This looks nice,would be really cool if this has a huge adoption in the future because i find SEO frustrating on github,does it have it's own custom SEO system for ranking repos?
The frustration that started this: I kept context-switching between Grafana dashboards, log files, and docs every time something broke in production. I wanted to just ask what was wrong and get an actual answer.
Argus is an open-source AI agent that monitors your infrastructure and investigates anomalies autonomously using a ReAct loop with 18+ tools, reading logs, querying metrics, tracing requests then proposes fixes for your approval before anything executes. The human-in-the-loop part was intentional; I didn't want an agent that could nuke a database without asking first.
It's LLM-agnostic (OpenAI, Anthropic, Gemini) and runs entirely in one Docker container. SQLite + DuckDB under the hood, no external dependencies.
Still early but already handles the case I built it for — would love brutal feedback, especially from anyone running their own infra.
I got tired of AI agents (Cursor, Claude Code) instantly writing hundreds of lines of bad code from a misunderstood prompt, burning tokens and time.
We built Overture: an open-source MCP server that intercepts the agent's plan and renders it as an interactive graph. You can review dependencies, inject context into specific nodes, and approve the flowchart before execution starts.
Would love any technical feedback or thoughts on the approach.
I'm a visual learner. Whenever I try to understand something hard like dynamic programming, vector calculus, how attention mechanisms work, reading about it only gets me so far. I need to see it move.
So I built Prism AI. When you ask it to explain something, it doesn't just return a report. If the topic calls for it, it generates an interactive visualization inline. Ask it to explain dynamic programming and you get a 2D animation with the code on one side and a decision tree on the other, recursively solving subproblems as a highlighter steps through each line. Ask it how a vector field works and it renders an interactive 3D field you can rotate and probe. Ask it how the attention mechanism in a transformer works and it shows you the actual weight matrix lighting up across tokens.
The research pipeline underneath is a Plan-and-Execute setup , a PlanningAgent breaks your query into a roadmap, then multiple Researcher Agents crawl sources in parallel via asyncio, with a LangGraph state machine handling retries when sources are weak. But the viz generation is honestly the part I care about most and the part I'm still iterating on hardest.
Feedback I'd value: 1. What complex topic would you most want explained this way? 2. Has anyone found a clean way to decide when an agent should generate a visual vs just write prose — that decision boundary is still the messiest part of my pipeline.
No yet,but it's a feature that can be added to allow the planning phase have a human in the loop because currently the planning agent handles all the planning,will definitely look into this thanks.
I built Prism AI because I was frustrated with the "wall of text" output typical of most AI research tools. While current LLMs are great at synthesis and citations, they often fail to communicate complex structural or numerical data effectively.
Prism AI is an open-source attempt to solve this by making the research process inherently visual.
Key Technical Details:
Orchestration: I'm using a "Plan-and-Execute" pattern powered by LangGraph. This allows the system to maintain a persistent state and perform recursive "gap analysis" on its own research.
Concurrency: The research nodes are built with Python’s asyncio, allowing it to scrape, crawl, and synthesize multiple sections of a report in parallel.
Visualization Engine: Rather than just generating Markdown, the agents are equipped with tools to generate 2D/3D illustrations, interactive animations, and dynamic charts. The system determines when a concept is better explained visually and generates the corresponding code on the fly.
Self-Hostable: Fully Dockerized and runs with a Next.js frontend.
I’m particularly interested in hearing how others are handling the "context drift" that happens in high-concurrency multi-agent systems. The code is MIT licensed.
GitHub: https://github.com/precious112/prism-ai-deep-research
About three weeks ago, I made a mistake that cost me years of traction on my VSCode extension.
A bad update led to user complaints, so I thought unpublishing for a bit was a good idea. Turns out, once an extension is unpublished, the publishing ID is gone forever.
Microsoft support? Nothing they could do.
This means there's no real way to "pause" an extension without permanently destroying its install base. I had to rebuild from scratch.
Two weeks later, my new extension is already at 9,000+ installs. The resilience of the dev community amazes me.
Maybe we need a better rollback system for VSCode Marketplace? Or at least a warning before unpublishing?
If you’re curious, here’s my new extension → https://marketplace.visualstudio.com/items?itemName=Sixth.si...
A few years? Sonnet 3.5 is a better API-mediated solution today, LLaMA 3.2 is one full turn of the crank from GPT-4-1106-preview parity.
It’s dramatically easier to list the researchers or executives of any note who remain than the ones that have left.
This is like when John Romero did Ion Storm without Carmack. Maybe, maybe, GPT-5 is tracking as a viable family of training runs today, it certainly wasn’t 6 months ago when it was already badly late.
And even if it is: there won’t be another on the handful of world-class research staff they have left. Now you get Canvas and shit that even they don’t think will stick.
OpenAI seems to have been losing it. They now cannot even get basic accounting to work correctly. They look to have lost all Credit Grants entries for users for Sept 2024. They then went out of their way to give active users a free credit of nearly $100 to make up for the loss of this accounting data. They didn't even issue a formal announcement of this screwup. As a user, it makes me worried.
I don't want to overlook that they introduced Canvas today, but all parts have to work correctly for the system to work. It's not a fully cybernetic system yet.
well you're kind of correct though,but we do know that it works,and as long as it's something that can be repeated over and over and still remains true,i thinks that's all that matters,we just have to figure out in our way how the algorithm works.
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