Antigravity SDK's hybrid architecture separates planning from execution: a cloud model plans, local models run most tokens on-device. See how routing, execution, and the data boundary fit together.
Antigravity SDK connects one agent loop to two execution targets: a cloud model that plans, and local models such as Gemma 4 via Google AI Edge's LiteRT that do the bulk of the token work. A routing layer picks the target per request, and any OpenAI-compatible server such as Ollama, LM Studio, or vLLM can serve the local path without changing agent orchestration. The split matters at the data boundary: on-device execution keeps code and requests local and drops API costs and rate limits, while the cloud path buys capability local hardware cannot reach. Treating routing as an architecture decision, not a config flag, is what lets you size GPU memory, place privacy controls, and keep working offline.
Local execution removes API costs and rate limits and keeps code and requests on-device, but it wants hardware like 24GB VRAM or unified memory. In the SDK's own code-auditing example, 3,322 of 3,417 tokens ran locally while the cloud model only planned. Short, interactive, context-heavy work favors local; long-horizon reasoning may still need the cloud.
No. The SDK's LocalOpenAIAgentConfig works with any OpenAI-compatible server such as Ollama, LM Studio, or vLLM, so the agent loop and its tool calls stay unchanged. Only the execution target behind the routing layer changes.
The posts behind it
Meta · · AnnouncementMeta AI glasses require cloud processing for advanced AI features like streaming transcription and long-term recall, but traditional cloud architectures expose data during use. Private Processing uses confidential virtual machines with Trusted Execution Environments (TEEs) on CPUs and GPUs to keep data encrypted even while being computed. Before sending context, glasses perform remote attestation, verifying the server’s TEE certificate against a public transparency ledger that records all deployed binary images. Non-targetable routing via blind-signed tokens and third-party OHTTP relays prevents tying requests to specific users, while encrypted storage ensures data is only accessible with user-provided keys. This infrastructure, already proven for WhatsApp and Meta AI, extends to glasses, with security audits by firms like NCC Group and a bug bounty program for independent research.
Databricks · · ExplainerConcurrence processes 100.8 billion input tokens and 11.2 million LLM calls every 30 days, at an annualized rate of 1.2 trillion tokens. To govern these workflows, the company consolidates on Databricks with Lakebase for operational state, Unity Catalog for governance, and Unity Gateway for centralized AI access. Patient data is recorded as immutable events in Delta tables, from which a current patient state—called the world model—is computed while preserving provenance. All agents are tested against simulated patients before deployment; simulation traffic is approximately seven times production traffic, using immutable event history to replay states. Concurrence routes all coding-agent traffic through Unity Gateway, attributing each request to the engineer, and enforces compliance-gated model routing where HIPAA requirements are met first.
Red Hat · · Problem & solutionDefault inference configurations for large language models often leave significant performance unused, even on expensive hardware like 16 NVIDIA H200 GPUs across two nodes. The author tested this setup with vLLM and Red Hat AI Inference 3.4 on a CoreWeave Kubernetes cluster, using enterprise RAG workloads with 2,000-token inputs and 100 concurrent users. By systematically comparing aggregated and prefill-decode (PD) disaggregated architectures, and tuning tensor parallelism (TP) and PD split, latency was dramatically reduced. For Qwen3-32B, time-to-first-token (TTFT) on the best aggregated configuration (2xTP8) dropped from 995ms to 304ms, and with optimized routing to 287ms — a 71% improvement without hardware changes. For Llama-70B, TTFT fell from 1,292ms to 463ms, a 64% gain. The author also built ServeIt Studio, an open-source tool that automates the full optimization pipeline on Kubernetes.
Google · · AnnouncementThe Antigravity SDK now supports local AI models, starting with Gemma 4 26B A4B via Google AI Edge’s LiteRT, enabling offline agentic assistance on local GPU and RAM with a recommended machine of 24GB VRAM or unified memory. Local execution eliminates API costs and rate limits while keeping code and requests on-device, offering privacy and offline resiliency. The SDK supports hybrid workflows where a cloud model like Gemini 3.8 Flash plans while local Gemma 4 instances execute, as in a code auditing task where 97.2% of tokens (3,322 of 3,417) ran locally. A built-in example generates a live-updating CLI resource monitor using psutil and rich from a single prompt, with the agent autonomously writing the script, creating requirements.txt, and testing the code entirely offline. The SDK also supports any OpenAI-compatible server like Ollama, LM Studio, or vLLM via LocalOpenAIAgentConfig without changing agent orchestration.
Jetbrains · · AnnouncementJetBrains Air is a system of products for agentic software development, built on the premise that no single model, agent, or service fits every developer, team, or task. It connects products for individual work, team coordination, organizational control, context, and process automation, while remaining open to tools and agents developers choose, including those JetBrains does not build. This openness lets developers select the agent, model, or service suited to a task without forcing the organization to give up context, visibility, or control. The Post notes that every new model, agent, or service an organization adds reduces visibility into its own development work. Because intelligence does not belong to one model, the benefit can extend across supported agents and services.
More like this
AI-DLC is an AI-driven development lifecycle that runs delivery through Inception, Design, and Construction, with specs, steering files, and hooks keeping agents aligned to agreed intent.
Apple Silicon's defining choice is one shared memory pool for CPU, GPU, and Neural Engine. This diagram maps that fabric, the on-package bandwidth it buys, and the scaling limits it imposes.
Antimicrobial resistance surveillance is a layered data architecture: raw reads and demand signals flow in, get normalized into governed tables, and answer plain-language queries. The diagram maps each boundary.
The Good Stuff
Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.
One useful email. Unsubscribe anytime.
