Hazumi News

Hey HN,

I’m Henry, cofounder and CTO at Span (https://span.app/). Today we’re launching AI Code Detector, an AI code detection tool you can try in your browser.

The explosion of AI generated code has created some weird problems for engineering orgs. Tools like Cursor and Copilot are used by virtually every org on the planet – but each codegen tool has its own idiosyncratic way of reporting usage. Some don’t report usage at all.

Our view is that token spend will start competing with payroll spend as AI becomes more deeply ingrained in how we build software, so understanding how to drive proficiency, improve ROI, and allocate resources relating to AI tools will become at least as important as parallel processes on the talent side.

Getting true visibility into AI-generated code is incredibly difficult. And yet it’s the number one thing customers ask us for.

So we built a new approach from the ground up.

Our AI Code Detector is powered by span-detect-1, a state-of-the-art model trained on millions of AI- and human-written code samples. It detects AI-generated code with 95% accuracy, and ties it to specific lines shipped into production. Within the Span platform, it’ll give teams a clear view into AI’s real impact on velocity, quality, and ROI.

It does have some limitations. Most notably, it only works for TypeScript and Python code. We are adding support for more languages: Java, Ruby, and C# are next. Its accuracy is around 95% today, and we’re working on improving that, too.

If you’d like to take it for a spin, you can run a code snippet here (https://code-detector.ai/) and get results in about five seconds. We also have a more narrative-driven microsite (https://www.span.app/detector) that my marketing team says I have to share.

Would love your thoughts, both on the tool itself and your own experiences. I’ll be hanging out in the comments to answer questions, too.

Hi HN! We are Arjun, Ramnique, and Akhilesh, the founders of Rowboat (https://www.rowboatlabs.com), an AI-assisted IDE for building and managing multi-agent systems with a copilot. Using Rowboat, you can build both deterministic automation agents (e.g. automatically summarizing emails) and more agentic systems (e.g. a meeting prep assistant or a customer support bot).

Here are some examples:

- Meeting-prep assistant: https://www.youtube.com/watch?v=KZTP4xZM2DY

- Customer support assistant: https://www.youtube.com/watch?v=Xfo-OfgOl8w

- Gmail and Reddit assistant: https://www.youtube.com/watch?v=6r7P4Vlcn2g

Rowboat is open-source (https://github.com/rowboatlabs/rowboat) and has a growing community. We first launched it on Show HN a few months ago (https://news.ycombinator.com/item?id=43763967).

Today we are launching a major update along with a cloud offering. We’ve added built-in tool integrations for 100s of tools like Gmail, Github and Slack, RAG with documents and URLs, and triggers to invoke your assistant based on external events.

Our cloud version includes all the features of the open-source IDE, but runs instantly with no setup or API keys. For launch, we're offering $10 free usage with Gemini models so you can start building right away for free without adding any card details. Paid plans start at $20/month and give you access to additional models (OpenAI, Anthropic, Gemini, with more coming) and higher usage limits.

There’s a growing view that some tasks are better handled by single agents (https://news.ycombinator.com/item?id=45096962), while others benefit from multi-agent systems for higher accuracy ( https://www.anthropic.com/engineering/multi-agent-research-s...). The difference often comes down to scope: a focused task like coding suits a single agent, but juggling multiple domains such as email, Slack, and LinkedIn is better split across agents. Multi-agent systems also help avoid context pollution, since LLMs lose focus when asked to handle unrelated tasks. In addition, cleanly dividing responsibilities makes each agent easier to test, debug, and improve.

However, splitting work into multiple agents and getting their prompts right is challenging. OpenAI and others have published patterns that work well for different scenarios (https://cdn.openai.com/business-guides-and-resources/a-pract...). We’ve added agent abstractions, built on top of OpenAI’s Agents SDK, to support these patterns. These include user-facing agents that can decide to hand off to another agent when needed; task agents that perform internal tasks; and pipelines that deterministically call a sequence of agents.

Rowboat’s copilot (‘Skipper’) is aware of these patterns and has been seeded with tested patterns, such as a manager‑worker setup for a customer support bot, a pipeline for automated document summarization, and multi‑agent workflows for combining web search with RAG. It can:

- Build multi-agent systems from a high-level request and decide how work must be delegated across agents

- Edit agent instructions to make correct tool calls using Composio tools or any connected MCP server

- Observe your playground chat and improve agents based on your tests

We see agentic systems as a spectrum. On one end are deterministic workflows with a few LLM calls. On the other end are fully agentic systems where the LLM makes all control flow decisions - we focus on this end of the spectrum, while still allowing deterministic control where necessary for real-world assistant use cases. We intentionally avoided flowchart-style editors (like n8n) because they become unwieldy when building and maintaining highly agentic systems.

We look forward to hearing your thoughts!