AI coding session context lost when switching tools

“Show HN: Total Recall – write-gated memory for Claude Code built this because I got tired of re-teaching Claude Code the same context every session. Preferences, decisions, “we already tried X,” “don’t touch this file,” etc. After a few days it starts to feel like onboarding the same coworker every morning. Most “agent memory” tools auto-save everything. That feels good briefly, then memory turns into a junk drawer and retrieval gets noisy. Total Recall takes the opposite approach: a write gate.”

“Show HN: Unpack – a lightweight way to steer Codex/Claude with phased docs I've been using LLMs for long discovery and research chats (papers, repos, best practices), then distilling that into phased markdown (build plan + tests), then handing those phases to Codex/Claude to implement and test phase by phase. The annoying part was always the distillation and keeping docs and architecture current, so I built Unpack: a lightweight GitHub template plus docs structure and a few commands th”

“Show HN: OpenGem – A Load-Balanced Gemini API Proxy (No API Key Required) Hi HN! I built OpenGem, an open-source, load-balanced proxy for the Gemini API that requires absolutely no paid API keys. GitHub: https://github.com/arifozgun/OpenGem The Context: Like many developers, I was constantly hitting 429 Quota Exceeded errors while building AI agents and processing large payloads on free tiers. I wanted to build freely without calculating API costs for every test request. How ”

“Show HN:`npx continues` – resume same session Claude, Gemini, Codex when limited i kept hitting rate limits in Claude Code mid-debugging, then hopping to Gemini or Codex. the annoying part wasn't switching tools (copy-pasting terminal output doesn't bring tool-use context with it) — it was losing the full conversation and spending 10 minutes re-explaining what i was doing. so i built *continues*. it finds your existing AI coding sessions across five tools (Claude Code, GitHub Copilot, ”

“Best architecture for integrating Python deep learning prototype into C++ production pipeline? I’m working on a deep learning module intended to be deployed on an edge device. Our situation: The production application is written in C++ . The research team develops models and pipelines in Python (PyTorch, NumPy, etc.). Customers are requesting a prototype of the full inference pipeline (preprocessing - inference - post-processing) as soon as possible. The research team has very limited C++ experi”