To recap: On-Premise: your data center, Confidential Computing infrastructure with GPUs required. On-Device: your hardware, fully offline, built for edge. VPC (AWS/GCP): all models and ElevenAgents, your cloud boundary, data stays in your environment. Cloud API: all models
SOFTWARE
-
On-Premise and On-Device AI Access Launches Mid-2026
By
–
On-Premise and On-Device are in early access, with initial releases expected in the first half of 2026. VPC deployments are available now. Join the waitlist:
-
ElevenLabs Cloud API: Fast Production Deployment with Multiple Voice Models
By
–
For everyone else, our cloud API is the fastest path to production. You get access to all our models and voices, and automatic scaling – managed entirely by ElevenLabs. We support data residency in the US, EU and India; Zero Retention Mode for enhanced privacy; and all of the
-
On-Device AI Inference for Offline Embedded Applications
By
–
On-Device runs directly on the hardware itself and is built for offline inference on constrained compute. This is best suited to use cases that require offline inference, such as automotive manufacturers embedding voice into vehicles or wearables.
-
ElevenLabs Expands Deployment Options On-Premise and On-Device
By
–
ElevenLabs can now be deployed on-premise and on-device. This expands our deployment options beyond cloud and VPC, to cover the full range of enterprise environments.
-
Claude Agent SDK Tracing LangSmith Upgrade Features
By
–
Claude Agent SDK tracing in LangSmith just got an upgrade. Now you can trace:
→ Subagents
→ Child runs inside MCP tools
→ Cost tracking + more Update to the latest Python SDK to try it out. Docs: https://
docs.langchain.com/langsmith/trac
e-claude-agent-sdk
… -
Anthropic’s Daily Marketing Strategy with Claude Cowork Launch
By
–
Anthropic is really using this as a marketing strategy to keep the conversation going every single day, with a daily update. I'd be happy if they continued like this! Claude (@claudeai) Claude Cowork is now generally available to all paid plans. For Enterprise, we are adding role-based access controls, group spend limits, usage analytics, and expanded OpenTelemetry to give admins what they need to deploy it across the org. — https://nitter.net/claudeai/status/2042273755485888810#m
→ View original post on X — @kimmonismus, 2026-04-09 16:38 UTC
-
Engramme’s Large Memory Models augment human memory with perfect recall
By
–
Huge: Engramme is building what they call "Large Memory Models" – designed to connect to your entire digital life and surface relevant context automatically.
— Chubby♨️ (@kimmonismus) 9 avril 2026
Open an email, join a call, send a message. The system recalls what you need without searching or prompting.
Founded by… https://t.co/QsNu6b3E5WHuge: Engramme is building what they call "Large Memory Models" – designed to connect to your entire digital life and surface relevant context automatically. Open an email, join a call, send a message. The system recalls what you need without searching or prompting. Founded by @gkreiman, the idea is to augment human memory rather than replace it. Your data, surfaced at the right moment. Gabriel Kreiman (@gkreiman) Imagine a future where you can REMEMBER EVERYTHING. Every email, every person, every conversation. Introducing Engramme. Our vision is to endow humans with perfect and infinite memory. All your memories come to you. No more searching or prompting. engramme.com 🧠 — https://nitter.net/gkreiman/status/2042271382265053537#m
→ View original post on X — @kimmonismus, 2026-04-09 16:27 UTC
-
AI agent autonomously probes its own multi-GPU setup and stats
By
–
running Qwen3.5 397B MoE (17B active/token)
— Ahmad (@TheAhmadOsman) 9 avril 2026
on 4x DGX Sparks in FP8 (~400GB)
> OpenCode driving
> agent exploring its own config
> probing all 4 Sparks (via ssh) + reporting thermals
> inspecting how vLLM is serving it
> collecting + analyzing its own stats
local AI is awesome https://t.co/KU9u30GgXk pic.twitter.com/yPWSbSKto8running Qwen3.5 397B MoE (17B active/token) on 4x DGX Sparks in FP8 (~400GB) > OpenCode driving
> agent exploring its own config
> probing all 4 Sparks (via ssh) + reporting thermals
> inspecting how vLLM is serving it
> collecting + analyzing its own stats local AI is awesome -
Deep Agents Deploy: Model Optionality and Sandbox Flexibility
By
–
One of the benefits of Deep Agents deploy is model optionality Choose from models from @OpenAI @GeminiApp @AnthropicAI @FireworksAI_HQ @baseten @OpenRouter @ollama @nvidia and many others Another is you can bring your own sandbox – @daytonaio @modal @RunloopDev