Compare/Stash vs TurboQuant WASM

AI tool comparison

Stash vs TurboQuant WASM

Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.

S

Infrastructure

Stash

Open-source memory layer that teaches AI agents to remember and learn

Ship

75%

Panel ship

Community

Paid

Entry

Stash is an open-source persistent memory infrastructure for AI agents built on PostgreSQL and pgvector. Unlike retrieval-augmented generation, which searches static documents, Stash actively learns from agent experience — consolidating raw observations into facts, relationships, causal links, and higher-order patterns over time. The system exposes 28 MCP tools covering the full cognitive stack: episode storage, fact synthesis, entity graph management, goal tracking, failure pattern recognition, and self-correction when contradictions emerge. It deploys via Docker Compose in three steps and works with any OpenAI-compatible API — Claude, GPT, local models via Ollama. Hierarchical namespaces let agents keep user facts separate from project facts separate from self-knowledge. This fills a real gap in the agent ecosystem. Most agent frameworks treat each session as stateless, which means agents repeat the same mistakes and lose hard-won context. Stash gives agents a persistent cognitive layer that compounds. It surfaced on Hacker News this week to notable developer interest and is worth watching as MCP adoption accelerates.

T

AI Infrastructure

TurboQuant WASM

6x vector compression in your browser — search compressed embeddings without unpacking

Mixed

50%

Panel ship

Community

Free

Entry

TurboQuant WASM ports the ICLR 2026 TurboQuant algorithm (Google Research) into a browser-native npm package using Zig, WASM, and WGSL compute shaders. It compresses embedding vectors ~6x (3–4.5 bits per dimension) and runs similarity search directly on compressed data — no decompression step. WebGPU acceleration delivers 30+ tok/s in Chrome. The demo shows Gemma 4 E2B generating Excalidraw diagrams from prompts with KV-cache compression cutting memory by 2.4x, enabling longer conversations inside browser GPU limits.

Decision
Stash
TurboQuant WASM
Panel verdict
Ship · 3 ship / 1 skip
Mixed · 2 ship / 2 skip
Community
No community votes yet
No community votes yet
Pricing
Open Source
Free / Open Source (MIT)
Best for
Open-source memory layer that teaches AI agents to remember and learn
6x vector compression in your browser — search compressed embeddings without unpacking
Category
Infrastructure
AI Infrastructure

Reviewer scorecard

Builder
80/100 · ship

The 28 MCP tools are the right abstraction level — my Claude Desktop agents can now actually remember what I've told them across sessions without me writing my own memory layer. The Docker Compose setup is clean and the pgvector backend is production-ready.

80/100 · ship

Searching directly on compressed vectors without decompression is a real algorithmic win, not a marketing trick. The npm package with embedded WASM binary means integration is literally one import. The Excalidraw demo proving KV-cache compression in-browser is compelling proof that this works in production-like conditions.

Skeptic
45/100 · skip

The consolidation pipeline sounds elegant in theory but in practice you're letting an LLM synthesize 'causal links' and 'higher-order patterns' from raw observations. That's a recipe for hallucinated beliefs that compound over time. I'd want rigorous testing before trusting this in any production agent.

45/100 · skip

Chrome 134+ and WebGPU requirement kills a significant fraction of potential users — Safari and iOS aren't supported at all. This is research-grade code with 264 stars, not a production library. Zig as the core language also means limited community support if something breaks.

Futurist
80/100 · ship

Persistent memory is the missing piece between 'AI assistant' and 'AI colleague.' Stash's self-correction and failure pattern recognition are early implementations of what agents will need to become genuinely reliable over long time horizons.

80/100 · ship

Browser-native LLM inference with compressed KV-caches is the path to private, local AI that actually fits in commodity hardware. TurboQuant is solving a memory wall problem that will matter more as models get longer context windows. The ICLR 2026 backing means the math is sound.

Creator
80/100 · ship

Finally an agent that remembers my brand guidelines, tone preferences, and past feedback without me repeating myself every session. The namespace hierarchy means I can have separate memories for different clients.

45/100 · skip

The Excalidraw diagram demo is legitimately impressive as a creative tool — prompt to architecture diagram in seconds, no server required. But until Safari/iOS support lands, this is a power-user curiosity. Most creative workflows aren't running on Chrome 134+ with WebGPU enabled.

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