AskMyFinance
Your AI personal finance advisor — no agenda, no ads
What it does
AskMyFinance is an AI that reads your financial situation — your credit score, your debt, your goal — and tells you exactly what to do. Ask it whether to pay off debt or invest. Ask it which credit card actually makes sense for you. Ask it whether you need life insurance. It will tell you, in plain language. Built for people who are tired of googling personal finance questions and landing on content that's really just a sales pitch.
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- AOAsk our algorithm and real financial expert anything about your money2022 · app.uprise.us · ▲38
It’s pretty simple: ask us absolutely anything about your finances (it’s okay to be really specific to your situation!) and our algorithm backed by a Certified Financial Planner will give you a hyper-personalized spot-on answer, for free. Go ahead and stump us! Let’s really kick the tires on this thing :) Up until now, we've been doing comprehensive financial advising for free. Our most popular feature is the ability to just ask us anything so we figured why not make that feature available for everyone? Also for free :) Some more context: Uprise’s mission is to make wealth…
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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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Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
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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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