# I Built a Smarter Pumpkin Than Alexa > Source: > Published: 2026-10-30 07:07:37+00:00 ## The Pumpkin That Outsmarted a Basic Voice Prompt Conventional voice assistants frustrate users. Ask Alexa to plan a weekend, and it replies, "sorry, I don't know that." This common limitation spurred the development of a smarter pumpkin. This pumpkin, however, understood. It offered to "check what's on round here," then suggested: - A murder mystery at the Pen Museum - Live music at the Jam House - A silent disco at the Botanical Gardens Crucially, it kept suggestions "all in the quarter, no city center needed," respecting the stated constraint. When asked for a Halloween joke instead, it delivered: "What do you call a pumpkin that's been sat on the doorstep all week? A bit of a squash." The useful upgrade isn't a novelty shell or a magic model. It's a **voice pipeline** combining fast local input with capable cloud reasoning. This architecture runs a **local speech-to-text (STT)** model on a Raspberry Pi for minimal latency, then sends transcriptions to a remote **LLM** (e.g., Gemini Flash, DeepSeek Flash) for complex queries. This hybrid approach allows the LLM to access external tools like web search and geolocation, enabling advanced planning and context-aware responses. The system then synthesizes speech, either on-device or via external high-fidelity neural voice APIs, depending on the need for speed or quality. ## Soldering the Face—and Surviving the Wiring Build started with a **Raspberry Pi**, microphone, speaker, and an amplifier HAT. Two LEDs served as glowing pumpkin eyes. A Perma-Proto board provided the soldering surface, avoiding direct Pi modifications. Before assembly, the creator practiced soldering on a test kit, a crucial step for beginners. The initial LED wiring was the most challenging: two wires threaded into a single, tight hole per eye, resulting in hours of fiddly work and snapped wires. Next, components were soldered onto the **Perma-Proto board**: - 40 pins for Raspberry Pi connection - 6-pin strip for the amplifier - Eight or nine additional wires for various connections Connections were verified with a **multimeter** to prevent issues. The active cooler and Perma-Proto board then mounted onto the Raspberry Pi, followed by the plug-and-play microphone and speaker. Debugging hit quickly: the speaker failed. Investigation revealed poorly soldered pins on the Perma-Proto board were touching the active cooler, causing a short circuit. An improvised card spacer temporarily resolved the issue, a functional but not ideal fix. The pumpkin powered on. ## Why the Smartest Parts Live in Two Places Pumpkin’s intelligence didn't live solely on the Pi. Architecture mapped a hybrid approach: a wake word such as “hey pumpkin” triggered local speech-to-text (STT) on the Raspberry Pi. This transcription then streamed to a remote **Large Language Model (LLM)** for reasoning, with the response converted back to speech either locally or via a cloud service. This split design balanced latency and capability. Local recognition, using lightweight models like **parakeet**, kept voice processing near the device, reducing perceived delay. Cloud reasoning, leveraging powerful LLMs such as Gemini or [DeepSeek Flash](https://www.stork.ai/en/deepseek), provided access to advanced models and external tools like web search and geolocation. Crucially, the Pi was not running a large reasoning model. Its role was limited to initial voice capture and text-to-speech synthesis. The heavy lifting of understanding complex queries and generating informed responses occurred off-device, where computational resources were abundant. This distributed intelligence ensured both responsiveness and depth. For a deeper dive into this build, check out the resources at [Better Stack](https://betterstack.com). Enjoying this? Get one like it in your inbox each morning. one email a day · unsubscribe in two clicks · no third-party tracking ## A Better Assistant Needs More Than a Better Model Tool access transforms a voice assistant from a generic idea generator into a practical planner. Integrating **web search** and real-time location context allows the pumpkin to suggest specific, nearby activities—like a murder mystery at the Pen Museum or a silent disco at the Botanical Gardens—instead of merely brainstorming abstract concepts. Remember, event listings, opening hours, and prices are dynamic. The pumpkin’s suggestions are a demonstration of capability, not a guaranteed itinerary. Always verify details before making firm plans. A truly better assistant hinges on more than just the underlying LLM. Judge DIY voice assistants by: - **Latency** - Useful context integration - User privacy choices - Dependable hardware This holistic approach defines a superior experience. For further exploration, consult [Raspberry Pi documentation](https://www.raspberrypi.com/documentation/) and [OpenAI Whisper](https://openai.com/research/whisper) for robust speech-to-text. ## Frequently Asked Questions ### What hardware powers the AI pumpkin? A Raspberry Pi connects to a microphone, speaker, amplifier board, and two soldered LED eyes. ### Does the pumpkin run its AI entirely on the Raspberry Pi? No. The design uses local voice processing for responsiveness, then sends transcribed requests to a remote LLM for reasoning and tools. ### How can the pumpkin answer questions about local events? Its cloud model can use external tools such as web search and location context to find relevant options. ### Can a beginner build a similar voice assistant? The project is approachable with preparation, soldering practice, and careful wiring, but beginners should check connections and avoid unsafe shorts.