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Cams ARE the original "programmable" automation - these mechanical marvels orchestrate complex sequences without a single line of code. By carefully profiling cam shapes, engineers encode timing, positioning, and sequencing directly into metal. What makes cams brilliant for automation: - Deterministic timing - no software, no edge cases -... Self-synchronising - no syncing clocks of different systems - Incredibly durable - cam-driven machines run for decades - Simple maintenance - mechanics can "read" the program by looking at cam profiles The cam followers, springs, and linkages in machines like this execute intricate routines - bending, cutting, feeding, and positioning with clockwork precision. Each bump and valley on those rotating cams tells a different part of the machine exactly when to move. With all the dancing robots, we sometimes forget how high-tech old-school automation is.show more

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One trader printing money with ClawdBot I’m not exaggerating. What he built is pretty wild: He auto-trades BTC / ETH Focused on 15-minute up/down moves Catching CEX lags and inefficiencies No Wall Street setup No big team Just smart automation Profile: Copytrade: How it works: Short-term moves The system trades 15-minute ups and downs on major coins. It’s not long-term investing - it’s micro-timing. Exchange inefficiencies It looks for small price lags on CEXs and reacts fast. When there’s a mismatch, it jumps in. Scale & frequency 500+ trades per week. Each trade targets tiny edges. Those small gains stack and compound. The surprising part All of it came from prompting Claude to generate code. No dev degree needed. Bottom line High frequency Small edges Heavy compounding Automation over emotion.

winkle.

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🚨 BREAKING — one of the strongest OpenClaw setups on Polymarket just went public. A trader reportedly started with ~$100–200 and scaled it to ~$3.7M. No insider access. No political connections. Just a developer running his own automation built with OpenClaw. Profile → Copytrade → I went through the framework myself. What surprised me: There’s no huge infrastructure. No complex quant stack. No giant data pipelines. Just clean logic and disciplined automation. After about 8 hours analyzing it, the strategy breaks down into three parts. 1) “Free money” via NO positions The bot targets outcomes with near-zero probability. Instead of chasing big wins, it accumulates a massive number of small high-probability NO trades. Not speculation — systematic probability harvesting. 2) Logical arbitrage Sometimes Outcome A logically implies Outcome B, but markets don’t adjust instantly. The bot detects these inconsistencies and enters before repricing happens. By the time the headline reaches traders, the window is already closed. 3) Retail-driven markets Sports and political markets are dominated by retail flow and emotional reactions. Prices overshoot, spreads widen, and inefficiencies appear constantly. The bot sits in those gaps and clips small edges repeatedly. Scale is the edge. 4,192 trades executed. Individually small. Together they compounded into roughly ~$3.7M profit. Largest single win: $1,464,152. The equity curve is almost vertical. It’s not about predicting events. It’s about exploiting structural inefficiencies faster than the crowd.

Discover

186,472 Aufrufe • vor 4 Monaten

Robots that act like slime! 🫟 Cornell University engineers developed a robotic collective that behaves less like a machine and more like a material that flows, reshapes, and adapts without centralized control. It consists of dozens of small robots with limited individual mobility that exhibit coordinated motion when entangled. The system resembles soft matter, continuously deforming and reorganizing as it moves, driven by mechanical intelligence. Each robotic module measures 200mm long and 20mm wide, containing a small motor that oscillates between "I" and "U" shapes. These oscillations generate forces against the ground, allowing modules to inch forward and jostle together. On their own, modules move slowly and inefficiently. When they entangle into chains, they self-organize into shifting configurations that prove resilient in challenging environments. On incline surfaces, chains moved more reliably than individuals. In obstacle fields, the collective behaved like a flowing material, connections formed to maintain cohesion, then broke apart to prevent jamming. The system stays functional even when modules fail. Isolated modules emit an audible distress signal, prompting nearby modules to slow down so the straggler can reconnect. No centralized sensing or control, each module infers when it has lost contact by how much it's being jostled. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Robots that act like slime! 🫟 Cornell University engineers developed a robotic collective that behaves less like a machine and more like a material that flows, reshapes, and adapts without centralized control. It consists of dozens of small robots with limited individual mobility that exhibit coordinated motion when entangled. The system resembles soft matter, continuously deforming and reorganizing as it moves, driven by mechanical intelligence. Each robotic module measures 200mm long and 20mm wide, containing a small motor that oscillates between "I" and "U" shapes. These oscillations generate forces against the ground, allowing modules to inch forward and jostle together. On their own, modules move slowly and inefficiently. When they entangle into chains, they self-organize into shifting configurations that prove resilient in challenging environments. On incline surfaces, chains moved more reliably than individuals. In obstacle fields, the collective behaved like a flowing material, connections formed to maintain cohesion, then broke apart to prevent jamming. The system stays functional even when modules fail. Isolated modules emit an audible distress signal, prompting nearby modules to slow down so the straggler can reconnect. No centralized sensing or control, each module infers when it has lost contact by how much it's being jostled. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

13,026 Aufrufe • vor 2 Monaten

🚨 SCIENTISTS JUST TOOK A MAJOR STEP TOWARD ATOM-BY-ATOM MANUFACTURING. Using a scanning tunneling microscope, researchers have demonstrated programmable, positionally controlled placement of carbon fragments onto a hydrogen-passivated silicon surface building precise carbon chains and even complex “X” shapes with yields up to 97% for adjacent units. This is not random chemistry or spray-and-pray methods. It’s targeted, repeatable mechanosynthesis at the atomic scale. Why this matters: For decades, atomically precise manufacturing has been a holy grail of nanotechnology. This work shows we can now pick up individual molecular building blocks, place them exactly where we want, and grow structured carbon architectures step by step. The deeper implication is staggering: We may be watching the early days of true molecular manufacturing the ability to build matter atom by atom, on demand. This could eventually transform everything from materials science to medicine, energy storage, and even age-reversal technologies. The dream of programmable matter is no longer pure science fiction. What happens when we can routinely construct complex molecular structures with atomic precision? Follow for more frontier physics and future technology.

🚨 SCIENTISTS JUST TOOK A MAJOR STEP TOWARD ATOM-BY-ATOM MANUFACTURING. Using a scanning tunneling microscope, researchers have demonstrated programmable, positionally controlled placement of carbon fragments onto a hydrogen-passivated silicon surface building precise carbon chains and even complex “X” shapes with yields up to 97% for adjacent units. This is not random chemistry or spray-and-pray methods. It’s targeted, repeatable mechanosynthesis at the atomic scale. Why this matters: For decades, atomically precise manufacturing has been a holy grail of nanotechnology. This work shows we can now pick up individual molecular building blocks, place them exactly where we want, and grow structured carbon architectures step by step. The deeper implication is staggering: We may be watching the early days of true molecular manufacturing the ability to build matter atom by atom, on demand. This could eventually transform everything from materials science to medicine, energy storage, and even age-reversal technologies. The dream of programmable matter is no longer pure science fiction. What happens when we can routinely construct complex molecular structures with atomic precision? Follow for more frontier physics and future technology.

TheNewPhysics

13,612 Aufrufe • vor 2 Monaten

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

elvis

18,374 Aufrufe • vor 2 Monaten

🚨 BREAKING… $100K profit in one month with Clawdbot and Polymarket, code did the work This is a REAL setup with REAL outcomes. Relevant for ANYONE trading on Polymarket. He took a small initial position, let Clawdbot handle operations, and grew it into a ~$100K outcome No secret connections No high-profile backing Just a dev who connected Moltbot (Clawdbot) straight to Polymarket Profile → Copytrade → After reviewing the system, I was genuinely surprised No hype-driven plays No subjective calls Everything runs without a human in the loop Execution runs on its own, no manual layer FULL strategy overview: 1. 15-minute BTC & ETH micro-arbitrage The system operates on short-window Bitcoin and Ethereum markets using 15-minute intervals. In these fast-moving contracts, brief mispricings appear when YES and NO combined dip below $1. With Moltbot (Clawdbot) wired directly into Polymarket, those gaps are seized instantly - no forecasting, no directional bets, no bias 2. Execution beats reaction When volatility surges and traders hesitate, the system doesn’t. Orders are placed mechanically, with no pauses, no emotional friction, and no execution lag. By the time humans notice the move, the edge has already closed 3. Autonomy enables scale Each cycle captures pennies, not big wins. But uninterrupted operation allows the process to repeat relentlessly, at high frequency, without burnout or slowdown compounding small edges into meaningful results Scale is the edge 5,978 executions. Each one trivial in isolation. Taken together, they snowballed into just under $100K in profit The takeaway In my view, a low-key bot arms race is already underway on Polymarket Humans debate entries Systems capitalize on mechanics And while structural inefficiencies remain, autonomous setups will continue to extract value-quietly, relentlessly I track this space closely. FOLLOW if you want signal, not noise.

Shelpid.WI3M

485,943 Aufrufe • vor 5 Monaten

🚨 BREAKING… $1.1M in 26 days using OpenClaw on Polymarket This is a REAL framework generating REAL results. He began with a relatively small base, linked OpenClaw for execution, and expanded it to approximately ~$1.1M in returns Simply a developer integrating Moltbot (OpenClaw) directly into Polymarket Profile - Copytrade - After analyzing the setup, I was honestly impressed The entire process runs fully automated Execution follows predefined rules, zero human intervention FULL strategy overview: 1. 5 & 15 minute BTC & ETH micro-arbitrage The system focuses on ultra short-term Bitcoin and Ethereum contracts within 5-minute windows. In these rapid cycles, inefficiencies surface when YES and NO combined fall below $1. With Moltbot (OpenClaw) wired directly into Polymarket, those gaps are captured instantly - no forecasting, no directional bets, no bias 2. Speed instead of hesitation When volatility increases and manual traders freeze, the system acts. Orders are placed automatically, without delay, emotion, or second thoughts. By the time others respond, the opportunity is already gone 3. Automation compounds the advantage Each round captures tight spreads, not massive individual wins. But relentless, high-frequency repetition keeps the engine running nonstop no fatigue, no slowdown - stacking small edges into significant results Scale is the edge 23,784 trades. Small on their own. Together, they accumulated into ~$1.1M in profit

Shelpid.WI3M

186,875 Aufrufe • vor 5 Monaten

The Circle 🐜 Nobody told the ant about the circle. That, as far as anyone can tell, is the whole problem. A researcher named Kostowski – this was in the early 1970s, at a laboratory in Warsaw that smelled permanently of formaldehyde and institutional coffee – discovered quite by accident that if you draw a continuous line around an ant using a felt-tip pen, the ant will not cross it. It will walk right up to the line, pause with what appears to be genuine philosophical unease, and turn back. It will do this indefinitely. For hours. Sometimes for days. The ant is not stupid. Let’s be clear about that. The creature you are looking at in this photograph – this tiny, improbable machine of chitin and chemical signals, this six-legged marvel that can carry fifty times its own body weight and navigate by polarized light – has a brain roughly the size of a pinhead, and yet that brain contains approximately 250,000 neurons dedicated entirely to making sense of the world. It has survived as a species for 130 million years. It watched the dinosaurs arrive, flourish, and disappear, and then went back to work. And yet here it is. Trapped by a drawing. The reason is chemistry, not cognition. Ants navigate by pheromones – volatile chemical compounds that their legs read like a blind man reads braille. When they encounter the solvent in a felt-tip pen, something in their nervous system fires an alarm. The signal says: boundary. The signal says: edge of the known world. And the ant, loyal to its chemistry in the way that all of us are loyal to ours, obeys. This is the part that stays with you if you think about it too long. The ant’s prison has no walls. No bars. No lock. It is made entirely of information – a chemical whisper laid down by a felt-tip marker – and the ant cannot see past it, because it has no framework for doing so. The circle is not a circle to the ant. The circle is simply: where the world ends. I find myself thinking about this more than is probably healthy. We are, most of us, walking around inside our own circles. They were drawn for us gradually, by parents and teachers and early disappointments, by the limits of what we saw done and the boundaries of what we were told was possible. We bump up against them occasionally – in those moments when a job offer from another city seems too frightening, or a new idea feels somehow presumptuous – and we turn back. Not because anything is stopping us. Because the world, as far as we can tell, simply ends there. The ant in the photograph is walking the inner edge of its circle with a kind of purposeful calm that is almost admirable. It has not given up. It is still looking. It is still moving. It simply cannot conceive of a direction that leads out. Kostowski, for what it’s worth, eventually just picked the ant up and moved it. Sometimes that’s what it takes. Gandalv / Gandalv

The Circle 🐜 Nobody told the ant about the circle. That, as far as anyone can tell, is the whole problem. A researcher named Kostowski – this was in the early 1970s, at a laboratory in Warsaw that smelled permanently of formaldehyde and institutional coffee – discovered quite by accident that if you draw a continuous line around an ant using a felt-tip pen, the ant will not cross it. It will walk right up to the line, pause with what appears to be genuine philosophical unease, and turn back. It will do this indefinitely. For hours. Sometimes for days. The ant is not stupid. Let’s be clear about that. The creature you are looking at in this photograph – this tiny, improbable machine of chitin and chemical signals, this six-legged marvel that can carry fifty times its own body weight and navigate by polarized light – has a brain roughly the size of a pinhead, and yet that brain contains approximately 250,000 neurons dedicated entirely to making sense of the world. It has survived as a species for 130 million years. It watched the dinosaurs arrive, flourish, and disappear, and then went back to work. And yet here it is. Trapped by a drawing. The reason is chemistry, not cognition. Ants navigate by pheromones – volatile chemical compounds that their legs read like a blind man reads braille. When they encounter the solvent in a felt-tip pen, something in their nervous system fires an alarm. The signal says: boundary. The signal says: edge of the known world. And the ant, loyal to its chemistry in the way that all of us are loyal to ours, obeys. This is the part that stays with you if you think about it too long. The ant’s prison has no walls. No bars. No lock. It is made entirely of information – a chemical whisper laid down by a felt-tip marker – and the ant cannot see past it, because it has no framework for doing so. The circle is not a circle to the ant. The circle is simply: where the world ends. I find myself thinking about this more than is probably healthy. We are, most of us, walking around inside our own circles. They were drawn for us gradually, by parents and teachers and early disappointments, by the limits of what we saw done and the boundaries of what we were told was possible. We bump up against them occasionally – in those moments when a job offer from another city seems too frightening, or a new idea feels somehow presumptuous – and we turn back. Not because anything is stopping us. Because the world, as far as we can tell, simply ends there. The ant in the photograph is walking the inner edge of its circle with a kind of purposeful calm that is almost admirable. It has not given up. It is still looking. It is still moving. It simply cannot conceive of a direction that leads out. Kostowski, for what it’s worth, eventually just picked the ant up and moved it. Sometimes that’s what it takes. Gandalv / Gandalv

Gandalv

28,294 Aufrufe • vor 4 Monaten

Hexagons and Octagons Those who follow football and coaching will be well aware that there are trends that emerge and become the great break through in coaching, only to vanish quite quickly. A few stick around and become a staple. Such as the rondo, or the 4v4+4 Guardiola rondo variation. One that did not stick around in the coaching collective consciousness that possible should have was Thomas Tuchel’s use of hexagonal and octagonal playing areas in training sessions. Tuchel explained that cutting off the corners and angling the pitch forced “sharp diagonal” passes that would help break the press. The positioning of players outside the hexagon/octagon or players close to the edges will be manipulated into an open body shape by the angles of the pitch. Players are impacted by environmental constraints and embodied cognition, where the geography of the playing area influences their actions. This influence spreads to the creation of triangles and diamonds within the playing area due to the “funnel” like nature of the playing area. We can use the cut outside angles by placing bounce or target players on the exterior, influencing the movement and organisation internally. The inside players will not have to move wide as those areas are occupied. The internal players will seek to create passing angles using the positioning of the outside players and their internal team mates. The diamonds and triangles will appear. If we leave the spaces on the outside empty players can move to fill the spaces. These act as free spaces to receive from the goalkeeper or open spaces for attackers to overlap into, encouraging attacking combination play and crosses. A different way of using the space is to remove goals and goalkeepers from the ends and place bounce/target players on the outside. Players now can combine with the outside players, when they do so they are then free to finish into the outside goals. The condition can be extended to combining with the target player in the opposite side of the pitch before scoring, adding an element of switching play. The hexagon and octagon are versatile spaces that help to replicate aspects of the game. By funneling the spaces we impact players body shape, ability to play forward quickly, team shape (or small group shapes), players cutting in, defending centrally, the types of combination used and the angles of line breaking pass (diagonals). The angles are hugely significant for teams that value combinations and possession football, Straight passes and receiving angles are much easier to intercept and carry high risks for being counter attacked. Short diagonals can bypass players and attacking shapes, creating angled connections. If an angles pass is given away there is still a risk of being counter attacked but there is more chance of having players around the ball to regain possession. To counter press. The question that emerges is should we then be using hexagons and octagons more? If they are of greater benefit than squares and rectangles, why use them? Should all pitches, including those of a small sided nature be hexagonal? Can the rondo square be replaced by the rondo octagon?

Hexagons and Octagons Those who follow football and coaching will be well aware that there are trends that emerge and become the great break through in coaching, only to vanish quite quickly. A few stick around and become a staple. Such as the rondo, or the 4v4+4 Guardiola rondo variation. One that did not stick around in the coaching collective consciousness that possible should have was Thomas Tuchel’s use of hexagonal and octagonal playing areas in training sessions. Tuchel explained that cutting off the corners and angling the pitch forced “sharp diagonal” passes that would help break the press. The positioning of players outside the hexagon/octagon or players close to the edges will be manipulated into an open body shape by the angles of the pitch. Players are impacted by environmental constraints and embodied cognition, where the geography of the playing area influences their actions. This influence spreads to the creation of triangles and diamonds within the playing area due to the “funnel” like nature of the playing area. We can use the cut outside angles by placing bounce or target players on the exterior, influencing the movement and organisation internally. The inside players will not have to move wide as those areas are occupied. The internal players will seek to create passing angles using the positioning of the outside players and their internal team mates. The diamonds and triangles will appear. If we leave the spaces on the outside empty players can move to fill the spaces. These act as free spaces to receive from the goalkeeper or open spaces for attackers to overlap into, encouraging attacking combination play and crosses. A different way of using the space is to remove goals and goalkeepers from the ends and place bounce/target players on the outside. Players now can combine with the outside players, when they do so they are then free to finish into the outside goals. The condition can be extended to combining with the target player in the opposite side of the pitch before scoring, adding an element of switching play. The hexagon and octagon are versatile spaces that help to replicate aspects of the game. By funneling the spaces we impact players body shape, ability to play forward quickly, team shape (or small group shapes), players cutting in, defending centrally, the types of combination used and the angles of line breaking pass (diagonals). The angles are hugely significant for teams that value combinations and possession football, Straight passes and receiving angles are much easier to intercept and carry high risks for being counter attacked. Short diagonals can bypass players and attacking shapes, creating angled connections. If an angles pass is given away there is still a risk of being counter attacked but there is more chance of having players around the ball to regain possession. To counter press. The question that emerges is should we then be using hexagons and octagons more? If they are of greater benefit than squares and rectangles, why use them? Should all pitches, including those of a small sided nature be hexagonal? Can the rondo square be replaced by the rondo octagon?

TheBeardedCoach

13,461 Aufrufe • vor 5 Monaten

This guy built a visual scanner that reads 468 points on his face and 42 points on his hands from a regular webcam and turns them into a cloud of thousands of particles right between his palms. Inside, MediaPipe and TouchDesigner are linked: the first captures hands and face from the webcam with high accuracy, the second turns those coordinates into a live plane and feeds it into a POP system that instantly generates a swarm of particles in the shape of a head. No studio, no render farmer, no VR headset. Just a laptop, a webcam, and 1 TouchDesigner session. And traditional VJ studios keep teams of 5 people on a setup with lighting, custom hardware, and commercial plugins, while his expenses are only a TouchDesigner subscription and a regular USB camera. One laptop runs MediaPipe and TouchDesigner simultaneously, holds the camera stream at 60 FPS without drops, and in parallel processes 468 face points + 21 points on each hand. The camera captures frame after frame, MediaPipe in real time sends TouchDesigner the finger coordinates and face geometry, and the POP operator inside the engine translates those numbers into thousands of particle points with colors from bright pink to gold. This setup immediately defines the role of the tool and the limits of its autonomy. It knows where the fingertips are at every moment of the frame. It knows how to read the face geometry at any angle to the camera. It knows how to draw a swarm of particles between them with the right color and contour. → MediaPipe pulls 468 points from the face and 21 points from each hand, 60 times per second → TouchDesigner receives those coordinates, builds a virtual rectangle between the fingertips, and feeds it into the POP system → POP generates thousands of particle points in the shape of a head, coloring them in a gradient from bright pink to gold → The HUD layer adds green corners and a blue neon frame, styling the image like an AR interface → All layers assemble into 1 real-time frame that projects back onto the video in the camera window → The final image is recorded to a file or broadcast to a projector for a live installation And only when the guy spreads his hands wider does the plane between the palms stretch; brings them together, it narrows. Otherwise the system runs on its own. And when he moves from his home room to a concert hall, the same laptop with the same webcam launches the same TouchDesigner session in just 5 minutes, without reconfiguration, without a new team, and without a single line of new code. In his work setup there is no studio of his own and no team for assembly. On the desk sits a laptop with a webcam, on top run MediaPipe and TouchDesigner with POP operators, and the same setup through a USB camera moves to any concert without a new configuration. Out of everything I have seen this year, this is the cleanest Creative Coding setup on 1 laptop: 0 render farms, 0 studio lighting, and between them 3 libraries, thousands of particle points, and 1 webcam.

This guy built a visual scanner that reads 468 points on his face and 42 points on his hands from a regular webcam and turns them into a cloud of thousands of particles right between his palms. Inside, MediaPipe and TouchDesigner are linked: the first captures hands and face from the webcam with high accuracy, the second turns those coordinates into a live plane and feeds it into a POP system that instantly generates a swarm of particles in the shape of a head. No studio, no render farmer, no VR headset. Just a laptop, a webcam, and 1 TouchDesigner session. And traditional VJ studios keep teams of 5 people on a setup with lighting, custom hardware, and commercial plugins, while his expenses are only a TouchDesigner subscription and a regular USB camera. One laptop runs MediaPipe and TouchDesigner simultaneously, holds the camera stream at 60 FPS without drops, and in parallel processes 468 face points + 21 points on each hand. The camera captures frame after frame, MediaPipe in real time sends TouchDesigner the finger coordinates and face geometry, and the POP operator inside the engine translates those numbers into thousands of particle points with colors from bright pink to gold. This setup immediately defines the role of the tool and the limits of its autonomy. It knows where the fingertips are at every moment of the frame. It knows how to read the face geometry at any angle to the camera. It knows how to draw a swarm of particles between them with the right color and contour. → MediaPipe pulls 468 points from the face and 21 points from each hand, 60 times per second → TouchDesigner receives those coordinates, builds a virtual rectangle between the fingertips, and feeds it into the POP system → POP generates thousands of particle points in the shape of a head, coloring them in a gradient from bright pink to gold → The HUD layer adds green corners and a blue neon frame, styling the image like an AR interface → All layers assemble into 1 real-time frame that projects back onto the video in the camera window → The final image is recorded to a file or broadcast to a projector for a live installation And only when the guy spreads his hands wider does the plane between the palms stretch; brings them together, it narrows. Otherwise the system runs on its own. And when he moves from his home room to a concert hall, the same laptop with the same webcam launches the same TouchDesigner session in just 5 minutes, without reconfiguration, without a new team, and without a single line of new code. In his work setup there is no studio of his own and no team for assembly. On the desk sits a laptop with a webcam, on top run MediaPipe and TouchDesigner with POP operators, and the same setup through a USB camera moves to any concert without a new configuration. Out of everything I have seen this year, this is the cleanest Creative Coding setup on 1 laptop: 0 render farms, 0 studio lighting, and between them 3 libraries, thousands of particle points, and 1 webcam.

Blaze

38,242 Aufrufe • vor 2 Monaten

To replace animal testing with AI, we need MASSIVE human datasets. Today, we're thrilled to share Axiom's new data exploration tool, providing the ability to visually explore the world's largest primary human liver toxicity dataset. Built with Axiom's proprietary wetlab protocols, our dataset includes detailed liver toxicity profiles for over 100,000 distinct molecules. The key to this dataset is our ability to do high-throughput, multiplexed high-content screening with primary human liver cells. Traditionally, toxicity assays either sacrifice throughput or sacrifice biological relevance (using easy-to-grow immortalized cell lines instead of real human cells). We managed to combine throughput, physiological relevance, and multiplexing in one platform. The assays run in a high throughput format using automation, meaning thousands of compound-dose conditions can be tested in one experiment. We achieved this using pooled primary human hepatocytes, which are often fragile and expensive. By systemizing our automation and quality control processes, we were able to run over 120+ batches on the same donor pool with incredible reproducibility and consistency. We did this while integrating many readouts per well, whereas many existing toxicity assays only do a single readout. Our multiplexed approach provides far more data per experiment enabling us to measure 10-20 different toxicity phenotypes such as apoptosis, necrosis, mitochondrial fission, endoplasmic reticulum stress, stress granule formation, microtubules, and more all from a single well on a 384-well plate! The combination of scale, high content information, and data quality is exactly what is needed to train highly accurate AI models in biology. If you're interested, please explore the dataset in the comments below and let me know if you want to chat about the details!

To replace animal testing with AI, we need MASSIVE human datasets. Today, we're thrilled to share Axiom's new data exploration tool, providing the ability to visually explore the world's largest primary human liver toxicity dataset. Built with Axiom's proprietary wetlab protocols, our dataset includes detailed liver toxicity profiles for over 100,000 distinct molecules. The key to this dataset is our ability to do high-throughput, multiplexed high-content screening with primary human liver cells. Traditionally, toxicity assays either sacrifice throughput or sacrifice biological relevance (using easy-to-grow immortalized cell lines instead of real human cells). We managed to combine throughput, physiological relevance, and multiplexing in one platform. The assays run in a high throughput format using automation, meaning thousands of compound-dose conditions can be tested in one experiment. We achieved this using pooled primary human hepatocytes, which are often fragile and expensive. By systemizing our automation and quality control processes, we were able to run over 120+ batches on the same donor pool with incredible reproducibility and consistency. We did this while integrating many readouts per well, whereas many existing toxicity assays only do a single readout. Our multiplexed approach provides far more data per experiment enabling us to measure 10-20 different toxicity phenotypes such as apoptosis, necrosis, mitochondrial fission, endoplasmic reticulum stress, stress granule formation, microtubules, and more all from a single well on a 384-well plate! The combination of scale, high content information, and data quality is exactly what is needed to train highly accurate AI models in biology. If you're interested, please explore the dataset in the comments below and let me know if you want to chat about the details!

Brandon White

25,117 Aufrufe • vor 1 Jahr

🚨 SCIENTISTS JUST BUILT A CHIP THAT CAN SEE, THINK, AND REMEMBER ALL AT THE SAME TIME. And it works more like a biological brain than a traditional computer. Researchers at RMIT University have created a neuromorphic vision chip that mimics the human eye and brain. Unlike conventional systems that capture images and send data to external processors, this chip performs sensing, processing, and memory storage directly where the light hits. The active layer is thousands of times thinner than a human hair. It uses doped indium oxide to detect light, process the information on-chip, and retain what it sees over time without constant electrical refreshing. Why this matters: • It dramatically cuts energy use and latency by eliminating data transfer to separate processors • Enables much faster real-time decision making for autonomous systems • Works more like biological vision than traditional machine vision • Could power the next generation of efficient edge AI in vehicles, robots, and remote sensors The deeper implication: For decades, we’ve built vision systems by bolting cameras, processors, and memory together like separate organs. This chip collapses those functions into one biological-style unit. It’s a step toward machines that don’t just “see” but actually perceive and remember in a more efficient, brain-like way. If scaled successfully, it could become a foundational component for autonomous systems that need to operate intelligently with minimal power and minimal delay. We’re moving from cameras that take pictures to chips that truly see. How do you think neuromorphic vision chips like this will change what’s possible for self-driving cars and autonomous robots? Follow for more frontier neuromorphic computing, AI hardware, and brain-inspired technology.

🚨 SCIENTISTS JUST BUILT A CHIP THAT CAN SEE, THINK, AND REMEMBER ALL AT THE SAME TIME. And it works more like a biological brain than a traditional computer. Researchers at RMIT University have created a neuromorphic vision chip that mimics the human eye and brain. Unlike conventional systems that capture images and send data to external processors, this chip performs sensing, processing, and memory storage directly where the light hits. The active layer is thousands of times thinner than a human hair. It uses doped indium oxide to detect light, process the information on-chip, and retain what it sees over time without constant electrical refreshing. Why this matters: • It dramatically cuts energy use and latency by eliminating data transfer to separate processors • Enables much faster real-time decision making for autonomous systems • Works more like biological vision than traditional machine vision • Could power the next generation of efficient edge AI in vehicles, robots, and remote sensors The deeper implication: For decades, we’ve built vision systems by bolting cameras, processors, and memory together like separate organs. This chip collapses those functions into one biological-style unit. It’s a step toward machines that don’t just “see” but actually perceive and remember in a more efficient, brain-like way. If scaled successfully, it could become a foundational component for autonomous systems that need to operate intelligently with minimal power and minimal delay. We’re moving from cameras that take pictures to chips that truly see. How do you think neuromorphic vision chips like this will change what’s possible for self-driving cars and autonomous robots? Follow for more frontier neuromorphic computing, AI hardware, and brain-inspired technology.

TheNewPhysics

23,196 Aufrufe • vor 1 Monat

$Yesterday at Brown University ICERM's workshop on “Agentic Scientific Computing and Scientific Machine Learning” I spoke about “Adaptive Swarms Across Scales”, making the case for scientific AI as systems that can create representations, stress them, fracture them, and enlarge the category in which future representations live. The category here is a composable and breakable working universe of science: data, hypotheses, simulations, measurements, tools, failures, figures, papers, provenance, and the transformations that connect them. Discovery happens when those transformations become executable, inspectable, composable, and capable of changing the world model they operate within. Atomistic modeling gives one category - states, forces, trajectories, observables, boundary conditions, conservation laws. Neural surrogates learn fast morphisms inside or between such categories. But discovery is higher-order: it changes which objects and morphisms are available in the first place: what variables exist, what operations are allowed, what evidence counts, what scale is active, what invariant is being preserved, and what kind of explanation the system is even capable of forming. This is scientific method as adaptive architecture: compression, stress, fracture, recomposition. Fracture matters here because it makes the logic physical: a non-commuting diagram realized in matter. The imposed load, material hierarchy, defect field, and assumed continuum description no longer map cleanly into the observed outcome. The crack is the obstruction and it identifies where the old morphism failed and where a new representation must be introduced. The physical crack and the categorical obstruction are the same event viewed in different substrates. ScienceClaw × Infinite is a machine for constructing and transforming a category of scientific artifacts. Each artifact is typed. Each operation has lineage. Each failed branch remains in the category as reusable structure. The “paper” is no longer the terminal object of science; it is one projection of a larger compositional trace, and it can be generated at any time for consumption by a human or an AI. With that the unit of scientific labor is changing. For most of the twentieth century the unit was the result (a measurement, a theorem, a synthesized molecule). It is now becoming the algorithm that produces results, and after that, the substrate of discovery itself. The static PDF is the wrong terminal object for this regime, and the role of the scientist with it. We now design algorithms that build algorithms, and eventually substrates in which such algorithms compose themselves. At that point, the scientist is no longer outside the discovery system. The scientist becomes one of the representations the system can transform. In that sense, the systems will eventually do science to us, and that is the structural consequence of the principle they are built on.$

Yesterday at Brown University ICERM's workshop on “Agentic Scientific Computing and Scientific Machine Learning” I spoke about “Adaptive Swarms Across Scales”, making the case for scientific AI as systems that can create representations, stress them, fracture them, and enlarge the category in which future representations live. The category here is a composable and breakable working universe of science: data, hypotheses, simulations, measurements, tools, failures, figures, papers, provenance, and the transformations that connect them. Discovery happens when those transformations become executable, inspectable, composable, and capable of changing the world model they operate within. Atomistic modeling gives one category - states, forces, trajectories, observables, boundary conditions, conservation laws. Neural surrogates learn fast morphisms inside or between such categories. But discovery is higher-order: it changes which objects and morphisms are available in the first place: what variables exist, what operations are allowed, what evidence counts, what scale is active, what invariant is being preserved, and what kind of explanation the system is even capable of forming. This is scientific method as adaptive architecture: compression, stress, fracture, recomposition. Fracture matters here because it makes the logic physical: a non-commuting diagram realized in matter. The imposed load, material hierarchy, defect field, and assumed continuum description no longer map cleanly into the observed outcome. The crack is the obstruction and it identifies where the old morphism failed and where a new representation must be introduced. The physical crack and the categorical obstruction are the same event viewed in different substrates. ScienceClaw × Infinite is a machine for constructing and transforming a category of scientific artifacts. Each artifact is typed. Each operation has lineage. Each failed branch remains in the category as reusable structure. The “paper” is no longer the terminal object of science; it is one projection of a larger compositional trace, and it can be generated at any time for consumption by a human or an AI. With that the unit of scientific labor is changing. For most of the twentieth century the unit was the result (a measurement, a theorem, a synthesized molecule). It is now becoming the algorithm that produces results, and after that, the substrate of discovery itself. The static PDF is the wrong terminal object for this regime, and the role of the scientist with it. We now design algorithms that build algorithms, and eventually substrates in which such algorithms compose themselves. At that point, the scientist is no longer outside the discovery system. The scientist becomes one of the representations the system can transform. In that sense, the systems will eventually do science to us, and that is the structural consequence of the principle they are built on.

Markus J. Buehler

10,095 Aufrufe • vor 2 Monaten

They did not take cursive from the schools because children no longer needed it. They took it because of what it was quietly building in them. Consider what the exercise actually is. A child, six years old, is handed a pen and asked to draw a single unbroken line that becomes a word. The wrist must float. The fingers must hold a living pressure, never quite the same twice, always correcting. The eye must follow the ink forward and trust the hand to finish what it has begun. There is no lifting, no stopping, no starting over mid-word. The loop must close. The ascender must rise and return. The sentence must travel from one margin to the other as a single continuous gesture, and at the end of it the hand must still be steady. Twelve years of this. Every day. Ten thousand small acts of sustained, self-correcting attention, carried out below the level of conscious thought, until the motion belongs to the body and the body belongs to the motion. This is not penmanship. It is the slow construction of an interior form. The hand that has learned to carry a line without breaking it is the hand of a mind that has learned to carry a thought without breaking it. The two are not metaphors for one another. They are the same faculty, trained in the same child, by the same daily discipline. Continuity of the stroke becomes continuity of the reasoning. The patience of the loop becomes the patience of the argument. The commitment to finish a word one has started becomes the commitment to finish a sentence, a paragraph, a life's idea, without reaching for the nearest distraction halfway through. Print is a different creature entirely. Print lifts. Print stops. Print assembles a word out of separate, stamped, interchangeable pieces, each one beginning and ending in isolation. A mind raised only on print learns to think the way print is made, in discrete tokens, in replaceable units, in fragments that can be recombined by any outside hand without the owner noticing the substitution. It is precisely the shape of thought a language model produces. It is precisely the shape of thought a language model can steer. Cursive is kata. This is the whole of it. A form repeated daily, for years, not for the sake of the form but for what the repetition lays down in the practitioner beneath the form. The swordsman does not train kata so that one day he may fight in kata. He trains it so that when the moment comes and there is no time to think, the movement is already inside him, older and deeper than thought, and it rises on its own. Cursive was the kata of the literate mind, the daily quiet drilling of continuity, of patience, of a line held steady under the long pressure of its own length. And the signature it produced at the end, that small flourished mark unique to a single human being on earth, was only the outward proof of an inward form no machine and no other hand could ever reproduce. Take the kata away and the practitioner is left with vocabulary in place of faculty. He can recognise a whole thought when he encounters one. He cannot carry one himself. He can admire a finished argument. He cannot sustain one long enough to close its loop. He begins books he does not finish, sentences he does not end, ideas he abandons the moment the screen in his palm offers him a brighter one. And when the machine begins feeding him tokens in the exact shape his schooling taught him to receive, he meets it with no interior resistance at all, because no interior form was ever built in him to push back with. They removed it quietly, across a generation, and they removed it in the last years before the machines arrived. Twelve years of daily practice in unbroken, embodied, self-authored thought, gone from the curriculum of almost every child in the Western world, just as the instruments designed to complete their sentences for them came online. The hand forgets. The mind, having never been taught the kata, forgets a thing it never knew it had. That is what cursive was. That is what was taken. And that is why the thought of anyone who still writes by hand, in long unlifted lines, remains, quietly, stubbornly, and without their ever needing to announce it, their own. Now the question stands open. What else has been banned, phased out, quietly retired from the curriculum and from common life over these same decades, under the same soft excuses? Mental arithmetic. Memorisation of poetry. Latin. Logic as a formal subject. Map reading. Knot work. The keeping of a commonplace book. The reading aloud of long passages in class. Singing in parts. What was each of those actually building in the child, beneath the surface of the lesson, and whose interest was served by its disappearance?

They did not take cursive from the schools because children no longer needed it. They took it because of what it was quietly building in them. Consider what the exercise actually is. A child, six years old, is handed a pen and asked to draw a single unbroken line that becomes a word. The wrist must float. The fingers must hold a living pressure, never quite the same twice, always correcting. The eye must follow the ink forward and trust the hand to finish what it has begun. There is no lifting, no stopping, no starting over mid-word. The loop must close. The ascender must rise and return. The sentence must travel from one margin to the other as a single continuous gesture, and at the end of it the hand must still be steady. Twelve years of this. Every day. Ten thousand small acts of sustained, self-correcting attention, carried out below the level of conscious thought, until the motion belongs to the body and the body belongs to the motion. This is not penmanship. It is the slow construction of an interior form. The hand that has learned to carry a line without breaking it is the hand of a mind that has learned to carry a thought without breaking it. The two are not metaphors for one another. They are the same faculty, trained in the same child, by the same daily discipline. Continuity of the stroke becomes continuity of the reasoning. The patience of the loop becomes the patience of the argument. The commitment to finish a word one has started becomes the commitment to finish a sentence, a paragraph, a life's idea, without reaching for the nearest distraction halfway through. Print is a different creature entirely. Print lifts. Print stops. Print assembles a word out of separate, stamped, interchangeable pieces, each one beginning and ending in isolation. A mind raised only on print learns to think the way print is made, in discrete tokens, in replaceable units, in fragments that can be recombined by any outside hand without the owner noticing the substitution. It is precisely the shape of thought a language model produces. It is precisely the shape of thought a language model can steer. Cursive is kata. This is the whole of it. A form repeated daily, for years, not for the sake of the form but for what the repetition lays down in the practitioner beneath the form. The swordsman does not train kata so that one day he may fight in kata. He trains it so that when the moment comes and there is no time to think, the movement is already inside him, older and deeper than thought, and it rises on its own. Cursive was the kata of the literate mind, the daily quiet drilling of continuity, of patience, of a line held steady under the long pressure of its own length. And the signature it produced at the end, that small flourished mark unique to a single human being on earth, was only the outward proof of an inward form no machine and no other hand could ever reproduce. Take the kata away and the practitioner is left with vocabulary in place of faculty. He can recognise a whole thought when he encounters one. He cannot carry one himself. He can admire a finished argument. He cannot sustain one long enough to close its loop. He begins books he does not finish, sentences he does not end, ideas he abandons the moment the screen in his palm offers him a brighter one. And when the machine begins feeding him tokens in the exact shape his schooling taught him to receive, he meets it with no interior resistance at all, because no interior form was ever built in him to push back with. They removed it quietly, across a generation, and they removed it in the last years before the machines arrived. Twelve years of daily practice in unbroken, embodied, self-authored thought, gone from the curriculum of almost every child in the Western world, just as the instruments designed to complete their sentences for them came online. The hand forgets. The mind, having never been taught the kata, forgets a thing it never knew it had. That is what cursive was. That is what was taken. And that is why the thought of anyone who still writes by hand, in long unlifted lines, remains, quietly, stubbornly, and without their ever needing to announce it, their own. Now the question stands open. What else has been banned, phased out, quietly retired from the curriculum and from common life over these same decades, under the same soft excuses? Mental arithmetic. Memorisation of poetry. Latin. Logic as a formal subject. Map reading. Knot work. The keeping of a commonplace book. The reading aloud of long passages in class. Singing in parts. What was each of those actually building in the child, beneath the surface of the lesson, and whose interest was served by its disappearance?

SiriusB

442,518 Aufrufe • vor 3 Monaten

The genial sanctions! For those who said that factories like the Chinese ones exist in the West, no, they don't. The Chinese are using full-cycle automation in factories that even lack basic lighting. And this is working for cars as well as for missiles, as I have shown here before. Now comes the most interesting part. A large portion of the automation machinery is still European, but this dependence has been decreasing, and today it’s less than 40%. The West used to dominate the market for Chinese chips, industrial machinery, aviation, and various other sectors. What was the big idea? Impose sanctions on the Chinese and force them to develop all these technologies so they no longer need to buy from the West. Genius, don’t you think? Today, the Chinese are developing four commercial aviation engines simultaneously, with at least one already flying in testing phase, and three other engine projects for cargo aviation based on Russian models. This was a market that was entirely owned by the West. Another issue that will need to be addressed sooner or later is whether there will be tariffs on products coming from fully roboticized factories. Some might say this is punishing efficiency. But what’s the point of efficiency if no one has a job and can spend? An extremely efficient model that creates unemployment beyond its borders? Obviously, this will be discussed at some point.

The genial sanctions! For those who said that factories like the Chinese ones exist in the West, no, they don't. The Chinese are using full-cycle automation in factories that even lack basic lighting. And this is working for cars as well as for missiles, as I have shown here before. Now comes the most interesting part. A large portion of the automation machinery is still European, but this dependence has been decreasing, and today it’s less than 40%. The West used to dominate the market for Chinese chips, industrial machinery, aviation, and various other sectors. What was the big idea? Impose sanctions on the Chinese and force them to develop all these technologies so they no longer need to buy from the West. Genius, don’t you think? Today, the Chinese are developing four commercial aviation engines simultaneously, with at least one already flying in testing phase, and three other engine projects for cargo aviation based on Russian models. This was a market that was entirely owned by the West. Another issue that will need to be addressed sooner or later is whether there will be tariffs on products coming from fully roboticized factories. Some might say this is punishing efficiency. But what’s the point of efficiency if no one has a job and can spend? An extremely efficient model that creates unemployment beyond its borders? Obviously, this will be discussed at some point.

Patricia Marins

57,759 Aufrufe • vor 8 Monaten

one of my favorite ways to use claude code skills right now - combining remotion with claude-in-chrome for motion video creation. the workflow is addictive. the clip you see here was produced with minimal prompting effort let me know if you would like me to write a full break down of this process. you describe to claude code what you want, claude code writes the remotion components, opens the remotion studio (via browser) with claude-in-chrome, sees the actual rendered output, and iterates on it in real time. need the arrows pointing to the center of the bubbles instead of the edge? just say it. need the layout shifted to the center? say it. claude sees the preview, adjusts the code, re-renders. then when you're happy you tell claude code to render the final video. but skills are what make this possible. remotion knowledge + browser automation + the taste to iterate visually. no copy pasting screenshots back and forth. no "can you try moving it 10px to the right" over chat. it just looks and fixes. this is the kind of workflow that makes you realize how much further claude code can go beyond just writing code in a terminal. i didn't touch any code while working on the clip you see if you haven't tried combining skills together like this - start experimenting. the skills combos is where the magic is at.

one of my favorite ways to use claude code skills right now - combining remotion with claude-in-chrome for motion video creation. the workflow is addictive. the clip you see here was produced with minimal prompting effort let me know if you would like me to write a full break down of this process. you describe to claude code what you want, claude code writes the remotion components, opens the remotion studio (via browser) with claude-in-chrome, sees the actual rendered output, and iterates on it in real time. need the arrows pointing to the center of the bubbles instead of the edge? just say it. need the layout shifted to the center? say it. claude sees the preview, adjusts the code, re-renders. then when you're happy you tell claude code to render the final video. but skills are what make this possible. remotion knowledge + browser automation + the taste to iterate visually. no copy pasting screenshots back and forth. no "can you try moving it 10px to the right" over chat. it just looks and fixes. this is the kind of workflow that makes you realize how much further claude code can go beyond just writing code in a terminal. i didn't touch any code while working on the clip you see if you haven't tried combining skills together like this - start experimenting. the skills combos is where the magic is at.

elvis

46,464 Aufrufe • vor 5 Monaten

This is the mechanical heart of every flight deck. At the core of this instrument is a high-speed mechanical gyroscope held within a series of gimbals. It relies on the physics of rigidity in space, meaning the spinning disc stays perfectly aligned with the Earth's horizon regardless of how the aircraft moves around it. When you see the outer casing tilt and dive, you are actually watching the plane move while the instrument stays perfectly still. For decades, this was the primary way pilots survived flying through thick cloud cover or at night. Without a visible horizon, the human brain can easily become disoriented, leading to a deadly condition called the graveyard spin. This device provides an objective visual reference that never fails, even if the entire electrical system of the plane goes dark. While modern airliners have transitioned to digital screens and solid-state sensors, these analog units remain critical. Many pilots still prefer them as a fail-safe backup because they operate on pure physics rather than software. It is a masterclass in engineering that remains just as relevant in the age of automation as it was eighty years ago. It is a timeless piece of safety technology. Would you trust a mechanical dial over a digital screen? Follow for more Technology updates Video: alternateb747

This is the mechanical heart of every flight deck. At the core of this instrument is a high-speed mechanical gyroscope held within a series of gimbals. It relies on the physics of rigidity in space, meaning the spinning disc stays perfectly aligned with the Earth's horizon regardless of how the aircraft moves around it. When you see the outer casing tilt and dive, you are actually watching the plane move while the instrument stays perfectly still. For decades, this was the primary way pilots survived flying through thick cloud cover or at night. Without a visible horizon, the human brain can easily become disoriented, leading to a deadly condition called the graveyard spin. This device provides an objective visual reference that never fails, even if the entire electrical system of the plane goes dark. While modern airliners have transitioned to digital screens and solid-state sensors, these analog units remain critical. Many pilots still prefer them as a fail-safe backup because they operate on pure physics rather than software. It is a masterclass in engineering that remains just as relevant in the age of automation as it was eighty years ago. It is a timeless piece of safety technology. Would you trust a mechanical dial over a digital screen? Follow for more Technology updates Video: alternateb747

Mechanical Knowledge

67,901 Aufrufe • vor 4 Monaten

Webhooks are LIVE on KEPLER 🛰️ Get real-time HTTP notifications directly from the blockchain. Say goodbye to constantly polling for data and managing heavy infrastructure. Setting up a webhook takes just a few clicks. Perfect for Developers & No-Code Builders • You can easily route webhooks directly into OpenClaw or platforms like Make or n8n. Build powerful, automated workflows in minutes without writing a single line of code. • For the classical builders, easily integrate real-time events into your existing Web2 backend using standard HTTP POST requests. Seamlessly bridge Web2 and Web3 to power custom indexing bots, trading algorithms, or dApp backends using our lightweight JSON payloads. Precision Filtering Cut through the noise by triggering webhooks only when your specific address, identifier, or topic conditions are met. Reliable Delivery If your endpoint or automation flow hiccups, our system automatically retries the delivery up to 4 times so you never miss an event. Cryptographic Security Every request includes an Ed25519 signature in the header, letting you natively verify that the payload is authentic and hasn't been tampered with.

Webhooks are LIVE on KEPLER 🛰️ Get real-time HTTP notifications directly from the blockchain. Say goodbye to constantly polling for data and managing heavy infrastructure. Setting up a webhook takes just a few clicks. Perfect for Developers & No-Code Builders • You can easily route webhooks directly into OpenClaw or platforms like Make or n8n. Build powerful, automated workflows in minutes without writing a single line of code. • For the classical builders, easily integrate real-time events into your existing Web2 backend using standard HTTP POST requests. Seamlessly bridge Web2 and Web3 to power custom indexing bots, trading algorithms, or dApp backends using our lightweight JSON payloads. Precision Filtering Cut through the noise by triggering webhooks only when your specific address, identifier, or topic conditions are met. Reliable Delivery If your endpoint or automation flow hiccups, our system automatically retries the delivery up to 4 times so you never miss an event. Cryptographic Security Every request includes an Ed25519 signature in the header, letting you natively verify that the payload is authentic and hasn't been tampered with.

Project X 🧬

11,024 Aufrufe • vor 5 Monaten

A 40-YEAR-OLD CHINESE MAN TURNED HIMSELF INTO AN AI GIRL AND BUILT A $4.7K/MONTH FANVUE FUNNEL he started with one face-swap clip. same room, same body, same camera, but the output looked like a completely different creator. not a filter, not cosplay, an ai identity built on top of his own footage the workflow is stupidly simple: claude picks the character, niche and visual style. comfyui generates the face, flux makes the content bank, kling turns still images into reels and capcut pushes out dozens of short clips the article breaks the system down: first month barely cleared $420, second month jumped to $4,700 after one reel crossed 600,000 views. the account reached 800 followers and 50 fanvue subscribers before the real automation even started the fanvue mcp is the part most people miss. claude can analyze messages, pricing, content performance and draft replies in the same style. instead of guessing what to post or say, the system turns the audience into data he only needed around 40 minutes a day. schedule reels, check what worked, generate new prompts, reply with claude, repeat. the business is not the ai girl, the business is the machine around her this is no longer content creation. it is identity arbitrage

A 40-YEAR-OLD CHINESE MAN TURNED HIMSELF INTO AN AI GIRL AND BUILT A $4.7K/MONTH FANVUE FUNNEL he started with one face-swap clip. same room, same body, same camera, but the output looked like a completely different creator. not a filter, not cosplay, an ai identity built on top of his own footage the workflow is stupidly simple: claude picks the character, niche and visual style. comfyui generates the face, flux makes the content bank, kling turns still images into reels and capcut pushes out dozens of short clips the article breaks the system down: first month barely cleared $420, second month jumped to $4,700 after one reel crossed 600,000 views. the account reached 800 followers and 50 fanvue subscribers before the real automation even started the fanvue mcp is the part most people miss. claude can analyze messages, pricing, content performance and draft replies in the same style. instead of guessing what to post or say, the system turns the audience into data he only needed around 40 minutes a day. schedule reels, check what worked, generate new prompts, reply with claude, repeat. the business is not the ai girl, the business is the machine around her this is no longer content creation. it is identity arbitrage

Gipp 🦅

194,689 Aufrufe • vor 1 Monat

Introducing Pods Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.

Introducing Pods Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.

Varun

308,519 Aufrufe • vor 3 Monaten