E2B

Devin’s 80% Moment: Background Agents, 7x PRs, & End of Hand-Held Coding — Walden Yan & Cole Murray

May 28, 2026 · 1:09:33

Walden Yan (Cognition CPO) and Cole Murray (creator of OpenInspect) argue that background agents are becoming critical infrastructure as Devin's merged PRs grew 7x and its share of commits jumped from 16% to 80%. They explain why architecture matters, particularly the decision to separate the agent's brain from the machine (harness out of the box) for security and permissions, and why full VMs beat Docker for running real applications. Testing, they stress, is a harder problem than computer use—requiring orchestration of services, feature flags, and multi-model coordination. Memory remains unsolved, with Devin using auto-generated 'Knowledge' and exploring file-system-like approaches. Both caution against uncontrolled vibe coding, which regresses codebases to the worst engineer's style, and advocate for hybrid frontier/sub-frontier systems to balance cost and capability.

Terminal-Bench 2.0: the most impt coding agent benchmark of 2025 gets a v2! Launch + Q&A w/ founders

Nov 8, 2025 · 35:01

Terminal-Bench co-creators Mike Merrill, Alex Shaw, and collaborators Andy Konwinski, Ludwig Schmidt, and Aatash launch Terminal-Bench 2.0, an 89-task coding benchmark with rigorous human and LM-assisted verification to test agent performance on real-world tasks like designing DNA primers and installing Windows 3.11. They reflect on Terminal-Bench 1's problems—easy tasks and reproducibility—and detail spending 300 hours manually auditing each task. They introduce Harbor, an open-source package for running sandboxed agent rollouts, standardizing task format and integrating with agents like Codex and Claude Code. Aatash shows using Harbor to generate thousands of training traces for DataComp Agent, running 1,000 parallel sandboxes. Future plans include expanding to scientific computing.

Why Every Agent needs Open Source Cloud Sandboxes

Apr 24, 2025 · 1:06:39

Vasek Mlejnsky, CEO of E2B, explains how his company grew from 40,000 sandboxes in March 2024 to 15 million in March 2025 by providing cloud sandboxes for AI agents. Originally built from DevBook's interactive docs playground, E2B now serves use cases from code interpreting and data analysis to Computer Use and reinforcement learning with Hugging Face's Open R1. The sandboxes are general Linux VMs with fast startup, security isolation, and persistence, handling untrusted code and allowing forking/checkpoints for parallel agent execution. Mlejnsky discusses the LLMOS landscape, pricing challenges (moving from token-based to value-based), MCPs as higher-order tools, and why he relocated from Prague to San Francisco to be closer to users. E2B is hiring across engineering and customer success as it aims to become full-lifecycle infrastructure for LLMs.

Building Manus AI (first ever Manus Meetup)

Mar 27, 2025 · 48:59

Tao Zhang, co-founder of Manus, explains how Manus gives LLMs 'hands' to take real-world actions, inspired by MIT's 'mens et manus' (mind and hand). Manus assigns each task its own cloud virtual machine via E2B, provides pre-paid data APIs (stock, social media), and a knowledge system to remember user preferences. Their previous product, Monica.im, grew to 20M monthly active users and $50M ARR before they pivoted from a failed AI browser project to Manus. On the GAIA benchmark, Manus achieves a per-task cost of ~$2, far cheaper than OpenAI's Deep Research (~$20) and prior SOTA. Zhang emphasizes a 'less structure, more intelligence' philosophy, opting not to predefine workflows but to give the model rich context and tools. He also discusses plans to partner with Cloudflare, pay paywalls on behalf of users, and integrate more APIs based on usage patterns, while ruling out building their own foundation model.

smol agents are all you need

Feb 13, 2025 · 22:31

This episode features Aymeric Roucher of Hugging Face discussing smol agents, a lightweight agent framework, and argues that code-based agents outperform JSON-based agents. Roucher explains that agents are defined by LLM control over execution flow, and smol agents simplifies this with a core file under 1000 lines. He describes the origin from transformers.agents and the security-focused custom Python interpreter. The library achieved third place on the GAIA benchmark, using o1 and Sonnet models, and Roucher projects 90% accuracy by 2026 based on a sigmoid trend line. Hugging Face is launching an agent course and plans to build GUI agents for computer use, while also fine-tuning R1 for agentic tasks.

The Winds of AI Winter (Q2 Four Wars of the AI Stack Recap)

Aug 2, 2024 · 1:23:36

Swyx and Alessio recap Q2 2024 through their 'Four Wars' framework, arguing that the AI landscape is shifting from frontier model dominance to commoditization and vertical applications. They highlight Claude 3.5 Sonnet overtaking OpenAI on coding benchmarks, Llama 3.1's synthetic data approach enabling 7B models to rival GPT-4, Mistral Large 2's non-commercial license and lost open-source crown, and on-device models like Gemini Nano and Apple Intelligence. The Quality Data Wars see NYT suing OpenAI, Reddit licensing data for $200M+, and synthetic data proving real for math (AlphaProof near IMO gold) and code. The Multimodality War includes ChatGPT Voice Mode delayed, Meta's Chameleon for native fusion, and Google's PaliGemma for PDF extraction. The renamed LLM OS War covers agent protocols, memory databases, and the collapse of model cost by an order of magnitude every four months, pushing startups toward vertical services like Brightwave and Dropzone that sell labor, not tools. The episode ends with a CrowdStrike joke about agent safety.