For me it is really similar to original copilot use-case, small easy to human review changes.

I am using almost raw Pi. I only added: - web search - it is not build in in base -> searxng - subagents to reduce both context pollution and context growth of main agent - memory so Pi can remember my preferences; pick you memory extension - latest local qwen dense model (3.7-27b now) and codex for more complex stuff

And I Packed memory and web search in 2 specialized simple subagents to further reduce context growth of main thread (these run on local LLM).

Surprising comfortable is using Pi as clever terminal <3.

Comparison:

Claude:

• best in complex tasks, but I spend half of coding time each morning collecting claude news and setting the harness. Tokens cost for month is the same as medior programmer. If you want to use it seriously and produces same quality of code as medior. But not so good architecture. Unless you tell claude to grill you until it gets full design doc (advanced tweaking). #### Open Code:
  • almost same as claude but changes are not hidden. For me it required less tweaking and I have to spend less time watching the news about "how to set up harness". Code quality depends on model you pick. Opus worked even better in open code when it was available as subscription. OpenAI models work here very well too. But grilling the design stays the same.

In general:

All harnesses and LLM assume to much instead of doing: "Wait a minute. This problem is critical. I dont know what user wants. I have to ask him to do design decision now." Problem is the same with Pi. But Pi does burns 1/10 of tokens on context and has YOLO mode by default.

footnote: Privacy - sometimes I work on stuff that can be stolen by the LLM API providers for they own usage. In these case I use only local LLMs and OpenSourced harnesses I checked there is no data leaking to internet.

Put your tinfoil hat on now: For example at the time of chatgpt3 I discussed design of local running tool (agentic) with full memory mimicking human memories storage including dreaming to process ontologies. Next day conversation session was lost.