Claude Code has a full execution environment: Run Python/pandas between tool calls to transform data Read/write files for intermediate results Run bash (jq, csvkit, curl, etc.) Write and execute multi-step scripts Debug iteratively by printing intermediate state Claude Desktop has only: Call MCP tool → get response → call next MCP tool Every workflow that requires intermediate data processing (filter, join, dedup, add columns, merge results) … Continue reading Claude Code vs Claude Desktop

provided by claude itself: General tips for effective prompting 1. Be clear and specific Clearly state your task or question at the beginning of your message. Provide context and details to help Claude understand your needs. Break complex tasks into smaller, manageable steps. Bad prompt: <prompt> "Help me with a presentation." </prompt> Good prompt: <prompt> … Continue reading Claude Prompting Guide

Using the same MCP server I created across different AI platforms, such as a coding assistant and Claude Desktop, I've noticed that the responses vary significantly, particularly when it comes to "finding alpha signals using RBICS data." Claude consistently provides stronger answers to finance-related questions. The primary reason for this difference lies in the MCP … Continue reading Same MCP Server, Different AI Can Give Different Answer and Claude is Strong in Finance

Fifteen days ago, SpaceX completed the largest initial public offering in financial history, raising $75 billion at a $1.77 trillion valuation. On its first day of trading, the stock blew past $160. Within 48 hours it had nearly reached $225. Then, just as quickly, it fell — shedding more than 30% in a week, settling … Continue reading SpaceX at $153: Three Companies, One Stock, and a $1.2 Trillion Question

With 29 tools in my MCP server, it's still just a collection of tool execution. User experience is not good. Phase 1 (Tool Execution Model): classify_by_hierarchy() → returns 334 IDs Claude Desktop streams all 334 to chat (agonizing) No continuity → lose context, restart from scratch No branching → can't compare two weighting approaches No … Continue reading Upgrading My AI Platform

The main speed bottlenecks are from tool design, remote deployment, snowflake/API latency, and how much data the MCP server returns. Tool ambiguity: if tool names/docs are unclear, the aagent spends extra turns choosing tools, validating results, or calling exploratory tools like run_sql_query. Reponses verbosity: returning both final IDs and full audit detail every time makes … Continue reading User Experience of Index_Plugin: Prompt and Precise

As I grow older, I increasingly believe that the difference between people who live wise and prosperous lives and those who do not comes down to one thing: how many fundamental truths they have discovered, truly understood, and consistently acted upon. Knowledge alone is not enough. Many people encounter truth, but few believe in it … Continue reading First Principle: Achieve Crystal Clarity About Reality

An agent to reply messages from email and TEAM is not difficult to build. Once it's equipped with MCP servers, plugins and skills it could be powerful and actually useful. There is a question to answer: should this agent have an interface/UI? Maybe it could be floating there like many apps do, it does the … Continue reading An Agent to Reply Messages from Email and TEAM

Use claude [options] [command] [prompt], how? here are explanation with examples: Basic prompt (interactive vs. one-shot) # Start an interactive session claude # Start interactive with an initial prompt claude "explain the architecture of this project" # One-shot, print result and exit (non-interactive) claude -p "what does src/main.py do?" # Pipe input in cat error.log … Continue reading Use Claude Effectively

No vendor uses plain SQL as the primary interface for terminal users — they all have proprietary query languages optimized for financial workflows. Behind the scenes, they may use SQL databases, Hadoop, or columnar stores (e.g., kdb+, Arctic, Parquet) internally for storage and processing, then expose data through their own APIs. Bloomberg is the most "closed" — almost everything stays … Continue reading How FinTech Store and Process Their Data