OpenAI Models, Codex, and Managed Agents Land on AWS Bedrock L1
Confidence: High
Key Points: OpenAI and AWS announced a major partnership: the latest OpenAI models, the Codex coding agent (4 million weekly users), and the new Amazon Bedrock Managed Agents service are now available on AWS Bedrock in limited preview. Enterprises can call OpenAI models directly through their existing Bedrock API, IAM, PrivateLink, CloudTrail, and compliance frameworks. Codex is available via Bedrock API, CLI, desktop app, and VS Code extension. This is the first major cross-cloud deployment following the revision of the Microsoft-OpenAI partnership, marking OpenAI's official break from Azure exclusivity.
Impact: This partnership reshapes the cloud AI market landscape. AWS customers (approximately 30% of global public cloud market share) can now use GPT models and Codex directly within their own VPCs with their security controls, without cross-cloud data transfer. For existing Azure OpenAI customers, it means a functionally equivalent service on AWS with multi-cloud strategy options. For enterprise AI platform selection teams, OpenAI models are no longer locked to Azure. Bedrock Managed Agents also formally competes with Google Vertex AI Agent Builder and Azure AI Foundry for the enterprise agent platform market.
Detailed Analysis
Trade-offs
Pros:
OpenAI models benefit from enterprise-grade AWS security controls: IAM, PrivateLink, and CloudTrail
Data stays within AWS, meeting existing compliance frameworks
Codex integrates with VS Code and desktop app for a consistent developer experience
Reduces vendor lock-in risk with a single cloud provider
Cons:
Currently in limited preview; access requires application
Pricing has not been announced and may differ from Azure OpenAI
Available OpenAI model versions (e.g., GPT-5, o-series) are not explicitly listed
Regional availability not announced; may be limited to US regions initially
Quick Start (5-15 minutes)
Log into AWS Console, navigate to Amazon Bedrock, and apply for limited preview access to OpenAI models
Read the AWS announcement at https://aws.amazon.com/about-aws/whats-new/2026/04/bedrock-openai-models-codex-managed-agents/ for the list of available models
If using Codex, install the VS Code extension and sign in with AWS credentials to test
Evaluate API compatibility (request format and authentication differences) when planning a migration from Azure OpenAI
Recommendation
If your company primarily runs on AWS, apply for the limited preview immediately and begin planning a proof of concept. Existing AWS customers no longer need to build a cross-cloud architecture just to access OpenAI models, which can significantly simplify security and billing workflows. Existing Azure OpenAI users should wait for pricing announcements before deciding on a multi-cloud deployment to avoid paying double for equivalent capabilities in the short term.
Microsoft and OpenAI Revise Partnership: IP License Extended to 2032 as Non-Exclusive, Cloud-Neutral L1
Confidence: High
Key Points: Microsoft and OpenAI announced a revised partnership agreement with key changes: (1) Microsoft's license to OpenAI's intellectual property is extended to 2032, but changed from exclusive to non-exclusive; (2) Microsoft remains OpenAI's primary cloud partner, with OpenAI products launching on Azure first, but OpenAI is free to operate on other cloud platforms; (3) Microsoft will no longer pay OpenAI revenue share, but OpenAI will continue paying Microsoft through 2030 at the original proportions (subject to a total cap); (4) Microsoft retains its position as a major shareholder. OpenAI's same-day launch on AWS (see previous item) confirms the freedom granted by the new agreement.
Impact: This is the most significant commercial restructuring in the AI industry in recent years. For Microsoft, it loses exclusivity but retains long-term returns through IP rights and equity. For OpenAI, it gains operational independence to deploy on AWS and GCP and serve enterprise customers across different clouds. For enterprise procurement teams, Azure OpenAI is no longer the only commercial channel for OpenAI models, greatly expanding negotiating leverage. For other AI providers (Anthropic, Google), this means their largest competitor's market reach has effectively doubled.
Microsoft retains long-term returns via IP licensing through 2032
Azure still enjoys a first-launch time advantage for OpenAI products
Simplified structure for both parties, reducing operational uncertainty
Cons:
Microsoft loses OpenAI model exclusivity, reducing Azure's differentiation
OpenAI must continue paying Microsoft revenue share through 2030, sustaining financial pressure
The announcement does not address the current status of AGI provisions, leaving room for interpretation
License details (e.g., which IP can be sublicensed to third parties) are not fully disclosed
Quick Start (5-15 minutes)
Read the Microsoft blog at https://blogs.microsoft.com/blog/2026/04/27/the-next-phase-of-the-microsoft-openai-partnership/ for the official statement
Reassess whether current Azure OpenAI usage remains the best option; compare pricing for OpenAI services soon to launch on AWS and GCP
If planning a multi-cloud architecture, design an SDK abstraction layer for OpenAI API calls in advance to enable future switching
Monitor potential OpenAI-GCP collaboration announcements, expected to materialize in the second half of 2026
Recommendation
Enterprise AI procurement teams should reassess cloud lock-in strategy: customers who previously chose Azure due to OpenAI exclusivity can re-evaluate pricing, regional availability, and integration depth between AWS Bedrock OpenAI and Azure OpenAI; new procurement decisions do not necessarily need to favor Azure. Additionally, abstract LLM calls at the SDK layer to preserve future multi-cloud switching flexibility.
DeepSeek V4 Preview Released: 1.6T Parameters, 1M Context, Native Support for Agentic Tool Workflows L1
Confidence: High
Key Points: DeepSeek released the V4 preview with two models: DeepSeek-V4-Pro (1.6T total parameters, 49B active) and DeepSeek-V4-Flash (284B total parameters, 13B active). The core innovation is a hybrid attention mechanism (Compressed Sparse Attention CSA + Hyper-Compressed Attention HCA) that reduces KV cache size to only 7–10% of V3.2's. Native support for 1M token context window. Three new agentic features: (1) Interleaved Thinking preserving reasoning traces across tool calls, (2) XML-format tool schema replacing JSON to reduce parsing failures, and (3) a Rust sandbox (DSec) supporting RL training. Terminal Bench 2.0 score is 67.9; SWE Verified 80.6 ties with Opus-4.6-Max. Compatible with both OpenAI ChatCompletions and Anthropic API formats, and already integrated with Claude Code. Legacy endpoints deepseek-chat and deepseek-reasoner will be retired on 2026-07-24.
Impact: V4 is the first flagship open-source model to maintain agent-quality performance at 1M context. For AI application developers, it means open-source options are now approaching Claude Opus and GPT-5 levels for tool calling, long conversations, and software engineering tasks. For existing DeepSeek users, legacy endpoints must be migrated within 3 months. For self-hosting teams, FP4/FP8 quantization allows V4-Flash to run on fewer GPUs. Dual compatibility with the Anthropic/OpenAI API formats dramatically lowers the barrier to adoption.
Detailed Analysis
Trade-offs
Pros:
1M context with efficient attention delivers a major leap in long-document agent task performance
Terminal Bench and SWE scores approaching Claude Opus — the first time an open-source option reaches this level
API compatibility with OpenAI/Anthropic minimizes migration cost
New XML tool schema reduces parsing errors
Cons:
Legacy models (deepseek-chat, deepseek-reasoner) retire on 7/24; migration must be completed within 3 months
Local deployment of 1.6T parameters has a high barrier; most teams will need to use Flash or the cloud API
Preview may still have stability issues and rate limit adjustments
New Think Max mode requires 384K+ context, with high memory consumption
Quick Start (5-15 minutes)
Log into the DeepSeek API console and change the model ID to deepseek-v4-pro or deepseek-v4-flash for A/B testing
For existing integrations, update the SDK and configure the thinking_mode parameter (non-think / think-high / think-max)
For agent applications: convert tool schema from JSON to XML format (DSML tokens) and measure the improvement in tool-use success rate
For local deployment: download DeepSeek-V4-Flash (284B/13B) from Hugging Face Hub; inference is feasible with 8x H100
Recommendation
Existing DeepSeek users should immediately validate V4 in a test environment and schedule a migration plan before 7/24. New adopters can start with the V4-Flash API to evaluate agent tasks (e.g., multi-step tool calling, long-document analysis). If performance approaches Claude Opus at significantly lower cost, it can serve as the primary model. A fallback path is still recommended in production until the preview reaches GA.
Anthropic Launches Claude for Creative Work: Direct Connectors for Blender, Adobe, Autodesk, Ableton, and 5 More Creative Tools L1
Confidence: High
Key Points: Anthropic released "Claude for Creative Work," providing 9 connectors designed for creative professionals: Blender, Adobe Creative Cloud, Autodesk Fusion, Ableton, Affinity, SketchUp, Resolume, and Splice. Two companion products were also launched: Claude Design (for exploring and iterating on software experience concepts) and Claude Code (for writing scripts and plugins). Anthropic emphasizes the positioning as "integrating into existing professional workflows, not replacing creativity." Educational partnerships have been established with Rhode Island School of Design, Ringling College, and Goldsmiths, University of London.
Impact: This marks the first time an LLM has been systematically integrated into mainstream creative workflows (3D, image, audio, design). For 3D artists, music producers, and designers, it enables controlling Blender nodes, Ableton MIDI, and Autodesk models through natural language. For game development, film/TV post-production, and industrial design teams, repetitive workflows (batch processing, cross-tool asset conversion, procedural generation) can be automated with Claude scripts. It also creates direct competition with Adobe Firefly, Autodesk Bernini AI, and similar integrations.
Detailed Analysis
Trade-offs
Pros:
Native integration with 9 major creative tools; no need to build custom MCP plugins
Claude Code can generate Blender Python and Ableton Max scripts
Educational institutions have adopted it, with rich course materials available
Emphasizes augmentation rather than replacement, aligning with creative professionals' expectations
Cons:
Pricing not announced (may be bundled with Claude Pro/Team or offered as a separate subscription)
Each connector requires individual permission and API key setup, resulting in high initial configuration cost
Heavy reliance on host OS IPC mechanisms may result in inconsistent experiences on Mac vs. Windows
Sending sensitive creative files to Claude requires a security policy review
Quick Start (5-15 minutes)
Visit https://anthropic.com/news/claude-for-creative-work to apply for early access or join the waitlist
Blender users: install the Claude connector and try natural language commands like "please unify all textures to 4K and re-bake"
Ableton users: have Claude generate Max for Live scripts and load them into a project for testing
When rolling out to a team, first test the connector's access scope and data transfer behavior in a sandbox project
Recommendation
Creative studios and game art pipeline teams are advised to schedule a one-month proof of concept: select 1–2 high-repetition workflows (e.g., batch format conversion, texture/mesh automation) to test and quantify time savings. Individual creators can try Claude Pro subscription with Blender/SketchUp connectors first, and evaluate an upgrade if the experience is positive. Pay attention to data transfer policies for sensitive creative files to avoid violating client NDAs.
GitHub Copilot Switches to Usage-Based Billing: GitHub AI Credits to Meter Token Consumption Starting June 1 L1
Confidence: High
Key Points: GitHub announced a major overhaul of Copilot's billing structure: starting 2026-06-01, the "premium request" unit will be retired and replaced by GitHub AI Credits, which meter actual usage of all input, output, and cached tokens. Base monthly fees remain unchanged: Pro $10 (includes $10 credits), Pro+ $39 (includes $39 credits), Business $19/seat (includes $19 credits), Enterprise $39/seat (includes $39 credits). Business and Enterprise customers receive a 6–8 month transition with additional $30 and $70 promotional credits respectively. Code completions remain free; admins gain budget controls; organizations can pool unused credits. GitHub explains: "Copilot is not the product it was a year ago — it has evolved from a completion tool to an agent platform, and compute costs have risen significantly."
Impact: This marks a turning point in the AI coding assistant pricing war. Heavy users (agent mode, long conversations) may see monthly costs exceed the original $10/$39. Light users will see little change. For enterprises, the previous per-seat pricing was predictable; the new credit-quota model requires active monitoring. Admins must set budget caps to avoid overruns. This also mirrors the pricing model of Cursor and Anthropic Claude Code, signaling the entire AI coding tool market is converging on usage-based billing.
Detailed Analysis
Trade-offs
Pros:
Light users see no price change; heavy users receive extra credits during transition
Admin budget controls and org-level credit sharing improve enterprise governance
Encourages developers to be mindful of token consumption and write more precise prompts
Cons:
Monthly costs for agent mode and long-conversation users may become unpredictable
The fallback experience will be removed; exceeding credits requires waiting or purchasing top-ups
Individual users must track their own usage, adding cognitive overhead
Impact on GitHub Copilot Free users is not yet clear
Quick Start (5-15 minutes)
Log into GitHub Settings → Copilot to review current usage history and estimate post-June 1 costs
Read the official documentation at https://docs.github.com/copilot/concepts/billing/usage-based-billing-for-individuals
Admins: set budget caps and notification thresholds in Organization settings and communicate the changes to your team
Heavy users: evaluate whether upgrading to Pro+ ($39) and enabling a spending limit makes sense
Recommendation
Individual developers: review your usage over the past month in May (especially agent mode interaction frequency). If you mainly use completions, staying on Pro is fine; if you use chat and agent mode heavily, consider upgrading to Pro+ and setting a spending limit. Enterprises: admins should complete budget modeling and policy communication by May 15, and enable org-level credits pooling to balance usage differences across individual developers.
Mistral Workflows in Public Preview: A Temporal-Based Enterprise AI Workflow Orchestration Engine L2
Confidence: High
Key Points: Mistral AI officially launched Workflows in public preview: an enterprise AI workflow orchestration layer built on Temporal. Key features include durable execution (auto-resume), step-level observability in Studio, single-line human-in-the-loop integration, and native integration with agents and connectors in Mistral Studio. The deployment model is hybrid: Mistral hosts the Temporal cluster, API, and Studio, while customers deploy workers via Helm on their own Kubernetes clusters to maintain data sovereignty. Already used in enterprise cases such as customs clearance, KYC document review, and customer service ticket triage.
Impact: Workflows brings production-grade AI workflow concerns — state management, retries, and human review — into the platform layer, reducing the integration complexity of building custom LangGraph/Airflow + LLM solutions. This is especially critical for European, financial, and government customers, as workers execute inside the customer's K8s environment so sensitive data never leaves the secure perimeter. It also competes with LangChain LangGraph Cloud and AWS Bedrock Agents.
Detailed Analysis
Trade-offs
Pros:
Temporal is a battle-tested workflow engine with high reliability
Workers execute inside the customer's K8s environment, meeting data sovereignty requirements
Human-in-the-loop integrated in a single line of code, simplifying enterprise review workflows
Native integration with Mistral models and Studio
Cons:
Requires an existing Kubernetes environment; higher barrier than pure SaaS
Locks into the Mistral platform; cross-model support scope not disclosed
Preview-period pricing and SLAs not yet clarified
Temporal has a steep learning curve for teams unfamiliar with distributed systems
Quick Start (5-15 minutes)
Read https://mistral.ai/news/workflows for links to official documentation
Deploy the worker Helm chart in an internal K8s sandbox environment to try a minimal example
Select one existing manual workflow (e.g., customer email classification) as a proof of concept
Evaluate the differences from existing LangGraph/n8n setups to avoid duplicating work
Recommendation
Teams already using Mistral Studio or with data sovereignty requirements in European, financial, or government contexts should evaluate this first. Other teams can wait for preview feedback and pricing announcements. If you already have a self-hosted LangGraph/Temporal solution, migration gains are limited; if workflows are still scattered scripts, Workflows can serve as a solid starting point.
NVIDIA Nemotron 3 Nano Omni: 30B Multimodal Long-Context Model for Document, Audio, and Video Agents L2
Confidence: High
Key Points: NVIDIA released Nemotron 3 Nano Omni (30B parameters, hybrid Mamba-Transformer-MoE architecture) with full modality coverage: text, vision, audio, and video. Three quantization variants are available: BF16, FP8, and NVFP4 (4-bit equivalent, ~18B effective). Benchmark scores: OCR Bench V2-En 65.8, Video-MME 72.2, VoiceBench 89.4, ASR WER 5.95. Capable of processing 5+ hours of multimodal content and 100+ page documents, with dynamic visual resolution from 1,024 to 13,312 patches and native audio integration. Optimized for agentic computer use (GUI automation), long-document analysis, meeting comprehension, and multimodal reasoning — claimed to be 7.4x more efficient on multi-document tasks and 9.2x on video compared to similar solutions.
Impact: Brings the Mamba-MoE architecture into the multimodal mainstream, particularly well-suited for enterprise document agent applications requiring long context (contract review, financial report analysis), multimodal customer service understanding, and desktop automation. The 30B parameter count combined with NVFP4 quantization enables deployment on a single high-end GPU, significantly lowering the barrier compared to prior-generation 70B+ models.
Detailed Analysis
Trade-offs
Pros:
Native multimodal coverage (text/vision/audio/video) in a single model
NVFP4 quantization enables 4-bit inference on a single GPU deployment
Impressive long-context performance (5+ hours of content, 100+ page documents)
OCR, ASR, and video benchmark scores approach specialist models
Cons:
NVIDIA proprietary license terms; commercial use requires review
Hybrid Mamba architecture requires a compatible inference engine (NeMo or TRT-LLM)
30B is relatively small but still requires H100/H200 for full-spec inference
Competes with Llama 4 and Qwen-VL in the same tier; independent benchmark validation needed
Quick Start (5-15 minutes)
Download the 4-bit version from huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4
Read the technical report at research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Omni-report.pdf
Use NeMo or TensorRT-LLM on a single H100 to test OCR and long-document inference
Benchmark against Qwen2.5-VL and Llama 4 on your own tasks
Recommendation
Teams with agent applications that heavily involve long documents and multimodal content (especially audio and video) should add this to their candidate list. Run independent tests with your own data for OCR, ASR, and video understanding, and confirm the license terms meet commercial requirements. Customers already in the NVIDIA AI Enterprise ecosystem should prioritize this evaluation.
OpenAI Achieves FedRAMP Moderate Authorization: Federal Agencies Can Now Adopt ChatGPT Enterprise and API L2
Confidence: High
Key Points: OpenAI announced that both ChatGPT Enterprise and the API have received FedRAMP Moderate authorization. FedRAMP Moderate is the most common security level for cloud services used by US federal agencies, covering most non-classified but sensitive government workloads. This authorization allows federal agencies (including GSA, HHS, and some DoD sub-agencies) to procure and use OpenAI services within the government compliance framework, without needing to conduct independent third-party security assessments.
Impact: For US government agencies, contractors, and state governments, the regulatory barrier to procuring ChatGPT and the OpenAI API is significantly lowered. For enterprise compliance purposes, FedRAMP Moderate also serves as a high-standard reference that increases trust among commercial customers. It also highlights OpenAI's progress in the government market (having previously obtained certain IL5 certifications) and puts competitive pressure on Anthropic and Google.
Detailed Analysis
Trade-offs
Pros:
Federal procurement process significantly simplified
FedRAMP Moderate serves as a valuable compliance reference for enterprise customers
Both ChatGPT Enterprise and the API are covered
Cons:
Moderate level does not cover classified or top-secret workloads (requires IL5/IL6)
Regional availability and data residency details not announced
Quick Start (5-15 minutes)
Federal agencies: find the OpenAI listing on the FedRAMP Marketplace
Read https://openai.com/index/openai-available-at-fedramp-moderate for the scope of authorization
Coordinate with internal security/procurement teams to update the approved cloud services list
Recommendation
US federal and state government agencies and their contractors can begin procurement evaluation immediately. Highly regulated industries such as financial services and healthcare can use this authorization as one compliance data point supporting OpenAI adoption.
OpenAI Publishes Five-Part Intelligence Age Cybersecurity Strategy: Democratizing AI-Powered Defense and Protecting Critical Infrastructure L2
Confidence: Medium
Key Points: OpenAI published a five-part cybersecurity strategy in "Cybersecurity in the Intelligence Age": (1) democratizing AI defense tools to make them affordable for SMBs; (2) protecting critical infrastructure (power grid, healthcare, finance); (3) collaborating with government and civilian red teams to identify risks; (4) investing in AI and cybersecurity talent; (5) strengthening built-in model defenses against misuse. The article emphasizes that both offensive and defensive AI capabilities are accelerating, and OpenAI will adopt a proactive defense posture.
Impact: This is a strategic commentary rather than an immediate product change, but it reveals OpenAI's long-term direction in the security market: likely future ChatGPT-based red team and blue team tooling, and enhanced built-in detection of social engineering and malicious prompts in models like GPT-5. For CISOs and government security units, this is a policy signal.
Detailed Analysis
Trade-offs
Pros:
Explicitly calls out democratizing defense, which may catalyze tools accessible to SMBs
Public commitment to protecting critical infrastructure
Cons:
No specific products or timelines
Strategy documents are easily interpreted as public relations
Quick Start (5-15 minutes)
Security teams: read the full article and map it against your company's existing AI risk assessment framework
Monitor whether OpenAI subsequently launches an enterprise security-specific product line
Recommendation
Useful as policy material for AI security governance discussions; no immediate action required. CISOs can observe over the next six months whether OpenAI delivers concrete defensive tools before evaluating procurement.
Xbox Re-Evaluates AI, Exclusivity, and Pricing Strategy Under New Leadership Asha Sharma L2GameDev - Code/CI
Confidence: Medium
Key Points: New Xbox head Asha Sharma issued a public memo 62 days into the role, explicitly committing to "re-evaluate exclusivity, windowing, and AI" as three major strategic pillars. Actions already taken include: reducing Game Pass Ultimate monthly fee from $29.99 to $22.99, rebranding the organization from "Microsoft Gaming" back to "Xbox," and establishing daily active players as the core metric. AI and Games analysis suggests that, relative to CEO Satya Nadella's strong push for generative AI visions like Microsoft Muse, Xbox under new leadership is adopting a more conservative, player-trust-first stance toward AI.
Impact: For game developers and publishers partnering with Microsoft/Xbox, this is an important strategic signal: do not expect Xbox to broadly integrate generative AI into game content in the near term. A visible gap has emerged between the parent company's AI push and the gaming division's priorities. Integration timelines for projects like Microsoft Muse and Copilot for Gaming may be delayed. Players may see fewer AI-generated content features and more adjustments to subscription and release models.
Detailed Analysis
Trade-offs
Pros:
Xbox refocuses on player trust and subscription economics
A slower AI integration pace avoids the risks of following hype blindly
Cons:
A visible gap from Microsoft's parent-company AI strategy creates internal coordination challenges
Planning uncertainty for partners invested in Muse and Copilot for Gaming
Quick Start (5-15 minutes)
Xbox partner developers: follow Sharma's subsequent official interviews and Microsoft Build 2026 announcements
AI integration projects: pause heavy bets on Xbox platform AI features; prioritize validating PC and PlayStation integrations first
Recommendation
If your studio is planning AI-generated content (NPC dialogue, procedural content) as a core selling point, avoid making Xbox your primary launch platform in the near term. Prioritize PC (Steam) and PlayStation as primary platforms, with Xbox as a follow-on. Continue monitoring Sharma's official statements on Muse and Copilot for Gaming.
Convai Releases Unity + Meta Quest Mixed Reality AI Character Tutorial: Build an NPC That Sees the Real World in 30 Minutes L2GameDev - Animation/Voice
Confidence: High
Key Points: Convai published an official tutorial demonstrating how to integrate Convai NPCs into Meta Quest passthrough mixed reality within a Unity URP Android project, with Quest camera vision enabled so AI characters can "see" and respond to the real-world environment. Required packages: Convai Unity SDK, Meta MR Utility Kit v85, OVR Interaction v85. Key steps: change the Convai Manager connection to Video, add Convai Vision Publisher and Quest Vision Frame Source components, replace the Unity default camera with Passthrough Camera Access, change Canvas to World Space and scale to 0.001. The tutorial claims completion is possible "within 30 minutes in a pre-configured URP Android project."
Impact: This is the first official template on an MR platform to fully demonstrate LLM NPC integration with passthrough vision, bringing AI characters that can see the player's environment from research-level to within reach of mid-sized studios. For VR/MR content developers, this is a compelling proof-of-concept starting point. It also puts competitive pressure on Inworld, Charisma.ai, and similar competitors.
Detailed Analysis
Trade-offs
Pros:
Complete steps with explicit version numbers; easy to replicate
Native integration with Meta Quest passthrough; no custom vision pipeline needed
Full demo can be up and running from scratch in 30 minutes
Cons:
Locked to the Convai platform and Meta Quest hardware
Latency and cost of real-time vision API calls require real-world testing
MR apps require URP Android; existing HDRP projects must be converted first
Quick Start (5-15 minutes)
Download the Convai Unity SDK and follow the tutorial at https://convai.com/blog/how-to-build-mixed-reality-ai-characters-in-unity-with-convai-on-meta-quest-2026
Confirm Meta MR Utility Kit v85 and OVR Interaction v85 are installed
Create a blank URP Android project, apply the OpenXR plugin, and configure it for XR
Follow the tutorial to configure Convai Vision Publisher and test NPC responses to real-world objects on a desk using the Quest camera
Recommendation
VR/MR studios and Quest developers are strongly encouraged to set aside half a day to implement the tutorial demo and understand the latency and interaction viability of LLM + passthrough vision. If results are positive, this can serve as a differentiating feature for the next prototype project. If latency is too high (>1.5 seconds), hold off until Convai releases a lower-latency solution.