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Tech Optimizer
September 1, 2026

Connecting My LangGraph AI Agent to Postgres

A stateful LangGraph agent was developed to streamline a 15-minute booking process, featuring a user-friendly Streamlit UI and a backend powered by a Postgres database. The agent responds to customer queries, calculates service pricing, manages service acceptance, suggests time slots, and confirms appointment details. It operates in two modes: in-memory for rapid testing and Postgres for persistent storage. Testing can be done locally using Docker, which simulates a Postgres environment, or with a hosted Postgres instance. The project includes a docker-compose.yml file to initiate a PostgreSQL 16 container, allowing the application to connect to the database. Data persistence is ensured through Docker volumes, and the application can be run with specific commands after setting up the environment variables. The system has been tested successfully, demonstrating its ability to manage bookings and maintain data integrity across sessions. Future enhancements are planned to improve the booking workflow and integrate additional communication channels.
Overcoming the Barriers of Commercial DBs with Open Source and AI… EDB Hosts 'PostgreSQL AI Summit'
Tech Optimizer
August 29, 2026

Overcoming the Barriers of Commercial DBs with Open Source and AI… EDB Hosts ‘PostgreSQL AI Summit’

A company's approach to artificial intelligence (AI) begins with determining the optimal structure for data storage and management, often facing challenges such as high costs of commercial databases and reliance on specific enterprise technologies. To effectively utilize AI, companies need to establish new infrastructures, including vector search capabilities, Search Augmentation Generative (RAG) systems, and data lakehouses. EDB is notable for connecting companies and technology partners that have transitioned to open-source databases. On September 3, EDB will host the 'EDB Postgres AI Summit Seoul 2026' at the Sofitel Ambassador Seoul, starting at 10:00 AM. This event is the largest PostgreSQL conference in Korea, themed 'Change the Game,' focusing on the shift from commercial databases to open-source and AI-driven frameworks, featuring real-world case studies. Approximately 300 C-level executives and IT decision-makers from various sectors are expected to attend, with participation by invitation only. The summit will include 15 sessions with customer case studies and technical presentations. Notable discussions will include IBK Industrial Bank of Korea's migration of 15 core systems to PostgreSQL, a semiconductor company's diversification of MPP databases on a DBaaS platform, and Kyobo Book Centre's database modernization strategy. Shopcast will present its development of an 'Agentic Lakehouse' integrating AI technology with a data lakehouse framework. The keynote address will be given by Kim Deok-joong, discussing organizational management strategies for integrating AI agents. The technical sessions will cover the architecture of the 'EDB Postgres AI' platform, which supports AI vector search and RAG, with live demonstrations of the analytics engine ClickHouse and LakeHouse technology for analyzing petabyte-scale data. EDB's domestic distributors and international partners will participate as sponsors. The summit aims to showcase technologies and case studies from the domestic ecosystem, addressing PostgreSQL adoption, system migration, operations, data analysis, and AI implementation. EDB manages transaction, analytics, and AI workloads using Postgres in cloud environments, serving over 1,500 global customers. Herve Timsit, EDB's Chief Revenue Officer, emphasized the event's focus on sharing tangible results and addressing the challenges of commercial databases while investing in AI infrastructure.
Microsoft denies Windows 11 secretly spies on you every 15 minutes
Winsage
August 5, 2026

Microsoft denies Windows 11 secretly spies on you every 15 minutes

Scott Hanselman, deputy technical director at Microsoft, explained that a service within the Windows diagnostic system collects local performance data to help users identify PC slowdowns. This data remains on the user's device unless the user opts to share it with Microsoft. Hanselman acknowledged that the service's description is unclear and that Microsoft plans to revise it based on user feedback. Concerns about data collection have been raised by Windows users, particularly regarding services like Recall, which faced backlash despite its local data storage promise. Microsoft is reconsidering its data collection approach in light of these concerns.
BitTorrent исполнилось 25 лет. Как программа одного человека изменила интернет и поставила Голливуд в тупик
TrendTechie
July 24, 2026

BitTorrent исполнилось 25 лет. Как программа одного человека изменила интернет и поставила Голливуд в тупик

On July 2, 2001, Bram Cohen announced the operational status of his program, BitTorrent, on a mailing list for peer-to-peer network developers. BitTorrent revolutionized file sharing by segmenting files into smaller pieces, allowing users to download from multiple sources simultaneously and share already downloaded data, which improved download speeds as file popularity increased. Unlike earlier peer-to-peer networks, BitTorrent did not have a built-in catalog, relying on external websites for users to locate torrents. The first major torrent catalog, Suprnova, emerged in 2002, followed by The Pirate Bay in 2003, making the protocol widely accessible. By 2004, BitTorrent accounted for nearly a third of all internet traffic. In 2004, Cohen founded BitTorrent Inc., which acquired the client µTorrent in 2006. The company launched the BitTorrent Entertainment Network in 2007, but it closed in 2008 due to competition from free pirated content. BitTorrent Inc. later explored various technologies but struggled to establish a sustainable business model. The protocol evolved with decentralization features like the distributed hash table (DHT) and magnet links. In 2018, BitTorrent was acquired by the blockchain project TRON for an estimated 0 million. BitTorrent Inc. claims around 54 million active users per month, with additional users on independent clients. Despite a decline in torrent traffic from its peak, the network has been experiencing growth, attributed to rising streaming costs and fragmented content availability.
I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer
Tech Optimizer
July 21, 2026

I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer

The author transitioned from a data analyst role to a data engineer, creating a 12-month self-study roadmap focused on learning by building projects. The first project was a GitHub ETL pipeline, which evolved from a simple Python script to a more complex system using SQLite and GitHub Actions for automation. The author realized that the challenges of data engineering lay in system design and orchestration rather than just writing ETL logic. For the second project, the author built an automated RSS ingestion pipeline to explore engineering decisions in creating a reliable data pipeline. The project emphasized the importance of separating application responsibilities from orchestration tasks, leading to the use of Docker for consistent execution environments and Kestra for orchestration. The author validated each component of the project incrementally: first the Python ETL, then PostgreSQL, followed by Docker, and finally Kestra. This approach ensured that each layer functioned correctly before adding complexity. The final architecture included distinct responsibilities for each component: Python for data processing, PostgreSQL for data storage, Docker for execution, and Kestra for orchestration. The author learned that effective engineering involves considering system reliability, error handling, and observability, shifting the focus from merely writing code to designing entire systems. The experience transformed the author's mindset, emphasizing the importance of incremental building and the separation of execution and orchestration responsibilities.
What Is Serverless PostgreSQL?
Tech Optimizer
July 12, 2026

What Is Serverless PostgreSQL?

Serverless PostgreSQL is a fully managed cloud database model that separates compute and storage, allowing them to scale independently and automatically based on demand. It eliminates the need for manual infrastructure provisioning and capacity planning, charging only for active usage. Unlike traditional PostgreSQL setups, which require continuous resource allocation and manual scaling, serverless PostgreSQL provisions resources on demand and can scale down to zero during idle periods. Serverless PostgreSQL integrates with serverless compute platforms, enabling analytical queries to access the same data within a unified architecture. Key differences between traditional and serverless PostgreSQL include manual versus automatic provisioning and scaling, fixed versus usage-based billing, and high versus reduced operational overhead. Lakebase architecture is an emerging model that combines transactional databases with lakehouse foundations, allowing operational and analytical workloads to coexist on a single platform. This architecture minimizes data duplication and simplifies access, enhancing data management and analysis. Serverless PostgreSQL operates on a cloud-native architecture that enhances efficiency by allowing compute and storage to scale autonomously. It features scale-to-zero behavior, where compute resources are suspended when inactive and reactivated upon new queries. Major providers include Databricks Lakebase, Amazon Aurora Serverless v2, and Neon, each offering varying capabilities and integrations. Pricing for serverless PostgreSQL typically includes charges for compute resources, storage, and data transfer, with costs fluctuating based on workload activity. Cold start latency is a performance consideration, as reactivating compute resources can introduce delays. Strategies to mitigate this include keeping resources partially active or selecting providers with minimal cold start impacts. Serverless PostgreSQL is well-suited for OLTP workloads, while lakebase architecture is better for AI development, variable workloads, and environments requiring rapid iteration. Setting up serverless PostgreSQL involves choosing a provider, creating a database instance, and configuring access settings. It can also be used alongside serverless compute platforms for analytics, further extending its capabilities.
All Android users just got an important backup feature
AppWizard
July 2, 2026

All Android users just got an important backup feature

Google is introducing a selective backup feature for Android users, allowing granular control over app data management. This update is initially rolling out to Pixel devices and users of Google Play Services version 26.24. Users can now selectively manage backups for each app, which helps conserve cloud storage space. The feature is available for those using Android 16 and Android 17, with other manufacturers expected to adopt it soon. Once activated, data is securely backed up to the cloud, but users should be aware that disabling backup for an app will permanently delete its previously backed-up data. To manage backups, users can go to their device’s settings under Google Services > Backup > Backup details. The rollout is gradual, primarily targeting Google Pixel smartphones. Most users will receive the update automatically, and they can check their version of Google Play Services in the settings.
The database storage problem is solved. Here’s what comes next.
Tech Optimizer
June 25, 2026

The database storage problem is solved. Here’s what comes next.

Postgres has been a reliable transactional database for three decades, used for managing customer records and financial transactions. Innovations in the Postgres ecosystem are now focused on minimizing data movement rather than just data storage. The challenge of interoperability is becoming crucial, as organizations seek to share operational data seamlessly across various systems without creating additional copies or pipelines. Many organizations are spending as much effort on data movement as on data storage. Postgres is increasingly viewed as the authoritative system for critical information, and its role is evolving to facilitate better interaction with operational data. Technologies like logical replication and change data capture are enhancing Postgres's integration within data ecosystems. The rise of AI has highlighted the need for real-time access to operational data and has prompted organizations to reconsider the necessity of maintaining multiple copies of the same data. The database industry is shifting focus from optimizing storage to enabling effortless data sharing across systems. Postgres continues to adapt to new workloads and architectural patterns, maintaining its reputation as a stable foundation for operational data while expanding its capabilities through innovative extensions.
The database that refused to die: How Postgres survived its own creators
Tech Optimizer
June 22, 2026

The database that refused to die: How Postgres survived its own creators

Postgres, originally developed by Michael Stonebraker in the early 1980s, is an open-source database system that evolved from Ingres. It was designed to handle complex data types and introduced user-defined data types, operators, and functions, leading to the support for abstract data types (ADTs). The initial commercialization of Postgres occurred through a startup named Illustra, later acquired by Informix. In 1995, graduate students Andrew Yu and Jolly Chen revived Postgres, transitioning it from QUEL to SQL, resulting in Postgre95, which evolved into PostgreSQL. Today, Postgres is one of the most popular database systems globally, known for its extensibility and high code quality. However, it currently lacks features like file-level encryption (TDE), which are standard in commercial systems, relying instead on the operating system for encryption. Efforts to implement TDE have faced challenges due to the complexity of required code changes.
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