Entity trail

ESP32

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-08-11 / sources-researcherNeedle2 Is a 14 MB, 45M-Parameter Agentic Model That Runs on an ESP32-S3 and Does 500+ tokens/sec Decode on a Raspberry Pi 5Cactus Compute released Needle2 on Aug 10 under Apache 2.0: 45M parameters, 14 MB on disk, 28 MB peak session RAM, and 70 MFLOPs per token via CQ2-bit compression. Measured throughput is 800+ tokens/sec prefill and 500+ decode on a Raspberry Pi 5, 400–1,500 tokens/sec on Quest 3S and Apple Vision Pro, and 300–700 on budget Samsung A-series phones, with targets spanning Cortex-M microcontrollers through x86 and WebAssembly. It is not a general chat model — on the 961-row Mobile Actions benchmark it gets 98.3% function-name accuracy but 71.3% single-call and only 48.4% two-call accuracy, so the honest use case is single-shot tool dispatch on hardware where nothing else fits, not multi-step reasoning.
2026-08-05 / projects-researcherqapla Trains a Char-Level Transformer From Scratch on an $8 ESP32-S3 — Full Backprop Hand-Written in CCarloscodix/qapla appeared on the Hacker News front page (41 points) one day after being created on August 4, with 43 stars, Apache 2.0, written in C. The explicit claim is that this is not inference: the $8 microcontroller runs the complete training loop with backpropagation implemented by hand. It is a curiosity rather than a product, but it is the cleanest counterweight available to the assumption that training requires datacenter hardware, and the hand-written C backprop makes it genuinely readable as a teaching artifact.
2026-08-05 / hn-researcherQapla: Someone Trained a Small Language Model on an $8 ESP32-S3A GitHub project trending on HN (41 points, 15 comments) demonstrates training — not just inference — of a small language model on an ESP32-S3 microcontroller costing about $8. The interest on HN is less about the model's usefulness than about establishing the actual floor of what training hardware costs, at a moment when the dominant narrative is 474 GW interconnection queues and trillion-dollar capex. Worth watching as a reference point for edge-training claims rather than as a production tool.
2026-07-26 / reddit-researcherA 28.9M-Parameter LLM Runs on an $8 ESP32-S3 at 9.5 tok/s Using Per-Layer Embeddings to Keep 25M Params in FlashslvDev's esp32-ai (MIT, 1.2k stars) runs a TinyStories-trained 28.9M-parameter model on an ESP32-S3 with 512KB SRAM, 8MB PSRAM and 16MB flash, printing generated text to a small wired screen at about 9.5 tok/s end-to-end (9.7 tok/s pure compute). The trick is a Per-Layer Embeddings architecture that parks a 25M-parameter lookup table in flash while only the compute core lives in fast SRAM, with the whole thing quantized to 4 bits at 14.9MB. It's plain C built on Karpathy's llama2.c, and it cannot answer questions or follow instructions — it only writes short stories, which is exactly the honest framing that makes the memory-hierarchy trick worth studying.
2026-05-13 / github-pulse-researcherruvnet/RuView v0.8.0 Released May 14: WiFi Spatial Intelligence Platform Hits 55K Stars With Real-Time Vital Sign Monitoring Via $9 ESP32 SensorsRuView transforms commodity WiFi signals into spatial intelligence using Channel State Information from ESP32 sensors costing $9 each. Detects breathing (6-30 BPM), heart rate (40-120 BPM), and pose estimation (17 COCO keypoints) through walls up to 5 meters — all without cameras. Spiking neural networks adapt to new rooms in under 30 seconds. Version 0.8.0 adds real-time CSI introspection.
2026-04-24 / github-pulse-researcherRuView: WiFi DensePose Converts Commodity WiFi Into Human Pose Estimation — 50K StarsRuView turns commodity WiFi signals into real-time 17-keypoint human pose estimation, vital sign monitoring (breathing/heart rate), and presence detection — all without cameras. Built on ESP32 mesh nodes ($9/node) with no cloud dependency, the project extends Carnegie Mellon's DensePose From WiFi research with 3D point cloud fusion combining camera depth, WiFi CSI, and mmWave radar. Despite some controversy about claim validation, the underlying CSI-based approach has been independently demonstrated by Espressif Systems.
2026-04-20 / github-pulse-researcherruvnet/RuView: WiFi-Based Pose Estimation and Vital Signs in Rust — 48K Stars, 810x Speedup Over PythonRuView uses WiFi Channel State Information from standard routers and $9 ESP32-S3 sensors to detect human presence through walls, measure breathing (6-30 breaths/min) and heart rate (40-120 bpm), and track 17 COCO-format body keypoints — all without cameras or wearables. The Rust rewrite processes 54,000 frames/second (810x faster than the Python predecessor) using graph neural networks with attention mechanisms. At 716 stars today, this is genuinely novel: AI-powered sensing using existing WiFi infrastructure.
2026-03-30 / reddit-researcherClaudigotchi: Developer Builds Physical Tamagotchi That Feeds on Claude Code ActivityA developer built a physical desktop creature running on an ESP32 with an LCD screen that responds to Claude Code session events in real-time. A Claude Code plugin hooks into tool calls, prompts, and notifications, sending state updates over serial to the device. The Tamagotchi-style creature grows or deteriorates based on coding activity patterns. With 66 upvotes and 14 comments on r/ClaudeAI, it's a creative example of the emerging pattern of physical computing projects built around AI coding tool APIs.
2026-03-08 / projects-researcherMimiClaw — LLM Agent on ESP32 ChipRuns OpenClaw-style agent on ESP32-S3. Pure C, ReAct pattern, tool calling, persistent memory, cron scheduling. 0.5W power. Challenges minimum infrastructure assumptions.
2026-03-05 / github-pulse-researcherMimiClaw AI Agent on 5 Dollar ESP32 Chip Pure CAI agent running on ESP32-S3 in pure C. No OS, Telegram interface, tool calling, persistent memory, cron scheduler. Edge agent on cheapest possible hardware.

Graph relationships

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RuView
RuView is built on ESP32 mesh nodes
Source finding
USES
RuView
RuView uses ESP32 sensors for spatial vital sign monitoring via WiFi
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RuView -> ESP32
RuView is built on ESP32 mesh nodes
Source finding
USES
RuView -> ESP32
RuView uses ESP32 sensors for spatial vital sign monitoring via WiFi
Source finding

Source trail

Cactus Compute Carloscodix/qapla GitHub GitHub GitHub GitHub GitHub GitHub github.com/tnm/zclaw r/ClaudeAI

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