Original workflows for websites, images, presentations, and everything in motion.
Pair the Claude-Mem Cowork plugin with a personal cmem.ai account and verify its masked configuration status.
Interview for a useful memory taxonomy, validate and install a custom mode, and verify it in startup context.
Save the decisions, facts, dead ends and architecture this conversation established to Atlas's shared memory, so other agents and future sessions can build on them.
Recall relevant context, summarize sessions and preserve durable memories through a configured Memori MCP connection.
Recall useful context and record durable insights with the connected Honcho memory integration.
Find previous decisions and solutions with a bounded index → context → selected-details search over Claude-Mem history.
Build a focused corpus from project observations, prime an AI session, and query that corpus conversationally.
Configure or diagnose Claude-Mem SyncHub through the official installer and verify authenticated hub reachability.
Set up Claude-Mem in Cursor with a local or remote worker and choose the appropriate observation provider.
Install or remove memU through host-specific adapters that connect session recording and memory retrieval.
Distill a conversation or completed task into reusable lessons for later agent work.
Split project memory into ISO weeks and generate connected narrative chapters with a carry-forward between weeks.
Integrate persistent memory, inspect workspaces, and recall or save conversation context.
Reconcile project documentation, agent rules, and authorized memory with current code and runtime evidence.
Design and refine dashboards, SaaS apps and admin interfaces with deliberate hierarchy, shared tokens, complete states and reusable design memory.
Inspect and debug Honcho workspaces via the `honcho` CLI.
Persistent memory for coding agents, with 21 curated workflows for recall, code exploration, planning, reviews, reports, and integrations. Install the upstream plugin; hosted memory and model usage may cost extra.
Build and debug Python agents and multi-agent workflows with Swarms tools, memory and orchestration architectures.
Search Screenpipe OCR or audio history and optionally preserve selected findings in Claude-Mem.
Keep multi-step work organized with persistent plan, findings, and progress files.
Combine seeded dungeons, turn scheduling, field of view, explored memory, permadeath, and loot tables.
Integrate Honcho memory into existing Python or TypeScript codebases.
Configure, manage, and use DeepTutor through its CLI, including capabilities, knowledge bases, partners, memory, sessions, notebooks, providers, skills, and the server or Web app.
Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training instabilities, NaN/Inf, and hardware/job failures; checkpointing and fault tolerance; inference…