creating internal organizational conflict. The average enterprise AI consulting implementation costs $228,000 in year one versus $77,000 for platform-based approaches and most still stall before reaching production. Anthropic immediately matched OpenAI with a competing $1.5 billion consulting venture backed by Blackstone, Goldman Sachs, and Hellman & Friedman bringing the combined spend by the two leading AI labs on human powered enterprise deployment to $5.5 billion in a single month Chamath's read is that the high end, the large enterprise platforms like Salesforce with proprietary data flywheels, Palantir with its FDE model already proven at scale, Oracle with vertical specific data moats will survive and consolidate. The mid-market point solutions, the single function tools, the lightweight enterprise apps without defensible data assets, those are on the conveyor belt. The AI industry is not just disrupting the companies that use software but rather disrupting the companies that sell it.
1:05

Chamath just delivered the clearest diagnosis of what is happening to enterprise software and the OpenAI Deployment Company is the most damning piece of evidence he could have picked. "The low end of the market is basically finished. There is no safe space." 90% of public SaaS stocks are down 30-80% from their 52 week highs, the median software stock is now negative over the last 3-6 months. Goldman Sachs reported that software forward P/E multiples fell from 35x to 20x, the lowest absolute level since 2014 and the smallest premium to the S&P 500 since 2010. The low end died first and fastest, because AI replaced it most directly. The small business tools, the lightweight project managers, the single function SaaS products that charged $49 a month per seat, those are being replaced by AI agents that do the same work as a workflow, not a product. You do not buy an AI powered tool, you describe what you need and it builds it and the seat based model that created the SaaS industry simply does not apply to that transaction. But Chamath's more interesting argument is about the high end and the tell he points to is perfect. OpenAI just raised $4 billion from 19 investors including TPG, Brookfield, Bain, and McKinsey to launch a consulting company and guaranteed those investors a 17.5% annual return to do it. On $4 billion in committed capital, that is roughly $700 million per year in guaranteed payouts, owed by a company that is projected to lose $14 billion in 2026. The goal of this venture is to compete directly with Deloitte, PwC, Ernst & Young, Andersen, and Cognizant. Think about what that structure reveals. OpenAI lost half of its enterprise LLM API market share from 50% to 25% between late 2023 and mid-2025, with Anthropic now leading at 32%. Its response was not to build a better model but rather to raise $4 billion, offer guaranteed PE-tier returns and hire embedded engineers to physically sit inside client organizations and make AI actually work in production. The reason, as Chamath identified, is that the high end of the market is not easy. "It's not like boop boop boop, put in a prompt and beep bap boop, it all works," he said and the data confirms exactly that. 88% of organizations running AI agents reported a security incident in the past year, 42% of C-suite executives say AI adoption is creating internal organizational conflict. The average enterprise AI consulting implementation costs $228,000 in year one versus $77,000 for platform-based approaches and most still stall before reaching production. Anthropic immediately matched OpenAI with a competing $1.5 billion consulting venture backed by Blackstone, Goldman Sachs, and Hellman & Friedman bringing the combined spend by the two leading AI labs on human powered enterprise deployment to $5.5 billion in a single month Chamath's read is that the high end, the large enterprise platforms like Salesforce with proprietary data flywheels, Palantir with its FDE model already proven at scale, Oracle with vertical specific data moats will survive and consolidate. The mid-market point solutions, the single function tools, the lightweight enterprise apps without defensible data assets, those are on the conveyor belt. The AI industry is not just disrupting the companies that use software but rather disrupting the companies that sell it.

Milk Road AI

1,660,474 次观看 • 4 个月前

Excited to share TERRA, a tissue world model 🧬 Over ~1.5 years we ran a large data-generation + modelling effort to build a world model for human tissues, pretrained on 112M cells from spatial transcriptomics (mostly Xenium 5000-plex + public data). It's built on one of the largest human spatial transcriptomics corpora assembled to date, spanning 20 tissues across development, health and 26 disease conditions, ~two-thirds newly generated in-house. Why a "world model" for tissue? Images have universal representations (ViT/DINOv3), so do proteins (ESM, Alex Rives) and pathology (UNI, Faisal Mahmood). We've worked hard to build something similar for human tissue: one model that captures its multi-scale logic, genes → cells → their native microenvironments. Like the JEPA approach Yann LeCun has championed, TERRA learns by prediction in embedding space, but for human tissue. How it works: it tokenises each cell together with its nearest neighbours into one sequence while keeping every gene's identity, then masks part of a neighbourhood and predicts the representation of the hidden part, not raw noisy counts. From one backbone it reads out three scales, gene embeddings (what a gene is doing in a cell and its niche), cell embeddings (cell type and state) and neighbourhood embeddings (the niche), and because it keeps gene-level resolution it can knock a gene out in silico and predict the response. Applied entirely zero-shot, TERRA maps and perturbs human tissue across unseen organs, diseases and technologies, outperforming existing spatial approaches. Three take-homes: 1️⃣ One model, any tissue. A single pretrained backbone provides tissue representations zero-shot, handling genes, cells and niches across organs and platforms, off the shelf. 2️⃣ New biology, development to clinic. We built a new spatial atlas of the developing human pancreas and found an islet-associated capillary state that looks like a precursor of mature islet vasculature. In kidney, TERRA's in silico knockouts predicted the tissue-injury programme from cancer immunotherapy (checkpoint blockade), confirmed in treated kidneys, detected in blood, and linked to declining kidney function. 3️⃣ A grammar of tissue architecture. By coupling each cell's state to its niche, TERRA defines recurring cross-organ "archetypes" of macrophage neighbourhoods, including a tumour-boundary niche that tracks poor survival in kidney cancer. TERRA is already in use: it powered our recent skin atlas of hidden immune-memory niches ( with more studies coming soon. This was an amazing collaboration between clinicians, machine-learning scientists and cell biologists 🙏 Led by Sebastian Birk, Vali Sanian Amirhosein Vahidi, Samuel Ogden, Daniyal Jafree, Adib Miraki, Carlo Leonardi and Arpit Merchant, with Lassi Paavolainen, Menna Clatworthy, Omer Ali Bayraktar, Muzlifah Haniffa, Tom Mitchell and Mostafa Bakhti. Huge thanks too to everyone who shared data and helped along the way. What excites me most is seeing how the community builds on this. The model, code and tutorials are all public, so anyone can run TERRA on their own tissues, extend it, or build new models on top. Huge thanks to the whole team across Wellcome Sanger Institute and our many collaborators. 📄 Paper: 💻 Code: 🤗 Model: #SpatialTranscriptomics #SpatialGenomics #FoundationModels #AI4Science #MachineLearning #ComputationalBiology #SingleCell #WorldModels
2:15

Excited to share TERRA, a tissue world model 🧬 Over ~1.5 years we ran a large data-generation + modelling effort to build a world model for human tissues, pretrained on 112M cells from spatial transcriptomics (mostly Xenium 5000-plex + public data). It's built on one of the largest human spatial transcriptomics corpora assembled to date, spanning 20 tissues across development, health and 26 disease conditions, ~two-thirds newly generated in-house. Why a "world model" for tissue? Images have universal representations (ViT/DINOv3), so do proteins (ESM, Alex Rives) and pathology (UNI, Faisal Mahmood). We've worked hard to build something similar for human tissue: one model that captures its multi-scale logic, genes → cells → their native microenvironments. Like the JEPA approach Yann LeCun has championed, TERRA learns by prediction in embedding space, but for human tissue. How it works: it tokenises each cell together with its nearest neighbours into one sequence while keeping every gene's identity, then masks part of a neighbourhood and predicts the representation of the hidden part, not raw noisy counts. From one backbone it reads out three scales, gene embeddings (what a gene is doing in a cell and its niche), cell embeddings (cell type and state) and neighbourhood embeddings (the niche), and because it keeps gene-level resolution it can knock a gene out in silico and predict the response. Applied entirely zero-shot, TERRA maps and perturbs human tissue across unseen organs, diseases and technologies, outperforming existing spatial approaches. Three take-homes: 1️⃣ One model, any tissue. A single pretrained backbone provides tissue representations zero-shot, handling genes, cells and niches across organs and platforms, off the shelf. 2️⃣ New biology, development to clinic. We built a new spatial atlas of the developing human pancreas and found an islet-associated capillary state that looks like a precursor of mature islet vasculature. In kidney, TERRA's in silico knockouts predicted the tissue-injury programme from cancer immunotherapy (checkpoint blockade), confirmed in treated kidneys, detected in blood, and linked to declining kidney function. 3️⃣ A grammar of tissue architecture. By coupling each cell's state to its niche, TERRA defines recurring cross-organ "archetypes" of macrophage neighbourhoods, including a tumour-boundary niche that tracks poor survival in kidney cancer. TERRA is already in use: it powered our recent skin atlas of hidden immune-memory niches ( with more studies coming soon. This was an amazing collaboration between clinicians, machine-learning scientists and cell biologists 🙏 Led by Sebastian Birk, Vali Sanian Amirhosein Vahidi, Samuel Ogden, Daniyal Jafree, Adib Miraki, Carlo Leonardi and Arpit Merchant, with Lassi Paavolainen, Menna Clatworthy, Omer Ali Bayraktar, Muzlifah Haniffa, Tom Mitchell and Mostafa Bakhti. Huge thanks too to everyone who shared data and helped along the way. What excites me most is seeing how the community builds on this. The model, code and tutorials are all public, so anyone can run TERRA on their own tissues, extend it, or build new models on top. Huge thanks to the whole team across Wellcome Sanger Institute and our many collaborators. 📄 Paper: 💻 Code: 🤗 Model: #SpatialTranscriptomics #SpatialGenomics #FoundationModels #AI4Science #MachineLearning #ComputationalBiology #SingleCell #WorldModels

Mo Lotfollahi

51,100 次观看 • 1 个月前

Today's Training Data episode takes us BTS on the infrastructure challenges required to do large RL runs at scale, featuring Federico Cassano (Composer Lead at Cursor) and Dmytro Dzhulgakov (Co-Founder at Fireworks). The Cursor team trained Composer 2 on Fireworks by starting with a strong base model (Kimi 2.5) and performing large-scale mid-training on code tokens and web data to learn common patterns and libraries, followed by a large-scale Reinforcement Learning run to learn how to navigate the Cursor harness, call tools, and write correct code. Today's episode dives into the systems and infrastructure challenges of making that large RL run happening, and there were many (!!), from numerical mismatch to global distribution to synchronizing rollouts across asynchronous pipelines to keeping track of expert activation across runs and more. Extremely nerdy in-the-weeds challenges that Federico and Dima were delighted to nerd out on together :) Beyond RL infra, we also discussed Online vs Simulated rollouts, self-summarization for long-horizon agents, environment design ("the most powerful RL environment is the product itself"), and other technical nuggets. PS: We filmed this episode before the SpaceX news, while the Cursor team was still compute-constrained. While Cursor now has *all* the flops, the takeaways and hurdles crossed ring true for any serious application-level company that is racing to post-train their own models. I believe that more serious application companies will go the way of Cursor and post-train their own models. 00:00 Introduction 00:53 Why Cursor Trained Composer 2 04:55 Specialization vs Bitter Lesson 06:16 Composer 2 Training Recipe 16:32 Scaling RL Infrastructure Globally 23:32 Floating Point Drift 25:11 MoE Sensitivity Explained 26:25 Router Replay Fix 27:19 Real Time RL Loop 31:49 Long Horizon Agents 34:29 Why RL Everywhere 37:34 LLM as Judge Rewards 39:14 RL in Hard Domains 40:13 Build Your Own Environments 44:34 Closing Thoughts
45:33

Today's Training Data episode takes us BTS on the infrastructure challenges required to do large RL runs at scale, featuring Federico Cassano (Composer Lead at Cursor) and Dmytro Dzhulgakov (Co-Founder at Fireworks). The Cursor team trained Composer 2 on Fireworks by starting with a strong base model (Kimi 2.5) and performing large-scale mid-training on code tokens and web data to learn common patterns and libraries, followed by a large-scale Reinforcement Learning run to learn how to navigate the Cursor harness, call tools, and write correct code. Today's episode dives into the systems and infrastructure challenges of making that large RL run happening, and there were many (!!), from numerical mismatch to global distribution to synchronizing rollouts across asynchronous pipelines to keeping track of expert activation across runs and more. Extremely nerdy in-the-weeds challenges that Federico and Dima were delighted to nerd out on together :) Beyond RL infra, we also discussed Online vs Simulated rollouts, self-summarization for long-horizon agents, environment design ("the most powerful RL environment is the product itself"), and other technical nuggets. PS: We filmed this episode before the SpaceX news, while the Cursor team was still compute-constrained. While Cursor now has *all* the flops, the takeaways and hurdles crossed ring true for any serious application-level company that is racing to post-train their own models. I believe that more serious application companies will go the way of Cursor and post-train their own models. 00:00 Introduction 00:53 Why Cursor Trained Composer 2 04:55 Specialization vs Bitter Lesson 06:16 Composer 2 Training Recipe 16:32 Scaling RL Infrastructure Globally 23:32 Floating Point Drift 25:11 MoE Sensitivity Explained 26:25 Router Replay Fix 27:19 Real Time RL Loop 31:49 Long Horizon Agents 34:29 Why RL Everywhere 37:34 LLM as Judge Rewards 39:14 RL in Hard Domains 40:13 Build Your Own Environments 44:34 Closing Thoughts

Sonya Huang 🐥

79,834 次观看 • 3 个月前

📺🎓How ATR Reveals The Best Stock Entry Points Please ❤️like, 🔖bookmark, and 🔁share with fellow growth stock traders/investors In this educational Short, Don Vandenbord explains why Average True Range (ATR) is one of the most valuable tools for improving stock entries and avoiding costly FOMO trades. * After analyzing roughly 1,000 completed trades at Revere Asset Management, we found a clear pattern: the farther a stock has already moved off its intraday low before you buy it, the lower your probability of success. Instead of chasing momentum after a stock has already made a large move, our data shows traders should aim to enter within roughly half an ATR of the day's low whenever possible. Once a stock has already traveled around three-quarters of its ATR for the day, the odds of an immediate pullback increase sharply. In our study, those late entries produced only about a 29% win rate and were far more likely to finish the day in the red. * The lesson isn't that strong stocks should be avoided—it’s that entry timing matters just as much as stock selection. Even fundamentally strong leaders can become poor trades if they're purchased after an extended intraday rally. Better entries improve win rates, first-day performance, and overall trade expectancy. * The discussion also highlights how today's leading growth stocks have much wider ATRs than in previous years. Traditional 7–8% stop losses can now represent just one normal day's movement, making traders more vulnerable to being stopped out during routine pullbacks if they chase entries. That makes both patience and proper position sizing even more important. * To adapt, our team is incorporating these findings directly into our trading process. Half of the portfolio remains invested in index exposure $SPX $QQQ to capture market rotations, while the other half focuses on leading stocks and sectors. Position sizes are adjusted based on volatility so that one highly volatile stock cannot disproportionately impact overall portfolio performance. * The bottom line is that don't let FOMO dictate your entries. Use ATR to determine whether a stock is extended, wait for higher-probability entry points, size positions according to volatility, and let data drive your trading decisions. Better entries won't guarantee every trade is a winner, but they can significantly improve the odds over hundreds of trades. * You can find more details about Revere Asset Management in the FAQ section on our website, along with additional insights into our investment process, portfolio structure, and onboarding. ▶️
2:58

📺🎓How ATR Reveals The Best Stock Entry Points Please ❤️like, 🔖bookmark, and 🔁share with fellow growth stock traders/investors In this educational Short, Don Vandenbord explains why Average True Range (ATR) is one of the most valuable tools for improving stock entries and avoiding costly FOMO trades. * After analyzing roughly 1,000 completed trades at Revere Asset Management, we found a clear pattern: the farther a stock has already moved off its intraday low before you buy it, the lower your probability of success. Instead of chasing momentum after a stock has already made a large move, our data shows traders should aim to enter within roughly half an ATR of the day's low whenever possible. Once a stock has already traveled around three-quarters of its ATR for the day, the odds of an immediate pullback increase sharply. In our study, those late entries produced only about a 29% win rate and were far more likely to finish the day in the red. * The lesson isn't that strong stocks should be avoided—it’s that entry timing matters just as much as stock selection. Even fundamentally strong leaders can become poor trades if they're purchased after an extended intraday rally. Better entries improve win rates, first-day performance, and overall trade expectancy. * The discussion also highlights how today's leading growth stocks have much wider ATRs than in previous years. Traditional 7–8% stop losses can now represent just one normal day's movement, making traders more vulnerable to being stopped out during routine pullbacks if they chase entries. That makes both patience and proper position sizing even more important. * To adapt, our team is incorporating these findings directly into our trading process. Half of the portfolio remains invested in index exposure $SPX $QQQ to capture market rotations, while the other half focuses on leading stocks and sectors. Position sizes are adjusted based on volatility so that one highly volatile stock cannot disproportionately impact overall portfolio performance. * The bottom line is that don't let FOMO dictate your entries. Use ATR to determine whether a stock is extended, wait for higher-probability entry points, size positions according to volatility, and let data drive your trading decisions. Better entries won't guarantee every trade is a winner, but they can significantly improve the odds over hundreds of trades. * You can find more details about Revere Asset Management in the FAQ section on our website, along with additional insights into our investment process, portfolio structure, and onboarding. ▶️

Revere Asset Management

60,913 次观看 • 2 个月前

🚀 Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase — one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isn’t experimental AI infrastructure — Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. 🌐 DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone — not just Big Tech. 🤝 Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects don’t compete — they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. 🔥 The future of AI is open, composable, and community-owned — and it’s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? 👇 Share your thoughts and join the conversation.
0:38

🚀 Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase — one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isn’t experimental AI infrastructure — Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. 🌐 DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone — not just Big Tech. 🤝 Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects don’t compete — they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. 🔥 The future of AI is open, composable, and community-owned — and it’s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? 👇 Share your thoughts and join the conversation.

Hồng Ngọc | Ruby💎

12,837 次观看 • 7 个月前

Padawans! We are excited to announce the return of Jediswap with concentrated liquidity, full audits, points, and incentives. Check it out at Since our last update two months ago, we have been working hard on a fresh new version of Jediswap, focused on bringing capital efficiency and the best price execution to our users. After two months of dedicated efforts, a full audit by Nethermind Starknet , and passing rigorous security tests, we are excited to announce the launch of Jediswap v2. Jediswap v2 significantly enhances user experience and performance while introducing new features and surprises. Our commitment to community and user growth remains strong, and we have exciting plans to expand the Jediswap ecosystem. Take a look at key updates coming with the launch. A points system that empowers genuine, loyal users: Jediswap's origins go back to early 2020 when we started our journey not as a product but as a community known as the Mesh community. Our mission was clear: bring Open Finance to billions of people. Recognising the strength of community-driven efforts, we understood that collective belief and collaboration, rather than individual or corporate endeavours, would be the most effective path forward. Early loyal users are the most crucial pillars of any community and product. This point system is Jediswap’s first step in recognising and rewarding the value each user has added to the protocol. We have prepared separate point systems for liquidity providers and traders. In short, as an LP, you can maximise your points by earning more fees on your LP positions and maintaining your liquidity in Jediswap over the long term. You can check out the complete math behind points here. For traders, use Jediswap when you genuinely need to swap tokens. There is no need to do any wash trading. We have published the points system for Jediswap v2 and will soon release points for all the activity that has occurred on Jediswap v1 to date with a boost. Check out the points logic on our docs: Improved performance and user experience: We have significantly enhanced Jediswap's performance, making it faster and more user-friendly. One notable improvement is the integration of pool analytics directly within the Pool page, eliminating the need for users to navigate to a separate analytics page. Additionally, balance fetching has been optimised for smoother operation. Any liquidity added to pools now updates the My Positions page in real-time. Battle-tested security For this launch, we implemented several security measures. We underwent a rigorous 7-week audit process with Nethermind. With the help of the Nethermind team, we also created a test framework for Jediswap to compare security against Uniswap v3, which has been operational for 3+ years and is one of the most battle-tested smart contracts available. We simulated real data from different Uniswap v3 pools on Jediswap. We achieved a 100% match in the contract state after each on-chain action, such as swaps and liquidity adjustments, bolstering our confidence in our code's security. We will announce many cool things over the next few weeks. Keep an out JediSwap ;) Mint a Galxe NFT: To commemorate this launch, we have published a new campaign on Galxe, which rewards users with an NFT for being an early user of Jediswap v2. To earn the Galxe NFT, add at least $25 worth of liquidity to one of the pools listed in the Galxe quest.
0:22

Padawans! We are excited to announce the return of Jediswap with concentrated liquidity, full audits, points, and incentives. Check it out at Since our last update two months ago, we have been working hard on a fresh new version of Jediswap, focused on bringing capital efficiency and the best price execution to our users. After two months of dedicated efforts, a full audit by Nethermind Starknet , and passing rigorous security tests, we are excited to announce the launch of Jediswap v2. Jediswap v2 significantly enhances user experience and performance while introducing new features and surprises. Our commitment to community and user growth remains strong, and we have exciting plans to expand the Jediswap ecosystem. Take a look at key updates coming with the launch. A points system that empowers genuine, loyal users: Jediswap's origins go back to early 2020 when we started our journey not as a product but as a community known as the Mesh community. Our mission was clear: bring Open Finance to billions of people. Recognising the strength of community-driven efforts, we understood that collective belief and collaboration, rather than individual or corporate endeavours, would be the most effective path forward. Early loyal users are the most crucial pillars of any community and product. This point system is Jediswap’s first step in recognising and rewarding the value each user has added to the protocol. We have prepared separate point systems for liquidity providers and traders. In short, as an LP, you can maximise your points by earning more fees on your LP positions and maintaining your liquidity in Jediswap over the long term. You can check out the complete math behind points here. For traders, use Jediswap when you genuinely need to swap tokens. There is no need to do any wash trading. We have published the points system for Jediswap v2 and will soon release points for all the activity that has occurred on Jediswap v1 to date with a boost. Check out the points logic on our docs: Improved performance and user experience: We have significantly enhanced Jediswap's performance, making it faster and more user-friendly. One notable improvement is the integration of pool analytics directly within the Pool page, eliminating the need for users to navigate to a separate analytics page. Additionally, balance fetching has been optimised for smoother operation. Any liquidity added to pools now updates the My Positions page in real-time. Battle-tested security For this launch, we implemented several security measures. We underwent a rigorous 7-week audit process with Nethermind. With the help of the Nethermind team, we also created a test framework for Jediswap to compare security against Uniswap v3, which has been operational for 3+ years and is one of the most battle-tested smart contracts available. We simulated real data from different Uniswap v3 pools on Jediswap. We achieved a 100% match in the contract state after each on-chain action, such as swaps and liquidity adjustments, bolstering our confidence in our code's security. We will announce many cool things over the next few weeks. Keep an out JediSwap ;) Mint a Galxe NFT: To commemorate this launch, we have published a new campaign on Galxe, which rewards users with an NFT for being an early user of Jediswap v2. To earn the Galxe NFT, add at least $25 worth of liquidity to one of the pools listed in the Galxe quest.

JediSwap

107,540 次观看 • 2 年前

Announcing the DVM Terminal Presale! 01/ We are excited to formally announce the next step in our journey: our AI and Signal based trading Terminal. See ALL details on our website, including product, tech, deposit address, and tech documentation: Deposit Address (SOL only): 4pyVRFX56MdqtREcxWnf6XuEGfRNCQaKm1LA4xmHeccv By contributing, you agree to our Terms & Privacy Policy – full docs on site. 02/ We are building ‘DVM Terminal’, a signal and AI powered trading platform for the Solana trenches (initially). The first multi-agent AI trading terminal designed as an institutional-grade dashboard – turning market noise into actionable alpha with agent summaries, live signals, rigid filters, and a full multi-agent system. 03/ The problem. Trench hunting is far too inefficient with real data and insights lacking. - Dashboards are noisy (not even sortable), - No AI agents (in an AI world) - No narratives (a critical component to a thesis), - VERY limited signals (only DB/DS), - No advanced trading (no TP, SL, or VWAP), - No portfolio alert/management system post-trade etc. - The list goes on… Products from major competitors are all just homogeneous, even down to the 3-frame design. We have to piece everything together like broken lego blocks, building a weak matrix from existing platforms, X, FNFs, telegram and discord for little to no alpha. 04/ The solution & moat. We rebuild this from the ground up, leveraging signals and AI. - Clean institutional-like dashboards (we can sort and navigate thru a proper terminal, like Bloomberg or Messari) - AI agents (thank goodness for intelligence, distilling all the important info upfront across 2k+ tokens/day) - A Narrative engine (no need to ask “what is this token about?”; additionally, our engine can identify the newest metas like AI, ICM, Cards, etc.) - 100s of value-add Signals overlaid live on charts (momentum, smart money, sentiment, event data; all of it; tell us what’s happening in real-time) - Advanced trading system (finally, SL, TP, VWAP etc.) - Live portfolio monitoring (AI will give us pertinent live info on our holdings, so we can go live life and not look at screens all day) - All in one place. At a higher-level, our advantage will be managing the massive on/off-chain data pipeline being processed by thousands or millions of context-aware AI agents that recognize patterns, filter noise and deliver only the most actionable insights to a trader with which it can execute a trade effectively. 05/ The opportunity. The Industry leader on Solana makes $600m+ in fees annually, with total industry near $1b on Solana alone, according to Adam. Yet, the entire industry gives us total burnout, fragmented data, either little info or info overload, no real signals, no narratives, no personalized AI-driven strategies, and zero incentives (like buybacks or a flywheel). We’ll flip the script, designing a high-powered scalable signal and AI driven intelligence platform with a flywheel (50-100% fee buy-back & burn). Simply put, we want to be tops. 06/ Development. Our product is MVP. We are building this to scale beyond Solana, into multi-chain. V1 is expected in 4-6 weeks. Our approach to building is an open feedback loop with community members, building to the demands of our users. 07/ Pre-sale terms & Valuation. We are offering 50% public sale, with min $100, no max. Ending valuation is susceptible to change based on amount raised, but will be fixed at 2x raise - i.e. $1m raised=$2m val, $50m raised=$100m val. We are seeking to raise $25m on a $50m valuation, which represents 1% of Solana bot market-share. At TGE event, expect ~65% of our tokens to be floating (or outstanding), with 25% in treasury and 10% of the team allocation locked. Tokens are expected to be distributed just ahead of v1 rollout. Again, find more details on our webpage. 08/ Tailwinds. AI input costs are declining 90%/yr also, so the operational model could become very accretive over time, as we scale our tech to other chains. Solana outputs the most tokens (~35k per day), so we start here, where the challenge is the greatest. 09/ Advisors. Big thanks to our advisors, who’ve been part of this community since inception. Austin Barack, JK 🛡️, cryptic, Tachi, , ZoeyLoo and Chetan Badhe. 10/ The end. Thank you for your consideration; and make sure the SOL address posted here is the same as on our website.
0:52

Announcing the DVM Terminal Presale! 01/ We are excited to formally announce the next step in our journey: our AI and Signal based trading Terminal. See ALL details on our website, including product, tech, deposit address, and tech documentation: Deposit Address (SOL only): 4pyVRFX56MdqtREcxWnf6XuEGfRNCQaKm1LA4xmHeccv By contributing, you agree to our Terms & Privacy Policy – full docs on site. 02/ We are building ‘DVM Terminal’, a signal and AI powered trading platform for the Solana trenches (initially). The first multi-agent AI trading terminal designed as an institutional-grade dashboard – turning market noise into actionable alpha with agent summaries, live signals, rigid filters, and a full multi-agent system. 03/ The problem. Trench hunting is far too inefficient with real data and insights lacking. - Dashboards are noisy (not even sortable), - No AI agents (in an AI world) - No narratives (a critical component to a thesis), - VERY limited signals (only DB/DS), - No advanced trading (no TP, SL, or VWAP), - No portfolio alert/management system post-trade etc. - The list goes on… Products from major competitors are all just homogeneous, even down to the 3-frame design. We have to piece everything together like broken lego blocks, building a weak matrix from existing platforms, X, FNFs, telegram and discord for little to no alpha. 04/ The solution & moat. We rebuild this from the ground up, leveraging signals and AI. - Clean institutional-like dashboards (we can sort and navigate thru a proper terminal, like Bloomberg or Messari) - AI agents (thank goodness for intelligence, distilling all the important info upfront across 2k+ tokens/day) - A Narrative engine (no need to ask “what is this token about?”; additionally, our engine can identify the newest metas like AI, ICM, Cards, etc.) - 100s of value-add Signals overlaid live on charts (momentum, smart money, sentiment, event data; all of it; tell us what’s happening in real-time) - Advanced trading system (finally, SL, TP, VWAP etc.) - Live portfolio monitoring (AI will give us pertinent live info on our holdings, so we can go live life and not look at screens all day) - All in one place. At a higher-level, our advantage will be managing the massive on/off-chain data pipeline being processed by thousands or millions of context-aware AI agents that recognize patterns, filter noise and deliver only the most actionable insights to a trader with which it can execute a trade effectively. 05/ The opportunity. The Industry leader on Solana makes $600m+ in fees annually, with total industry near $1b on Solana alone, according to Adam. Yet, the entire industry gives us total burnout, fragmented data, either little info or info overload, no real signals, no narratives, no personalized AI-driven strategies, and zero incentives (like buybacks or a flywheel). We’ll flip the script, designing a high-powered scalable signal and AI driven intelligence platform with a flywheel (50-100% fee buy-back & burn). Simply put, we want to be tops. 06/ Development. Our product is MVP. We are building this to scale beyond Solana, into multi-chain. V1 is expected in 4-6 weeks. Our approach to building is an open feedback loop with community members, building to the demands of our users. 07/ Pre-sale terms & Valuation. We are offering 50% public sale, with min $100, no max. Ending valuation is susceptible to change based on amount raised, but will be fixed at 2x raise - i.e. $1m raised=$2m val, $50m raised=$100m val. We are seeking to raise $25m on a $50m valuation, which represents 1% of Solana bot market-share. At TGE event, expect ~65% of our tokens to be floating (or outstanding), with 25% in treasury and 10% of the team allocation locked. Tokens are expected to be distributed just ahead of v1 rollout. Again, find more details on our webpage. 08/ Tailwinds. AI input costs are declining 90%/yr also, so the operational model could become very accretive over time, as we scale our tech to other chains. Solana outputs the most tokens (~35k per day), so we start here, where the challenge is the greatest. 09/ Advisors. Big thanks to our advisors, who’ve been part of this community since inception. Austin Barack, JK 🛡️, cryptic, Tachi, , ZoeyLoo and Chetan Badhe. 10/ The end. Thank you for your consideration; and make sure the SOL address posted here is the same as on our website.

Deep Value Memetics

23,075 次观看 • 11 个月前

The July 4th weekend All-In The All-In Podcast turned into a long argument about who owns the intelligence layer. The besties think enterprises just woke up to a trap they had been walking into, here's how the conversation went (save this): ◽️ The Palantir-Nvidia deal is a bet against the model-layer duopoly. Palantir will use Nvidia's Nemotron open models to build a custom frontier-quality model for US government agencies, and the agencies own the hardware, the data, and the weights. Sacks framed it as structural: an application company and a chip company both want a competitive model layer, so they are natural partners against a two-provider middle. ◽️ Alex Karp's CNBC "crashout" was actually the thesis. Karp argued enterprises have lost trust in the frontier labs and want to own their compute, models, data, and alpha. Sacks translated it as a new definition of enterprise AI safety: safety means the model provider cannot hoover up your proprietary knowledge and turn it into its next product. ◽️ Figma is the cautionary tale that made it real. Anthropic launched Claude Design into Figma's category, its chief product officer sat on Figma's board and resigned only 3 days before launch, and Figma's stock is down about 50% this year while Anthropic's valuation surged. Sacks listed Claude Science, Security, Legal, Financial, and Code as the same move: dominate the model layer, then take the lucrative verticals. ◽️ The playbook has a name, and it is Microsoft and Google. Sacks argued Anthropic is running the operating-system strategy: own the layer everyone builds on, then walk up the stack. His Google receipt is that fewer than half of searches now send you off-site, versus an early Google that prided itself on how fast it kicked you away. ◽️ The BCG number is what raises the stakes. Chamath cited a BCG return-on-capital-employed study: the cost of capital is back to its long-run 8 to 11%, and half of large US companies cannot earn returns above it. If you are already teetering on your cost of capital, handing your alpha to a provider that may compete with you is not a luxury risk, it is fatal. ◽️ The 16.4x number is the whole argument in one data point. Chamath ran a code-migration task through 8090's harness. Wrapping Claude was 1.4x cheaper and 1.5x faster than Claude Opus alone. Wrapping the best open-source model was 16.4x cheaper, at about 3x slower. For a background task, three extra hours to cut cost by 16x is not a close call. ◽️ Even at 100x cheaper, enterprises were saying no for the wrong reason. Chamath relayed an ex-Meta PM's point that companies reject open models over China and safety fears, when they could host those same open weights on their own GPUs in US data centers with nothing flowing back. The safety objection, she argued, is backwards: the leak is the data you hand the frontier labs. ◽️ Friedberg says the frontier labs are trying to commoditize their own customers. Anthropic has been signing up life-sciences companies to feed a new life-focused model in exchange for early access, and nearly everyone he has talked to now refuses, recognizing that data they spent billions generating becomes worthless once it is pooled with everyone else's. ◽️ The deployment topology is shifting from big hubs to distributed spokes. Friedberg's map: the old assumption was a few capital-advantaged mega-clusters plus inference clouds. The new one is large hubs, medium hubs (enterprise training clusters), and distributed spokes, including on-prem inference in your own building. Owning your weights is the point. ◽️ Chamath's endgame is running GLM himself. An industry contact told him that with harness post-training and telemetry, an open Chinese model like GLM could get as good as Anthropic's Mythos. His conclusion: take GLM, control it soup-to-nuts on US hardware with only US citizens touching it, and pay a fraction. ◽️ The Apple analogy sharpens why renting intelligence is different from renting distribution. Chamath argued Apple is the only platform that respected developers, deliberately keeping its stock apps basic to protect the ecosystem and collect its 30% tax. There is no 30% tax on open models, and worse, you cannot rent intelligence from the same place that rents it to your competitor without ending up identical to them. ◽️ Nvidia's open model is now good enough to matter. Calacanis claimed you cannot tell Jensen Huang's Nemotron from Claude on 95% of searches, and that Nvidia downplayed the model until now to avoid alarming its top customers. The gloves came off once OpenAI, Anthropic, and Elon all signaled their own silicon ambitions. ◽️ Sacks sized the duopoly: roughly $60B and $40B in ARR. Anthropic is around ~$60 billion of ARR, OpenAI at ~$40 billion, and no one else generates meaningful model-layer revenue. Sacks's policy line: the US does not ban monopolies, only anti-competitive tactics, but the government should do nothing to make the duopoly more likely. ◽️ The token deflation call: 90% a year for three years. Calacanis predicted token costs fall 90% annually for three years, putting the price of intelligence near free and making it rational to waste tokens on hardware you already own. Friedberg's version is a 70/20/10 split between big cloud, local, and other clouds. ◽️ A wave of platform lock-in spending is already landing. Calacanis flagged Microsoft standing up a roughly $2.5 billion forward-deployed-engineer effort and Amazon spending about $1 billion on the same, plus OpenAI's version. His read: enterprises will slam the door, because letting a provider's engineers study your business is how it ends up in their model. ◽️ The server-per-employee prediction. Calacanis expects every employee to get $10,000 to $20,000 of local compute, a Mac Studio or a high-RAM Dell, running a personal local model that syncs to a thin laptop. A server per person, so nothing leaks. ◽️ On jobs, the data does not show present-tense loss. Sacks cited a RAMP and Revelio Labs study of over 21,000 US firms: the heaviest AI spenders grew headcount about 10% over two years, and entry-level headcount grew even faster at 12%. Friedberg's harder claim: there is no AI job loss yet, only clunky, gradual value creation, and the media will not reverse its narrative because that destroys its credibility. ◽️ The displacement case is real but forward-dated. The counterpoint on the show was that customer support, entry-level data entry and BPO, and driving are the near-term displacements, with Waymo cited as present-tense evidence: in markets where it hits critical mass, Uber and Lyft stop recruiting drivers. Sacks noted most US entry-level support was already offshored, so the acute risk sits in those countries first. ◽️ The human-premium counternarrative. Friedberg argued that as automation spreads, human interaction gets a premium: the skilled bartender, the real driver, the human-in-the-loop tier. He cited the company (referenced as Klarna) that hyped replacing its whole support team with AI, then reversed a year later on brand grounds. ◽️ The export-control episode needed three conditions, and Sacks says do not over-read it. Commerce lifted controls on Anthropic's Fable 5 after two weeks, with Mythos 5 restored to US customers around June 26 once co-founder Tom Brown replaced Dario as lead negotiator. Sacks's three conditions: Dario boasting for months about a cyber weapon, Amazon reporting failed guardrails in testing, and Dario refusing to roll Fable back. His message to allies: this was a particular set of circumstances rather than the debut of a standing lever. ◽️ The import question nobody answered cleanly. Calacanis pressed on why the US blocks Chinese cars and drones but not Chinese open models like DeepSeek and Kimi. Sacks's answer: a forked open model run on US hardware stops being Chinese, and banning open source would isolate the US and impose a token tax on American enterprises, so let the market decide if American open models win. ◽️ The California fiscal story is a business-climate story. Friedberg walked through the numbers behind Newsom's "balanced" $351B budget: expenses exceed revenue and $20-40B is borrowed to close the gap, the budget grew 65% in six years ($215B to $355B), personal income tax is $142B of ~$211B revenue with the top 1% (150,000 people) paying $70B of it, and the corporate rate of 8.9% sits far above Texas at zero. ◽️ The tax base is leaving, and the state is now taxing everyone else. Friedberg cited 1 to 1.5% of adjusted gross income leaving each year (about 15% over a decade), at least 15 Fortune 500 HQs and ~2,100 firms gone since 2019, and a new 8% software sales tax hitting Word, Gmail, and ChatGPT subscriptions plus a health-insurance tax, on top of a now-permanent 14.4% top bracket. The liabilities behind it run $1.4T in debt, up to $1.5T in unfunded pensions senior to state bonds, and ~$40B/year in out-year deficits. Lastly, the line that framed the whole show: "You can't rent intelligence from the same place that rents it to your competitor." That is the sovereignty thesis in one sentence, and every number in this episode is an argument for it. ____ Follow Fireside Alpha for more summaries on key business and technology conversations.
1:33

The July 4th weekend All-In The All-In Podcast turned into a long argument about who owns the intelligence layer. The besties think enterprises just woke up to a trap they had been walking into, here's how the conversation went (save this): ◽️ The Palantir-Nvidia deal is a bet against the model-layer duopoly. Palantir will use Nvidia's Nemotron open models to build a custom frontier-quality model for US government agencies, and the agencies own the hardware, the data, and the weights. Sacks framed it as structural: an application company and a chip company both want a competitive model layer, so they are natural partners against a two-provider middle. ◽️ Alex Karp's CNBC "crashout" was actually the thesis. Karp argued enterprises have lost trust in the frontier labs and want to own their compute, models, data, and alpha. Sacks translated it as a new definition of enterprise AI safety: safety means the model provider cannot hoover up your proprietary knowledge and turn it into its next product. ◽️ Figma is the cautionary tale that made it real. Anthropic launched Claude Design into Figma's category, its chief product officer sat on Figma's board and resigned only 3 days before launch, and Figma's stock is down about 50% this year while Anthropic's valuation surged. Sacks listed Claude Science, Security, Legal, Financial, and Code as the same move: dominate the model layer, then take the lucrative verticals. ◽️ The playbook has a name, and it is Microsoft and Google. Sacks argued Anthropic is running the operating-system strategy: own the layer everyone builds on, then walk up the stack. His Google receipt is that fewer than half of searches now send you off-site, versus an early Google that prided itself on how fast it kicked you away. ◽️ The BCG number is what raises the stakes. Chamath cited a BCG return-on-capital-employed study: the cost of capital is back to its long-run 8 to 11%, and half of large US companies cannot earn returns above it. If you are already teetering on your cost of capital, handing your alpha to a provider that may compete with you is not a luxury risk, it is fatal. ◽️ The 16.4x number is the whole argument in one data point. Chamath ran a code-migration task through 8090's harness. Wrapping Claude was 1.4x cheaper and 1.5x faster than Claude Opus alone. Wrapping the best open-source model was 16.4x cheaper, at about 3x slower. For a background task, three extra hours to cut cost by 16x is not a close call. ◽️ Even at 100x cheaper, enterprises were saying no for the wrong reason. Chamath relayed an ex-Meta PM's point that companies reject open models over China and safety fears, when they could host those same open weights on their own GPUs in US data centers with nothing flowing back. The safety objection, she argued, is backwards: the leak is the data you hand the frontier labs. ◽️ Friedberg says the frontier labs are trying to commoditize their own customers. Anthropic has been signing up life-sciences companies to feed a new life-focused model in exchange for early access, and nearly everyone he has talked to now refuses, recognizing that data they spent billions generating becomes worthless once it is pooled with everyone else's. ◽️ The deployment topology is shifting from big hubs to distributed spokes. Friedberg's map: the old assumption was a few capital-advantaged mega-clusters plus inference clouds. The new one is large hubs, medium hubs (enterprise training clusters), and distributed spokes, including on-prem inference in your own building. Owning your weights is the point. ◽️ Chamath's endgame is running GLM himself. An industry contact told him that with harness post-training and telemetry, an open Chinese model like GLM could get as good as Anthropic's Mythos. His conclusion: take GLM, control it soup-to-nuts on US hardware with only US citizens touching it, and pay a fraction. ◽️ The Apple analogy sharpens why renting intelligence is different from renting distribution. Chamath argued Apple is the only platform that respected developers, deliberately keeping its stock apps basic to protect the ecosystem and collect its 30% tax. There is no 30% tax on open models, and worse, you cannot rent intelligence from the same place that rents it to your competitor without ending up identical to them. ◽️ Nvidia's open model is now good enough to matter. Calacanis claimed you cannot tell Jensen Huang's Nemotron from Claude on 95% of searches, and that Nvidia downplayed the model until now to avoid alarming its top customers. The gloves came off once OpenAI, Anthropic, and Elon all signaled their own silicon ambitions. ◽️ Sacks sized the duopoly: roughly $60B and $40B in ARR. Anthropic is around ~$60 billion of ARR, OpenAI at ~$40 billion, and no one else generates meaningful model-layer revenue. Sacks's policy line: the US does not ban monopolies, only anti-competitive tactics, but the government should do nothing to make the duopoly more likely. ◽️ The token deflation call: 90% a year for three years. Calacanis predicted token costs fall 90% annually for three years, putting the price of intelligence near free and making it rational to waste tokens on hardware you already own. Friedberg's version is a 70/20/10 split between big cloud, local, and other clouds. ◽️ A wave of platform lock-in spending is already landing. Calacanis flagged Microsoft standing up a roughly $2.5 billion forward-deployed-engineer effort and Amazon spending about $1 billion on the same, plus OpenAI's version. His read: enterprises will slam the door, because letting a provider's engineers study your business is how it ends up in their model. ◽️ The server-per-employee prediction. Calacanis expects every employee to get $10,000 to $20,000 of local compute, a Mac Studio or a high-RAM Dell, running a personal local model that syncs to a thin laptop. A server per person, so nothing leaks. ◽️ On jobs, the data does not show present-tense loss. Sacks cited a RAMP and Revelio Labs study of over 21,000 US firms: the heaviest AI spenders grew headcount about 10% over two years, and entry-level headcount grew even faster at 12%. Friedberg's harder claim: there is no AI job loss yet, only clunky, gradual value creation, and the media will not reverse its narrative because that destroys its credibility. ◽️ The displacement case is real but forward-dated. The counterpoint on the show was that customer support, entry-level data entry and BPO, and driving are the near-term displacements, with Waymo cited as present-tense evidence: in markets where it hits critical mass, Uber and Lyft stop recruiting drivers. Sacks noted most US entry-level support was already offshored, so the acute risk sits in those countries first. ◽️ The human-premium counternarrative. Friedberg argued that as automation spreads, human interaction gets a premium: the skilled bartender, the real driver, the human-in-the-loop tier. He cited the company (referenced as Klarna) that hyped replacing its whole support team with AI, then reversed a year later on brand grounds. ◽️ The export-control episode needed three conditions, and Sacks says do not over-read it. Commerce lifted controls on Anthropic's Fable 5 after two weeks, with Mythos 5 restored to US customers around June 26 once co-founder Tom Brown replaced Dario as lead negotiator. Sacks's three conditions: Dario boasting for months about a cyber weapon, Amazon reporting failed guardrails in testing, and Dario refusing to roll Fable back. His message to allies: this was a particular set of circumstances rather than the debut of a standing lever. ◽️ The import question nobody answered cleanly. Calacanis pressed on why the US blocks Chinese cars and drones but not Chinese open models like DeepSeek and Kimi. Sacks's answer: a forked open model run on US hardware stops being Chinese, and banning open source would isolate the US and impose a token tax on American enterprises, so let the market decide if American open models win. ◽️ The California fiscal story is a business-climate story. Friedberg walked through the numbers behind Newsom's "balanced" $351B budget: expenses exceed revenue and $20-40B is borrowed to close the gap, the budget grew 65% in six years ($215B to $355B), personal income tax is $142B of ~$211B revenue with the top 1% (150,000 people) paying $70B of it, and the corporate rate of 8.9% sits far above Texas at zero. ◽️ The tax base is leaving, and the state is now taxing everyone else. Friedberg cited 1 to 1.5% of adjusted gross income leaving each year (about 15% over a decade), at least 15 Fortune 500 HQs and ~2,100 firms gone since 2019, and a new 8% software sales tax hitting Word, Gmail, and ChatGPT subscriptions plus a health-insurance tax, on top of a now-permanent 14.4% top bracket. The liabilities behind it run $1.4T in debt, up to $1.5T in unfunded pensions senior to state bonds, and ~$40B/year in out-year deficits. Lastly, the line that framed the whole show: "You can't rent intelligence from the same place that rents it to your competitor." That is the sovereignty thesis in one sentence, and every number in this episode is an argument for it. ____ Follow Fireside Alpha for more summaries on key business and technology conversations.

Fireside Alpha

55,816 次观看 • 2 个月前

This was one of my favorite interviews of 2025... Founders often underestimate how much freedom they actually have. Anil Varanasi and Meter is a reminder of what happens when you use all of it. They ignored the usual advice and built the company their way. It’s no surprise their story doesn’t resemble anyone else’s. Here are just a few examples: 1. They spent four and a half years pre–revenue, just two people. It was essentially Anil and Sunil, alone, for four and a half years before they had a sales ready product and their first customers. They even scrapped an entire year of operating system work once they realized a different technical approach (inspired by an open source project) was better. 2. They literally moved to Shenzhen to learn how the physical world is made. They were blocked by slow hardware iteration in San Francisco, so they just relocated to Shenzhen for over a year. 3. Full vertical integration as a day one decision, not an afterthought. Meter decided from the start to own the entire stack: hardware, software, installation, and ongoing service. This is in a market where most entrants pick one slice (just switches, just access points, etc.) and get trapped as point solutions that end up acquired. 4. Business model treated as part of the product, not a pricing afterthought. They moved networking from “buy hardware” to: Meter provides the hardware, the software, the installation and ongoing support. The customer pays recurring, per square foot, and effectively “don’t pay us if the network doesn’t work.” Anil thinks about business model innovation on the same level as product and technology innovation. 5. Choosing a massive, incumbent dominated market on purpose. Networking is controlled by a few giants like Cisco. They were pulled toward that exact dynamic: a huge, durable market where the initial ramp is brutal, but if you get through it, there are very few new players alongside you. 6. Deliberately avoided the channel in a channel dominated industry. Roughly 90 percent of networking is sold through the channel.Meter refused to use the channel until they were convinced the product was dramatically better in every way, because incumbents could weaponize the channel with discounts to block them. Only after they had hundreds of happy customers and strong tools did they fully embrace channel sales. 7. The team has an extreme time horizon, paired with extreme urgency. Anil thinks in decades: “I care about where Meter ends up in 25 years, not five.” At the same time, he is obsessively focused on what happens in the next few hours and where every report spends time. That “barbell” between multi decade vision and hour by hour intensity is very explicit for him. 8. An allergy to “meta work” and most conventional management. No OKRs or goals at all. They have a strong skepticism of spending time on docs, processes, and coordination that feel like work but do not move the product forward.
1:14:53

This was one of my favorite interviews of 2025... Founders often underestimate how much freedom they actually have. Anil Varanasi and Meter is a reminder of what happens when you use all of it. They ignored the usual advice and built the company their way. It’s no surprise their story doesn’t resemble anyone else’s. Here are just a few examples: 1. They spent four and a half years pre–revenue, just two people. It was essentially Anil and Sunil, alone, for four and a half years before they had a sales ready product and their first customers. They even scrapped an entire year of operating system work once they realized a different technical approach (inspired by an open source project) was better. 2. They literally moved to Shenzhen to learn how the physical world is made. They were blocked by slow hardware iteration in San Francisco, so they just relocated to Shenzhen for over a year. 3. Full vertical integration as a day one decision, not an afterthought. Meter decided from the start to own the entire stack: hardware, software, installation, and ongoing service. This is in a market where most entrants pick one slice (just switches, just access points, etc.) and get trapped as point solutions that end up acquired. 4. Business model treated as part of the product, not a pricing afterthought. They moved networking from “buy hardware” to: Meter provides the hardware, the software, the installation and ongoing support. The customer pays recurring, per square foot, and effectively “don’t pay us if the network doesn’t work.” Anil thinks about business model innovation on the same level as product and technology innovation. 5. Choosing a massive, incumbent dominated market on purpose. Networking is controlled by a few giants like Cisco. They were pulled toward that exact dynamic: a huge, durable market where the initial ramp is brutal, but if you get through it, there are very few new players alongside you. 6. Deliberately avoided the channel in a channel dominated industry. Roughly 90 percent of networking is sold through the channel.Meter refused to use the channel until they were convinced the product was dramatically better in every way, because incumbents could weaponize the channel with discounts to block them. Only after they had hundreds of happy customers and strong tools did they fully embrace channel sales. 7. The team has an extreme time horizon, paired with extreme urgency. Anil thinks in decades: “I care about where Meter ends up in 25 years, not five.” At the same time, he is obsessively focused on what happens in the next few hours and where every report spends time. That “barbell” between multi decade vision and hour by hour intensity is very explicit for him. 8. An allergy to “meta work” and most conventional management. No OKRs or goals at all. They have a strong skepticism of spending time on docs, processes, and coordination that feel like work but do not move the product forward.

Brett Berson

27,529 次观看 • 9 个月前

Gojek completes more food deliveries than GrubHub, Uber Eats, and DoorDash combined, and more rides per day than Lyft. Vikrama Dhiman (Vikrama Dhiman) heads all things product at Gojek Tech, including product management, design, and research, across teams in Indonesia, Singapore and India. He is among the most well-known and respected product leaders in all of Asia, and one of my most requested guests. In our conversation, we discuss: 🔸 The most common traits of successful PMs 🔸 The 3 W’s framework for PM career growth 🔸 The right way to push back as a PM 🔸 The Four A’s of leveling up in product management 🔸 Common pitfalls that stall PM careers 🔸 Advice for transitioning into PM 🔸 Why intent alone is not enough 🔸 Much more Listen now 👇 - YouTube: - Spotify: - Apple: Some key takeaways: 1. As a PM—especially early in your career—make sure you’re nailing execution before you spend too much time on strategy. Effective strategic thinking requires context on the company, product, and stakeholders that you might not have yet. But you always have opportunities to execute and make your team members’ lives easier. As you execute more, you’ll begin to understand the strategic needs more deeply, and you’ll have more opportunities to contribute at that level. 2. Three traits that make you a great PM to work with: a. Raise difficult issues without being difficult to work with b. Bring up important topics without drawing importance to yourself c. Remember that you are in charge of getting decisions made, not making the decisions yourself 3. Good PMs produce good artifacts: PRDs, product notes, design briefs, etc. These demonstrate your proficiency across core PM skills, and they are your opportunity to “have impact on the impact”—to add value to a team that is adding value to the company. If you’re not sure what to focus on next in your PM career, go scrutinize your recent artifacts and ask how they could be improved. 4. To push back on ideas without being difficult to work with, ask clarifying questions and bring the conversation to a logical space rather than an emotional one. Here are some of Vikrama’s favorites: a. Why has this become suddenly important? b. So that I am clear, can you help clarify xyz? c. What would success look like? d. What time frame do you need this in? 5. Vikrama’s 3 W’s framework for PM career growth comprises (1) what you produce, (2) what you bring to the table, and (3) what your operating model is. Strong product managers excel at two of the three W’s. Product managers who rise in their career excel at all three. 6. Three mindset shifts that can enable faster growth: a. Focus on things you can control: Concentrate your efforts on aspects of your role that you can influence or control, especially as you advance in your career. b. Always embrace change: As an IC PM, you experience rapid change. It’s important that as you get to mid/senior level, you never stop looking for ways to improve. c. How you see yourself: Cultivate a positive and growth-oriented mindset about yourself and your capabilities. 7. Three core competencies to focus on when transitioning into PM roles are (1) data, (2) design/research, and (3) technology. Someone from a design background should focus on strengthening data or tech skills to complement their existing expertise. Similarly, someone from a tech background should pick a design skill to focus on. This approach allows for maximum leverage.
0:49

Gojek completes more food deliveries than GrubHub, Uber Eats, and DoorDash combined, and more rides per day than Lyft. Vikrama Dhiman (Vikrama Dhiman) heads all things product at Gojek Tech, including product management, design, and research, across teams in Indonesia, Singapore and India. He is among the most well-known and respected product leaders in all of Asia, and one of my most requested guests. In our conversation, we discuss: 🔸 The most common traits of successful PMs 🔸 The 3 W’s framework for PM career growth 🔸 The right way to push back as a PM 🔸 The Four A’s of leveling up in product management 🔸 Common pitfalls that stall PM careers 🔸 Advice for transitioning into PM 🔸 Why intent alone is not enough 🔸 Much more Listen now 👇 - YouTube: - Spotify: - Apple: Some key takeaways: 1. As a PM—especially early in your career—make sure you’re nailing execution before you spend too much time on strategy. Effective strategic thinking requires context on the company, product, and stakeholders that you might not have yet. But you always have opportunities to execute and make your team members’ lives easier. As you execute more, you’ll begin to understand the strategic needs more deeply, and you’ll have more opportunities to contribute at that level. 2. Three traits that make you a great PM to work with: a. Raise difficult issues without being difficult to work with b. Bring up important topics without drawing importance to yourself c. Remember that you are in charge of getting decisions made, not making the decisions yourself 3. Good PMs produce good artifacts: PRDs, product notes, design briefs, etc. These demonstrate your proficiency across core PM skills, and they are your opportunity to “have impact on the impact”—to add value to a team that is adding value to the company. If you’re not sure what to focus on next in your PM career, go scrutinize your recent artifacts and ask how they could be improved. 4. To push back on ideas without being difficult to work with, ask clarifying questions and bring the conversation to a logical space rather than an emotional one. Here are some of Vikrama’s favorites: a. Why has this become suddenly important? b. So that I am clear, can you help clarify xyz? c. What would success look like? d. What time frame do you need this in? 5. Vikrama’s 3 W’s framework for PM career growth comprises (1) what you produce, (2) what you bring to the table, and (3) what your operating model is. Strong product managers excel at two of the three W’s. Product managers who rise in their career excel at all three. 6. Three mindset shifts that can enable faster growth: a. Focus on things you can control: Concentrate your efforts on aspects of your role that you can influence or control, especially as you advance in your career. b. Always embrace change: As an IC PM, you experience rapid change. It’s important that as you get to mid/senior level, you never stop looking for ways to improve. c. How you see yourself: Cultivate a positive and growth-oriented mindset about yourself and your capabilities. 7. Three core competencies to focus on when transitioning into PM roles are (1) data, (2) design/research, and (3) technology. Someone from a design background should focus on strengthening data or tech skills to complement their existing expertise. Similarly, someone from a tech background should pick a design skill to focus on. This approach allows for maximum leverage.

Lenny Rachitsky

115,463 次观看 • 2 年前

The 15-Minute Polymarket Sniper: Making $332,565 on "Boring" Bitcoin Fluctuations Profile Statistics: Total Profit: $332,565.20 The Biggest Win: $4,480.76 Total Forecasts: 39,222 The Origin Story: Free Claude meets Prediction Markets This trader didn't have a team of developers or a high-priced algorithmic terminal. In April 2026, he found a completely free way to access Claude's advanced coding capabilities. Instead of using it for basic tasks, he spent a weekend prompting the AI to write a highly specialized script. The result? An automated trading bot designed for one specific task: scanning Polymarket's 15-minute Bitcoin intervals for mispriced odds and executing trades with zero human latency. The Strategy: 15-Minute Scalping & Mathematical Edge Buying Mispriced Probabilities: The bot targets hyper-short-term "Bitcoin Up or Down" contracts, identifying moments where shares are heavily discounted (frequently buying between 8¢ and 49¢). It essentially bets on outcomes where the market has temporarily overreacted. Pure Math over Luck: Making a few large bets is gambling. Executing over 39,000 forecasts is mathematics. By running a high-volume, systematic execution model, the bot relies on the Law of Large Numbers to turn minor, cheap market inefficiencies into a massive, compounding curve of over $332K in total profit. Absolute Discipline: Free from fear or greed, the script runs 24/7, catching micro-trends while human traders are asleep. Top Deals from the Dashboard: June 3 (5:45 AM - 5:50 AM): The ultimate sniper shot. Bought "Up" at just 8.4¢ ➔ Invested $219.21 ➔ Won $2,617.25 (+$2,398.04 / +1,093.96%) May 28 (11:05 AM - 11:10 AM): Bought "Down" at a massive discount of 9.9¢ ➔ Invested $244.67 ➔ Won $2,482.63 (+$2,237.96 / +914.67%) June 1 (9:15 AM - 9:30 AM): High-conviction scalp. Bought "Down" at 48.3¢ ➔ Invested $2,317.79 ➔ Won $4,798.19 (+$2,480.39 / +107.02%) Why does this work? On ultra-short 15-minute charts, order books constantly fracture. Retail traders and standard execution bots panic-sell or over-leverage based on minor, noise-level BTC ticks. This AI-driven bot filters out the noise, calculates the mathematically optimal risk-to-reward ratio in milliseconds, locks in undervalued shares, and systematically extracts $2,200+ in net profit per cycle.
0:35

The 15-Minute Polymarket Sniper: Making $332,565 on "Boring" Bitcoin Fluctuations Profile Statistics: Total Profit: $332,565.20 The Biggest Win: $4,480.76 Total Forecasts: 39,222 The Origin Story: Free Claude meets Prediction Markets This trader didn't have a team of developers or a high-priced algorithmic terminal. In April 2026, he found a completely free way to access Claude's advanced coding capabilities. Instead of using it for basic tasks, he spent a weekend prompting the AI to write a highly specialized script. The result? An automated trading bot designed for one specific task: scanning Polymarket's 15-minute Bitcoin intervals for mispriced odds and executing trades with zero human latency. The Strategy: 15-Minute Scalping & Mathematical Edge Buying Mispriced Probabilities: The bot targets hyper-short-term "Bitcoin Up or Down" contracts, identifying moments where shares are heavily discounted (frequently buying between 8¢ and 49¢). It essentially bets on outcomes where the market has temporarily overreacted. Pure Math over Luck: Making a few large bets is gambling. Executing over 39,000 forecasts is mathematics. By running a high-volume, systematic execution model, the bot relies on the Law of Large Numbers to turn minor, cheap market inefficiencies into a massive, compounding curve of over $332K in total profit. Absolute Discipline: Free from fear or greed, the script runs 24/7, catching micro-trends while human traders are asleep. Top Deals from the Dashboard: June 3 (5:45 AM - 5:50 AM): The ultimate sniper shot. Bought "Up" at just 8.4¢ ➔ Invested $219.21 ➔ Won $2,617.25 (+$2,398.04 / +1,093.96%) May 28 (11:05 AM - 11:10 AM): Bought "Down" at a massive discount of 9.9¢ ➔ Invested $244.67 ➔ Won $2,482.63 (+$2,237.96 / +914.67%) June 1 (9:15 AM - 9:30 AM): High-conviction scalp. Bought "Down" at 48.3¢ ➔ Invested $2,317.79 ➔ Won $4,798.19 (+$2,480.39 / +107.02%) Why does this work? On ultra-short 15-minute charts, order books constantly fracture. Retail traders and standard execution bots panic-sell or over-leverage based on minor, noise-level BTC ticks. This AI-driven bot filters out the noise, calculates the mathematically optimal risk-to-reward ratio in milliseconds, locks in undervalued shares, and systematically extracts $2,200+ in net profit per cycle.

Ridark

33,395 次观看 • 3 个月前

INTERVIEW: Patrick Gruhn was the CEO of FTX Europe. He sold his company to FTX for $400 million before it collapsed, watched it implode, then bought it back for $30 million. Now he's built something that might be the most interesting idea in trading right now. His company UpsideOnly flips the model. You trade with their money, lose nothing if you're wrong, split the profits 50/50 if you're right. He also gets into why 95% of traders lose, why even AI falls for the same behavioral traps humans do, and what FTX could have been if SBF hadn't gotten greedy. His take: it wasn't outright fraud from day one, it was embezzlement, the same thing banks do legally, except FTX wasn't a bank. Full interview below. Patrick Gruhn Disclaimer: This content was produced in collaboration with the other party and is intended for informational purposes only. It does not constitute financial or investment advice. Always conduct your own research before making any decisions 01:43 - FTX would probably be bigger than Binance today if Sam Bankman-Fried hadn’t destroyed the company. 03:01 - The new wave of pre-IPO perpetual futures on companies like SpaceX is basically gambling on steroids. 04:19 - Trading platforms sell desperate people the dream of escaping financial pressure overnight. 04:59 - Why retail traders are structurally doomed against market makers and professional liquidity providers. 06:41 - Traders lock in tiny wins but refuse to accept losses until they get wiped out. 09:13 - Trading algorithms are specifically built to exploit human emotional weaknesses. 14:26 - Upside Only trained AI on more than 22 billion trades. 18:38 - The core twist behind Upside Only: users never risk their own money, the company absorbs all losses and only shares profits. 19:58 - Humans are actually much better at identifying entry points than exit points, which is where AI takes over. 20:45 - Leveraged traders often become worse over time because losses psychologically destroy their discipline. 27:05 - Old users eventually go bankrupt and need to be replaced by new victims. 28:54 - Retail traders cannot beat markets unless they have insider information. 31:59 - Suicides, addiction and financial ruin caused by predatory trading platforms and influencers. 35:30 - Casinos actually give people far better odds than crypto leverage trading platforms. 45:40 - FTX would still dominate the industry today if customer funds had never been touched.
50:41

INTERVIEW: Patrick Gruhn was the CEO of FTX Europe. He sold his company to FTX for $400 million before it collapsed, watched it implode, then bought it back for $30 million. Now he's built something that might be the most interesting idea in trading right now. His company UpsideOnly flips the model. You trade with their money, lose nothing if you're wrong, split the profits 50/50 if you're right. He also gets into why 95% of traders lose, why even AI falls for the same behavioral traps humans do, and what FTX could have been if SBF hadn't gotten greedy. His take: it wasn't outright fraud from day one, it was embezzlement, the same thing banks do legally, except FTX wasn't a bank. Full interview below. Patrick Gruhn Disclaimer: This content was produced in collaboration with the other party and is intended for informational purposes only. It does not constitute financial or investment advice. Always conduct your own research before making any decisions 01:43 - FTX would probably be bigger than Binance today if Sam Bankman-Fried hadn’t destroyed the company. 03:01 - The new wave of pre-IPO perpetual futures on companies like SpaceX is basically gambling on steroids. 04:19 - Trading platforms sell desperate people the dream of escaping financial pressure overnight. 04:59 - Why retail traders are structurally doomed against market makers and professional liquidity providers. 06:41 - Traders lock in tiny wins but refuse to accept losses until they get wiped out. 09:13 - Trading algorithms are specifically built to exploit human emotional weaknesses. 14:26 - Upside Only trained AI on more than 22 billion trades. 18:38 - The core twist behind Upside Only: users never risk their own money, the company absorbs all losses and only shares profits. 19:58 - Humans are actually much better at identifying entry points than exit points, which is where AI takes over. 20:45 - Leveraged traders often become worse over time because losses psychologically destroy their discipline. 27:05 - Old users eventually go bankrupt and need to be replaced by new victims. 28:54 - Retail traders cannot beat markets unless they have insider information. 31:59 - Suicides, addiction and financial ruin caused by predatory trading platforms and influencers. 35:30 - Casinos actually give people far better odds than crypto leverage trading platforms. 45:40 - FTX would still dominate the industry today if customer funds had never been touched.

Mario Nawfal

465,851 次观看 • 3 个月前

77 Reasons Why I’ve Invested Over $8,000,000+ in MultiversX (EGLD) and Why EGLD Will Crush It in 2025 (My Investment Thesis). I publicly shared my portfolio on X. EGLD is A) Better than BTC B) Everything that ETH wants to be C) The GameStop of Crypto 1. EGLD is verifiably the most scalable (theoretically unlimited) L1 chain in the world, theoretically capable of over 10 million TPS (thanks to adaptive state sharding). 2. e-Gold is digital gold. It has the best tokenomics among all L1s, similarly scarce to BTC, with a maximum supply of 31.4 million coins. Currently, 27.68 million coins are in circulation. 3. EGLD will be the most decentralized cryptocurrency in the world thanks to sharding and minimal hardware requirements for running nodes. It’s already second only to Ethereum with 3,618 validator nodes. 4. EGLD has extremely low fees, around ~$0.002 per transaction. 5. EGLD is extremely secure. No wallet drains like on ETH/SOL; assets are owned natively (not via a smart contract). There is no MEV risk (front-running bots). 6. EGLD is the only chain in the world with an on-chain Guardian (two-phase verification), making it impossible for a hacker to steal your funds—even if they have your private keys (seed phrase). 7. EGLD is carbon-neutral and eco-friendly, not wasting energy like BTC and other PoW chains. It’s exceptionally efficient, scalable, global, and sustainable. 8. EGLD has the best UX in crypto. Download the xPortal wallet—it’s like discovering Apple in Web3. The interface is simple, flawless, and you barely realize you’re using crypto. Instead of addresses, you use HeroTags. The app features all dApps, everything runs smoothly, and the visuals are beautifully designed. The explorer, web wallet, etc. follow the same high-quality user experience. 9. EGLD supports native assets, unlike Ethereum, for example. 10. EGLD is the first chain to fully implement horizontal (theoretically unlimited) sharding without compromising on decentralization—unlike Solana and others that attempt vertical scaling, leading to multiple network downtimes (11+ times) and huge hardware demands for validators, ultimately harming decentralization. 11. EGLD makes setting up a validator agency extremely easy. Even complete IT beginners can do it. The UX and documentation are superb. I personally set up the “EGLDSqueeze” agency in about 30 minutes. Managing it is straightforward via the web wallet, which feels like managing a Facebook page. This simplifies decentralization enormously. 12. EGLD allows literally anyone (even your grandma) to participate in decentralization, since nodes can run on a Raspberry Pi or a relatively affordable phone. Imagine millions of people worldwide securing the network, validating transactions without even knowing it. This can’t be done with BTC, where setting up profitable mining operations is prohibitively expensive. 13. WASM-Based Virtual Machine: You can write smart contracts in your favorite language, compile them, and run them via the fastest VM in the world. 14. EGLD has been tested at an incredible 263,000 TPS using its sharding mechanism and low hardware requirements. Allegedly, by mid-next year (April), they’ll demonstrate 1,000,000 TPS. (For context: Mastercard handles around 5,000 TPS; BTC handles 5–7 TPS.) 15. EGLD is currently the most advanced L1 in terms of scalability, security, decentralization, UX, eco-friendliness, and tokenomics. It’s the only chain that has genuinely solved the Blockchain Trilemma and is ready to onboard 1 billion people into crypto—users who won’t even realize they’re interacting with crypto. 16. EGLD is perfectly positioned for AI projects—AI agents, AI tools, or a so-called “Truth Machine” that monitors other AIs on-chain, documenting what’s true and comparing different AI outputs (some of which may be censored or biased), ensuring people don’t get confused or scammed in an AI-driven world. 17. The EGLD team is the hardest-working team I’ve ever encountered. I had the honor of meeting many of them personally, and can attest that their pace—even during a bear market—is extraordinary. 18. EGLD’s development team is exceptionally active on GitHub, continually improving their network and actively committing code. 19. EGLD plans to introduce an update reducing block time to 600ms (down from ~6 seconds), which would make the chain essentially unrivaled. 20. EGLD is effectively the only usable L1 in Europe, and the team has direct connections within the EU government—extremely bullish for the project. 21. EGLD provides top-tier on-chain governance not only for the MultiversX (EGLD) protocol but also for DeFi projects (e.g., xExchange, MEX). 22. EGLD plans to expand to the US, likely opening offices in Austin, Texas. This could put them in direct contact with Elon Musk (if it hasn’t happened already), as he’s involved with If he’s done his research, he’d discover there’s simply no better L1 worldwide. 23. EGLD solved fully implemented sharding, perfect tokenomics, and top-tier architecture with just $5M, whereas other chains failed to do so even with $100M+. The second-best sharding network, NEAR, needed $100M, has worse tokenomics, and its sharding isn’t fully implemented yet. Its UX also doesn’t compare. Owning NEAR was like comparing a VW Golf R to a Porsche GT3—EGLD is the Porsche GT3. 24. According to Similarweb, EGLD has significantly high traffic relative to other chains with market caps 100x larger. The market cap vs. web traffic discrepancy is huge, which is a strong indicator of EGLD’s potential. 25. EGLD has the most active and dedicated community relative to its user base, with users who believe in the technology, have full faith in the team, and remain loyal despite price volatility—because they use the chain and know there’s nothing better. 26. Check other chains’ active user counts on X (Twitter) and compare it with the followers of EGLD’s founders and main network accounts, versus those with 30x, 50x, or 100x larger market caps. 27. Visit the MultiversX website to observe the futuristic design and presentation, then compare it to other chains that appear nearly a decade behind in design and branding. 28. EGLD hosts the xDay Global event, showcasing updates, new builders, projects in the ecosystem, and major announcements—similar to Apple’s Keynotes—delivered in a highly professional, goosebump-inducing atmosphere. The next event is in Korea, the second-biggest crypto market after the US. Check out their previous xDay after-movie to see why this is extremely bullish. 29. EGLD is moving forward with plans for the first regulated, audited EU stablecoin under MiCa regulation, made possible by acquiring xMoney, which I view as a “Stripe” for crypto/fiat, offering everything from user solutions to merchant services—potentially the future of payments. 30. Greg Siourouni recently joined EGLD, having been an executive director at SUI Foundation. He’s now co-founder of xMoney Global. xMoney (formerly UTrust, with token UTK) is owned and founded by the MultiversX Labs team. A stablecoin might be introduced soon, which would be massively bullish given xMoney’s roadmap. They recently announced integrations with Binance Pay—both ways. 31. EGLD prioritizes user safety, believing it’s the only feasible approach once the network scales to serve a billion people—many of whom are retail users with little to no security awareness. 32. EGLD offers “Sovereign Chains,” letting you effectively clone their chain without heavy development, set up your own validators, and leverage their unlimited scalability. Any blockchain (ETH, BTC, SOL) struggling with scalability, decentralization, or security could run an ultra-fast, scalable, and secure L2 on EGLD’s Sovereign Chain, meeting top enterprise requirements. No one else has really done this. The Sovereign Chain demo achieved astonishing TPS and has an SDK. 33. No downtime since inception. 34. No shard takeover attacks have occurred. 35. Extremely fast—soon 600ms block time will be in place. 36. ESDTs – The best token standard available: fungible, non-fungible, semi-fungible, DeFi assets—everything is native and highly customizable. 37. Top-tier composability of assets and smart contracts. 38. Integrated DNS at protocol level with HeroTags (nicknames) instead of long addresses. 39. Asynchronous calls are supported. 40. Cross-shard transfers, execution, reverts, and calls are seamlessly integrated. 41. The best staking system in the space. Secure Proof of Stake (SPoS) is far more efficient than Proof of Work (PoW). 42. Built-in Delegation and Staking Provider system, with over 125K delegators. 43. Complete support for liquid staked assets, fostering decentralization rather than centralization. 44. TransferRoles for ESDT and other advanced operations. 45. Composable tasks on-chain for more sophisticated DeFi workflows. 46. MultiTransfer and asset execution within one transaction. 47. Re-entrancy protection is built-in by design. 48. Storage for ESDT assets goes beyond a linear approach, optimizing performance. 49. No integer overflows thanks to integrated safeMath operations. 50. Integrated crypto opcodes in the VM, enhancing security and performance. 51. Support for BigFloats, BigInts, and BigDecimals, enabling advanced financial calculations on-chain. 52. No sandwich attacks, plus front-running and MEV protection. 53. Relayed Transactions, simplifying user interactions and fees. 54. Smart Accounts featuring data tries and multiple built-in functions. 55. Generalized Paymaster solutions, enabling flexible fee models. 56. Subscriptions for recurring or automated on-chain payments. 57. Web2-like usability with Web3 functionality, bridging mainstream adoption. 58. StakingV4 for improved decentralization. 59. Enhanced MEV protection rolling out to safeguard users. 60. Parallel execution is coming soon, boosting throughput. 61. 1 million TPS is on the roadmap, targeted for demonstration. 62. 600ms block time is also coming soon. 63. Reduced cross-shard processing is planned to improve efficiency. 64. ZK everywhere (PI²): “prove everything” approach is coming. 65. AsyncV3 is in development for more complex cross-contract interactions. 66. Scalability enhancements for Merkle Tries or a new data model are being explored. 67. Linear storage on the VM is forthcoming. 68. A dynamic language interpreter at the VM is also planned. 69. Rumors suggest that MultiversX (EGLD) is building a “Truth Machine” on their L1—an essential, game-changing tool for AI verification and societal impact. 70. The entire team features individuals with PhDs in mathematics and physics, and many are former engineers at Google, IBM, and similar companies. 71. Over 56% of the network’s supply is staked, showcasing strong community involvement. 72. More than 6,772,347 accounts have been created on the network. 73. A total of 476,627,710 transactions have been processed on-chain without any outages or hacks. 74. EGLD has built a massive ecosystem over time. While not as numerous in project count as Solana, its market cap is ~100x smaller, yet it has far superior tokenomics and technology. The projects that do exist, like Hatom Protocol, are top-tier in UX, security, and advanced features. Hatom will soon introduce USH, a truly high-quality, decentralized stablecoin. 75. On competing chains, automated transactions aren’t easily or cheaply executed, whereas on MultiversX, tools like let you do this for free (with near-zero fees). 76. No other chain combines such a strong team and long-term vision where every product meets extreme security and UX standards like MultiversX does. This is why I see it as the “next Apple” in Web3. 77. MultiversX has a new CMO – Adam Bates, a former CMO at the Cardano Foundation. He was behind the success of Cardano’s huge marketing campaign and has a very good relationship with Charles Hoskinson. Thanks to him, Beniamin Mincu (the founder of MultiversX) was likely introduced, and now they will probably discuss how both blockchains can help each other, as well as any other potential collaborations we don’t yet know about. This is also extremely bullish. #EGLD is undeniably the most Scalable, Advanced, Secure, and User-friendly L1 supercomputer ever created. It’s built to SHAPE THE FUTURE. 1) 2) 3) 4) 5) 27/6/2024 - EGLDSqueeze - SUMMARY: HERE IS NO 2ND BEST. EGLD IS ONLY ONE BLOCKCHAIN THAT CAN RULE THEM ALL. ✅ UNLIMITED SCALING ✅ SCARCE AS BTC ✅ PROGRAMMABLE AS ETH ✅ NO DOWNTIME AS SOL ✅ UI/UX OF Apple ✅ SHARDING DONE BEFORE NEAR & TON ✅ BEST WALLET xPortal WITH GUARDIAN Price prediction (NFA|DYOR): My reasoning is that the real market cap as of December 23, 2024...if we take into account the value of other cryptocurrencies such as BTC, SOL, ETH, AVAX, NEAR, TON, Cardano, BNB, XRP, and so forth, plus the existence of meme coins with valuations above 20 billion USD, or even games nobody plays anymore that still have valuations above 800 million shows that EGLD’s current market cap of approximately 942 million USD is incredibly low. From a technological standpoint, user experience, and other relevant aspects, compared to SOL, NEAR, TON, AVAX, and other L1 protocols, EGLD’s market cap should realistically be around 100 billion USD. Therefore, my prediction and investment thesis is a minimum of a 100x increase from its current price (+-SOL marketcap). MultiversX is ready to onboard 1 billion people to the blockchain. From a long-term perspective, it could even reach a market cap of 1 trillion USD, which is roughly half of where BTC is right now. That would be approximately a 1060x gain from the current market cap. 1 EGLD (MultiversX) is for $34 (only 31.4M max supply) think about this. Not financial advice. Again. There is no 2nd best L1. Position yourself where the puck is going, then wait at the goal until the goal gets there Apes together, strong. Ape alone, weak. We Don't Worry. We Just Win. Shape The Future
5:34

77 Reasons Why I’ve Invested Over $8,000,000+ in MultiversX (EGLD) and Why EGLD Will Crush It in 2025 (My Investment Thesis). I publicly shared my portfolio on X. EGLD is A) Better than BTC B) Everything that ETH wants to be C) The GameStop of Crypto 1. EGLD is verifiably the most scalable (theoretically unlimited) L1 chain in the world, theoretically capable of over 10 million TPS (thanks to adaptive state sharding). 2. e-Gold is digital gold. It has the best tokenomics among all L1s, similarly scarce to BTC, with a maximum supply of 31.4 million coins. Currently, 27.68 million coins are in circulation. 3. EGLD will be the most decentralized cryptocurrency in the world thanks to sharding and minimal hardware requirements for running nodes. It’s already second only to Ethereum with 3,618 validator nodes. 4. EGLD has extremely low fees, around ~$0.002 per transaction. 5. EGLD is extremely secure. No wallet drains like on ETH/SOL; assets are owned natively (not via a smart contract). There is no MEV risk (front-running bots). 6. EGLD is the only chain in the world with an on-chain Guardian (two-phase verification), making it impossible for a hacker to steal your funds—even if they have your private keys (seed phrase). 7. EGLD is carbon-neutral and eco-friendly, not wasting energy like BTC and other PoW chains. It’s exceptionally efficient, scalable, global, and sustainable. 8. EGLD has the best UX in crypto. Download the xPortal wallet—it’s like discovering Apple in Web3. The interface is simple, flawless, and you barely realize you’re using crypto. Instead of addresses, you use HeroTags. The app features all dApps, everything runs smoothly, and the visuals are beautifully designed. The explorer, web wallet, etc. follow the same high-quality user experience. 9. EGLD supports native assets, unlike Ethereum, for example. 10. EGLD is the first chain to fully implement horizontal (theoretically unlimited) sharding without compromising on decentralization—unlike Solana and others that attempt vertical scaling, leading to multiple network downtimes (11+ times) and huge hardware demands for validators, ultimately harming decentralization. 11. EGLD makes setting up a validator agency extremely easy. Even complete IT beginners can do it. The UX and documentation are superb. I personally set up the “EGLDSqueeze” agency in about 30 minutes. Managing it is straightforward via the web wallet, which feels like managing a Facebook page. This simplifies decentralization enormously. 12. EGLD allows literally anyone (even your grandma) to participate in decentralization, since nodes can run on a Raspberry Pi or a relatively affordable phone. Imagine millions of people worldwide securing the network, validating transactions without even knowing it. This can’t be done with BTC, where setting up profitable mining operations is prohibitively expensive. 13. WASM-Based Virtual Machine: You can write smart contracts in your favorite language, compile them, and run them via the fastest VM in the world. 14. EGLD has been tested at an incredible 263,000 TPS using its sharding mechanism and low hardware requirements. Allegedly, by mid-next year (April), they’ll demonstrate 1,000,000 TPS. (For context: Mastercard handles around 5,000 TPS; BTC handles 5–7 TPS.) 15. EGLD is currently the most advanced L1 in terms of scalability, security, decentralization, UX, eco-friendliness, and tokenomics. It’s the only chain that has genuinely solved the Blockchain Trilemma and is ready to onboard 1 billion people into crypto—users who won’t even realize they’re interacting with crypto. 16. EGLD is perfectly positioned for AI projects—AI agents, AI tools, or a so-called “Truth Machine” that monitors other AIs on-chain, documenting what’s true and comparing different AI outputs (some of which may be censored or biased), ensuring people don’t get confused or scammed in an AI-driven world. 17. The EGLD team is the hardest-working team I’ve ever encountered. I had the honor of meeting many of them personally, and can attest that their pace—even during a bear market—is extraordinary. 18. EGLD’s development team is exceptionally active on GitHub, continually improving their network and actively committing code. 19. EGLD plans to introduce an update reducing block time to 600ms (down from ~6 seconds), which would make the chain essentially unrivaled. 20. EGLD is effectively the only usable L1 in Europe, and the team has direct connections within the EU government—extremely bullish for the project. 21. EGLD provides top-tier on-chain governance not only for the MultiversX (EGLD) protocol but also for DeFi projects (e.g., xExchange, MEX). 22. EGLD plans to expand to the US, likely opening offices in Austin, Texas. This could put them in direct contact with Elon Musk (if it hasn’t happened already), as he’s involved with If he’s done his research, he’d discover there’s simply no better L1 worldwide. 23. EGLD solved fully implemented sharding, perfect tokenomics, and top-tier architecture with just $5M, whereas other chains failed to do so even with $100M+. The second-best sharding network, NEAR, needed $100M, has worse tokenomics, and its sharding isn’t fully implemented yet. Its UX also doesn’t compare. Owning NEAR was like comparing a VW Golf R to a Porsche GT3—EGLD is the Porsche GT3. 24. According to Similarweb, EGLD has significantly high traffic relative to other chains with market caps 100x larger. The market cap vs. web traffic discrepancy is huge, which is a strong indicator of EGLD’s potential. 25. EGLD has the most active and dedicated community relative to its user base, with users who believe in the technology, have full faith in the team, and remain loyal despite price volatility—because they use the chain and know there’s nothing better. 26. Check other chains’ active user counts on X (Twitter) and compare it with the followers of EGLD’s founders and main network accounts, versus those with 30x, 50x, or 100x larger market caps. 27. Visit the MultiversX website to observe the futuristic design and presentation, then compare it to other chains that appear nearly a decade behind in design and branding. 28. EGLD hosts the xDay Global event, showcasing updates, new builders, projects in the ecosystem, and major announcements—similar to Apple’s Keynotes—delivered in a highly professional, goosebump-inducing atmosphere. The next event is in Korea, the second-biggest crypto market after the US. Check out their previous xDay after-movie to see why this is extremely bullish. 29. EGLD is moving forward with plans for the first regulated, audited EU stablecoin under MiCa regulation, made possible by acquiring xMoney, which I view as a “Stripe” for crypto/fiat, offering everything from user solutions to merchant services—potentially the future of payments. 30. Greg Siourouni recently joined EGLD, having been an executive director at SUI Foundation. He’s now co-founder of xMoney Global. xMoney (formerly UTrust, with token UTK) is owned and founded by the MultiversX Labs team. A stablecoin might be introduced soon, which would be massively bullish given xMoney’s roadmap. They recently announced integrations with Binance Pay—both ways. 31. EGLD prioritizes user safety, believing it’s the only feasible approach once the network scales to serve a billion people—many of whom are retail users with little to no security awareness. 32. EGLD offers “Sovereign Chains,” letting you effectively clone their chain without heavy development, set up your own validators, and leverage their unlimited scalability. Any blockchain (ETH, BTC, SOL) struggling with scalability, decentralization, or security could run an ultra-fast, scalable, and secure L2 on EGLD’s Sovereign Chain, meeting top enterprise requirements. No one else has really done this. The Sovereign Chain demo achieved astonishing TPS and has an SDK. 33. No downtime since inception. 34. No shard takeover attacks have occurred. 35. Extremely fast—soon 600ms block time will be in place. 36. ESDTs – The best token standard available: fungible, non-fungible, semi-fungible, DeFi assets—everything is native and highly customizable. 37. Top-tier composability of assets and smart contracts. 38. Integrated DNS at protocol level with HeroTags (nicknames) instead of long addresses. 39. Asynchronous calls are supported. 40. Cross-shard transfers, execution, reverts, and calls are seamlessly integrated. 41. The best staking system in the space. Secure Proof of Stake (SPoS) is far more efficient than Proof of Work (PoW). 42. Built-in Delegation and Staking Provider system, with over 125K delegators. 43. Complete support for liquid staked assets, fostering decentralization rather than centralization. 44. TransferRoles for ESDT and other advanced operations. 45. Composable tasks on-chain for more sophisticated DeFi workflows. 46. MultiTransfer and asset execution within one transaction. 47. Re-entrancy protection is built-in by design. 48. Storage for ESDT assets goes beyond a linear approach, optimizing performance. 49. No integer overflows thanks to integrated safeMath operations. 50. Integrated crypto opcodes in the VM, enhancing security and performance. 51. Support for BigFloats, BigInts, and BigDecimals, enabling advanced financial calculations on-chain. 52. No sandwich attacks, plus front-running and MEV protection. 53. Relayed Transactions, simplifying user interactions and fees. 54. Smart Accounts featuring data tries and multiple built-in functions. 55. Generalized Paymaster solutions, enabling flexible fee models. 56. Subscriptions for recurring or automated on-chain payments. 57. Web2-like usability with Web3 functionality, bridging mainstream adoption. 58. StakingV4 for improved decentralization. 59. Enhanced MEV protection rolling out to safeguard users. 60. Parallel execution is coming soon, boosting throughput. 61. 1 million TPS is on the roadmap, targeted for demonstration. 62. 600ms block time is also coming soon. 63. Reduced cross-shard processing is planned to improve efficiency. 64. ZK everywhere (PI²): “prove everything” approach is coming. 65. AsyncV3 is in development for more complex cross-contract interactions. 66. Scalability enhancements for Merkle Tries or a new data model are being explored. 67. Linear storage on the VM is forthcoming. 68. A dynamic language interpreter at the VM is also planned. 69. Rumors suggest that MultiversX (EGLD) is building a “Truth Machine” on their L1—an essential, game-changing tool for AI verification and societal impact. 70. The entire team features individuals with PhDs in mathematics and physics, and many are former engineers at Google, IBM, and similar companies. 71. Over 56% of the network’s supply is staked, showcasing strong community involvement. 72. More than 6,772,347 accounts have been created on the network. 73. A total of 476,627,710 transactions have been processed on-chain without any outages or hacks. 74. EGLD has built a massive ecosystem over time. While not as numerous in project count as Solana, its market cap is ~100x smaller, yet it has far superior tokenomics and technology. The projects that do exist, like Hatom Protocol, are top-tier in UX, security, and advanced features. Hatom will soon introduce USH, a truly high-quality, decentralized stablecoin. 75. On competing chains, automated transactions aren’t easily or cheaply executed, whereas on MultiversX, tools like let you do this for free (with near-zero fees). 76. No other chain combines such a strong team and long-term vision where every product meets extreme security and UX standards like MultiversX does. This is why I see it as the “next Apple” in Web3. 77. MultiversX has a new CMO – Adam Bates, a former CMO at the Cardano Foundation. He was behind the success of Cardano’s huge marketing campaign and has a very good relationship with Charles Hoskinson. Thanks to him, Beniamin Mincu (the founder of MultiversX) was likely introduced, and now they will probably discuss how both blockchains can help each other, as well as any other potential collaborations we don’t yet know about. This is also extremely bullish. #EGLD is undeniably the most Scalable, Advanced, Secure, and User-friendly L1 supercomputer ever created. It’s built to SHAPE THE FUTURE. 1) 2) 3) 4) 5) 27/6/2024 - EGLDSqueeze - SUMMARY: HERE IS NO 2ND BEST. EGLD IS ONLY ONE BLOCKCHAIN THAT CAN RULE THEM ALL. ✅ UNLIMITED SCALING ✅ SCARCE AS BTC ✅ PROGRAMMABLE AS ETH ✅ NO DOWNTIME AS SOL ✅ UI/UX OF Apple ✅ SHARDING DONE BEFORE NEAR & TON ✅ BEST WALLET xPortal WITH GUARDIAN Price prediction (NFA|DYOR): My reasoning is that the real market cap as of December 23, 2024...if we take into account the value of other cryptocurrencies such as BTC, SOL, ETH, AVAX, NEAR, TON, Cardano, BNB, XRP, and so forth, plus the existence of meme coins with valuations above 20 billion USD, or even games nobody plays anymore that still have valuations above 800 million shows that EGLD’s current market cap of approximately 942 million USD is incredibly low. From a technological standpoint, user experience, and other relevant aspects, compared to SOL, NEAR, TON, AVAX, and other L1 protocols, EGLD’s market cap should realistically be around 100 billion USD. Therefore, my prediction and investment thesis is a minimum of a 100x increase from its current price (+-SOL marketcap). MultiversX is ready to onboard 1 billion people to the blockchain. From a long-term perspective, it could even reach a market cap of 1 trillion USD, which is roughly half of where BTC is right now. That would be approximately a 1060x gain from the current market cap. 1 EGLD (MultiversX) is for $34 (only 31.4M max supply) think about this. Not financial advice. Again. There is no 2nd best L1. Position yourself where the puck is going, then wait at the goal until the goal gets there Apes together, strong. Ape alone, weak. We Don't Worry. We Just Win. Shape The Future

Daniel Veroc

50,587 次观看 • 1 年前

Weekly Briefing: The 39th Iran "Deal", The SpaceX IPO, and Hollow Midterms This week, Tony Nash and Albert Marko tear through Trump's latest cyclical "peace deal" headlines, unpack the structural fallout of SpaceX officially hitting public markets, and deliver a brutal reality check on the hollowed-out national identities of both political parties heading into the midterms. We pull no punches on the macro forces squeezing the everyday price taker, from post-pandemic consumer compression to the financial world's worst-kept secret: the absolute disdain for the consumer coming out of the Treasury. Thematic Takeaways - The Friday Peace Script: Why Trump's 39th claimed agreement with Iran is pure algorithmic bait, weaponized to spike the markets on headlines before institutional players dump the top on retail investors. - Weekend Missile Odds: We peg the likelihood of weekend kinetic action at a 1-in-3 chance, driven by Israel's ongoing containment in Lebanon rather than direct US involvement. - Starving the IRGC: The underlying mechanics of the US energy blockade, targeting the illicit arms, narcotics, and oil revenues that funded the axis out of Venezuela. - The Iraq Level Disaster: A look at the post-conflict roadmap, exploring whether regional powers will simply pay off the IRGC elite with backdoor amnesties to retire quietly to Dubai or Switzerland. - Skunk Works vs. The Ukraine Myth: Why mainstream commentary claiming the US needs drone lessons from Ukraine is fundamentally ridiculous, and how public displays of unmanned naval tech prove the US is 10 years ahead in its black-budget divisions. - The SpaceX Market Siphon: SpaceX's public debut surged 25%, causing a massive liquidity drain as small institutions and retail investors actively liquidated bloated positions in Nvidia and Broadcom to chase Elon Musk's portfolio. - The Orbiting Data Center Trap: Why space-cooled data centers are a plausible 5-to-6 year business line, but face the catastrophic structural threat of high-velocity space junk and microscopic space dust ripping hardware apart. - The Hollow Midterm Platforms: The complete degradation of national political identities, where the Democrats' entire platform has devolved into "we are not Trump," while the GOP has entirely surrendered its leadership structure to a single man. - The 5.2% Inflation Trap: Why local redistricting won't save the House for Republicans if centrist voters in swing states like Michigan stay home due to unrelenting post-pandemic margin compression. - The $40 Hamburger Economy: The reality of a working-class family of four being completely priced out of basic fast-casual dining, exacerbated by a highly sensitive outbreak within the Texas beef herd. Timestamps 00:00 – Friday afternoon briefing: Overcoming audio-only slip-ups and mapping out the 3-topic agenda. 01:00 – The 39th Agreement: Reuters headlines, algo microseconds, and the repetition of the Trump peace pump. 03:00 – Weekend Missile Checklist: Israel, Lebanon, and the structural impossibility of an instant nuclear solution. 04:00 – Cutting off the Revenue Base: Illicit arms, narcotics trafficking, and the real reason the US went into Venezuela. 05:45 – The Oil Game of Chicken: Trump's midterm market calculations vs. a pathway to a multi-stage agreement. 07:00 – Sub-$75 Crude: Why breaking $75 triggers an immediate buy signal while the European and Asian economies sit at a standstill. 08:45 – The Dubai Amnesty Plan: Paying off the IRGC elite to take a 20-year vacation from geopolitics. 10:10 – The Iraq Model: Disrupting political elites to transform regional powers into subordinate oil-selling states. 10:50 – Advanced Defense Tech: Unmanned naval boats, corporate technology testing, and the 10-year Skunk Works lead. 12:00 – Debunking the Commentators: Why the US military doesn't need "hodgepodge DJI" drone advice from Ukraine. 13:00 – SpaceX Siphons the Market: Gaining 25% on day one and forcing fund managers to liquidate Nvidia and Broadcom. 14:45 – Orbiting Data Centers: High-speed space junk, harvesting the sun, and Starlink’s direct threat to global mobile providers. 17:00 – Midterm Primaries: James Carville's Clinton-era legacy and how fringe litmus tests infected the modern Democratic identity. 18:40 – Radical Micro-Issues: Why ceding the center prevents the nomination of winning candidates like Andy Beshear. 19:15 – The GOP Identity Vacuum: The Thomas Massie miscalculation and the danger of Republican voters staying home. 20:30 – The 5.2% Inflation Trap: AI data centers suing for Michigan farmlands and the loss of the House. 22:00 – The Slow Downside Pump: Department of Energy failures and the absolute myth of gasoline refinery delays. 23:00 – The $40 Hamburger: The Texas beef herd outbreak and the rapid destruction of consumer disposable income. 24:45 – "They don't care": Exposing Treasury Secretary Bessent's well-known disdain for the American consumer. 25:40 – Fake National Security: The rise of online demonization and who actually controls Trump's policy. 27:00 – The Gold Panic: Why breaking $4,000 triggers a massive liquidation wave straight down to $3,300. Join the Patreon for unvarnished macro analysis: Follow Albert Marko Live Monitor
29:59

Weekly Briefing: The 39th Iran "Deal", The SpaceX IPO, and Hollow Midterms This week, Tony Nash and Albert Marko tear through Trump's latest cyclical "peace deal" headlines, unpack the structural fallout of SpaceX officially hitting public markets, and deliver a brutal reality check on the hollowed-out national identities of both political parties heading into the midterms. We pull no punches on the macro forces squeezing the everyday price taker, from post-pandemic consumer compression to the financial world's worst-kept secret: the absolute disdain for the consumer coming out of the Treasury. Thematic Takeaways - The Friday Peace Script: Why Trump's 39th claimed agreement with Iran is pure algorithmic bait, weaponized to spike the markets on headlines before institutional players dump the top on retail investors. - Weekend Missile Odds: We peg the likelihood of weekend kinetic action at a 1-in-3 chance, driven by Israel's ongoing containment in Lebanon rather than direct US involvement. - Starving the IRGC: The underlying mechanics of the US energy blockade, targeting the illicit arms, narcotics, and oil revenues that funded the axis out of Venezuela. - The Iraq Level Disaster: A look at the post-conflict roadmap, exploring whether regional powers will simply pay off the IRGC elite with backdoor amnesties to retire quietly to Dubai or Switzerland. - Skunk Works vs. The Ukraine Myth: Why mainstream commentary claiming the US needs drone lessons from Ukraine is fundamentally ridiculous, and how public displays of unmanned naval tech prove the US is 10 years ahead in its black-budget divisions. - The SpaceX Market Siphon: SpaceX's public debut surged 25%, causing a massive liquidity drain as small institutions and retail investors actively liquidated bloated positions in Nvidia and Broadcom to chase Elon Musk's portfolio. - The Orbiting Data Center Trap: Why space-cooled data centers are a plausible 5-to-6 year business line, but face the catastrophic structural threat of high-velocity space junk and microscopic space dust ripping hardware apart. - The Hollow Midterm Platforms: The complete degradation of national political identities, where the Democrats' entire platform has devolved into "we are not Trump," while the GOP has entirely surrendered its leadership structure to a single man. - The 5.2% Inflation Trap: Why local redistricting won't save the House for Republicans if centrist voters in swing states like Michigan stay home due to unrelenting post-pandemic margin compression. - The $40 Hamburger Economy: The reality of a working-class family of four being completely priced out of basic fast-casual dining, exacerbated by a highly sensitive outbreak within the Texas beef herd. Timestamps 00:00 – Friday afternoon briefing: Overcoming audio-only slip-ups and mapping out the 3-topic agenda. 01:00 – The 39th Agreement: Reuters headlines, algo microseconds, and the repetition of the Trump peace pump. 03:00 – Weekend Missile Checklist: Israel, Lebanon, and the structural impossibility of an instant nuclear solution. 04:00 – Cutting off the Revenue Base: Illicit arms, narcotics trafficking, and the real reason the US went into Venezuela. 05:45 – The Oil Game of Chicken: Trump's midterm market calculations vs. a pathway to a multi-stage agreement. 07:00 – Sub-$75 Crude: Why breaking $75 triggers an immediate buy signal while the European and Asian economies sit at a standstill. 08:45 – The Dubai Amnesty Plan: Paying off the IRGC elite to take a 20-year vacation from geopolitics. 10:10 – The Iraq Model: Disrupting political elites to transform regional powers into subordinate oil-selling states. 10:50 – Advanced Defense Tech: Unmanned naval boats, corporate technology testing, and the 10-year Skunk Works lead. 12:00 – Debunking the Commentators: Why the US military doesn't need "hodgepodge DJI" drone advice from Ukraine. 13:00 – SpaceX Siphons the Market: Gaining 25% on day one and forcing fund managers to liquidate Nvidia and Broadcom. 14:45 – Orbiting Data Centers: High-speed space junk, harvesting the sun, and Starlink’s direct threat to global mobile providers. 17:00 – Midterm Primaries: James Carville's Clinton-era legacy and how fringe litmus tests infected the modern Democratic identity. 18:40 – Radical Micro-Issues: Why ceding the center prevents the nomination of winning candidates like Andy Beshear. 19:15 – The GOP Identity Vacuum: The Thomas Massie miscalculation and the danger of Republican voters staying home. 20:30 – The 5.2% Inflation Trap: AI data centers suing for Michigan farmlands and the loss of the House. 22:00 – The Slow Downside Pump: Department of Energy failures and the absolute myth of gasoline refinery delays. 23:00 – The $40 Hamburger: The Texas beef herd outbreak and the rapid destruction of consumer disposable income. 24:45 – "They don't care": Exposing Treasury Secretary Bessent's well-known disdain for the American consumer. 25:40 – Fake National Security: The rise of online demonization and who actually controls Trump's policy. 27:00 – The Gold Panic: Why breaking $4,000 triggers a massive liquidation wave straight down to $3,300. Join the Patreon for unvarnished macro analysis: Follow Albert Marko Live Monitor

Tony Nash

17,175 次观看 • 3 个月前