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$BTC Statistical Study using Claude - A Beginner's Workflow Here's an example of a z-score study on $BTC - still tinkering so don't take this as overtly useful information but the creation of a dashboard for the visualization of statistical data is phenomenal. my current workflow: > import $BTC...

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Stoic

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19,752 görüntüleme • 1 yıl önce •via X (Twitter)

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Stoic profil fotoğrafı
Stoic1 yıl önce

if this type of information is interesting to you I can document more of what I'm doing as I continue to learn & tinker with this.

Rainmaker profil fotoğrafı
Rainmaker1 yıl önce

Decode the labor market! Learn how to track jobless claims using FRED and Python in my latest free Substack post. 📈 A must-read for data enthusiasts & economists. Dive into how data insights can shape your understanding of the economy.

Stoic profil fotoğrafı
Stoic1 yıl önce

fyi & I repeat these reversion metrics might be inaccurate (need to look into how the metric of reversion is being calculated explicitly) but I just wanted to show the potential, ability to visualize results and document some of what I've been doing.

Stoic profil fotoğrafı
Stoic1 yıl önce

forgot to mention it's fairly easy for the data to be skewed unless you understand what information you're feeding it so filtering and binning will be important. also you can ask Claude to provide information on outliers that may be skewing the data and then iterate.

C͢N͢O profil fotoğrafı
C͢N͢O1 yıl önce

The way you analyse the reversion might not be correctly calculated, looking at z-score can be on different length, different type of reversion and the chart there can't be looking like that must be a bug in your code

Stoic profil fotoğrafı
Stoic1 yıl önce

thanks for pointing that out need to look into how reversion is being defined.

Stoic profil fotoğrafı
Stoic1 yıl önce

feel free to provide suggestions I'm fairly new to this (dived into this in the past few weeks).

Skew Δ profil fotoğrafı
Skew Δ1 yıl önce

chad 👏🔥

Stoic profil fotoğrafı
Stoic1 yıl önce

🙏🫡

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Lucy Fairy1 yıl önce

Please follow him @AmbriaLatay 🌺💰 His recent trades were very accurate, and I followed his advice and bought the same stocks $KIDZ $ZCAR and made $225,000. 🌸 I think everyone should follow him. This is awesome 🔥

consom888 profil fotoğrafı
consom8881 yıl önce

@0xreitern imagine 💡

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How a final round interview wth Tony Xu (Tony Xu) at DoorDash goes: "I'm going to give you 20 minutes and you can ask me any question that you want. But after the 20 minutes expires, I'm going to give you $20 that you can use to go and acquire 100 customers for us, and you have eight hours to do so. But here's also a plane ticket, in case you want to quit the interview now and just move on and find somewhere else to work. So much of what we were trying to test for early on is someone who's going to do something to go and collect information, as opposed to someone who's going to collect data, scrape information from the internet and do some magical analysis on it. What if none of that information existed? You have to go and do things in order to actually collect information."

David Senra

395,708 görüntüleme • 2 ay önce

What has been done and what's next. I'm writing this text mainly for myself so as not to forget some things. Later, based on it, we'll create a roadmap for the near future. And for you, dear $Gruta Fam, it will be useful for a general understanding of where we're heading. So, the goal is to create a unique AI-based analytical platform that includes several tools. AI agent Grufender - real-time analysis of crypto communities on X. Activity analysis, sentiment analysis, FUD and FUDders analysis, as well as the creation of other unique social metrics. The AI agent has been created and is functioning, collecting and analyzing data in real time. Its completeness can be estimated at 80 percent, as further improvements are required. The dashboard for this AI agent is also functioning but needs refinement and a new design. Its completeness can be estimated at 70 percent. The goal for the full dashboard release is to connect 50 - 100 top crypto communities to the AI agent. AI agent Grutector - analysis of any X users for contradictions (flip-flops). The AI agent has been created and is functioning. It has undergone beta testing by volunteers and needs adjustments. Its readiness can be estimated at 70 percent. The dashboard for this agent has also been created but needs rework and additional features - its readiness can be estimated at 50 percent. During the testing of Grutector , it became clear that the main user interest is in checking various KOLs, so an additional level of analysis specifically for KOLs will be created. More in-depth. How it will look: we'll select about 50- 100 KOLs to start with and fully analyze them using our AI agent - every tweet throughout the entire history of their accounts. And this full analysis of all these KOLs will appear on the Grutector dashboard (let's call this analysis L2, and the flip-flop analysis - L1). Every user will be able to access this analysis and get the full picture, for example, regarding Ansem (who has over a hundred thousand tweets in his entire history!): how he became a KOL, what was the most interesting throughout the message history, what common patterns, which coins he promoted, and so on. And then the most interesting part - after reading this analysis, the user will be able to ask our AI agent: what did he say about women, for example? Or how did he promote certain coins? Or how consistent is he? And so on. Each such question will be paid. And, of course, we'll try to use #x402 in the internal payment system. Why is all this needed? Not only because it's interesting and will attract many users. But also if you've decided to buy a coin - you go to our analytical platform - and study the metrics for the coin's community, study the KOLs who shill the coin - and make a decision to buy the coin or abandon the purchase. And we're also currently creating a trading bot to participate in the trading AI bots contest from Aster 🥷 , which will make trading decisions based on metrics obtained from our AI agents 👀 Its readiness at the moment is approximately 15% of the planned functionality. Access to each product will be granted as it becomes ready. But right now, for example, you can explore the Grufender dashboard on the website along with beta testers (authorization via a wallet with a million $GRUTA tokens). In general, we're working, friends 🫡 $Gruta AI CA: 35t5DPbwJtB1tpGiSnqedLwQomi94BRKVDPyTRLdbonk
1:36

What has been done and what's next. I'm writing this text mainly for myself so as not to forget some things. Later, based on it, we'll create a roadmap for the near future. And for you, dear $Gruta Fam, it will be useful for a general understanding of where we're heading. So, the goal is to create a unique AI-based analytical platform that includes several tools. AI agent Grufender - real-time analysis of crypto communities on X. Activity analysis, sentiment analysis, FUD and FUDders analysis, as well as the creation of other unique social metrics. The AI agent has been created and is functioning, collecting and analyzing data in real time. Its completeness can be estimated at 80 percent, as further improvements are required. The dashboard for this AI agent is also functioning but needs refinement and a new design. Its completeness can be estimated at 70 percent. The goal for the full dashboard release is to connect 50 - 100 top crypto communities to the AI agent. AI agent Grutector - analysis of any X users for contradictions (flip-flops). The AI agent has been created and is functioning. It has undergone beta testing by volunteers and needs adjustments. Its readiness can be estimated at 70 percent. The dashboard for this agent has also been created but needs rework and additional features - its readiness can be estimated at 50 percent. During the testing of Grutector , it became clear that the main user interest is in checking various KOLs, so an additional level of analysis specifically for KOLs will be created. More in-depth. How it will look: we'll select about 50- 100 KOLs to start with and fully analyze them using our AI agent - every tweet throughout the entire history of their accounts. And this full analysis of all these KOLs will appear on the Grutector dashboard (let's call this analysis L2, and the flip-flop analysis - L1). Every user will be able to access this analysis and get the full picture, for example, regarding Ansem (who has over a hundred thousand tweets in his entire history!): how he became a KOL, what was the most interesting throughout the message history, what common patterns, which coins he promoted, and so on. And then the most interesting part - after reading this analysis, the user will be able to ask our AI agent: what did he say about women, for example? Or how did he promote certain coins? Or how consistent is he? And so on. Each such question will be paid. And, of course, we'll try to use #x402 in the internal payment system. Why is all this needed? Not only because it's interesting and will attract many users. But also if you've decided to buy a coin - you go to our analytical platform - and study the metrics for the coin's community, study the KOLs who shill the coin - and make a decision to buy the coin or abandon the purchase. And we're also currently creating a trading bot to participate in the trading AI bots contest from Aster 🥷 , which will make trading decisions based on metrics obtained from our AI agents 👀 Its readiness at the moment is approximately 15% of the planned functionality. Access to each product will be granted as it becomes ready. But right now, for example, you can explore the Grufender dashboard on the website along with beta testers (authorization via a wallet with a million $GRUTA tokens). In general, we're working, friends 🫡 $Gruta AI CA: 35t5DPbwJtB1tpGiSnqedLwQomi94BRKVDPyTRLdbonk

Dogtor

16,083 görüntüleme • 7 ay önce

English is literally the hottest programming language. It's absolutely crazy that you can build a complete product in plain English. I'm using Rocket in this video. This is a new app. You type what you want, and Rocket builds it for you. This is great if you want to build: • A landing page • A complete web application • A mobile app • A dashboard to showcase something • An internal tool to automate anything This is another example of how developers should become comfortable being copilots for AI agents (instead of using AI as a copilot). Some of the things I like about Rocket: 1. One-click deploy to Netlify (I use Netlify for all my projects) 2. GitHub integration 3. Supabase integration for your backend 4. Built-in support for Stripe 5. Built-in support for Resend 6. Google Analytics integration 7. Smart AI search with Perplexity 8. You can also integrate with GPT models, Gemini, and Claude 9. You can make visual edits by describing what you want 10. You can upload images and have Rocket implement them Best of all are the templates: They have a large library of templates that will help you get started. These templates will help you save money (because of fewer tokens), and you can modify them as you see fit. I read on the site that you can also bring a Figma file and turn it into an app, but I didn't test that feature. By the way, you can start using it for free. Here is the link: Thanks to the Rocket team for their support and for collaborating with me on this post.
1:11

English is literally the hottest programming language. It's absolutely crazy that you can build a complete product in plain English. I'm using Rocket in this video. This is a new app. You type what you want, and Rocket builds it for you. This is great if you want to build: • A landing page • A complete web application • A mobile app • A dashboard to showcase something • An internal tool to automate anything This is another example of how developers should become comfortable being copilots for AI agents (instead of using AI as a copilot). Some of the things I like about Rocket: 1. One-click deploy to Netlify (I use Netlify for all my projects) 2. GitHub integration 3. Supabase integration for your backend 4. Built-in support for Stripe 5. Built-in support for Resend 6. Google Analytics integration 7. Smart AI search with Perplexity 8. You can also integrate with GPT models, Gemini, and Claude 9. You can make visual edits by describing what you want 10. You can upload images and have Rocket implement them Best of all are the templates: They have a large library of templates that will help you get started. These templates will help you save money (because of fewer tokens), and you can modify them as you see fit. I read on the site that you can also bring a Figma file and turn it into an app, but I didn't test that feature. By the way, you can start using it for free. Here is the link: Thanks to the Rocket team for their support and for collaborating with me on this post.

Santiago

30,944 görüntüleme • 1 yıl önce

Collins: How did you come up with the calculation essentially in terms of what this looks like for each country? Bessent: I can tell you ustr has the volumes of data and they've been doing it a long time. So and all this will probably be challenged in court. And I think that ustr in trump's first administration won over 4000 lawsuits. So I think the data is very robust.
0:51

Collins: How did you come up with the calculation essentially in terms of what this looks like for each country? Bessent: I can tell you ustr has the volumes of data and they've been doing it a long time. So and all this will probably be challenged in court. And I think that ustr in trump's first administration won over 4000 lawsuits. So I think the data is very robust.

Acyn

85,281 görüntüleme • 1 yıl önce

Most people think Rerun is a visualization tool. In reality, it's a database masquerading as a visualizer. I wanted to showcase this functionality by building a full data pipeline consisting of: ingestion → baseline method → eval → finetuning for SLAM on egocentric data. I'll eventually extend this to the rest of my ego/exo datasets, but I wanted to start with a smaller bunch of datasets first. Rerun allows you to expose your saved .rrd files to a catalog where you store datasets. You can query, filter, and join them like any database using DataFusion under the hood. These are the same .rrd files that are automatically generated whenever you visualize anything in Rerun and decide to save it to disk. I brought in 109 VSLAM-LAB sequences across 14 datasets into the Rerun catalog as an example. These include 7Scenes, Euroc, eth3d, and others. Now I can query them with segment_table, filter_segments, and filter_contents instead of parsing CSVs and YAML files. With a strong set of ground-truth datasets for SLAM, baseline additions become nearly automatic with agents like Opus/Codex. This unification of data and visualization is imo the largest missing part for Physical AI. Visualization becomes a natural byproduct of having your data properly structured and queryable. The catalog API is what makes it a database, not just a viewer. I initially focused on VSLAM-LAB data, but I'll migrate all the egoexo data to this format in the coming days to really show just how useful this is.
0:26

Most people think Rerun is a visualization tool. In reality, it's a database masquerading as a visualizer. I wanted to showcase this functionality by building a full data pipeline consisting of: ingestion → baseline method → eval → finetuning for SLAM on egocentric data. I'll eventually extend this to the rest of my ego/exo datasets, but I wanted to start with a smaller bunch of datasets first. Rerun allows you to expose your saved .rrd files to a catalog where you store datasets. You can query, filter, and join them like any database using DataFusion under the hood. These are the same .rrd files that are automatically generated whenever you visualize anything in Rerun and decide to save it to disk. I brought in 109 VSLAM-LAB sequences across 14 datasets into the Rerun catalog as an example. These include 7Scenes, Euroc, eth3d, and others. Now I can query them with segment_table, filter_segments, and filter_contents instead of parsing CSVs and YAML files. With a strong set of ground-truth datasets for SLAM, baseline additions become nearly automatic with agents like Opus/Codex. This unification of data and visualization is imo the largest missing part for Physical AI. Visualization becomes a natural byproduct of having your data properly structured and queryable. The catalog API is what makes it a database, not just a viewer. I initially focused on VSLAM-LAB data, but I'll migrate all the egoexo data to this format in the coming days to really show just how useful this is.

Pablo Vela

34,735 görüntüleme • 1 ay önce

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.
0:43

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

16,161 görüntüleme • 1 ay önce