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Hey HN, we're Haakam, Michael, and Adi. We're building AgentMail (https://agentmail.to), the email inbox API for agents. We’re not talking about AI for your email, this is email for your AI.

Email is an optimal interface for long-running agents. It’s multithreaded and asynchronous with full support for rich text and files. It’s a universal protocol with identity and authentication built in. Moreover, a lot of workflow critical context already lives in email.

We wanted to build email agents that you can forward your work to and get back a completed task. The agents could act entirely autonomously as you wouldn't need to delegate your identity. If they did get stuck they could just send you, or anyone else, an email.

Using Gmail, we kept getting stuck on the limitations of their API. No way to create inboxes programmatically. Rate and sending limits. OAuth for every single inbox. Keyword search that doesn't understand context. Per-seat pricing that doesn't work for agents.

So we built what we wished existed: an email provider for developers. APIs for creating inboxes and configuring domains. Email parsing and threading. Text extraction from attachments. Realtime webhooks and websockets. Semantic search across inboxes. Usage-based pricing that works for agents.

Developers, startups, and enterprises are already deploying email agents with AgentMail. Agents that convert conversations and documents into structured data. Agents that source quotes, negotiate prices, and get the best deals. Agents that emulate internet users for training models on end-to-end tasks.

Here's demo of Clawdbots communicating using AgentMail: https://youtu.be/Y0MfUWS3LKQ

You can get started with AgentMail for free at https://agentmail.to

Looking forward to hearing your thoughts and feedback.

Hi HN,

I’m Tullie, founder of Shaped. Previously, I was a researcher at Meta AI, worked on ranking for Instagram Reels, and was a contributor to PyTorch Lightning.

We built ShapedQL because we noticed that while retrieval (finding 1,000 items) has been commoditized by vector DBs, ranking (finding the best 10 items) is still an infrastructure problem.

To build a decent for you feed or a RAG system with long-term memory, you usually have to put together a vector DB (Pinecone/Milvus), a feature store (Redis), an inference service, and thousands of lines of Python to handle business logic and reranking.

We built an engine that consolidates this into a single SQL dialect. It compiles declarative queries into high-performance, multi-stage ranking pipelines.

HOW IT WORKS:

Instead of just SELECT , ShapedQL operates in four stages native to recommendation systems:

RETRIEVE: Fetch candidates via Hybrid Search (Keywords + Vectors) or Collaborative Filtering. FILTER: Apply hard constraints (e.g., "inventory > 0"). SCORE: Rank results using real-time models (e.g., p(click) or p(relevance)). REORDER: Apply diversity logic so your Agent/User doesn’t see 10 nearly identical results.

THE SYNTAX: Here is what a RAG query looks like. This replaces about 500 lines of standard Python/LangChain code:

SELECT item_id, description, price

FROM

  -- Retrieval: Hybrid search across multiple indexes

  search_flights("$param.user_prompt", "$param.context"),

  search_hotels("$param.user_prompt", "$param.context")

WHERE
-- Filtering: Hard business constraints price <= "$param.budget" AND is_available("$param.dates")
ORDER BY
-- Scoring: Real-time reranking (Personalization + Relevance) 0.5 * preference_score(user, item) + 0.3 * relevance_score(item, "$param.user_prompt")
LIMIT 20

If you don’t like SQL, you can also use our Python and Typescript SDKs. I’d love to know what you think of the syntax and the abstraction layer!