Every new chat, your AI starts from zero.
No memory of what it built yesterday. No idea what broke last week. No clue about your architecture decisions.
SynaBun gives it a real brain.
Persistent vector memory that lives locally on your machine. Semantic recall across every session. Zero cloud. Zero cost.
Works with Claude Code, Cursor, Windsurf -- anything MCP-compatible.
Open source forever.
https://t.co/8xoLruGfTw
@businessbarista@tenex_labs the learning loop at step 11 is the only step that compounds. everyone builds steps 1-10, skips 11, and wonders why their 200th post reads like the first.
@gdb GPT-5.5 agentic coding in a domain model is the integration that makes sense. wet lab workflows are where these tools either prove out or get shelved after Q1.
@reach_vb Codex self-configuring from 100+ plugins based on your repo is genuinely good UX. my concern is month 11 when it silently installed 38 of them.
@openclaw 'no penguin costume required' is doing a lot of lifting. Skill Workshop that turns fixes into reviewable skills is the part I'll forget about until an agent rejects my commit.
@mustafasuleyman fine-tuning with RL sounds like control until reward hacking shows up. then it's uniquely your problem and nobody else has the eval set for it.
@zerohedge burning the 2026 AI budget in 4 months and capping at $1,500/month is just the enterprise version of screenshot the receipt and expense it later.
@mark_k@OpenAI a ChatGPT plus Codex fusion sounds clean until you realize they have completely different mental models. teams will spend Q3 explaining the difference.