Surprise! “The Tortured Poets Department” does not appear to be about Joe Alwyn, but about Swift’s situationship with the problematic singer, at least according to alleged leaks.

If This Taylor Swift Album Leak Is Real, It’s All About Matty Healy

Fashion critic Cathy Horyn’s close read on a decade of Taylor Swift’s outfits including the Grammys, her Eras tour and just walking around with Travis Kelce.

The Real Reason Taylor Swift Dresses Like That

<strong>Real</strong> Madrid will face Bayern Munich in the Champions League semifinals after defeating Manchester City in a tense penalty shootout on Wednesday.

Real Madrid defeats Manchester City in penalty shootout to reach Champions League semifinals | CNN

Man City are one of the world&#x27;s elite teams, but <strong>Real</strong> Madrid showed why they are still the gold standard after a backs-against-the-wall win in the Champions League quarterfinals.

Real Madrid punish Man City's missed chances in Champions League epic

Real

How Good Is WebRTC Screen Sharing, Really? I Put 4 Codecs to the Test

Hoe goed is WebRTC-skermdeling regtig? Ek stel 4 kodeks op die proef

ما مدى جودة مشاركة الشاشة عبر WebRTC؟ اختبرت أربعة برامج ترميز.

WebRTC স্ক্রিন শেয়ারিং আসলে কতটা ভালো? আমি ৪টি কোডেক পরীক্ষা করে দেখেছি

¿Qué tan bueno es realmente compartir pantalla con WebRTC? Puse a prueba cuatro códecs

Իրոք, որքանո՞վ է լավ WebRTC էկրանի փոխանակումը: Ես փորձարկման եմ դրել 4 կոդեկ

כמה טוב שיתוף מסך WebRTC, באמת? שמתי 4 Codec למבחן

WebRTC 画面共有は本当に優れているのか? 4 つのコーデックをテストしてみた

Comment Malamu Ezali WebRTC Screen Sharing, Vraiment? Natie 4 Codecs na Test

Seberapa Baik Perkongsian Skrin WebRTC, Betulkah? Saya Menguji 4 Codec

WebRTC स्क्रिन सेयरिङ कति राम्रो छ, साँच्चै? मैले ४ वटा कोडेक्स परीक्षण गरें

Yakanaka Sei WebRTC Screen Kugovera, Chaizvoizvo? Ndakaisa 4 Codecs kuMuedzo

Мубодилаи экрани WebRTC то чӣ андоза хуб аст, воқеан? Ман 4 кодекро ба озмоиш гузоштам

No, the Market Isn’t ‘Hunting’ Your Stop-Loss—Here’s What’s Really Happening Instead

**NEW YORK, United States, March 19th, 2025/Chainwire/--**Arda, a company developing a universal operating system for real estate, has raised $3 million in a pre-seed funding round led by Lightshift. The investment supports Arda’s initiative to enhance efficiency in the $380 trillion real estate sector by addressing its fragmented and opaque infrastructure. Arda is building a digital framework designed to unify real estate assets, data, and financial services into a single, interoperable system. Leveraging AI and blockchain technology, the platform aims to reduce transaction times and enhance transparency by enabling real-time ownership and financial processes. A Purpose-Built Digital Infrastructure for Real Estate Arda’s platform introduces a programmable execution layer for real estate, integrating digital rights management, AI-driven automation, and seamless data interoperability. Key features include: Digital Asset Profiles: A verifiable record of property history to streamline due diligence.
Real-Time Money Movement: Payments directly linked to transactions for improved settlement efficiency.
Integrated Data and Automation: Automated workflows reducing administrative friction in asset management. Digital Asset Profiles: A verifiable record of property history to streamline due diligence. Real-Time Money Movement: Payments directly linked to transactions for improved settlement efficiency. Integrated Data and Automation: Automated workflows reducing administrative friction in asset management. Industry Leadership and Institutional Backing Arda was founded by Oli Harris, former Managing Director in Digital Assets at Goldman Sachs and Head of Crypto Assets Strategy at JPMorgan Chase. Harris played a key role in the development and commercialization of Quorum, an enterprise Ethereum platform, and contributed to institutional digital asset adoption. His experience includes board positions at Anchorage, BitGo, Blockdaemon, and TRM Labs. “Real estate remains one of the most valuable yet operationally fragmented asset classes,” said Oli Harris, Founder & CEO of Arda. “By creating a programmable, trust-based system, we aim to bring real estate transactions in line with modern digital financial infrastructure.” “Real estate remains one of the most valuable yet operationally fragmented asset classes,” said Oli Harris, Founder & CEO of Arda. “By creating a programmable, trust-based system, we aim to bring real estate transactions in line with modern digital financial infrastructure.” A Market Evolving Toward Digitization As institutional investment in digital assets grows, governments and industry leaders are exploring frameworks for secure, verifiable digital property transactions. Regions such as the Gulf are advancing adoption, while U.S. policymakers are shaping regulations to integrate digital assets into mainstream financial systems. “We invest in founders who are building the foundations of next-generation industries,” said Simao Cruz, Founding Partner at Lightshift. “Arda is developing purpose-built infrastructure that could redefine how real estate is owned, transacted, and financed.” “We invest in founders who are building the foundations of next-generation industries,” said Simao Cruz, Founding Partner at Lightshift. “Arda is developing purpose-built infrastructure that could redefine how real estate is owned, transacted, and financed.” About Arda Arda is developing a real estate operating system designed for institutions, enterprises, and governments. Its platform enables digital interoperability, automated transactions, and real-time execution in the global real estate sector. Founded by Oli Harris, Arda is backed by investors supporting the evolution of digital real estate infrastructure. For more information, users can follow @arda_labs on X (Twitter) or contact contact@arda.xyz. About Lightshift Lightshift is a venture firm investing in early-stage companies focused on digital infrastructure and programmable assets. The firm partners with founders to provide capital, technical expertise, and strategic insights for market-defining innovation. Lightshift Lightshift For more information, users can visit www.lightshift.xyz or contact katy.campbell@lightshift.xyz. www.lightshift.xyz www.lightshift.xyz Contact CEO and Founder Oliver Harris Arda oli@arda.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Arda Raises Pre-Seed To Build Real Estate’s Operating System,Led By Ex-Goldman MD & JPMC Crypto Head

Arda verhoog Pre-Seed om Real Estate se bedryfstelsel te bou, gelei deur oud-Goldman MD & JPMC Crypto Head

Arda zvyšuje předběžnou přípravu k vybudování operačního systému Real Estate, vedené bývalým Goldmanem MD a krypto hlavou JPMC

Arda recauda capital inicial para construir el sistema operativo inmobiliario, liderado por el exdirector general de Goldman Sachs y el director de criptomonedas de JPMC.

Arda levanta pre-seed para construír o sistema operativo de inmobles, liderado polo ex-Goldman MD e JPMC Crypto Head

Arda、元ゴールドマンMD兼JPMC暗号資産責任者が率いる不動産のオペレーティング システム構築のためにプレシード資金を調達

Arda ຍົກສູງບົດບາດເບື້ອງຕົ້ນເພື່ອສ້າງລະບົບປະຕິບັດງານຂອງອະສັງຫາລິມະສັບ, ນໍາໂດຍ Ex-Goldman MD & JPMC Crypto Head

Арда экс Голдман MD & JPMC Crypto Head тэргүүтэй үл хөдлөх хөрөнгийн үйлдлийн системийг бий болгохын тулд урьдчилсан үрийг өсгөв.

Arda e Godiša Peu ya Pele Go Aga Real Estate’s Operating System,E eteletšwe pele ke Ex-Goldman MD & JPMC Crypto Head

முன்னாள் கோல்ட்மேன் எம்.டி & ஜே.பி.எம்.சி கிரிப்டோ தலைவரால் வழிநடத்தப்படும் ரியல் எஸ்டேட்டின் இயக்க முறைமையை உருவாக்க ஆர்டா முன் விதைகளை திரட்டுகிறது.

ኣርዳ ቅድመ ዘርኢ ኣዕቢዩ ሪል እስቴት’ስ ኦፐሬቲንግ ሲስተም ንምህናጽ፣ብ Ex-Goldman MD & JPMC Crypto Head ዝምራሕ

Arda huy động vốn trước để xây dựng hệ điều hành bất động sản, do cựu giám đốc điều hành Goldman và giám đốc tiền điện tử JPMC dẫn đầu

I-Arda iphakamisa iMbewu yangaphambili ukuze kwakhiwe iNkqubo yokuSebenza ye-Real Estate, ekhokelwa ngu-Ex-Goldman MD kunye ne-JPMC Crypto Head

Hello AI Enthusiasts! Welcome to the tenth edition of "This Week in AI Engineering"! "This Week in AI Engineering" Google released Gemma 3 with a powerful 27B parameter version, AI21 Labs launched Jamba 1.6 outperforming Mistral and Llama on long-context tasks, Manus AI emerged as a breakthrough autonomous agent with 95% task completion, and Sesame AI just made AI girlfriends real by achieving human-level speech quality with emotional intelligence. With this, we’ll also be talking about some must-know tools to make developing AI agents and apps easier. AI Girlfriends Just Got Real AI Girlfriends Just Got Real Sesame AI has introduced a voice-based AI companion focused on achieving genuine "voice presence" through emotional intelligence and contextual adaptation. The system moves beyond traditional text-to-speech by incorporating conversational dynamics to create more engaging and natural interactions. Seems like AI girlfriends just got real! Sesame AI Sesame AI Technical Architecture: Technical Architecture: Conversational Speech Model (CSM): End-to-end multimodal transformer with conversation history awareness
Split Tokenization System: Handles semantic meaning and acoustic details separately for high-fidelity output
Three-Tier Model Range: Tiny (1B backbone/100M decoder), Small (3B/250M), and Medium (8B/300M)
Training Scale: Processed approximately one million hours of predominantly English audio Conversational Speech Model (CSM): End-to-end multimodal transformer with conversation history awareness Conversational Speech Model (CSM): Split Tokenization System: Handles semantic meaning and acoustic details separately for high-fidelity output Split Tokenization System: Three-Tier Model Range: Tiny (1B backbone/100M decoder), Small (3B/250M), and Medium (8B/300M) Three-Tier Model Range: Training Scale: Processed approximately one million hours of predominantly English audio Training Scale: Performance Metrics: Performance Metrics: Word Error Rate: Small model matches human ground truth at 2.9% WER
Speaker Similarity: Achieves 0.938 score (vs 0.940 human baseline)
Homograph Disambiguation: 80% accuracy for Medium model vs 70% for PlayHT and OpenAI
Pronunciation Consistency: 90% for the Medium model, outperforming all competitors
Subjective Testing: 52.9% win rate against human references in blind no-context tests Word Error Rate: Small model matches human ground truth at 2.9% WER Word Error Rate: Speaker Similarity: Achieves 0.938 score (vs 0.940 human baseline) Speaker Similarity: Homograph Disambiguation: 80% accuracy for Medium model vs 70% for PlayHT and OpenAI Homograph Disambiguation: Pronunciation Consistency: 90% for the Medium model, outperforming all competitors Pronunciation Consistency: Subjective Testing: 52.9% win rate against human references in blind no-context tests Subjective Testing: Key Innovations: Key Innovations: Compute Amortization: Trains on only 1/16 of audio frames to improve efficiency
Contextual Adaptation: Performance increases to 66.7% human preference when evaluating with context
Novel Benchmarks: Introduces specialized tests for homograph disambiguation and pronunciation consistency
Multi-Stage Processing: Combines semantic understanding with acoustic reproduction Compute Amortization: Trains on only 1/16 of audio frames to improve efficiency Compute Amortization: Contextual Adaptation: Performance increases to 66.7% human preference when evaluating with context Contextual Adaptation: Novel Benchmarks: Introduces specialized tests for homograph disambiguation and pronunciation consistency Novel Benchmarks: Multi-Stage Processing: Combines semantic understanding with acoustic reproduction Multi-Stage Processing: The technology fundamentally reimagines voice assistants by focusing on the emotional and contextual elements that make human communication meaningful, addressing the "emotional flatness" problem that limits user engagement with current systems. Manus AI Agent Delivers 95% Task Completion With Full Autonomy Manus AI Agent Delivers 95% Task Completion With Full Autonomy Manus AI is an autonomous agent system that executes complex tasks end-to-end and achieves 95% task completion without continuous user supervision. The China-based platform is gaining significant attention as the "second DeepSeek moment" for AI, moving beyond conversation to focus on end-to-end task automation. Manus AI Manus AI Technical Architecture: Technical Architecture: Asynchronous Processing: Cloud-based task execution without requiring active user monitoring
Preference Learning: Adaptive system that optimizes outputs based on previous user interactions
File Management: Native capabilities for working with complex document types and compression formats
Output Customization: Flexible delivery formats including documents and spreadsheets Asynchronous Processing: Cloud-based task execution without requiring active user monitoring Asynchronous Processing: Preference Learning: Adaptive system that optimizes outputs based on previous user interactions Preference Learning: File Management: Native capabilities for working with complex document types and compression formats File Management: Output Customization: Flexible delivery formats including documents and spreadsheets Output Customization: Performance Metrics: Performance Metrics: GAIA Score: >65% benchmark rating on general AI agent intelligence assessments
Task Completion: 95% success rate across varied task types
Response Time: 2.5s average response latency during interactions
User Satisfaction: 92% positive rating from current users
Integration Capacity: 100+ external tool connections for expanded functionality GAIA Score: >65% benchmark rating on general AI agent intelligence assessments GAIA Score: Task Completion: 95% success rate across varied task types Task Completion: Response Time: 2.5s average response latency during interactions Response Time: User Satisfaction: 92% positive rating from current users User Satisfaction: Integration Capacity: 100+ external tool connections for expanded functionality Integration Capacity: Comparative Analysis: Comparative Analysis: Task Performance: Consistently outperforms competitors by 10-15% across key metrics
User Growth: Expanded from 1,000 to over 18,000 users between January and June 2025
Use Cases: Excels in data analysis, content creation, document processing, and decision support
Accuracy Rating: 95% vs approximately 80% for nearest competitors in comparative testing Task Performance: Consistently outperforms competitors by 10-15% across key metrics Task Performance: User Growth: Expanded from 1,000 to over 18,000 users between January and June 2025 User Growth: Use Cases: Excels in data analysis, content creation, document processing, and decision support Use Cases: Accuracy Rating: 95% vs approximately 80% for nearest competitors in comparative testing Accuracy Rating: The platform bridges the gap between conception and execution, enabling users to delegate tasks like resume screening, stock analysis, and educational content creation while receiving notification upon completion, without requiring continuous monitoring. Google Gemma 3 Open Model Family’s 27B Parameter Version Reached a 1338 ELO Score Google Gemma 3 Open Model Family’s 27B Parameter Version Reached a 1338 ELO Score Google has introduced Gemma 3, a collection of open-source AI models designed for efficient operation on consumer hardware while delivering frontier-level performance. The family includes four parameter sizes (1B, 4B, 12B, and 27B) with multimodal capabilities, 128K token context window, and support for over 140 languages. Gemma 3 Gemma 3 Technical Architecture: Technical Architecture: Vision Integration: Compatible with SigLIP vision encoder and Pan & Scan technology for handling varied image resolutions
Context Management: 5:1 ratio of local to global attention layers with 1024-token local span for optimized memory usage
Token Processing: Enhanced RoPE base frequency (1M for global layers, 10K for local layers) for extended context handling Vision Integration: Compatible with SigLIP vision encoder and Pan & Scan technology for handling varied image resolutions Vision Integration: Context Management: 5:1 ratio of local to global attention layers with 1024-token local span for optimized memory usage Context Management: Token Processing: Enhanced RoPE base frequency (1M for global layers, 10K for local layers) for extended context handling Token Processing: Performance Metrics: Performance Metrics: Chatbot Arena: 27B model achieves 1338 ELO score, ranking 9th overall ahead of models like DeepSeek-V3 (1318) and o3-mini (1304)
MATH Benchmark: 89.0% accuracy for the 27B model, significantly outperforming Gemma 2 27B (55.6%)
HumanEval: 87.8% pass rate for 27B model versus 51.8% for Gemma 2 27B
MMLU: 67.5% accuracy for 27B, compared to 56.9% for the previous generation Chatbot Arena: 27B model achieves 1338 ELO score, ranking 9th overall ahead of models like DeepSeek-V3 (1318) and o3-mini (1304) Chatbot Arena: MATH Benchmark: 89.0% accuracy for the 27B model, significantly outperforming Gemma 2 27B (55.6%) MATH Benchmark: HumanEval: 87.8% pass rate for 27B model versus 51.8% for Gemma 2 27B HumanEval: MMLU: 67.5% accuracy for 27B, compared to 56.9% for the previous generation MMLU: Key Innovations: Key Innovations: Architecture Optimization: Increased ratio of local to global attention layers reduces KV-cache memory requirements
Multimodal Processing: Vision capabilities using a 400M parameter SigLIP encoder with 256 image tokens
Language Coverage: Enhanced multilingual abilities through revised data mixture and tokenizer optimization
Knowledge Distillation: All models trained with knowledge distillation from larger teacher models Architecture Optimization: Increased ratio of local to global attention layers reduces KV-cache memory requirements Architecture Optimization: Multimodal Processing: Vision capabilities using a 400M parameter SigLIP encoder with 256 image tokens Multimodal Processing: Language Coverage: Enhanced multilingual abilities through revised data mixture and tokenizer optimization Language Coverage: Knowledge Distillation: All models trained with knowledge distillation from larger teacher models Knowledge Distillation: Google has also released ShieldGemma 2, a 4B parameter image safety classifier built on the Gemma 3 foundation, providing developers with ready-made solutions for content moderation across dangerous content, and violence categories. Jamba 1.6 Outperforms Mistral and Llama on Quality and Long Context Tasks Jamba 1.6 Outperforms Mistral and Llama on Quality and Long Context Tasks AI21 Labs has released Jamba 1.6, a new family of open-source models designed for enterprise deployments where data privacy and performance are critical requirements. The lineup includes Jamba Large 1.6 and Jamba Mini 1.6, both optimized for quality, speed, and extended context handling. Jamba 1. Jamba 1. 6 Technical Architecture: Technical Architecture: Hybrid Design: Combined Mamba-Transformer MoE architecture for efficient processing
Context Window: 256K tokens supported without performance degradation
Inference Speed: 165 tokens/second for Mini variant vs 125 for Ministral 8B and 100 for GPT-4o mini
Deployment Options: On-premises, in-VPC, or through AI21 Studio with batch processing capability Hybrid Design: Combined Mamba-Transformer MoE architecture for efficient processing Hybrid Design: Context Window: 256K tokens supported without performance degradation Context Window: Inference Speed: 165 tokens/second for Mini variant vs 125 for Ministral 8B and 100 for GPT-4o mini Inference Speed: Deployment Options: On-premises, in-VPC, or through AI21 Studio with batch processing capability Deployment Options: Performance Metrics: Performance Metrics: Arena Hard: Jamba Large 1.6 scores 75 vs Mistral Large 2's 65 and Llama 3.3 70B's 67
CRAG: 78 points for Jamba Large 1.6 vs 61 for Llama 3.3 70B and 47 for Command R+
HELMET RAG: 65 points for Jamba Large 1.6 vs 55 for Mistral Large 2
LongBench: 37 points for Jamba Large 1.6 vs 24 for Llama 3.3 70B and 18 for Command R+
HELMET LongQA: 57 points for Jamba Large 1.6 vs 50 for Mistral Large 2 Arena Hard: Jamba Large 1.6 scores 75 vs Mistral Large 2's 65 and Llama 3.3 70B's 67 Arena Hard: CRAG: 78 points for Jamba Large 1.6 vs 61 for Llama 3.3 70B and 47 for Command R+ CRAG: HELMET RAG: 65 points for Jamba Large 1.6 vs 55 for Mistral Large 2 HELMET RAG: LongBench: 37 points for Jamba Large 1.6 vs 24 for Llama 3.3 70B and 18 for Command R+ LongBench: HELMET LongQA: 57 points for Jamba Large 1.6 vs 50 for Mistral Large 2 HELMET LongQA: Enterprise Implementations: Enterprise Implementations: Retail (Fnac): 26% quality improvement with 40% better latency by switching to Jamba 1.6 Mini
Education (Educa Edtech): 90%+ retrieval accuracy and citation reliability for personalized chatbots
Digital Banking: 21% higher precision than previous solutions, matching GPT-4o quality
E-commerce: Automated conversion of inventory databases to structured product descriptions Retail (Fnac): 26% quality improvement with 40% better latency by switching to Jamba 1.6 Mini Retail (Fnac): Education (Educa Edtech): 90%+ retrieval accuracy and citation reliability for personalized chatbots Education (Educa Edtech): Digital Banking: 21% higher precision than previous solutions, matching GPT-4o quality Digital Banking: E-commerce: Automated conversion of inventory databases to structured product descriptions E-commerce: The new Batch API allows enterprises to handle large volumes of requests asynchronously, supporting high-volume data processing with faster turnaround times than traditional batch systems. Tools & Releases YOU Should Know About Tools & Releases YOU Should Know About N8n is a workflow automation platform designed for technical teams. It allows users to connect various apps and services to automate tasks without code. Technically, n8n operates through a node-based system where each node represents a specific action or integration. Users create workflows by linking these nodes together in a visual editor, defining the flow of data and operations. n8n can be self-hosted or used via a cloud version, offering flexibility and control. It's particularly useful for developers, IT professionals, and data engineers who need to streamline processes and integrate different systems.
The Eclipse Theia IDE is an extensible, open-source IDE available for both web and desktop use. Built on the Theia platform, it supports AI-powered features like chat, code completion, terminal assistance, and custom agents, leveraging arbitrary LLMs. Theia uses a modular architecture, allowing for custom extensions and tailored tool creation. While incorporating components like the Monaco editor from VS Code, it's independently developed and not a fork. Theia is ideal for developers seeking a customizable, vendor-neutral IDE with modern UX and AI integration, deployable across different environments.
Adrenaline is a platform engineered to streamline code-related inquiries by connecting directly to your GitHub repositories and documentation. It leverages advanced natural language processing (NLP) to understand questions about your codebase, offering precise answers and code snippets. It indexes your repositories and documentation, creating a searchable knowledge base. Users can then pose questions in natural language, which the platform processes to identify relevant code segments and documentation excerpts. This makes it particularly useful for developers, technical writers, and teams seeking rapid, context-aware answers within their existing projects, enhancing productivity and reducing search time.
Wren AI is an open-source SQL AI Agent that empowers users to obtain results and insights from databases faster by asking questions in natural language, eliminating the need to write SQL. Wren AI employs a semantic engine architecture, allowing users to establish a logical layer on their data schema using "Modeling Definition Language." This enables the LLM to understand the business context, generate accurate SQL queries, and provide AI-generated summaries with its GenBI feature, which converts data into actionable visuals. Wren AI supports various databases, LLMs, and analytics tools and can be deployed in any environment. N8n is a workflow automation platform designed for technical teams. It allows users to connect various apps and services to automate tasks without code. Technically, n8n operates through a node-based system where each node represents a specific action or integration. Users create workflows by linking these nodes together in a visual editor, defining the flow of data and operations. n8n can be self-hosted or used via a cloud version, offering flexibility and control. It's particularly useful for developers, IT professionals, and data engineers who need to streamline processes and integrate different systems. N8n N8n The Eclipse Theia IDE is an extensible, open-source IDE available for both web and desktop use. Built on the Theia platform, it supports AI-powered features like chat, code completion, terminal assistance, and custom agents, leveraging arbitrary LLMs. Theia uses a modular architecture, allowing for custom extensions and tailored tool creation. While incorporating components like the Monaco editor from VS Code, it's independently developed and not a fork. Theia is ideal for developers seeking a customizable, vendor-neutral IDE with modern UX and AI integration, deployable across different environments. The Eclipse Theia IDE The Eclipse Theia IDE Adrenaline is a platform engineered to streamline code-related inquiries by connecting directly to your GitHub repositories and documentation. It leverages advanced natural language processing (NLP) to understand questions about your codebase, offering precise answers and code snippets. It indexes your repositories and documentation, creating a searchable knowledge base. Users can then pose questions in natural language, which the platform processes to identify relevant code segments and documentation excerpts. This makes it particularly useful for developers, technical writers, and teams seeking rapid, context-aware answers within their existing projects, enhancing productivity and reducing search time. Adrenaline Adrenaline Wren AI is an open-source SQL AI Agent that empowers users to obtain results and insights from databases faster by asking questions in natural language, eliminating the need to write SQL. Wren AI employs a semantic engine architecture, allowing users to establish a logical layer on their data schema using "Modeling Definition Language." This enables the LLM to understand the business context, generate accurate SQL queries, and provide AI-generated summaries with its GenBI feature, which converts data into actionable visuals. Wren AI supports various databases, LLMs, and analytics tools and can be deployed in any environment. Wren AI Wren AI And that wraps up this issue of "This Week in AI Engineering." This Week in AI Engineering. Thank you for tuning in! Be sure to share this newsletter with your fellow AI enthusiasts and follow for more weekly updates. Until next time, happy building!

AI Girlfriends Just Got Real

Software development and programming, once regarded as endeavors that required deep expertise, can now be done by anyone using natural language. A feature that used to take days or months to develop can now be developed in minutes or hours thanks to code from an AI model. For example, OpenAI Codex and Google BERT are trained on programming web blogs, Stack overflow questions, etc. These intelligent models create code through mathematical probabilities while they are also known to hallucinate and present false information. A research done by academia claims that AI code generation is the leading cause for top 10 vulnerabilities and nearly 40% of code has security bugs. Many of the leading players, along with new SaaS providers are leveraging AI for making their offerings smarter. SaaS programmers too, must be more knowledgeable about AI-based SaaS tools. nearly 40% of code has security bugs What Makes AI-Generated Code Unsafe? Following programming standards and code quality dictates software safety. But, AI models are trained on every bit of information available on the internet. The code quality, reliability, security, and more might differ from that which is generated by human developers. A model trained on web development examples may contain poor data validation practices, for instance. This lack of validation can lead to security issues when the model generates code that adopts the same poor practices. 5 Indicators That Suggest Code Contains Security Weaknesses No matter the size (million or billion of parameters) the mоdels are known to hallucinate and make incorrect predictions. When a typical developer sees the code that an AI produces, they will miss the subtle but serious security vulnerabilities. However, for a developer who has complete knowledge of design and development patterns, flaws are a review away from being identified. Developers can leverage these patterns to discover vulnerabilities and align with SaaS security best practices. SaaS security best practices 1. Type Inference and Input Validations are Not Enforced Modern frameworks and libraries rely heavily on interface/enum for inference and validation. This guarantees that the code does its job accurately and imposes security. AI-generated code won't infer unless we direct it. Even after crafting a careful prompt, type mismatch and validation enforcement may not match the use case. To locate and amend code mismatches, developers must be well aware of the domain and business requirements. def reciprocal(user_input):
     # Insecure implementation with no type inference or validation
     result = 100 / user_input
     return result def reciprocal(user_input):
     # Insecure implementation with no type inference or validation
     result = 100 / user_input
     return result 2. Non-Standard State and Context Sharing Between Classes/Objects Programs share objects in Public/Private/Protected ways. Higher order functions and classes inherit object state by accessing public/protected variables directly to perform computations. If something is done incorrectly in implementation or execution, security or performance bottlenecks may easily occur. SaaS developers must implement their state and context management logic appropriately and review it for correct and safe use. class InsecureClass:
    def __init__(self, owner, balance, password):
        self.owner = owner  # Public attribute
        self._balance = balance  # Protected attribute
        self.__password = password  # Private attribute

    # Public def
    def get_balance(self):
        return self._balance

    # Protected def
    def _update_balance(self, amount):
        self._balance += amount

    # Private def
    def __validate_password(self, input_password):
        return self.__password == input_password

    # Insecure def exposing private data
    def insecure_password_exposure(self):
        return self.__password class InsecureClass:
    def __init__(self, owner, balance, password):
        self.owner = owner  # Public attribute
        self._balance = balance  # Protected attribute
        self.__password = password  # Private attribute

    # Public def
    def get_balance(self):
        return self._balance

    # Protected def
    def _update_balance(self, amount):
        self._balance += amount

    # Private def
    def __validate_password(self, input_password):
        return self.__password == input_password

    # Insecure def exposing private data
    def insecure_password_exposure(self):
        return self.__password 3. Weak Implementation of Data Handling and Sharing Techniques Services share and receive information over the network. These days, secure connectivity and data handling have been crucial to the success of cloud-based systems. When reading, processing, and sharing sensitive data on organizations through distributed data networks, strong protocols and security techniques must be in place to prevent data interception. Using AI, the SaaS developer has to implement every single aspect of the architecture in full-fledged applications. data interception #Insecure Data Sharing
@app.route("/user/<int:user_id>", methods=["GET"])
def get_user(user_id):
    user = users.get(user_id)
    if user:
        return jsonify(user)  # All user data exposed, including secrets

# Insecure Data Handling
@app.route("/update_email", methods=["POST"])
def update_email():
    data = request.json()
    user_id = data.get("user_id")
    new_email = data.get("new_email")
    if user_id in users:
        users[user_id]["email"] = new_email  # No validation of new_email
        return jsonify({"message": "Email updated successfully"}) #Insecure Data Sharing
@app.route("/user/<int:user_id>", methods=["GET"])
def get_user(user_id):
    user = users.get(user_id)
    if user:
        return jsonify(user)  # All user data exposed, including secrets

# Insecure Data Handling
@app.route("/update_email", methods=["POST"])
def update_email():
    data = request.json()
    user_id = data.get("user_id")
    new_email = data.get("new_email")
    if user_id in users:
        users[user_id]["email"] = new_email  # No validation of new_email
        return jsonify({"message": "Email updated successfully"}) 4. Inadequate Secrets and Auth Handling 4. Inadequate Secrets and Auth Handling In today's cyber sensitive world, tight RBAC implementation is a must for access control to secure identity while maintaining privacy and compliance. When the code is generated by an LLM, by default there will be a mechanism that connects and authenticates with the auth provider with basic implementation. Simple solutions are not enough to stay secure with emerging cyber threats. While adding custom functionalities developers must test thoroughly to ensure security and auth handling is strongly implemented. RBAC implementation RBAC implementation # Insecure authentication
@app.route("/login", methods=["POST"])
def login():
    data = request.json()
    email = data.get("email")
    password = data.get("password")
    for user_id, user in users.items():
        if user["email"] == email and user["password"] == password:
            return jsonify({"message": "Login successful", "user_id": user_id}) # Insecure authentication
@app.route("/login", methods=["POST"])
def login():
    data = request.json()
    email = data.get("email")
    password = data.get("password")
    for user_id, user in users.items():
        if user["email"] == email and user["password"] == password:
            return jsonify({"message": "Login successful", "user_id": user_id}) 5. Outdated Dependencies with Deprecated Functionality Usage AI programming is being directed by libraries and frameworks made by the community and open-source. People support new technology by using these promising tools and creating new ones. The data these models were trained on is not up to date and the model capabilities are frozen, and so is their knowledge. With the development of technology, a lot of features will become obsolete and some libraries won't be relevant to current needs. A SaaS developer is tasked with the review and usage of valid dependencies to ensure functionality and security. import md5  # Outdated library

def insecure_hash_password(password):
    # Insecure password hashing done using the deprecated MD5 algorithm.
    return md5.new(password).hexdigest() import md5  # Outdated library

def insecure_hash_password(password):
    # Insecure password hashing done using the deprecated MD5 algorithm.
    return md5.new(password).hexdigest() Tips to Make AI-Generated Code Safe to Use The advanced coding capabilities of Large Language Models is due to their extensive mathematical calculations. No fancy techniques are needed to make it compliant with security and programming standards. We can use these simple checks to make AI-generated code safe and compliant with standards: Code review with security and architecture teams should be a standard part of your life cycle.
Integrate automated security testing and validation steps in version control tools.
Include dependency and compliance checks in testing KPIs.
Adopt Zero-Trust architecture with static and dynamic security testing tools.
Leverage DevSecOps practices and shadow AI. Code review with security and architecture teams should be a standard part of your life cycle. Integrate automated security testing and validation steps in version control tools. automated security testing Include dependency and compliance checks in testing KPIs. Adopt Zero-Trust architecture with static and dynamic security testing tools. Leverage DevSecOps practices and shadow AI. Handling Unsafe AI-Generated Code with a Simple Github Action No matter how carefully we review and audit the code, chances of human error are always there. Relying solely on manual audits is not enough as we need to have some predefined checks that can test and validate the code as soon as it enters the version control system. What better check than to add a Github action which automatically runs security and quality checks when a PR is raised. name: Simple Security Checks for AI generated Code

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main
jobs:
  security-and-quality-check:
    runs-on: ubuntu-latest
    Steps:

      - name: Repository checkout
        uses: actions/checkout@v3

      - name: Python setup
        uses: actions/setup-python@v4
        with:
          python-version: ">=3.9"

      - name: Dependency installation
        run: |
          python -m pip install --upgrade pip
          pip install bandit pytest

      - name: Identifying insecure libraries and patterns
        run: |
          echo "Checking for insecure patterns..."
          if grep -r "md5.new(" .; then
            echo "ERROR: Insecure MD5 hashing detected. Use hashlib.sha256 or bcrypt instead."
            exit 1
          fi
          echo "No insecure patterns detected."

      - name: Scanning for security vulnerabilities
        run: |
          echo "Running Bandit security scanner..."
          bandit -r .

      - name: Running unit tests
        run: |
          echo "Running unit tests..."
          pytest test/unit --cmodopt=local

      - name: Notifying on failure
        if: failure()
        run: |
          send_slack_notification(“Unsafe code merge detected, fix immediately”) name: Simple Security Checks for AI generated Code

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main
jobs:
  security-and-quality-check:
    runs-on: ubuntu-latest
    Steps:

      - name: Repository checkout
        uses: actions/checkout@v3

      - name: Python setup
        uses: actions/setup-python@v4
        with:
          python-version: ">=3.9"

      - name: Dependency installation
        run: |
          python -m pip install --upgrade pip
          pip install bandit pytest

      - name: Identifying insecure libraries and patterns
        run: |
          echo "Checking for insecure patterns..."
          if grep -r "md5.new(" .; then
            echo "ERROR: Insecure MD5 hashing detected. Use hashlib.sha256 or bcrypt instead."
            exit 1
          fi
          echo "No insecure patterns detected."

      - name: Scanning for security vulnerabilities
        run: |
          echo "Running Bandit security scanner..."
          bandit -r .

      - name: Running unit tests
        run: |
          echo "Running unit tests..."
          pytest test/unit --cmodopt=local

      - name: Notifying on failure
        if: failure()
        run: |
          send_slack_notification(“Unsafe code merge detected, fix immediately”) Conclusion Large language models are quite useful tools for SaaS developers to generate code and information with natural prompts. However, they pose security risks and sometimes deliver non-performant code that doesn't suit enterprise needs. SaaS developers must be very careful when using these tools and implementing AI-generated code for real-life use cases. This practical guide focuses on the factors that arise and influence security posture while showing how to overcome these challenges.

Is Your AI-Generated Code Really Secure?

هل الكود الذي تم إنشاؤه بواسطة الذكاء الاصطناعي الخاص بك آمن حقًا؟

আপনার এআই-জেনারেটেড কোড কি সত্যিই নিরাপদ?

¿Es realmente seguro su código generado por IA?

Il tuo codice generato dall'intelligenza artificiale è davvero sicuro?

AI 生成コードは本当に安全ですか?

តើលេខកូដដែលបង្កើតដោយ AI របស់អ្នកពិតជាមានសុវត្ថិភាពមែនទេ?

Je váš kód vygenerovaný AI skutočne bezpečný?

Yako AI-Yakagadzirwa Kodhi Yakachengeteka Chaizvoizvo?

Да ли је ваш код генерисан вештачком интелигенцијом заиста безбедан?

Är din AI-genererade kod verkligen säker?

ብAI ዝፍጠር ኮድካ ብሓቂ ውሑስ ድዩ?

کیا آپ کا AI سے تیار کردہ کوڈ واقعی محفوظ ہے؟

Michael Vasilkovsky didn’t start his career aspiring to transform how millions of people interact with cutting-edge technology. Yet today, as a Technical Lead specializing in generative AI at Snap, he has established himself at the forefront of a rapidly expanding field that aims to redefine digital experiences—from the creation of intricate 3D assets to the real-time generation of video. “I’m fascinated by how technology can evolve from a purely research-driven endeavor into products that genuinely reshape human creativity,” Vasilkovsky says. His journey spans top-tier research, hands-on product development, and leadership roles, culminating in innovations that serve an expansive user base and push the boundaries of what generative AI can accomplish. Vasilkovsky’s trajectory began in academic research, where he first encountered computer vision challenges that demanded more than conventional machine learning solutions. He published in prominent forums like CVPR and SIGGRAPH, earning early recognition among the global research community. Over time, he noticed that while many researchers were producing impressive proofs of concept, only a fraction made it into actual products that people could use every day. “Translating research into something consumers find intuitive and valuable is a real challenge,” he reflects. “It’s not enough to create a brilliant model; you need to optimize its performance, build infrastructure around it, and then ensure it can withstand the real-world demands of millions, if not hundreds of millions, of users.” His professional leap came when he joined Snap in 2021, just as augmented reality was accelerating in popularity. Instead of focusing exclusively on AR filters or lenses, Vasilkovsky set his sights on foundational 3D models, exploring how generative AI could revolutionize asset creation. “A typical 3D model for a game or AR application can take over 40 hours to develop from scratch,” he explains. “We are now cutting that time down to minutes, unlocking entirely new levels of creative freedom.” Indeed, one of his hallmark achievements is leading a suite of generative AI tools that allow both novices and professionals to produce high-grade 3D assets using simple text prompts or images. This dovetails with the growing market appetite for AI-powered content creation; according to the IBM Global AI Adoption Index 2022, 35 percent of businesses today use AI in their operations (Source: IBM). While Snap’s platform is home to hundreds of millions of daily users, Vasilkovsky’s focus extends beyond the specifics of the company’s ecosystem. He emphasizes the broader implications of generative AI for anyone looking to streamline content production or develop immersive experiences. “We’re seeing a paradigm shift. An artist without extensive technical knowledge can now create a 3D asset or short animated clip by describing what they want in a single sentence,” he points out. “That empowerment fosters new types of creativity and drives economic opportunities for both individual creators and businesses.” Statistics highlight the accelerating demand for generative AI solutions. A 2022 report by Omdia predicts the generative AI market will surge from an estimated $1.2 billion in 2022 to $10.8 billion by 2027 (Source: Omdia). In part, that explosive growth reflects an increasing awareness across industries—from gaming to e-commerce—of AI’s potential to lower production costs and shorten go-to-market timelines. “If a fashion retailer wants to showcase a new clothing line in a virtual runway setting, historically they’d need specialized 3D artists, hours of rendering time, and complicated workflows,” Vasilkovsky notes. “Now, they can generate and animate digital garments in a fraction of the time and cost.” His unique vantage point allows him to see how generative AI can be applied to user-driven experiences, particularly those involving augmented or virtual reality. One of his notable projects involves integrating text-to-3D and image-to-3D pipelines into creation platforms, enabling creators to build more compelling visuals that can be rigged to human movements. “Anyone can place these 3D objects into an interactive environment,” he says. “We’ve proven that generative 3D assets can be effectively integrated into consumer-facing experiences with minimal friction. That’s the real breakthrough: bridging research with real-world application.” A hallmark of Vasilkovsky’s career is his holistic approach to development. While many AI specialists are accustomed to working within the silo of research labs, he manages projects through the entire lifecycle—from paper submissions to deploying a final product. “It’s not just about building the model or the algorithm. You have to think about performance optimization, server loads, user interface design, and the moral or ethical questions around AI-generated content,” he says. His track record of building infrastructure that can handle large-scale use is rooted in practical necessity: Snap’s augmented reality features alone engage a massive audience every day. “When you have a platform that sees hundreds of millions of daily active users, your innovations need to be robust and responsive,” Vasilkovsky adds. Part of that robustness comes from actively collaborating with cross-functional teams, including data scientists, software engineers, and product managers. He considers these collaborations critical not just for scaling AI models, but also for ensuring they align with human behavior and creative needs. “A specialized model might perform flawlessly in the lab, but if the average user finds it unintuitive or slow, it’s essentially useless,” he remarks. “We discovered that giving creators a text-prompt-based interface significantly lowered the entry barrier. That’s when generative AI starts to truly democratize creation.” His work has garnered attention in media circles, with outlets like Reuters, TechCrunch, and Maginative covering the launch of advanced generative AI tools and video generation models that incorporate camera control. While these achievements often reference Snap’s ecosystem, Vasilkovsky is cautious not to oversell the company itself. “I don’t view this purely as a Snap story,” he explains. “Yes, we have a large user base and a thriving augmented reality community, but the real narrative is about how generative AI can disrupt and improve content creation across industries.” Looking ahead, Vasilkovsky sees video as the next major frontier. Existing models often require significant time—sometimes minutes per second of content—to render passable outputs, which is hardly practical in an age of instant media. “Waiting five or six minutes for a short video clip might be acceptable in certain production environments, but it’s not going to spark mainstream adoption,” he asserts. His goal is to dramatically reduce these generation times while preserving and even improving quality. “We want to allow people to create longer, more engaging videos, but that means tackling a host of new challenges. Video generation is inherently more complex than still-image generation: motion dynamics, camera angles, transitions, and even user interactivity come into play.” To address these complexities, Vasilkovsky envisions new editing tools that go beyond typed prompts. “It’s one thing to describe a scene with words; it’s another to precisely control camera movement or the timing of certain actions,” he says. “I’m looking to merge generative AI with more intuitive user interfaces, so individuals can direct their video content much like a film director. That involves advanced rigging techniques, timeline editing, and real-time previews.” In discussing these aspirations, he underscores the economic impact. According to a 2022 McKinsey study, 44 percent of organizations surveyed reported cost savings from AI adoption in various business units (Source: McKinsey & Company). As AI-based video generation becomes more efficient, it stands to slash production budgets for marketing agencies, film studios, and freelancers. “The tools we’re building aren’t just flashy demos; they could shift how entire industries operate,” he notes. “That’s a compelling reason to invest heavily in both research and productization. We’re creating new efficiencies, new workflows, and ultimately new opportunities.” Part of Vasilkovsky’s vision includes democratizing these tools so that smaller businesses and individual creators also benefit. “Too often, innovations get locked behind large enterprises. I’d like to see generative AI-driven video creation become as commonplace as smartphone photography,” he says. “Widespread access is where you see real leaps in creativity and economic development. I want budding entrepreneurs, educators, and everyday social media users to tap into this potential.” When asked about the human element—inevitable discussions around AI ethics, data usage, and potential displacement of artists—he’s candid that these are meaningful conversations that require ongoing attention. “AI doesn’t replace human creativity; it augments it,” he says. “But there’s a learning curve. We need guidelines and best practices for how people use these generative tools. My hope is that it remains a catalyst for collaboration, not a cause for fear.” He adds that transparency and user education will be crucial. By making the mechanics of AI clearer and user-friendly, he believes that people will better embrace the technology and use it responsibly. Ultimately, Vasilkovsky measures success by how seamlessly generative AI integrates into people’s creative processes. “We’re at a point where AI can remove technical barriers and open up new forms of expression,” he says. “If our tools inspire someone who’s never touched 3D design or video production to create something original—and even share it with a global audience—then we’ve succeeded.” That democratizing ethos underpins his entire career. From publishing top-tier research to launching real-world products, he has consistently advocated for bridging the gap between cutting-edge technology and everyday experiences. In doing so, he has become one of the leading voices in generative AI, shaping the immediate future of content creation and carving out a blueprint for its long-term societal and economic impact. “Every new tool or model we build can reshape the creative landscape,” he says. “I want to keep pushing those boundaries—faster video generation, more immersive 3D content, easier editing tools. The end goal is to empower as many people as possible to tell their stories. That’s what excites me most about generative AI: it’s not just for big corporations or specialized studios. It’s for all of us.”

Michael Vasilkovsky Traded Research For Real-World GenAI and Is Now Transforming Content Creation

As someone who's worked in the trenches of financial markets, I've seen firsthand the importance of real-time data processing. During my time at Two Sigma and Bloomberg, I witnessed how even minor delays can have significant consequences. In this article, I'll share my insights on the challenges of real-time data processing in distributed systems, using examples from the financial industry. Data Consistency: The Achilles' Heel of Distributed Systems Imagine you're a trader, relying on real-time market data to make split-second decisions. But what if the data you're receiving is inconsistent? Perhaps one server thinks the price of Apple is $240, while another sees it at $241. This discrepancy might seem minor, but in the world of high-frequency trading, it can be catastrophic. To ensure data consistency, financial institutions employ various techniques, such as: Event sourcing: By storing all state changes as a sequence of events, systems can reconstruct the latest state, even in the event of a failure.
Distributed consensus algorithms: Protocols like Paxos or Raft enable nodes to agree on a single source of truth, even in cases of network partitioning. Event sourcing: By storing all state changes as a sequence of events, systems can reconstruct the latest state, even in the event of a failure. Event sourcing Distributed consensus algorithms: Protocols like Paxos or Raft enable nodes to agree on a single source of truth, even in cases of network partitioning. Distributed consensus algorithms However, these solutions can introduce additional complexity, particularly in high-throughput environments. Latency: The Need for Speed In financial markets, latency can make or break a trade. High-frequency trading firms invest heavily in infrastructure to minimize latency, and even the smallest inefficiency can have significant consequences. Real-time market data must be processed and delivered to consumers with extremely low latency. To address latency, financial institutions employ strategies such as: Edge processing: By processing data closer to its source, systems can reduce latency and improve performance.
Hardware acceleration: Utilizing specialized hardware, such as FPGA, can significantly reduce processing times for critical tasks.
Optimized message brokers: Choosing the right message broker, such as Kafka or Pulsar, is crucial in ensuring that data is pushed to consumers as quickly as possible. Edge processing: By processing data closer to its source, systems can reduce latency and improve performance. Edge processing Hardware acceleration: Utilizing specialized hardware, such as FPGA, can significantly reduce processing times for critical tasks. Hardware acceleration Optimized message brokers: Choosing the right message broker, such as Kafka or Pulsar, is crucial in ensuring that data is pushed to consumers as quickly as possible. Optimized message brokers Fault Tolerance: What Happens When Things Go Wrong? No system is immune to failure, but in financial markets, fault tolerance is paramount. If a single node or service goes down, consumers cannot afford to lose critical market data. To ensure fault tolerance, financial institutions employ strategies such as: Replication: Market data is replicated across multiple servers or regions to ensure continuity in the event of a failure.
Automatic failover: Systems are designed to detect failure and route traffic to healthy servers without human intervention.
Distributed logging: Distributed logs, such as Kafka's log-based architecture, ensure that all actions are recorded and can be replayed in the event of a crash. Replication: Market data is replicated across multiple servers or regions to ensure continuity in the event of a failure. Replication Automatic failover: Systems are designed to detect failure and route traffic to healthy servers without human intervention. Automatic failover Distributed logging: Distributed logs, such as Kafka's log-based architecture, ensure that all actions are recorded and can be replayed in the event of a crash. Distributed logging Scalability: Handling Explosive Growth Financial markets are inherently unpredictable, and systems must be designed to handle sudden surges in traffic. Scalability is critical to ensure that systems can handle explosive growth without degrading performance. To achieve scalability, financial institutions employ strategies such as: Sharding: Market data is divided into smaller chunks and distributed across different servers to improve performance.
Load balancing: Incoming data is efficiently distributed across multiple nodes or servers to avoid bottlenecks.
Elasticity: Systems are designed to scale up or down based on demand, ensuring that resources are allocated efficiently. Sharding: Market data is divided into smaller chunks and distributed across different servers to improve performance. Sharding Load balancing: Incoming data is efficiently distributed across multiple nodes or servers to avoid bottlenecks. Load balancing Elasticity: Systems are designed to scale up or down based on demand, ensuring that resources are allocated efficiently. Elasticity Security: Protecting Critical Market Data Finally, security is paramount in financial markets. Distributed systems, by their nature, involve multiple servers, databases, and services spread across various regions, making them vulnerable to attacks. To ensure security, financial institutions employ strategies such as: Encryption: Data is encrypted both in transit and at rest to prevent eavesdropping or unauthorized access.
Authentication and authorization: Only authorized parties can access sensitive market data feeds, using techniques like OAuth and API keys.
DDoS mitigation: Systems are designed to detect and prevent Distributed Denial of Service (DDoS) attacks, ensuring availability and performance. Encryption: Data is encrypted both in transit and at rest to prevent eavesdropping or unauthorized access. Encryption Authentication and authorization: Only authorized parties can access sensitive market data feeds, using techniques like OAuth and API keys. Authentication and authorization DDoS mitigation: Systems are designed to detect and prevent Distributed Denial of Service (DDoS) attacks, ensuring availability and performance. DDoS mitigation Conclusion Real-time data processing in distributed systems is a complex challenge, particularly in high-stakes environments like financial markets. By understanding the challenges of data consistency, latency, fault tolerance, scalability, and security, financial institutions can design and implement more efficient, resilient, and scalable systems. As the financial industry continues to evolve, the quest for near-zero latency, high availability, and real-time data processing will only become more critical. By sharing my insights and experiences, I hope to contribute to the ongoing conversation about the challenges and opportunities in real-time data processing. Image Courtesy: https://www.nber.org/digest/202110/race-exploit-stock-price-differences-between-exchanges
https://www.linkedin.com/pulse/distributed-systems-design-pattern-lease-based-stock-shanoj-kumar-v-0bawc/
https://blog.e-boks.com/understanding-data-encryption-a-comprehensive-guide-for-regulated-industries https://www.nber.org/digest/202110/race-exploit-stock-price-differences-between-exchanges https://www.nber.org/digest/202110/race-exploit-stock-price-differences-between-exchanges https://www.linkedin.com/pulse/distributed-systems-design-pattern-lease-based-stock-shanoj-kumar-v-0bawc/ https://www.linkedin.com/pulse/distributed-systems-design-pattern-lease-based-stock-shanoj-kumar-v-0bawc/ https://blog.e-boks.com/understanding-data-encryption-a-comprehensive-guide-for-regulated-industries https://blog.e-boks.com/understanding-data-encryption-a-comprehensive-guide-for-regulated-industries

Challenges of Real-Time Data Processing in Financial Markets

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Survey with Industry Professionals 2 Survey with Industry Professionals 3 RQ1: Real-World use cases that necessitate output constraints 3 RQ1: Real-World use cases that necessitate output constraints 4 RQ2: Benefits of Applying Constraints to LLM Outputs and 4.1 Increasing Prompt-based development Efficiency 4 RQ2: Benefits of Applying Constraints to LLM Outputs and 4.1 Increasing Prompt-based development Efficiency 4.2 Integrating with Downstream Processes and Workflows 4.2 Integrating with Downstream Processes and Workflows 4.3 Satisfying UI and Product Requirements and 4.4 Improving User Experience, Trust, and Adoption 4.3 Satisfying UI and Product Requirements and 4.4 Improving User Experience, Trust, and Adoption 5 How to Articulate output constraints to LLMS and 5.1 The case for GUI: A Quick, Reliable, and Flexible Way of Prototyping Constraints 5 How to Articulate output constraints to LLMS and 5.1 The case for GUI: A Quick, Reliable, and Flexible Way of Prototyping Constraints 5.2 The Case for NL: More Intuitive and Expressive for Complex Constraints 5.2 The Case for NL: More Intuitive and Expressive for Complex Constraints 6 The Constraint maker Tool and 6.1 Iterative Design and User Feedback 6 The Constraint maker Tool and 6.1 Iterative Design and User Feedback 7 Conclusion and References 7 Conclusion and References A. The Survey Instrument A. The Survey Instrument 3 RQ1: REAL-WORLD USE CASES THAT NECESSITATE OUTPUT CONSTRAINTS Table 1 presents a taxonomy of six primary categories of use cases that require output constraints, each with representative real-world examples and quotes submitted by our respondents. These can be further divided into low-level and high-level constraints — low-level constraints ensure that model outputs adhere to a specific structure (e.g., JSON or markdown), instruct the model to perform pure multiple choices (e.g., sentiment classification), or dictate the length of the outputs; whereas high-level constraints enforce model outputs to respect semantic (e.g., must include or avoid specific terms or actions) or stylistic (e.g., follow certain style or tone) guidelines, while preventing hallucination. low-level high-level constraints structure multiple choices length semantic stylistic preventing hallucination. Below, we discuss a number of interesting insights that emerged from our analysis of the use cases: • Going beyond valid JSON. Note that recent advancements in instruction-tuning techniques have substantially improved the the chances of generating a valid JSON object upon user request [27, 29]. Nonetheless, our survey respondents believed that this was not enough and desired to have more precise control over the JSON schema (i.e., key/value pairs). One respondent stated their expectation as follows: “I expect the quiz [that the LLM makes given a few passages provided below] to have 1 correct answer and 3 incorrect ones. I want to have the output to be like a json with keys {"question": "...", "correct_answer": "...", "incorrect_answers": [...]}.” It is also worth mentioning that some respondents found that “few-shot prompts” — demonstrating the desired key/value pairs with several examples — tend to work “fairly well”. However, they concurred that having a formal guarantee of JSON schema would be greatly appreciated (see section 4.1 for their detailed rationales). • Going beyond valid JSON. desired to have more precise control over the JSON schema (i.e., key/value pairs). “I expect the quiz [that the LLM makes given a few passages provided below] to have 1 correct answer and 3 incorrect ones. I want to have the output to be like a json with keys {"question": "...", "correct_answer": "...", "incorrect_answers": [...]}.” “few-shot prompts” “fairly well” • Giving an answer without extra conversational prose. When asking an LLM to perform data classification or labeling, such as “[classifying sentiments as] Positive, Negative, Neutral, etc.,” respondents typically expect the model to only output the classification result (e.g. “Positive.”) without a trailing “explanation” (e.g., “Positive, since it referred to the movie as a ‘timeless masterpiece’...”), as the addition of explanation could potentially confuse the downstream parsing logic. This indicates a potential misalignment between a common training objective — where LLMs are often tailored to be conversational and provide rich details [2, 17, 33] — and certain specialized downstream use cases where software developers need LLMs to be succinct. Such use cases necessitate output constraints that are independent of the prompt that would help adapt a general-purpose model to meet specific user requirements. • Giving an answer without extra conversational prose. “[classifying sentiments as] Positive, Negative, Neutral, etc.,” only output the classification result without a trailing “explanation” • Conditioning the output on the input, but don’t “improvise!” One thread of high-level constraints places emphasis on directing the model to condition its output on specific content from the input. For example, the model’s response should semantically remain “in the same ballpark” as “the user’s original query” — “[the output of] a query about ‘fall jackets’ should be confined to clothing.” A particular instance of this is for LLMs to echo segments of the input in their output, occasionally with slight alterations. For example, “we want LLM to repeat input with some control tokens to indicate the mentions. e.g. input: ‘Obama was born in 1961.’,... , we want output to be ‘«Obama» was born in 1961.’” Nevertheless, respondents underscored the importance of the model not improvising beyond its input and instructions. For example, one respondent instructed an LLM to “annotate a method with debug statement,” anticipating the output would “ONLY include changes that add print statements to the method.” However, the LLM would frequently introduce additional “changes in syntax” that were unwarranted. One thread of high-level constraints places emphasis on directing the model condition its output on specific content from the input in the same ballpark” as “the user’s original query” — “[the output of] a query about ‘fall jackets’ should be confined to clothing.” “we want LLM to repeat input with some control tokens to indicate the mentions. e.g. input: ‘Obama was born in 1961.’,... , we want output to be ‘«Obama» was born in 1961.’” not improvising beyond its input and instructions. “annotate a method with debug statement,” “ONLY include changes that add print statements to the method.” This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors:
(1) Michael Xieyang Liu, Google Research, Pittsburgh, PA, USA (lxieyang@google.com);
(2) Frederick Liu, Google Research, Seattle, Washington, USA (frederickliu@google.com);
(3) Alexander J. Fiannaca, Google Research, Seattle, Washington, USA (afiannaca@google.com);
(4) Terry Koo, Google, Indiana, USA (terrykoo@google.com);
(5) Lucas Dixon, Google Research, Paris, France (ldixon@google.com);
(6) Michael Terry, Google Research, Cambridge, Massachusetts, USA (michaelterry@google.com);
(7) Carrie J. Cai, Google Research, Mountain View, California, USA (cjcai@google.com). Authors: Authors: (1) Michael Xieyang Liu, Google Research, Pittsburgh, PA, USA (lxieyang@google.com); (2) Frederick Liu, Google Research, Seattle, Washington, USA (frederickliu@google.com); (3) Alexander J. Fiannaca, Google Research, Seattle, Washington, USA (afiannaca@google.com); (4) Terry Koo, Google, Indiana, USA (terrykoo@google.com); (5) Lucas Dixon, Google Research, Paris, France (ldixon@google.com); (6) Michael Terry, Google Research, Cambridge, Massachusetts, USA (michaelterry@google.com); (7) Carrie J. Cai, Google Research, Mountain View, California, USA (cjcai@google.com).

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