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- Sandboxing AI Agents, 100x Faster
Sandboxing AI Agents, 100x Faster
+ Make Claude Code and Codex talk to each other
Shubham Saboo & Gargi Gupta
March 30, 2026
Today’s top AI Highlights:
& so much more!
Read time: 3 mins
AI Tutorial
Six AI agents run my entire life while I sleep.
Not a demo. Not a weekend project.
A real team that works 24/7, making sure I'm never behind. Research done. Content drafted. Code reviewed. Newsletter ready. By the time I open Telegram in the morning, they've already put in a full shift.
By the end of this, you will understand exactly how to build an autonomous AI agent team that runs while you sleep.
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Latest Developments
What if you could spin up a brand-new sandbox for every single user request, run one snippet of AI-generated code, and trash it? All at a million requests per second?
That's what Cloudflare just shipped. Dynamic Worker Loader is now in open beta, giving any paid Workers user the ability to create V8 isolate sandboxes on the fly, with code specified at runtime.
It's the execution layer that Code Mode always needed - isolates that start in milliseconds, use a few megabytes of memory, and run on the same thread as the parent Worker with zero routing latency.
Key Highlights:
100x faster than containers: Isolates boot in single-digit milliseconds vs. hundreds for containers, with 10-100x better memory efficiency. No need to keep warm pools or reuse sandboxes across tasks.
Security by default: Network access is off. Filesystem doesn't exist. Env variables can't leak. Outbound HTTP can be intercepted for credential injection so agents never see secrets.
TypeScript over OpenAPI: Agent tools are defined as TypeScript interfaces rather than verbose REST specs, bridged automatically across the sandbox boundary via RPC. The result: dramatically fewer tokens for both the API definition and the agent's code.
Production-ready SDK:
@cloudflare/codemodeships withDynamicWorkerExecutorfor plug-and-play sandbox execution, plus server-side utilities to wrap MCP servers or OpenAPI specs into Code Mode tools.
Just launched: Voxtral TTS is Mistral's first text-to-speech model, 3x faster than the industry standard, with voice cloning that goes beyond reading text aloud. It models personality: natural pauses, rhythm, intonation, and emotional range.
Try it now via API, Mistral Studio, or on Hugging Face under Apache 2.0.
Key highlights:
Human-level naturalness: Outperforms ElevenLabs Flash v2.5 on naturalness in human evaluations, and matches ElevenLabs v3 quality with emotion-steering for more lifelike, context-aware speech.
Voice cloning that captures personality: Provide a 3-25 second voice prompt and the model replicates not just tone but rhythm, pauses, and emotional dexterity. Zero-shot, no fine-tuning required.
9 languages, cross-lingual out of the box: Supports English, French, German, Spanish, Dutch, Portuguese, Italian, Hindi, and Arabic, including cross-lingual cloning (For example, French voice prompt + English text = English speech with a French accent).
Built for real-time voice agents: 90ms TTFA with a ~6x real-time factor. Streams natively, handles arbitrarily long generations, and slots into any STT + LLM stack.
Full audio pipeline with Voxtral Transcribe: Pair with Voxtral Transcribe for end-to-end speech-to-speech, or slot into any existing speech-to-text and LLM stack.
The team behind one of the most popular vector databases just shipped their first LLM.
Chroma has released Context-1, a 20B parameter open-weights model that's trained to do one thing exceptionally well: find the right documents so a bigger model doesn't have to waste time looking.
Context-1 works as a retrieval subagent. You pair it with a frontier reasoning model, and it handles the entire multi-hop search loop autonomously. It decomposes complex queries into subqueries, searches iteratively using hybrid BM25 + dense search, regex, and document reads. And here's the cool part: it actively prunes its own context mid-search, throwing out irrelevant documents to make room for better ones.
The result is frontier-level retrieval performance at 10x the speed and 25x lower cost, released under Apache 2.0 with the full synthetic training pipeline on GitHub.
Key Highlights:
Self-Editing Context: Context-1 is trained to selectively discard retrieved documents mid-search with 0.94 prune accuracy, letting a 20B model with a 32k token budget outperform frontier models running on much larger context windows.
Frontier-Competitive Retrieval: Matches models like GPT-5.4 on benchmarks like BrowseComp-Plus, FRAMES, and HotpotQA, and a 4x parallel config (four agents + reciprocal rank fusion) closes the gap further at a fraction of the compute.
Staged RL Training: Fine-tuned from gpt-oss-20B using a curriculum that starts recall-heavy (16x recall over precision) and gradually shifts toward precision, trained on 8,000+ synthetic tasks across web, SEC filings, patents, and email domains.
Fully Open-Source: Model weights on HuggingFace and the complete synthetic data generation pipeline on GitHub, both under Apache 2.0.
Quick Bites
Local document parsing for AI Agents
LlamaIndex just open-sourced LiteParse, the core of their LlamaParse engine, stripped down into a fast, local-first CLI and TS-native library for parsing PDFs, Office docs, and images. The interesting bit is that instead of trying to detect tables and convert to markdown (hello, failure modes), it preserves spatial layout on a text grid that LLMs already know how to read. Zero Python dependencies, runs entirely locally, and ships with a ready-made skill for coding agents like Claude Code.
Use Plugins in Codex for workflows with Skills and integrations
OpenAI's Codex now supports plugins that let you install bundles of skills, MCP servers, and app integrations into a single unit. Think Sentry error logs, Datadog dashboards, and Linear project context flowing directly into your agent's sandbox while it works. You can build custom plugins locally, load them from a repo or personal marketplace, and the official public Plugin Directory is coming soon.
Quick tips to reduce your Claude Code token usage by upto 60%
If your Claude Code token bill has been creeping up, this thread walks through a surprisingly effective optimization workflow that can reduce the token usage by upto 60%. The highlights: /context reveals how much of your window is gone before you even start (unused MCP servers are the silent culprit), concise CLAUDE.md files and precise prompts shrink search scope, and RTK filters noisy command output before it hits your context. Good quick read.
Pi: Ollama's take on AI coding agents
Ollama just shipped Pi, a minimal coding agent harness that lets you spin up your own coding agent with a single command. It comes bundled with primitives for building, and supports extensions, skills, prompt templates, and themes, all interoperable between Pi and Ollama. First cloud model out of the gate: Kimi K2.5.
Tools of the Trade
Smux: A tmux configuration that lets AI coding agents collaborate directly in a shared terminal. It turns the terminal into a simple interface where agents like Claude Code and Codex can read each other’s outputs, respond, and work together on tasks without APIs or custom protocols.
Agent Reach: A plug-and-play Python toolkit that gives AI agents access to platforms they normally can't reach like X, YouTube, Reddit, Xiaohongshu, Bilibili, and more. It bundles free, open-source tools behind a unified interface so you skip the per-platform config grind and just tell your agent "read this tweet" or "summarize this video."
Notchy: A macOS menu bar app that turns your MacBook's notch into a Claude Code terminal. Hover to reveal it, and it auto-detects your open Xcode projects. It also tracks Claude's status live in the notch, plays a sound on task completion, prevents your Mac from sleeping mid-session, and lets you snapshot code with Cmd+S.
Awesome LLM Apps - A curated collection of LLM apps with RAG, AI Agents, multi-agent teams, MCP, voice agents, and more. The apps use models from OpenAI, Anthropic, Google, and open-source models like DeepSeek, Qwen, and Llama that you can run locally on your computer.
(Now accepting GitHub sponsorships)
Hot Takes
Whoever is taking the time to craft an insanely organized and well-documented Obsidian vault will experience personal AGI faster than anyone else. Arguably months before.
The easiest way to make money fast from a superhuman artificial intelligence would be in the financial markets, almost by definition. So the first lab to develop one, if AGI is possible, would almost certainly keep it quiet for as long as they could. Beats charging for API access
That’s all for today! See you tomorrow with more such AI-filled content.
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