C3.ai

C3.ai AI

C3.ai AI

C3.ai AI

C3.ai AI

C3.ai AI

C3.ai AI

C3.ai AI

C3.ai AI

C3.ai AI

C3.ai AI

Why claude code doesn’t have SSH?

submitted by /u/Alternative-Way-3685 [link] [comments]

An AI Equity Research Framework: Design Principles and Setup

I used Claude Code + two MCP tools + a ~400-line protocol file to run deep equity research on Chinese A-share stocks. The workflow starts by enabling Plan Mode, then giving the agent a single instruction: "Follow Deep Research.md strictly, and conduct deep research on [ticker]." This post covers: the core principles behind the protocol design, and the two MCP tools that support it. Note — A-share specific: This setup is built around the Chinese A-share market. The protocol (Deep Research.md) is market-agnostic, but the two MCP tools are not. For US equities, you'll need to swap out ashare-mcp for a different financial data pipeline (SEC EDGAR, a brokerage API, or something like SimFin / Financial Modeling Prep). NotebookLM MCP works as-is for any language. A few other differences worth considering: A-share filings are in Chinese, so your retrieval and parsing tools need to handle that; liquidity and disclosure standards differ; and the grassroots signal sources (Phase 3.5 in the full protocol) will need to be substituted with US equivalents. Part 1: Core Principles The protocol is built around four principles. Each one exists to counter a specific failure mode of LLMs doing open-ended research. 1. Define "Done" Before You Start LLMs are next-token predictors. Without a concrete target, "analyze this company" matches the most statistically common pattern in training data: introduce the company, list metrics, discuss competitive landscape, give a vague conclusion. That output is probable, not useful. The protocol requires a falsifiable research question with a measurable threshold before any retrieval begins — locked in a research contract the agent cannot skip. "CAGR ≥8%" is verifiable. "Decent growth" is not. The agent needs to know what counts as done, or it will optimize for looking like a complete analysis rather than being one. 2. Anchor Hypotheses Before Seeing the Data Once you've read everything, rationalization is easy. The protocol requires the agent to write 3–5 hypotheses and assign prior probabilities to each before any retrieval — and to specify, for each one, what evidence would trigger a revision. Using probabilities rather than just listing hypotheses matters for a specific reason: a list of hypotheses costs nothing — the model can write five and "confirm" all of them. A probability externalizes judgment. Before any evidence arrives, you've committed to a degree of belief for each hypothesis. This forces belief updating to be explicit. If H2 starts at 40% and ends at 40% after a full investigation, that's a finding. If it drops to 15%, that's the conclusion. The prior is the anchor that prevents the model from reading everything and then working backwards to fit the hypotheses to the answer. 3. Grade Claims, Not Just Sources Not all assertions carry the same weight. The protocol classifies every claim into three tiers: C1 (Critical): Revenue, profit, cashflow figures; market share; core thesis; valuation inputs — requires ≥2 independent sources, or must be explicitly flagged as "single source, high uncertainty" C2 (Supporting): Industry trends, event timelines, non-critical comparisons C3 (Background): Definitions and common knowledge "Independent" means different organizations and different data collection methods. Two sell-side analysts citing the same company filing are not two independent sources. Citation drift in practice looks like this: the agent writes "the company holds approximately 35% market share," attaches a link to an analyst report, and all subsequent reasoning builds on that 35% — but that report was itself citing the company's own IR materials, and management's market share definition differs from third-party research firms by 8 percentage points. This rule exists to catch that before it propagates. 4. Isolate the Red Team Confirmation bias in LLMs is structural, not incidental. An agent that built the bull case has its context loaded with supporting reasoning chains. Ask it to challenge itself and its next-token predictions are already anchored toward the thesis it just constructed. The protocol runs a mandatory adversarial review using a separate subagent with a clean context — one that reads only raw data and working notes, never the main report. This subagent must produce ≥3 substantive rebuttals with evidence. The main context then adjudicates each challenge with a formal verdict (accept / partial accept / reject), and "reject" requires ≥2 sourced counter-evidence entries. Context isolation is a structural defense against confirmation bias, not a stylistic choice. Part 2: The Two MCP Tools The principles define what the protocol should do. The tools determine what data it can reach. Both are necessary: a rigorous protocol running on patchwork data will exhaust its context window assembling financials before any analysis begins; abundant data without protocol constraints just produces wrong answers faster and with more confidence. The two

Comparison of AI code generation: looking for insights

Supposedly C3 Code won an AI coding shootout. I’d be very interested in anyone who’s got a knowledgeable critique of this. The box score (in the story) rates Claude lower than I’d personally expect but this is not my wheelhouse. Other parts of the comparison also make me wonder about the objectively of it, so anyone who is familiar with comparisons of code generation capabilities… what say you?? https://aithority.com/robots/automation/c3-ai-announces-c3-code/ submitted by /u/Special-Steel [link] [comments]

Skill Seekers v3.5: 10 new source types, 12 LLM platforms, marketplace pipeline, agent-agnostic AI, and prompt injection scanner

Hey r/ClaudeAI — sharing the latest update on Skill Seekers, the open-source tool that converts documentation into Claude Code skills. A lot has changed since the v3.2 post, so here's what's new across 3 releases (v3.3 → v3.5.1). What's new 10 new source types (17 total) You can now generate skills from Notion, Confluence, HTML files, OpenAPI specs, AsciiDoc, PowerPoint, RSS feeds, man pages, chat exports (Slack/Discord), and unified multi-source configs — on top of the original web, GitHub, PDF, Word, EPUB, video, and local codebase sources. 12 LLM platforms Skills now package for Claude, OpenAI, Gemini, Kimi, DeepSeek, Qwen, OpenRouter, Together AI, Fireworks AI, OpenCode, Markdown, and MiniMax. Plus RAG framework exports for LangChain, LlamaIndex, Haystack, ChromaDB, FAISS, Weaviate, Qdrant, and Pinecone. Agent-agnostic AI enhancement Enhancement is no longer locked to Claude. The new AgentClient abstraction supports Claude, Kimi, Codex, Copilot, OpenCode, and custom agents. It auto-detects which agent to use from your API keys, or you can specify with --agent. Marketplace pipeline You can now publish skills directly to Claude Code plugin marketplace repositories and manage multiple marketplace registries. Config sources can be pushed and synced across repos. Prompt injection scanner A built-in workflow scans scraped content for injection patterns — role assumption, instruction overrides, delimiter injection, hidden instructions. Runs automatically as the first stage in default and security-focused workflows. Flags suspicious content without removing it so you can review. One-command auto-detection skill-seekers create https://docs.example.com/ skill-seekers create owner/repo skill-seekers create ./my-project skill-seekers create document.pdf One command figures out the source type and routes to the right scraper. No more separate subcommands. Headless browser rendering JavaScript SPA sites (React, Vue, etc.) that return empty HTML shells now work with --browser. Uses Playwright under the hood. Other highlights skill-seekers doctor health check command Kotlin language support in the C3.x codebase analysis pipeline Smart SPA discovery (sitemap.xml + llms.txt + browser nav) Unlimited pages by default (was capped at 500) 3100+ tests passing Full MCP server with 40 tools (works in Claude Code and Cursor/Windsurf) Links GitHub: github.com/yusufkaraaslan/Skill_Seekers PyPI: pip install skill-seekers Free and open source Built with Claude Code. Happy to answer questions or take feedback. submitted by /u/Critical-Pea-8782 [link] [comments]