SambaNova

SambaNova AI

SambaNova AI

SambaNova AI

SambaNova AI

SambaNova AI

SambaNova AI

SambaNova AI

SambaNova AI

SambaNova AI

SambaNova AI

I built a router that automatically sends your AI tasks to the most appropriate model to handle them at low cost - 9,200 tasks in, $21 saved at $0.14 actual cost

The observation that started this: most of what people use AI for every day - summarising, drafting, classifying, extracting etc doesn't actually require a frontier model. Any competent 8-70B model handles those just as well. But most people run everything through Claude or ChatGPT out of habit. I built Followloop (followloop.app) to solve this automatically. It classifies each task by complexity and routes it: - Simple tasks → Cerebras Llama (2000 TPS, 1M tokens/day free), Groq, Gemini Flash - Moderate tasks → Groq 70B, SambaNova - Complex tasks → Claude Haiku as fallback The dashboard shows your actual cost alongside what you'd have paid running everything on Claude Sonnet. I've been running it on my own AI workflow for two weeks: 9,200 tasks routed, $21.24 saved, $0.1360 actual cost. About 157× cheaper per token than Sonnet on average. Works with any AI setup via MCP (Model Context Protocol) - Claude Desktop, Cursor, Claude Code, or anything MCP-compatible. Also has a library of 1,300+ safety-screened MCP servers as a bonus feature. $5/month at followloop.app submitted by /u/QueefLatinahOG [link] [comments]

I built a local-first MCP server that gives Claude Code persistent memory, a knowledge graph, and a consent framework — and Claude is just the first client

I've been building this for a couple of years. It started as "what if my AI assistant actually remembered things," and it became something bigger. The short version: I built a local AI infrastructure layer that runs entirely on my machine. No cloud. No exposed ports. My data stays on my hardware. And this week it's finally at a point where I can share it. --- What it is willow-1.7 is a Model Context Protocol server. Claude Code connects to it at session start via stdio — no HTTP, no ports, no supervisor. A direct pipe. Through that connection, Claude gets 44 tools: - Persistent memory — a Postgres knowledge graph (atoms, entities, edges) that survives sessions - Local storage — SQLite per collection, with a full audit trail and soft-delete - Inference routing — local Ollama first, then Groq / Cerebras / SambaNova as free-tier fallback if Ollama is down - Task queue — Claude submits shell tasks to Kart, a worker that polls Postgres and executes them - SAFE authorization — every agent that wants knowledge graph access must present a GPG-signed manifest. No valid signature = access denied. Revoke an agent by deleting its folder. The filesystem is the ACL. - Session handoffs — structured handoff documents written to disk and indexed in Postgres, so the next session can pick up from where the last one ended --- The authorization model This part is unusual enough that it's worth explaining. Each application that wants to access the knowledge graph has a folder on a separate partition (/media/willow/SAFE/Applications/ /). That fo - safe-app-manifest.json — declares permissions and data streams - safe-app-manifest.json.sig — a GPG detached signature of the manifest On every access attempt, the gate checks: folder exists → manifest present → signature present → gpg --verify passes. All four must pass. Any failure → deny + log. No code changes to revoke access. Delete the folder, and that agent is done. I've been running 17 AI professors through this gate for months. Each one has its own signed folder, its own permitted data streams, its own context. None of them can access data outside their declared scope. --- What powers it locally Ollama runs the inference. Currently using qwen2.5:3b as the default. The system routes there first and falls back to free cloud APIs only if Ollama is unavailable. But Claude is just the first client. The MCP server speaks stdio MCP. Any agent that understands the protocol can connect — Gemini, local models, anything. The longer plan: Yggdrasil. A small model trained on the operational patterns this system generates — session handoffs, ratified knowledge atoms, governance logs. When that model is trained, it replaces the cloud fleet entirely. The system becomes fully air-gappable. And after that: an open-source Claude Code equivalent. A terminal AI agent that boots from your local repo, connects to willow via stdio, and has no dependencies you don't control. No telemetry. No cloud account required. Just you and the tools you built. willow-1.7 is the bus everything else rides. The client is just the first thing attached to it. --- Why local-first matters to me I have two daughters. I'm building this so they grow up with tools that help them think instead of thinking for them. That don't own their journals. That don't optimize their attention. That expire when they close the app. The current model is: agree once, we own everything forever. Your notes train our models. Your data lives in our building. Local-first is the other way. Your data lives on your machine. Consent is session-based — the system asks every time, and that permission expires when you're done. If you walk away, it stops. --- The bootstrap There's a separate installer repo, willow-seed, that handles the full setup from scratch — clones the repo, creates the Postgres database, scaffolds the first SAFE agent entry, writes the MCP config. Stdlib only, no dependencies. Consent gates before every action. python seed.py That's it. Tested it this week on a fresh partition. It works. --- Links - willow-1.7: https://github.com/rudi193-cmd/willow-1.7 - willow-seed: https://github.com/rudi193-cmd/willow-seed - SAFE spec: https://github.com/rudi193-cmd/SAFE --- Happy to answer questions. Still building. ΔΣ=42 submitted by /u/BeneficialBig8372 [link] [comments]