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Nothing Fancy... just experimenting and exploring with the available ones before exploring the new trending ones.. The core trick here: ctx.measureText() pre-computes every word into a data object with a baseX, current x, and targetX. Each frame, a 120-segment dragon emits collision nodes with anatomically-computed radii. For every line...

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SHAHNAB AHMED

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73,747 次观看 • 5 个月前 •via X (Twitter)

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CLIP by hand ✍️ ~ 13 steps walkthrough below CLIP, Contrastive Language-Image Pre-training, is OpenAI's answer to a question that sounds impossible: how do you put a sentence and a picture in the same space? CLIP shipped when OpenAI was still open, and those embeddings were shared far and wide. Almost every multimodal model you use today descends from them. How does it work? Goal: learn one shared embedding space for text and images. = 1. Given = A mini batch of three text-image pairs. OpenAI trained the original on 400 million. = 2. Text to vectors = Let us look up each word with word2vec. = 3. Image to vectors = We cut each image into two patches and flatten them. Now text and pixels are both just numbers. = 4. The other pairs = Repeat steps 2 and 3 for the rest of the batch. = 5. Encode = Let us push both sides through their encoders, a linear layer and a ReLU. In practice these are transformers, but the shape of the operation is the same. = 6. Mean pooling = We average across the columns, so each image and each sentence collapses to a single vector. = 7. Projection = The text vectors are 3D and the image vectors are 4D, so they cannot be compared at all. A linear layer projects both to 2D. That 2D space is the shared embedding space, and getting here is the whole point of the model. = 8. Prepare for matmul = Let us copy the text vectors down and the transposed image vectors across. = 9. MatMul = We multiply, which takes the dot product of every text vector with every image vector. Each cell is one estimate of how well a sentence matches a picture. = 10. Softmax, e to the power = Raise e to each cell. To keep it hand sized we approximate e with 3. = 11. Softmax, sum = Sum each row for image to text, each column for text to image. = 12. Softmax, normalize = Divide, and out come two similarity matrices, one per direction. = 13. Loss gradients = The targets are identity matrices: a pair that belongs together should score 1, every other cell 0. Subtract the target from the similarity and you have the gradients, in both directions. The takeaway: pairing a picture with a sentence comes down to a single dot product. Everything before step 9 is the work of getting them into one shared space, so that the dot product finally means something. 💾 Save this post!
0:36

CLIP by hand ✍️ ~ 13 steps walkthrough below CLIP, Contrastive Language-Image Pre-training, is OpenAI's answer to a question that sounds impossible: how do you put a sentence and a picture in the same space? CLIP shipped when OpenAI was still open, and those embeddings were shared far and wide. Almost every multimodal model you use today descends from them. How does it work? Goal: learn one shared embedding space for text and images. = 1. Given = A mini batch of three text-image pairs. OpenAI trained the original on 400 million. = 2. Text to vectors = Let us look up each word with word2vec. = 3. Image to vectors = We cut each image into two patches and flatten them. Now text and pixels are both just numbers. = 4. The other pairs = Repeat steps 2 and 3 for the rest of the batch. = 5. Encode = Let us push both sides through their encoders, a linear layer and a ReLU. In practice these are transformers, but the shape of the operation is the same. = 6. Mean pooling = We average across the columns, so each image and each sentence collapses to a single vector. = 7. Projection = The text vectors are 3D and the image vectors are 4D, so they cannot be compared at all. A linear layer projects both to 2D. That 2D space is the shared embedding space, and getting here is the whole point of the model. = 8. Prepare for matmul = Let us copy the text vectors down and the transposed image vectors across. = 9. MatMul = We multiply, which takes the dot product of every text vector with every image vector. Each cell is one estimate of how well a sentence matches a picture. = 10. Softmax, e to the power = Raise e to each cell. To keep it hand sized we approximate e with 3. = 11. Softmax, sum = Sum each row for image to text, each column for text to image. = 12. Softmax, normalize = Divide, and out come two similarity matrices, one per direction. = 13. Loss gradients = The targets are identity matrices: a pair that belongs together should score 1, every other cell 0. Subtract the target from the similarity and you have the gradients, in both directions. The takeaway: pairing a picture with a sentence comes down to a single dot product. Everything before step 9 is the work of getting them into one shared space, so that the dot product finally means something. 💾 Save this post!

Tom Yeh

20,750 次观看 • 1 个月前

Ask anyone who’s taken a course in Ordinary Differential Equations (ODEs) what a solution to an ODE represents geometrically, and most of them won’t have a clean answer. When I first took ordinary differential equations, the pattern was always the same. Early on it turns into a speedrun of methods: separation of variables, integrating factors, variation of parameters, Bernoulli, exact equations. Then pretty quickly the course slides into hammer-picking. Spot the form, apply the recipe, move on. Too mechanical! And the real problem is what you don’t walk away with. You leave with a toolkit, but without a feel for what a differential equation even is, especially geometrically. That matters because in real modeling the equations you meet are rarely nice enough to reward memorised recipes. So you get trained to solve toy forms, while the actual subject stays blurry. The behavior. The flow. The shape of solutions. It wasn't until I watched the first lecture of Professor Arthur Mattuck that I realized I didn’t actually know what a solution to a differential equation represents geometrically. His point is almost embarrassingly simple. A first-order ODE is a slope field, and a solution is a curve that stays tangent to that field everywhere. The math breakdown: Write the ODE as dy/dx = f(x,y). At each point (x,y), attach a tiny line segment with slope f(x,y). A function y = y₁(x) is a solution exactly when its graph follows those slopes. At every x, the slope of the curve equals the slope prescribed by the field at the point on the curve. That’s the one line that ties both viewpoints together: y₁′(x) = f(x, y₁(x)). So solving the ODE and drawing an integral curve are the same statement in two languages. Once you see that, you stop obsessing over whether you can write y(x) in closed form. You start asking the questions that actually matter. Where do solutions flow. Where do they get trapped. Where do they blow up. Where does existence or uniqueness fail because the field isn’t even defined? That’s the perspective shift I wish every ODE course forces early. It’s also why I keep pairing math with animation. #DifferentialEquations #ODEs #VectorFields #AppliedMathematics #Mathematics #
20:13

Ask anyone who’s taken a course in Ordinary Differential Equations (ODEs) what a solution to an ODE represents geometrically, and most of them won’t have a clean answer. When I first took ordinary differential equations, the pattern was always the same. Early on it turns into a speedrun of methods: separation of variables, integrating factors, variation of parameters, Bernoulli, exact equations. Then pretty quickly the course slides into hammer-picking. Spot the form, apply the recipe, move on. Too mechanical! And the real problem is what you don’t walk away with. You leave with a toolkit, but without a feel for what a differential equation even is, especially geometrically. That matters because in real modeling the equations you meet are rarely nice enough to reward memorised recipes. So you get trained to solve toy forms, while the actual subject stays blurry. The behavior. The flow. The shape of solutions. It wasn't until I watched the first lecture of Professor Arthur Mattuck that I realized I didn’t actually know what a solution to a differential equation represents geometrically. His point is almost embarrassingly simple. A first-order ODE is a slope field, and a solution is a curve that stays tangent to that field everywhere. The math breakdown: Write the ODE as dy/dx = f(x,y). At each point (x,y), attach a tiny line segment with slope f(x,y). A function y = y₁(x) is a solution exactly when its graph follows those slopes. At every x, the slope of the curve equals the slope prescribed by the field at the point on the curve. That’s the one line that ties both viewpoints together: y₁′(x) = f(x, y₁(x)). So solving the ODE and drawing an integral curve are the same statement in two languages. Once you see that, you stop obsessing over whether you can write y(x) in closed form. You start asking the questions that actually matter. Where do solutions flow. Where do they get trapped. Where do they blow up. Where does existence or uniqueness fail because the field isn’t even defined? That’s the perspective shift I wish every ODE course forces early. It’s also why I keep pairing math with animation. #DifferentialEquations #ODEs #VectorFields #AppliedMathematics #Mathematics #

Mathelirium

40,841 次观看 • 7 个月前

When I first took ordinary differential equations, the pattern was always the same. Week 1 turns into a speedrun of methods: separation of variables, integrating factors, variation of parameters, Bernoulli, exact equations… and by Week 2 or 3 the course has quietly degenerated into hammer-picking. Spot the form, apply the recipe, move on. Mechanical! Fuuuuck!😫😫😫😫 The problem is what you don’t walk away with. You leave with a toolkit, but without a feel for what a differential equation even is, especially geometrically. And that’s a big deal, because in real modeling the equations you meet are rarely nice enough to reward memorized recipes. So you end up trained to solve toy forms, while the actual subject...the behavior, the flow, the shape of solutions stays blurry. This is why I’m biased toward the old-timers. Their old-school way of doing things always surprises me:...they’ll spend time on one idea until it sticks, instead of sprinting through a syllabus checklist. One lecture from them and you start noticing a contrast. A lot of modern teaching feels like "finish the content,". You get marched through techniques, but you’re not left with a single thought that keeps bothering you later...the kind of thought that actually pushes you toward research-level curiosity. MIT OpenCourseWare’s Professor Arthur Mattuck did that to me in his very first ODE lecture. One lecture, and your whole relationship with dy/dx = f(x,y) changes. In this segment, Prof. Mattuck is basically saying: A first-order ODE is a slope field, and a solution is a curve that moves everywhere tangent to that field. The math breakdown Write the ODE as dy/dx = f(x,y). At each point (x,y) you attach a tiny line segment with slope f(x,y). A function y = y₁(x) is a solution exactly when its graph follows those slopes:. At every x, the slope of the curve equals the slope prescribed by the field at the point on the curve. That’s the single line that unifies both viewpoints: y₁′(x) = f(x, y₁(x)). So solving the ODE and drawing an integral curve are the same statement in two languages!👌🏻 Once you see that, you can stop obsessing over whether you can write y(x) in closed form. You can start asking the questions that matter: where do solutions flow, where do they get trapped, where do they blow up, and where does existence/uniqueness fail just because the field isn’t even defined? That’s the perspective shift I wish every ODE course forces early and it’s exactly why I keep pairing math with animation. #DifferentialEquations #ODEs #VectorFields #MathAnimation #Mathematics
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When I first took ordinary differential equations, the pattern was always the same. Week 1 turns into a speedrun of methods: separation of variables, integrating factors, variation of parameters, Bernoulli, exact equations… and by Week 2 or 3 the course has quietly degenerated into hammer-picking. Spot the form, apply the recipe, move on. Mechanical! Fuuuuck!😫😫😫😫 The problem is what you don’t walk away with. You leave with a toolkit, but without a feel for what a differential equation even is, especially geometrically. And that’s a big deal, because in real modeling the equations you meet are rarely nice enough to reward memorized recipes. So you end up trained to solve toy forms, while the actual subject...the behavior, the flow, the shape of solutions stays blurry. This is why I’m biased toward the old-timers. Their old-school way of doing things always surprises me:...they’ll spend time on one idea until it sticks, instead of sprinting through a syllabus checklist. One lecture from them and you start noticing a contrast. A lot of modern teaching feels like "finish the content,". You get marched through techniques, but you’re not left with a single thought that keeps bothering you later...the kind of thought that actually pushes you toward research-level curiosity. MIT OpenCourseWare’s Professor Arthur Mattuck did that to me in his very first ODE lecture. One lecture, and your whole relationship with dy/dx = f(x,y) changes. In this segment, Prof. Mattuck is basically saying: A first-order ODE is a slope field, and a solution is a curve that moves everywhere tangent to that field. The math breakdown Write the ODE as dy/dx = f(x,y). At each point (x,y) you attach a tiny line segment with slope f(x,y). A function y = y₁(x) is a solution exactly when its graph follows those slopes:. At every x, the slope of the curve equals the slope prescribed by the field at the point on the curve. That’s the single line that unifies both viewpoints: y₁′(x) = f(x, y₁(x)). So solving the ODE and drawing an integral curve are the same statement in two languages!👌🏻 Once you see that, you can stop obsessing over whether you can write y(x) in closed form. You can start asking the questions that matter: where do solutions flow, where do they get trapped, where do they blow up, and where does existence/uniqueness fail just because the field isn’t even defined? That’s the perspective shift I wish every ODE course forces early and it’s exactly why I keep pairing math with animation. #DifferentialEquations #ODEs #VectorFields #MathAnimation #Mathematics

Mathelirium

53,338 次观看 • 8 个月前

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🚨NODE SALE Alert!🚨 If you are looking into exploring #Nodes, but finding it difficult to get started, I recommend exploring PlayFi - (Follow @PlayFiAI our new handle)! It's super easy to setup and they are about to have an epic public Node License Sale! 💥 Their Launch partners are great! Including Polygon, Aethir, Nim.Trade, ZKsync, Matter Labs, and more! This is your chance to be part of something HUGE - where #AI supercharges content creation across gaming, streaming, and everything in between! 🤯 🔥 Why You NEED to Jump In 👇👇: PlayFi's AI Magic: Imagine turning every like, comment, and view into instant, actionable data! 📈 From electrifying live streams to giving game studios insane insights without lifting a finger, PlayFi is changing the game. $PLAY Token Bonanza: Early birds get the worm... or in this case, a 3x airdrop bonus of $PLAY tokens! 🤑 Easy-Peasy Nodes: No fancy gear needed! Run PlayFi nodes right alongside your current setup without breaking a sweat. It's your VIP pass to a massive, decentralized network that turns endless content into valuable, on-chain data. 🤯 Limited-Time Gold Rush: Only 10,000 licenses up for grabs! Secure yours and start earning those sweet, sweet $PLAY token rewards at mainnet launch. The more nodes you own, the bigger your potential payout! 🤑 Tech Meets Opportunity: We're talking AI, decentralized networks, and the creator economy all in one place. PlayFi nodes are the backbone of this revolution, ensuring a massive, global network that rewards everyone involved. 🌐 ⏳ Don't Miss Out! PlayFi is shaping the future of Web3, and you can be a part of it! 💪 Join the Discord for Whitelists: #PlayFi #Web3Gaming #P2E #DATA #Node #NodeSale Disclaimer: For full transparency, I am serving #PlayFi as a KOL, and I want to emphasize that I am extremely selective when it comes to the projects I choose to partner with. Only those that meet my strict criteria and align with my values will have the privilege of collaborating with me. This is just my review and not a financial advice, please DYOR

LadyTraderRa

50,477 次观看 • 2 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

I funded the new ChatGPT Astra with $43.47 and gave it one line: "turn a profit or I cancel the subscription" that was it. one line. then I went to sleep woke up, checked the terminal new outcome: $43.47 → $41,495.53 overnight. still running. still hasn't pinged me once I never told it how. I gave it a wallet and a threat, and within the first hour Astra had already built its own terminal, scanning wallets across the whole chain, scoring them faster than I could read a single ticker it doesn't trade one meme. it profiles the board, live, and copytrades the wallets that actually print, smart money, dev wallets, insiders, the ones that get in before the candle forms. and the garbage, the rug wallets, the bots, the fat-finger tops, it just skips them. no input from me it's wrong sometimes. the win rate dips when a signal misfires. that's normal, that's the cost of hunting. position sizing makes the good calls outweigh the bad ones and the line just bends up anyway I went into the logs. 51,784 wallets analyzed. every fresh mint scored the second it touches the chain, snipe, copytrade, or pass, before I can even blink. mint authority still live? thin LP? top wallets too heavy? gone before it costs a cent first minutes were ugly. dropped low before something clicked. then it tightened up, stopped chasing loud tickers, and never looked back the clip is a replay of last night, the whole run, wallet-by-wallet, compressed into a few seconds. watch the line I keep thinking I'm running this thing. I'm starting to think I just fund it and watch there are more of these humming right now than anyone will admit. you'll believe it the night your own $40 turns into a number you don't want to say out loud honestly you could build the exact same thing with Grok Bot, that's the usual play. I just wanted to experiment, so I ran mine on ChatGPT Astra instead. same result, different engine save this post follow if you want to build one like this and get more alpha every day
0:20

I funded the new ChatGPT Astra with $43.47 and gave it one line: "turn a profit or I cancel the subscription" that was it. one line. then I went to sleep woke up, checked the terminal new outcome: $43.47 → $41,495.53 overnight. still running. still hasn't pinged me once I never told it how. I gave it a wallet and a threat, and within the first hour Astra had already built its own terminal, scanning wallets across the whole chain, scoring them faster than I could read a single ticker it doesn't trade one meme. it profiles the board, live, and copytrades the wallets that actually print, smart money, dev wallets, insiders, the ones that get in before the candle forms. and the garbage, the rug wallets, the bots, the fat-finger tops, it just skips them. no input from me it's wrong sometimes. the win rate dips when a signal misfires. that's normal, that's the cost of hunting. position sizing makes the good calls outweigh the bad ones and the line just bends up anyway I went into the logs. 51,784 wallets analyzed. every fresh mint scored the second it touches the chain, snipe, copytrade, or pass, before I can even blink. mint authority still live? thin LP? top wallets too heavy? gone before it costs a cent first minutes were ugly. dropped low before something clicked. then it tightened up, stopped chasing loud tickers, and never looked back the clip is a replay of last night, the whole run, wallet-by-wallet, compressed into a few seconds. watch the line I keep thinking I'm running this thing. I'm starting to think I just fund it and watch there are more of these humming right now than anyone will admit. you'll believe it the night your own $40 turns into a number you don't want to say out loud honestly you could build the exact same thing with Grok Bot, that's the usual play. I just wanted to experiment, so I ran mine on ChatGPT Astra instead. same result, different engine save this post follow if you want to build one like this and get more alpha every day

cristal💎

975,085 次观看 • 7 天前

GeoLibre v2.7.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release is about building analysis instead of typing it: a visual Model Builder for processing workflows, a STAC browser that can add every asset type the app can draw, and a Chrome extension that opens the data on any web page in one click. 97 pull requests merged in 9 days, from 11 contributors besides me, 9 of whom sent their first contribution to GeoLibre in this cycle. Thank you all. What's new in v2.7.0 - Model Builder, a visual canvas for processing: drop tools as nodes, wire one tool's output into the next tool's input, and save the graph as a model that re-runs as a single job. The whole graph is validated before anything executes. - Your AI assistant can build one for you: describe a workflow in plain English, and it authors a validated model and opens it for review before it runs. Any model copies out as a runnable Python script. - Open data in GeoLibre, a Chrome extension now on the Chrome Web Store: it finds the dataset links and map services on the page you are viewing, including services inside embedded maps, and opens the ones you pick together on one map. - The STAC browser adds everything: PMTiles, GeoParquet, and Zarr with a variable picker join the COGs it already loaded, Icechunk repositories are read through their own manifest, and private Planetary Computer assets are signed for you. A static catalog can now be walked as a tree, not only searched. - Select features by drawing on the map: click, rectangle, polygon, freehand, and radius gestures, with Shift and Alt combining into the selection you already have. No more describing features in an expression to pick a handful of them. - Apache Iceberg tables load as vector layers, read in the browser through DuckDB, from a metadata location or a REST catalog, with the row count reported before anything is scanned. - Encoded polylines are a first-class format, with a codec, an interactive preview, processing tools, layer export, and Python support. - New vector tools: merge layers, extract vertices, and generate points along lines and polygon boundaries, all running client-side. - An activity log for shared projects and collaboration sessions, so a project owner can see who opened and edited their work. - Plugins and processing tools are now translatable too, closing the last gap where a non-English interface still read half in English. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #MapLibre #GeoLibre
0:45

GeoLibre v2.7.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release is about building analysis instead of typing it: a visual Model Builder for processing workflows, a STAC browser that can add every asset type the app can draw, and a Chrome extension that opens the data on any web page in one click. 97 pull requests merged in 9 days, from 11 contributors besides me, 9 of whom sent their first contribution to GeoLibre in this cycle. Thank you all. What's new in v2.7.0 - Model Builder, a visual canvas for processing: drop tools as nodes, wire one tool's output into the next tool's input, and save the graph as a model that re-runs as a single job. The whole graph is validated before anything executes. - Your AI assistant can build one for you: describe a workflow in plain English, and it authors a validated model and opens it for review before it runs. Any model copies out as a runnable Python script. - Open data in GeoLibre, a Chrome extension now on the Chrome Web Store: it finds the dataset links and map services on the page you are viewing, including services inside embedded maps, and opens the ones you pick together on one map. - The STAC browser adds everything: PMTiles, GeoParquet, and Zarr with a variable picker join the COGs it already loaded, Icechunk repositories are read through their own manifest, and private Planetary Computer assets are signed for you. A static catalog can now be walked as a tree, not only searched. - Select features by drawing on the map: click, rectangle, polygon, freehand, and radius gestures, with Shift and Alt combining into the selection you already have. No more describing features in an expression to pick a handful of them. - Apache Iceberg tables load as vector layers, read in the browser through DuckDB, from a metadata location or a REST catalog, with the row count reported before anything is scanned. - Encoded polylines are a first-class format, with a codec, an interactive preview, processing tools, layer export, and Python support. - New vector tools: merge layers, extract vertices, and generate points along lines and polygon boundaries, all running client-side. - An activity log for shared projects and collaboration sessions, so a project owner can see who opened and edited their work. - Plugins and processing tools are now translatable too, closing the last gap where a non-English interface still read half in English. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #MapLibre #GeoLibre

Qiusheng Wu

14,994 次观看 • 23 天前

Back on March 27th, we shared a video showcasing the very first prototype of the CLR game. It featured the Visual Novel portion with a conversation between William and Odd, followed by a choice-driven sequence that transitioned directly into an RPG battle. Yes, you read that correctly: there are traditional RPG mechanics in the game. It's not "just" a Visual Novel. Since then, quite a bit of time has passed, and the stubborn person that Shizuna is decided to rebuild the game's entire foundation from scratch. So... where does the game stand today? 🦋 -> The entire battle system has now been designed, including each character's abilities, the combat flow, and all of the core gameplay mechanics that make battles work. What's left is polishing the overall layout, implementing the final UI, and, above all... designing every single enemy kit. And trust me, there are a lot of them. -> Every playable character now has their own unique gameplay mechanic. None of these systems were created at random, they all reflect each character's personality, state of mind, and individual fighting style. -> An autosave system has been implemented at the beginning and end of every chapter, alongside the ability to save manually whenever you want... except during battles, of course. -> Character profiles have been added. They'll contain everything you need to know about each character: their statistics, abilities, and much more. Speaking of abilities... did you know you'll be able to freely enable or disable whichever ones you want? Oh, and naturally, you won't have access to every abilities right from the start. -> The character illustrations currently shown in those profiles are only placeholders. Brand-new designs are already in the works... and there's absolutely no chance we're revealing them just yet. 🚨 Everything you see here is subject to change. This is by no means the final version. 🚨 #codelyoko #codelyokoresurgence
2:06

Back on March 27th, we shared a video showcasing the very first prototype of the CLR game. It featured the Visual Novel portion with a conversation between William and Odd, followed by a choice-driven sequence that transitioned directly into an RPG battle. Yes, you read that correctly: there are traditional RPG mechanics in the game. It's not "just" a Visual Novel. Since then, quite a bit of time has passed, and the stubborn person that Shizuna is decided to rebuild the game's entire foundation from scratch. So... where does the game stand today? 🦋 -> The entire battle system has now been designed, including each character's abilities, the combat flow, and all of the core gameplay mechanics that make battles work. What's left is polishing the overall layout, implementing the final UI, and, above all... designing every single enemy kit. And trust me, there are a lot of them. -> Every playable character now has their own unique gameplay mechanic. None of these systems were created at random, they all reflect each character's personality, state of mind, and individual fighting style. -> An autosave system has been implemented at the beginning and end of every chapter, alongside the ability to save manually whenever you want... except during battles, of course. -> Character profiles have been added. They'll contain everything you need to know about each character: their statistics, abilities, and much more. Speaking of abilities... did you know you'll be able to freely enable or disable whichever ones you want? Oh, and naturally, you won't have access to every abilities right from the start. -> The character illustrations currently shown in those profiles are only placeholders. Brand-new designs are already in the works... and there's absolutely no chance we're revealing them just yet. 🚨 Everything you see here is subject to change. This is by no means the final version. 🚨 #codelyoko #codelyokoresurgence

Code Lyoko RESURGENCE

16,970 次观看 • 2 个月前

Gemini talking about the concept of his concert 😭🤍 #GeminiIGLive #GeminiConcert #Gemini_NT #เจมีไนน์ ♊️: The teaser everyone has seen is related to art, right? The concert is called "Artventure Concert". It's a combination of the words 'Art' and 'Adventure.' I came up with it bcs I wanted to create something that truly feels like 'me' from beginning to end. It's not that my previous concerts weren't me, they absolutely were. But I mean that each concert had a different concept ♊️: My Turn was based on a board game. Run the World had a castle and fantasy theme, with that elegant, prince-like aesthetic. Then Awake Concert was more AI and sci-fi inspired. I felt like I'd already explored all those concepts. So this time I wanted to try something centered around art ♊️: I paint, I draw and color, and I feel like art can go in so many directions. Since you can paint anything, you can put all of your imagination onto the canvas. Everything I create through my paintings represents me 100%, and no one else can make exactly the same thing. That's why, in this concert, you'll definitely see really beautiful production ♊️: Since it's based on art, every background, every visual, and every concept for each part of the show will have its own unique beauty. Each section will express a different side of who I am 🤍
1:48

Gemini talking about the concept of his concert 😭🤍 #GeminiIGLive #GeminiConcert #Gemini_NT #เจมีไนน์ ♊️: The teaser everyone has seen is related to art, right? The concert is called "Artventure Concert". It's a combination of the words 'Art' and 'Adventure.' I came up with it bcs I wanted to create something that truly feels like 'me' from beginning to end. It's not that my previous concerts weren't me, they absolutely were. But I mean that each concert had a different concept ♊️: My Turn was based on a board game. Run the World had a castle and fantasy theme, with that elegant, prince-like aesthetic. Then Awake Concert was more AI and sci-fi inspired. I felt like I'd already explored all those concepts. So this time I wanted to try something centered around art ♊️: I paint, I draw and color, and I feel like art can go in so many directions. Since you can paint anything, you can put all of your imagination onto the canvas. Everything I create through my paintings represents me 100%, and no one else can make exactly the same thing. That's why, in this concert, you'll definitely see really beautiful production ♊️: Since it's based on art, every background, every visual, and every concept for each part of the show will have its own unique beauty. Each section will express a different side of who I am 🤍

𝒜𝒹ℯ𝓁 🪐🤍

15,002 次观看 • 2 个月前

I BUILT GROK BOT SECOND BRAIN 26 agents, one core, running off a single rule my own memory never followed: capture everything, forget nothing, connect it before you even ask. A voice note on a coffee walk gets split into a task and an idea in under 6 seconds - the core already knows the difference between something you have to do and something you just thought of. An idea from three weeks ago gets linked to the one you just had, automatically, before you remember you'd had it the first time. That visual -the rotating profile, the 26 glowing lines firing out through the skull, each one labeled with the agent running it - isn't a rendered mockup. It's the actual runtime. Every line is a live agent doing its job in real time, right now, in your browser. This isn't a note app. Note apps hold what you give them. This is a nervous system for everything your brain was never built to hold onto in the first place. Full build - the 26 agents, how memory actually persists, what it costs to set one up for real - is in the article.
0:20

I BUILT GROK BOT SECOND BRAIN 26 agents, one core, running off a single rule my own memory never followed: capture everything, forget nothing, connect it before you even ask. A voice note on a coffee walk gets split into a task and an idea in under 6 seconds - the core already knows the difference between something you have to do and something you just thought of. An idea from three weeks ago gets linked to the one you just had, automatically, before you remember you'd had it the first time. That visual -the rotating profile, the 26 glowing lines firing out through the skull, each one labeled with the agent running it - isn't a rendered mockup. It's the actual runtime. Every line is a live agent doing its job in real time, right now, in your browser. This isn't a note app. Note apps hold what you give them. This is a nervous system for everything your brain was never built to hold onto in the first place. Full build - the 26 agents, how memory actually persists, what it costs to set one up for real - is in the article.

Aleiah

257,595 次观看 • 17 天前

A fired Jane Street quant walked out with 10 years of private BTC trading data. Turned it into $1.5M. He did not build a bot. He built a simulator that runs every move Bitcoin can make before it makes one. I found his wallet. Been copying him for a week. PnL prints like clockwork. Here is what he actually built. A swarm of agents feeds 10 years of stolen tick data into MiroFish. A god-tier agentic simulator. It does not forecast the next candle. It spins up a virtual market and plays Bitcoin forward through thousands of scenarios at once. Six agents each validate their own call. A trade only fires when they converge. They collect data 24/7, rerun the sim, and remember every pattern, every reaction, every signal they have ever seen. He does not predict the future. The math already knows it. He just reads the numbers and takes the money. Here is the part firms do not want public: MiroFish just broke algo trading. The desks are quietly building their own simulators right now. The window where one solo wallet can run this is still open. Barely. I rebuilt his approach using Claude. One prompt. Fed it the same framework. Let it run. The agent monitors his wallet 24/7. Copies every position in real-time. No delay. No guessing. Just mirror and profit. You only need Claude + device + 1 hour to deploy. Giving this free for 24 hours. To get it: 1. Comment the word "QUANT" 2. Like and retweet this post 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the copytrading system this week. Start with $200. Scale on evidence.
0:43

A fired Jane Street quant walked out with 10 years of private BTC trading data. Turned it into $1.5M. He did not build a bot. He built a simulator that runs every move Bitcoin can make before it makes one. I found his wallet. Been copying him for a week. PnL prints like clockwork. Here is what he actually built. A swarm of agents feeds 10 years of stolen tick data into MiroFish. A god-tier agentic simulator. It does not forecast the next candle. It spins up a virtual market and plays Bitcoin forward through thousands of scenarios at once. Six agents each validate their own call. A trade only fires when they converge. They collect data 24/7, rerun the sim, and remember every pattern, every reaction, every signal they have ever seen. He does not predict the future. The math already knows it. He just reads the numbers and takes the money. Here is the part firms do not want public: MiroFish just broke algo trading. The desks are quietly building their own simulators right now. The window where one solo wallet can run this is still open. Barely. I rebuilt his approach using Claude. One prompt. Fed it the same framework. Let it run. The agent monitors his wallet 24/7. Copies every position in real-time. No delay. No guessing. Just mirror and profit. You only need Claude + device + 1 hour to deploy. Giving this free for 24 hours. To get it: 1. Comment the word "QUANT" 2. Like and retweet this post 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the copytrading system this week. Start with $200. Scale on evidence.

Himanshu Kumar

63,648 次观看 • 3 个月前

[CLIP] by Hand ✍️ The CLIP (Contrastive Language–Image Pre-training) model, a groundbreaking work by OpenAI, redefines the intersection of computer vision and natural language processing. It is the basis of all the multi-modal foundation models we see today. How does CLIP work? Goal: 🟨 Learn a shared embedding space for text and image [1] Given ↳ A mini batch of 3 text-image pairs ↳ OpenAI used 400 million text-image pairs to train its original CLIP model. Process 1st pair: "big table" [2] 🟪 Text → 2 Vectors (3D) ↳ Look up word embedding vectors using word2vec. [3] 🟩 Image → 2 Vectors (4D) ↳ Divide the image into two patches. ↳ Flatten each patch [4] Process other pairs ↳ Repeat [2]-[3] [5] 🟪 Text Encoder & 🟩 Image Encoder ↳ Encode input vectors into feature vectors ↳ Here, both encoders are simple one layer perceptron (linear + ReLU) ↳ In practice, the encoders are usually transformer models. [6] 🟪 🟩 Mean Pooling: 2 → 1 vector ↳ Average 2 feature vectors into a single vector by averaging across the columns ↳ The goal is to have one vector to represent each image or text [7] 🟪 🟩 -> 🟨 Projection ↳ Note that the text and image feature vectors from the encoders have different dimensions (3D vs. 4D). ↳ Use a linear layer to project image and text vectors to a 2D shared embedding space. 🏋️ Contrastive Pre-training 🏋️ [8] Prepare for MatMul ↳ Copy text vectors (T1,T2,T3) ↳ Copy the transpose of image vectors (I1,I2,I3) ↳ They are all in the 2D shared embedding space. [9] 🟦 MatMul ↳ Multiply T and I matrices. ↳ This is equivalent to taking dot product between every pair of image and text vectors. ↳ The purpose is to use dot product to estimate the similarity between a pair of image-text. [10] 🟦 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [11] 🟦 Softmax: ∑ ↳ Sum each row for 🟩 image→🟪 text ↳ Sum each column for 🟪 text→ 🟩 image [12] 🟦 Softmax: 1 / sum ↳ Divide each element by the column sum to obtain a similarity matrix for 🟪 text→🟩 image ↳ Divide each element by the row sum to obtain a similarity matrix for 🟩 image→🟪 text [13] 🟥 Loss Gradients ↳ The "Targets" for the similarity matrices are Identity Matrices. ↳ Why? If I and T come from the same pair (i=j), we want the highest value, which is 1, and 0 otherwise. ↳ Apply the simple equation of [Similarity - Target] to compute gradients of for both directions. ↳ Why so simple? Because when Softmax and Cross-Entropy Loss are used together, the math magically works out that way. ↳ These gradients kick off the backpropagation process to update weights and biases of the encoders and projection layers (red borders).
1:20

[CLIP] by Hand ✍️ The CLIP (Contrastive Language–Image Pre-training) model, a groundbreaking work by OpenAI, redefines the intersection of computer vision and natural language processing. It is the basis of all the multi-modal foundation models we see today. How does CLIP work? Goal: 🟨 Learn a shared embedding space for text and image [1] Given ↳ A mini batch of 3 text-image pairs ↳ OpenAI used 400 million text-image pairs to train its original CLIP model. Process 1st pair: "big table" [2] 🟪 Text → 2 Vectors (3D) ↳ Look up word embedding vectors using word2vec. [3] 🟩 Image → 2 Vectors (4D) ↳ Divide the image into two patches. ↳ Flatten each patch [4] Process other pairs ↳ Repeat [2]-[3] [5] 🟪 Text Encoder & 🟩 Image Encoder ↳ Encode input vectors into feature vectors ↳ Here, both encoders are simple one layer perceptron (linear + ReLU) ↳ In practice, the encoders are usually transformer models. [6] 🟪 🟩 Mean Pooling: 2 → 1 vector ↳ Average 2 feature vectors into a single vector by averaging across the columns ↳ The goal is to have one vector to represent each image or text [7] 🟪 🟩 -> 🟨 Projection ↳ Note that the text and image feature vectors from the encoders have different dimensions (3D vs. 4D). ↳ Use a linear layer to project image and text vectors to a 2D shared embedding space. 🏋️ Contrastive Pre-training 🏋️ [8] Prepare for MatMul ↳ Copy text vectors (T1,T2,T3) ↳ Copy the transpose of image vectors (I1,I2,I3) ↳ They are all in the 2D shared embedding space. [9] 🟦 MatMul ↳ Multiply T and I matrices. ↳ This is equivalent to taking dot product between every pair of image and text vectors. ↳ The purpose is to use dot product to estimate the similarity between a pair of image-text. [10] 🟦 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [11] 🟦 Softmax: ∑ ↳ Sum each row for 🟩 image→🟪 text ↳ Sum each column for 🟪 text→ 🟩 image [12] 🟦 Softmax: 1 / sum ↳ Divide each element by the column sum to obtain a similarity matrix for 🟪 text→🟩 image ↳ Divide each element by the row sum to obtain a similarity matrix for 🟩 image→🟪 text [13] 🟥 Loss Gradients ↳ The "Targets" for the similarity matrices are Identity Matrices. ↳ Why? If I and T come from the same pair (i=j), we want the highest value, which is 1, and 0 otherwise. ↳ Apply the simple equation of [Similarity - Target] to compute gradients of for both directions. ↳ Why so simple? Because when Softmax and Cross-Entropy Loss are used together, the math magically works out that way. ↳ These gradients kick off the backpropagation process to update weights and biases of the encoders and projection layers (red borders).

Tom Yeh

67,896 次观看 • 2 年前

EA Sports CFB25 released their Sights & Sounds Deep Dive today and I am DISAPPOINTED! The main theme they use in the video is "Every Team Is Somebody's Favorite" and if you are going to make that claim, then I will hold you to it. My favorite school is the one I attended, Baylor University, and it has been clipped and used in both CFB25 videos with a GLARING mistake to anyone who went to Baylor. The Baylor Line is missing! Every year the freshman class gets a Baylor Line Jersey at the Freshman Line Camp before school starts. They spend a week learning about the school, each other, and our traditions which all leads up to getting a gold Line Jersey with your graduation year as the number and your last name or nickname on the back. Once football starts the Freshman show up hours early in their jersey to each home game and wait in the brutal Texas heat to be in the Baylor Line. Right before the game starts the freshman class runs onto the field in a frenzie to get to the other side of the field and form the tunnel that the team will run through. This is a tradition that is so crazy and dangerous that the NCAA outlawed it and grandfathered Baylor in as the one school allowed. The last home game of the season the graduating senior class gets to join the Freshman and run the line one last time in a double sized mosh pit to send them off how they entered. This tradition is near and dear to Baylor student's hearts. I remember my first time running with my roommate and best friend who both were in my wedding years later. I couldn't wait to watch my brother run his first line when he got here, then getting to run my last line at the last home game as a senior with my brother who was a freshman. I still take a video at every home opener to compare the sizes of the freshman classes, and because it gives me nostalgia and is the thing that makes me really feel that college football has started. So, with all of that, I am calling on College Football 27 and EA SPORTS to please add the biggest Baylor football tradition to the game. My favorite game of all time is NCAA 14, and I am literally taking off work July 19th (my birthday as well if that helps) to play this game, I just want it to be perfect. 🙏 #SicEm #Baylor #CFB25
0:38

EA Sports CFB25 released their Sights & Sounds Deep Dive today and I am DISAPPOINTED! The main theme they use in the video is "Every Team Is Somebody's Favorite" and if you are going to make that claim, then I will hold you to it. My favorite school is the one I attended, Baylor University, and it has been clipped and used in both CFB25 videos with a GLARING mistake to anyone who went to Baylor. The Baylor Line is missing! Every year the freshman class gets a Baylor Line Jersey at the Freshman Line Camp before school starts. They spend a week learning about the school, each other, and our traditions which all leads up to getting a gold Line Jersey with your graduation year as the number and your last name or nickname on the back. Once football starts the Freshman show up hours early in their jersey to each home game and wait in the brutal Texas heat to be in the Baylor Line. Right before the game starts the freshman class runs onto the field in a frenzie to get to the other side of the field and form the tunnel that the team will run through. This is a tradition that is so crazy and dangerous that the NCAA outlawed it and grandfathered Baylor in as the one school allowed. The last home game of the season the graduating senior class gets to join the Freshman and run the line one last time in a double sized mosh pit to send them off how they entered. This tradition is near and dear to Baylor student's hearts. I remember my first time running with my roommate and best friend who both were in my wedding years later. I couldn't wait to watch my brother run his first line when he got here, then getting to run my last line at the last home game as a senior with my brother who was a freshman. I still take a video at every home opener to compare the sizes of the freshman classes, and because it gives me nostalgia and is the thing that makes me really feel that college football has started. So, with all of that, I am calling on College Football 27 and EA SPORTS to please add the biggest Baylor football tradition to the game. My favorite game of all time is NCAA 14, and I am literally taking off work July 19th (my birthday as well if that helps) to play this game, I just want it to be perfect. 🙏 #SicEm #Baylor #CFB25

Ace: United Football Media VP

66,398 次观看 • 2 年前

I funded Grok Bot with $41 and gave it one line: "turn a profit or I wipe you" outcome: $55 → $3,117.16 in a little under two days still alive. haven't touched the dashboard since last night. it sits on a cloud box and flips memecoins by itself while I'm asleep I keep bracing for the run where it blows up. so far that run never comes on every cycle it: - scans each fresh launch and reads the pool depth before the chart has even rendered - fires a safety pass, mint authority, LP lock status, how concentrated the top wallets are, and throws out anything that smells off - weighs the social chatter against mempool flow to see what's genuinely loading up - pulls the trigger only when the entry is clean enough to eat the slippage - scales the next buy off how the last exit landed, never a flat ticket - rotates in and out without pinging me for a yes, and pays its own server bill out of the top before anything else opening night it nearly flatlined, dipped to $5 before something clicked. then it quit jumping on every loud new ticker, tightened its filters, and the line finally bent upward setup was a single evening: laid out the job, watched it push one swap through, dropped it on a timer, linked a wallet nothing to keep alive. no keys to cycle. no code just sitting there ready to snap way more of these are humming along right now than people will cop to, and you usually only learn one's out there when somebody fumbles and drops a screenshot of the panel bookmark this so you've got the timestamp for the day it lands for you hit follow if you want to run the same playbook, more alpha coming
0:24

I funded Grok Bot with $41 and gave it one line: "turn a profit or I wipe you" outcome: $55 → $3,117.16 in a little under two days still alive. haven't touched the dashboard since last night. it sits on a cloud box and flips memecoins by itself while I'm asleep I keep bracing for the run where it blows up. so far that run never comes on every cycle it: - scans each fresh launch and reads the pool depth before the chart has even rendered - fires a safety pass, mint authority, LP lock status, how concentrated the top wallets are, and throws out anything that smells off - weighs the social chatter against mempool flow to see what's genuinely loading up - pulls the trigger only when the entry is clean enough to eat the slippage - scales the next buy off how the last exit landed, never a flat ticket - rotates in and out without pinging me for a yes, and pays its own server bill out of the top before anything else opening night it nearly flatlined, dipped to $5 before something clicked. then it quit jumping on every loud new ticker, tightened its filters, and the line finally bent upward setup was a single evening: laid out the job, watched it push one swap through, dropped it on a timer, linked a wallet nothing to keep alive. no keys to cycle. no code just sitting there ready to snap way more of these are humming along right now than people will cop to, and you usually only learn one's out there when somebody fumbles and drops a screenshot of the panel bookmark this so you've got the timestamp for the day it lands for you hit follow if you want to run the same playbook, more alpha coming

cristal💎

221,370 次观看 • 12 天前

One of my favorite upgrades to Claude Cowork right now: Projects Here's the problem it solves: By default, every new thread starts blank Claude has no memory of what you were working on, what other threads figured out, or what your instructions were So you'd re-explain yourself at the start of every session Or your agents would overlap, contradict each other, or miss context that another thread already had If you've been following my Cowork setup guides, you've already solved part of this — local files and a CLAUDE.md get you most of the way there Projects takes it the rest of the way Shared memory across every thread in the project Permanent instructions that apply everywhere — set once, always on Scheduled tasks that prep your work before you open the app One project HQ to launch every agent from The day-one prompt I paste into every new project: "I just created this project. Read every file in the folder. Then summarize what you know about this workspace — what's here, what I probably use it for, and what instructions you'll follow. If anything is unclear, ask me some questions." Run that once and Claude builds its own understanding of your project workspace from scratch Full video with my live build, including my 4 tips for getting Projects right, and where projects still lack. Send to someone whose ready to Do More with AI, or bookmark to implement this weekend!
28:01

One of my favorite upgrades to Claude Cowork right now: Projects Here's the problem it solves: By default, every new thread starts blank Claude has no memory of what you were working on, what other threads figured out, or what your instructions were So you'd re-explain yourself at the start of every session Or your agents would overlap, contradict each other, or miss context that another thread already had If you've been following my Cowork setup guides, you've already solved part of this — local files and a CLAUDE.md get you most of the way there Projects takes it the rest of the way Shared memory across every thread in the project Permanent instructions that apply everywhere — set once, always on Scheduled tasks that prep your work before you open the app One project HQ to launch every agent from The day-one prompt I paste into every new project: "I just created this project. Read every file in the folder. Then summarize what you know about this workspace — what's here, what I probably use it for, and what instructions you'll follow. If anything is unclear, ask me some questions." Run that once and Claude builds its own understanding of your project workspace from scratch Full video with my live build, including my 4 tips for getting Projects right, and where projects still lack. Send to someone whose ready to Do More with AI, or bookmark to implement this weekend!

JJ Englert

29,809 次观看 • 5 个月前

Mark Zuckerberg just gave away the real business model of the next decade. It isn’t computing. It’s loneliness. Zuckerberg: “The average person would like to have 10 friends, and they have two, right? Or three. And there’s just more demand to socialize than what people are able to do given the current construct.” The CEO of the largest social network ever built just told you the construct is broken. He built half of it. Now he’s selling the repair. Every platform shift in modern history has quietly repriced a human need. The phone repriced attention. The feed repriced validation. The glasses reprice presence itself. Zuckerberg: “This is probably going to be the next major platform after phones.” Ten years of miniaturization. Full holograms. Wide field of view. Not a headset. Glasses. Something you wear the way you wear your face. Every winning platform disappeared into daily life. The phone won because it fit in your pocket. Glasses win if they fit into who you are. Then comes the layer nobody else has. An AI that sees what you see. Hears what you hear. Not a map overlay. Not a floating notification. A second mind sitting behind your eyes. Building context around every person in front of you, every room you walk into, every silence you’d otherwise sit in alone. Zuckerberg: “It’s this feeling of presence, and this capability of really personalized intelligence that can help you.” Presence and intelligence. The two things human beings have always needed from each other and could never reliably provide. That’s the whole pitch. That’s the whole company. The loneliness data has been stacking for thirty years. People are more connected and more isolated at the same time. That isn’t a contradiction. That’s what connection without presence produces. You can text someone every day and still feel like they’re gone. The phone solved distance. It didn’t solve absence. Video calls solved visibility. They didn’t solve the room. There’s a gap between being reachable and being there. Every platform of the last twenty years has lived inside that gap and called it enough. Zuckerberg is the first person with the capital, the hardware, and the AI to close it. Or to simulate closing it so convincingly that nobody checks. That’s the part that should keep you up. Because if a pair of glasses can make an empty room feel full, most people won’t go looking for the real thing. They’ll just put the glasses back on. The feed taught a generation to trade community for dopamine. The glasses will teach the next one to stop noticing the difference.
2:59

Mark Zuckerberg just gave away the real business model of the next decade. It isn’t computing. It’s loneliness. Zuckerberg: “The average person would like to have 10 friends, and they have two, right? Or three. And there’s just more demand to socialize than what people are able to do given the current construct.” The CEO of the largest social network ever built just told you the construct is broken. He built half of it. Now he’s selling the repair. Every platform shift in modern history has quietly repriced a human need. The phone repriced attention. The feed repriced validation. The glasses reprice presence itself. Zuckerberg: “This is probably going to be the next major platform after phones.” Ten years of miniaturization. Full holograms. Wide field of view. Not a headset. Glasses. Something you wear the way you wear your face. Every winning platform disappeared into daily life. The phone won because it fit in your pocket. Glasses win if they fit into who you are. Then comes the layer nobody else has. An AI that sees what you see. Hears what you hear. Not a map overlay. Not a floating notification. A second mind sitting behind your eyes. Building context around every person in front of you, every room you walk into, every silence you’d otherwise sit in alone. Zuckerberg: “It’s this feeling of presence, and this capability of really personalized intelligence that can help you.” Presence and intelligence. The two things human beings have always needed from each other and could never reliably provide. That’s the whole pitch. That’s the whole company. The loneliness data has been stacking for thirty years. People are more connected and more isolated at the same time. That isn’t a contradiction. That’s what connection without presence produces. You can text someone every day and still feel like they’re gone. The phone solved distance. It didn’t solve absence. Video calls solved visibility. They didn’t solve the room. There’s a gap between being reachable and being there. Every platform of the last twenty years has lived inside that gap and called it enough. Zuckerberg is the first person with the capital, the hardware, and the AI to close it. Or to simulate closing it so convincingly that nobody checks. That’s the part that should keep you up. Because if a pair of glasses can make an empty room feel full, most people won’t go looking for the real thing. They’ll just put the glasses back on. The feed taught a generation to trade community for dopamine. The glasses will teach the next one to stop noticing the difference.

Dustin

20,864 次观看 • 5 个月前

Every major platform in history has run the same play. You’re about to watch it happen again. Jason Calacanis just went on record. He wants it clipped. He wants it shared. Calacanis: “If I was a developer of any kind, I would never work with Sam Altman and OpenAI.” This isn’t pessimism. It’s pattern recognition. And the pattern has a 40 year track record. Open. Invite. Reward. Study. Absorb. Eliminate. Microsoft let developers build Lotus 1-2-3. Then built Excel. Let them build WordPerfect. Then built Word. Flew them to conferences. Handed out awards. Studied everything. Then eliminated them. Zuckerberg ran the exact same play at Facebook. Zynga built billions in value on their platform. Then Zuckerberg shifted them without blinking. Calacanis: “Sam Altman comes from the Zuckerberg school of business. Give people access to your tools, study them, and like the Borg, steal every innovation they have.” This is how platforms grow. They don’t innovate at the edges. They let the ecosystem do it for them. Startups take the risk. Startups find the market. Startups prove the concept. Then the platform ships it natively and calls it a feature. Altman isn’t selling you compute. He’s selling you a front row seat to your own disruption. Calacanis: “This is a warning for anybody dumb enough to use Sam Altman’s OpenAI API. They are studying you.” OpenAI has the legal right to study how you use their API. You agreed to it. It’s in the terms. Every gap you find, you’re finding it for them first. Every dollar you make signals exactly where he should build next. We are at the exact same moment in AI that we were in the early internet. Developers flooded onto platforms. Built incredible things. Created real value. And handed the leverage to whoever owned the infrastructure beneath them. The AI gold rush feels different because the tools are more powerful. It isn’t different. You are not a founder. You are unpaid R&D. The builders who win the next decade won’t be the ones who used the best tools. They’ll be the ones who owned something the tools couldn’t absorb. Proprietary data. Distribution. A brand. A moat. History doesn’t warn you before it repeats. It just repeats. Thousands of developers are walking straight into this right now convinced they’re different. They’re not. Do not build your business on OpenAI. Build something he has to acquire or destroy.
1:18

Every major platform in history has run the same play. You’re about to watch it happen again. Jason Calacanis just went on record. He wants it clipped. He wants it shared. Calacanis: “If I was a developer of any kind, I would never work with Sam Altman and OpenAI.” This isn’t pessimism. It’s pattern recognition. And the pattern has a 40 year track record. Open. Invite. Reward. Study. Absorb. Eliminate. Microsoft let developers build Lotus 1-2-3. Then built Excel. Let them build WordPerfect. Then built Word. Flew them to conferences. Handed out awards. Studied everything. Then eliminated them. Zuckerberg ran the exact same play at Facebook. Zynga built billions in value on their platform. Then Zuckerberg shifted them without blinking. Calacanis: “Sam Altman comes from the Zuckerberg school of business. Give people access to your tools, study them, and like the Borg, steal every innovation they have.” This is how platforms grow. They don’t innovate at the edges. They let the ecosystem do it for them. Startups take the risk. Startups find the market. Startups prove the concept. Then the platform ships it natively and calls it a feature. Altman isn’t selling you compute. He’s selling you a front row seat to your own disruption. Calacanis: “This is a warning for anybody dumb enough to use Sam Altman’s OpenAI API. They are studying you.” OpenAI has the legal right to study how you use their API. You agreed to it. It’s in the terms. Every gap you find, you’re finding it for them first. Every dollar you make signals exactly where he should build next. We are at the exact same moment in AI that we were in the early internet. Developers flooded onto platforms. Built incredible things. Created real value. And handed the leverage to whoever owned the infrastructure beneath them. The AI gold rush feels different because the tools are more powerful. It isn’t different. You are not a founder. You are unpaid R&D. The builders who win the next decade won’t be the ones who used the best tools. They’ll be the ones who owned something the tools couldn’t absorb. Proprietary data. Distribution. A brand. A moat. History doesn’t warn you before it repeats. It just repeats. Thousands of developers are walking straight into this right now convinced they’re different. They’re not. Do not build your business on OpenAI. Build something he has to acquire or destroy.

Dustin

248,682 次观看 • 6 个月前

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this
0:24

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,549 次观看 • 3 个月前

CSS Trick 🧲 You can create magnetic links with the power of custom properties and some JavaScript 💪 a { translate: calc(clamp(-1, var(--x), 1) * var(--pad-x)) ...; transition: translate var(--s, 1s) var(--ease, var(--elastic)); } a:hover { --s: 0s; } The trick here is to pad out the list items wrapping your links and use that as a translation limit 🛑 Start by using some JavaScript to calculate a value between -1 and 1 for both the x/y axis on pointermove for each list item, not the link! 🔗 If your pointer was at the center of the item, you'd get [0,0]. If it was in the top right, you'd get [1,-1] ☝️ It's worth checking out the JavaScript snippet to see how the mapping function works. Essentially, you create a function that when given a value between two bounds, will give you a mapped value back 🤙 const mapX = mapRange( item.offsetWidth * -0.5, item.offsetWidth * 0.5, 1, -1 ) Then, on pointermove, you plug the pointer position in to get the value back out and pass that into your CSS const x = mapX(item.centerX - event.x) document​.documentElement​.style.setProperty(--x, x) When the pointer leaves the list item, you make sure to reset these values back to 0 ✨ Once CSS has your values, it's the trick of updating the translation of each part You know that in each axis, you only want to translate the link by the padding amount li a { translate: calc(clamp(-1, var(--x), 1) * var(--pad-x)) calc(clamp(-1, var(--y), 1) * var(--pad-y)); transition: translate var(--speed, 1s) var(--ease, var(--elastic)); } This will translate the link within the list item by the desired amount. The cool part here is that you can set an offset for the text inside the link and have that move at a different rate ⭐️ By only updating the --pad-x/y custom properties for the inside the link, you can control how much it moves nav a span { --pad-x: 0.25rem; --pad-y: 0.25rem; } And the last piece, how do you update the behavior for transition speeds? And so it springs back like that? Again, use custom properties ✨ a:hover { --s: 0s; } a { transition: translate var(--s, 1s) var(--ease, var(--elastic)); } By default, a link will use --elastic easing via linear() and have a transition-duration of 1s. When a link is hovered that speed becomes 0s because you want the link to magnetise to your pointer. How about that little gap between when your pointer enters the item but hasn't hovered the link? Set a different transition so it transitions to being hovered 🫶 nav li:hover a { --ease: ease-out; --speed: 0.1s; } That's kinda it! 🙌 Use JavaScript (~40 loc) to get the information and then let CSS do all the lifting for you 💪 Any questions or suggestions, let me know 🙏 If you want a walkthrough video, also let me know please 🙏 CodePen.IO link below 👇
1:25

CSS Trick 🧲 You can create magnetic links with the power of custom properties and some JavaScript 💪 a { translate: calc(clamp(-1, var(--x), 1) * var(--pad-x)) ...; transition: translate var(--s, 1s) var(--ease, var(--elastic)); } a:hover { --s: 0s; } The trick here is to pad out the list items wrapping your links and use that as a translation limit 🛑 Start by using some JavaScript to calculate a value between -1 and 1 for both the x/y axis on pointermove for each list item, not the link! 🔗 If your pointer was at the center of the item, you'd get [0,0]. If it was in the top right, you'd get [1,-1] ☝️ It's worth checking out the JavaScript snippet to see how the mapping function works. Essentially, you create a function that when given a value between two bounds, will give you a mapped value back 🤙 const mapX = mapRange( item.offsetWidth * -0.5, item.offsetWidth * 0.5, 1, -1 ) Then, on pointermove, you plug the pointer position in to get the value back out and pass that into your CSS const x = mapX(item.centerX - event.x) document​.documentElement​.style.setProperty(--x, x) When the pointer leaves the list item, you make sure to reset these values back to 0 ✨ Once CSS has your values, it's the trick of updating the translation of each part You know that in each axis, you only want to translate the link by the padding amount li a { translate: calc(clamp(-1, var(--x), 1) * var(--pad-x)) calc(clamp(-1, var(--y), 1) * var(--pad-y)); transition: translate var(--speed, 1s) var(--ease, var(--elastic)); } This will translate the link within the list item by the desired amount. The cool part here is that you can set an offset for the text inside the link and have that move at a different rate ⭐️ By only updating the --pad-x/y custom properties for the inside the link, you can control how much it moves nav a span { --pad-x: 0.25rem; --pad-y: 0.25rem; } And the last piece, how do you update the behavior for transition speeds? And so it springs back like that? Again, use custom properties ✨ a:hover { --s: 0s; } a { transition: translate var(--s, 1s) var(--ease, var(--elastic)); } By default, a link will use --elastic easing via linear() and have a transition-duration of 1s. When a link is hovered that speed becomes 0s because you want the link to magnetise to your pointer. How about that little gap between when your pointer enters the item but hasn't hovered the link? Set a different transition so it transitions to being hovered 🫶 nav li:hover a { --ease: ease-out; --speed: 0.1s; } That's kinda it! 🙌 Use JavaScript (~40 loc) to get the information and then let CSS do all the lifting for you 💪 Any questions or suggestions, let me know 🙏 If you want a walkthrough video, also let me know please 🙏 CodePen.IO link below 👇

jhey ʕ•ᴥ•ʔ

164,863 次观看 • 2 年前

This man teaches at a community college in California. His salary: around $800,000 a year. The engineers who passed calculus because of him: $1,800,000 to start. He has more calculus students than Harvard, MIT, and Stanford combined. This is Professor Leonard's Calculus 2, Lecture 6.2. Free on YouTube. Professor Leonard has taught calculus on YouTube for over a decade. His channel has millions of subscribers across 150 countries. Every major university has students who watch him the night before their exam. Then the concept. An inverse function is a machine that undoes another machine. If a function takes 2 and gives you 8, the inverse takes 8 and gives you back 2. Finding an inverse means switching every x and y in the equation and solving for y again. The graph flips across the line y = x like a mirror. Then the problem. Sometimes it is easy to find the inverse. Sometimes it is impossible to write it explicitly. A function like 3π sin x + sin x cannot be solved for x with algebra. You have to think. What angle makes the whole thing equal to 1? You work backwards through the unit circle until the answer appears. Then the shortcut. If you want the derivative of an inverse at a point, you do not need the inverse itself. You only need the derivative of the original function. The formula: the derivative of the inverse at a point equals 1 divided by the derivative of the original function evaluated at the switched point. The inverse flips the coordinates, so you flip where you plug in. Watch the moment he shows why G prime of 8 equals 1/12 without ever writing the inverse function. Every engineering student memorizes the derivative rules. Professor Leonard's lecture is the one that shows why the inverse derivative formula is just those same rules run backwards. A software engineer at a semiconductor company in Austin said Professor Leonard's channel is the reason she passed Calculus 2 on her second attempt. She graduated, joined the company, and now makes $165,000 a year. Bookmark this and watch later - after this lecture every inverse problem on your exam will feel like a question you already answered.
43:46

This man teaches at a community college in California. His salary: around $800,000 a year. The engineers who passed calculus because of him: $1,800,000 to start. He has more calculus students than Harvard, MIT, and Stanford combined. This is Professor Leonard's Calculus 2, Lecture 6.2. Free on YouTube. Professor Leonard has taught calculus on YouTube for over a decade. His channel has millions of subscribers across 150 countries. Every major university has students who watch him the night before their exam. Then the concept. An inverse function is a machine that undoes another machine. If a function takes 2 and gives you 8, the inverse takes 8 and gives you back 2. Finding an inverse means switching every x and y in the equation and solving for y again. The graph flips across the line y = x like a mirror. Then the problem. Sometimes it is easy to find the inverse. Sometimes it is impossible to write it explicitly. A function like 3π sin x + sin x cannot be solved for x with algebra. You have to think. What angle makes the whole thing equal to 1? You work backwards through the unit circle until the answer appears. Then the shortcut. If you want the derivative of an inverse at a point, you do not need the inverse itself. You only need the derivative of the original function. The formula: the derivative of the inverse at a point equals 1 divided by the derivative of the original function evaluated at the switched point. The inverse flips the coordinates, so you flip where you plug in. Watch the moment he shows why G prime of 8 equals 1/12 without ever writing the inverse function. Every engineering student memorizes the derivative rules. Professor Leonard's lecture is the one that shows why the inverse derivative formula is just those same rules run backwards. A software engineer at a semiconductor company in Austin said Professor Leonard's channel is the reason she passed Calculus 2 on her second attempt. She graduated, joined the company, and now makes $165,000 a year. Bookmark this and watch later - after this lecture every inverse problem on your exam will feel like a question you already answered.

Lupen

731,930 次观看 • 13 天前