Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
LLM TTFT is the single biggest latency line item, often 70% of total. We show how to cut it with prompt caching, smaller models, region pinning, and Realtime APIs that fuse STT + LLM.
Akamai's 2026 sensor.js inspects WebRTC stack alongside GPU and audio context to score every visitor. For AI voice apps, the trick is using the same signals against bots without nuking real users.
Voice latency budgets live or die under 800 ms. We show how OpenAI's Stored Completions + Distillation pipeline turns GPT-4o traces into a fine-tuned gpt-4o-mini that hits the same task accuracy at 1/8 the cost and 250 ms lower TTFT.
Inngest steps give every LLM call retries, sleeps, human-in-the-loop pauses, and replay-safe state. Build a research agent that survives 2-hour approvals.
Interactive onboarding lifts activation 50% over static tutorials and chat nudges boost re-engagement 47%. Here is the 2026 chat playbook D2C teams use to drive first-week activation.
Mozilla shipped AV1 by default, H.264 simulcast with dependency descriptors, and OS-integrated screen capture in Firefox during 2025. Here is what is locked in for 2026 and how it affects voice AI agents.
Few-shot examples are agent prompt steroids — but the wrong ones poison the run. We separate when 3 hand-picked shots beat 30 random ones, the new many-shot regime that scales to 1,000+ exemplars, and CallSphere's per-tool example bank for the Salon stack.
Gartner warns 40% of agentic AI projects will fail by 2027. Learn the governance frameworks, cost controls, and risk management needed to avoid the most common failure modes.
The trajectory of enterprise Claude agents — longer autonomy, deeper MCP, multi-agent orgs — and concrete moves to prepare your team and architecture now.
