What's the Deal with Autism Rates?
38 by paulpauper | 17 comments on Hacker News.
Sunday, April 27, 2025
New top story on Hacker News: Show HN: Logchef – Schema-agnostic log viewer for ClickHouse
Show HN: Logchef – Schema-agnostic log viewer for ClickHouse
7 by mr-karan | 2 comments on Hacker News.
Hey HN! I’m Karan, creator of Logchef ( https://logchef.app ), an open-source log viewer built specifically for exploring logs stored in ClickHouse. This tool grew directly out of my $day job managing massive log volumes. Like many orgs, we migrated our log workloads to ClickHouse for its performance, but found the ecosystem lacked dedicated UI tooling for actually browsing and analyzing those logs effectively. We were using Metabase, and while great for general BI, it wasn't designed for log exploration workflows. Common pain points included: - Clunky Ad-hoc Querying: Writing/modifying raw ClickHouse SQL for quick checks was slow and error-prone, especially during incidents. - Disconnect Between Viz & Raw Logs: Visualizing trends (like error counts) then drilling down to the specific raw logs often required separate, complex queries and wrestling with row limits. The intuitive "slice-and-dice" was missing. - UI Friction: Simple things like selecting precise time ranges ("last 90 minutes"), easily viewing surrounding log context, or dealing with truncated columns added unnecessary friction. Debugging sessions were taking longer than they should. So, over the last 3-4 months, I built Logchef to scratch this itch. Logchef's Core Ideas: - Purpose-Built for ClickHouse Logs: Designed from the ground up for the specific task of log exploration on top of ClickHouse, focusing on speed and intuitive workflows. - Schema-Agnostic: Logchef doesn't force OTEL or any other schema. Connect it directly to your existing ClickHouse log tables (it just needs a timestamp column). Bring your own schema! - Focus on Viewing/Querying: Logchef intentionally doesn't handle log collection/ingestion. It complements great tools like Vector, Fluentbit, etc., by focusing purely on the exploration layer once data is in ClickHouse. - Simple Search Syntax: Includes a simple query syntax (e.g., `status=200 and path~"/api/"`) that translates to efficient ClickHouse SQL behind the scenes, integrated with the Monaco editor. Tech Stack: Go backend, SQLite for metadata, Vue.js + shadcn/ui + Tailwind CSS frontend. You can try a live public demo here: https://ift.tt/C8exFvn (It's pre-populated with sample data using Vector, so you can dive right in. Uses Dex for OIDC auth - creds are on the login page). What's Next & Getting Involved: Logchef is already used internally at Zerodha, and I'm driving towards v1.0 this year. The roadmap includes features like Alerting, Live Tail Logs, and Enhanced Dashboarding. It's open source (AGPLv3), and I'd love to get more eyes on it and build a community. Check out the repo: https://ift.tt/UmxE7HS I’d love to hear your feedback, whether positive or negative. Please open issues on GitHub with suggestions or bug reports! Thanks so much, HN!
7 by mr-karan | 2 comments on Hacker News.
Hey HN! I’m Karan, creator of Logchef ( https://logchef.app ), an open-source log viewer built specifically for exploring logs stored in ClickHouse. This tool grew directly out of my $day job managing massive log volumes. Like many orgs, we migrated our log workloads to ClickHouse for its performance, but found the ecosystem lacked dedicated UI tooling for actually browsing and analyzing those logs effectively. We were using Metabase, and while great for general BI, it wasn't designed for log exploration workflows. Common pain points included: - Clunky Ad-hoc Querying: Writing/modifying raw ClickHouse SQL for quick checks was slow and error-prone, especially during incidents. - Disconnect Between Viz & Raw Logs: Visualizing trends (like error counts) then drilling down to the specific raw logs often required separate, complex queries and wrestling with row limits. The intuitive "slice-and-dice" was missing. - UI Friction: Simple things like selecting precise time ranges ("last 90 minutes"), easily viewing surrounding log context, or dealing with truncated columns added unnecessary friction. Debugging sessions were taking longer than they should. So, over the last 3-4 months, I built Logchef to scratch this itch. Logchef's Core Ideas: - Purpose-Built for ClickHouse Logs: Designed from the ground up for the specific task of log exploration on top of ClickHouse, focusing on speed and intuitive workflows. - Schema-Agnostic: Logchef doesn't force OTEL or any other schema. Connect it directly to your existing ClickHouse log tables (it just needs a timestamp column). Bring your own schema! - Focus on Viewing/Querying: Logchef intentionally doesn't handle log collection/ingestion. It complements great tools like Vector, Fluentbit, etc., by focusing purely on the exploration layer once data is in ClickHouse. - Simple Search Syntax: Includes a simple query syntax (e.g., `status=200 and path~"/api/"`) that translates to efficient ClickHouse SQL behind the scenes, integrated with the Monaco editor. Tech Stack: Go backend, SQLite for metadata, Vue.js + shadcn/ui + Tailwind CSS frontend. You can try a live public demo here: https://ift.tt/C8exFvn (It's pre-populated with sample data using Vector, so you can dive right in. Uses Dex for OIDC auth - creds are on the login page). What's Next & Getting Involved: Logchef is already used internally at Zerodha, and I'm driving towards v1.0 this year. The roadmap includes features like Alerting, Live Tail Logs, and Enhanced Dashboarding. It's open source (AGPLv3), and I'd love to get more eyes on it and build a community. Check out the repo: https://ift.tt/UmxE7HS I’d love to hear your feedback, whether positive or negative. Please open issues on GitHub with suggestions or bug reports! Thanks so much, HN!
Saturday, April 26, 2025
Friday, April 25, 2025
Thursday, April 24, 2025
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Tuesday, April 22, 2025
New top story on Hacker News: Show HN: Rowboat – Open-source IDE for multi-agent systems
Show HN: Rowboat – Open-source IDE for multi-agent systems
16 by segmenta | 0 comments on Hacker News.
Hi HN! We’re Arjun, Ramnique, and Akhilesh, and we are building Rowboat ( https://ift.tt/xKUkJRH ), an AI-assisted IDE for building and managing multi-agent systems. You start with a single agent, then scale up to teams of agents that work together, use MCP tools, and improve over time - all through a chat-based copilot. Our repo is https://ift.tt/5XziZCD , docs are at https://ift.tt/dTXpAGu , and there’s a demo video here: https://youtu.be/YRTCw9UHRbU It’s becoming clear that real-world agentic systems work best when multiple agents collaborate, rather than having one agent attempt to do everything. This isn’t too surprising - it’s a bit like how good code consists of multiple functions that each do one thing, rather than cramming everything into one function. For example, a travel assistant works best when different agents handle specialized tasks: one agent finds the best flights, another optimizes hotel selections, and a third organizes the itinerary. This modular approach makes the system easier to manage, debug, and improve over time. OpenAI’s Agents SDK provides a neat Python library to support this, but building reliable agentic systems requires constant iterations and tweaking - e.g. updating agent instructions (which can quickly get as complex as actual code), connecting tools, and testing the system and incorporating feedback. Rowboat is an AI IDE to do all this. Rowboat is to AI agents what Cursor is to code. We’ve taken a code-like approach to agent instructions (prompts). There are special keywords to directly reference other agents, tools or prompts - which are highlighted in the UI. The copilot is the best way to create and edit these instructions - each change comes with a code-style diff. You can give agents access to tools by integrating any MCP server or connecting your own functions through a webhook. You can instruct the agents on when to use specific tools via ‘@mentions’ in the agent instruction. To enable quick testing, we added a way to mock tool responses using LLM calls. Rowboat playground lets you test and debug the assistants as you build them. You can see agent transfers, tool invocations and tool responses in real-time. The copilot has the context of the chat, and can improve the agent instructions based on feedback. For example, you could say ‘The agent shouldn’t have done x here. Fix this’ and the copilot can go and make this fix. You can integrate agentic systems built in Rowboat into your application via the HTTP API or the Python SDK (‘pip install rowboat’). For example, you can build user-facing chatbots, enterprise workflows and employee assistants using Rowboat. We’ve been working with LLMs since GPT-1 launched in 2018. Most recently, we built Coinbase’s support chatbot after our last AI startup was acquired by them. Rowboat is Apache 2.0 licensed, giving you full freedom to self-host, modify, or extend it however you like. We’re excited to share Rowboat with everyone here. We’d love to hear your thoughts!
16 by segmenta | 0 comments on Hacker News.
Hi HN! We’re Arjun, Ramnique, and Akhilesh, and we are building Rowboat ( https://ift.tt/xKUkJRH ), an AI-assisted IDE for building and managing multi-agent systems. You start with a single agent, then scale up to teams of agents that work together, use MCP tools, and improve over time - all through a chat-based copilot. Our repo is https://ift.tt/5XziZCD , docs are at https://ift.tt/dTXpAGu , and there’s a demo video here: https://youtu.be/YRTCw9UHRbU It’s becoming clear that real-world agentic systems work best when multiple agents collaborate, rather than having one agent attempt to do everything. This isn’t too surprising - it’s a bit like how good code consists of multiple functions that each do one thing, rather than cramming everything into one function. For example, a travel assistant works best when different agents handle specialized tasks: one agent finds the best flights, another optimizes hotel selections, and a third organizes the itinerary. This modular approach makes the system easier to manage, debug, and improve over time. OpenAI’s Agents SDK provides a neat Python library to support this, but building reliable agentic systems requires constant iterations and tweaking - e.g. updating agent instructions (which can quickly get as complex as actual code), connecting tools, and testing the system and incorporating feedback. Rowboat is an AI IDE to do all this. Rowboat is to AI agents what Cursor is to code. We’ve taken a code-like approach to agent instructions (prompts). There are special keywords to directly reference other agents, tools or prompts - which are highlighted in the UI. The copilot is the best way to create and edit these instructions - each change comes with a code-style diff. You can give agents access to tools by integrating any MCP server or connecting your own functions through a webhook. You can instruct the agents on when to use specific tools via ‘@mentions’ in the agent instruction. To enable quick testing, we added a way to mock tool responses using LLM calls. Rowboat playground lets you test and debug the assistants as you build them. You can see agent transfers, tool invocations and tool responses in real-time. The copilot has the context of the chat, and can improve the agent instructions based on feedback. For example, you could say ‘The agent shouldn’t have done x here. Fix this’ and the copilot can go and make this fix. You can integrate agentic systems built in Rowboat into your application via the HTTP API or the Python SDK (‘pip install rowboat’). For example, you can build user-facing chatbots, enterprise workflows and employee assistants using Rowboat. We’ve been working with LLMs since GPT-1 launched in 2018. Most recently, we built Coinbase’s support chatbot after our last AI startup was acquired by them. Rowboat is Apache 2.0 licensed, giving you full freedom to self-host, modify, or extend it however you like. We’re excited to share Rowboat with everyone here. We’d love to hear your thoughts!