Show HN: Modulus – Cross-repository knowledge orchestration for coding agents
1 by dasubhajit | 0 comments on Hacker News.
Hello HN, we're Jeet and Husain from Modulus ( https://modulus.so ) - a desktop app that lets you run multiple coding agents with shared project memory. We built it to solve two problems we kept running into: - Cross-repo context is broken. When working across multiple repositories, agents don't understand dependencies between them. Even if we open two repos in separate Cursor windows, we still have to manually explain the backend API schema while making changes in the frontend repo. - Agents lose context. Switching between coding agents often means losing context and repeating the same instructions again. Modulus shares memory across agents and repositories so they can understand your entire system. It's an alternative to tools like Conductor for orchestrating AI coding agents to build product, but we focused specifically on multi-repo workflows (e.g., backend repo + client repo + shared library repo + AI agents repo). We built our own Memory and Context Engine from the ground up specifically for coding agents. Why build another agent orchestration tool? It came from our own problem. While working on our last startup, Husain and I were working across two different repositories. Working across repos meant manually pasting API schemas between Cursor windows — telling the frontend agent what the backend API looked like again and again. So we built a small context engine to share knowledge across repos and hooked it up to Cursor via MCP. This later became Modulus. Soon, Modulus will allow teams to share knowledge with others to improve their workflows with AI coding agents - enabling team collaboration in the era of AI coding. Our API will allow developers to switch between coding agents or IDEs without losing any context. If you wanna see a quick demo before trying out, here is our launch post - https://ift.tt/B86Jp7Z We'd greatly appreciate any feedback you have and hope you get the chance to try out Modulus.
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New Show Hacker News story: Show HN: What's my JND? – a colour guessing game
Show HN: What's my JND? – a colour guessing game
15 by Keithamus | 16 comments on Hacker News.
https://ift.tt/pjK4d0k
15 by Keithamus | 16 comments on Hacker News.
https://ift.tt/pjK4d0k
New ask Hacker News story: AI is to software as power tools are to woodworking
AI is to software as power tools are to woodworking
4 by danfunk | 2 comments on Hacker News.
Power tools did not remove people. They make woodworking accessible to more people. They make more complex projects possible. They make furniture less expensive. We don't have less jobs because of power tools. And with power tools came a proliferation of hardware stores to support all the people suddenly empowered to try their hand. To take the analogy further, agents are like factories. Yes the drill can do the work on it's own, when it's on an assembly line, getting exactly the right part at the right time at the right angle. But it is insanely hard and expensive to set up a factory, and when it is done, it produces one thing. Shit will change. But that is exactly what I liked about this industry to begin with. And people are highly motivated by fear, so the manipulators and influencers peddle it for all they are worth. There is nothing to fear here. It's just a new kind of tool for you to pick up, if you have the courage and heart to do so.
4 by danfunk | 2 comments on Hacker News.
Power tools did not remove people. They make woodworking accessible to more people. They make more complex projects possible. They make furniture less expensive. We don't have less jobs because of power tools. And with power tools came a proliferation of hardware stores to support all the people suddenly empowered to try their hand. To take the analogy further, agents are like factories. Yes the drill can do the work on it's own, when it's on an assembly line, getting exactly the right part at the right time at the right angle. But it is insanely hard and expensive to set up a factory, and when it is done, it produces one thing. Shit will change. But that is exactly what I liked about this industry to begin with. And people are highly motivated by fear, so the manipulators and influencers peddle it for all they are worth. There is nothing to fear here. It's just a new kind of tool for you to pick up, if you have the courage and heart to do so.
New ask Hacker News story: Ask HN: What game engine would you recommend for vibe coding?
Ask HN: What game engine would you recommend for vibe coding?
5 by general_reveal | 2 comments on Hacker News.
I feel like things like Unity and Unreal have a lot of UI-centric workflows that wouldn’t be great for vibing.
5 by general_reveal | 2 comments on Hacker News.
I feel like things like Unity and Unreal have a lot of UI-centric workflows that wouldn’t be great for vibing.
New ask Hacker News story: Ask HN: Read‑only LLM tool for email triage and knowledge extraction?
Ask HN: Read‑only LLM tool for email triage and knowledge extraction?
2 by maille | 1 comments on Hacker News.
I’m looking for an LLM‑powered tool to help organize and query my email, but with a strict boundary: it must not write, send, or modify anything. Standard filters miss the nuance of "middle-ground" emails, and existing AI email clients demand full inbox control or try to automate too much. What I’m looking for: - Strictly read‑only triage : Categorizes incoming mail (e.g., “important”, “maybe useful”, “likely promo”) with zero write permissions ie. no sending, moving, deleting, or auto-archiving - Knowledge retrieval : Builds a searchable knowledge base from scattered threads. I want to ask natural-language questions like: “What were all the decisions about Project X across the last 3 months?” - Follow-up & action item detection : surfaces email threads that have stalled and need a nudge, while extracting explicit tasks and deadlines buried in long chains - (optional) Privacy-first : Ideally runs locally or lets me plug in my own API keys to keep the data secure Has anyone seen a tool that focuses entirely on this read-only, triage + retrieval workflow?
2 by maille | 1 comments on Hacker News.
I’m looking for an LLM‑powered tool to help organize and query my email, but with a strict boundary: it must not write, send, or modify anything. Standard filters miss the nuance of "middle-ground" emails, and existing AI email clients demand full inbox control or try to automate too much. What I’m looking for: - Strictly read‑only triage : Categorizes incoming mail (e.g., “important”, “maybe useful”, “likely promo”) with zero write permissions ie. no sending, moving, deleting, or auto-archiving - Knowledge retrieval : Builds a searchable knowledge base from scattered threads. I want to ask natural-language questions like: “What were all the decisions about Project X across the last 3 months?” - Follow-up & action item detection : surfaces email threads that have stalled and need a nudge, while extracting explicit tasks and deadlines buried in long chains - (optional) Privacy-first : Ideally runs locally or lets me plug in my own API keys to keep the data secure Has anyone seen a tool that focuses entirely on this read-only, triage + retrieval workflow?
New ask Hacker News story: Ask HN: Any informed guesses on the actual size/architecture of GPT-5.4 etc.?
Ask HN: Any informed guesses on the actual size/architecture of GPT-5.4 etc.?
2 by dsrtslnd23 | 0 comments on Hacker News.
Does anyone have decent intuitions or hard clues on how big models like GPT-5.4, Gemini 3.1, and Opus 4.6 actually are, and how they compare to the best open models like GLM-5? Are they all roughly in the same range now (for example around 1T params, maybe MoE), or are the closed models still much bigger? Also curious about “pro” versions like GPT-5.4 Pro - is that likely a different model, or mostly the same model with more inference-time compute / longer reasoning / better orchestration?
2 by dsrtslnd23 | 0 comments on Hacker News.
Does anyone have decent intuitions or hard clues on how big models like GPT-5.4, Gemini 3.1, and Opus 4.6 actually are, and how they compare to the best open models like GLM-5? Are they all roughly in the same range now (for example around 1T params, maybe MoE), or are the closed models still much bigger? Also curious about “pro” versions like GPT-5.4 Pro - is that likely a different model, or mostly the same model with more inference-time compute / longer reasoning / better orchestration?