Ledger AI
Calibrated financial intelligence.
What it does
Most people don't know where their money goes. Bank apps show transactions — not understanding. Ledger AI fixes that. Upload any PDF or CSV statement — no bank login, no Plaid — and get instant AI insights: spending patterns, subscriptions, anomalies, and savings opportunities in plain English. The more you use it, the smarter it gets. Chat with your data. Ask anything. Built for anyone who wants to understand their money — not just track it.
Does the same job
all alternatives →- AAAn API to store balances and transactions2020 · ▲151
Hey everyone! I'm Rob and I want to share what I've built - a solid ledger behind simple API: https://decimals.app The ledger is a key piece of any financial system and is easy to get the design wrong, resulting in troubles for making sure money movements are adding up. I took a lot of inspiration from good designs from Square, Uber, and others for the scalability and security aspects. And on the usability side, I applied many concepts from the Ledger CLI, making it very easy to use. I'm really happy I get to show this to you all, thank you for reading about it! Please let me know…
WiseFlowJun 2026 · wiseflowapp.com · ▲13Your money finally makes sense ! Meet Your New AI comapnion.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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