Workshop: Building and optimizing dictation features
Learn how to build low-latency dictation features using the Sync API, connection warming, and LLM cleanup passes. The process is optimized to deliver results in under one second.
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Can AI help us better predict the weather?
DeepMind's WeatherNext 3 model revolutionizes forecasting by using raw satellite imagery and predicting raw station observations in a single, end-to-end AI architecture, moving beyond traditional NWP.
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Meta, Stanford & Odevo on Agentic Coding at Scale
Scaling agentic coding requires fixing fundamentals first: CI/CD, platform stability, and testing. The 2025 DORA report warns that agents make weak delivery worse.
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The Universal Remote Control for AI — Alex Hancock, Block
The Agent Client Protocol (ACP) proposes a universal standard for client-to-harness communication in AI. By standardizing the interface using JSON RPC, ACP allows multiple client applications to drive the same agent, making the entire agentic stack modular and interoperable.
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MCP Apps: Give the Model Data, Give the User a UI — Dustin Mihalik, Indeed
Building Model-Composable Platform (MCP) apps requires a data-first approach. To integrate UIs into LLMs, developers must separate data processing from rendering and ensure all displayed information is passed back to the model's context.
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Accelerate the self-improving AI loop with CoreWeave ARIA
CoreWeave ARIA is an AI research agent built into Weights & Biases that accelerates the self-improving AI loop. It automates auto-research, analyzes training runs, and optimizes agent performance.
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How long can your skills be before your agent forgets what you told it? — Laurie Voss, Arize AI
The instruction ceiling for LLMs has jumped 10x in a year, moving from 200 rules to 5,000. This shifts the engineering challenge from prompt compression to output verification.
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Your agents lack context: Here's how to fix "You're absolutely right!" — Brandon Waselnuk, Unblocked
AI agents need a 'context engine' to move beyond simple information retrieval. Learn how to fix the context gap, avoid the 'curated context trap,' and achieve massive token and time savings in your CI/CD pipelines.
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500 Skills, Zero Fine-Tuning: LinkedIn's Playbook for AI Agents — Ajay Prakash, LinkedIn
Learn how LinkedIn scaled AI agents to thousands of tools and playbooks using a Search/Get Schema/Execute meta-tool layer, enabling reliable, context-aware automation.
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Turn Flagged Traces Into a Dataset in 3 Minutes with the LangSmith CLI
Learn how to analyze thousands of production traces and build structured datasets of agent failures in minutes. This workflow uses the LangSmith CLI and a coding agent to classify perceived errors into reusable failure types.
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Why won’t AI agents just follow the rules?
Experts discuss why controlling AI agents is difficult due to their probabilistic nature, requiring hard deterministic controls at runtime. The segment also covers OWASP Top 10 for agentic skills and the use of ThreatXtension.
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From Video to Voice: Build Faster with TensorRT Model Connect
TensorRT Model Connect simplifies the path from open-source models to deployable applications. It provides a consistent workflow to convert models into optimized bundles, supporting everything from LLMs to video generation on multi-device setups.
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Multiplayer AI Manifesto
The Multiplayer AI Manifesto outlines five principles for collaborative agent work: agents must live next to the work, learning must be open, and sessions must persist in the cloud. This shifts AI from single-user chats to true co-working.
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Deep dive on LLM Inference at Scale — Harshul Jain, Audible & Tanmay Sah, Independent AI Researcher
This deep dive covers LLM inference optimization at scale, detailing how memory and compute costs are managed through techniques like Paged Attention, Continuous Batching, and model quantization.
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Run Local Models in Pi: llama.cpp, GGUF, and the /llama Command
Learn how to run local LLMs like Qwen3 8B on a Raspberry Pi using llama.cpp. This guide covers installation, selecting optimal GGUF quantization, and running models entirely offline.
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Specializing AI for Regulated Industries - How Domyn Uses NVIDIA Nemotron
Domyn outlines building sovereign AI models for regulated industries using advanced techniques like model compression, CPT, and RL. They detail custom tooling (Swarm, FinalFlows) essential for managing large-scale ML pipelines.
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Google, McKinsey & Dave Farley on AI Code Review
AI code generation creates a bottleneck in traditional review processes. The talk explores how rapid agent output strains human maintainers, arguing that review is about aligning mental models, not just quality control.
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AI News & Strategy Daily | Nate B Jones
There Are Jobs You Could Never Give AI. I Gave GPT-6 Astra 20 Hours Of Admin.
Advanced AI agents like Astra can handle massive administrative jobs (like a move) by using a 'Manager Loop' and structured 'Recipe Cards,' moving beyond simple prompts to automate complex workflows.
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Inside the Team That Killed Mandatory Code Review — Quinn Slack, AMP
AI agents are killing local dev environments and mandatory code reviews. Cloud-based platforms like AMP/Orbs enable massive parallelism, making secure, scalable development possible.
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Are Agent Swarms USEFUL? OpenAI’s GPT-6 Astra SWARM Takeaways
IndyDevDan demonstrates three multi-agent swarms (GLM 5.3, DeepSeek v4 Pro, Gemini 3.7 Flash) on an M4 Mac mini to prove that agent swarms are viable for complex engineering tasks when properly engineered.
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