From brainstorming to execution, AI will become a standard teammate, enhancing efficiency and productivity across industries. The future of work is almost here! DMs and waitlist open

Intelligence requires hierarchical planning — Humans plan at many timescales: “go to the airport → get a taxi → stand up from chair → open door.” AI needs world models that operate at multiple resolutions, each level predicting at its own timescale and abstraction. No one has

Full breakdown: https://t.co/31GMfK8NVT Who’s adding live sigmoid fitting to their RL dashboard this week? 🚀

This changes the game: → Kill doomed runs early → Know exactly which tricks raise ceiling vs just speed → Budget RL like LLM pre-training: “We need X compute to hit 0.63” 4/5

Real results from 400k+ GPU-hours: FP32 LM head → ceiling jumps 0.52 → 0.61 CISPO loss → much steeper slope, healthier grads PipelineRL → same ceiling, way faster Big batches = tortoise (slow but higher peak), small = hare (fast then flat) 3/5

The curve: Slow start → explosion → hard plateau Three knobs control everything: A: Ceiling (max score) B: Speed/efficiency C_mid: When takeoff actually happens 2/5

RL scaling just got predictable. New findings: Big RL runs (8B–17B models) follow a "perfect" sigmoid curve. Fit it at ~50% of training → predict final performance almost exactly. No more gambling millions of GPU-hours. 🔥 1/5

And in real organisations, that distinction is everything… If you need help navigating AI transformation get in touch https://t.co/I5mwumWxWD DMs open! 5/5

That’s the picture in the illustration: an arc from RPA to “AI Workflow” to “Open‑Ended Agent.” But here’s the problem: All three points on that curve are thinking about AI as the thing that does the activity. Almost nobody is thinking about AI as the thing that manages the

There’s a reason for that. If you plot reliability against flexibility, you get a familiar curve: - At one end, RPA is extremely reliable and painfully rigid. - At the other, open‑ended agents are wildly flexible and hard to trust. - In the middle, AI workflows promise a balance:

Why workflows aren’t the real endgame for enterprise AI.. Open LinkedIn today 😬 and it feels like every AI product has become some flavor of workflow builder: canvases, blocks, triggers, drag‑and‑drop agents. It’s the new “everyone is a platform” moment. 2/5

Is everything in the agent space really just a workflow?

Great time and amazing work from the @EWOR_official team 🙏

Experienced founders know this already: 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘦𝘤𝘰𝘴𝘺𝘴𝘵𝘦𝘮 𝘢𝘤𝘤𝘦𝘭𝘦𝘳𝘢𝘵𝘦𝘴 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨. @SlushHQ delivers that every single year. Earlier this week, EWOR Fellows, Partners, and community members validated this again in Helsinki. Highlights

The team is delivering this workshop today @KingsCollegeLon at DAI 2025, London (Nov 21–24)! DMs open #DAI #DistributedAI #AI

The team is growing! If you want to work on leading MARL systems and LLM-MAS get in touch! hiring@deepflow.com

Keynotes from RL legends: Michael I. Jordan, David Silver, Manuela Veloso, & Shimon Whiteson Join the convo in London or follow for recaps/code drops. Let’s build distributed AI together! 👏 https://t.co/e1zKsD3gUY #DeepFlow #AIResearch

🚀 Excited for DAI 2025 in London (Nov 21-24)? DeepFlow is making waves! Our ML team drops a tutorial on LLM-based Multi-Agent Systems, co-hosts a killer workshop w/ Shanghai Jiao Tong Uni, & presents our Manager Agent Challenge paper. Plus, we’re sponsors! #DAI2025

Check out our most recent paper on open challenges in the field:

World class management tooling is what we’re building at @DeepFlowAI

This is literally @DeepFlowAI