Feeds:
Posts
Comments

Posts Tagged ‘llm’

Since my last post I’ve been using Claude Code more, and I’m pleased to report it works well and is genuinely powerful. As a confirmed control freak, I was also pleased to find that I stayed in control throughout.

(more…)

Read Full Post »

After spending a year building fintechbenchmark.com the traditional way—writing code, testing it, iterating—I’d grown comfortable with GitHub Copilot as my AI assistant. It delivered a significant productivity boost, though I’d learned when to trust it and when to ignore its suggestions and push forward on my own.

Then, weeks before our soft launch, our lead contractor dropped a demo of Google Antigravity (Project IDX) that made our entire workflow look like stone tools and campfire stories.

(more…)

Read Full Post »

The assumption that bigger AI models always deliver better results is being quietly dismantled — and the economics behind this shift are compelling. Now we have Small Language Models (SLM) to distinguish them from Large language Models (LLM). How do we measure the size of a model?

(more…)

Read Full Post »

When I was seeding the database for fintechbenchmark.com with Claude-generated or Openai-generated content, I needed structured data that matched my database schema. My format of choice was JSON, and my approach was straightforward:

  • Request JSON in the prompt
  • Provide an example of the desired structure
  • Parse Claude’s response—a text string containing JSON wrapped in markdown code blocks with triple backticks
(more…)

Read Full Post »

MCP (Model Context Protocol) solves a fundamental problem: how do you give an AI model access to external data like databases, files, or APIs without embedding everything in each request? Introduced by Anthropic in November 2024, MCP is now mature enough (15+ months old) for serious production use. Think of it like a USB-C port for AI—a standardized way to connect AI models to external systems.

(more…)

Read Full Post »

When we planned fintechbenchmark.com (currently in beta), we realized that AI wasn’t optional—it was essential. We could either embrace this revolution or watch competitors leave us behind.

We faced a classic startup dilemma: build a custom AI solution (expensive and slow) or use an off-the-shelf product to launch quickly and learn what our visitors actually need. We chose the pragmatic route.

(more…)

Read Full Post »