VibeTrade – Trading Harness for Claude
Post-Opus 4.6, LLMs feel much better at using bash, code, local files, and tools. So I kept coming back to a simple question: if a model can use a computer reasonably well, why can’t I just give it my broker account, a strategy, and let it trade? My conclusion is that the blocker is not model capability in the abstract. It is the system around the model. A raw LLM breaks on a few practical things almost immediately: • no persistent operating memory across sessions • no trustworthy record of what it did and why • no hard approval boundary before money moves • no cheap always-on monitoring if…
VibeTrade – Trading Harness for Claude launched on March 13, 2026 with 7 votes, #1,458 of 23,380 launches that month and more than 75% of that year's launches.
What it does
VibeTrade is an open-source harness that gives LLMs everything they need to trade Indian markets autonomously. Six primitives: live market tooling (20 tools, Dhan broker), a heartbeat that monitors every 30s without running an LLM, triggers (code, time, event, or fuzzy), strategy playbooks that persist across sessions, an immutable trade journal, and a permission gate that ensures no money moves without your explicit consent. Describe a thesis in English. The agent handles the rest.
Contribute to vibetrade-ai/vibe-trade development by creating an account on GitHub.
Vibe Trade is a local AI trading agent powered by Claude. Describe your strategy in plain English — the agent writes the Playbook, builds the automation stack, monitors the market around the clock, and comes to you with a reasoned case before touching your account. Ask the agent anything. It has 20 built-in tools and a live broker connection. It fetches real prices, reads your open positions, pulls candles and indicators, and scans the news — then answers like a portfolio manager who's been watching all morning. you: what's the weakest position right now? agent: ONGC SHORT is the most at risk. RSI has climbed to 56 — short momentum is fading. You're at −1.02% with the hard SL only ₹1.29…from github.com
In the maker’s words, at launch
Post-Opus 4.6, LLMs feel much better at using bash, code, local files, and tools. So I kept coming back to a simple question: if a model can use a computer reasonably well, why can’t I just give it my broker account, a strategy, and let it trade? My conclusion is that the blocker is not model capability in the…
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ViberiaMay 2026 · getviberia.com · ▲146Hey HN, This is my take on the agent harness. Everything on an isometric map. Agents are grouped into "buildings" that run in a sequence or a loop; e.g., the CodeForge has an agent that writes a PRD, another one that implements, and a third that reviews. Everything is customizable, you build your own buildings/teams however you want. It's a Tauri app, really light (about 8x less energy than the closest competitor I benchmarked, so it actually runs from a coffee shop on battery). It's macOS only for now, but ping me if you are willing to test the Windows or Linux version. I've been…
Claude 22023 · claude.ai · ▲248It was quite interesting to vibe code this artifact. I learned some basic ideas about commitment scheme and hash cryptography in the half and hour spent on this. One of the few times vibe coding left me feeling enriched rather than drained. What do you think?
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LangAlpha – what if Claude Code was built for Wall Street?Apr 2026 · github.com · ▲148Some technical context on what we ran into building this. MCP tools don't really work for financial data at scale. One tool call for five years of daily prices dumps tens of thousands of tokens into the context window. And data vendors pack dozens of tools into a single MCP server, schemas alone can eat 50k+ tokens before the agent does anything useful. So we auto-generate typed Python modules from the MCP schemas at workspace init and upload them into the sandbox. The agent just imports them like a normal library. Only a one-line summary per server stays in the prompt. We have around 80…
Bindly2016 · bind.ly · ▲76Hi HN, I’m a product designer by background, not a traditional software engineer. Over the last year, tools like Claude and ChatGPT completely changed how I work. I started with small internal tools, and now I’m “vibe coding” multiple highly personalized apps. As I iterated between Claude Code (implementation) and ChatGPT (ideation / thinking), I kept running into the same problem. To think clearly, I had to repeatedly re-explain: - what the code currently does, - what changed recently, - and why certain decisions were made. That re-summarization step became a real bottleneck. So I…
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