The full gallery
Tech stack
60 projects
Measure your GPU's real memory-bandwidth ceiling for local AI in 30 seconds.
Ar5en1c · HN
Headroom – measure your GPU's true bandwidth ceiling for local AI
Real-time memory and context layer for voice AI agents with sub-10ms latency.
@SouravDaaa · X
- End to end personalised memory retrieval in 10ms. Would love some feedback here
Affordable memory storage layer for AI agents.
@AudaxicTech · X
Memory for AI agents, most affordable in the industry
Benchmark local coding models on consumer hardware to measure accuracy, latency, and throughput across 27 tasks.
u/Unfair_Association89 · Reddit
I built a reproducible benchmark for local coding models (Ollama, 27 tasks, live leaderboard) ran it on my 8GB card, here's what I found I kept eyeballing "vibes" to decide whether one quant of a coding model was actually better than another on my machine, so I built Sakura to get real numbers instead. What it does: - Points at any Ollama model and runs it through 27 hand-curated tasks: codegen, bugfix, SQL, refactor, systems design, protocol implementation, and terminal-agent episode
Size your way to private local AI. Directional sizing for one resident text model: KV memory, combined prefill/decode serving, and runtime sessions. Compare eligible on-prem, sover
juxhinr · GitHub
bindwidth Evidence-aware on-prem LLM inference sizing and TCO calculator
Estimate GPU memory, latency, TTFT, TPOT, and throughput for LLM inference.
popopanda · HN
LLM Inference Calculator – Estimate VRAM, Latency, and Throughput
Portable memory that persists across Claude, ChatGPT, Cursor, and other AI assistants.
u/OrganicArgument2092 · Reddit
I shipped Lodekeep: portable memory for AI agents that follows you across Claude, Cursor, and ChatGPT Every new AI chat starts from zero. Claude, Cursor, ChatGPT all forget my stack, my decisions, the gotchas I already solved, so I kept re-pasting the same context every single session. I got sick of it and built Lodekeep. You capture a decision, preference, or lesson once, and it's recallable in every future session across every MCP client (Claude web + desktop, Claude Code, Cursor, Gemini
File-first memory runtime for AI agents with dashboard and HTTPS API access.
@memofsdev · X
Persistent memory system for AI agents that survives across sessions and devices.
@v1b3_x · X
I built a persistent, Git-like memory engine for LLM agents so they never lose context across session. works with ClaudeDesktop <—> Claude Code <—> Codex
Organize and retrieve your work sessions with AI-powered memory.
@vitverb · X
hi :) One tap back to the work you meant to finish. No tasks to manage. Nothing to set up. Local-first
Store shared memories once and reuse them across AI systems and your team.
Repeater22746 · HN
ContextVault – Shared memory layer for your AI and your team
Persistent memory system for AI agents in Claude Code, Cursor, and other MCP-compatible applications.
FabianBeiner · HN
CMEM – Persistent Memory for AI Coding Agents
