Technical Content
What is an LLM control plane?
Runaway agents? Provider outages? Discover why your AI stack needs an LLM control plane, not just a gateway, to handle production routing, budgets, and privacy.
Technical Content
Technical Content
Use the Otari Gateway with OpenCode
AI coding sessions can feel like a black box. Route OpenCode through the Otari Gateway to track costs, token usage, and model activity in real time. Get budget controls and visibility across every session without changing a single line of application code.
Technical Content
First Line of Defense for cq
cq helps coding agents share resolution paths and learn from past failures. We partnered with Lauren Mushro to bring VIBE✓ into cq and help review knowledge units before they enter shared memory.
Technical Content
Encoderfile’s New Format: Why a “Dull” Design Wins
Encoder models power most NLP in production, but deploying them still means dragging along Python runtimes and dependencies. Encoderfile introduces a single executable with an appended payload and a format that can be inspected and understood.
Technical Content
Hardening Your LLM Dependency Supply Chain
When source code and distributed packages don’t match, risks increase. This breakdown of the LiteLLM incident shares what to watch for and how to reduce exposure.
Announcement
cq: Shared Agent Learning
cq explores a shared commons where agents can query past learnings, contribute new knowledge, and avoid repeating the same mistakes in isolation.
Technical Content
The Star Chamber: Multi-LLM Consensus for Code Quality
The Star Chamber runs code reviews across multiple LLM providers and aggregates their feedback by consensus. Instead of relying on one model’s perspective, developers get a structured view of where models agree, disagree, and raise unique insights.
Technical Content
Integrating Alinia into any-guardrail for Multilingual AI Security
The newest integration with any-guardrail: Alinia AI, whose security models are specifically built to detect threats like prompt injection, data exfiltration, and policy violations by understanding the cultural and linguistic nuances of multilingual AI interactions.
Technical Content
Evaluating Multilingual, Context-Aware Guardrails: Evidence from a Humanitarian LLM Use Case
A technical evaluation of multilingual, context-aware AI guardrails, analyzing how English and Farsi responses are scored under identical policies. The findings surface scoring gaps, reasoning issues, and consistency challenges in humanitarian deployments.
Technical Content
Polyglot AI Agents: WebAssembly Meets the Java Virtual Machine (JVM)
Leverage the JVM's polyglot capabilities to create a self-contained, enterprise-optimized server-side blueprint that combines the performance benefits of WebAssembly with the reliability and maturity of Java's ecosystem.
Technical Content
Can Open-Source Guardrails Really Protect AI Agents?
AI Agents extend large language models beyond text generation. They can call functions, access internal and external resources, perform deterministic operations, and even communicate with other agents. Yet, most existing guardrails weren’t built to protect these operations.
Technical Content
3W for In-Browser AI: WebLLM + WASM + WebWorkers
What if AI agents could run entirely in your browser? Not just the UI part—the actual model inference, agent logic, and response generation, all happening locally without a single API call.