Grindr's Chief Product Officer, AJ Balance, discusses the company's significant investment in AI, with 70% of its code now being checked via AI tools like Claude Code, OpenAI, and GitHub Copilot. This shift is changing the role of software engineers, moving them towards more code review and agent coordination. The company is also testing a premium "Edge" subscription tier at high price points, justifying the cost based on the value it delivers to users seeking enhanced connections. Balance also addressed concerns about ad density and subscription fatigue, outlining plans for ad format improvements and a focus on maintaining a positive free user experience.
This article advocates for wider adoption of Claude Code, an AI tool from Anthropic designed to write, edit, and fix code. Initially an internal tool for Anthropic developers, it's now publicly available as a command-line tool that operates within your terminal. It can understand natural language instructions to modify codebases, and even assists with non-programming tasks like file organization and research. While the terminal interface can be intimidating, the author suggests using it within an IDE or utilizing the Claude Desktop app's integrated Cowork interface, highlighting its potential for both developers and non-developers.
A new ETH Zurich study challenges the common practice of using `AGENTS.md` files with AI coding agents. LLM-generated context files decrease performance (3% lower success rate, +20% steps/costs).Human-written files offer small gains (4% success rate) but also increase costs. Researchers recommend omitting context files unless manually written with non-inferable details (tooling, build commands).They tested this using a new dataset, AGENTbench, with four agents.
This article discusses how to effectively utilize Large Language Models (LLMs) by acknowledging their superior processing capabilities and adapting prompting techniques. It emphasizes the importance of brevity, directness, and providing relevant context (through RAG and MCP servers) to maximize LLM performance. The article also highlights the need to treat LLM responses as drafts and use Socratic prompting for refinement, while acknowledging their potential for "hallucinations." It suggests formatting output expectations (JSON, Markdown) and utilizing role-playing to guide the LLM towards desired results. Ultimately, the author argues that LLMs, while not inherently "smarter" in a human sense, possess vast knowledge and can be incredibly powerful tools when approached strategically.
OpenCode is an open source agent that helps you write code in your terminal, IDE, or desktop.
It features LSP enabled, multi-session support, shareable links, GitHub Copilot and ChatGPT Plus/Pro integration, support for 75+ LLM providers, and availability as a terminal interface, desktop app, and IDE extension.
With over 120,000 GitHub stars, 800 contributors, and over 5,000,000 monthly developers, OpenCode prioritizes privacy by not storing user code or context data.
It also offers Zen, a curated set of AI models optimized for coding agents.
It features LSP enabled, multi-session support, shareable links, GitHub Copilot and ChatGPT Plus/Pro integration, support for 75+ LLM providers, and availability as a terminal interface, desktop app, and IDE extension.
With over 120,000 GitHub stars, 800 contributors, and over 5,000,000 monthly developers, OpenCode prioritizes privacy by not storing user code or context data.
It also offers Zen, a curated set of AI models optimized for coding agents.
This article presents findings from a survey of over 900 software engineers regarding their use of AI tools. Key findings include the dominance of Claude Code, the mainstream adoption of AI in software engineering (95% weekly usage), the increasing use of AI agents (especially among staff+ engineers), and the influence of company size on tool choice. The survey also reveals which tools engineers love, with Claude Code being particularly favored, and provides demographic information about the respondents. A longer, 35-page report with additional details is available for full subscribers.
Sarvam AI is releasing Sarvam 30B and Sarvam 105B as open-source models, trained from scratch on large-scale, high-quality datasets. These models demonstrate strong reasoning, programming, and agentic capabilities, with optimizations for efficient deployment across various hardware. Sarvam 30B powers Samvaad, while Sarvam 105B powers Indus. The release includes details on the model architecture, training process, benchmark results, and inference optimizations. The models are available on AI Kosh and Hugging Face, and the article details their performance across benchmarks and in real-world applications like webpage generation, JEE problem solving, and conversational agents.
Qwen3-Coder-Next is an 80-billion-parameter language model that activates only 3 billion parameters during inference, achieving strong coding capabilities through agentic training with verifiable task synthesis and reinforcement learning. It is an open-weight model specialized for coding agents, and both base and instruction-tuned versions are released to support research and real-world coding agent development.
An Emacs frontend for the pi coding agent. Compose prompts in a full Emacs buffer, chat history as markdown, live streaming output, and more.
An exploration of Claude 3 Opus's coding capabilities, specifically its ability to generate a functional CLI tool for the Minimax algorithm with a single prompt. The article details the prompt used, the generated code, and the successful execution of the tool, highlighting Claude's impressive one-shot learning and code generation abilities.
