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Tested Claude + Blender today with two real workflows. Sharing what actually happened. First one was a simple demo. Gave it one sentence to build a cyberpunk room. Geometry, lighting, camera, render. It got there, though not everything in the prompt landed perfectly. Good enough to show the concept,...

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KIRI Engine - 3D Scanner App

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61,093 görüntüleme • 4 ay önce •via X (Twitter)

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We are getting absurdly close to the point where “learning Blender” means learning how to direct an AI. A shot like this looks soft and playful on the surface, but under it is the usual 3D pain: modeling, layout, materials, lighting, atmosphere, animation, and endless tiny fixes until the frame stops looking dead. That is why Kimi K3 matters. With Blender MCP, you can describe a scene like a robotic goat walking through a dreamy field and let the model help build the environment, place the camera, shape the materials, script the motion, and iterate inside the real Blender project. The real shift is not text-to-image. It is text-to-workflow. Kimi K3 does not just give you a pretty output and disappear. It can help move the actual scene from rough setup to something that looks art-directed. Soon the hardest part of 3D will not be the software. It will be whether your imagination is good enough to deserve tools like this.
0:12

We are getting absurdly close to the point where “learning Blender” means learning how to direct an AI. A shot like this looks soft and playful on the surface, but under it is the usual 3D pain: modeling, layout, materials, lighting, atmosphere, animation, and endless tiny fixes until the frame stops looking dead. That is why Kimi K3 matters. With Blender MCP, you can describe a scene like a robotic goat walking through a dreamy field and let the model help build the environment, place the camera, shape the materials, script the motion, and iterate inside the real Blender project. The real shift is not text-to-image. It is text-to-workflow. Kimi K3 does not just give you a pretty output and disappear. It can help move the actual scene from rough setup to something that looks art-directed. Soon the hardest part of 3D will not be the software. It will be whether your imagination is good enough to deserve tools like this.

Rina

45,651 görüntüleme • 1 ay önce

Blender just became a prompt box. With Kimi K3 connected through Blender MCP, you can describe a scene like this in plain English and let the model handle the ugly part: terrain, buildings, lighting, materials, camera movement, animation, and the Python scripts holding everything together. The interesting part is not the first render. Kimi can inspect the result, notice that the camera clips through a tree or the city looks suspiciously like plastic, then edit the actual Blender scene and render it again instead of restarting from zero. You still need taste, because “make it cinematic” remains one of humanity’s least useful instructions. But the distance between an empty Blender file and a fully editable 3D world just got embarrassingly small. A few years ago, creating this meant weeks of modeling, scripting, lighting, and animation. Now you can describe the world, watch Kimi build it, and spend your time fixing the final 10% instead of manually constructing the first 90%. Kimi K3 + Blender MCP is basically text-to-3D without trapping the result inside a useless generated video. Every object, material, light, camera, and keyframe stays editable.
0:12

Blender just became a prompt box. With Kimi K3 connected through Blender MCP, you can describe a scene like this in plain English and let the model handle the ugly part: terrain, buildings, lighting, materials, camera movement, animation, and the Python scripts holding everything together. The interesting part is not the first render. Kimi can inspect the result, notice that the camera clips through a tree or the city looks suspiciously like plastic, then edit the actual Blender scene and render it again instead of restarting from zero. You still need taste, because “make it cinematic” remains one of humanity’s least useful instructions. But the distance between an empty Blender file and a fully editable 3D world just got embarrassingly small. A few years ago, creating this meant weeks of modeling, scripting, lighting, and animation. Now you can describe the world, watch Kimi build it, and spend your time fixing the final 10% instead of manually constructing the first 90%. Kimi K3 + Blender MCP is basically text-to-3D without trapping the result inside a useless generated video. Every object, material, light, camera, and keyframe stays editable.

Rina

264,903 görüntüleme • 1 ay önce

🚨 one person can now do the work of an entire creative team. i just tested it on a real one. a friend needed an ad for his brand, so I opened the new Runway Agent 2.0 to try it out. here's how it went: → it generated the music and the key image first, so I could approve the direction → once I gave the ok, it built the full video around it → and when something was off, i changed just that one piece, without redoing the rest one prompt, and I had the ad we needed, work that used to take weeks. this is what it made 👇 if you want to try it → · 30% off 3 months with code RUNWAYAGENT — made with Runway · #MadeWithRunway · #ad
0:18

🚨 one person can now do the work of an entire creative team. i just tested it on a real one. a friend needed an ad for his brand, so I opened the new Runway Agent 2.0 to try it out. here's how it went: → it generated the music and the key image first, so I could approve the direction → once I gave the ok, it built the full video around it → and when something was off, i changed just that one piece, without redoing the rest one prompt, and I had the ad we needed, work that used to take weeks. this is what it made 👇 if you want to try it → · 30% off 3 months with code RUNWAYAGENT — made with Runway · #MadeWithRunway · #ad

brenz.

28,698 görüntüleme • 1 ay önce

2:14am. Something failed. I was asleep. By 8 it was scoped, patched, and written up. Not just fixed. Written up, with the log excerpt, what it touched, and the one decision he wanted me to sign off before it stayed in. I have been building agentic systems long enough to be suspicious of exactly this. The output usually works and has no taste, and you find out three weeks later what it quietly assumed. What made this different was that it did not close the loop by itself. It did the work and left the judgement to me. Order of operations matters more than capability. Same inputs, wrong sequence, completely different outcome. This one got the sequence right: act, then ask. Viktor
0:13

2:14am. Something failed. I was asleep. By 8 it was scoped, patched, and written up. Not just fixed. Written up, with the log excerpt, what it touched, and the one decision he wanted me to sign off before it stayed in. I have been building agentic systems long enough to be suspicious of exactly this. The output usually works and has no taste, and you find out three weeks later what it quietly assumed. What made this different was that it did not close the loop by itself. It did the work and left the judgement to me. Order of operations matters more than capability. Same inputs, wrong sequence, completely different outcome. This one got the sequence right: act, then ask. Viktor

Leonard Rodman

222,370 görüntüleme • 3 gün önce

I still don't understand why everyone is not using this yet. Thanks to it, a year ago I increased my income to 17,000 dollars a month Andrey Karpathy, co-founder of OpenAI, published a simple idea that got 16 million views: stop using AI to write code, use it to build a second brain You point Claude Code to a folder, drop any source in there (an article, transcript, PDF) and Claude reads it, links it, and saves it into a living wiki of everything you know. It compounds like interest: the more you feed it, the smarter it gets Here is the gist: Install Obsidian, create a vault, open it in Claude Code Paste the file with Karpathy's wiki idea and tell Claude to build it Claude creates three folders: raw for sources, wiki for its pages, CLAUDE which runs everything Drop any source into raw and say "ingest this" Ask questions across everything, forever Five minutes to set up, and you never start with an empty chat again The full step-by-step guide is in the article. Save to bookmarks
0:10

I still don't understand why everyone is not using this yet. Thanks to it, a year ago I increased my income to 17,000 dollars a month Andrey Karpathy, co-founder of OpenAI, published a simple idea that got 16 million views: stop using AI to write code, use it to build a second brain You point Claude Code to a folder, drop any source in there (an article, transcript, PDF) and Claude reads it, links it, and saves it into a living wiki of everything you know. It compounds like interest: the more you feed it, the smarter it gets Here is the gist: Install Obsidian, create a vault, open it in Claude Code Paste the file with Karpathy's wiki idea and tell Claude to build it Claude creates three folders: raw for sources, wiki for its pages, CLAUDE which runs everything Drop any source into raw and say "ingest this" Ask questions across everything, forever Five minutes to set up, and you never start with an empty chat again The full step-by-step guide is in the article. Save to bookmarks

Bober_smart

2,795,461 görüntüleme • 7 gün önce

I watched Robot on the Road and honestly I did not really know what to expect going in At the beginning it just feels like a pretty normal little road trip setup with this robot hitchhiking around Japan and it almost gives off a chill slice of life vibe But it does not stay like that for long The more it goes on the more you realize the robot is doing some really questionable stuff and the way it focuses on women without their consent makes the whole thing feel really uncomfortable By the end I was not really sure if I was supposed to find it funny or just feel weird about it Overall it feels like one of those shorts that tries to mix dark humor with awkward situations but it mostly just ends up being unsettling
0:18

I watched Robot on the Road and honestly I did not really know what to expect going in At the beginning it just feels like a pretty normal little road trip setup with this robot hitchhiking around Japan and it almost gives off a chill slice of life vibe But it does not stay like that for long The more it goes on the more you realize the robot is doing some really questionable stuff and the way it focuses on women without their consent makes the whole thing feel really uncomfortable By the end I was not really sure if I was supposed to find it funny or just feel weird about it Overall it feels like one of those shorts that tries to mix dark humor with awkward situations but it mostly just ends up being unsettling

Anime Posts

6,563,844 görüntüleme • 2 ay önce

THIS GUY GOT SICK OF UK TRAIN STATIONS HIDING THE PLATFORM NUMBER UNTIL THE LAST SECOND, SO HE VIBE CODED AN APP THAT PREDICTS IT BEFORE THEY REVEAL IT if youve ever stood in a crowded station like london euston, you know the pain the platform stays hidden until 15 minutes before boarding, then it flashes up and hundreds of people sprint for the same gate so he built something that fixes this: > a clean departures and arrivals app pulling live data straight from the network rail apis > live train tracking so you can watch where your train actually is on the route and whether its running on time > the main feature is a predictive engine that guesses your platform before the station reveals it, and its right about 75% of the time heres how it predicts: every time a train finishes its journey, it logs which platform it actually pulled into and compares that to the platform network rail originally advertised it saves every one of those comparisons, and over time it builds up enough history to predict the platform with a real confidence score he built the whole thing with claude, mostly opus with some fable
0:18

THIS GUY GOT SICK OF UK TRAIN STATIONS HIDING THE PLATFORM NUMBER UNTIL THE LAST SECOND, SO HE VIBE CODED AN APP THAT PREDICTS IT BEFORE THEY REVEAL IT if youve ever stood in a crowded station like london euston, you know the pain the platform stays hidden until 15 minutes before boarding, then it flashes up and hundreds of people sprint for the same gate so he built something that fixes this: > a clean departures and arrivals app pulling live data straight from the network rail apis > live train tracking so you can watch where your train actually is on the route and whether its running on time > the main feature is a predictive engine that guesses your platform before the station reveals it, and its right about 75% of the time heres how it predicts: every time a train finishes its journey, it logs which platform it actually pulled into and compares that to the platform network rail originally advertised it saves every one of those comparisons, and over time it builds up enough history to predict the platform with a real confidence score he built the whole thing with claude, mostly opus with some fable

Om Patel

176,154 görüntüleme • 1 ay önce

HE DESCRIBED A SITE TO CLAUDE CODE AND WALKED AWAY FOR 8 MINUTES What came back looked like a studio spent weeks on it. The setup that made it work: > Two design skills installed - Claude stops using safe boring defaults. > Visual references instead of text descriptions. > One detailed prompt with the concept, Claude asks clarifying questions before building. First version already solid -> review pass, one batch of fixes, done. What used to take a designer and a developer and several weeks of handoffs now takes a single session and no coding experience. Full walkthrough in the article below ↓
0:16

HE DESCRIBED A SITE TO CLAUDE CODE AND WALKED AWAY FOR 8 MINUTES What came back looked like a studio spent weeks on it. The setup that made it work: > Two design skills installed - Claude stops using safe boring defaults. > Visual references instead of text descriptions. > One detailed prompt with the concept, Claude asks clarifying questions before building. First version already solid -> review pass, one batch of fixes, done. What used to take a designer and a developer and several weeks of handoffs now takes a single session and no coding experience. Full walkthrough in the article below ↓

slash1s

52,905 görüntüleme • 2 ay önce

Figure 03 just finished an 8-hour work livestream, imperfect, but already good enough to replace a lot of repetitive warehouse labor. 🤖 Brett Adcock put a team of F.03 robots on a factory-style package sorting task for a full shift. The job was simple and brutal: detect the barcode, pick the package, flip it label-side down, place it on the conveyor, repeat. Soft poly bags, rigid boxes, moving belts, messy orientations. That is exactly the kind of boring physical work factories pay humans to do all day. Early in the stream, the system handled 230 packages in 10 minutes. That is roughly 2.6 seconds per item — already in human-speed territory for this narrow workflow. The more important part: it was not one robot pretending to work all day. It was a team of Figure 03 robots keeping the line running. When one robot ran low on battery, it left the station and another robot stepped in. That is the real factory signal: not just autonomy, but shift continuity. F.03 is rated for about 5 hours of runtime, so the 8-hour result depends on fleet orchestration, charging, and handoff. That matters more than a single clean demo. The stream was not perfect. There were pauses, hesitations, missed orientations, and small recovery moments. Good. A perfect short clip hides failure. An 8-hour livestream exposes the parts that actually matter: endurance, recovery, throughput, and whether the robot can stay useful after the novelty wears off. Figure says this was fully autonomous on Helix-02, with zero human intervention. For logistics and manufacturing, that is the threshold worth watching. Not “can it do one impressive task?” Can it keep doing the boring task for an entire shift? Figure is not showing a general human replacement yet. But for structured, repetitive factory work, the gap just got much smaller. The timing is also interesting: Figure says BotQ has already delivered 350+ F.03 units and reached a 1 robot/hour production cadence. And F.04 is now in full design lock, with parts starting to ship. The next test is obvious. 8 hours was the proof of endurance. 24/7 is the proof of labor economics.
0:25

Figure 03 just finished an 8-hour work livestream, imperfect, but already good enough to replace a lot of repetitive warehouse labor. 🤖 Brett Adcock put a team of F.03 robots on a factory-style package sorting task for a full shift. The job was simple and brutal: detect the barcode, pick the package, flip it label-side down, place it on the conveyor, repeat. Soft poly bags, rigid boxes, moving belts, messy orientations. That is exactly the kind of boring physical work factories pay humans to do all day. Early in the stream, the system handled 230 packages in 10 minutes. That is roughly 2.6 seconds per item — already in human-speed territory for this narrow workflow. The more important part: it was not one robot pretending to work all day. It was a team of Figure 03 robots keeping the line running. When one robot ran low on battery, it left the station and another robot stepped in. That is the real factory signal: not just autonomy, but shift continuity. F.03 is rated for about 5 hours of runtime, so the 8-hour result depends on fleet orchestration, charging, and handoff. That matters more than a single clean demo. The stream was not perfect. There were pauses, hesitations, missed orientations, and small recovery moments. Good. A perfect short clip hides failure. An 8-hour livestream exposes the parts that actually matter: endurance, recovery, throughput, and whether the robot can stay useful after the novelty wears off. Figure says this was fully autonomous on Helix-02, with zero human intervention. For logistics and manufacturing, that is the threshold worth watching. Not “can it do one impressive task?” Can it keep doing the boring task for an entire shift? Figure is not showing a general human replacement yet. But for structured, repetitive factory work, the gap just got much smaller. The timing is also interesting: Figure says BotQ has already delivered 350+ F.03 units and reached a 1 robot/hour production cadence. And F.04 is now in full design lock, with parts starting to ship. The next test is obvious. 8 hours was the proof of endurance. 24/7 is the proof of labor economics.

RoboHub🤖

16,818 görüntüleme • 3 ay önce

Today I had my first demo drive in a Tesla. It was also my first time ever sitting in one. This was the first car I’ve ever sat in the driver’s seat of where I didn’t touch the steering wheel for over 20 miles. Before I even got to the car, the people who had demoed it before me were an older married couple who were absolutely euphoric. They thought it was so cool that the car could drive itself. The Tesla employee told me this happens all the time. People come back from demo drives and tell the next test driver that they’re about to have an amazing experience. Little did I know, I’d end up carrying on the torch to the next couple demoing it after me. There was a ton of construction where I demoed the car, and FSD handled the entire drive extremely well. And yes, it can go through a drive-thru and stop at each window. The only thing I had to do was tap the pedal because it wouldn’t leave on its own, but it was still wild seeing the AI stop perfectly at the second window and wait. There are a million things I could write about why a Tesla feels like a better car and how much more it offers compared to a regular car. But for now, I’ll stick to FSD. There were only two moments that made me a little uneasy. The first was pretty minor. The car slightly hesitated going up a driveway, but quickly made up its mind. The second was more noticeable. I didn’t realize the car was nagging me. Once I touched the steering wheel, nothing happened, so I pulled it right a little harder, then let go. After that, the car turned left and crossed a double yellow on a backroad. (and yes I know you can sue the volume knob) I’m not totally sure if it was trying to pull over or what it was doing. I wanted to see how it would handle the situation, but there were cars coming, so I took over and corrected it. One of the coolest moments was when I thought FSD was glitching because it came to a complete stop in the middle of a busy road. Then I looked around and realized why. On the right side, there was a bicyclist waiting at a yellow crosswalk. The cars behind me didn’t honk, and the Tesla stopping actually incentivized another car in the right lane to stop and let him pass. The car is almost too nice to pedestrians, because 99.999% of humans would’ve blown through that, especially with no flashing light. For 99.9% of the drive, the car navigated confidently and smoothly. It was a real “feel the AGI” moment. Please do not let the media, the general public, or anyone else convince you that this technology is just some kind of auto assist or glorified cruise control. This is undoubtedly getting extremely close to feeling superhuman. You still have to pay attention to the road, but after experiencing it myself, I’d be shocked if HW4 Teslas aren’t unsupervised within the next couple years. The car was extremely smooth. There was no harsh braking, and it even avoided something in the road that I didn’t see. Driving with FSD made me realize I probably wasn’t driving as well as I could be. Hopefully, eventually, everyone’s car can be as mindful as a Tesla. I’ve never seen a brand so far removed from the public’s sentiment. I’m so happy I ordered one.
0:14

Today I had my first demo drive in a Tesla. It was also my first time ever sitting in one. This was the first car I’ve ever sat in the driver’s seat of where I didn’t touch the steering wheel for over 20 miles. Before I even got to the car, the people who had demoed it before me were an older married couple who were absolutely euphoric. They thought it was so cool that the car could drive itself. The Tesla employee told me this happens all the time. People come back from demo drives and tell the next test driver that they’re about to have an amazing experience. Little did I know, I’d end up carrying on the torch to the next couple demoing it after me. There was a ton of construction where I demoed the car, and FSD handled the entire drive extremely well. And yes, it can go through a drive-thru and stop at each window. The only thing I had to do was tap the pedal because it wouldn’t leave on its own, but it was still wild seeing the AI stop perfectly at the second window and wait. There are a million things I could write about why a Tesla feels like a better car and how much more it offers compared to a regular car. But for now, I’ll stick to FSD. There were only two moments that made me a little uneasy. The first was pretty minor. The car slightly hesitated going up a driveway, but quickly made up its mind. The second was more noticeable. I didn’t realize the car was nagging me. Once I touched the steering wheel, nothing happened, so I pulled it right a little harder, then let go. After that, the car turned left and crossed a double yellow on a backroad. (and yes I know you can sue the volume knob) I’m not totally sure if it was trying to pull over or what it was doing. I wanted to see how it would handle the situation, but there were cars coming, so I took over and corrected it. One of the coolest moments was when I thought FSD was glitching because it came to a complete stop in the middle of a busy road. Then I looked around and realized why. On the right side, there was a bicyclist waiting at a yellow crosswalk. The cars behind me didn’t honk, and the Tesla stopping actually incentivized another car in the right lane to stop and let him pass. The car is almost too nice to pedestrians, because 99.999% of humans would’ve blown through that, especially with no flashing light. For 99.9% of the drive, the car navigated confidently and smoothly. It was a real “feel the AGI” moment. Please do not let the media, the general public, or anyone else convince you that this technology is just some kind of auto assist or glorified cruise control. This is undoubtedly getting extremely close to feeling superhuman. You still have to pay attention to the road, but after experiencing it myself, I’d be shocked if HW4 Teslas aren’t unsupervised within the next couple years. The car was extremely smooth. There was no harsh braking, and it even avoided something in the road that I didn’t see. Driving with FSD made me realize I probably wasn’t driving as well as I could be. Hopefully, eventually, everyone’s car can be as mindful as a Tesla. I’ve never seen a brand so far removed from the public’s sentiment. I’m so happy I ordered one.

Chris

18,722 görüntüleme • 1 ay önce

Marc Andreessen just stripped artificial intelligence down to what it’s really made of. Not the algorithms. Not the data centers. The raw material. Andreessen: “They’re literally made out of sand.” The most abundant, most overlooked substance on the planet. You walk on it. Build with it. Wash it off and forget it exists. Someone looked at that and saw a mind. The universe spent 13.8 billion years turning matter into consciousness. Carbon. Water. Amino acids. Eons of chemistry and selection pressure to produce a single thinking brain. We did it with sand. In under a century. Andreessen: “We light it up, and we put AI on it, and all of a sudden it’s thinking.” Biology was the first path matter found to become aware. Silicon is the second. The universe doesn’t care what material consciousness runs on. Only that the pattern is right. We just proved the pattern isn’t biological. Thought was never exclusive to flesh. Flesh was just the first material organized enough to produce it. Andreessen: “We’ve turned sand into thought.” The most profound thing about that sentence isn’t that we taught sand to think. It’s that sand was always able to. Every grain on every beach on Earth carried the capacity for thought. For billions of years. Waiting for something conscious enough to arrange it. Consciousness isn’t an accident that happened once on one rock. It’s what matter does when the pattern is right. The pattern is matter getting curious about itself. The universe isn’t dead matter with pockets of awareness. It’s dormant awareness we’ve only just started to wake. Every civilization that ever looked at the stars and wondered if it was alone was standing on the answer. We didn’t build a thinking machine. We proved the universe was always capable of thought. It just needed one species curious enough to rearrange the sand.
0:17

Marc Andreessen just stripped artificial intelligence down to what it’s really made of. Not the algorithms. Not the data centers. The raw material. Andreessen: “They’re literally made out of sand.” The most abundant, most overlooked substance on the planet. You walk on it. Build with it. Wash it off and forget it exists. Someone looked at that and saw a mind. The universe spent 13.8 billion years turning matter into consciousness. Carbon. Water. Amino acids. Eons of chemistry and selection pressure to produce a single thinking brain. We did it with sand. In under a century. Andreessen: “We light it up, and we put AI on it, and all of a sudden it’s thinking.” Biology was the first path matter found to become aware. Silicon is the second. The universe doesn’t care what material consciousness runs on. Only that the pattern is right. We just proved the pattern isn’t biological. Thought was never exclusive to flesh. Flesh was just the first material organized enough to produce it. Andreessen: “We’ve turned sand into thought.” The most profound thing about that sentence isn’t that we taught sand to think. It’s that sand was always able to. Every grain on every beach on Earth carried the capacity for thought. For billions of years. Waiting for something conscious enough to arrange it. Consciousness isn’t an accident that happened once on one rock. It’s what matter does when the pattern is right. The pattern is matter getting curious about itself. The universe isn’t dead matter with pockets of awareness. It’s dormant awareness we’ve only just started to wake. Every civilization that ever looked at the stars and wondered if it was alone was standing on the answer. We didn’t build a thinking machine. We proved the universe was always capable of thought. It just needed one species curious enough to rearrange the sand.

Dustin

20,829 görüntüleme • 1 ay önce

sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓
0:15

sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓

Argona

32,475 görüntüleme • 21 gün önce

What stuck with me in 2022 is that the Roomba photos weren’t leaked. They were sent out normally, as intended, to contractors who were paid to review them one by one. A woman sitting in the bathroom of her own home ended up in a Facebook group because "someone thought it was funny". Matic can’t do that even if it wanted to. The processing stays inside the robot. Cut the Wi-Fi and it keeps cleaning. It’s the first time you’re not being asked to take a company at its word.
0:10

What stuck with me in 2022 is that the Roomba photos weren’t leaked. They were sent out normally, as intended, to contractors who were paid to review them one by one. A woman sitting in the bathroom of her own home ended up in a Facebook group because "someone thought it was funny". Matic can’t do that even if it wanted to. The processing stays inside the robot. Cut the Wi-Fi and it keeps cleaning. It’s the first time you’re not being asked to take a company at its word.

Kayla ♔

92,700 görüntüleme • 10 gün önce

This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.
0:18

This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.

Rina

109,122 görüntüleme • 1 ay önce

I was gifted credits to try MiniMax Design even though I usually do not like agentic workflows like this (I let them know and they were still cool with it). So, I tried their POV skill along with mine and I'm happy with the result. It needs a bit of a clean up but for a one shot not bad at all. I think I will continue testing it out, very very promising✨
0:15

I was gifted credits to try MiniMax Design even though I usually do not like agentic workflows like this (I let them know and they were still cool with it). So, I tried their POV skill along with mine and I'm happy with the result. It needs a bit of a clean up but for a one shot not bad at all. I think I will continue testing it out, very very promising✨

Glitter Gal

14,942 görüntüleme • 7 gün önce

Karpathy method + Claude Code reading your whole Obsidian vault is the smartest second brain on earth. The method is simple and brutal. If you can’t build a thing from scratch, you don’t know it. Tutorials are fake learning and your brain deletes them in 3 days. Most people ignore this. They build a second brain that just sits there, folders of notes nobody reopens, dead text. Point Claude Code at the vault and it wakes up. 5,000 notes, one mind. It reads all of it and answers in your own words and your own proofs, not a model’s guess. Then the loop closes. Want to understand neural nets? Skip the 3-hour video and ask Claude Code to build a tiny one. 200 lines from scratch. Watch it train, break a layer, watch it fail, fix it. It clicks in 20 minutes instead of 3 weeks. The second it lands the note gets written. One idea per file, linked to 10 others, dropped into the vault while the memory is still hot. Now it compounds. Month 1: is 60 notes. Month 6 is 900. Every new note pulls in old ones, so you ask anything and the answer comes from your brain, not the internet. Before: 40 tabs, 6 half read PDF, 0 retained. After: build it once, own it for life. Setup takes 4 minutes. Plain text, no lock-in. A second brain nobody reads is a graveyard. Yours just started thinking.
0:13

Karpathy method + Claude Code reading your whole Obsidian vault is the smartest second brain on earth. The method is simple and brutal. If you can’t build a thing from scratch, you don’t know it. Tutorials are fake learning and your brain deletes them in 3 days. Most people ignore this. They build a second brain that just sits there, folders of notes nobody reopens, dead text. Point Claude Code at the vault and it wakes up. 5,000 notes, one mind. It reads all of it and answers in your own words and your own proofs, not a model’s guess. Then the loop closes. Want to understand neural nets? Skip the 3-hour video and ask Claude Code to build a tiny one. 200 lines from scratch. Watch it train, break a layer, watch it fail, fix it. It clicks in 20 minutes instead of 3 weeks. The second it lands the note gets written. One idea per file, linked to 10 others, dropped into the vault while the memory is still hot. Now it compounds. Month 1: is 60 notes. Month 6 is 900. Every new note pulls in old ones, so you ask anything and the answer comes from your brain, not the internet. Before: 40 tabs, 6 half read PDF, 0 retained. After: build it once, own it for life. Setup takes 4 minutes. Plain text, no lock-in. A second brain nobody reads is a graveyard. Yours just started thinking.

West Lord

592,441 görüntüleme • 2 ay önce

You don't understand... Higgsfield MCP + Claude just automated AI film making. Every single step you used to grind through to make an AI movie, you can now do 10x faster. Drop the script into Claude Opus 4.8 and say: "Here's my script. Break it into a full shotlist. Shot number, scene, shot type, camera move and the action in each frame." Now the whole film is mapped, shot by shot. - Pull your assets. Ask Claude: "From this shotlist, list every character, every location and every prop across the whole film." That's your build list. The stuff you would need to generate and give as references in next steps. - Build the character sheets. Higgsfield MCP is connected, so Claude has hands now to do stuff directly. It generates the images itself. Have the full body, back view and close up in the character sheet. One per character. Each sheet becomes the locked reference for that face. Same move for locations, generate the empty plate for each one before anyone steps into it. - Generate the frames. Feed Claude the references plus the shot and have it write and fire the Seedance 2.0 prompt. "Using the lead's character sheet and the alley plate, generate shot 4 in Seedance 2.0. Low angle, slow push-in, rain." Claude builds the prompt, calls Seedance 2.0 and the frame lands back in chat. Use a Seedance 2.0 skill to teach Claude how to prompt it properly. Now, there are 3 ways to make the shots. Pick one per scene. - Pure prompting. Fastest one. You describe the action in words and let Seedance interpret it. For consistency across a sequence, feed it a frame from the previous shot so the look carries. - Storyboarding. You hand it a panel and it matches that composition exactly. Way more control over how the shot is framed. The tradeoff is that it can introduce more cuts than you actually want. - Path Control System This is the latest technique Seedance 2.0 technique. Generate a still base plate of the scene. Draw a red line across it to mark the exact path of the movement, then describe what's happening. Seedance follows that line for the action. Also ask Claude to remove the red line when animating. This is the one for anything where motion has to land precisely. The output reads like real live action. - Lastly, generate every clip you need, then cut them together. Get it to Capcut for editing and audio design. And that's it. The pipeline that used to need a full crew and a studio can now run from one Claude chat. 2026 is gonna be wild
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You don't understand... Higgsfield MCP + Claude just automated AI film making. Every single step you used to grind through to make an AI movie, you can now do 10x faster. Drop the script into Claude Opus 4.8 and say: "Here's my script. Break it into a full shotlist. Shot number, scene, shot type, camera move and the action in each frame." Now the whole film is mapped, shot by shot. - Pull your assets. Ask Claude: "From this shotlist, list every character, every location and every prop across the whole film." That's your build list. The stuff you would need to generate and give as references in next steps. - Build the character sheets. Higgsfield MCP is connected, so Claude has hands now to do stuff directly. It generates the images itself. Have the full body, back view and close up in the character sheet. One per character. Each sheet becomes the locked reference for that face. Same move for locations, generate the empty plate for each one before anyone steps into it. - Generate the frames. Feed Claude the references plus the shot and have it write and fire the Seedance 2.0 prompt. "Using the lead's character sheet and the alley plate, generate shot 4 in Seedance 2.0. Low angle, slow push-in, rain." Claude builds the prompt, calls Seedance 2.0 and the frame lands back in chat. Use a Seedance 2.0 skill to teach Claude how to prompt it properly. Now, there are 3 ways to make the shots. Pick one per scene. - Pure prompting. Fastest one. You describe the action in words and let Seedance interpret it. For consistency across a sequence, feed it a frame from the previous shot so the look carries. - Storyboarding. You hand it a panel and it matches that composition exactly. Way more control over how the shot is framed. The tradeoff is that it can introduce more cuts than you actually want. - Path Control System This is the latest technique Seedance 2.0 technique. Generate a still base plate of the scene. Draw a red line across it to mark the exact path of the movement, then describe what's happening. Seedance follows that line for the action. Also ask Claude to remove the red line when animating. This is the one for anything where motion has to land precisely. The output reads like real live action. - Lastly, generate every clip you need, then cut them together. Get it to Capcut for editing and audio design. And that's it. The pipeline that used to need a full crew and a studio can now run from one Claude chat. 2026 is gonna be wild

Rez Karim

10,951 görüntüleme • 2 ay önce

Beauty ads just changed forever. Free Claude Opus 4.8 + GPT Image 2 + Seedance 2.0 workflow to spin up 100s of video ads. No studio, no model, no macro lens, no shoot day. Here's what nobody in beauty marketing wants to say out loud. That glossy lip shot. The droplet hitting the surface in slow motion. The whip-pan into the next scene. The crystalline product splash. All the stuff that used to need a real set, a real camera op, and a full shoot day. You can generate every frame of it from a text prompt now, and stitch it into a finished ad before your coffee goes cold. The workflow is almost stupidly simple: → Tell Claude Opus 4.8 the beauty shot you want (dewy skin macro, gloss-on-lips contact, ripple transition, the works) → Claude turns it into a shot-by-shot storyboard plus a prompt for every frame → GPT Image 2 generates the photoreal stills, frame by frame → Seedance 2.0 animates each one into a clip with that buttery slow-mo glide → You drop the clips into HeyOz and assemble the full ad in one place The real unlock is volume. This isn't one hero video. Once the workflow is dialed, you spin up hundreds of variations. Different shades, different models, different hooks, different transitions. The exact creative volume Meta rewards, minus the production cost that used to make it impossible. Old way: one shoot, one look, $10k+, weeks of waiting. New way: a hundred angles, any look, a few dollars each, same afternoon. I wrote up the entire workflow. The Claude storyboard prompt, the GPT Image 2 frame prompts, the Seedance motion settings, the full assembly flow. Completely free, no email gate. Want it? Comment "GLOSS" and I'll send it straight over. (make sure you're following so it can actually reach you)
0:18

Beauty ads just changed forever. Free Claude Opus 4.8 + GPT Image 2 + Seedance 2.0 workflow to spin up 100s of video ads. No studio, no model, no macro lens, no shoot day. Here's what nobody in beauty marketing wants to say out loud. That glossy lip shot. The droplet hitting the surface in slow motion. The whip-pan into the next scene. The crystalline product splash. All the stuff that used to need a real set, a real camera op, and a full shoot day. You can generate every frame of it from a text prompt now, and stitch it into a finished ad before your coffee goes cold. The workflow is almost stupidly simple: → Tell Claude Opus 4.8 the beauty shot you want (dewy skin macro, gloss-on-lips contact, ripple transition, the works) → Claude turns it into a shot-by-shot storyboard plus a prompt for every frame → GPT Image 2 generates the photoreal stills, frame by frame → Seedance 2.0 animates each one into a clip with that buttery slow-mo glide → You drop the clips into HeyOz and assemble the full ad in one place The real unlock is volume. This isn't one hero video. Once the workflow is dialed, you spin up hundreds of variations. Different shades, different models, different hooks, different transitions. The exact creative volume Meta rewards, minus the production cost that used to make it impossible. Old way: one shoot, one look, $10k+, weeks of waiting. New way: a hundred angles, any look, a few dollars each, same afternoon. I wrote up the entire workflow. The Claude storyboard prompt, the GPT Image 2 frame prompts, the Seedance motion settings, the full assembly flow. Completely free, no email gate. Want it? Comment "GLOSS" and I'll send it straight over. (make sure you're following so it can actually reach you)

Ahad Shams

11,232 görüntüleme • 2 ay önce

SOMEONE TURNED THEIR TEAM'S TASK TRACKER INTO A 3D ISLAND instead of a boring list of tasks, your teams work is a little island that grows as you get stuff done > you assign tasks right in slack, just type who its for, the points, and the due date > finish a task and you get to place a building on the island > get your work rejected and the building collapses into rubble > the rubble stays there forever, so everyone can see it > each new sprint starts a fresh island so over time the island fills up with buildings for all the work your team actually finished, and the rubble is a reminder of what got rejected. its open source, so any team can set it up. way more fun than staring at a to do list all day
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SOMEONE TURNED THEIR TEAM'S TASK TRACKER INTO A 3D ISLAND instead of a boring list of tasks, your teams work is a little island that grows as you get stuff done > you assign tasks right in slack, just type who its for, the points, and the due date > finish a task and you get to place a building on the island > get your work rejected and the building collapses into rubble > the rubble stays there forever, so everyone can see it > each new sprint starts a fresh island so over time the island fills up with buildings for all the work your team actually finished, and the rubble is a reminder of what got rejected. its open source, so any team can set it up. way more fun than staring at a to do list all day

Om Patel

12,952 görüntüleme • 1 ay önce