TuckMeIn
Create personalized bedtime stories in seconds
What it does
Punchy / PH-native Bedtime stories, starring your kid as the hero. 🌙 TuckMeIn uses your child's age, interests, friends, and pets to generate a fully personalized story in seconds — with audio narration and a printable version included. Play it. Print it. Skip the screen. Try your first story free. No signup needed.
Does the same job
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- TATaleBot – AI-Generated Personalized Bedtime Stories for Kids2023 · talebotai.com · ▲17
Hello HN, I'm excited to share a project my co-founder and I have been working on - TaleBot. What It Does: TaleBot creates a personalized bedtime story based on parent/caregiver's inputs. You can customize the theme, the challenges (kid-friendly obstacles) the characters face, and even the moral lesson it teaches. Once the story is ready, you'll receive a PDF file, and an AI narrated voiceover of it. Try It Out: We've made it as barrier-free as possible for you to test. Use the promo code HNFREETALE on our story creation form, and you can get a story written for free, no sign-ups…
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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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