well...I'm just giving my opinion as an old noob: if you're precise about the requests it's really a great tool to improve your settings. Of course you have to know the basic map names...it helped me understand my timing reduction because of kfwms. Log analysis, screen shot, ecuxplot or csv. Sometimes I tell him to redirect him to the S4wiki. Of course you can provide him with .bin, damos...make comparisons...I think it's cool for noobs

yes sorry I always use Google translate, my English is average. but sometimes I call her 'She' and I imagine her with big... coming from Eastern countries . seriously it helped me understand KFZWMS and other 'limitations' maps. even if the free version is quickly limited.

im using it for my projects usually, good for coding
but as for the ecu things most of the time gpt fckd up totally

import pandas as pd
import matplotlib.pyplot as plt

# === CONFIG ===
log_file = "log_me7.csv"   # <-- put your ME7Logger .csv file here

# === READ FILE ===
df = pd.read_csv(log_file, sep="\t", encoding="latin1")
cols = df.columns.tolist()
print("Columns found in log:", cols)

# === HELPER FUNCTION TO FIND COLUMN NAMES ===
def find_col(keywords):
    for col in cols:
        for k in keywords:
            if k.lower() in col.lower():
                return col
    return None

# === DETECT RELEVANT COLUMNS ===
rpm_col   = find_col(["rpm", "engine speed"])
ldreq_col = find_col(["specified load", "requested load"])
ldact_col = find_col(["engine load", "load actual"])
boost_req_col = find_col(["boost pressure request", "specified boost"])
boost_act_col = find_col(["boost pressure actual", "boost actual"])
ign_col   = find_col(["ignition angle", "ign angle"])
cf_cols   = [find_col([f"cylinder {i}", f"cf{i}", f"knock retard {i}"]) for i in range(1,5)]
lam_col   = find_col(["lambda", "afr"])

# === EXTRACT DATA ===
rpm   = df[rpm_col] if rpm_col else None
ldreq = df[ldreq_col] if ldreq_col else None
ldact = df[ldact_col] if ldact_col else None
boost_req = df[boost_req_col] if boost_req_col else None
boost_act = df[boost_act_col] if boost_act_col else None
ign   = df[ign_col] if ign_col else None
cf    = [df[c] for c in cf_cols if c] if cf_cols else []
lam   = df[lam_col] if lam_col else None

# === PLOTS ===
if boost_req is not None and boost_act is not None:
    plt.figure(figsize=(10,6))
    plt.plot(rpm, boost_req, label="Boost requested")
    plt.plot(rpm, boost_act, label="Boost actual")
    plt.xlabel("Engine Speed (RPM)")
    plt.ylabel("Boost (mbar)")
    plt.title("Boost Requested vs Actual")
    plt.legend()
    plt.grid()
    plt.show()

if ign is not None and len(cf) > 0:
    plt.figure(figsize=(10,6))
    plt.plot(rpm, ign, label="Ignition advance")
    for i, c in enumerate(cf, start=1):
        plt.plot(rpm, c, label=f"CF Cyl {i}")
    plt.xlabel("Engine Speed (RPM)")
    plt.ylabel("Degrees")
    plt.title("Ignition Timing & Knock Retard")
    plt.legend()
    plt.grid()
    plt.show()

if lam is not None:
    plt.figure(figsize=(10,6))
    plt.plot(rpm, lam, label="Lambda")
    plt.xlabel("Engine Speed (RPM)")
    plt.ylabel("Lambda")
    plt.title("Air-Fuel Ratio (Lambda)")
    plt.legend()
    plt.grid()
    plt.show()

# === AUTOMATIC ANALYSIS ===
print("\n=== LOG ANALYSIS REPORT ===")

# Knock
if len(cf) > 0:
    for i, c in enumerate(cf, start=1):
        max_knock = c.max()
        if max_knock > 3:
            rpm_event = rpm[c.idxmax()]
            print(f"⚠ Cylinder {i}: knock retard {max_knock:.1f}° at {rpm_event:.0f} rpm")
        else:
            print(f"✓ Cylinder {i}: knock OK (max {max_knock:.1f}°)")

# Load
if ldreq is not None and ldact is not None:
    diff = (ldact - ldreq) / ldreq * 100
    max_over = diff.max()
    if max_over > 10:
        rpm_event = rpm[diff.idxmax()]
        print(f"⚠ Load overshoot: {max_over:.1f}% at {rpm_event:.0f} rpm")
    else:
        print("✓ Engine load close to requested")

# Lambda
if lam is not None:
    min_lam = lam.min()
    max_lam = lam.max()
    if min_lam < 0.78:
        print(f"⚠ Mixture too rich (lambda {min_lam:.2f})")
    if max_lam > 0.95:
        print(f"⚠ Mixture too lean (lambda {max_lam:.2f})")
    if 0.78 <= min_lam and max_lam <= 0.95:
        print("✓ Lambda within expected range")

I did it. If you're very careful with what you give it, it can help you understand your logs. You really need to trim them to what you want it to focus on, though, and you really ought to be able to interpret them yourself anyway. You can have a live sort-of chat with something to bounce ideas off of it.

I actually forced GPT 5 to make an entire tune for me that I ran on my car. It didn't work great, but I wanted to know if it could do it. I'll document it here in a bit. I do not recommend that anybody does this unless you're willing to buy new connecting rods. I thought the risk was worth it to find out though.

In the end, what's running on my car is something that I wrote and then had refined on a dyno by a professional. I'll try to capture that.