The state of the art, 2026

printf and a blinking LED.

// the default firmware debugger, still
printf("got here %d\n", x); HAL_GPIO_TogglePin(LED);

Every other software domain got observability, tracing, and an AI copilot. The 30 billion microcontrollers the cloud forgot got almost none of it.

Today's AI can write firmware, compile, and flash onto a device. Then it goes blind.

ViewAlyzer closes that gap. A rewind button for embedded systems: recorded time-travel through real execution paths. ViewAlyzer is the observability layer for embedded systems serving any AI agent or engineer that needs to understand what happened on a device.

Why it matters

01 The wedge: the 5-figure wall

Program flow has been gated behind a 5-figure price tag.

For the last 20 years, embedded trace tooling has lived in a high-priced, low-velocity world of specialized bench instruments. A modern, software-defined approach is overdue: always-on observability, powerful tracing, and AI-native workflows. But not AI-dependent.

The only thing standing between engineers and a full recording of what their code did has been the cost of capturing and decoding it: bench instruments priced like capital equipment.

ViewAlyzer collapses that cost into software, so the market multiplies instead of splits. The probe gets developers in; the software and agent are where the value compounds.

Incumbent instruction traceLauterbach / SEGGER, hardware + seat

$5,000–$30,000+

ViewAlyzerthe runtime data + AI layer that becomes the embedded standard

a fraction

02 The problem

The engineers programming the physical world are flying blind.

There are tens of billions of microcontrollers running real-time code in the world. The people who debug them are still using tools from another decade, and the act of looking destroys the very behavior they're chasing.

// 01

Breakpoints lie.

Halting a chip to inspect it freezes timers, drops interrupts, and destroys the timing-dependent bug you were chasing. Race conditions, priority inversions, missed deadlines, jitter: they vanish the moment you stop to look.

// 02

printf is the state of the art.

The default debugging tool in 2026 firmware is still printing strings over a serial port, which itself perturbs timing and falls over under load. The blinking LED is the backup.

// 03

Real tracing costs a fortune.

Instruction-trace tools run into the thousands-to-tens-of-thousands of dollars per seat, with hardware to match and a UX from another era. Program-flow visibility has been a luxury good.

// 04

AI copilots are blind here.

Copilots can read your firmware source, but they have no idea what the chip actually did at runtime: the task that overran, the ISR that fired 10× too often, the mutex nobody released. They're guessing.

03 The solution

Three layers. One coordinate system.

Each layer reinforces the next: the app feeds the agent, the trace engine feeds both, and frozen interfaces let all three move independently. Together they do for firmware what cloud observability did for web services.

The Visualizer: see the chip without stopping it

A native desktop app that connects to any standard debug probe and streams live runtime data over SWO/RTT, rendering a real-time picture of what the chip is doing as it runs.

Swim-lane timeline of RTOS tasks, context switches, and ISR bursts
Blocked tasks, mutexes, semaphores & priority inversion made visual
Live memory map, symbol resolution, call graph & event table
Runs headless with a clean JSON query interface

The killer property: low-friction. For FreeRTOS and Zephyr it taps the trace hooks the RTOS already exposes, so task switches and sync events show up with no code changes. Your own custom signals add a lightweight, low-overhead recorder. For true zero-instrumentation capture, see the Trace Engine below.

timeline · 17/17 tasks · live

Live RTOS timeline, CPU load and task details in ViewAlyzer

The Agent: the copilot that sees what you see

An MCP server + IDE integration gives an AI agent purpose-built tools to query a recording, and, critically, to query the exact view the engineer is looking at, in the same coordinate system.

Ask "why did this spike?": the agent resolves "this" to the literal data on screen
Three-tier, token-budgeted queries reason over millions of events
Heavy analysis pre-computed natively: jitter, inversion, CPU stats
Bidirectional attention: the agent can move your cursor to the spike at 23.4s
Model-agnostic by design: any team's agent, any model plugs into the same runtime tools (proven with Claude & DeepSeek)

Same data, same language. The hard part of an AI debugging copilot isn't the model, it's grounding it in exactly what the human sees. That's architected in from the start, not bolted on, and it's open to any agent, not just ours.

editor · firmware-agent (MCP)

AI agent diagnosing a priority inversion, calling ViewAlyzer record and read tools in an agentic workflow

The Trace Engine: time-travel debugging for firmware

The deepest layer, and the moat. A capture probe sniffs the chip's 4-bit hardware trace port and a host decoder reconstructs the complete nested call tree of what actually ran, with zero firmware instrumentation, verified on STM32 F4/F7-class silicon.

Flame-graph timeline + dynamic call graph of real execution
Source-level navigation with executed file:line
A debugger-like step-through of the recorded run: next call, next branch
Scales to millions of invocations, the view queries SQL, not a giant tree

A debugger that never had to stop the chip, because the run already happened. Walk the path the code actually took, after the fact.

call graph · NucleoU575.elf · 256 nodes

Hardware trace flame graph, step-through call stack, source view and dynamic call graph reconstructed from recorded execution

04 The architecture moat

Two frozen seams that outlast any single chip.

Two locked interfaces separate the chip-specific capture layer from everything above it. The hardware details stay below the line and the visualizer and agent stay portable, so new silicon and new probes slot in without disturbing the rest of the stack.

// seam 01

New silicon = a new decoder.

Supporting Cortex-M7 / M33 swaps one module behind the seam. The entire view, agent, and app are untouched. New chips are a module, not a rewrite.

// seam 02

New hardware = a transport swap.

The planned ViewAlyzer Core 1 lifts throughput ~100× over the proof-of-concept probe, and emits the exact same capture format, so the whole stack ships unchanged.

// compounding

The roadmap is one query away.

WCET hunting, interrupt-latency jitter, a concurrency bug finder, and cross-capture diff: timing-regression testing for firmware in CI, because the hard data already exists.

trace · flame graph + step-through of recorded execution

ViewAlyzer desktop app overview: live RTOS timeline, signal traces and memory map

30B+

microcontrollers running real-time code in the physical world

~100×

throughput lift from ViewAlyzer Core 1, same capture output

30min

from install to a beta tester's first live trace

0×

code changes to the target for RTOS task & sync capture

05 Why now

The window to own runtime context for embedded AI is open, briefly.

AI is eating software, but blind on embedded.

The copilots developers now expect have no runtime ground truth for firmware. We supply it, in a form an agent can actually reason over.

The incumbents are expensive and stagnant.

The incumbents have run a high-priced, low-velocity bench-instrument market for two decades, with little pressure to modernize. That leaves the field wide open for a software-defined, AI-native challenger.

The trace hardware is already in the silicon.

Hardware tracing ships in the chips engineers already use. The bottleneck was never the chip, it was affordable capture and modern software. We built both.

RTOS everywhere.

FreeRTOS / Zephyr adoption keeps rising, and concurrency is exactly where visibility matters most and printf fails hardest.

06 Why us: the unfair advantages

Capture hardware, decoder, visualizer, and an AI agent, built to one contract, by one team.

Vertical integration nobody else has. Each layer makes the others better; the frozen seams let all three move independently.
The "same data, same language" agent. Grounded in exactly what the human sees, architected in, not retrofitted.
An architecture built to outlast any chip. New cores and new capture hardware are modules, not rewrites.
Already real. The visualizer ships. The agent is wired end to end. The trace engine runs against physical silicon. This isn't a deck.

07 The ecosystem today

This is running code against real hardware, not a deck.

ViewAlyzer Appnative JUCE / C++ visualizer

Live RTOS / ISR / memory tracing over a debug probe; RTOS task & sync capture needs no code changes. Headless + JSON query mode.

firmware-agentMCP server + IDE integration

Gives an AI agent the engineer's exact view; token-budgeted three-tier queries; shared bidirectional attention.

Hardware Trace Enginecapture → decoder → trace store

Capture container to host decoder to trace store: flame graph, dynamic call graph, source-level step-through.

Capture hardwareproof-of-concept probe today · Core 1 next

Proof-of-concept probe working; ViewAlyzer Core 1 (VA-C1) specced for ~100× throughput, emits the same capture format.

Observability and an AI copilot for the chips everyone else forgot.

Every serious software domain got both, except the tens of billions of chips running the physical world. ViewAlyzer is building them, on hardware we control, for the engineers everyone else overlooked.

And it isn't only our agent. Any team building AI for the physical world plugs into the same runtime ground truth. In a gold rush, ViewAlyzer sells the shovels.

Talk to the founders