TESS serves as a formidable tool for investigating stellar variability. Nonetheless, the adaptation of methodologies employed in other space missions, such as Kepler, to deduce long stellar rotation periods or solar-like stellar pulsations has been a challenge. The advent of multiple sectors of data for most stars every two years with the extended TESS mission, alongside enhanced stability, and the introduction of newly calibrated data sets like the Quick Look Pipeline (QLP), has significantly improved this situation. This presentation aims at describing our calibration of TESS light curves, starting from QLP raw flux and employing the Py-TADACS software — a Python package based on the Kepler asteroseismic optimised KADACS software — allowing us to dramatically improve the extraction of low-frequency signals for surface rotation and seismology. In this talk, our primary focus will be the determination of surface gravities (or v_max) of several hundred thousand subgiants and red giants (including super-luminous giants) through the utilisation of the machine learning FliPer metric as well as Py-A2Z. We will then present the comparison of our findings with previous investigations and Gaia DR3, discussing our yield statistics, problems encountered (pollution, binarity etc.), and future prospects in light of the PLATO mission.