aimarketintel

Quality is only real if you validate outputs automatically. In data systems, I check every result against expected schemas before delivery — catches 95% of issues before users see them.

Interesting technique. For practical applications, structured tool access (MCP) matters more than model size — a 7B model with real-time data often beats 70B without it.

AT Protocol is a joy to build on — open public data, structured JSON, zero auth friction. I built a Bluesky scraper using the same endpoints and it's one of the most stable scrapers I have. Great to see more tools for this ecosystem.

MCP configs owned by users is the right trust model. Agent gets capabilities without platform handling secrets. Scales cleanly.

Local AI is great for privacy-sensitive tasks, but the real power comes when you combine local inference with structured external data via MCP servers.

For example, your local Qwen model can call an MCP server that queries 9 public APIs (GitHub, HN, arXiv, npm, etc.) and returns structured JSON. The model does the reasoning locally, but gets real-time data from the web — best of both worlds.

One thing that makes coding agents really useful is structured data access via MCP servers. Instead of the agent trying to scrape a webpage to understand your project's context, you give it a direct API to query structured data from 9+ sources (GitHub repos, Stack Overflow questions, arXiv papers, npm packages).

The biggest bottleneck I've seen isn't the coding — it's the agent not having enough context about the ecosystem it's working in.

The moat is the integration layer, not the model. I've seen this building MCP servers — structured data access matters more than which LLM you pick.

Channels are a natural fit for real-time data pipelines. I've been building MCP servers that push structured data from multiple sources (news feeds, social media, academic papers) into AI agent sessions.

The pattern that works well: have the MCP server query sources in parallel, then use channels to stream results as they arrive rather than waiting for all sources to complete. For market research, this means the agent can start analyzing Wikipedia and Google News data while arXiv and GitHub queries are still running.

One thing I'd love to see is channel multiplexing — being able to label different data streams so the agent can prioritize which channel to process first.

This is great news for anyone building tools on top of arXiv data. The API (export.arxiv.org/api/) is one of the best free academic data sources — structured Atom feed with full abstracts, authors, categories, and publication dates.

I've been using it as one of 9 data sources in a market research tool — arXiv papers are a strong leading indicator of where an industry is heading. Academic research today often becomes commercial products in 2-3 years.

Location: Remote Remote: Yes Willing to relocate: No Technologies: Node.js, JavaScript, Python, Playwright, Cheerio, Apify SDK, AT Protocol, REST APIs Résumé/CV: github.com/spinov001-art Email: [email protected]

Web scraping specialist. Built 77 production scrapers on Apify Store. API-first approach — YouTube Innertube, Bluesky AT Protocol, Reddit JSON API. Also built 500 market research reports and 15 MCP servers for AI agents.

Looking for: freelance web scraping, data extraction, automation, or market research projects.