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We built an open-source Bloomberg that you talk to like ChatGPT🤯 It lets you query financial data in English: - Markets (stocks, crypto, FX) data📈 - Deep dives into SEC filings📁 - Instant charts & reports✅ - Insider transactions for companies💰 🔗

Odogwu Machalla

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32,915 просмотров • 6 месяцев назад •via X (Twitter)

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Bloomberg sells Wall Street a financial command center that costs $32,000 a year. So, a guy named Didier built a free alternative for ordinary investors like you and me. It is called OpenBB. Open it and you get a customizable financial workspace pulling market data, company fundamentals, filings, earnings transcripts, estimates, news, and economic indicators into one screen. > Stocks and ETFs > Options and futures > Forex and crypto > Financial statements > SEC filings > Global economic data Turn any dataset into interactive charts, tables, watchlists, and dashboards. Then ask its built-in AI Copilot to compare companies, explain price movements, summarize filings, or find patterns hidden inside the numbers. It reads the data already sitting on your dashboard and cites the exact sources behind its answers. You can also connect your own files, APIs, databases, research models, and custom AI agents. The underlying Open Data Platform is open source, has more than 70,000 GitHub stars, and installs with one command: pip install openbb The Community workspace is free for individual investors. Wall Street pays nearly $32,000 a year for a financial command center. OpenBB puts one in your browser.

Bloomberg sells Wall Street a financial command center that costs $32,000 a year. So, a guy named Didier built a free alternative for ordinary investors like you and me. It is called OpenBB. Open it and you get a customizable financial workspace pulling market data, company fundamentals, filings, earnings transcripts, estimates, news, and economic indicators into one screen. > Stocks and ETFs > Options and futures > Forex and crypto > Financial statements > SEC filings > Global economic data Turn any dataset into interactive charts, tables, watchlists, and dashboards. Then ask its built-in AI Copilot to compare companies, explain price movements, summarize filings, or find patterns hidden inside the numbers. It reads the data already sitting on your dashboard and cites the exact sources behind its answers. You can also connect your own files, APIs, databases, research models, and custom AI agents. The underlying Open Data Platform is open source, has more than 70,000 GitHub stars, and installs with one command: pip install openbb The Community workspace is free for individual investors. Wall Street pays nearly $32,000 a year for a financial command center. OpenBB puts one in your browser.

Ihtesham Ali

16,515 просмотров • 27 дней назад

Claude Opus 4.6 is so strong at financial analysis, including crunching data and reading SEC filings, that after it launched, financial data stocks sold off, with FactSet dropping nearly 10%. Claude Opus 4.6 can analyze company data, filings, and market info to produce deep financial analysis in hours instead of days. On an internal Real-World Finance evaluation of about 50 tasks spanning spreadsheets, slide decks, and word document creation and review, it scores over 23 percentage points higher than Claude Sonnet 4.5. On Vals AI benchmarks, it reports 60.7% on Finance Agent, which tests research over SEC filings, and 76.0% on TaxEval, which tests tax reasoning, with Finance Agent up 5.47% over Opus 4.5.

Claude Opus 4.6 is so strong at financial analysis, including crunching data and reading SEC filings, that after it launched, financial data stocks sold off, with FactSet dropping nearly 10%. Claude Opus 4.6 can analyze company data, filings, and market info to produce deep financial analysis in hours instead of days. On an internal Real-World Finance evaluation of about 50 tasks spanning spreadsheets, slide decks, and word document creation and review, it scores over 23 percentage points higher than Claude Sonnet 4.5. On Vals AI benchmarks, it reports 60.7% on Finance Agent, which tests research over SEC filings, and 76.0% on TaxEval, which tests tax reasoning, with Finance Agent up 5.47% over Opus 4.5.

Rohan Paul

465,744 просмотров • 6 месяцев назад

A snapshot of the Rogo Spreadsheets Agent. We’re proud to have built an agent that produces deliverables you’ll actually use. With it, you can: • Build complex, multi-tab DCFs / LBOs • Generate cohort, retention, P&L & KPI analyses • Convert filings / uploaded docs into structured data Every output is grounded in high-fidelity financial data — with every number attributed back to its source. Available in Rogo today.

A snapshot of the Rogo Spreadsheets Agent. We’re proud to have built an agent that produces deliverables you’ll actually use. With it, you can: • Build complex, multi-tab DCFs / LBOs • Generate cohort, retention, P&L & KPI analyses • Convert filings / uploaded docs into structured data Every output is grounded in high-fidelity financial data — with every number attributed back to its source. Available in Rogo today.

Rogo

12,083 просмотров • 6 месяцев назад

Minara is now open to everyone! 🔥 No more invite codes — just dive straight into the best-in-class financial AI assistant that works like a personal CFO in your pocket. ✅ Ask anything about crypto, stocks, DeFi, airdrops, trading strategies, events prediction, and more. ✅ Vibe trading crypto and tokenized stocks with a high-win-rate AI copilot. ✅ Create data table, charts and agentic financial workflow with simple prompts. ✅ 10x better in digital finance than generic AI chatbots like ChatGPT — at the same price. Special thanks to our launching partners Circle, Particle Network, CoinGecko, KITE AI, RootData | We're Hiring, DMind AI and imToken for powering a seamless experience from data to trading to automation. Minara is here to redefine how AI + humans interact with money, and we’re just getting started⚡️

Minara is now open to everyone! 🔥 No more invite codes — just dive straight into the best-in-class financial AI assistant that works like a personal CFO in your pocket. ✅ Ask anything about crypto, stocks, DeFi, airdrops, trading strategies, events prediction, and more. ✅ Vibe trading crypto and tokenized stocks with a high-win-rate AI copilot. ✅ Create data table, charts and agentic financial workflow with simple prompts. ✅ 10x better in digital finance than generic AI chatbots like ChatGPT — at the same price. Special thanks to our launching partners Circle, Particle Network, CoinGecko, KITE AI, RootData | We're Hiring, DMind AI and imToken for powering a seamless experience from data to trading to automation. Minara is here to redefine how AI + humans interact with money, and we’re just getting started⚡️

Minara AI

116,555 просмотров • 8 месяцев назад

Introducing Minara AI - your ultimate AI assistant for digital finance, like having a 24/7 personal CFO. Just ask your questions, get powerful market insights, and turn them into actionable strategies—backed by real-time data. Instantly. Overall, Minara can provides: ✅ Profressional analysis & insights as you need on crypto, stocks, RWA, DeFi,NFTs and more. (Far better than ChatGPT-5 and Grok-4) ✅ 50+ real-time data sources, including on-chain, financial, social and prediction data. ✅ Vibe trading crypto and tokenized stocks in one place. ✅ Create any agentic financial workflow with prompts. ✅ Manage your portfolio and track profit&loss ✅ Financial planning & forecasting (integrating Polymarket) No learning curve. For everyone. Join the waitlist and experience the revolution NOW👇

Introducing Minara AI - your ultimate AI assistant for digital finance, like having a 24/7 personal CFO. Just ask your questions, get powerful market insights, and turn them into actionable strategies—backed by real-time data. Instantly. Overall, Minara can provides: ✅ Profressional analysis & insights as you need on crypto, stocks, RWA, DeFi,NFTs and more. (Far better than ChatGPT-5 and Grok-4) ✅ 50+ real-time data sources, including on-chain, financial, social and prediction data. ✅ Vibe trading crypto and tokenized stocks in one place. ✅ Create any agentic financial workflow with prompts. ✅ Manage your portfolio and track profit&loss ✅ Financial planning & forecasting (integrating Polymarket) No learning curve. For everyone. Join the waitlist and experience the revolution NOW👇

Minara AI

201,403 просмотров • 1 год назад

Introducing Index. Turn your trading idea into a live perps trading agent. → Index lets you create AI agents that trade perpetual futures. You can describe a trading idea in plain English, backtest it on historical data, improve it through an experimentation loop, and deploy an agent that follows the strategy in live markets. Our community has already been testing Index in beta for a few weeks. Now it's open to everyone. Here’s how it works:👇

Introducing Index. Turn your trading idea into a live perps trading agent. → Index lets you create AI agents that trade perpetual futures. You can describe a trading idea in plain English, backtest it on historical data, improve it through an experimentation loop, and deploy an agent that follows the strategy in live markets. Our community has already been testing Index in beta for a few weeks. Now it's open to everyone. Here’s how it works:👇

Fraction AI

64,460 просмотров • 3 месяцев назад

We built a neat tool that lets you convert a directory of Powerpoint files into clean, structured markdown - that Claude Code / agent SDK / any generalized agent wrapper can easily understand. The pptx skill in Claude Code is quite basic and doesn’t have high-fidelity understanding over graphics/charts/tables. Our project Surreal Slides uses LlamaParse to convert presentations into clean structured data that you can put into a db (SurrealDB) for simple retrieval, without having to take screenshots of the data on the fly. Thanks to Clelia Bertelli (🦙/acc) for this project, check it out:

We built a neat tool that lets you convert a directory of Powerpoint files into clean, structured markdown - that Claude Code / agent SDK / any generalized agent wrapper can easily understand. The pptx skill in Claude Code is quite basic and doesn’t have high-fidelity understanding over graphics/charts/tables. Our project Surreal Slides uses LlamaParse to convert presentations into clean structured data that you can put into a db (SurrealDB) for simple retrieval, without having to take screenshots of the data on the fly. Thanks to Clelia Bertelli (🦙/acc) for this project, check it out:

Jerry Liu

39,872 просмотров • 5 месяцев назад

.Josh Wolfe: Anybody Using DeepSeek App Is 'Absolute Fool' "Anybody using the DeepSeek app is an absolute fool. If you're using DeepSeek on companies like Together Compute, one of Lux's companies, which can get rid of the CCP censorship, then it's probably okay. But remember, the open-source movement is something we deeply believe in. Most great technologists, entrepreneurs, and venture capitalists are on the side of open source. The closed-source models that have consumed tens of billions of dollars are the ones that are really going to be at risk. When you look at Hugging Face, a major repository, or Together Compute, Runway ML, and a lot of Lux's companies, they have been pioneers in open source. Now, why am I not worried about open source, even with the DeepSeek model? As long as you don't have the CCP censorship on it, the models with their open weights allow people to run on their proprietary data. This means companies like pharma or defense companies that have their own siloed, proprietary data—think about Bloomberg with their proprietary longitudinal data, or Meta with their data—are the ones who will have the edge. Even as open source takes hold, these companies will still dominate. I’m not worried about open source being the problem. I’m more concerned about people overfunding closed models with no proprietary source. A lot of capital is going to be burned there, and we’re already seeing that with people worried about OpenAI in some aspects."

.Josh Wolfe: Anybody Using DeepSeek App Is 'Absolute Fool' "Anybody using the DeepSeek app is an absolute fool. If you're using DeepSeek on companies like Together Compute, one of Lux's companies, which can get rid of the CCP censorship, then it's probably okay. But remember, the open-source movement is something we deeply believe in. Most great technologists, entrepreneurs, and venture capitalists are on the side of open source. The closed-source models that have consumed tens of billions of dollars are the ones that are really going to be at risk. When you look at Hugging Face, a major repository, or Together Compute, Runway ML, and a lot of Lux's companies, they have been pioneers in open source. Now, why am I not worried about open source, even with the DeepSeek model? As long as you don't have the CCP censorship on it, the models with their open weights allow people to run on their proprietary data. This means companies like pharma or defense companies that have their own siloed, proprietary data—think about Bloomberg with their proprietary longitudinal data, or Meta with their data—are the ones who will have the edge. Even as open source takes hold, these companies will still dominate. I’m not worried about open source being the problem. I’m more concerned about people overfunding closed models with no proprietary source. A lot of capital is going to be burned there, and we’re already seeing that with people worried about OpenAI in some aspects."

Josh Caplan

40,039 просмотров • 1 год назад

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 просмотров • 9 месяцев назад

By 2030, the data science field is projected to grow by 30% -- this is the tool I’d add to my learning path if I were re-skilling into data science: 𝐩𝐢𝐩 𝐢𝐧𝐬𝐭𝐚𝐥𝐥 𝐦𝐢𝐭𝐨-𝐚𝐢 It’s an open-source toolkit that adds AI to your Jupyter Notebooks, and it’s surprisingly helpful for learning by doing. Try now It lets you: • Use natural language to generate full data workflows • Upload an Excel file and watch it convert to clean Python code • Work with top models (ChatGPT, Claude, Gemini) right inside your notebook with no setup One thing I really like about Mito AI: it shows you exactly what it’s doing. (Which is super helpful for both beginners and debugging.) -The agent explains its assumptions - Every result links back to editable, transparent code That makes it a great companion if you're: • Transitioning from Excel • Exploring data automation or dashboards • Or just brushing up on how real data science pipelines work

By 2030, the data science field is projected to grow by 30% -- this is the tool I’d add to my learning path if I were re-skilling into data science: 𝐩𝐢𝐩 𝐢𝐧𝐬𝐭𝐚𝐥𝐥 𝐦𝐢𝐭𝐨-𝐚𝐢 It’s an open-source toolkit that adds AI to your Jupyter Notebooks, and it’s surprisingly helpful for learning by doing. Try now It lets you: • Use natural language to generate full data workflows • Upload an Excel file and watch it convert to clean Python code • Work with top models (ChatGPT, Claude, Gemini) right inside your notebook with no setup One thing I really like about Mito AI: it shows you exactly what it’s doing. (Which is super helpful for both beginners and debugging.) -The agent explains its assumptions - Every result links back to editable, transparent code That makes it a great companion if you're: • Transitioning from Excel • Exploring data automation or dashboards • Or just brushing up on how real data science pipelines work

Python Coding

107,495 просмотров • 10 месяцев назад

We're currently rebranding APItoolkit to Monoscope (monoscope ). This is because the scope of the platform was beyond API monitoring (Even if it still can do that; 3rd party or own API monitoring). But Monoscope is now a general open telemetry vendor. So, anything that can be instrumented via OpenTelemetry can be monitored using Monoscope. You can also create all sorts of dashboards (and yes, you can use AI to make the charts). And you can have all your data be stored in your own s3 bucket (you own the data. We don't charge for retention), so you can query years of data via the platform. No extra cost. Let me know what you think. Cheers.

We're currently rebranding APItoolkit to Monoscope (monoscope ). This is because the scope of the platform was beyond API monitoring (Even if it still can do that; 3rd party or own API monitoring). But Monoscope is now a general open telemetry vendor. So, anything that can be instrumented via OpenTelemetry can be monitored using Monoscope. You can also create all sorts of dashboards (and yes, you can use AI to make the charts). And you can have all your data be stored in your own s3 bucket (you own the data. We don't charge for retention), so you can query years of data via the platform. No extra cost. Let me know what you think. Cheers.

Anthony Alaribe {···}

17,825 просмотров • 10 месяцев назад

Problem: After working for a few months in open source, I noticed that understanding issues is often very tough, and random OS files make it a nightmare for newbies. Solution: So here we go I created It explains any open-source repo file line by line with flowcharts, explains open-source issues, and provides a dedicated step-by-step solution plan. It also helps you write PR content in multiple languages. Built for the Apify × Lingo.dev Multilingual Actors Hackathon You can start using it by adding a Gemini or Groq API key. For more detailed explanations and better data fetching, you can add an Apify API key. For in-app translations, it uses Lingo. Even if you don’t believe in the app, you can check the repo code or fork it and deploy it yourself (SQL query provided) and use it unlimited times. Enjoy. Your Problem solved.

Problem: After working for a few months in open source, I noticed that understanding issues is often very tough, and random OS files make it a nightmare for newbies. Solution: So here we go I created It explains any open-source repo file line by line with flowcharts, explains open-source issues, and provides a dedicated step-by-step solution plan. It also helps you write PR content in multiple languages. Built for the Apify × Lingo.dev Multilingual Actors Hackathon You can start using it by adding a Gemini or Groq API key. For more detailed explanations and better data fetching, you can add an Apify API key. For in-app translations, it uses Lingo. Even if you don’t believe in the app, you can check the repo code or fork it and deploy it yourself (SQL query provided) and use it unlimited times. Enjoy. Your Problem solved.

Sk Akram

30,264 просмотров • 7 месяцев назад

Build a loan underwriting agent workflow in ~5 mins 💵🤖 (No code, just English) I built an agentic workflow that can process incoming PDFs of financial statements: checking statements, bank statements, brokerage accounts, and extract/normalize the financials in each source of data. I did this by specifying the schemas and classification logic in plain English; our agent compiles this into a deterministic workflow with calls to agentic parsing to extract out all the information that you need. Our agent builder is now in LlamaCloud, you can sign up and try it yourself! Blog: Sign up:

Build a loan underwriting agent workflow in ~5 mins 💵🤖 (No code, just English) I built an agentic workflow that can process incoming PDFs of financial statements: checking statements, bank statements, brokerage accounts, and extract/normalize the financials in each source of data. I did this by specifying the schemas and classification logic in plain English; our agent compiles this into a deterministic workflow with calls to agentic parsing to extract out all the information that you need. Our agent builder is now in LlamaCloud, you can sign up and try it yourself! Blog: Sign up:

Jerry Liu

30,015 просмотров • 6 месяцев назад

i wish i could just upload a CSV and it would automatically create a dashboard so we built this today we're launching graphed .com graphed is an AI data analytics and dashboard generator upload data into Graphed and it's gonna one shot generate a dashboard you can chat with the entire dashboard to make changes and you can click into the graph and chat with it to make updates everything you create in Graphed can easily be shared with your team by sending a link or you can download a screenshot and drop it into a Slack channel or add it to a presentation the Graphed source data can either be a CSV or a data integration data integrations allow you to create live dashboards that are constantly updating in the background we're launching with Google Analytics 4 as our first data integration but we have Shopify, Facebook ads, Google Ads, MongoDB, and hundreds more are on our product roadmap you can sign up for free at graphed .com and if you want a 40% discount code that will only be available during launch comment "graphed" below

i wish i could just upload a CSV and it would automatically create a dashboard so we built this today we're launching graphed .com graphed is an AI data analytics and dashboard generator upload data into Graphed and it's gonna one shot generate a dashboard you can chat with the entire dashboard to make changes and you can click into the graph and chat with it to make updates everything you create in Graphed can easily be shared with your team by sending a link or you can download a screenshot and drop it into a Slack channel or add it to a presentation the Graphed source data can either be a CSV or a data integration data integrations allow you to create live dashboards that are constantly updating in the background we're launching with Google Analytics 4 as our first data integration but we have Shopify, Facebook ads, Google Ads, MongoDB, and hundreds more are on our product roadmap you can sign up for free at graphed .com and if you want a 40% discount code that will only be available during launch comment "graphed" below

Cody Schneider

47,333 просмотров • 1 год назад

David Sacks: Anthropic Is Trying to Crush Open Source AI and the American Developers Who Use It David Sacks: “I know people don't have a lot of sympathy for Chinese companies, that's fine. I'm not defending Chinese companies. I'm defending American developers who need to be able to use everything in the public domain. And let me give you an example. Cursor rolled out its new product, Composer 2. They were able to post-train that model using Kimi K2.5 on their own proprietary coding data. They started with a Chinese open source model, and then they used their own data, and they came up with a new derivative product. This is the way that open source works. You take things that are in the public domain, you fork them, you make them your own. And by the way, once it's in the public domain, it's not a Chinese model anymore. No data is going back to China, nothing's going back to China. An American company has taken open source contributions in the public domain, made it their own, and then developed their own model. And if you say that American companies can't do that, or that somehow it's tainted with IP theft, you are basically going to put a dagger through the heart of the entire American open source ecosystem. And that is exactly what Anthropic wants, because they do not want to have the competition.”

David Sacks: Anthropic Is Trying to Crush Open Source AI and the American Developers Who Use It David Sacks: “I know people don't have a lot of sympathy for Chinese companies, that's fine. I'm not defending Chinese companies. I'm defending American developers who need to be able to use everything in the public domain. And let me give you an example. Cursor rolled out its new product, Composer 2. They were able to post-train that model using Kimi K2.5 on their own proprietary coding data. They started with a Chinese open source model, and then they used their own data, and they came up with a new derivative product. This is the way that open source works. You take things that are in the public domain, you fork them, you make them your own. And by the way, once it's in the public domain, it's not a Chinese model anymore. No data is going back to China, nothing's going back to China. An American company has taken open source contributions in the public domain, made it their own, and then developed their own model. And if you say that American companies can't do that, or that somehow it's tainted with IP theft, you are basically going to put a dagger through the heart of the entire American open source ecosystem. And that is exactly what Anthropic wants, because they do not want to have the competition.”

The All-In Podcast

277,774 просмотров • 26 дней назад

$Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.$

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.

Akshay 🚀

39,331 просмотров • 6 месяцев назад

What if your data worked for you instead of against you? In one of the most gripping talks at ZK Hub ETHDenver, Juan Angel (Juan), Ecosystem Growth at , made the case for “open but private” data - arguing that blind computation can break data out of corporate silos and let users lease it securely without ever revealing the raw inputs. Through real examples like encrypted genomic data (via Monatic DNA) and incentivized health prediction markets (Stadium Science), Juan painted a future where users hold the encryption keys, opt in to share data selectively, and get paid every time it's used. The biggest takeaway? Not your keys, not your data. This talk is also available to view on the House of ZK YouTube:

What if your data worked for you instead of against you? In one of the most gripping talks at ZK Hub ETHDenver, Juan Angel (Juan), Ecosystem Growth at , made the case for “open but private” data - arguing that blind computation can break data out of corporate silos and let users lease it securely without ever revealing the raw inputs. Through real examples like encrypted genomic data (via Monatic DNA) and incentivized health prediction markets (Stadium Science), Juan painted a future where users hold the encryption keys, opt in to share data selectively, and get paid every time it's used. The biggest takeaway? Not your keys, not your data. This talk is also available to view on the House of ZK YouTube:

House of ZK

65,143 просмотров • 1 год назад

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

Santiago

142,696 просмотров • 1 год назад