Tied Crosscoders: Tracing How Chat LLM Behavior Emerges from Base Model
3 by aranguri | 0 comments on Hacker News.
Sunday, March 23, 2025
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Thursday, March 20, 2025
New top story on Hacker News: Show HN: AgentKit – JavaScript Alternative to OpenAI Agents SDK with Native MCP
Show HN: AgentKit – JavaScript Alternative to OpenAI Agents SDK with Native MCP
32 by tonyhb | 9 comments on Hacker News.
Hi HN! I’m Tony, co-founder of Inngest. I wanted to share AgentKit, our Typescript multi-agent library we’ve been cooking and testing with some early users in prod for months. Although OpenAI’s Agents SDK has been launched since, we think an Agent framework should offer more deterministic and flexible routing, work with multiple model providers, embrace MCP (for rich tooling), and support the unstoppable and growing community of TypeScript AI developers by enabling a smooth transition to production use cases. This is why we are building AgentKit, and we’re really excited about it for a few reasons: Firstly, it’s simple. We embrace KISS principles brought by Anthropic and HuggingFace by allowing you to gradually add autonomy to your AgentKit program using primitives: - Agents: LLM calls that can be combined with prompts, tools, and MCP native support. - Networks: a simple way to get Agents to collaborate with a shared State, including handoff. - State: combines conversation history with a fully typed state machine, used in routing. - Routers: where the autonomy lives, from code-based to LLM-based (ex: ReAct) orchestration The routers are where the magic happens, and allow you to build deterministic, reliable, testable agents. AgentKit routing works as follows: the network calls itself in a loop, inspecting the State to determine which agents to call next using a router. The returned agent runs, then optionally updates state data using its tools. On the next loop, the network inspects state data and conversation history, and determines which new agent to run. This fully typed state machine routing allows you to deterministically build agents using any of the effective agent patterns — which means your code is easy to read, edit, understand, and debug. This also makes handoff incredibly easy: you define when agents should hand off to each other using regular code and state (or by calling an LLM in the router for AI-based routing). This is similar to the OpenAI Agents SDK but easier to manage, plan, and build. Then comes the local development and moving to production capabilities. AgentKit is compatible with Inngest’s tooling, meaning that you can test agents using Inngest’s local DevServer, which provides traces, inputs, outputs, replay, tool, and MCP inputs and outputs, and (soon) a step-over debugger so that you can easily understand and visually see what's happening in the agent loop. In production, you can also optionally combine AgentKit with Inngest for fault-tolerant execution. Each agent’s LLM call is wrapped in a step, and tools can use multiple steps to incorporate things like human-in-the-loop. This gives you native orchestration, observability, and out-of-the-box scale. You will find the documentation as an example of an AgentKit SWE-bench and multiple Coding Agent examples. It’s fully open-source under the Apache 2 license. If you want to get started: - npm: npm i @inngest/agent-kit - GitHub: https://ift.tt/mWMq13F - Docs: https://ift.tt/kCgoYu9 We’re excited to finally launch AgentKit; let us know what you think!
32 by tonyhb | 9 comments on Hacker News.
Hi HN! I’m Tony, co-founder of Inngest. I wanted to share AgentKit, our Typescript multi-agent library we’ve been cooking and testing with some early users in prod for months. Although OpenAI’s Agents SDK has been launched since, we think an Agent framework should offer more deterministic and flexible routing, work with multiple model providers, embrace MCP (for rich tooling), and support the unstoppable and growing community of TypeScript AI developers by enabling a smooth transition to production use cases. This is why we are building AgentKit, and we’re really excited about it for a few reasons: Firstly, it’s simple. We embrace KISS principles brought by Anthropic and HuggingFace by allowing you to gradually add autonomy to your AgentKit program using primitives: - Agents: LLM calls that can be combined with prompts, tools, and MCP native support. - Networks: a simple way to get Agents to collaborate with a shared State, including handoff. - State: combines conversation history with a fully typed state machine, used in routing. - Routers: where the autonomy lives, from code-based to LLM-based (ex: ReAct) orchestration The routers are where the magic happens, and allow you to build deterministic, reliable, testable agents. AgentKit routing works as follows: the network calls itself in a loop, inspecting the State to determine which agents to call next using a router. The returned agent runs, then optionally updates state data using its tools. On the next loop, the network inspects state data and conversation history, and determines which new agent to run. This fully typed state machine routing allows you to deterministically build agents using any of the effective agent patterns — which means your code is easy to read, edit, understand, and debug. This also makes handoff incredibly easy: you define when agents should hand off to each other using regular code and state (or by calling an LLM in the router for AI-based routing). This is similar to the OpenAI Agents SDK but easier to manage, plan, and build. Then comes the local development and moving to production capabilities. AgentKit is compatible with Inngest’s tooling, meaning that you can test agents using Inngest’s local DevServer, which provides traces, inputs, outputs, replay, tool, and MCP inputs and outputs, and (soon) a step-over debugger so that you can easily understand and visually see what's happening in the agent loop. In production, you can also optionally combine AgentKit with Inngest for fault-tolerant execution. Each agent’s LLM call is wrapped in a step, and tools can use multiple steps to incorporate things like human-in-the-loop. This gives you native orchestration, observability, and out-of-the-box scale. You will find the documentation as an example of an AgentKit SWE-bench and multiple Coding Agent examples. It’s fully open-source under the Apache 2 license. If you want to get started: - npm: npm i @inngest/agent-kit - GitHub: https://ift.tt/mWMq13F - Docs: https://ift.tt/kCgoYu9 We’re excited to finally launch AgentKit; let us know what you think!
Wednesday, March 19, 2025
Tuesday, March 18, 2025
Monday, March 17, 2025
New top story on Hacker News: Show HN: OpenTimes – Free travel times between U.S. Census geographies
Show HN: OpenTimes – Free travel times between U.S. Census geographies
13 by dfsnow | 1 comments on Hacker News.
Hi HN! Today I'm launching OpenTimes, a free database of roughly 150 billion pre-computed, point-to-point travel times between United States Census geographies. In addition to letting you visualize travel isochrones on the homepage, OpenTimes also lets you download massive amounts of travel time data for free and with no limits. The primary goal here is to enable research and fill a gap I noticed in the open-source spatial ecosystem. Researchers (social scientists, economists, etc.) use large travel time matrices to quantify things like access to healthcare, but they often end up paying Google or Esri for the necessary data. By pre-calculating times between commonly-used research geographies (i.e. Census) and then making those times easily accessible via SQL, I hope to make large-scale accessibility research cheaper and simpler. Some technical bits that may be of interest to HN folks: - The entire OpenTimes backend is just static Parquet files on R2. There's no RDBMS or running service. The whole thing costs about $10/month to host and is free to serve. - All travel times were calculated by pre-building the inputs (OSM, OSRM networks) and then distributing the compute over hundreds of GitHub Actions jobs. - The query/SQL layer uses a setup I haven't seen before: a single DuckDB database file with views that point to static Parquet files via HTTP. Finally, the driving times are optimistic since they don't (yet) account for traffic. This is something I hope to work on in the near future. Enjoy!
13 by dfsnow | 1 comments on Hacker News.
Hi HN! Today I'm launching OpenTimes, a free database of roughly 150 billion pre-computed, point-to-point travel times between United States Census geographies. In addition to letting you visualize travel isochrones on the homepage, OpenTimes also lets you download massive amounts of travel time data for free and with no limits. The primary goal here is to enable research and fill a gap I noticed in the open-source spatial ecosystem. Researchers (social scientists, economists, etc.) use large travel time matrices to quantify things like access to healthcare, but they often end up paying Google or Esri for the necessary data. By pre-calculating times between commonly-used research geographies (i.e. Census) and then making those times easily accessible via SQL, I hope to make large-scale accessibility research cheaper and simpler. Some technical bits that may be of interest to HN folks: - The entire OpenTimes backend is just static Parquet files on R2. There's no RDBMS or running service. The whole thing costs about $10/month to host and is free to serve. - All travel times were calculated by pre-building the inputs (OSM, OSRM networks) and then distributing the compute over hundreds of GitHub Actions jobs. - The query/SQL layer uses a setup I haven't seen before: a single DuckDB database file with views that point to static Parquet files via HTTP. Finally, the driving times are optimistic since they don't (yet) account for traffic. This is something I hope to work on in the near future. Enjoy!
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