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Что такое торрент, как он работает и можно ли им пользоваться
TrendTechie
June 20, 2026

Что такое торрент, как он работает и можно ли им пользоваться

Downloading a Linux distribution from the official website typically takes about half an hour, but during high-demand periods, such as new Ubuntu releases, speeds can drastically decrease due to server overload. Torrents, which use a peer-to-peer file-sharing protocol, allow users to download and share data simultaneously, reducing the load on a single server. Torrents operate by enabling direct exchanges between users through the BitTorrent protocol, where files are divided into smaller pieces and downloaded in parallel from multiple sources. Key terms include peers (users with parts of the file), seeds (users who have fully downloaded the file), .torrent files (metadata files), and magnet links (text-based alternatives to .torrent files). The BitTorrent protocol does not rely on a central server, enhancing resilience as files can continue to circulate as long as at least one seed is available. To use torrents, a torrent client is needed, with recommended options for 2026 including qBittorrent, Transmission, and BiglyBT. The BitTorrent technology itself is legal, but legality depends on the content being shared. Legal uses include downloading Linux distributions, open-source software, and scientific datasets, while unlicensed movies and cracked software are illegal. In Russia, while torrent technology is not prohibited, downloading copyrighted content without permission is a violation. Risks of using torrents include exposure to viruses and malware, copyright infringement, fake distributions, and IP address exposure. To protect oneself, users should choose moderated trackers, avoid suspicious files, use antivirus software, and limit upload speeds. Common problems include slow speeds, connection issues, and disappearing files, which can often be resolved by checking settings or ensuring there are seeds available. Sharing files after downloading is not legally mandatory but is considered good etiquette in the torrent community.
Tiger Data launches PostgreSQL extension designed for AI agents
Tech Optimizer
June 9, 2026

Tiger Data launches PostgreSQL extension designed for AI agents

Tiger Data has launched a managed PostgreSQL database service called Ghost, designed for AI agents, addressing the limitations of traditional database architectures for autonomous software. The service is now generally available and allows agents to create unlimited databases quickly through a feature called "fast forking." Ghost utilizes Tiger Data's Fluid Storage technology, which employs a copy-on-write storage layer, enabling multiple database instances to share data blocks while charging users only for changed data. The service supports popular PostgreSQL extensions and is positioned as an evolution of PostgreSQL, maintaining its compatibility with the existing ecosystem. Tiger Data has raised 0 million in funding and employs 200 individuals across 25 countries.
You already know Russia’s Max messenger spies on users. You probably don’t know just how many surveillance tools it hides, including even a neural network for eavesdropping.
AppWizard
May 25, 2026

You already know Russia’s Max messenger spies on users. You probably don’t know just how many surveillance tools it hides, including even a neural network for eavesdropping.

Concerns about the Russian messaging application Max have grown due to vulnerabilities and surveillance features identified by IT specialists. An analysis revealed that Max can disable encryption on conversations with a single command and includes a forced-update feature that restricts user communication until the app is updated. Max collects extensive user data, including a list of installed applications and checks for VPNs, and has the ability to disable TLS session validation. Additionally, a version of Max included a neural network for speech recognition, although these features were later removed. Despite these concerns, VK, the parent company of Max, reported rapid user growth, surpassing 120 million users as of early May, with a daily reach of 68 million users. Many users are switching from Telegram to Max due to accessibility issues with Telegram and pressure from authorities. Major mobile carriers in Russia have collaborated with VK to send authentication messages through Max, further embedding the app in users' daily lives. However, Max has faced challenges in becoming a primary source for news and entertainment, hosting around 300,000 public channels compared to Telegram's 1.6 million, which has hindered advertising growth and led to an increase in scams.
Query billion-scale vectors with SQL: Integrating Amazon S3 Vectors and Aurora PostgreSQL
Tech Optimizer
May 10, 2026

Query billion-scale vectors with SQL: Integrating Amazon S3 Vectors and Aurora PostgreSQL

For managing relational data within Amazon Aurora PostgreSQL and enhancing capabilities with similarity search over large embedding collections, an integration with Amazon S3 Vectors is available. This integration utilizes the pgvector extension for low-latency similarity searches on vectors stored in the database. Amazon S3 Vectors provides cost-effective storage for extensive datasets, scaling to hundreds of millions or billions of embeddings. The integration, facilitated through AWS Lambda, allows users to query S3 Vectors directly from Aurora PostgreSQL using standard SQL, enabling the combination of vector similarity results with relational filters in a single query. The integration offers benefits such as basic key-value metadata support in S3 Vectors for simple filtering, while Aurora PostgreSQL is optimal for complex SQL filters, multi-table joins, and access-control policies. The architecture separates concerns, with Lambda handling API integration and Aurora managing relational data. Security is maintained through IAM role separation and network security measures. Data consistency considerations arise due to the distribution of data across Aurora PostgreSQL and S3 Vectors, necessitating explicit synchronization processes to manage potential stale results. The integration is suitable for use cases that can tolerate eventual consistency, such as recommendations and content discovery. Performance expectations include Lambda invocation latencies of 100-500 milliseconds, while Aurora pgvector typically provides single-digit millisecond response times. S3 Vectors achieves sub-second performance for cold queries and less than 100 milliseconds for warm queries. From a cost perspective, S3 Vectors is more economical than Aurora, making it suitable for high-volume vector data that requires archiving and queryability. The integration architecture consists of three components: Aurora PostgreSQL, AWS Lambda for API translation, and S3 Vectors for executing similarity searches. A typical query involves several stages, including SQL query invocation, function processing, API translation, similarity search, and result processing. Prerequisites for this integration include familiarity with Aurora PostgreSQL, AWS Lambda, vector databases, and SQL. Required AWS resources include an Aurora PostgreSQL cluster, a VPC with internet access, and appropriate permissions for IAM roles and Lambda functions. To deploy the integration, users must gather configuration values from their Aurora cluster, deploy a CloudFormation stack, associate the IAM role, update the Lambda function code, and install the necessary PostgreSQL schema. Sample data can be uploaded to the S3 Vectors index, allowing for testing of vector operations and combined queries. When combining relational and vector queries, developers should be aware of the trade-offs involved in pre-filtering data by metadata, which can improve performance but may reduce recall. Troubleshooting may involve addressing connectivity issues, IAM role misconfigurations, and performance optimizations. To clean up resources after testing, users should remove the PostgreSQL schema, disassociate the Lambda role from the Aurora cluster, and delete the CloudFormation stack to ensure all resources are removed.
Veteran Windows dev shows off AI running on 47-year-old PDP11 with 6 MHz CPU and 64KB of RAM — 'gloriously absurd' project runs transformer model written in PDP-11 assembly language
Winsage
April 14, 2026

Veteran Windows dev shows off AI running on 47-year-old PDP11 with 6 MHz CPU and 64KB of RAM — ‘gloriously absurd’ project runs transformer model written in PDP-11 assembly language

Veteran Windows developer Dave Plummer is using a 47-year-old PDP-11 system with a 6 MHz CPU and 64KB of RAM to train a neural network called ‘Attention 11,’ developed in PDP-11 assembly language. The model is tasked with reversing a sequence of eight digits, requiring it to understand structural rules similar to those used in contemporary large language models like ChatGPT. Plummer emphasizes the importance of optimizing for the system's constraints, stating that constraints drive creative engineering. The model operates with 1,216 parameters and uses fixed-point math with 8-bit precision. After approximately 350 training steps, the model achieved 100% accuracy in the number-reversing task in about 3.5 minutes. Plummer argues that modern AI operates on the same mechanical principles as this vintage machine, just at a much larger scale, and suggests that companies focusing on efficiency and optimization may gain an advantage in the AI landscape.
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