Launching today
VELA
Securely execute AI-generated & untrusted code
36 followers
Securely execute AI-generated & untrusted code
36 followers
Autonomous AI agents are writing and executing code, but running it on your host server is a massive security risk. Vela (powered by the Aegis runtime) solves this. It’s a policy-driven execution guard that uses Firecracker micro-VMs and HMAC capability tokens to safely run untrusted code. Get structured results, fine-grained filesystem/network restrictions, and a full JSONL audit trail. Open-source, MIT licensed, and built for LangChain/LlamaIndex.
Products used by VELA
Explore the tech stack and tools that power VELA. See what products VELA uses for development, design, marketing, analytics, and more.
Productivity 1
Productivity 1
Lineage LensTrack which lines of your code came from AI
5.0 (1 review)
Most of the alternatives here (like CodeRabbit, Bito, Kilo Code, and Codex) are excellent for generating or reviewing code, but they treat AI as just another advanced autocomplete. Lineage Lens solves a much deeper and increasingly critical problem: provenance.
As LLMs write more of our codebases, tracking exactly which lines are human-authored versus AI-generated is becoming vital for security auditing, IP compliance, and debugging. While tools like CodeRabbit are great for reviewing PRs, Lineage Lens gives you a persistent map of your code's actual DNA. As a solo founder building secure execution runtimes for autonomous AI agents (my project, Vela), I know firsthand how dangerous untracked and uncontained AI code can be. That’s why I deeply appreciate Lineage Lens's focus on visibility and lineage, rather than just raw generation speed. It fills a gap the others completely ignore.
LLMs 2
LLMs 2
Claude Opus 4.7Claude’s most capable model for reasoning and agentic coding
5.0 (6 reviews)
For complex agentic coding, you need more than just fast autocomplete or massive parameter counts; you need deep, multi-step architectural reasoning.
While GPT-5 and Gemini 2.5 are incredibly fast and highly capable, Opus 4.7 feels like a true senior staff engineer. When my autonomous agents need to plan a complex data pipeline, debug a tricky Python traceback, or write multi-file scripts that will eventually be executed safely inside my Vela micro-VM sandbox, Opus 4.7 consistently maps out the logic without losing the thread. Its ability to hold massive context, reason through edge cases, and self-correct before outputting the final code makes it the undisputed choice for heavy-lifting agent tasks. The other models are great for sprints, but Opus 4.7 is what I use when the architecture actually matters.
Claude by AnthropicA family of foundational AI models
5.0 (853 reviews)
When building autonomous agent workflows (like the ones my project, Vela, secures and sandboxes), the biggest bottleneck isn't just raw intelligence—it's reliability and instruction following.
While platforms like Flowise or GPTBots are great for orchestrating pipelines, and DeepSeek is fantastic for raw code generation, Claude by Anthropic is in a league of its own when it comes to agentic behavior. When you give an LLM the ability to write and execute code, you need a model that strictly adheres to system prompts, outputs perfectly formatted JSON for tool calls, and doesn't hallucinate mid-reasoning. Claude consistently respects constraints and boundaries better than the alternatives. It feels less like a chatbot and more like a reliable junior engineer that actually reads the documentation before acting. For any maker building serious AI agents, that reliability is non-negotiable.