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: Mesa – A collaborative canvas IDE built for agent-first development Hi HN - I'm Ryan a product designer who codes, and I built Mesa. Current IDEs feel wrong for the type of development being done now - the focus is still on files. Mesa puts the focus on the full workflow: your agent, terminal, browser, and files all live as equal nodes on a canvas with full multiplayer support. (think figma but for code) I was tired of the overhead of switching windows, tabs, and terminals across m”

“Ask HN: $50 monthly budget, which coding models would you recommend now? I currently have a claude pro monthly subscription ($20) which I use for coding. It's been useful but I'm fatigued from optimising my work around it's session limits. There are so many choices and providers out there today but hard to get a good signal about what's good. I'm not looking for another Opus-level model but something reliable enough that it can follow TDD well.”

“Ask HN: How do you cope with the broken rythm of agentic coding? I used to seek focus and concentration while coding. It was not always easy to reach this flow state but I knew it was possible. I am now using agentic coding quite a lot. The honeymoon is finishing and I am starting to dislike some facets of it. I think the main setback is the rythm. Writing some specs/prompts, launching the agent, confirming quite atomic actions and waiting 10 to 30 seconds until the next question/confi”

“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, ”

“Show HN: Unify memory across agents and improve context rot, written in Rust I was frustrated that memory is usually tied to a specific tool. They’re useful inside one session but I have to re-explain the same things when I switch tools or sessions. Furthermore, most agents' memory systems just append to a markdown file and dump the whole thing into context. Eventually, it's full of irrelevant information that wastes tokens. So I built this local memory layer that unifies memory across”

“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”