u/remoteinspace

Try papr ai harness. Get a Claude subscription and plug it in. Auto routes to modules including local and uses a search model vs having Claude read every file to find and edit things.

the biggest thing is a linkedin automation campaign. It finds leads based on keywords/roles and connects with them and messages them. I just let it run in the backend.

I also build an x-posting engine that picks 10 trending posts a day and generates replies i can review and post.

“We” 5.6 maybe?

do you spend a lot of time maintaining it?

we wrote this

Try papr ai for quickly building internal automation tools

We’ve heard really similar thoughts from CIOs at papr.

Everyone starts with giving AI MCPs. Then they realize that’s not an efficient or proper way to access data. One CIO mentioned that employees are finishing up their monthly token budgets in a week when Claude tries to search and analyze gdrive PDFs.

After MCPs then they throw everything in a. Vector db and hope it works. It breaks quickly.

Try papr.ai, the Mac app is second brain + Claude + n8n and handles all this stuff for you. Uses papr’s memory infra and graph as the backend

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if i'm following you are basically creating a cached vector store with the full history then a summary in KV cache?

At Papr, we try to predict relevant memories and cache those. If there's a hit great, if not it goes to a full context search. Our model improves so it's a hit moving forward.

tbh, a 1.5GB download is large for a mobile app, especially for folks with limited storage. Might work better for a mac app. $5 is fair, especially since you're avoiding those annoying subscriptions.

The main question is how good is gemma 2b for this use case. How are you handling RAG on device - is there another embedding model you need to add?

Also i'd test this out quickly before apple announces their own version of a free agent that runs on device

this sounds super interesting! i love how you’re integrating macro data into the analytics

it’s a real challenge to get accurate performance metrics, especially with things like cashflow awareness. tagging deposits and withdrawals correctly is key, but can be a pain for users.

the AI copilot feature is cool. how are you handling memory? you may want to check out Papr ai - lets you combine structured analytics data + unstructured chat memory in one memory db that agents can tab as they help users.

awesome to see these accuracy jumps

how did you handled the data preprocessing for multi-turn tasks. did you find any particular techniques that worked better than others? also, any tips for avoiding overfitting during fine-tuning?

Nice! I’d love to learn more.

Good stuff! What’s your thought on having this in a GitHub repo vs putting skills in a memory MCP server?

claude: https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview

papr ai assistant memory - skills category : https://platform.papr.ai/apis/memory/add_memory

you can put md files in a repo and git-sync them. Then each person in your team/world needs to pick and choose the skills they want, and copy them into every local environment they control. If it's an app they don't control, they can't add skills to it since it's all at the file system level. If you have a ton of skills, you start having a search problem.

What i'm mentioning is to put the skills in 'memory' so they are portable across agents, environments, and more easily sharable (i.e. another user can add/share 3 skills they want vs. the entire skills repo) + you resolve search when you have a ton of skills (cheaper and more accurate than having all skills in the llm context window).

A memory can belong to two different launches (or nodes in a graph). We also track updates to memories for temporal search.

yea, vector search is much faster. For knowledge graphs, what we're doing now is predicting what users want next and prepping that context and caching it. Helps us make graph search feel super fast <100ms when we get the prediction right.

Nice, how are you traversing the graph? Are you using templates queries?