GPT‑5.6 Sol sets a new state of the art on Terminal‑Bench 2.1, which tests complex command-line workflows requiring planning, iteration, and tool coordination.
The Lakers are in desperate need of a reliable lob threat big man this summer…
Imagine if the Lakers try and bring back AD from Washington to coincide with Luka while resigning LeBron to a team-friendly deal. 😳
AD has also been liking a lot of posts on socials about when he was first traded to LA 6 years ago.
LangSmith Fleet template spotlight: Software Engineer
Ships code from Slack, Linear, and GitHub in a sandbox
A coding agent that takes issues from @Linear, writes and verifies the code, and opens a PR.
Triggered directly from Slack.
We're building this at LangChain
Fleet lets you create and manage a fleet of agents. Each agent specializes in a workflow, e.g. inbox management, blog writing, competitor research, candidate recruiting. These are Deep Agents with custom instructions, skills, tools, subagents, and memory. They continually improve with feedback. You can share them with your coworkers. You can configure them to run on a schedule. You can export their context files should you ever want to host them yourself
I think Fleet strikes a great balance: easy to use and still highly capable
We've put an inordinate amount of thought into the UX patterns that make that possible. For example, I love our 'channels' concept: you can configure your agent's communication channel (e.g. Slack, Teams, email, etc.) so it meets you where you work instead of forcing you into Fleet's UI
It's free to try out so give it a spin and share feedback: https://t.co/TRYcK32IBB
I'm bullish on agent swarms (aka workflows). Agents are increasingly being used to analyze and collate massive amounts of unstructured data in repetitive ways (e.g. document extraction, reading emails, parsing logs), but as these tasks and data inputs scale we've seen reliable execution decrease, even from the most capable models. Specifically, the consistency of sub agent dispatches from filesystem-based agents drops dramatically when attempting to deploy more than 30+ sub agents in parallel.
So… how can you harness the best of an agent's intelligent decision making with reliable sub agent task execution at scale? Here's how 👇 1/5
The agent development lifecycle has been manual for too long.
We’re building a future where it runs continuously, without manual triggers.
Where well-understood issue types resolve without human review.
Where your harnesses get smarter about your agents over time.
LangSmith Engine is just the beginning.