Background: Co-founder and CTO of Sixth AI, an AI coding agent VSCode extension I built from Lagos to 350k installs and $27k in monthly revenue, with users organically switching from GitHub Copilot. Architected the full backend including a client-side agentic layer with a pure LLM proxy backend that handles thousands of concurrent requests. Owned everything from infrastructure decisions to VSCode marketplace growth and distribution.
Looking for a founding engineer or early engineer role at an AI-native startup where I can bring more than just code, product thinking, distribution instincts, and the kind of context that only comes from having built and shipped something real under real constraints. That's the sweet spot for me but open to other serious engineering roles where this kind of background is valued.
Open to full-time remote roles or startups willing to sponsor relocation/visa processing.
I have gone back to manual coding to remove this AI induced laziness because i discovered immediately the LLM starts making some useless engineering decisions it does it like it knows what it's doing and also very deceptive and sometimes it's just easier to do it by myself other than trying to get the AI to do it,LLMS get so confidently wrong.
I think group dynamics comes with a turn taking ambiguity. unlike in one-on-one dialogue that's structurally clean since there's a clear prompt, a clear response, and a clear feedback signal for RLHF.
Isn't windsurf now antigravity? because i still used antigravity for a while this year because of how the agent can natively try to test web pages using chrome which helps it find UI bugs,but i use mostly claude code now though
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