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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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11,971 Aufrufe • vor 3 Monaten •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

3 Kommentare

Profilbild von 0x
0xvor 3 Monaten

KEEP BUILDING LFG ethereum:0x4647e1fe715c9e23959022c2416c71867f5a6e80 will all be worth it

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TECAvor 3 Monaten

This is the real deal. Can't wait to test the demo.

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Validatus.comvor 3 Monaten

Looking forward to the open-source demo. Seeing the architecture in practice will answer a lot of questions.

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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 Aufrufe • vor 5 Monaten

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
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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

12,129 Aufrufe • vor 1 Jahr

Extremely honored to announce that we will be 1/4 schools in the state of Ohio and the only football team in Columbus who will be wearing Jordan jerseys. We will work to live up to the high standards of the brand! Thanks to BSN SPORTS for making it possible! #WeAreJordan
1:10

Extremely honored to announce that we will be 1/4 schools in the state of Ohio and the only football team in Columbus who will be wearing Jordan jerseys. We will work to live up to the high standards of the brand! Thanks to BSN SPORTS for making it possible! #WeAreJordan

Josh Franke

48,190 Aufrufe • vor 11 Monaten

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
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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

24,672 Aufrufe • vor 8 Monaten

With Alpenglow, there will be no onchain voting anymore for achieving consensus. This removes the cost of including votes into blocks. However, in order to avoid abrupt change in economics for the validators and maintain same equilibrium, there is an ongoing discussion for this Solana upgrade to have a small form of incentive (like a tax) where the validators will have to pay to be part of the system next epoch. When determining the set of validators for the new epoch, the fee is deducted, and if a validator cannot pay the fee, they are not included in the next set of validators. Here is the writeup: Listen to the full episode for more details on Alpenglow!
2:41

With Alpenglow, there will be no onchain voting anymore for achieving consensus. This removes the cost of including votes into blocks. However, in order to avoid abrupt change in economics for the validators and maintain same equilibrium, there is an ongoing discussion for this Solana upgrade to have a small form of incentive (like a tax) where the validators will have to pay to be part of the system next epoch. When determining the set of validators for the new epoch, the fee is deducted, and if a validator cannot pay the fee, they are not included in the next set of validators. Here is the writeup: Listen to the full episode for more details on Alpenglow!

Soubhik Deb

11,045 Aufrufe • vor 8 Monaten

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.🩷
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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 Aufrufe • vor 6 Monaten

Claude Mythos will be RELEASED within a WEEK Polymarket estimates this at a 40% chance just 4 DAYS ago, the probability was 3% the model ALREADY appeared for a MINUTE in Anthropic and triggered an insane INCREASE in probabilities this will be the MOST POWERFUL model in the history of AI and very soon we will be able to work with this MONSTER
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Claude Mythos will be RELEASED within a WEEK Polymarket estimates this at a 40% chance just 4 DAYS ago, the probability was 3% the model ALREADY appeared for a MINUTE in Anthropic and triggered an insane INCREASE in probabilities this will be the MOST POWERFUL model in the history of AI and very soon we will be able to work with this MONSTER

renewable 🌏

46,275 Aufrufe • vor 5 Monaten

Criminal Netanyahu in Hebron: We will do something that has not been implemented until now. We will apply the successful and offensive model that we implemented in the refugee camps, which are in fact strongholds of terrorism. And this is exactly what we will do here, inside the neighborhood in Hebron from which the saboteurs emerged. We will evacuate the residents, and we will go from house to house, and confiscate the weapons there, and all the infrastructure of terrorism will be dealt with and destroyed. At the same time, we will impose a cordon on the cities of Hebron and Halhul, and we will consider expanding this model to include other cities as well.
0:31

Criminal Netanyahu in Hebron: We will do something that has not been implemented until now. We will apply the successful and offensive model that we implemented in the refugee camps, which are in fact strongholds of terrorism. And this is exactly what we will do here, inside the neighborhood in Hebron from which the saboteurs emerged. We will evacuate the residents, and we will go from house to house, and confiscate the weapons there, and all the infrastructure of terrorism will be dealt with and destroyed. At the same time, we will impose a cordon on the cities of Hebron and Halhul, and we will consider expanding this model to include other cities as well.

Middle East Observer

10,988 Aufrufe • vor 15 Tagen

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 Aufrufe • vor 5 Monaten

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

287,432 Aufrufe • vor 2 Monaten

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

910,500 Aufrufe • vor 3 Jahren

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 Bo🍊🍊

912,003 Aufrufe • vor 2 Jahren

🧠 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 Aufrufe • vor 1 Jahr

Yes indeed, Perplexity's decider is the best decision model. That's why we use it in EVE, our inference stack for decision models. Here it is speedrunning "Who Wants to Be a Millionaire?"
0:23

Yes indeed, Perplexity's decider is the best decision model. That's why we use it in EVE, our inference stack for decision models. Here it is speedrunning "Who Wants to Be a Millionaire?"

Sanchit monga

34,126 Aufrufe • vor 1 Monat

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.
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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

37,163 Aufrufe • vor 2 Jahren