Parse notes
SQLite can be used as the foundation for file formats like OpenDocument Presentation (ODP) instead of the current ZIP archive structure. SQLite has advantages such as smaller file sizes, faster file saving and startup times, and reduced memory usage due to its ability to store and retrieve data in smaller, incremental pieces. Furthermore, SQLite allows for features like document versioning.
Memory is not always faster than disk, as disk I/O can outperform memory caching in specific scenarios. Experiments involving counting integers in a large dataset show that traditional memory access methods like `mmap()` introduce overhead due to memory latency. By using an `io_uring` based I/O engine to stream data directly from disk, faster data processing is achieved compared to accessing data from the page cache.
Bitrig built a Swift interpreter to dynamically run Swift apps on iPhones without requiring compilation and signing through Xcode. The interpreter uses SwiftSyntax to parse Swift code and represents runtime values using an `InterpreterValue` enum, handling both native and custom types. For framework APIs like SwiftUI, it pre-compiles calls to initializers and other functions, parsing `.swiftinterface` files to automate the process.
Claims of increased developer productivity due to AI coding tools are unfounded. Data shows no real surge in software releases despite widespread AI adoption. If these claims were true, there would be a noticeable increase in new software releases and a flood of "shovelware”.
This dev was initially drawn to Electrical Engineering by the promise of hardware innovation and better human-computer interaction. However, their experience in a Computer Science and Engineering program led to disillusionment with the memorization-heavy engineering coursework and a lack of connection to real-world innovation. They found more satisfaction in software development, web applications, and research in physics, eventually switching their major to CS + Physics.
Saying no in software engineering, while important for work-life balance, comes with psychological and social costs, making it harder than it seems. People often avoid refusing requests due to negative connotations, potential career risks, and the desire to please others. Instead of simply telling people to "just say no," we should understand the burden of refusal and create a supportive culture that encourages saying no when necessary.
Terragon is a platform to help devs run Claude Code and OpenAI Codex in the background. It has features such as isolated dev environments, Git workflows, cross-platform access, monitoring, automation, and security.
Open SWE is an open-source, cloud-based coding agent built with LangGraph that autonomously understands codebases and implements solutions, from planning to pull requests. It has human-in-the-loop feedback, parallel task execution, and automatic issue/pull request management. Users can initiate tasks via a UI or directly from GitHub issues with specific labels.
Most companies are failing to see a return on their AI investments. Fewer than 10% of AI pilot projects generate real profit. This shows that the value lies not in flashy AI applications, but in practical uses like streamlining operations and automating tasks that free up human employees for higher-value work. Startups focusing on solving specific problems with AI are seeing more success.
Atlassian is acquiring The Browser Co., the startup behind the Arc and Dia browsers, for $610 million in cash. The Browser Co. debuted Arc in 2022 and attracted interest from OpenAI and Perplexity before the acquisition. The acquisition is expected to close in Atlassian's fiscal second quarter.
There are four layers of agent architecture: context & memory, data & integration, skills & capabilities, and evaluation & trust. Most AI agent flows should start with a simple, single-agent architecture and prioritize graceful escalation to humans over striving for constant correctness to build user trust.
One can become an expert at anything by learning through real projects on demand and teaching what you learn.
To avoid AI-driven code complexity, devs should prioritize thorough problem understanding and structured planning before using AI tools for implementation.
While LLMs have reduced the cost of writing code, understanding, testing, and maintaining that code, especially in a team setting, still remains the true bottleneck in software engineering.
This is an interactive visualization of an 85,000-parameter LLM that sorts a sequence of letters and predicts the next token, explaining the process from token embedding to transformer layers and probability output.