COBALTGPT + Cobalt-TunersX – AI-assisted telemetry, diagnostics, and tuning stack for a 2010 Cobalt SS Turbo

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[Project]

TL;DR

I’m building a real-world, end-to-end project that fuses:

  • A domain-locked AI expert (“COBALTGPT”) running on top of OpenAI models, and
  • A CANbus/telemetry stack (“Cobalt-TunersX”) for a 2010 Chevrolet Cobalt SS Turbo (LNF / F35 / G85).

The goal: turn an old GM performance car into a living lab for AI-assisted diagnostics, telemetry, and safe, data-driven tuning – not to flash ECUs directly, but to create a smart co-pilot that understands the platform deeply and helps prevent dumb, expensive mistakes.

I’m sharing the concept, architecture, and roadmap here to get feedback from the OpenAI dev community on:

  • Agent design
  • Tooling patterns
  • Safety boundaries
  • How far it’s reasonable to push this kind of “AI + hardware + legacy vehicle” integration.

Context and motivation

The platform:

  • Car: 2010 Chevrolet Cobalt SS Turbo
  • Powertrain: 2.0L LNF, F35 5-speed, G85 LSD
  • Use case: Performance street car, occasional hard pulls, future track use
  • Owner (me): Mechanic + carpenter who likes building tools, not just buying them

There’s an obvious gap between:

  1. Locked-down OEM systems (dealer tools, black-box ECUs), and
  2. Power user ECU tools (HP Tuners, etc.) that assume you already know how not to blow the engine up.

I wanted an AI-backed “expert in the loop” that:

  • Understands this exact platform (LNF / F35 / G85),
  • Tracks the actual car’s mods, logs, and history over time, and
  • Can reason about risk (“this pull is safe”, “this is how you crack a ringland”, “don’t do that with stock clutch/axles”).

That became COBALTGPT (the AI side) and Cobalt-TunersX (the hardware/data side).

High-level concept

Think of the project as a two-layer system:

  1. Cobalt-TunersX (vehicle + data layer)

    • A Python-based stack that:
      • Talks to the car’s CANbus via CANable/CANtact-style hardware.
      • Uses DBC files to decode GM Delta platform signals.
      • Logs and analyzes:
        • Engine load, boost, KR, IAT2, coolant, fuel pressure
        • Torque management and traction events
        • Wheel speed / ABS data, etc.
      • Provides a simple live dashboard (PyQt) + log recorder + analysis tools.

  2. COBALTGPT (AI expert + memory layer)

    • A domain-locked OpenAI agent with:
      • A strict system prompt: only talk about this car, this platform, this build.
      • Long-term memory for:
        • Mod list, maintenance history, issues found, parts used
        • Baseline tune assumptions (fuel, boost, power goals)
        • DBC mappings and custom sensor configs.
      • Tools (conceptual for now) to:
        • Ingest new log files (CSV/JSON) from Cobalt-TunersX
        • Run analysis (KR vs RPM vs load, boost creep, spool time, etc.)
        • Propose changes or limits in a human-readable, safety-first way.

The idea is not to have the model push binary directly to the ECU. Instead, the AI acts as a tuning and diagnostics consultant with full context and a live data feed.

System architecture (developer view)

1. Vehicle & data acquisition

  • Hardware:

    • CANable / CANtact-style USB–CAN interface
    • OBD-II breakout harness
    • Optional extra sensors (wideband AFR, additional pressure/temperature sensors)

  • Software stack (Python):

    • python-can – bus interface
    • cantools – DBC parsing
    • Custom modules:
      • live_dashboard.py – real-time gauges
      • data_recorder.py – log to JSON/CSV
      • afr_boost_ve_calc.py – basic VE/boost/AFR calculations
      • tune_advisor.py – analysis hooks that COULD be driven by AI

  • DBC layer:

    • Hand-built / sniffed DBC for:
      • ECM high-speed CAN signals
      • ABS/traction wheel speed
      • Some BCM signals when relevant (lighting, status, etc.)
    • Focus on signals that matter for:
      • Engine safety (KR, temps, fuel pressure)
      • Drivetrain stress (torque, wheel slip, LSD behavior)
      • Driver feedback (boost, AFR, etc.)

2. AI / COBALTGPT layer

  • Core ideas:

    • The AI agent is locked to one domain:
      • 2010 Cobalt SS Turbo
      • LNF engine
      • F35 transmission
      • G85 LSD
    • It treats the car as an ongoing “project” with:
      • A persistent build sheet
      • A diagnostics log
      • A telemetry history

  • Model behavior:

    • Follows a structured diagnostic workflow:
      1. Symptoms
      2. Likely causes (platform-specific)
      3. Verification steps (sensors, tests, logs)
      4. Recommended actions (with torque specs, fluid types, etc. where safe/known)
    • Provides stage-by-stage upgrade paths:
      • “If you want X power on pump gas with this clutch and these axles, here’s the safe route.”

  • Planned tools / function calling:

    • load_build_sheet() – read current configuration
    • save_build_sheet(changes) – persist mod/maintenance updates
    • analyze_log(file_ref) – parse a log, compute:
      • KR heatmaps
      • Boost creep indicators
      • Spool time per gear
      • Virtual dyno estimates
    • generate_safety_report() – summarize risk:
      • “KR looks clean up to X RPM/Y load”
      • “Fuel pressure dropped here – investigate before adding more boost”

Right now, some of this is conceptual; the architecture is designed to support tool calling and log-driven reasoning with an OpenAI model in the loop.

Safety and boundaries

Because this touches a real vehicle, safety boundaries are a first-class design constraint.

What the AI does NOT do:

  • It does not:
    • Connect directly to the ECU to flash tunes.
    • Generate proprietary .HPT, .BIN, or OEM calibration files.
    • Bypass immobilizers, security, or emissions controls.

What the AI CAN do:

  • Interpret owner-generated logs and provide:
    • Warnings (“this KR pattern is bad”, “this ECT/IAT2 pattern is heat-soak territory”)
    • Guidelines (“for stock rods/clutch, stay under ~X torque in 3rd/4th”)
    • Maintenance recommendations (“your symptoms + mileage + logs point to X before you chase Y”)
  • Help organize and document:
    • Repairs
    • Part choices
    • Baseline vs changed behavior after mods

Think of it as a coach with a multimeter and a whiteboard, not as a hacking tool or self-driving ECU flasher.

Why involve OpenAI at all?

This could have been “just another logger,” but the pain points it’s trying to solve are inherently cognitive:

  • Interpreting messy logs over time
  • Connecting symptoms, sensors, history, and platform quirks
  • Reasoning about trade-offs:
    • Power vs reliability
    • Cost vs benefit
    • Stock hardware limits vs future goals

OpenAI models are ideal here because they can:

  1. Digest structured + unstructured data together

    • Build sheet, past conversations, service manual snippets, forum knowledge, sensor logs.

  2. Act as a long-term “project brain”

    • Remember what’s already been replaced, what failed before, what fuel is used, etc.

  3. Explain decisions in plain language

    • Not just “your AFR is lean,” but:
      • “Given your injectors, pump, fuel type, and this boost level, that lean spike at 5800 RPM in 3rd gear is a red flag; here’s the likely cause and how to confirm it.”

The idea is to treat the car as a real-world AI testbed where the model’s reasoning actually matters, and it’s easy to tell when advice is good or bad.

Current status

Pieces that exist or are partially built:

  • A detailed COBALTGPT system prompt locked to:
    • This vehicle
    • This platform
    • This user (owner) and their build history
  • A build sheet / provenance record for the car:
    • VIN, options (including G85), color, rarity
    • Mod list (intake, exhaust, suspension, etc.)
    • Maintenance/repair history
  • Architecture + partial code for:
    • CAN logging (python-can, cantools, etc.)
    • Basic analysis (KR, boost, spool)
    • GUI dashboard concept
  • A design for:
    • Hidden/undocumented ECM/BCM channels mapped in a DBC
    • Future “safety advisory” layer based on those channels

Where I’d love feedback from the OpenAI dev community

I’m interested in opinions / patterns on:

  1. Agent design & memory

    • Best practices for a single-domain, long-lived agent that follows one project (this car) over months/years.
    • How to structure memory so it’s:
      • Persistent and accurate
      • Not bloated or redundant
      • Easy to diff over time (e.g., “versioned build sheet”)

  2. Tooling and log ingestion

    • Clean patterns for:
      • Uploading log files
      • Having a tool parse and summarize them
      • Feeding condensed results back into the model for reasoning
    • Guardrails so the model doesn’t overstate certainty when log data is noisy or incomplete.

  3. Safety frameworks

    • How to encode hard constraints into the agent:
      • Never recommend disabling core safety systems.
      • Always assume conservative limits unless explicitly overridden.
      • Require multiple confirmations before suggesting any risky change.

  4. Generalization

    • If this works for a 2010 Cobalt SS Turbo, what’s a sane path to:
      • Extend to “GM Delta I performance cars” in general?
      • Or design a framework where each platform gets its own domain-locked agent with its own build sheet + DBC?

  5. Best practices for “AI + real hardware”

    • Any patterns, lessons, or horror stories from others who’ve integrated OpenAI agents with:
      • Vehicles
      • Robotics
      • Industrial systems
    • What additional telemetry, logging, or human-in-the-loop design would you insist on?

Closing

This started as “I want my Cobalt to have its own personal AI race engineer,” but it’s grown into a broader experiment:

Can we build safe, domain-locked, hardware-aware AI agents that live with one real object over time, remember its history, and help humans make better decisions?

If any of this resonates, I’d love feedback on:

  • Architecture improvements
  • Safety constraints I might be missing
  • Tool designs that would make this cleaner or more robust

…and if you’ve done anything similar (cars, CANbus, robots, industrial control), I’d be very interested in what worked and what absolutely did not.

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In regards to that specific terminology, NO, an AI agent is:

a call to an Large Language Model endpoint either locally or on an external server.

It does not exist independently, it has no memory, it cannot be domain-locked, it cannot be hardware-aware, etc. It is just a black box that you send your prompt/conversation/data too, and it outputs data in return.

In regards to your concept YES. You can build:

  1. A safe, domain-locked, hardware-aware ENVIRONMENT AND APPLICATION that you then CONNECT TO AN LLM.

The AI or “agent” itself has nothing to do with your program, your hardware, your memory retention, your safety features, etc. The “AI Agent” (LLM), is just an endpoint that you integrate into your application. This is the most common misconception and misapplication of terminology today in regards to AI. The AI Agent is not the “doer” - the program/application/environment that you build for it to interact with is the “doer”.

The AI Agent is just “one service of a multifaceted program/application” that provides the generative-text layer for achieving the users end-goals.

So you have to decide:

  1. How your going to save data and build a store/database of information/conversation history/etc.
  2. How your going to “configure” and “save” (for this project and or extensible for future projects) your “environment configuration” (i.e. your hardware specifics, etc.).
  3. How your going to connect external tools (i.e. to the car computer)/data/etc. and save that/maintain it/etc.
  4. How your going to contact the LLM/AI endpoint (i.e. using API, or using existing web interfaces like chat GPT, etc.).

Making use of existing interfaces like OpenAI/ChatGPT conversation history, “memory” features, etc., means you are limited completely to their interface, though with development of MCP (Model Context Protocol) that’s extended a bit now.

But their environments (I.e. chatGPT) are also non-specific, and you cannot guarantee safety or reliability in this way, because their tools are general purpose and not specific-to-your-use-case.

In this circumstance, I would recommend that you:

  • Develop a standalone application, most likely one that would involve a cross-device interface (like a web app, or a native app that can run both on smartphone and on laptop) so that you could actually use the app from whatever device you happened to wanted to plug your car-computer-reader-whatever into. This way you can control the environment, the memory, the integration with the LLM, etc. Otherwise, you are trying to bootstrap someone elses “app that’s built for coding and conversation” into a “hardware-specific and real-world-impacting” circumstance that frankly, would be definitely unsafe!
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Most of this is mentioned or I have said an idea of how to connect to said vehicle depending course you’d need either a external ssd or a mini pc or laptop with then enough space but I already have it ready to expand and have it ready to implement to it if you have any questions feel free to ask me more or can discuss further with me directly thank you for your input I am definitely open to more information