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Your ETL pipeline has more orchestration code than transformation logic. AWS Glue 6.0: Delivers 30% lower price, Iceberg v3 with VARIANT shredding, Spark 4.1 and Python 3.13 runtimes, Spark Declarative Pipelines and Real-Time Mode

Amazon Web Services

2,235,024 subscribers

27,908 次观看 • 7 天前 •via X (Twitter)

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Upcoming Releases for Alchemist AI We’re rolling out a series of infrastructure and product upgrades to further enhance the Alchemist AI platform: • New Generation Pipeline Improved orchestration, lower latency, and optimized resource handling for faster and more scalable world generation. • 3D Engine Enhancements Upgraded rendering capabilities, improved material fidelity, and smoother in-app performance across devices. • App Testing Agent Automated simulation and behavioral testing across generated environments to ensure logic consistency and playability. • Expanded 3D Model Library Hundreds of new assets added across key categories, optimized for real-time use and fully integrated into the creation workflow. • Texturized 3D Model Generation Models now ship with context-aware textures and material mapping, reducing setup time and enhancing visual realism.

Upcoming Releases for Alchemist AI We’re rolling out a series of infrastructure and product upgrades to further enhance the Alchemist AI platform: • New Generation Pipeline Improved orchestration, lower latency, and optimized resource handling for faster and more scalable world generation. • 3D Engine Enhancements Upgraded rendering capabilities, improved material fidelity, and smoother in-app performance across devices. • App Testing Agent Automated simulation and behavioral testing across generated environments to ensure logic consistency and playability. • Expanded 3D Model Library Hundreds of new assets added across key categories, optimized for real-time use and fully integrated into the creation workflow. • Texturized 3D Model Generation Models now ship with context-aware textures and material mapping, reducing setup time and enhancing visual realism.

ALCHEMIST AI 🔮

10,899 次观看 • 1 年前

Building Data Pipelines has levels to it: - level 0 Understand the basic flow: Extract → Transform → Load (ETL) or ELT This is the foundation. - Extract: Pull data from sources (APIs, DBs, files) - Transform: Clean, filter, join, or enrich the data - Load: Store into a warehouse or lake for analysis You’re not a data engineer until you’ve scheduled a job to pull CSVs off an SFTP server at 3AM! level 1 Master the tools: - Airflow for orchestration - dbt for transformations - Spark or PySpark for big data - Snowflake, BigQuery, Redshift for warehouses - Kafka or Kinesis for streaming Understand when to batch vs stream. Most companies think they need real-time data. They usually don’t. level 2 Handle complexity with modular design: - DAGs should be atomic, idempotent, and parameterized - Use task dependencies and sensors wisely - Break transformations into layers (staging → clean → marts) - Design for failure recovery. If a step fails, how do you re-run it? From scratch or just that part? Learn how to backfill without breaking the world. level 3 Data quality and observability: - Add tests for nulls, duplicates, and business logic - Use tools like Great Expectations, Monte Carlo, or built-in dbt tests - Track lineage so you know what downstream will break if upstream changes Know the difference between: - a late-arriving dimension - a broken SCD2 - and a pipeline silently dropping rows At this level, you understand that reliability > cleverness. level 4 Build for scale and maintainability: - Version control your pipeline configs - Use feature flags to toggle behavior in prod - Push vs pull architecture - Decouple compute and storage (e.g. Iceberg and Delta Lake) - Data mesh, data contracts, streaming joins, and CDC are words you throw around because you know how and when to use them. What else belongs in the journey to mastering data pipelines?

Building Data Pipelines has levels to it: - level 0 Understand the basic flow: Extract → Transform → Load (ETL) or ELT This is the foundation. - Extract: Pull data from sources (APIs, DBs, files) - Transform: Clean, filter, join, or enrich the data - Load: Store into a warehouse or lake for analysis You’re not a data engineer until you’ve scheduled a job to pull CSVs off an SFTP server at 3AM! level 1 Master the tools: - Airflow for orchestration - dbt for transformations - Spark or PySpark for big data - Snowflake, BigQuery, Redshift for warehouses - Kafka or Kinesis for streaming Understand when to batch vs stream. Most companies think they need real-time data. They usually don’t. level 2 Handle complexity with modular design: - DAGs should be atomic, idempotent, and parameterized - Use task dependencies and sensors wisely - Break transformations into layers (staging → clean → marts) - Design for failure recovery. If a step fails, how do you re-run it? From scratch or just that part? Learn how to backfill without breaking the world. level 3 Data quality and observability: - Add tests for nulls, duplicates, and business logic - Use tools like Great Expectations, Monte Carlo, or built-in dbt tests - Track lineage so you know what downstream will break if upstream changes Know the difference between: - a late-arriving dimension - a broken SCD2 - and a pipeline silently dropping rows At this level, you understand that reliability > cleverness. level 4 Build for scale and maintainability: - Version control your pipeline configs - Use feature flags to toggle behavior in prod - Push vs pull architecture - Decouple compute and storage (e.g. Iceberg and Delta Lake) - Data mesh, data contracts, streaming joins, and CDC are words you throw around because you know how and when to use them. What else belongs in the journey to mastering data pipelines?

Zach Wilson

16,688 次观看 • 1 年前

Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:

Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:

Santiago

296,637 次观看 • 10 个月前

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 次观看 • 9 个月前

Jupiter prides itself as the consumer face of Defi lend, perps, pro, prediction markets,jup has it all! now it gets even better with the ULTRA V3! the ultra V3 is Crypto’s ultimate trading engine! It introduces;👇 •Iris, our new router which uses Brent’s method for route splitting and capable of more granular splitting, up to 0.01%. •ShadowLane - Jupiter’s in-house transaction landing engine which leverages on its own validator stake and dedicated R&D efforts to consistently process transactions within sub-second latency It operates entirely on Jupiter’s infrastructure instead of relying on external providers, this eliminates the risk of artificial delays and front-running ensuring faster and more secure execution for jupiter users. With shadowlane, Jupiter minimizes your exposure to susceptible MEV risks by ensuring trades are never handed off to any external providers for onchain execution. The Ultra V3 introduces Predictive Execution. The Ultra V3 introduces JupiterZ which facilitates about $100M daily volume all with ZERO SLIPPAGE! Ultra V3 also introduces Ultra Signaling, which allows Prop AMMs to distinguish between different types of user flow when submitting quotes to Ultra. The ultra signaling differentiates Ls between “non-toxic” and “toxic” order flow when quoting. With Ultra V3, Jupiter accounts for slippage when deciding which AMM to use. This ensures that the provided quotes reflect actual onchain execution, not theoretical or inflated “best prices”. The ultra v3 also has gasless support coverage! With the Gasless Support acting as a just-in-time fee payer, you can make trades on ULTRA V3 without holding $SOL to cover gas fees, Ultra automatically calculates and covers the gas fee, deducting it from the swap amount. Ultra v3 fundamentally transforms swap execution with top tier improvements at every level resulting in •the best price, •the best execution (3x more trades landed) •the best price protection (34x better sandwich protection) note: meta aggregation with the new iris router means you literally get the best price across dflow, hashflow, okx, and more Ultra V3’s execution is insane too - 34x better sandwich protection, lower slippage, and 8-10x cheaper fees. The predictive routing + gasless support combo also make your trading experience seamless! The ULTRA v3 runs across the web, Jupiter mobile, desktop wallet and jup pro tools. It is majorly a spot construct meaning you can swap studio tokens with v3 using the jup mobile (every swap in the jup mobile is powered by the ultra V3). Try the ULTRA V3 here - or through the swap ui or pro ui in the Jupiter Mobile!

Jupiter prides itself as the consumer face of Defi lend, perps, pro, prediction markets,jup has it all! now it gets even better with the ULTRA V3! the ultra V3 is Crypto’s ultimate trading engine! It introduces;👇 •Iris, our new router which uses Brent’s method for route splitting and capable of more granular splitting, up to 0.01%. •ShadowLane - Jupiter’s in-house transaction landing engine which leverages on its own validator stake and dedicated R&D efforts to consistently process transactions within sub-second latency It operates entirely on Jupiter’s infrastructure instead of relying on external providers, this eliminates the risk of artificial delays and front-running ensuring faster and more secure execution for jupiter users. With shadowlane, Jupiter minimizes your exposure to susceptible MEV risks by ensuring trades are never handed off to any external providers for onchain execution. The Ultra V3 introduces Predictive Execution. The Ultra V3 introduces JupiterZ which facilitates about $100M daily volume all with ZERO SLIPPAGE! Ultra V3 also introduces Ultra Signaling, which allows Prop AMMs to distinguish between different types of user flow when submitting quotes to Ultra. The ultra signaling differentiates Ls between “non-toxic” and “toxic” order flow when quoting. With Ultra V3, Jupiter accounts for slippage when deciding which AMM to use. This ensures that the provided quotes reflect actual onchain execution, not theoretical or inflated “best prices”. The ultra v3 also has gasless support coverage! With the Gasless Support acting as a just-in-time fee payer, you can make trades on ULTRA V3 without holding $SOL to cover gas fees, Ultra automatically calculates and covers the gas fee, deducting it from the swap amount. Ultra v3 fundamentally transforms swap execution with top tier improvements at every level resulting in •the best price, •the best execution (3x more trades landed) •the best price protection (34x better sandwich protection) note: meta aggregation with the new iris router means you literally get the best price across dflow, hashflow, okx, and more Ultra V3’s execution is insane too - 34x better sandwich protection, lower slippage, and 8-10x cheaper fees. The predictive routing + gasless support combo also make your trading experience seamless! The ULTRA v3 runs across the web, Jupiter mobile, desktop wallet and jup pro tools. It is majorly a spot construct meaning you can swap studio tokens with v3 using the jup mobile (every swap in the jup mobile is powered by the ultra V3). Try the ULTRA V3 here - or through the swap ui or pro ui in the Jupiter Mobile!

DUKE 🇲🇾

85,423 次观看 • 10 个月前

The IRS has a designation called Real Estate Professional Status (REPS) If your spouse works 750+ hours a year in real estate and it's their primary occupation — they qualify. Once they do, the paper losses from depreciation on your properties can offset your W-2 income taxes directly. But if you own a short-term rental, the bar is even lower. It's called the STR Loophole — and both spouses can have full-time jobs and still qualify. You just need 100+ hours spent working on your property and more time than anyone else involved. That's 2 hours a week. My wife and I used this to save over $1M in taxes last year across our Airbnb portfolio. The tax code is working for you. You just have to know how to use it. Comment "bnb" and I'll send you some info on this exact strategy

The IRS has a designation called Real Estate Professional Status (REPS) If your spouse works 750+ hours a year in real estate and it's their primary occupation — they qualify. Once they do, the paper losses from depreciation on your properties can offset your W-2 income taxes directly. But if you own a short-term rental, the bar is even lower. It's called the STR Loophole — and both spouses can have full-time jobs and still qualify. You just need 100+ hours spent working on your property and more time than anyone else involved. That's 2 hours a week. My wife and I used this to save over $1M in taxes last year across our Airbnb portfolio. The tax code is working for you. You just have to know how to use it. Comment "bnb" and I'll send you some info on this exact strategy

Michael Elefante

86,340 次观看 • 3 个月前

Still relying on AI tools that can’t turn your ideas into real, working apps? Most can write code snippets but fall apart when it’s time to actually build something complete. That’s where Replit ⠕ changes everything. I simply asked it: “Build a responsive calculator app with a clean UI in light/dark mode.” Within minutes, it built the entire app with a beautiful UI, logic, light/dark mode, animations, and full responsiveness. Literally no setup. No debugging headache. Just vibe coding. Here’s why Replit stands out 👇 ✅ Build in Plain English: Just describe what you want and Replit turns it into a production-ready app. ✅ All-in-One Platform: Code, AI help, hosting, auth, and database, everything you need, already built in. ✅ Vibe Code Anywhere: Build on the go with the Replit mobile app for iOS & Android. ✅ Connected Ecosystem: Integrate with tools you already use like Notion, Slack, Google Drive, Dropbox, and more. ✅ Design to Live App: Import directly from Figma and bring your ideas to life in minutes. No micromanaging. No chaos. Just you, your idea, and Replit doing the heavy lifting. 👉 Try it now: 🎁 Get $10 credit when you purchase Replit Core via my referral link. → #Replit #VibeCoding #AIApps #NoCode #BuildWithReplit #AItools #ReplitCore

Still relying on AI tools that can’t turn your ideas into real, working apps? Most can write code snippets but fall apart when it’s time to actually build something complete. That’s where Replit ⠕ changes everything. I simply asked it: “Build a responsive calculator app with a clean UI in light/dark mode.” Within minutes, it built the entire app with a beautiful UI, logic, light/dark mode, animations, and full responsiveness. Literally no setup. No debugging headache. Just vibe coding. Here’s why Replit stands out 👇 ✅ Build in Plain English: Just describe what you want and Replit turns it into a production-ready app. ✅ All-in-One Platform: Code, AI help, hosting, auth, and database, everything you need, already built in. ✅ Vibe Code Anywhere: Build on the go with the Replit mobile app for iOS & Android. ✅ Connected Ecosystem: Integrate with tools you already use like Notion, Slack, Google Drive, Dropbox, and more. ✅ Design to Live App: Import directly from Figma and bring your ideas to life in minutes. No micromanaging. No chaos. Just you, your idea, and Replit doing the heavy lifting. 👉 Try it now: 🎁 Get $10 credit when you purchase Replit Core via my referral link. → #Replit #VibeCoding #AIApps #NoCode #BuildWithReplit #AItools #ReplitCore

Manish Kumar Shah

52,314 次观看 • 9 个月前

gemini 3.7 flash vs deepseek v4 pro 0813 vs muse spark 1.2 – on voxel city dioramas three models each built three crossy road-style 3d scenes – a construction site, a nyc intersection, a river with a drawbridge – as single self-contained html files the setup: Nous Research's hermes agent cli on OpenRouter, three.js skills preloaded, identical prompts per scene tasks: 1. construction site – tower crane on a working lift loop, paver laying fresh road, roller compacting it behind 2. nyc crossing – four-way intersection with a traffic light state machine, queuing cars, pedestrians crossing on the walk signal 3. river drawbridge – double-leaf bascule that lifts for tall boats, cars queuing at the barriers, animated water every scene: Three.js r185, box geometry only, a locked 20-color palette, four camera presets, and a day/night mode with bloom. one file, no build step, no assets models: Google DeepMind gemini 3.7 flash, DeepSeek v4 pro 0813, AI at Meta muse spark 1.2 muse and gemini finished every scene in two to three minutes. deepseek took 15 to 41 minutes per scene - build time, all three scenes #1 gemini 3.7 flash – 6m 43s #2 muse spark 1.2 – 7m 20s #3 deepseek v4 pro – 91m 25s - total tokens #1 muse spark 1.2 – 440,279 #2 gemini 3.7 flash – 713,855 #3 deepseek v4 pro – 20,957,568 - total price #1 muse spark 1.2 – $0.53 #2 gemini 3.7 flash – $0.56 #3 deepseek v4 pro – $4.57 - agent calls across the three builds #1 muse spark 1.2 – 12 #2 gemini 3.7 flash – 18 #3 deepseek v4 pro – 143 observations: • muse won two of the three scenes on looks with the smallest files in the test – 887 to 1,042 lines against gemini's 1,934 to 2,377. cheapest, fastest to a good frame, and shortest turned out to be the same column • deepseek burned 20.96m tokens – 29x gemini, 48x muse – across 143 agent calls. prompt caching is the only reason that cost $4.57: the cache discount absorbed roughly $30 of resent context • gemini was the only model whose files needed zero fixes to render – and the only one whose night mode is cosmetic. the sky never darkens and one camera button does nothing. clean code for a scene it never looked at follow thehype. for 24/7 ai news, analysis and breakdowns

gemini 3.7 flash vs deepseek v4 pro 0813 vs muse spark 1.2 – on voxel city dioramas three models each built three crossy road-style 3d scenes – a construction site, a nyc intersection, a river with a drawbridge – as single self-contained html files the setup: Nous Research's hermes agent cli on OpenRouter, three.js skills preloaded, identical prompts per scene tasks: 1. construction site – tower crane on a working lift loop, paver laying fresh road, roller compacting it behind 2. nyc crossing – four-way intersection with a traffic light state machine, queuing cars, pedestrians crossing on the walk signal 3. river drawbridge – double-leaf bascule that lifts for tall boats, cars queuing at the barriers, animated water every scene: Three.js r185, box geometry only, a locked 20-color palette, four camera presets, and a day/night mode with bloom. one file, no build step, no assets models: Google DeepMind gemini 3.7 flash, DeepSeek v4 pro 0813, AI at Meta muse spark 1.2 muse and gemini finished every scene in two to three minutes. deepseek took 15 to 41 minutes per scene - build time, all three scenes #1 gemini 3.7 flash – 6m 43s #2 muse spark 1.2 – 7m 20s #3 deepseek v4 pro – 91m 25s - total tokens #1 muse spark 1.2 – 440,279 #2 gemini 3.7 flash – 713,855 #3 deepseek v4 pro – 20,957,568 - total price #1 muse spark 1.2 – $0.53 #2 gemini 3.7 flash – $0.56 #3 deepseek v4 pro – $4.57 - agent calls across the three builds #1 muse spark 1.2 – 12 #2 gemini 3.7 flash – 18 #3 deepseek v4 pro – 143 observations: • muse won two of the three scenes on looks with the smallest files in the test – 887 to 1,042 lines against gemini's 1,934 to 2,377. cheapest, fastest to a good frame, and shortest turned out to be the same column • deepseek burned 20.96m tokens – 29x gemini, 48x muse – across 143 agent calls. prompt caching is the only reason that cost $4.57: the cache discount absorbed roughly $30 of resent context • gemini was the only model whose files needed zero fixes to render – and the only one whose night mode is cosmetic. the sky never darkens and one camera button does nothing. clean code for a scene it never looked at follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

28,407 次观看 • 10 天前

New short course: Serverless Agentic Workflows with Amazon Bedrock. Learn to build and deploy serverless agents in this course created with Amazon Web Services and taught by Mike G Chambers, a Senior Developer Advocate at AWS specializing in GenAI. (Disclosure: I serve on Amazon's board.) Generative AI applications are becoming more complex, sophisticated, and agentic. Agentic applications have workloads that can be hard to predict in advance -- for example, what tools will it decide to call? -- and a serverless architecture helps you efficiently providing on-demand resources. This course teaches you to build and deploy a serverless agentic application. You’ll learn to create agents with tools, code execution, and guardrails, and build responsible agents for business use cases: - Build a customer service bot for a fictional tea mug business that can answering questions, retrieve information, and process orders. - Connect your customer service agent to a CRM to get customer info and log support tickets in real-time. - Explore how you invoke the agent, and see the trace to review the agent’s thought process and observation loop until it reaches its final output. - Attach a code interpreter to your agent, giving it the ability to perform accurate calculations by writing and running its own Python code. - Implement guardrails to prevent your agent from revealing sensitive information or using inappropriate language. By the end, you will have built a sophisticated AI agent capable of handling real-world customer support scenarios. Please sign up here!

New short course: Serverless Agentic Workflows with Amazon Bedrock. Learn to build and deploy serverless agents in this course created with Amazon Web Services and taught by Mike G Chambers, a Senior Developer Advocate at AWS specializing in GenAI. (Disclosure: I serve on Amazon's board.) Generative AI applications are becoming more complex, sophisticated, and agentic. Agentic applications have workloads that can be hard to predict in advance -- for example, what tools will it decide to call? -- and a serverless architecture helps you efficiently providing on-demand resources. This course teaches you to build and deploy a serverless agentic application. You’ll learn to create agents with tools, code execution, and guardrails, and build responsible agents for business use cases: - Build a customer service bot for a fictional tea mug business that can answering questions, retrieve information, and process orders. - Connect your customer service agent to a CRM to get customer info and log support tickets in real-time. - Explore how you invoke the agent, and see the trace to review the agent’s thought process and observation loop until it reaches its final output. - Attach a code interpreter to your agent, giving it the ability to perform accurate calculations by writing and running its own Python code. - Implement guardrails to prevent your agent from revealing sensitive information or using inappropriate language. By the end, you will have built a sophisticated AI agent capable of handling real-world customer support scenarios. Please sign up here!

Andrew Ng

81,048 次观看 • 1 年前

🚨 FREEPIK HAS RESET AI PRICING This isn't a flash sale or a limited-time deal. Freepik has quietly changed the rules. With the same subscription, you get more generations and a lower cost per output. Unlike most platforms that rely on discounts and countdowns, Freepik has redesigned its pricing structure. Here's what that means: ✅ Permanently reduced credit costs for top AI models ✅ More generations without upgrading plans ✅ Designed for high-volume creators and teams ✅ No gimmicks, no urgency traps One subscription now gives you access to: → Nano Banana Pro (now unlimited 🍌) → Google Veo 3.1 → Kling 2.5 & 2.6 → 30+ leading AI models → Lowest credit cost in the market Same price. More output. Long-term value. If your workflow relies on generating AI content at scale, this matters—a lot.Why pay more to generate less? 🔗 Freepik

🚨 FREEPIK HAS RESET AI PRICING This isn't a flash sale or a limited-time deal. Freepik has quietly changed the rules. With the same subscription, you get more generations and a lower cost per output. Unlike most platforms that rely on discounts and countdowns, Freepik has redesigned its pricing structure. Here's what that means: ✅ Permanently reduced credit costs for top AI models ✅ More generations without upgrading plans ✅ Designed for high-volume creators and teams ✅ No gimmicks, no urgency traps One subscription now gives you access to: → Nano Banana Pro (now unlimited 🍌) → Google Veo 3.1 → Kling 2.5 & 2.6 → 30+ leading AI models → Lowest credit cost in the market Same price. More output. Long-term value. If your workflow relies on generating AI content at scale, this matters—a lot.Why pay more to generate less? 🔗 Freepik

James Bond

27,427 次观看 • 7 个月前

We've completely overhauled the trading experience with real-time infrastructure and professional-grade tooling: • Shipped Helius webhook integration — trades index the moment they hit chain • Built StatsCollector service — live market data updates every 30 seconds across 51 markets • Added professional candlestick charts with full timeframe support (1m/5m/15m/1h/4h/1d) • Implemented global USD/token toggle across the entire platform • Crushed 14 bugs including critical security exploits, mobile UI overhaul, and loading state fixes • Upgraded database pipeline with fully automated market stats and oracle price tracking • & so much more! 37 commits, 10 PRs merged, +4.3k lines of new code this sprint alone You can view every update on Percolator through GitHub, everything is completely open-sourced: Try it live: Tutorial: Create mint ->Launch market ->Open account -> Deposit -> Set Trade -> Active trade -> Close trade

We've completely overhauled the trading experience with real-time infrastructure and professional-grade tooling: • Shipped Helius webhook integration — trades index the moment they hit chain • Built StatsCollector service — live market data updates every 30 seconds across 51 markets • Added professional candlestick charts with full timeframe support (1m/5m/15m/1h/4h/1d) • Implemented global USD/token toggle across the entire platform • Crushed 14 bugs including critical security exploits, mobile UI overhaul, and loading state fixes • Upgraded database pipeline with fully automated market stats and oracle price tracking • & so much more! 37 commits, 10 PRs merged, +4.3k lines of new code this sprint alone You can view every update on Percolator through GitHub, everything is completely open-sourced: Try it live: Tutorial: Create mint ->Launch market ->Open account -> Deposit -> Set Trade -> Active trade -> Close trade

Percolator

35,763 次观看 • 6 个月前

‘Truflation': Trump’s Actual Inflation Rate | E.J. Antoni, Ph.D. President Trump’s “clamoring for the Federal Reserve to lower interest rates” may finally pay off later this year when Kevin Warsh takes over as the new Fed chair. Trump “shouldn’t have to wait that long,” however, because his appeal to the Fed has been “on the grounds that inflation is much lower than what’s being officially reported,” explains E.J. Antoni, Ph.D., Ph.D., Heritage Foundation's chief economist. “ It turns out Trump is spot on with today's real inflation rate being only one-third the official metrics. In fact, these numbers come from the real-time price aggregator Truflation, which monitors millions of prices every single day. That is orders of magnitude more than the Bureau of Labor Statistics, or BLS, which only observes a few thousand prices just three times per month.” (00:00) Trump's Push for Lower Interest Rates (01:49) Understanding Real Inflation Rates (03:40) Comparing Truflation and CPI (06:26) Housing Costs and Inflation Metrics (09:02) The Impact of Lower Interest Rates (09:38) Conclusion and Next Steps 👉For more videos like this, subscribe to The Daily Signal’s YouTube channel and enable notifications to be alerted the second a new video drops:

‘Truflation': Trump’s Actual Inflation Rate | E.J. Antoni, Ph.D. President Trump’s “clamoring for the Federal Reserve to lower interest rates” may finally pay off later this year when Kevin Warsh takes over as the new Fed chair. Trump “shouldn’t have to wait that long,” however, because his appeal to the Fed has been “on the grounds that inflation is much lower than what’s being officially reported,” explains E.J. Antoni, Ph.D., Ph.D., Heritage Foundation's chief economist. “ It turns out Trump is spot on with today's real inflation rate being only one-third the official metrics. In fact, these numbers come from the real-time price aggregator Truflation, which monitors millions of prices every single day. That is orders of magnitude more than the Bureau of Labor Statistics, or BLS, which only observes a few thousand prices just three times per month.” (00:00) Trump's Push for Lower Interest Rates (01:49) Understanding Real Inflation Rates (03:40) Comparing Truflation and CPI (06:26) Housing Costs and Inflation Metrics (09:02) The Impact of Lower Interest Rates (09:38) Conclusion and Next Steps 👉For more videos like this, subscribe to The Daily Signal’s YouTube channel and enable notifications to be alerted the second a new video drops:

The Daily Signal

31,658 次观看 • 6 个月前

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

Andrew Ng

1,225,302 次观看 • 2 年前

🎬 Global Launch: Dreamina Seedance 2.5 is now live! Our most cinematic video model yet has arrived, and Dreamina AI is your official platform. Step into a new era of precision control and long-take storytelling: · Native 30s videos in a single generation · A new interactive editing experience: reshape any part of the frame · Long video mode: up to 3 minutes, with consistency that holds throughout · Dreamina AI plugins for Maya and Blender, built for film-grade production pipelines · Up to 50 multimodal references, now with support for 3D white models and green-screen footage — bringing AI generation into real production workflows · More true-to-life lighting and shadows, richer motion detail, and precise timestamp control down to 1 second Dreamina Seedance 2.5 is more than a model. It's an upgrade to what you can create. Now open to Dreamina AI subscribers. 🕐 Available now across Southeast Asia, the Middle East, Africa, Europe, and South America. More countries and regions coming soon. ✨ Watch Aura Design in action with the new model ⏳ Limited time: save up to 32% on credits per generation 🎁 Repost and comment – first 300 users get 1,000 credits in 12 hours #DreaminaSeedance25 #DreaminaAI

🎬 Global Launch: Dreamina Seedance 2.5 is now live! Our most cinematic video model yet has arrived, and Dreamina AI is your official platform. Step into a new era of precision control and long-take storytelling: · Native 30s videos in a single generation · A new interactive editing experience: reshape any part of the frame · Long video mode: up to 3 minutes, with consistency that holds throughout · Dreamina AI plugins for Maya and Blender, built for film-grade production pipelines · Up to 50 multimodal references, now with support for 3D white models and green-screen footage — bringing AI generation into real production workflows · More true-to-life lighting and shadows, richer motion detail, and precise timestamp control down to 1 second Dreamina Seedance 2.5 is more than a model. It's an upgrade to what you can create. Now open to Dreamina AI subscribers. 🕐 Available now across Southeast Asia, the Middle East, Africa, Europe, and South America. More countries and regions coming soon. ✨ Watch Aura Design in action with the new model ⏳ Limited time: save up to 32% on credits per generation 🎁 Repost and comment – first 300 users get 1,000 credits in 12 hours #DreaminaSeedance25 #DreaminaAI

Dreamina AI

616,254 次观看 • 28 天前

Opendoor $OPEN CEO Carrie Wheeler: • 65% of first-time home sellers willing to take 20% less for their home to avoid the stress while prioritizing convenience and certainty • Delistings are at a decade level high right now. 1 in 4 sellers are not getting a transaction done and pulling themselves back off the market. • More softness to home prices then may be reported today. • Advice for home buyers this spring is: Be Ready • The market is very dynamic, Even small changes in mortgage rates right now spark buyer demand • Inventory is 20% higher this time than it was last year, and the market is slow. Homes are staying on the market for longer. Things are shifting in the home-buyers favor. • Advice for home sellers: Be ready, there’s increasing competition. Your home’s price is the first impression, those first 2 weeks are really critical to gauge buyers demand. • Home sellers: Start with an #Opendoor cash offer, it’s the best way to understand the value of your home and start your selling journey. And you can get more insight on this way.

Opendoor $OPEN CEO Carrie Wheeler: • 65% of first-time home sellers willing to take 20% less for their home to avoid the stress while prioritizing convenience and certainty • Delistings are at a decade level high right now. 1 in 4 sellers are not getting a transaction done and pulling themselves back off the market. • More softness to home prices then may be reported today. • Advice for home buyers this spring is: Be Ready • The market is very dynamic, Even small changes in mortgage rates right now spark buyer demand • Inventory is 20% higher this time than it was last year, and the market is slow. Homes are staying on the market for longer. Things are shifting in the home-buyers favor. • Advice for home sellers: Be ready, there’s increasing competition. Your home’s price is the first impression, those first 2 weeks are really critical to gauge buyers demand. • Home sellers: Start with an #Opendoor cash offer, it’s the best way to understand the value of your home and start your selling journey. And you can get more insight on this way.

Art Jonak

12,147 次观看 • 1 年前

the V3 system is my best work yet.. i literally spent under 30 cents to create this AI UGC video i am literally mindblown by this i think it will be a while until we see something better than this.. this system fixes everything that was broken with AI UGC: > price per video is now cheaper than ever (and no it's not any of the models you've seen like seedance, kling, veo, grok, heygen.. etc) > realism has hit a whole new level (i can barely tell that this is AI) > AI voice has been fixed (no robotic kling / veo voice anymore) > the movement is the best it has ever been & you can control it however you want this is literally the best time to be alive and scale creatives without anyone stopping you except your imagination here's a recent vid i did:

the V3 system is my best work yet.. i literally spent under 30 cents to create this AI UGC video i am literally mindblown by this i think it will be a while until we see something better than this.. this system fixes everything that was broken with AI UGC: > price per video is now cheaper than ever (and no it's not any of the models you've seen like seedance, kling, veo, grok, heygen.. etc) > realism has hit a whole new level (i can barely tell that this is AI) > AI voice has been fixed (no robotic kling / veo voice anymore) > the movement is the best it has ever been & you can control it however you want this is literally the best time to be alive and scale creatives without anyone stopping you except your imagination here's a recent vid i did:

Miko

17,100 次观看 • 3 个月前

$I just compared Claude Code vs Codex vs Cursor CLI The task was to build a Next.js app with Tailwind 4 and shadcn components to collect customer feedback and showcase it with a widget. I gave all three the same prompt and let them go for 30 minutes to see what they came up with. Claude Code with Opus 4.1 Even though I told it to set up the app in the existing project folder, it tried to create a directory for it. After I interrupted and told it not to do that, it built a demo form and landing page with no errors. I had to ask it to make the demo interactive so users could submit a testimonial and preview it. The landing page looked like AI and was pretty basic, but it worked and it was done in a fraction of the time of the others. Total tokens used: 33k Codex with GPT-5 At the end of the 30 minutes I just could not get Codex to produce a working app. It got stuck in a loop of not being able to set up Tailwind 4 and despite many, MANY, attempts, I ended up with a "failed to compile" error. Total tokens used: 102k Cursor Agent with GPT-5 This was the slowest agent by far and a couple of times I actually thought it got stuck in a loop and was close to Ctrl+C'ing to cancel it. The TUI is really nice though, especially how it shows diffs and it did eventually build a working app (after one or two slight errors that needed fixing) The demo was interactive and it had a very minimal design that looked bare but also a lot less like an "AI generated" app than the Opus 4.1 design. It also wasn't too chatty and just did what it needed to do! Code quality was on a par with Opus 4.1, but it did use 5.5x as many tokens to get there. Still cheaper than Opus on a direct comparison but not when you factor in a Claude Code Max subscription. Total tokens: 188k I'll be able to do a proper comparison and record some videos when I'm back from holiday but for now, Opus is still the more capable model out of the box and Claude Code is the more complete CLI product. It will be interesting to see how Cursor evolve their CLI though with commands and subagents because I think with GPT-5 they have a real shot at providing competition for Claude Code if they can optimise output to get similar quality with less tokens. Jump to 0:40 in the video to see the two apps. Which do you think is which? ;)$

I just compared Claude Code vs Codex vs Cursor CLI The task was to build a Next.js app with Tailwind 4 and shadcn components to collect customer feedback and showcase it with a widget. I gave all three the same prompt and let them go for 30 minutes to see what they came up with. Claude Code with Opus 4.1 Even though I told it to set up the app in the existing project folder, it tried to create a directory for it. After I interrupted and told it not to do that, it built a demo form and landing page with no errors. I had to ask it to make the demo interactive so users could submit a testimonial and preview it. The landing page looked like AI and was pretty basic, but it worked and it was done in a fraction of the time of the others. Total tokens used: 33k Codex with GPT-5 At the end of the 30 minutes I just could not get Codex to produce a working app. It got stuck in a loop of not being able to set up Tailwind 4 and despite many, MANY, attempts, I ended up with a "failed to compile" error. Total tokens used: 102k Cursor Agent with GPT-5 This was the slowest agent by far and a couple of times I actually thought it got stuck in a loop and was close to Ctrl+C'ing to cancel it. The TUI is really nice though, especially how it shows diffs and it did eventually build a working app (after one or two slight errors that needed fixing) The demo was interactive and it had a very minimal design that looked bare but also a lot less like an "AI generated" app than the Opus 4.1 design. It also wasn't too chatty and just did what it needed to do! Code quality was on a par with Opus 4.1, but it did use 5.5x as many tokens to get there. Still cheaper than Opus on a direct comparison but not when you factor in a Claude Code Max subscription. Total tokens: 188k I'll be able to do a proper comparison and record some videos when I'm back from holiday but for now, Opus is still the more capable model out of the box and Claude Code is the more complete CLI product. It will be interesting to see how Cursor evolve their CLI though with commands and subagents because I think with GPT-5 they have a real shot at providing competition for Claude Code if they can optimise output to get similar quality with less tokens. Jump to 0:40 in the video to see the two apps. Which do you think is which? ;)

Ian Nuttall

195,173 次观看 • 1 年前