copilot 4

I run AI coding agents inside a Docker container for safety. The setup is dev.dockerfile: builds the image dev.sh: launches the container with the right mounts and env vars dev.test.sh: verifies everything works. I wrote them semi-manually and it had bugs. I had GitHub Copilot + GPT-5.4 High update tests and actually run the commands to verify the setup. Here’s what I learned from the process. 1. Make it easier to review. The first run took long. I pressed Ctrl+C, told Copilot to “add colored output, timing, and a live status line”. Then I re-ran. Instead of a bunch of ERROR: lines, I now got a color-coded output with timing + a live status line showing what’s running. ...

I tried 2 experiments. Can I code a visual data story only using LLMs? Does this make me faster? How much? Has GitHub Copilot caught up with Cursor? How far behind is it? Can I recommend it? So I built a visual story for Lech Mazur’s elimination game benchmark (it’s like LLMs playing Survivor) using only the free GitHub Copilot as the AI code editor. SUMMARY: using LLMs and AI code editors make me a bit faster. It took me 7 hours instead of 10-12. But more importantly: ...

This is an email I sent to our leadership team a few minutes ago. We may be witnessing the third major leap in computing productivity, after high-level languages in the 1960s and spreadsheets in the 1980s In the last few weeks, AI coding really took off. Cursor, Cody, Replit Agents are FAR better than GitHub Copilot. Research on ~5,000 devs in Fortune 100 shows that even GitHub Copilot makes them ~25% more productive. ...

This week, I learned: Karya.in is creating high quality datasets. Suhel mentioned them An 8-year old uses Cursor.ai to code Hermes 3 has special tokens like <SCRATCHPAD>, <RESTATEMENT>, <THOUGHT_*>, <PYDANTIC_SCHEMAS>, <SCHEMA_*>, <REASONING>, <INNER_MONOLOGUE>, <PLAN>, <EXECUTION>, <REFLECTION>, <THINKING>, <SOLUTION>, <EXPLANATION>, <UNIT_TEST>, etc. This extends the capability dramatically. Lumentis creates docs from transcripts and text LLMs write worse code in JSON than Markdown Copilot’s system prompt calls a search_enterprise(query: str) tool and a hint(M365Copilot_language: str) tool as assistants. Anthropic Prompt Caching is 90% cheaper to use and 25% costlier to create. So if there’s a 27% chance it’ll be re-used, cache it.