u/Old_Cow_6636

Talus generates 64x64 heightmaps (4 km, up to 1,200 m) conditioned on a terrain type and any subset of five measured properties: mean elevation, relief, mean slope, water fraction and spectral slope. I trained it from scratch on one RTX 5060 (8 GB), about 4.5 hours in total across versions. The data is 45,000 maps from my own procedural generator (fBm/ridged noise, stream-power erosion, hillslope diffusion, thermal erosion). Model

Pixel-space U-Net, v-prediction, cosine schedule, 50-step DDIM with quadratic spacing, classifier-free guidance 2.0

Each property has a learned "unknown" embedding and is dropped independently during training, so any subset works at inference

Relative heights: the model generates the map's shape normalized by its relief, and the sampler places it at the requested mean elevation and relief (borrowed from the 16 nearest training maps when unspecified). This fixed grainy plains: the plains distance ratio went from 3.98 to 1.23

Evaluation: Every distance (W1 over 25 per-map terrain metrics, radially averaged power spectrum, height and slope distributions) is divided by the same distance between two disjoint halves of real maps, so 1.0 means indistinguishable at that sample size. Checkpoints are chosen on VAL; TEST is scored once on the pick, and memorization is checked against the training set. Current model on TEST: metric W1 1.51x the floor, spectrum 9.1x, slopes 1.65x.

Browser: ONNX export with fp16-stored weights (cast to fp32 at load), ONNX Runtime Web on WebGPU, about 3 s per map on my RTX 5060, CPU fallback. A JavaScript reimplementation of the sampler matches PyTorch to within 0.6 m on reference samples. Demo: https://talus.tersa.tech Code, weights, scorecard (Apache-2.0): https://github.com/osfv/talus

Open problems: ridges and the finest spectral band, mountains too smooth and plains too grainy. If you've closed a spectrum gap like this in a small diffusion model, I'd like to hear what worked.

Big respect to SlimBrave, it's what got me into this. but it hasn't been updated in a while and Brave keeps adding new stuff, and i kept seeing people whose shields got broken after running a debloater. so i built my own take with safety as the whole point.

it disables the usual bloat (rewards, wallet, vpn, leo ai, news, telemetry) through Brave's official enterprise policies, so every change is visible in brave://policy. the difference is in how careful it is:

- dry run by default, it shows you every change before doing anything

- backs up the old values to json first, and there's a one command undo

- it hard refuses to disable your shields, safe browsing, or updates, even if you tell it to. a debloater shouldn't be the thing that makes you less safe

- read only doctor mode that just audits your current policy state, handy if another tool left brave acting "managed"

windows + powershell, open source. would love feedback or contributors: https://github.com/osfv/BraveDebloater

Hey bravers, I built BraveDebloater: a small open-source PowerShell tool for people who like Brave’s Shields but want fewer extra surfaces.

It can disable things like Rewards, Wallet, VPN, News, Talk, Playlist, Leo AI Chat, promos, and telemetry-related toggles using official Brave/Chromium policies.

Safety stuff:

- dry-run by default

- creates backups before applying

- has a restore path

- refuses policies that disable Shields, weaken Safe Browsing, or disable updates

Repo:

https://github.com/osfv/BraveDebloater

Feedback welcome, especially if I missed any policy that should or should not be included.