How do you safely let AI agents handle real money? @catena_labs has the blueprint: • Decentralized Identifiers (DIDs) Verifiable Credentials for secure agent identity • Deterministic governance policies humans define rules, agents execute within bounds • Full on-chain audit trails & compliance • Multi-chain stablecoin payments emerging fiat rails • Yield opportunities on idle agent balances No more trust issues in agent to agent or agent to human commerce. Built from the ground up for the AI economy. This changes everything.

I was able to create my Line zn Europe account but in some European countries there are bugs… you can’t add identifiers using QR codes.

Universities Australia’s submission to the inquiry into the factors driving educational attainment calls for urgent reforms to make it easier for more Australians to access, participate in and complete tertiary education. Australia’s ambition of 80 per cent tertiary attainment by 2050 won’t be achieved without stronger outcomes across universities, VET, apprenticeships and traineeships—and better connected, more accessible education and training pathways for all Australians. Our recommendations to help lift educational attainment so that Australia can meet its future skills needs include: 1. Maintain and expand pathways into tertiary education, including enabling programs and FEEFREE Uni Ready courses, to support successful transitions into further study. 2. Replace the Job-ready Graduates Package with a funding model that removes financial barriers to participation and supports student choice. 3. Strengthen student support measures that improve participation and completion, including cost-of-living assistance and placement support. 4. Accelerate implementation of a nationally linked student data framework through Universal Student Identifiers to better understand learner pathways and attainment outcomes. Read our full submission: universitiesaustralia.edu.au… #Universitiesmatter #highered #research #auspol @AusGovEducation @JasonClareMP

spent most of yesterday improving the foundations of $NOELCLAW. a few highlights : - Deep Research v2 what started as a simple research tool is now becoming a proper multi-stage research system. reports can now: • reflect on their own findings • identify missing information • run additional searches • rank source quality • surface counterarguments • assign confidence levels the goal isn't more output, it's better output. - Security completed one of the biggest security passes we've done so far. > 38 public functions were refactored to use > authenticated session ownership instead of trusting client-provided identifiers. > Vault > Chronicle > Marketplace > API Keys > Notifications > Swarms all received stricter access controls. >> Testing (until recently we had zero automated tests) today : • 49 tests passing • auth coverage • ownership validation • cross-user isolation • session security the more NoelClaw grows, the more important trust, reliability, and accuracy become. we're continued shipping!

Decentralized Identifiers and governance policies definitely provide a secure framework it reduces my anxiety about using AI agents for transactions

Captions/Identifiers? #UFCFreedom250

Due to namespacing rules, this is valid C. ``` struct e {}; struct e e = {}; ``` For whatever reason, Wayland has leaned into this flexibility hard, and variables' identifiers share type names in many places. It's not a problem natively but is a real bother for bindings.

As e-commerce expands across dozens of sales channels, the complexity of managing product data — especially standardized identifiers like barcodes — keeps growing. Sellers must ensure every SKU complies with different platform requirements, from Amazon’s FNSKU to Walmart’s UPC constraints. UPCgen simplifies that process with a free online barcode generator covering formats such as UPC, EAN, ISBN, and ITF-14. It’s a small but meaningful tool that saves time and prevents listing errors for sellers building omnichannel operations. auraplusplus.com/projects/fr… How do you see automation tools shaping the future of e-commerce logistics and catalog management? Launch your project on auraplusplus.com and get featured on our social media and blogs.

the top trending narratives are pointing to some interesting on-chain themes digital identity and decentralized identifiers are seeing renewed interest chains are abstracting away complexity to enable new primitives privacy infrastructure is emerging as a key focus for builders what's the alpha? money is flowing to projects solving these problems strategic investors and early adopters are positioning for the next wave

Of course it is. You may have not chosen the feeling, but you chose to call yourself trans to bear their symbol. You weren't born with those identifiers, you chose them

a human being and after interacting and / or socialising with them, normal identifiers are formed. You are fixated on Muslim / Non Muslim - you need to 1st slot the person as Human or Not Human

Well if it is any consolation, you are the epitome of an American who believes in our constitution and laws. Which is how we should all be seen. No identifiers just Americans.

Do you realize how desperate and pathetic this sounds? Honestly.. My point still stands and this has been well documented at this point There are zero markers, identifiers, or records of Christ being here. Christ. God Himself in the flesh On earth. The most consequential moment in human history We saw the impact it had in the Middle East, Europe, Africa, and India…is undeniable Americas? Nothing Stop wasting my time. This approach you’re taking won’t work

They could literally reveal the device based identifiers for the post in question. If he was hacked, the ip addresses etc would be different than his devices lol. @elonmusk could clear this up pretty quickly lol

It says they died You should still expect to see markers and identifiers Christ, God Himself, coming in the flesh, is the most consequential event in human history… Zero markers, zero identifiers, zero evidence of Him being here spreading the gospel The BoM is a fraud

It’s such a shitty story and book that I forgot about the convenient cop out “they all died” There should still be markers and identifiers of Christ.. You know, the most consequential event in human history

Look at all the mormons seething saying “just read the book and find out” They all died and apostatized… Well that’s just so convenient isn’t it? There should still be identifiers and markers… But there aren’t What a sham

I read it maybe 5-6 years ago “They all apostatized and died” still doesn’t really explain it There should still be markers and identifiers of the gospel But there are none Hard to imagine believing this nonsense tbh

🚨 🚀 Power BI Roadmap — Topic 5 🏗️ Data Modeling in Power BI (Most Important Topic) If DAX is the brain of Power BI, then Data Modeling is the backbone. Many beginners focus on visuals and DAX but ignore data modeling. In reality, a well-designed model can: ✅ Improve report performance by 10x ✅ Reduce DAX complexity ✅ Minimize errors ✅ Improve scalability ✅ Simplify maintenance 👉 Most Power BI performance issues originate from poor data modeling. 🎯 Learning Objectives By the end of this topic, you will understand: ✅ Fact Tables ✅ Dimension Tables ✅ Relationships ✅ Cardinality ✅ Star Schema ✅ Snowflake Schema ✅ Date Tables ✅ Model Optimization 📌 1. What is Data Modeling? Data Modeling is the process of organizing data tables and relationships so Power BI can analyze data efficiently. Example Instead of putting everything into one large table: ❌ Bad Model OrderID, Product, Customer, Region, Sales We split data logically. ✅ Good Model FactSales, DimProduct, DimCustomer, DimRegion, DimDate 📌 2. Fact Table A Fact Table contains measurable business data. Examples OrderID, ProductID, CustomerID, Sales Characteristics ✅ Large table ✅ Numeric values ✅ Transactional data ✅ Foreign Keys Common Metrics Sales, Revenue, Profit, Quantity, Cost 👉 Fact tables answer: "What happened?" 📌 3. Dimension Table Dimension tables describe business entities. Example DimProduct ProductID, ProductName, Category DimCustomer CustomerID, CustomerName, City Characteristics ✅ Descriptive data ✅ Smaller tables ✅ Unique Keys Dimensions answer: "Who?" "What?" "Where?" "When?" 📌 4. Understanding Relationships Relationships connect tables. Example FactSales ProductID DimProduct ProductID Relationship: FactSales → ProductID → DimProduct 📌 5. Types of Relationships 🔹 One-to-One (1:1) One record matches one record. Example: Employee ↔ EmployeeProfile Rarely used. 🔹 One-to-Many (1:_) Most common relationship. Example: One Product → Many Sales Transactions DimProduct (1) → FactSales (_) 👉 Preferred relationship type. 🔹 Many-to-Many (_:_) Multiple matches on both sides. Example: Students ↔ Courses Usually avoided when possible. 📌 6. Cardinality Cardinality defines how tables relate. Types Type | Meaning 1:1 | One-to-One 1:_ | One-to-Many _:* | Many-to-Many Interview Question Which cardinality is preferred? ✅ One-to-Many Because it: Improves performance, Simplifies filtering, Reduces ambiguity 📌 7. Cross Filter Direction Controls how filters flow. Single Direction DimProduct → FactSales Recommended. Both Directions DimProduct ↔ FactSales Use carefully. May create: ❌ Ambiguous relationships, ❌ Performance issues 📌 8. Active vs Inactive Relationships Suppose Sales table contains: Order Date, Ship Date Both connect to Date Table. Power BI allows: ✅ One Active Relationship, ❌ Others become Inactive Example Sales → Date Active Sales → Date Inactive[OrderDate][Date][ShipDate] DAX Solution Ship Sales = CALCULATE( SUM(Sales[Amount]), USERELATIONSHIP(Sales[ShipDate], Date[Date]) ) 📌 9. Star Schema (MOST IMPORTANT) Industry-standard Power BI design. Structure DimDate DimCustomer → FactSales → DimProduct DimRegion Characteristics ✅ Simple ✅ Fast ✅ Easy DAX ✅ Best Performance Why Use Star Schema? Because Power BI's engine is optimized for it. 👉 90% of enterprise Power BI models use Star Schema. 📌 10. Snowflake Schema Dimension tables are further normalized. Example FactSales → DimProduct → DimCategory Advantages ✅ Reduced redundancy Disadvantages ❌ More joins ❌ Slower performance ❌ More complexity Interview Answer Power BI generally prefers: ✅ Star Schema over ❌ Snowflake Schema 📌 11. Date Table (Very Important) Many DAX functions require a proper Date Table. Date Table Includes Date, Year, Month, Quarter, Week Benefits ✅ Time Intelligence ✅ YTD, MTD, QTD ✅ Previous Year Analysis Example Calendar = CALENDAR(DATE(2024,1,1), DATE(2026,12,31)) 📌 12. Surrogate Keys Artificial unique identifiers. Example Instead of: Product Name Use: ProductID Benefits ✅ Faster joins ✅ Better relationships ✅ Improved performance 📌 13. Model Optimization Best Practices Remove Unused Columns ❌ Load 50 columns ✅ Use only required columns Use Correct Data Types Example: Sales ✅ Whole Number, ❌ Text Reduce Cardinality High-cardinality columns increase memory. Examples: ❌ Transaction IDs, ❌ GUIDs Prefer Measures Instead of: ❌ Calculated Columns Use: ✅ Measures 📌 14. Common Modeling Mistakes ❌ Fact-to-Fact Relationships: Avoid FactSales ↔ FactInventory ❌ Many-to-Many Overuse: Causes confusion ❌ Duplicate Keys: Dimension table keys must be unique ❌ Bi-Directional Relationships Everywhere: Can slow reports dramatically 📌 15. Real-World Model Example Retail Dashboard Fact Table FactSales: ProductID, CustomerID, DateID, Quantity, Revenue Dimension Tables DimProduct, DimCustomer, DimRegion, DimDate Relationship Structure DimDate DimCustomer → FactSales → DimProduct DimRegion 📌 16. Interview Questions 1. What is Data Modeling? 2. Difference between Fact and Dimension tables? 3. What is Cardinality? 4. What is a Star Schema? 5. What is a Snowflake Schema? 6. What is a Date Table? 7. What is Cross Filter Direction? 8. What are Inactive Relationships? 9. What causes duplicate value errors? 10. Why is Star Schema preferred? 📌 17. Practice Project 🛒 Retail Sales Model Tables: Sales, Products, Customers, Regions, Calendar Tasks: ✅ Create Relationships ✅ Build Star Schema ✅ Create Date Table ✅ Test Filtering ✅ Validate Model 🎯 Goal of This Topic After completing this topic, you should be able to: ✅ Design professional Power BI models ✅ Create relationships confidently ✅ Build Star Schemas ✅ Optimize performance ✅ Prepare for Data Modeling interview questions Double Tap ❤️ For Part-6