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Meta launches Muse Code AI coding agent

Meta released Muse Code (beta) on August 5, 2026 — a terminal-installable, single-command tool that coordinates multi-agent coding workflows across repositories while leaving the user’s working copy untouched. It runs on Muse Spark 1.2 (two variants), available inside Muse Code and via the Meta Model API with pay-as-you-go access. Muse Code launches parallel sub-agents in isolated worktrees, runs persistent background agents that resume after crashes, can access local files, GitHub and the web, and logs model calls, tool use, approvals and changes locally; integrated skills include /plan, /grill and /goal. Pricing includes a lower-cost Contributor tier (data opt-in for model training) and a private tier that disallows training, and Meta accepts zero-data-retention enterprise requests. Mark Zuckerberg announced the launches as part of a developer-tools push.

Snap posts strong revenue and profit improvement in Q2 2026

Snap Inc. reported significantly improved financial results for Q2 2026: revenue rose to $1.599 billion (up 19% year-over-year), net loss narrowed to $164 million, and adjusted EBITDA increased to $250 million. Operating cash flow nearly doubled to $176 million and free cash flow reached $121 million. Snap said revenue growth was driven by improved ad performance and expanded direct revenue sources, while margins and operational efficiency improved. The company reported about 971 million monthly active users and emphasized disciplined investment to further increase free cash flow per share and sustainable profitability.

ContextOS: AST-aware Retrieval for AI in Large Codebases

The article argues that failures of AI coding assistants in large repositories are retrieval problems, not model reasoning issues. The author introduces ContextOS, a local-first context engine that preserves code structure by using Tree-sitter to extract AST-aware chunks (functions, classes, interfaces), prioritizes BM25 lexical search via SQLite FTS5 with a MiniLM ONNX fallback for semantic matching, and applies query-aware context compression. In benchmarks, ContextOS reached 98% file-level recall on 100 exact-function queries against the Redis 7.x C codebase with an average 589 tokens per query, and ~100% accuracy on React/Next.js with ~280 tokens per query. ContextOS exposes a Model Context Protocol (MCP) server and is available on GitHub.