DayBe — AI Daily Planner
Dump everything. Get a real plan for today.
What it does
Most planning apps hand you a blank canvas. DayBe hands you a plan. You write down everything that's in your head — tasks, appointments, random thoughts and DayBe builds your actual day around it. What to do, when, and a short reason why. Fresh every single day. Each task gets a time window. Miss one? One tap moves it to a "later" bucket. No guilt. Those tasks automatically surface as suggestions tomorrow, ready to roll over instantly. The plan bends around your day. Not the other way around.
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- AEAn Efficient Daily Planner2024 · apps.apple.com · ▲5
I made this app because I have such flexible schedule that I needed a tool where I can organise and structure my days efficiently. All apps I tried had title based events (you had to type manually what you want to do each time). My app is based on the premise that most people do the same activities/routines every day: So when creating an event in this app, the user can just select the from the pre-created activities/routines instead of having to type it in manually. After you download the app you get 1 day completely free testing, after that you can use the code LAUNCH for another…
DAYPICK — Your day, already picked.May 2026 · daypick.day · ▲2AI picks your food, outfit & daily plan for you
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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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