☕️ DeepSeek launches V4 Pro

Hi there, this is your daily ☕️ DevShot.

In today's DevShot:

🐋 DeepSeek launches V4 Pro

🎵 Spotify speeds up its data lake queries

🐳 Docker's new VMM enters public beta

⚡ Astro 7 builds up to 61% faster

🌐 Databricks configures tens of millions of VMs

🐍 Simon Willison releases alchemy-utils 0.1

Plus: 🎁 5 other news you might like, 🛠️ 5 tutorials, 🧰 6 tools, and 📚 5 papers.

• DeepSeek quietly finalized its flagship V4 Pro, shipping the DeepSeek-V4-Pro-0813 build with no blog post-the only signal being an updated version string on its API pricing page after months of the model running as a preview.
• Pricing holds steady at roughly $0.44 per million input tokens and $0.87 per million output, but the weights underneath changed; the earlier scores every independent lab tested described the unfinished preview, so nobody has externally benchmarked 0813 yet.
• DeepSeek's own numbers across 10 agent benchmarks trail Fable 5 by about 5% on average, but on blended rates V4 Pro costs around 46 times less; the MIT-licensed weights sit on Hugging Face for independent verification.

• Spotify built Random Access Parquet (RAP), a storage layer that serves low-latency point queries directly against its data lake, letting online services and AI apps fetch individual records without replicating datasets into operational databases like Bigtable.
• RAP adds an external index mapping keys such as user IDs to specific Parquet files and row locations, so a lookup resolves through the index and issues a targeted ranged read instead of scanning thousands of files.
• Storage optimizations-sorting by lookup key, interleaving columns, and covering indexes-trade modest size increases for fewer operations, serving some point queries in a single few-kilobyte read, while secondary indexes enable multi-dimension queries without rewriting data.

• Docker has launched a public beta of Docker VMM, a first-party virtualization layer it built from scratch to replace the third-party VMM under Docker Desktop, available on Mac and Windows in v4.86.
• Developers get faster container startup, quicker host-container file sharing, and memory that returns to the host when containers idle, while Windows gains Hyper-V-level isolation with WSL2-like speed for the first time under a Docker-maintained VMM.
• Mac users already on Docker VMM upgrade automatically; Windows users opt in via Settings > General with no waitlist, Linux support arrives at GA, targeted for late October 2026 when Docker VMM becomes the default for new installs.

• Astro 7 shipped with its build pipeline rewritten in native code, cutting build times by 15 to 61 percent, with the astro.build site dropping from 63s to 24s and Cloudflare's 8,400-page docs falling from 387s to 262s.
• The compiler moves from Go to a Rust build on oxc and Lightning CSS, Markdown now runs through a Rust processor called Sätteri, and the framework sits on Vite 8 with Rolldown, though remark and unified pipelines remain available.
• Compilation turned stricter, erroring on unclosed tags and passing invalid nesting through as written, while point releases added CSP directives for inline styles, per-route incremental builds in 7.2, and JSON logging for AI coding agents.

• Databricks re-architected how it delivers network configuration to the tens of millions of VMs its serverless platform launches daily, cutting p99 RPC latency by 97.5% from 5,000ms to 125ms while hitting 99.99% availability.
• The old design synchronously fetched config from multiple upstream services on the cluster-start critical path; the rewrite subscribes to change events via a message queue and pre-computes per-workspace snapshots, so serving becomes a single storage read.
• A periodic reconciler re-syncs all workspaces as a safety net for missed events, and calling upstream only on actual changes rather than every request dropped upstream call volume by 86% across billions of daily requests.

• Simon Willison has shipped alchemy-utils 0.1a0, an early alpha of a database-agnostic library and CLI that ports his sqlite-utils API to SQLAlchemy so it works across PostgreSQL, SQLite, and DuckDB.
• The tool mirrors sqlite-utils core methods like insert, upsert, insert_all, upsert_all, create, update, and table introspection, and runs via uvx with per-engine extras such as alchemy-utils[postgresql] or alchemy-utils[duckdb].
• Willison built the prototype using Codex and GPT-5.6 with red/green TDD and pytest; a DuckDB CSV insert of every San Francisco tree initially took nearly an hour, but Codex optimized it down to about 35 seconds.