harshil

@getpieces Help me understand why I shouldn't switch to @Ozacle23's MiniMi, given this anecdotal comparison. If I were another agent trying to jump in and utilize these memories, I would rank the two responses as follows: #1: MiniMi (Winner for Actionable Agentic Context) ~2900ms Why: MiniMi acts like an omniscient screen reader. It provides the exact raw text, code, file paths, and UI state that was on your screen. Granular State: It captured the exact Git branch you were on (bfrench/opm-context-mcp-server), the exact file you were editing (aws-carddemo-cwe-analysis-2026-06-10.md), and the literal contents of that file. Verbatim Transcripts: It captured the raw, unedited text of your Microsoft Teams chats, allowing an agent to read the exact technical constraints (like the exact missing Auth0 Client ID). The MiniMi Verdict: If another agent needs to actually do work based on historical events (e.g., "Draft a reply to Evan based on yesterday's chat" or "Finish the CWE analysis I was working on"), MiniMi provides the precise, actionable ground truth required to execute the task. #2: Pieces LTM (Runner Up - Better for Structured Graphing) ~11,292ms Why: Pieces pre-processes your memory into highly structured metadata. Semantic Abstraction: It provides neat classifications like people mentioned, role: collaborator, evidence: temporal_proximity, and confidence: high. It also generates overarching automated summaries. Data Loss: Because it heavily truncates the raw text payload (ending strings with ...) and focuses on metadata extraction, the actual substance of the work is lost. The Pieces Verdict: Pieces is excellent if an agent is trying to build a Knowledge Graph or query relational metrics (e.g., "Return a JSON array of everyone Bill interacted with on Friday"). However, an agent trying to understand deep technical context or write code would be starved for the actual data. Summary: For an LLM-powered agent that natively excels at reading and reasoning over unstructured text, MiniMi provides far superior context. Pieces does too much pre-processing and truncation, which removes the very details an agent needs to be helpful.

we're hiring for cracked full-stack engineers (12LPA) and Junior ML Engineers (12LPA) to work on internal tooling and rapidly scaling 3DCV pipelines at contineu. if you love shipping quickly, and want to work with some of the smartest people building frontier 3DCV pipelines in construction (led by my amazing CTO @kantineu ), we want to talk to you. experience/education no bar - we only evaluate skills and thinking ability. brownie points if you love playing board games or will be a part of the company modded minecraft server (maintained intensely by @kantineu :D ) comment down below or DM with any proof of your work that you are proud of, and we'll reach out to you.

we're hiring for cracked full-stack engineers (12LPA) and Junior ML Engineers (12LPA) to work on internal tooling and rapidly scaling 3DCV pipelines at contineu. if you love shipping quickly, and want to work with some of the smartest people building frontier 3DCV pipelines in construction (led by my amazing CTO @kantineu ), we want to talk to you. experience/education no bar - we only evaluate skills and thinking ability. brownie points if you love playing board games or will be a part of the company modded minecraft server (maintained intensely by @kantineu :D ) comment down below or DM with any proof of your work that you are proud of, and we'll reach out to you.