#3239 Multi-ESP32 Applications; AI Chatbot

#3239 What would be interesting things to do with 8 "ESP32 Development Board 16MB Integrated 1.9 inch ST7789 170x320 TFT LCD Display,WiFi+BL Wireless Module,CH340 Driver USB Type-C for Arduino Micropython"?

Below is a set of twelve project concepts that take advantage of having eight identical ESP32 + 1.9" TFT boards. I’ve grouped them by whether they use the boards together as a system or individually but in parallel, and I’ve sprinkled in notes on parts, difficulty, and “what makes it interesting” from an innovative-thinking standpoint.

A. Use all eight boards as a coordinated system

#	Project	Core Idea & Why It’s Interesting	Key Tech Elements	Stretch / Iteration Ideas
1	Wi-Fi Mesh Notifier Wall	Mount the eight boards in a 4 × 2 frame; each node forms an ESP-NOW mesh. Push a JSON packet (e.g., GitHub build status, stock tickers, room sensor data) and let every node light up a custom icon or short message.	ESP-NOW for low-latency broadcast; simple bitmap blitting to ST7789; OTA update for graphics.	Add a small click-cap battery + hall sensor so lifting a board off the frame lets it become a handheld “detail viewer.”
2	Room-to-Room Voice Assistant Cluster	Each ESP32 runs a minimal offline keyword spotter (“Hey Node”) and streams short audio clips to a local RAG/Llama-factory on your LAN; replies are sent back as scrolling text bubbles.	I²S MEMS mic (≈$3) per board; UDP to your server; simple ChatGPT call or local Ollama.	Fuse presence detection (BLE RSSI of your phone) so answers follow you around the house.
3	Distributed Escape-Room Puzzle	Players must visit eight physical stations; each station shows a puzzle or clue. Completing one unlocks the next via a token published onto an MQTT topic.	MicroPython + paho-mqtt; local broker on a Pi; capacitive-touch pads or small rotary encoder for input.	After all eight clues solved, trigger a synchronized animation across every screen.
4	Interactive 8-Panel Art Installation	Run the boards in Sync-Sketch mode: one master streams compressed RGB565 frames over UDP multicast so all eight act like tiles of a larger 340 × 640 piece of generative art (e.g., GLSL-style noise).	LVGL (Micropython binding) double-buffer; multicast UDP; simple ArtNet-style protocol.	Allow visitors to draw on a tablet; strokes are partitioned to the relevant tile in real time.

B. Same codebase, parallel deployment (eight self-contained gadgets)

#	Project	What Each Board Does	Interesting Twists
5	Pocket-Size Network Analyzer	Shows Wi-Fi SSIDs, RSSI heat-bar, and BLE device count; perfect give-away tool for tech demos.	Long-press opens a “rogue AP alert” radar view; export captured scan logs over WebUSB.
6	Tiny LoRa-to-WiFi Gateways	Add an $8 LoRa module; place units around a property. They forward 915 MHz sensor packets onto Wi-Fi/MQTT.	Mesh-route between gateways when Wi-Fi is weak, using ESP-NOW as backhaul.
7	Personal Wearable Badges	3-D-print a slim case + LiPo; display dynamic QR codes, current conference talk, or animated pixel avatar.	Include a BLE Nordic UART service so a phone app can update the text on the fly.
8	DIY e-Paper Substitute	Flash a gray-scale dithering demo to show static dashboards (calendar, weather) that refresh a few times a day—use deep-sleep between updates.	Add a light sensor to auto-dim; solar trickle-charge the LiPo for a cable-free plaque.
9	Handheld Sensor Samplers	Plug in a STEMMA-QT port; try different I²C modules: particulate, CO₂, UV, bio-impedance, etc. Display live graphs with LVGL spark-lines.	One button long-press logs the data to SPIFFS; short-press starts BLE advertising of the sample.
10	Two-Player Retro Gaming Kits	Each board runs a MicroPython port of Pong, Snake, or TinyTetris; two boards discover each other over BLE GATT, syncing game state.	Make tournaments: a Pi scoreboard tracks wins via MQTT.
11	Micro-Kiosk Feedback Buttons	Mount beside lab equipment or office doors; screen asks “Did this tool work?” with 👍/👎 touch regions. Data posts to Google Sheets via HTTPS.	Rotate prompts hourly (satisfaction, cleanliness, queue length).
12	BLE Beacon Story-Trail	Place boards along a museum route; they broadcast an Eddystone URL + show an image teaser. Visitors’ phones open AR content; the board’s screen cycles fun facts.	When a visitor taps “Like,” the board flashes their avatar and logs an interaction count to Firebase.

Why these boards shine

Ili9341-class TFT but portrait & dense. 320 × 170 is ideal for portrait scrolling UIs and single-word status alerts.
16 MB flash gives room for multiple apps or OTA partitions (e.g., release + test).
USB-C w/ CH340 means plug-and-play flashing—even from Android.
ESP-NOW + BLE + Wi-Fi lets you mix infrastructure and p2p links without extra chips.
MicroPython lowers the time-to-first-pixel: REPL + in-field tweaks.

Implementation Starter Tips

Adopt a common component library (e.g., lvgl_micropython or TFT_eSPI for Arduino) so every idea re-uses fonts, icons, and drivers.
Create a “base firmware” that exposes a simple RPC (WebSocket / JSON-RPC over Wi-Fi). Each distinct project becomes a Python script pushed at runtime, not a full re-flash.
Leverage ESP-Now for node discovery. Even if the final data flows over Wi-Fi, ESP-Now lets devices exchange credentials and detect proximity without waking your router.
Design printable snap-fit enclosures early—nothing kills enthusiasm like a dangling PCB.
Automate provisioning. Use a tiny Python script on your PC that enumerates all eight USB devices, flashes the firmware, writes a unique JSON config (ID, Wi-Fi creds, icon theme), and reboots them.

Where to go next

AI Edge Extras: The 8-bit parallel camera interface is broken out on many ESP32 dev boards. Add an OV2640 to two units and you can prototype tiny-ML vision nodes feeding the display with bounding boxes.
Community Showcase: Post the art-installation or mesh-notifier frame on Hackaday.io or the r/esp32 subreddit; you’ll attract collaborators and new extension ideas.
Iterative Improvement Loop: After your first project, ask “Which manual step can the boards now automate for me?”—using them to improve their own dev workflow is meta-fun and fits your innovation ethos.

Happy hacking!

Would one of these units be suitable for applying voice AI-chat via WiFi with an online LLM?

Short answer

Yes — with a small microphone/speaker add-on, one of your ESP32-TFT boards can be turned into a Wi-Fi “voice chat terminal” that streams speech to an online speech-to-text (STT) → LLM → text-to-speech (TTS) pipeline and plays the reply aloud.
The heavy AI work (ASR, LLM, TTS) stays in the cloud; the ESP32 only needs to:

sample audio and push it over HTTPS/WebSocket,
receive the textual or audio response, and
display / play it.

Below are the practical details, trade-offs, and a minimal reference architecture.

1. Hardware checklist

Purpose	Recommended part(s)	Notes
Microphone	I²S MEMS mic module (e.g., INMP441, SPH0645)	Direct I²S ↔ ESP32; no ADC needed.
Speaker / audio out	3 W I²S class-D amp (MAX98357A) + 8 Ω micro-speaker or USB-C audio dongle	I²S keeps CPU free; PWM is possible but noisy.
User input	• One GPIO push-button for “push-to-talk” or • Capacitive touch on the bezel	Helps with voice-activity detection.
Power	Li-ion 500–800 mAh + TP4056 charger board	Enough for ~2 h continuous Wi-Fi streaming.

Everything fits behind the 1.9″ display in a 3-D-printed enclosure ≈ 65 × 35 × 20 mm.

2. ESP32 resource constraints and how to cope

Resource	Typical value	Why it’s still OK
RAM: 520 kB (internal) + a few hundred kB PSRAM (if present)	You only need a 1–2 s circular buffer: 16 kHz × 16-bit × mono ≈ 64 kB per second.
Flash: 16 MB	Plenty for MicroPython/Arduino firmware + fonts + OTA slot.
CPU: 240 MHz dual-core Xtensa	<3 % busy while DMA pushes I²S; leaves headroom for UI.
No HW FP32 / NN accelerator	All ML happens in the cloud.

3. Network & protocol sketch

┌──────────────┐    HTTPS (PCM chunks, 20–100 kB)       ┌──────────────┐
│  ESP32 node  │ ─────────────────────────────────────▶│  STT service │
│ (mic + TFT)  │                                        └────┬─────────┘
│              │ ◀────────── JSON w/ transcript ────────────┘
│              │               │
│              │   HTTPS (prompt) / WebSocket stream
│              │               ▼
│              │        Online LLM (OpenAI ChatGPT o3, etc.)
│              │               │
│              │ ◀── JSON / text ──┘
│              │               │
│              │   HTTPS (text → MP3/OPUS)               ┌──────────────┐
│              │ ◀──────────────────────────────────────│  TTS service │
└──────────────┘                                         └──────────────┘

Chunked upload: Post raw 16-kHz 16-bit PCM every 200–400 ms until the button is released (or VAD says stop).
STT options:
- POST /v1/audio/transcriptions (OpenAI Whisper)
- AssemblyAI real-time WebSocket
- Google Speech-to-Text streaming gRPC over HTTPS proxy
LLM call: Use the transcript as the user message; maintain conversation state in the ESP32 or on your server.
TTS options:
- OpenAI audio.speech.create (MP3/OPUS)
- ElevenLabs, Amazon Polly, Coqui TTS

Return a compressed audio file (< 50 kB) and stream it to the MAX98357A.

4. Minimal MicroPython/Arduino flow

# Pseudo-MicroPython
import i2s, uasyncio, urequests, audio_player, wifi

async def record_and_stream():
    buf = bytearray(32000)            # 0.5 s @ 16 kHz
    while button.value() == 0:
        i2s.readinto(buf)
        await post_chunk(buf)
    transcript = await close_stt()
    reply = await call_llm(transcript)
    tts_mp3 = await call_tts(reply)
    audio_player.play(tts_mp3)

Use ESP-IDF’s esp-sr I²S driver for glitch-free DMA; MicroPython’s i2s module if you prefer REPL prototyping.

5. Expected performance

Metric	Typical value (good Wi-Fi)
STT latency	300–700 ms
LLM response (gpt-4o, short answer)	300–800 ms
TTS synthesis	200–500 ms
End-to-end “ear-to-ear”	≈ 1.0–2.0 s

Good enough for conversational back-and-forth; not real-time duplex.

6. Extra polish ideas

On-device keyword: run tiny-ML wake-word (“Hey Nova”) with Espressif ESP-SR to avoid sending unintended audio.
Screen UX: while speaking, show a live waveform; while thinking, show a spinner; while replying, scroll the text and animate an equalizer.
Caching: hash the prompt; identical queries fetch the last response from flash, saving tokens.
OTA updates: deliver new prompt templates or wake-word models without USB.
Edge fallback: if Wi-Fi is lost, display “offline” and log queued prompts to SPIFFS to retry later.

Bottom line

With an I²S mic + amp, sensible buffering, and HTTP calls to online STT/LLM/TTS services, a single 16 MB ESP32-TFT module becomes a pocket-size voice chat client. The microcontroller acts mainly as a streaming front-end and user interface; the cloud handles the intelligent heavy lifting.

Would a "ESP32-S3 2inch Capacitive Touch Display Development Board, 240×320 Pixels, IPS Panel, 32-bit LX7 Dual-core Processor, Up to 240MHz Frequency" support AI chat via WiFi and online AI Chatbot account?

Short answer — Yes.

An ESP32-S3 board with a 2 inch 240 × 320 capacitive-touch IPS display can act as a thin-client “AI chat terminal” that talks to an online LLM (OpenAI, Gemini, Claude, etc.) over Wi-Fi. All AI inference runs in the cloud; the micro-controller only handles the UI, networking, and basic request / response handling.

Why it works

Capability	Relevance to cloud-chat
Dual-core Xtensa LX7 @ ≤ 240 MHz	Easily handles UI updates, JSON parsing, and TLS without noticeable lag.
Wi-Fi (802.11 b/g/n)	Secure HTTPS to the chatbot REST endpoint.
PSRAM (often 8 MB on dev boards)	Enough head-room for TLS buffers (≈50–200 kB), JSON payloads, scroll-back history, and bitmaps for the GUI.
240 × 320 IPS + capacitive touch	Small but usable for chat bubbles, soft keyboard, or gesture-based input.
USB-C & CH340	Simple flashing/debugging; can expose a CDC-serial console for logging.

A 2 inch screen limits how many past messages you show, but it’s more than sufficient for single-question/answer interactions.

Typical software stack

Firmware layer
Arduino-ESP32 or ESP-IDF (C/C++) -or- MicroPython
–> WiFiClientSecure (TLS), HTTP(S) client, SPI driver for TFT, I²C driver for touch.
UI layer
- LVGL 9.x (lightweight GUI lib) or TFT_eSPI + custom touch handler.
- Soft keyboard or swipe-to-type overlay; optional Bluetooth keyboard support.

Chat logic

capture_input()            # text or recorded audio
build_json_request()       # {model:"gpt-4o-mini", stream:true, ...}
https_post("/v1/chat/completions")
stream_parser()            # incremental JSON → display bubbles
allow_cancel_or_resume()

Optional speech I/O
- I²S MEMS microphone → send raw/encoded audio to Whisper / Google STT.
- I²S DAC or MAX98357A amp + speaker → play TTS stream returned from cloud.

Memory & performance tips

Tip	Reason
Compile with `-DBUFFER_SIZE_HTTPS=16384` and enable PSRAM for heap	TLS handshake and streaming responses need contiguous buffers.
Use HTTP chunked streaming (`stream=true`) and render as chunks arrive	Avoids buffering multi-KB completions in RAM.
Keep local history short (e.g., last 4 pairs) or store on SD-card	RAM is limited; large prompt contexts belong in the cloud.
Turn on ESP-IDF Wi-Fi power-save only after the request ends	Reduces latency spikes during active chat.

Voice-chat architecture (optional)

[TFT Touch UI]
     │ text or hold-to-record
     ▼
[ESP32-S3]
 ├─ stream Mic → cloud STT
 ├─ send user text → /chat/completions
 ├─ receive tokens → display + queue for TTS
 └─ stream TTS audio → I²S amp → speaker

All speech recognition and synthesis happens remotely, so the ESP32 only ships/receives small audio chunks (~20 kB min) and never does heavy DSP locally.

Limitations & work-arounds

Constraint	Mitigation
Screen real-estate	Auto-scroll bubbles; “tap-and-hold” to view full answer; off-load full logs to SD or serial.
HTTPS RAM usage	Always enable 8 MB PSRAM variant; avoid giant system prompts.
Battery life	Use deep-sleep between interactions; turn off back-light after idle.
Typing friction	Pair a Bluetooth keyboard, or rely on voice input.

Development quick-start

Flash MicroPython

esptool.py --chip esp32s3 write_flash 0x0 esp32s3-202402.bin

Pip-install openai==1.14.2 to your local dev PC only (ESP32 calls REST directly, no SDK needed).
In main.py on the board:

import urequests, network, json, ssl

WIFI_SSID, WIFI_PW = "...", "..."
OPENAI_KEY = "sk-..."

def chat(msg):
    url = "https://api.openai.com/v1/chat/completions"
    hdr = {"Authorization": f"Bearer {OPENAI_KEY}",
           "Content-Type": "application/json"}
    payload = {"model":"gpt-4o-mini",
               "stream": True,
               "messages":[{"role":"user","content":msg}]}
    resp = urequests.post(url, headers=hdr,
                          data=json.dumps(payload))
    for chunk in resp.iter_lines():
        # parse & render...

Link SPI pins to the TFT driver (e.g., ST7789) and initialize LVGL or TFT_eSPI.

Bottom line

Feasible: The ESP32-S3’s Wi-Fi + PSRAM + display is sufficient for a lightweight cloud-based chat experience.
Not local inference: All AI models, STT/TTS, and language reasoning remain on the server.
Adds value: Creates an inexpensive, always-on “pocket terminal” for AI assistance, smart-home control via chat, or kiosk-style deployments.

If you need pointers to open-source examples (Arduino or MicroPython) or want to extend this into a voice-first hands-free device, just let me know!

From <https://chatgpt.com/> ChatGPT o3

Search This Blog

Learning from ChatGPT