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Ex-Microsoft executive says he fought against the removal of the vertical taskbar in Windows 11
Winsage
February 18, 2026

Ex-Microsoft executive says he fought against the removal of the vertical taskbar in Windows 11

Microsoft is reportedly planning to reintroduce the ability to relocate and resize the Windows 11 taskbar, a feature that was removed and led to user dissatisfaction. Former Microsoft executive Mikhail Parakhin criticized the removal of the vertical taskbar, stating it enhances productivity. Other former employees, including Andy Young, have also expressed dissatisfaction with Windows 11's design, particularly the Start Menu's performance issues. Microsoft is acknowledging the need to address user concerns and "pain points" within Windows 11 as it considers restoring customization options.
Microsoft says Windows 11 File Explorer will soon use less RAM when you search files
Winsage
December 26, 2025

Microsoft says Windows 11 File Explorer will soon use less RAM when you search files

Windows 11 is updating its File Explorer to improve search efficiency and reduce memory usage during file searches, currently being tested in Windows 11 Build 26220.7523 for Windows Insider participants. The new search functionality eliminates duplicate file indexing operations, resulting in faster searches and lower system resource usage. The File Explorer Search relies on the Windows Search Indexer, which will now avoid redundant indexing, leading to reduced disk I/O, lower CPU cycles, and decreased RAM usage. Additionally, Microsoft is decluttering the context menu by relocating features like “Compress to” and “Copy as path” into a new sub-menu called “Manage file,” or “Other actions” on different machines. These updates are expected to be rolled out by the end of January or in February.
PostgreSQL 12 progress update
Tech Optimizer
December 6, 2025

PostgreSQL 12 progress update

The PostgreSQL development team provided a progress update on PostgreSQL 12, highlighting several enhancements: - Just-in-Time (JIT) compilation will be enabled by default, reducing memory consumption during COPY operations and function calls, and improving search performance for multi-byte characters. - Server configuration improvements will allow enabling/disabling cluster checksums via pg_checksums, reduce the default value for autovacuum_vacuum_cost_delay to 2ms, and support micro-seconds for time-based server variables. - Speed improvements for btree index insertions will enhance space efficiency of page splits, reducing locking overhead and optimizing performance for UPDATEs and DELETEs with many duplicates. - Recovery parameters can be modified during reloads, including archive_cleanup_command, promote_trigger_file, recovery_end_command, and recovery_min_apply_delay, along with a new streaming replication timeout feature. - The peculiar behavior of OID columns will likely be phased out, but they will still be defined as type OID, requiring adjustments for operations on tables with OID-named columns. - Data types such as abstime, reltime, and tinterval are expected to be removed. - Enhancements to geometric functions and operators will improve results, and geometric types will be restructured to manage NaN, underflow, overflow, and division by zero scenarios.
Optimizing Postgres Pro for 1C: what's new
Tech Optimizer
December 4, 2025

Optimizing Postgres Pro for 1C: what’s new

Postgres Professional has released Postgres Pro Enterprise 17.6, which includes enhancements for 1C users. Key features include: - Official documentation for configuring Postgres Pro with 1C solutions, consolidating best practices for performance tuning and cluster operation. - Suppression of excessive catalog cache invalidation messages during index creation or dropping on temporary tables and statistics gathering. - Introduction of the skip_temp_rel_lock parameter to bypass lock acquisition for temporary relations and their indexes, improving performance during frequent DDL operations. - Reengineered TRUNCATE operations for temporary tables, allowing for a single scan of the buffer pool, enhancing efficiency. - Improved substring search performance in 1C applications by enabling accurate cost estimation for LIKE operators on mvarchar fields. - Memoization support for parameterized ANTI JOIN nodes, reducing unnecessary data processing cycles in complex queries. - Reduced lock requests during query planning involving multiple relations, improving operation when new sessions connect. - Optimized VACUUM ANALYZE on catalog tables, ensuring the pg_internal.init file remains intact during analysis, allowing for swift new session connections.
silver Android smartphone
Tech Optimizer
August 5, 2025

Building a job search engine with PostgreSQL’s advanced search features

Job search platforms connect employers and candidates through advanced search engines that analyze structured and unstructured data. These platforms require robust database technologies for executing complex queries, full-text and semantic searches, and geospatial functionalities. A modern job search engine consists of a data repository that stores job listings and candidate profiles, and a search engine that facilitates bidirectional searches. Key features of an effective job search engine include: - **Full-text search**: Provides lexical matching for job titles and skills, supporting exact phrase matching and typo-tolerant searches. - **Semantic search**: Uses vector-based similarity to understand context and relationships beyond literal terms. - **Geospatial search**: Incorporates geographic considerations to find opportunities within specific distances. PostgreSQL serves as both a data repository and search engine, supporting full-text search, semantic search via the pgvector extension, and geospatial queries using the PostGIS extension. The job search engine utilizes PostgreSQL to manage job listings and candidate profiles, enabling real-time searches across millions of entries. The data model for a job search engine includes tables for jobs and resumes, with columns for full-text search vectors, semantic vectors, and geographical locations. PostgreSQL's full-text search capabilities include tokenization, dictionaries for language-aware parsing, and ranking functions for relevance. Advanced features such as proximity search, simple and weighted ranking, and fuzzy matching enhance search accuracy. Vector embeddings represent text in high-dimensional space, allowing for semantic searches that recognize similar roles or skills. PostgreSQL supports vector similarity searches with specialized indexing methods like IVFFlat and HNSW for efficient querying. Geospatial search capabilities in PostgreSQL enable location-aware job searches, allowing candidates to find jobs within commuting distances. Combining different search techniques, such as full-text and semantic searches, provides more relevant results. Performance optimization features in PostgreSQL address challenges related to computational complexity, indexing overhead, and concurrent query loads. The architecture discussed is applicable to various applications beyond job search platforms, including e-commerce, real estate, content recommendation systems, travel, and healthcare provider matching.
Why Instacart Moved to Postgres & pgvector to Boost Semantic Search
Tech Optimizer
June 10, 2025

Why Instacart Moved to Postgres & pgvector to Boost Semantic Search

Instacart serves 14 million daily users and manages billions of products, necessitating advanced search capabilities that go beyond keyword matching to understand user intent. The search system must reflect real-time inventory changes, leading to significant workloads on the database. Instacart transitioned from Elasticsearch and FAISS to a hybrid architecture using Postgres and pgvector, improving search performance and reducing write workloads by tenfold. This normalization allowed for better storage of machine learning features and improved flexibility. Moving compute closer to storage with NVMe resulted in a twofold increase in search performance. Instacart's migration to pgvector eliminated data duplication and operational complexity, enhancing search quality and user satisfaction, evidenced by a 6% decrease in searches with zero results.
Supercharging vector search performance and relevance with pgvector 0.8.0 on Amazon Aurora PostgreSQL
Tech Optimizer
June 1, 2025

Supercharging vector search performance and relevance with pgvector 0.8.0 on Amazon Aurora PostgreSQL

The Amazon Aurora PostgreSQL-Compatible Edition has integrated support for pgvector 0.8.0, enhancing vector search capabilities significantly. This version achieves up to 9x faster query processing and delivers 100x more relevant search results compared to previous iterations. Key improvements in pgvector 0.8.0 include: - Up to 5.7x improvement in query performance for specific patterns. - Introduction of the iterative_scan feature, enhancing recall for filter queries requiring approximate nearest neighbor (ANN) index searches. - Improved cost estimation for more efficient query planning, allowing the selection of traditional indexes for complex filtered searches. - Flexible performance tuning with two modes for iterative_scan: relaxed_order and strict_order. Previous versions faced challenges with overfiltering, where combining vector similarity search with traditional SQL filters resulted in fewer results than expected. The HNSW indexing algorithm accelerates vector similarity searches by creating a multi-layered graph structure for efficient navigation. Iterative index scans in pgvector 0.8.0 improve query reliability and performance by applying filters during the scanning process, reducing false negatives and enhancing performance. The configuration options for iterative scanning include off, strict_order, and relaxed_order, with relaxed_order providing the best balance between performance and accuracy. In performance tests, pgvector 0.8.0 demonstrated substantial improvements across various query patterns, with significant enhancements in result completeness and recall. The database can now efficiently handle enterprise-scale applications, such as e-commerce and recommendation systems, with high performance and result quality even as product catalogs expand. Best practices for deploying pgvector 0.8.0 include using HNSW with recommended parameters, creating additional indexes on commonly filtered metadata columns, and tuning query parameters based on specific use cases. Operational best practices involve using Graviton4-based instances for optimal performance, monitoring and tuning slow vector queries, and considering partitioning for very large tables.
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