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Hi HN,

I posted VaultSandbox two months ago and the main feedback was AGPL concerns. I've listened: it's now Apache 2.0. I've also overhauled the local experience to make it "just work" on localhost with zero config.

VaultSandbox is a self-hosted SMTP receiver and programmable debugger. It bridges the gap between "it sent" and "it was delivered" by simulating production-grade email lifecycles. It is architected for parallel testing: each test runs in its own isolated inbox with dedicated webhooks and chaos settings...no state leaks.

What’s New: -Local-First: Encryption (TLS) and email auth (SPF/DKIM) are now optional toggles. On localhost, they are off by default so you can test instantly. -Chaos Mode: Trigger greylisting, latency, or specific SMTP errors per-inbox to test your app's resilience. -Webhooks & Spam Scores: Inbox-scoped webhooks and integrated spam scoring (via rspamd) to predict deliverability before you send. -Instant DNS (vsx.email): Any public IP running VaultSandbox gets a subdomain with pre-configured MX, SPF, DKIM, and DMARC records for high-fidelity testing. -Deterministic Testing: Use our SDKs to "wait" for emails based on specific criteria instead of using sleep() in your tests.

Workflow: 1-Start local: One container gives you SMTP, Web UI, and SDKs. 2-Scale to production: Deploy to a public IP to test real flows from SES/SendGrid with the same test code. No mocks, just reality.

Repo: https://github.com/vaultsandbox/gateway (Apache 2.0) Quick start: https://vaultsandbox.dev/deployment/local-development/

What would it take for you to switch from your current email testing setup?

Hi HN, I’m Vincent, CTO of TeamOut (https://www.teamout.com/). We build an AI agent that plans company events from start to finish entirely through conversation. Similar to how Lovable helps build websites through chat, we apply that approach to event planning. Our system handles venue sourcing, vendor coordination, flight cost estimation, itinerary building, and overall project management.

Here’s a demo: https://www.youtube.com/watch?v=QVyc-x-isjI. The product is live at https://app.teamout.com/ai and does not require signup.

We went through YC in 2022 but did not launch on HN at the time. Back then, the product was more traditional, closer to an Airbnb-style search marketplace. Over the past two years, after helping organize more than 1,200 events, we rebuilt the core system around an agent architecture that directly manages the planning process. With this new version live, it felt like the right moment to share it here since it represents a fundamentally different approach to planning events.

The problem: Planning a company retreat usually means choosing between three imperfect options: (1) Hire an event planner and pay significant fees and venue markups; (2) Do it yourself and spend dozens of hours on research, emails, and negotiation; or (3) Use tools like Airbnb that are not designed for group logistics or meeting space.

The difficulty is not just finding a venue. Even for 30 to 50 people, planning turns into weeks of back-and-forth emails for quotes, comparing inconsistent pricing across PDFs, and tracking budgets in spreadsheets. It becomes an ongoing coordination problem with evolving constraints and slow, asynchronous vendor responses. Most existing software is form-driven, but the real workflow is conversational and stateful.

Offsites are expensive and high stakes. A single event can represent a significant chunk of a team’s annual budget, and mistakes show up directly as cost overruns or poor experiences. Founders and operators often end up spending time on event logistics instead of their actual work.

I ran into this while organizing retreats at a previous company. Before TeamOut, I worked as an AI researcher at IBM on NLP and machine learning systems. Sitting inside long email threads and cost spreadsheets, it did not look like a marketplace gap to me. It looked like a reasoning and state management problem. As large language models improved at multi-step reasoning and tool use, it became realistic to automate the coordination layer itself.

Our Solution: The core agent relies on a combination of models such as Gemini, Claude, and GPT. A central LLM-based agent maintains planning context across turns and decides which specialized tool to call next. Each tool has a specific responsibility: - Venue search and filtering - Cost estimations (accommodation + flights) - Budget comparisons - Quote and outreach flows - Communication tool with our team

For venue recommendations across more than 10,000 venues, we do not rely purely on the language model. We embed both user requirements and venues into vector representations and retrieve candidates using similarity search. Hard constraints such as capacity and dates are applied first, and results are ranked before being presented.

On the interface side, we use a split layout: conversation on the left and structured results on the right. As you refine the plan in chat, the event updates in real time, allowing an iterative workflow rather than a static search experience.

What is different is that we treat event planning as a stateful coordination problem rather than a one-shot search query. The agent orchestrates tools, manages evolving constraints, and surfaces trade-offs explicitly. It does not invent venues or fabricate pricing, and it is not designed to replace human planners for very large or highly customized events.

We make money from commissions on venue bookings. It is free for teams to explore options and plan. If you’ve organized an offsite or large meetup before, I’d genuinely value your perspective. Where would you expect this to fail? What edge cases are we underestimating? Where wouldn’t you trust an agent to handle the details?

My engineering team and I will be here all day to answer questions, happy to go deep on architecture, tradeoffs, and lessons learned. We’d really appreciate your candid feedback.