Powerlifting programming built around RPE autoregulation
RPE-based powerlifting autoregulation adjusts each session's weights and sets based on how you perform that day. Nothing is locked in weeks ahead on a spreadsheet. The main signal is RPE rate of perceived exertion, a 1 to 10 rating of how hard a set felt. Our guide to using RPE in powerlifting walks through how to rate a set.
LiftNlog's engine uses the RPE-based method from RTS (Reactive Training Systems). You train to a target RPE. Every number after that comes from the RPE you report. A strong day pushes your weights up. A rough day pulls them back. You never touch a formula.
How a LiftNlog session works
Weight, reps, RPE, and a quick technique rating.
LiftNlog compares the RPE you logged with the target RPE for that set.
Your back-off sets and your next top sets change from that comparison.
When a deload or peak week is close, the app lowers your volume and intensity for you.
Your powerlifting program adapts after every set
Top sets, back-off work, accessories, deloads, long-term progress every part of your training updates on its own. You have nothing to maintain.
Autoregulated loading
Walk up to the bar without second-guessing. The number on your screen is already right for the day you are having.
See how autoregulated loading works →Built-in peak taper
Hit your peak week sharp and on time. No deload spreadsheet, no counting weeks, no guessing whether you backed off too soon.
See how peak tapering works →Accessories, actually programmed
Every accessory has a real rep range, a way to progress, and alternatives you can swap in not just "pick something for legs."
See how accessory programming works →Full training history
Every e1RM and every set you have ever logged, charted per lift not just today's number.
See how training history works →The method behind LiftNlog
LiftNlog is built by Enrique and Collin, two competitive powerlifters. They started it out of one shared frustration: rebuilding the same periodization spreadsheet by hand at the start of every training block.
The app uses the autoregulation model they wanted for their own training: RPE-based loading from the RTS method, not a generic percentage table.