Ar5en1c · HN

Headroom – measure your GPU's true bandwidth ceiling for local AI

@SouravDaaa · X

- End to end personalised memory retrieval in 10ms. Would love some feedback here

@AudaxicTech · X

Memory for AI agents, most affordable in the industry

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

juxhinr · GitHub

bindwidth Evidence-aware on-prem LLM inference sizing and TCO calculator

popopanda · HN

LLM Inference Calculator – Estimate VRAM, Latency, and Throughput

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

@memofsdev · X

@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

@vitverb · X

hi :) One tap back to the work you meant to finish. No tasks to manage. Nothing to set up. Local-first

Repeater22746 · HN

ContextVault – Shared memory layer for your AI and your team

FabianBeiner · HN

CMEM – Persistent Memory for AI Coding Agents