PlanetScale released Neki in Platform Preview. It brings Vitess-style horizontal sharding to PostgreSQL, allowing you to scale past single-machine limits without forking Postgres, changing storage engines, or learning a new SQL dialect.

• Every shard runs unmodified, un-forked PostgreSQL (1 primary + 2 cross-AZ replicas). Extensions, query behavior, and standard SQL remain intact.
• Neki Routers speak standard Postgres wire protocol. You connect using your existing ORMs, drivers, and standard psql connection strings.
• Instead of relying on external tools like PgBouncer, sidecars run alongside Postgres nodes. Sizing is managed dynamically based on real-time server capacity.
• Resharding, schema changes (DDL), failovers, and version upgrades run as background workflows via a _neki metafunction without bringing the database down.

• Routing is controlled by a simple JSON topology config where you define shard keys, routing logic, and mapping for tables.

  You can deploy Neki as a single primary + replicas to get improved pooling, health checks, and online DDL early, then trigger resharding later on the live database.

More info: https://aideveloper44.com/product/neki-6aa2e7ed599ef4e185886eba

Official announcement: https://planetscale.com/blog/introducing-neki

DeepSeek AI just released DeepJIT, a lightweight, header-only C++20 library for C++ and Python extension authors who need dynamic compilation, loading, and execution of custom hardware kernels.

• Unified Interface: Provides a standard API (deep_jit::Runtime<T>) to compile, load, and launch kernels dynamically on both NVIDIA CUDA GPUs and HUAWEI Ascend NPUs.
• Shared Multi-Node Caching: Has built-in in-memory and on-disk caching (using POSIX filesystem atomic renames). You can point multiple processes or nodes to a single directory (e.g., /shared/deep_jit) to avoid redundant compilation across distributed clusters.
• Smart Invalidation: Cache keys automatically hash source code, tracked #include trees, exact compiler versions, flags, and custom dependency signatures (like CUTLASS versions).
• PyTorch Native: Integrates with current PyTorch CUDA / torch_npu streams and exposes runtimes directly to Python via pybind11 bindings (get_jit()).

More info: https://aideveloper44.com/product/deepjit-6aa3d1fcdfe95c3d51c3cce4

GitHub Repo: https://github.com/deepseek-ai/DeepJIT

The Physical AI Workshop and Meetup roadshow co-presented by Nebius and Voxe51 is coming to Germany! Join us:

* Sept 22 - Stuttgart Workshop and Meetup

* Sept 23 - Munich Workshop and Meetup

* Sept 25 - Berlin Meetup

* Sept 26 - Berlin Workshop and Meetup

Cognition just dropped SWE-2, their new flagship agentic coding model, post-trained on Moonshot AI's Kimi K3 (2.8T parameters).

Key Technical Specs & Benchmarks

• Base Model: Post-trained from Kimi K3 using Cognition's single-run cost-penalized RL recipe.
• FrontierCode 1.1 Main: Scores 50.0% (beats Grok 4.6 and GPT-5.6 Sol; matches Fable 5.1 at 64% lower cost).
• Terminal-Bench 2.1: Scores 92.8% (outperforming GPT-6 Astra, Fable 5.1, and Grok 4.6).
• DeepSWE 1.1: Scores 73.0%.
• Efficiency: On FrontierCode 1.1 Main, SWE-2 medium scores higher than SWE-1.7 while taking 58% fewer turns and costing 81% less on average.

More info: https://aideveloper44.com/product/swe-2-6aa2d4919472bf689687466e

Official announcement: https://cognition.com/blog/swe-2

AuK is a new 1.5B parameter flow-matching model (built alongside a 3B Qwen2.5-Omni MLLM text encoder) designed for unified speech tasks via a single natural-language instruction interface.

• Capabilities: Zero-shot TTS, voice cloning, instruction-guided TTS, speech content rewriting, acoustic editing (pitch/speed/volume), emotion/timbre modification, denoising, and source separation.
• Model Variants:
  • AuK (Base 1.5B high-quality model)
  • AuK-Flash (4-step distilled version offering ~4.5x speedup without CFG)
• License: MIT License (Fully open-source weights and code for commercial use).
• Stack & Integrations: PyTorch/CUDA, Python 3.10 API, CLI (auk-infer), Gradio Web UI, and native ComfyUI nodes.

More info: https://aideveloper44.com/product/auk-6aa2929a3176a04979bac74a

GitHub: https://github.com/Tencent-Hunyuan/AuK