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Foundry MCP server 🤝 Heimdall Heimdall is an EVM smart contract toolkit by jon becker specializing in bytecode analysis and extracting information from unverified contracts. Combining the power of foundry tools with heimdall's capabilities you can perform advanced solidity bytecode analysis. Here's an example analysis of Malicious Safe Implementation... show more

PraneshASP

1,449 subscribers

26,119 просмотров • 1 год назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 10

Фото профиля PraneshASP

PraneshASP1 год назад

You can try it out here 👇

Фото профиля jon becker

jon becker1 год назад

this is sick 🔥

Фото профиля PraneshASP

PraneshASP1 год назад

Thanks for building Heimdall 🤝

Фото профиля KOREVERSE🌐🖤

KOREVERSE🌐🖤1 год назад

@beckerrjon Crazy tech!

Фото профиля Antonio Viggiano

Antonio Viggiano1 год назад

@beckerrjon Really cool

Фото профиля PraneshASP

PraneshASP1 год назад

@beckerrjon Thanks!

Фото профиля Xyz

Xyz1 год назад

@beckerrjon this is sick

Фото профиля PETER d/acc

PETER d/acc1 год назад

@beckerrjon Thanks 🙏🏻

Фото профиля Edward

Edward1 год назад

@beckerrjon Whoa

Фото профиля Andoroid

Andoroid1 год назад

@beckerrjon That's sick!

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This guy works at coinbase. in his spare time he built the largest open dataset for polymarket and kalshi 72,100,000 trades. 7,680,000 markets. open source - figured out who actually makes money on prediction markets - wrote a research paper with the math to prove it - 3,100 stars on github just for that also built heimdall-rs - rust toolkit for evm bytecode analysis. 1,500 stars. serious dev 3,008 contributions in the last year while everyone's posting takes about polymarket - he's building the infrastructure underneath it → like + bookmark. you'll need this when you build your first polymarket bot

This guy works at coinbase. in his spare time he built the largest open dataset for polymarket and kalshi 72,100,000 trades. 7,680,000 markets. open source - figured out who actually makes money on prediction markets - wrote a research paper with the math to prove it - 3,100 stars on github just for that also built heimdall-rs - rust toolkit for evm bytecode analysis. 1,500 stars. serious dev 3,008 contributions in the last year while everyone's posting takes about polymarket - he's building the infrastructure underneath it → like + bookmark. you'll need this when you build your first polymarket bot

Archive

119,072 просмотров • 3 месяцев назад

PhD Students – Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs for data analysis. 5. AnswerThis will generate variety of graphs for you. 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE. AnswerThis link:

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Faheem Ullah

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Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs from data. 5. AnswerThis will generate variety of graphs 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE.

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Faheem Ullah

26,459 просмотров • 7 месяцев назад

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to and log in 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. E.g., generate graphs for exploratory data analysis. 5. Liner will generate a variety of graphs for you. 6. It also generates an insightful data analysis report. ✦ You can edit the report. For example, you can: ✦ Insert citations in the report ✦ Make changes to the text ✦ Add/remove any graphs After making changes, you can download the report. Try Liner today. It’s FREE. Liner link:

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Faheem Ullah

11,959 просмотров • 4 месяцев назад

Here's another reason why Microsoft Foundry is the BEST platform in the world to build and run your agents: Our durable, long running agents come with Toolbox 🔨 Toolbox allowed me to reduce input tokens 90%(!!) and significantly simplify the amount of code needed Check out the demo below and see how you can bring hundreds of tools through a single MCP endpoint -- all running in Foundry at cloud scale! ☁️

Here's another reason why Microsoft Foundry is the BEST platform in the world to build and run your agents: Our durable, long running agents come with Toolbox 🔨 Toolbox allowed me to reduce input tokens 90%(!!) and significantly simplify the amount of code needed Check out the demo below and see how you can bring hundreds of tools through a single MCP endpoint -- all running in Foundry at cloud scale! ☁️

Jeff Hollan

127,430 просмотров • 3 месяцев назад

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

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Sam Rodriques

61,934 просмотров • 8 месяцев назад

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Santiago

215,882 просмотров • 1 год назад

The Sanity MCP server is now available in v0! Build content-powered sites and apps without leaving the chat — structured, queryable, and scaleable from the get-go. Here's an example app of what you can build: A feature-rich pizza recipe app with advanced taxonomy, recommendations, 100s of landing pages, and LD-JSON schemas (great for SEO/AEO). All from chatting with the agent. Try it out now at →

The Sanity MCP server is now available in v0! Build content-powered sites and apps without leaving the chat — structured, queryable, and scaleable from the get-go. Here's an example app of what you can build: A feature-rich pizza recipe app with advanced taxonomy, recommendations, 100s of landing pages, and LD-JSON schemas (great for SEO/AEO). All from chatting with the agent. Try it out now at →

Sanity

12,315 просмотров • 7 месяцев назад

Screaming Frog now has the power to perform N-gram analysis! This step by step tutorial shows you how to use the new N-gram reports: Screaming Frog's latest update was a big one. Along with the ability to perform ChatGPT crawls, they also released a feature that analyzes the N-grams of a page. N-grams are sequences of words that can be used to find semantic concepts or relevant queries. The N-gram analysis feature can be used in a variety of different ways. You can use it better understand the optimization of a page, your site's topical authority or even using them to find internal linking opportunities. Here's the process:

Screaming Frog now has the power to perform N-gram analysis! This step by step tutorial shows you how to use the new N-gram reports: Screaming Frog's latest update was a big one. Along with the ability to perform ChatGPT crawls, they also released a feature that analyzes the N-grams of a page. N-grams are sequences of words that can be used to find semantic concepts or relevant queries. The N-gram analysis feature can be used in a variety of different ways. You can use it better understand the optimization of a page, your site's topical authority or even using them to find internal linking opportunities. Here's the process:

Chris Long

17,795 просмотров • 2 лет назад

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,129 просмотров • 1 год назад

In light of today’s Bybit incident, here’s how we secure our technical Safe.eth multisig at Gearbox Protocol: Continuous monitoring Whenever a new transaction is submitted or an existing one is signed, we immediately receive a notification in our alert channel. This alert shows the transaction hash and who proposed and signed the transaction. Detailed transaction report The most significant feature is the transaction report. It contains parsed transaction call data in a human-readable format, allowing all signers to see actions that occur understandably, both in plain text and in bytecode. Deployment check and whitelisted external calls Whenever the system deploys a new contract or interacts with an existing one, it checks the bytecode and compares it with code in our GitHub repository. Thus, the system constantly recompiles and compares the bytecode with the audited version, providing us with detailed information such as source links, audit reports, etc. This is how we have built a robust system to filter and check all transactions before they go to the technical multisig and affect changes to the protocol.

In light of today’s Bybit incident, here’s how we secure our technical Safe.eth multisig at Gearbox Protocol: Continuous monitoring Whenever a new transaction is submitted or an existing one is signed, we immediately receive a notification in our alert channel. This alert shows the transaction hash and who proposed and signed the transaction. Detailed transaction report The most significant feature is the transaction report. It contains parsed transaction call data in a human-readable format, allowing all signers to see actions that occur understandably, both in plain text and in bytecode. Deployment check and whitelisted external calls Whenever the system deploys a new contract or interacts with an existing one, it checks the bytecode and compares it with code in our GitHub repository. Thus, the system constantly recompiles and compares the bytecode with the audited version, providing us with detailed information such as source links, audit reports, etc. This is how we have built a robust system to filter and check all transactions before they go to the technical multisig and affect changes to the protocol.

0xmikko.eth

17,986 просмотров • 1 год назад

🚀 Big news: Cline just launched the MCP Marketplace -- now you can one-click install MCP servers and give Cline access to a whole new suite of tools! What's changing with the MCP Marketplace: • Browse AI capabilities like apps • One-click MCP server installation • Automatic setup and configuration • No more JSON wrestling Before: - Find GitHub repos - Read technical docs - Configure manually - Hope it works Now: - Open Cline - Browse marketplace - Click "Install" - Start using The impact: Making AI superpowers accessible to everyone, not just tech experts. Imagine giving your AI instant access to: • Web searching capabilities • Design tools integration • Code analysis powers • And much more For developers: Want to list your MCP server? Create an issue in our mcp-marketplace repo with: • GitHub repo URL • 400x400 PNG logo • Brief description The future of AI development isn't about complex integrations. It's about unlocking capabilities with a single click. Welcome to that future. 🔮

Cline

112,069 просмотров • 1 год назад

I've built it for you!! It's an automated AI system that analyzes AI case studies (you can change the use case) to identify and document enterprise-level AI implementations. It starts by reading URLs from a CSV file and uses web scraping (either through WebLoader or Firecrawl) to extract the content from each case study. The extracted content is then sent to Claude 3.5 Sonnet, which analyzes whether the case study represents a genuine enterprise AI implementation based on specific criteria like company maturity, implementation scale, and measurable business outcomes. For each URL, the system first saves the raw content and then performs this initial qualification analysis. If Claude determines that a case study qualifies as an enterprise AI implementation, the system proceeds to generate a detailed analysis. It creates three types of reports: - an individual case study report with sections like Executive Summary, AI Strategy Analysis, and Business Impact Assessment - a cross-case analysis that identifies patterns and trends across multiple case studies - and an executive dashboard summarizing key metrics and insights. All of these reports are saved in structured formats (markdown for individual reports, JSON for cross-case analysis and dashboard) in their respective directories. If a case study doesn't qualify as an enterprise AI implementation, the system logs the reason and moves on to the next URL. The entire process is asynchronous and provides detailed terminal feedback about its progress and decisions.

I've built it for you!! It's an automated AI system that analyzes AI case studies (you can change the use case) to identify and document enterprise-level AI implementations. It starts by reading URLs from a CSV file and uses web scraping (either through WebLoader or Firecrawl) to extract the content from each case study. The extracted content is then sent to Claude 3.5 Sonnet, which analyzes whether the case study represents a genuine enterprise AI implementation based on specific criteria like company maturity, implementation scale, and measurable business outcomes. For each URL, the system first saves the raw content and then performs this initial qualification analysis. If Claude determines that a case study qualifies as an enterprise AI implementation, the system proceeds to generate a detailed analysis. It creates three types of reports: - an individual case study report with sections like Executive Summary, AI Strategy Analysis, and Business Impact Assessment - a cross-case analysis that identifies patterns and trends across multiple case studies - and an executive dashboard summarizing key metrics and insights. All of these reports are saved in structured formats (markdown for individual reports, JSON for cross-case analysis and dashboard) in their respective directories. If a case study doesn't qualify as an enterprise AI implementation, the system logs the reason and moves on to the next URL. The entire process is asynchronous and provides detailed terminal feedback about its progress and decisions.

Muratcan Koylan

85,319 просмотров • 1 год назад

🔎 ICE Shooting of Renée Good : Preliminary 3D analysis On January 7, 2025, an agent of the US Immigration and Customs Enforcement (ICE) killed Renee Nicole Good in Minneapolis, firing three shots into her vehicle. Following the release of witness footage, Index conducted a preliminary visual analysis and 3D reconstruction of the event. Our findings contradict the claims made by federal authorities regarding the agent's safety: Trajectory: Our 3D model shows the agent was not in the path of the SUV when the shots were fired. Physical Contact: The analysis indicates the agent was not struck by the vehicle. Targeting: The shooter fired at lethal areas of Good’s body from a position where he was not in immediate danger. This preliminary investigation utilises photogrammetry, audiovisual analysis and 3D reconstruction to provide a factual account of the incident. Notice: Index is an independent, digital investigation NGO based in Paris, France. This is a preliminary analysis based on available footage and 3D digital reconstruction; it shall not be considered a finalized expert report. Usage Rights: This video can be freely embedded on webpages provided full credit is given: “Preliminary 3D analysis by Index Investigation” with a link to #Minneapolis #ReneeNicoleGood #ReneeNicoleMacklinGood #ReneeGood

🔎 ICE Shooting of Renée Good : Preliminary 3D analysis On January 7, 2025, an agent of the US Immigration and Customs Enforcement (ICE) killed Renee Nicole Good in Minneapolis, firing three shots into her vehicle. Following the release of witness footage, Index conducted a preliminary visual analysis and 3D reconstruction of the event. Our findings contradict the claims made by federal authorities regarding the agent's safety: Trajectory: Our 3D model shows the agent was not in the path of the SUV when the shots were fired. Physical Contact: The analysis indicates the agent was not struck by the vehicle. Targeting: The shooter fired at lethal areas of Good’s body from a position where he was not in immediate danger. This preliminary investigation utilises photogrammetry, audiovisual analysis and 3D reconstruction to provide a factual account of the incident. Notice: Index is an independent, digital investigation NGO based in Paris, France. This is a preliminary analysis based on available footage and 3D digital reconstruction; it shall not be considered a finalized expert report. Usage Rights: This video can be freely embedded on webpages provided full credit is given: “Preliminary 3D analysis by Index Investigation” with a link to #Minneapolis #ReneeNicoleGood #ReneeNicoleMacklinGood #ReneeGood

Index Investigation

72,962 просмотров • 7 месяцев назад

$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 time data - .csv file (can get this information from multiple venues - Binance is where I got mine) > creating bins - if you have a larger data set - you can use recent data to prevent overt bias in the long direction or filter consolidation and trending regime data into separate bins for statistical analysis - however, you will have to define thresholds and determine what that entails. > defining metrics in Claude that you want to use for statistical analysis e.g. for z-score what is it based on and what type of calculation? make sure you understand the calculations being performed for any metrics that you are doing a study for and modify them accordingly. > prompting Claude to do a statistical analysis with specific instructions and then tell it to create visualization for this. I've been messing around with this and I'm seriously impressed by the output.

$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 time data - .csv file (can get this information from multiple venues - Binance is where I got mine) > creating bins - if you have a larger data set - you can use recent data to prevent overt bias in the long direction or filter consolidation and trending regime data into separate bins for statistical analysis - however, you will have to define thresholds and determine what that entails. > defining metrics in Claude that you want to use for statistical analysis e.g. for z-score what is it based on and what type of calculation? make sure you understand the calculations being performed for any metrics that you are doing a study for and modify them accordingly. > prompting Claude to do a statistical analysis with specific instructions and then tell it to create visualization for this. I've been messing around with this and I'm seriously impressed by the output.

Stoic

19,757 просмотров • 1 год назад

You don’t need to browse YouTube to understand what’s performing. The YouTube Agent lets you query videos and extract structured data directly from the Teneo Agent Console. Example: Search “AI agents” sorted by view count Instead of scrolling through results, you get ranked videos with key metadata like views, titles, and channel information. From discovery to analysis, in one query. Explore it ↓

You don’t need to browse YouTube to understand what’s performing. The YouTube Agent lets you query videos and extract structured data directly from the Teneo Agent Console. Example: Search “AI agents” sorted by view count Instead of scrolling through results, you get ranked videos with key metadata like views, titles, and channel information. From discovery to analysis, in one query. Explore it ↓

Teneo Protocol

35,513 просмотров • 4 месяцев назад

We’re looking to expand our editing team for the JLA YouTube channel! You’ll need to be skilled in Premiere Pro and After Effects, ideally with an interest in football and a good understanding of our analysis content. If you think you can match of exceed the standard of the editing shown in this example, get in touch: jlavideoediting@gmail.com Please share or tag someone who fits! 👊🏼🫡

We’re looking to expand our editing team for the JLA YouTube channel! You’ll need to be skilled in Premiere Pro and After Effects, ideally with an interest in football and a good understanding of our analysis content. If you think you can match of exceed the standard of the editing shown in this example, get in touch: [email protected] Please share or tag someone who fits! 👊🏼🫡

James Allcott

24,558 просмотров • 1 год назад

“Chat with your PDF” still fails on ChatGPT/Claude when the doc is too complex 📉🧾 . If it’s a complicated financial report with scanned tables, you’re going to get back hallucinated values in your analysis! Check out the video 🎬 - in the first part, Claude gives back hallucinated values on the scanned tables in the equity research report. Having a proper document MCP server capable of high-accuracy parsing/indexing can help 💡. In the second part of the video I plug in LlamaCloud as an MCP tool to help with doc parsing and retrieval - it is fully able to render the scanned table in 2D format. You can set one up for yourself in 5 mins in the UI, no coding required. LlamaCloud: MCP Server:

“Chat with your PDF” still fails on ChatGPT/Claude when the doc is too complex 📉🧾 . If it’s a complicated financial report with scanned tables, you’re going to get back hallucinated values in your analysis! Check out the video 🎬 - in the first part, Claude gives back hallucinated values on the scanned tables in the equity research report. Having a proper document MCP server capable of high-accuracy parsing/indexing can help 💡. In the second part of the video I plug in LlamaCloud as an MCP tool to help with doc parsing and retrieval - it is fully able to render the scanned table in 2D format. You can set one up for yourself in 5 mins in the UI, no coding required. LlamaCloud: MCP Server:

Jerry Liu

21,369 просмотров • 1 год назад