Neil Sachdeva

New in Claude Code (research preview): dynamic workflows. Claude writes an orchestration script on the fly, then spins up a large fleet of coordinated subagents in parallel to take on your most complex tasks. Use the word "workflow" in a prompt to get started.

maybe hot take 👀 Improving Agents is a Data Mining Problem Harness Engineering, Post-Training, Continual Learning...these all boil down to the same underlying substrate - Mining Agent Traces 1. I need to run my agents to collect Traces 2. Understand behaviors from Traces at scale 3. Filter data for "improvement" 4. Do an "improvement experiment" There's a reason why every continual learning platform ends up looking like an observability platform. It's because Traces are the lifeblood of agent improvement and data is king 👑 The mechanism that we use to attempt improvement can vary - Harness Eng, SFT, etc. But without understanding the data agents produce, no algorithm will truly build better agents. The holy grail of Agent Improvement is Continual Learning. Consistently mining data and integrating it into the agent definition over infinitely long time horizons. Today, the easiest way to do that is to build an observability platform and constantly point agentic compute to understand the data that agents produce

⚕️ Introducing C2S-Scale 27B, our new Gemma open model that can translate complex single-cell gene expression data into “cell sentences” that LLMs can understand.