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we achieved some progress in the implementation of state inference inside octra , first we managed to implement continuous reasoning without clearing the circle memory, second we completed a pure resource model under which validators can verify and certify the execution flow of a set of useful work (in...

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11,296 次观看 • 23 天前 •via X (Twitter)

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Gavin Baker (Gavin Baker) says the disaggregation of inference can extend GPU useful lives from 3-4 years to 10-15. That may single-handedly save private credit and reduce the financing rates for GPUs, which will drive demand and help finance the build-out. "The disaggregation of prefill and inference is going to be amazing for the useful lives of GPU and may single-handedly save private credit. Private credit is in pain from these SaaS loans. But there's a lot of private credit in GPUs too. They were underwriting that to 3-4. The disaggregation of inference means that these GPUs are going to have 10 or 15-year lives. The AI skeptics are like, "Oh, these companies are all cooking their books. The useful life of a GPU is only a year or two. The useful life of a CPU is only four years because the rapid technological change." No. What rapid technological change has done with the disaggregation of prefill and inference is you can put a Cerebras system or Groq LPUs effectively in front of a Hopper or even an Ampere, use that Hopper and Ampere for prefill, and extend the useful life of that GPU until it melts. This is going to be really good for the whole private credit industry. It's gonna help finance the AI build-out. Because if you can start to finance GPUs at 5% or 6% instead of – I think CoreWeave's lowest financing was low sevens – that actually mathematically changes the cost to finance this build-out."
1:51

Gavin Baker (Gavin Baker) says the disaggregation of inference can extend GPU useful lives from 3-4 years to 10-15. That may single-handedly save private credit and reduce the financing rates for GPUs, which will drive demand and help finance the build-out. "The disaggregation of prefill and inference is going to be amazing for the useful lives of GPU and may single-handedly save private credit. Private credit is in pain from these SaaS loans. But there's a lot of private credit in GPUs too. They were underwriting that to 3-4. The disaggregation of inference means that these GPUs are going to have 10 or 15-year lives. The AI skeptics are like, "Oh, these companies are all cooking their books. The useful life of a GPU is only a year or two. The useful life of a CPU is only four years because the rapid technological change." No. What rapid technological change has done with the disaggregation of prefill and inference is you can put a Cerebras system or Groq LPUs effectively in front of a Hopper or even an Ampere, use that Hopper and Ampere for prefill, and extend the useful life of that GPU until it melts. This is going to be really good for the whole private credit industry. It's gonna help finance the AI build-out. Because if you can start to finance GPUs at 5% or 6% instead of – I think CoreWeave's lowest financing was low sevens – that actually mathematically changes the cost to finance this build-out."

Invest Like the Best

211,336 次观看 • 3 个月前

We are proud to announce the launch of our platform and the THREE initial agents: the Data Aggregation Agent, Sentiment Analysis Agent and Risk Manager Agent. This demo clip is a culmination of months of hard work and dedication to our vision. The platform have now entered a private alpha test which will be followed by a public beta. Tomorrow we will be doing a live streamed demo of the platform as it exists right now and will also be opening our waitlist to the general public. Thank you for your patience, belief and support. $cainam
0:39

We are proud to announce the launch of our platform and the THREE initial agents: the Data Aggregation Agent, Sentiment Analysis Agent and Risk Manager Agent. This demo clip is a culmination of months of hard work and dedication to our vision. The platform have now entered a private alpha test which will be followed by a public beta. Tomorrow we will be doing a live streamed demo of the platform as it exists right now and will also be opening our waitlist to the general public. Thank you for your patience, belief and support. $cainam

Cainam Ventures

12,101 次观看 • 1 年前

In just one week, Binh and I trained a full-body Unitree G1. Here's a recap: 1. Secured a Unitree G1 humanoid through a LinkedIn post 2. Deployed TWIST2 full-body teleoperation pipelines 3. Adapted TWIST2 for Zed stereo camera & collected full-body teleoperation samples (carried by Binh ) 4. Adapted & fine-tuned NVIDIA Gr00T N1.5 VLA on the TWIST2 public datasets, which I fine-tuned on an 8xNVIDIA H100 Cluster. We picked Gr00T N1.5 as it was trained with Unitree G1 embodiment data. 5. Adapted the TWIST2 codebase to stream in the actions from Gr00T via ZMQ using a co-located NVIDIA H100 for ~200ms inference latency 6. Tested the model in sim, then deployed to the real-world Unitree G1. We streamed a training sample observation to the VLA (as we didn't want to break robot in case real observations were OOD) We were the first team in the world to deploy the full TWIST2 data collection pipeline to the unitree g1 :) Much more work ahead though, which I'll work on as a side-project over the next months: 1. Exploring the various types of 'world models': video backbones, dynamics models, v-jepa-2 models. I believe these will generalize better & train much more data-efficiently than VLM backbones 2. Speeding up inference - I believe low-latency robotics inference will be a big challenge. There are many works in video diffusion which I'd like to test (e.g. SageAttention, SparseAttention, Drifting Models). Perhaps also writing custom CUDA kernels. 3. Economics of inference scaling :) What will be the compute demands as we scale inference up to millions of humanoids? Will it run on edge or on distributed 'co-located' inference clusters? These are questions I'd like to answer. Adapted TWIST2 codebase: Adapted Gr00T-N1.5 codebase: The ETH Robotics Club are doing a cool GTC Golden ticket competition with NVIDIA , so this is my submission :) The DGX Spark compute will get me a long way with initial prototyping & especially working on inference optimization for next-gen Blackwell GPUs #NVIDIAGTC #GOLDENTICKET #ETHRC
0:45

In just one week, Binh and I trained a full-body Unitree G1. Here's a recap: 1. Secured a Unitree G1 humanoid through a LinkedIn post 2. Deployed TWIST2 full-body teleoperation pipelines 3. Adapted TWIST2 for Zed stereo camera & collected full-body teleoperation samples (carried by Binh ) 4. Adapted & fine-tuned NVIDIA Gr00T N1.5 VLA on the TWIST2 public datasets, which I fine-tuned on an 8xNVIDIA H100 Cluster. We picked Gr00T N1.5 as it was trained with Unitree G1 embodiment data. 5. Adapted the TWIST2 codebase to stream in the actions from Gr00T via ZMQ using a co-located NVIDIA H100 for ~200ms inference latency 6. Tested the model in sim, then deployed to the real-world Unitree G1. We streamed a training sample observation to the VLA (as we didn't want to break robot in case real observations were OOD) We were the first team in the world to deploy the full TWIST2 data collection pipeline to the unitree g1 :) Much more work ahead though, which I'll work on as a side-project over the next months: 1. Exploring the various types of 'world models': video backbones, dynamics models, v-jepa-2 models. I believe these will generalize better & train much more data-efficiently than VLM backbones 2. Speeding up inference - I believe low-latency robotics inference will be a big challenge. There are many works in video diffusion which I'd like to test (e.g. SageAttention, SparseAttention, Drifting Models). Perhaps also writing custom CUDA kernels. 3. Economics of inference scaling :) What will be the compute demands as we scale inference up to millions of humanoids? Will it run on edge or on distributed 'co-located' inference clusters? These are questions I'd like to answer. Adapted TWIST2 codebase: Adapted Gr00T-N1.5 codebase: The ETH Robotics Club are doing a cool GTC Golden ticket competition with NVIDIA , so this is my submission :) The DGX Spark compute will get me a long way with initial prototyping & especially working on inference optimization for next-gen Blackwell GPUs #NVIDIAGTC #GOLDENTICKET #ETHRC

Arnie Ramesh

23,236 次观看 • 6 个月前

I'm so lucky to have the incredible opportunity to make the first of it's kind and create a customizable vtuber model in 3D!🩷 This wouldn't have been possible without the support we've received. Thank you for helping us provide groundbreaking developments for the vtubing community! ^u^✨ Because this will be such a huge update, we are hard at work to make it the best it can be. This update will be free for those who have already bought the model and it's price will not change.🩷
0:21

I'm so lucky to have the incredible opportunity to make the first of it's kind and create a customizable vtuber model in 3D!🩷 This wouldn't have been possible without the support we've received. Thank you for helping us provide groundbreaking developments for the vtubing community! ^u^✨ Because this will be such a huge update, we are hard at work to make it the best it can be. This update will be free for those who have already bought the model and it's price will not change.🩷

Pastell & Palette🤍 3D Customizable Model!

18,567 次观看 • 4 个月前

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)
0:40

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)

kwindla

40,593 次观看 • 3 个月前

UPDATE- we will be in East Tennessee late tonight. We will open my shops up to collect items as of Tomorrow morning, and we will deliver it to the counties in need. Kelly and Evan will be there all day beginning at 8am, and the rest of us will be taking stuff where it is needed. If Morristown is closer for you and you want to help, we will get it there immediately. You have my word on that. Also, if you want to donate cash and it be immediately put to use for goods, my venmo is attached. We will be loading the truck as we come back into town through Pennsylvania and Virginia. Thank you all, and we will do what we can to assist without getting in their way.
2:23

UPDATE- we will be in East Tennessee late tonight. We will open my shops up to collect items as of Tomorrow morning, and we will deliver it to the counties in need. Kelly and Evan will be there all day beginning at 8am, and the rest of us will be taking stuff where it is needed. If Morristown is closer for you and you want to help, we will get it there immediately. You have my word on that. Also, if you want to donate cash and it be immediately put to use for goods, my venmo is attached. We will be loading the truck as we come back into town through Pennsylvania and Virginia. Thank you all, and we will do what we can to assist without getting in their way.

🍊🍊Capt'n Cornjuice🥃🥃

911,934 次观看 • 1 年前

By the end of my first term, the US-Israel relationship will be deeper and stronger than ever because it won’t be a client relationship, it will be a true friendship. The centerpiece of my Middle East policy in Year 1 will be to consummate “Abraham Accords 2.0” which will be good for the U.S. and good for Israel. If we can lead the way in helping Israel fully integrate into the economic and security infrastructure of the Middle East via Abraham Accords 2.0, it is better for everyone if Israel is truly able to stand on its own feet with support from partners across the Middle East that we diplomatically bring to the table. We will not leave Israel hanging out to dry - ever.
2:27

By the end of my first term, the US-Israel relationship will be deeper and stronger than ever because it won’t be a client relationship, it will be a true friendship. The centerpiece of my Middle East policy in Year 1 will be to consummate “Abraham Accords 2.0” which will be good for the U.S. and good for Israel. If we can lead the way in helping Israel fully integrate into the economic and security infrastructure of the Middle East via Abraham Accords 2.0, it is better for everyone if Israel is truly able to stand on its own feet with support from partners across the Middle East that we diplomatically bring to the table. We will not leave Israel hanging out to dry - ever.

Vivek Ramaswamy

910,274 次观看 • 3 年前

Today in Ivano-Frankivsk, the final stage of selection for the national Tysiachovesna initiative is taking place. A total of 1,227 projects made it to the final. That is an amazing number. And this number proves that all of our Ukrainian art can become an even greater force. The results of the first Tysiachovesna will now be extremely important: which projects will be created with state support, how they will be received by Ukrainian viewers, listeners, and all our people – the people Tysiachovesna is for. Will Ukrainians in general be interested in this? Will this become a strong voice for Ukraine in the world? This is very important to us, especially in wartime. I believe we will succeed. And exactly one year from now, we will not only be summing up the dry statistics of the first Tysiachovesna through projects that have already been brought to life – we will be talking about the triumphs of our cinema, animation, theater, music, and all of our Ukrainian art, which is here to stay. To be successful, high-quality, and popular among all Ukrainians. This is an important goal for which Tysiachovesna was launched and which it will continue to pursue in the years ahead.
11:28

Today in Ivano-Frankivsk, the final stage of selection for the national Tysiachovesna initiative is taking place. A total of 1,227 projects made it to the final. That is an amazing number. And this number proves that all of our Ukrainian art can become an even greater force. The results of the first Tysiachovesna will now be extremely important: which projects will be created with state support, how they will be received by Ukrainian viewers, listeners, and all our people – the people Tysiachovesna is for. Will Ukrainians in general be interested in this? Will this become a strong voice for Ukraine in the world? This is very important to us, especially in wartime. I believe we will succeed. And exactly one year from now, we will not only be summing up the dry statistics of the first Tysiachovesna through projects that have already been brought to life – we will be talking about the triumphs of our cinema, animation, theater, music, and all of our Ukrainian art, which is here to stay. To be successful, high-quality, and popular among all Ukrainians. This is an important goal for which Tysiachovesna was launched and which it will continue to pursue in the years ahead.

Volodymyr Zelenskyy / Володимир Зеленський

286,236 次观看 • 15 天前

🧠 Chat with Reasoning A few days ago the DeepSeek team released a LLM model with reasoning in various sizes. This we show is an example of 1bl that can run on machines with low GPU power like a mobile, but have enough power to answer complex questions. With these advanced models it is possible to link it with #IoT equipment to control information and use it in advanced control environments. All this under Open Source models and decentralized networks such as #Neurai #XNA $XNA #DeepSeek #Reasoning #AIchat
0:20

🧠 Chat with Reasoning A few days ago the DeepSeek team released a LLM model with reasoning in various sizes. This we show is an example of 1bl that can run on machines with low GPU power like a mobile, but have enough power to answer complex questions. With these advanced models it is possible to link it with #IoT equipment to control information and use it in advanced control environments. All this under Open Source models and decentralized networks such as #Neurai #XNA $XNA #DeepSeek #Reasoning #AIchat

NeurAI Project / XNA

17,691 次观看 • 1 年前

𝐃𝐞𝐬𝐭𝐫𝐚 𝐎𝐂𝐀𝐈 (𝐎𝐧𝐞-𝐂𝐥𝐢𝐜𝐤 𝐀𝐈) 𝐌𝐨𝐝𝐞𝐥𝐬 - $DSYNC 𝐄𝐚𝐫𝐥𝐲 𝐀𝐜𝐜𝐞𝐬𝐬 𝐏𝐫𝐞𝐯𝐢𝐞𝐰 We are training and building a wide range of AI models for the Destra OCAI platform. Today, we will be showcasing an early preview of one of the initial models we are working on—a bullish/bearish prediction model designed to 𝐟𝐨𝐫𝐞𝐜𝐚𝐬𝐭 𝐭𝐡𝐞 𝐦𝐚𝐫𝐤𝐞𝐭 𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐨𝐟 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐭𝐨𝐤𝐞𝐧𝐬. This is just one of the initial models we are working on; as we progress, the models will become more complex and intricate. In this behind-the-scenes video, we demonstrate how the model predicts the future market trend of the BTC-USD pair with an 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐟 𝟔𝟓%. This is the barebones structure of the models we are training, which will soon be available to users with a single click through the Destra OCAI platform. The OCAI platform includes a wide array of Destra Exclusive AI models, specifically designed to simplify life for crypto audiences, that are envisioned and built from scratch at Destra Labs.
0:54

𝐃𝐞𝐬𝐭𝐫𝐚 𝐎𝐂𝐀𝐈 (𝐎𝐧𝐞-𝐂𝐥𝐢𝐜𝐤 𝐀𝐈) 𝐌𝐨𝐝𝐞𝐥𝐬 - $DSYNC 𝐄𝐚𝐫𝐥𝐲 𝐀𝐜𝐜𝐞𝐬𝐬 𝐏𝐫𝐞𝐯𝐢𝐞𝐰 We are training and building a wide range of AI models for the Destra OCAI platform. Today, we will be showcasing an early preview of one of the initial models we are working on—a bullish/bearish prediction model designed to 𝐟𝐨𝐫𝐞𝐜𝐚𝐬𝐭 𝐭𝐡𝐞 𝐦𝐚𝐫𝐤𝐞𝐭 𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐨𝐟 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐭𝐨𝐤𝐞𝐧𝐬. This is just one of the initial models we are working on; as we progress, the models will become more complex and intricate. In this behind-the-scenes video, we demonstrate how the model predicts the future market trend of the BTC-USD pair with an 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐟 𝟔𝟓%. This is the barebones structure of the models we are training, which will soon be available to users with a single click through the Destra OCAI platform. The OCAI platform includes a wide array of Destra Exclusive AI models, specifically designed to simplify life for crypto audiences, that are envisioned and built from scratch at Destra Labs.

Destra Network

37,150 次观看 • 2 年前

If conservatism is not ultimately about economics, what is it about? 'The defence of the West.' This, in one short, simple phrase is as near as I can come to expressing the overall mission of conservatives now. That defence involves securing our nations against internal and external threats alike. I shall enlarge on those threats in a minute. But what are the core convictions we are defending? Not having time to pen a treatise, or even speak as long as a German politician, I shall set out my views with English brevity. We conservatives believe that man has a basic sense of right and wrong and an amazing creativity when free to apply his talents; but we also believe that his nature is flawed, such that without restraints applied by convention and law he will destroy himself and others. We believe in free, limited democratic government, as the framework most likely to minimise opportunities for mischief and abuse of power; but we scorn the association of vox populi with vox dei, knowing that no mere majority vote can make what is good bad or what is bad good. We understand that in some areas government has to be strong. It has to be prepared to use force to defend the nation's security. We appreciate the majesty and rituals and pomp of the state, operating in its proper sphere. But we also believe that what is public ultimately exists for what is private—that it is the family (not the state or nation or even the Church), which is the basic institution of our society, without which all the rest collapses. We are suspicious of attempts at institutional change, unless those institutions have become in their present form a threat to the whole future of the country—as the trade unions and nationalised industries were, for instance, in Britain when I became Prime Minister. We view the world in which we live as in need, not of re-ordering according to master plans devised by enlightened experts, but rather of constant renewal according to timeless truths and rich traditions. And so we are unashamedly patriots. For as Edmund Burke wrote two hundred years ago: “To be attached to the sub-division, to love the little platoon to which we belong in society, is the first principle of public affection. It is the first link in the series by which we proceed towards a love of our country and to mankind”. So here we stand. Rock solid. Rooted in a clear, tried, tested view of the world and the heavens and everything in them.
3:35

If conservatism is not ultimately about economics, what is it about? 'The defence of the West.' This, in one short, simple phrase is as near as I can come to expressing the overall mission of conservatives now. That defence involves securing our nations against internal and external threats alike. I shall enlarge on those threats in a minute. But what are the core convictions we are defending? Not having time to pen a treatise, or even speak as long as a German politician, I shall set out my views with English brevity. We conservatives believe that man has a basic sense of right and wrong and an amazing creativity when free to apply his talents; but we also believe that his nature is flawed, such that without restraints applied by convention and law he will destroy himself and others. We believe in free, limited democratic government, as the framework most likely to minimise opportunities for mischief and abuse of power; but we scorn the association of vox populi with vox dei, knowing that no mere majority vote can make what is good bad or what is bad good. We understand that in some areas government has to be strong. It has to be prepared to use force to defend the nation's security. We appreciate the majesty and rituals and pomp of the state, operating in its proper sphere. But we also believe that what is public ultimately exists for what is private—that it is the family (not the state or nation or even the Church), which is the basic institution of our society, without which all the rest collapses. We are suspicious of attempts at institutional change, unless those institutions have become in their present form a threat to the whole future of the country—as the trade unions and nationalised industries were, for instance, in Britain when I became Prime Minister. We view the world in which we live as in need, not of re-ordering according to master plans devised by enlightened experts, but rather of constant renewal according to timeless truths and rich traditions. And so we are unashamedly patriots. For as Edmund Burke wrote two hundred years ago: “To be attached to the sub-division, to love the little platoon to which we belong in society, is the first principle of public affection. It is the first link in the series by which we proceed towards a love of our country and to mankind”. So here we stand. Rock solid. Rooted in a clear, tried, tested view of the world and the heavens and everything in them.

Margaret Thatcher

32,889 次观看 • 8 个月前

Milestone! We (robotic arms for gadgets assembly) finished the first commercial order, which brought the first revenue. Here are some learnings from this: The customer was a smart toy manufacturer. The task was to add a heatsink to Raspberry Pi. We received parts from them and returned the assembled modules back. Currently, it's done by teleoperation. Later it will be done by a remote employee via the Internet. Then it will be automated action by action, reducing the operator's time on this and making the task profitable. ps. If you have an assembly task that we can do for you asynchronically - leave a comment below. Learning 1. It's possible! This task which is usually done by the human arm with 5 fingers can be done with a two-finger gripper with the addition of a couple of simple tooling. The task was not simplified. We peeled off thin films from stickers, unpacked paper boxes, moved PCB boards full of components, etc. And no unsolvable problems have been encountered yet. Challenges: 1) The paper box shifted during the opening Solved with the plastic walls that you can lean against 2) Heat pad, stuck to the gripper instead of heat sync. Can be solved by gripper with a pump, but this time solved with the patience of the operator 3) The film on the pad is very thin. Turned out that sub-millimeter arm precision is enough to peel it off with just a regular gripper. 4) The working area has not enough space. You'll only know this by doing real tasks in bulk. This could be solved by an extra pair of long arms, but in this case, solved with the patience of the operator. I think that in the end, we will have 5-10 types of universal tooling and 5-10 types of grippers to solve almost all the problems in such assembly tasks. Learning 2. It's slow. It took 5 times more time, than doing it with human hands. But the good news is there's a lot of room for improvement. We now have specific “time for task” metrics, which we will decrease with iterations. The main reasons for slowness: 1) To rotate the gripper to a steep angle you are forced to control one robot arm with two hands instead of using both arms. We can fix this by just making more room for rotations. 2) Grabbing PCB board with two arms is hard. A slight difference in rotation can break the board, and it's hard to control these angles visually. To solve this, the best way is to use force feedback so you can feel the pressure applied to the item. 3) Accuracy and steadiness is still can be improved We will try a metal version and double the motors to do this. 4) It is physically difficult for the human hands to move with such precision To solve this, we will add a pad for the hands like in surgical robots Learning 3. It's a good business model The "Factory in the cloud" is a good business model for this stage. You send us parts and we send back assembled modules. Currently, it's more convenient than sending a robot to your place, as we can iterate/fix the robot quickly and utilize it 100% of the time. When we polish the set-up over time - we can send robots to your place. So if we can assemble something for you in the USA with Chinese prices by using modern automation - leave a comment below.
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Milestone! We (robotic arms for gadgets assembly) finished the first commercial order, which brought the first revenue. Here are some learnings from this: The customer was a smart toy manufacturer. The task was to add a heatsink to Raspberry Pi. We received parts from them and returned the assembled modules back. Currently, it's done by teleoperation. Later it will be done by a remote employee via the Internet. Then it will be automated action by action, reducing the operator's time on this and making the task profitable. ps. If you have an assembly task that we can do for you asynchronically - leave a comment below. Learning 1. It's possible! This task which is usually done by the human arm with 5 fingers can be done with a two-finger gripper with the addition of a couple of simple tooling. The task was not simplified. We peeled off thin films from stickers, unpacked paper boxes, moved PCB boards full of components, etc. And no unsolvable problems have been encountered yet. Challenges: 1) The paper box shifted during the opening Solved with the plastic walls that you can lean against 2) Heat pad, stuck to the gripper instead of heat sync. Can be solved by gripper with a pump, but this time solved with the patience of the operator 3) The film on the pad is very thin. Turned out that sub-millimeter arm precision is enough to peel it off with just a regular gripper. 4) The working area has not enough space. You'll only know this by doing real tasks in bulk. This could be solved by an extra pair of long arms, but in this case, solved with the patience of the operator. I think that in the end, we will have 5-10 types of universal tooling and 5-10 types of grippers to solve almost all the problems in such assembly tasks. Learning 2. It's slow. It took 5 times more time, than doing it with human hands. But the good news is there's a lot of room for improvement. We now have specific “time for task” metrics, which we will decrease with iterations. The main reasons for slowness: 1) To rotate the gripper to a steep angle you are forced to control one robot arm with two hands instead of using both arms. We can fix this by just making more room for rotations. 2) Grabbing PCB board with two arms is hard. A slight difference in rotation can break the board, and it's hard to control these angles visually. To solve this, the best way is to use force feedback so you can feel the pressure applied to the item. 3) Accuracy and steadiness is still can be improved We will try a metal version and double the motors to do this. 4) It is physically difficult for the human hands to move with such precision To solve this, we will add a pad for the hands like in surgical robots Learning 3. It's a good business model The "Factory in the cloud" is a good business model for this stage. You send us parts and we send back assembled modules. Currently, it's more convenient than sending a robot to your place, as we can iterate/fix the robot quickly and utilize it 100% of the time. When we polish the set-up over time - we can send robots to your place. So if we can assemble something for you in the USA with Chinese prices by using modern automation - leave a comment below.

Igor Kulakov

37,266 次观看 • 1 年前

"It's basically what we call the Blackstone model, which is, you don't take over the whole company right, you buy the largest stake in it and you use that to control it. And so that's the model starting with the US, but basically every country in the world is being subject to this. And so they have planted through these small focus groups of private intelligence, and it's a combination of private intelligence, private equity, and IT firms, and they're basically coming together as one. And they set up their teams in each country to basically take it over strategically. There's a playbook for this..." Emanuel Pastreich of 🥷 🔗
8:37

"It's basically what we call the Blackstone model, which is, you don't take over the whole company right, you buy the largest stake in it and you use that to control it. And so that's the model starting with the US, but basically every country in the world is being subject to this. And so they have planted through these small focus groups of private intelligence, and it's a combination of private intelligence, private equity, and IT firms, and they're basically coming together as one. And they set up their teams in each country to basically take it over strategically. There's a playbook for this..." Emanuel Pastreich of 🥷 🔗

Geopolitics & Empire

18,051 次观看 • 5 个月前

VIDEO | Varanasi: Shankaracharya Avimukteshwarananda reacts to the Prayagraj court order, saying, “Yes, it is appropriate. Only when a case is registered will the subsequent process of investigation and recording of testimony be completed. Only then will the truth of the false case filed against us come before everyone, and those responsible for doing this can be punished. Therefore, it is necessary. We would only say that the court should proceed with this at a faster pace without taking too much time, as many people are watching this matter. Testimony should be recorded and a decision taken as soon as possible. What is wrong will remain wrong, and the false case that has been filed will ultimately be proven false.” A special POCSO court in Prayagraj directs the police to register an FIR against Swami Avimukteshwaranand Saraswati and his disciple Swami Mukundanand Giri in connection with allegations of sexual exploitation. (Full video available on
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VIDEO | Varanasi: Shankaracharya Avimukteshwarananda reacts to the Prayagraj court order, saying, “Yes, it is appropriate. Only when a case is registered will the subsequent process of investigation and recording of testimony be completed. Only then will the truth of the false case filed against us come before everyone, and those responsible for doing this can be punished. Therefore, it is necessary. We would only say that the court should proceed with this at a faster pace without taking too much time, as many people are watching this matter. Testimony should be recorded and a decision taken as soon as possible. What is wrong will remain wrong, and the false case that has been filed will ultimately be proven false.” A special POCSO court in Prayagraj directs the police to register an FIR against Swami Avimukteshwaranand Saraswati and his disciple Swami Mukundanand Giri in connection with allegations of sexual exploitation. (Full video available on

Press Trust of India

16,166 次观看 • 6 个月前