diff --git a/studio/install_python_stack.py b/install_python_stack.py
similarity index 98%
rename from studio/install_python_stack.py
rename to install_python_stack.py
index 927dc625b7..78ddc571d0 100644
--- a/studio/install_python_stack.py
+++ b/install_python_stack.py
@@ -23,11 +23,11 @@ IS_WINDOWS = sys.platform == "win32"
 
 # ── Paths ──────────────────────────────────────────────────────────────
 SCRIPT_DIR = Path(__file__).resolve().parent
-REQ_ROOT = SCRIPT_DIR / "backend" / "requirements"
+REQ_ROOT = SCRIPT_DIR / "studio" / "backend" / "requirements"
 SINGLE_ENV = REQ_ROOT / "single-env"
 CONSTRAINTS = SINGLE_ENV / "constraints.txt"
 LOCAL_DD_UNSTRUCTURED_PLUGIN = (
-    SCRIPT_DIR / "backend" / "plugins" / "data-designer-unstructured-seed"
+    SCRIPT_DIR / "studio" / "backend" / "plugins" / "data-designer-unstructured-seed"
 )
 
 # ── Color support ──────────────────────────────────────────────────────
diff --git a/studio/setup.bat b/setup.bat
similarity index 100%
rename from studio/setup.bat
rename to setup.bat
diff --git a/studio/setup.ps1 b/setup.ps1
similarity index 98%
rename from studio/setup.ps1
rename to setup.ps1
index 6597fc6746..5a573126aa 100644
--- a/studio/setup.ps1
+++ b/setup.ps1
@@ -17,9 +17,9 @@ $ErrorActionPreference = "Stop"
 $ScriptDir = Split-Path -Parent $MyInvocation.MyCommand.Path
 $PackageDir = Split-Path -Parent $ScriptDir
 
-# Detect if running from pip install (no frontend/ dir in studio)
-$FrontendDir = Join-Path $ScriptDir "frontend"
-$OxcValidatorDir = Join-Path $ScriptDir "backend\core\data_recipe\oxc-validator"
+# Detect if running from pip install (no studio/frontend/ dir in repo)
+$FrontendDir = Join-Path $ScriptDir "studio\frontend"
+$OxcValidatorDir = Join-Path $ScriptDir "studio\backend\core\data_recipe\oxc-validator"
 $IsPipInstall = -not (Test-Path $FrontendDir)
 
 # ─────────────────────────────────────────────
@@ -587,7 +587,7 @@ if ($IsPipInstall) {
         Pop-Location
         $ErrorActionPreference = $prevEAP_npm
         Write-Host "[ERROR] npm install failed (exit code $LASTEXITCODE)" -ForegroundColor Red
-        Write-Host "   Try running 'npm install' manually in frontend/ to see errors" -ForegroundColor Yellow
+        Write-Host "   Try running 'npm install' manually in studio/frontend/ to see errors" -ForegroundColor Yellow
         exit 1
     }
     npm run build 2>&1 | Out-Null
@@ -599,7 +599,7 @@ if ($IsPipInstall) {
     }
     Pop-Location
     $ErrorActionPreference = $prevEAP_npm
-    Write-Host "[OK] Frontend built to frontend/dist" -ForegroundColor Green
+    Write-Host "[OK] Frontend built to studio/frontend/dist" -ForegroundColor Green
 }
 
 if (Test-Path $OxcValidatorDir) {
@@ -648,8 +648,8 @@ if (-not $PythonCmd) {
 Write-Host "[OK] Using $PythonCmd ($(& $PythonCmd --version 2>&1))" -ForegroundColor Green
 
 # Always create a .venv for isolation -- even for pip installs.
-# Created in the repo root (parent of studio/).
-$VenvDir = Join-Path (Split-Path -Parent $PSScriptRoot) ".venv"
+# Created in the current working directory (where user ran the command).
+$VenvDir = Join-Path (Get-Location) ".venv"
 if (-not (Test-Path $VenvDir)) {
     Write-Host "   Creating virtual environment at $VenvDir..." -ForegroundColor Cyan
     & $PythonCmd -m venv $VenvDir
@@ -722,7 +722,7 @@ $ErrorActionPreference = $prevEAP
 # The training subprocess just prepends .venv_t5/ to sys.path — instant switch.
 Write-Host ""
 Write-Host "   Pre-installing transformers 5.x for newer model support..." -ForegroundColor Cyan
-$VenvT5Dir = Join-Path (Split-Path -Parent $PSScriptRoot) ".venv_t5"
+$VenvT5Dir = Join-Path $PSScriptRoot ".venv_t5"
 if (Test-Path $VenvT5Dir) { Remove-Item -Recurse -Force $VenvT5Dir }
 New-Item -ItemType Directory -Path $VenvT5Dir -Force | Out-Null
 $prevEAP_t5 = $ErrorActionPreference
@@ -956,10 +956,10 @@ if (Test-Path $LlamaServerBin) {
 # Add shell aliases (PowerShell profile + cmd batch files)
 # ============================================
 Write-Host ""
-$RepoDir = Split-Path -Parent $PSScriptRoot
+$RepoDir = $PSScriptRoot
 $VenvPython = Join-Path $RepoDir ".venv\Scripts\python.exe"
 $CliScript = Join-Path $RepoDir "cli.py"
-$FrontendDist = Join-Path $PSScriptRoot "frontend\dist"
+$FrontendDist = Join-Path $RepoDir "studio\frontend\dist"
 $AliasAdded = $false
 
 # --- PowerShell profile: add functions ---
diff --git a/studio/setup.sh b/setup.sh
similarity index 93%
rename from studio/setup.sh
rename to setup.sh
index a1595fd935..b1ff403fbe 100755
--- a/studio/setup.sh
+++ b/setup.sh
@@ -5,7 +5,6 @@
 set -euo pipefail
 
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
 
 # ── Helper: run command quietly, show output only on failure ──
 run_quiet() {
@@ -29,9 +28,9 @@ echo "║     Unsloth Studio Setup Script      ║"
 echo "╚══════════════════════════════════════╝"
 
 # ── Clean up stale Unsloth compiled caches ──
-rm -rf "$REPO_ROOT/unsloth_compiled_cache"
-rm -rf "$SCRIPT_DIR/backend/unsloth_compiled_cache"
-rm -rf "$SCRIPT_DIR/tmp/unsloth_compiled_cache"
+rm -rf "$SCRIPT_DIR/unsloth_compiled_cache"
+rm -rf "$SCRIPT_DIR/studio/backend/unsloth_compiled_cache"
+rm -rf "$SCRIPT_DIR/studio/tmp/unsloth_compiled_cache"
 
 # ── Detect Colab (like unsloth does) ──
 IS_COLAB=false
@@ -100,13 +99,13 @@ echo "✅ Node $(node -v) | npm $(npm -v)"
 # ── 5. Build frontend ──
 echo ""
 echo "Building frontend..."
-cd "$SCRIPT_DIR/frontend"
+cd "$SCRIPT_DIR/studio/frontend"
 run_quiet "npm install" npm install
 run_quiet "npm run build" npm run build
-cd "$SCRIPT_DIR/backend/core/data_recipe/oxc-validator"
+cd "$SCRIPT_DIR/studio/backend/core/data_recipe/oxc-validator"
 run_quiet "npm install (oxc validator runtime)" npm install
 cd "$SCRIPT_DIR"
-echo "✅ Frontend built to frontend/dist"
+echo "✅ Frontend built to studio/frontend/dist"
 
 # ── 6. Python venv + deps ──
 echo ""
@@ -169,7 +168,7 @@ fi
 BEST_VER=$("$BEST_PY" --version 2>&1 | awk '{print $2}')
 echo "✅ Using $BEST_PY ($BEST_VER) — compatible (3.${MIN_PY_MINOR}.x – 3.${MAX_PY_MINOR}.x)"
 
-REQ_ROOT="$SCRIPT_DIR/backend/requirements"
+REQ_ROOT="$SCRIPT_DIR/studio/backend/requirements"
 SINGLE_ENV_CONSTRAINTS="$REQ_ROOT/single-env/constraints.txt"
 SINGLE_ENV_DATA_DESIGNER="$REQ_ROOT/single-env/data-designer.txt"
 SINGLE_ENV_DATA_DESIGNER_DEPS="$REQ_ROOT/single-env/data-designer-deps.txt"
@@ -184,13 +183,11 @@ if [ "$IS_COLAB" = true ]; then
     install_python_stack
 else
     # Local: create venv (always start fresh to preserve correct install order)
-    cd "$REPO_ROOT"
     rm -rf .venv
     rm -rf .venv_overlay  # Remove legacy overlay (no longer used)
     rm -rf .venv_t5       # Will be rebuilt below
     "$BEST_PY" -m venv .venv
     source .venv/bin/activate
-    cd "$SCRIPT_DIR"
     install_python_stack
 
     # ── 6b. Pre-install transformers 5.x into .venv_t5/ ──
@@ -199,7 +196,7 @@ else
     # The training subprocess just prepends .venv_t5/ to sys.path — instant switch.
     echo ""
     echo "   Pre-installing transformers 5.x for newer model support..."
-    VENV_T5_DIR="$REPO_ROOT/.venv_t5"
+    VENV_T5_DIR="$SCRIPT_DIR/.venv_t5"
     mkdir -p "$VENV_T5_DIR"
     run_quiet "pip install transformers 5.x" pip install --target "$VENV_T5_DIR" --no-deps "transformers==5.2.0"
     run_quiet "pip install huggingface_hub for t5" pip install --target "$VENV_T5_DIR" --no-deps "huggingface_hub==1.3.0"
@@ -309,30 +306,32 @@ rm -rf "$LLAMA_CPP_DIR"
 # This alias hardcodes the venv python path so users don't need to activate.
 if [ "$IS_COLAB" = false ]; then
 echo ""
-REPO_DIR="$REPO_ROOT"
+REPO_DIR="$SCRIPT_DIR"
 
 # Detect the user's default shell and pick the right rc file
 USER_SHELL="$(basename "${SHELL:-/bin/bash}")"
 case "$USER_SHELL" in
     zsh)
         SHELL_RC="$HOME/.zshrc"
-        ALIAS_BLOCK="alias unsloth-studio='${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${SCRIPT_DIR}/frontend/dist'
-alias unsloth-ui='${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${SCRIPT_DIR}/frontend/dist'"
+        ALIAS_BLOCK="alias unsloth-studio='${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${REPO_DIR}/studio/frontend/dist'
+alias unsloth-ui='${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${REPO_DIR}/studio/frontend/dist'"
         ;;
     fish)
         SHELL_RC="$HOME/.config/fish/config.fish"
-        ALIAS_BLOCK="alias unsloth-studio '${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${SCRIPT_DIR}/frontend/dist'
-alias unsloth-ui '${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${SCRIPT_DIR}/frontend/dist'"
+        # fish uses 'abbr' or 'function'; a simple alias works via 'alias' in config.fish
+        ALIAS_BLOCK="alias unsloth-studio '${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${REPO_DIR}/studio/frontend/dist'
+alias unsloth-ui '${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${REPO_DIR}/studio/frontend/dist'"
         ;;
     ksh)
         SHELL_RC="$HOME/.kshrc"
-        ALIAS_BLOCK="alias unsloth-studio='${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${SCRIPT_DIR}/frontend/dist'
-alias unsloth-ui='${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${SCRIPT_DIR}/frontend/dist'"
+        ALIAS_BLOCK="alias unsloth-studio='${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${REPO_DIR}/studio/frontend/dist'
+alias unsloth-ui='${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${REPO_DIR}/studio/frontend/dist'"
         ;;
     *)
+        # Default to bash for bash and any other POSIX-compatible shell
         SHELL_RC="$HOME/.bashrc"
-        ALIAS_BLOCK="alias unsloth-studio='${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${SCRIPT_DIR}/frontend/dist'
-alias unsloth-ui='${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${SCRIPT_DIR}/frontend/dist'"
+        ALIAS_BLOCK="alias unsloth-studio='${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${REPO_DIR}/studio/frontend/dist'
+alias unsloth-ui='${REPO_DIR}/.venv/bin/python ${REPO_DIR}/cli.py studio -f ${REPO_DIR}/studio/frontend/dist'"
         ;;
 esac
 
diff --git a/studio/backend/auth/storage.py b/studio/backend/auth/storage.py
index cb5df6e3f9..f72a7ed943 100644
--- a/studio/backend/auth/storage.py
+++ b/studio/backend/auth/storage.py
@@ -7,11 +7,10 @@ SQLite storage for authentication data (user credentials + JWT secret).
 import hashlib
 import sqlite3
 from datetime import datetime, timezone
+from pathlib import Path
 from typing import Optional, Tuple
 
-from utils.paths import auth_db_path, ensure_dir
-
-DB_PATH = auth_db_path()
+DB_PATH = Path(__file__).parent / "auth.db"
 
 
 def _hash_token(token: str) -> str:
@@ -21,7 +20,6 @@ def _hash_token(token: str) -> str:
 
 def get_connection() -> sqlite3.Connection:
     """Get a connection to the auth database, creating tables if needed."""
-    ensure_dir(DB_PATH.parent)
     conn = sqlite3.connect(DB_PATH)
     conn.row_factory = sqlite3.Row
     conn.execute(
@@ -258,3 +256,4 @@ def revoke_user_refresh_tokens(username: str) -> None:
         conn.commit()
     finally:
         conn.close()
+
diff --git a/studio/backend/core/data_recipe/jobs/worker.py b/studio/backend/core/data_recipe/jobs/worker.py
index 1d898f866f..fbe9d91b6d 100644
--- a/studio/backend/core/data_recipe/jobs/worker.py
+++ b/studio/backend/core/data_recipe/jobs/worker.py
@@ -16,9 +16,9 @@ from typing import Any
 from ..jsonable import to_jsonable, to_preview_jsonable
 from .constants import EVENT_JOB_COMPLETED, EVENT_JOB_ERROR, EVENT_JOB_STARTED
 from ..service import build_config_builder, create_data_designer
-from utils.paths import ensure_dir, recipe_datasets_root
 
-_ARTIFACT_ROOT = recipe_datasets_root()
+_PROJECT_ROOT = Path(__file__).resolve().parents[5]
+_ARTIFACT_ROOT = _PROJECT_ROOT / "studio" / "backend" / "assets" / "datasets"
 
 
 class _QueueLogHandler(logging.Handler):
@@ -88,7 +88,7 @@ def run_job_process(
             artifact_root=_ARTIFACT_ROOT,
         )
         merge_batches = bool(run.get("merge_batches"))
-        ensure_dir(_ARTIFACT_ROOT)
+        _ARTIFACT_ROOT.mkdir(parents=True, exist_ok=True)
         run_config_raw = run.get("run_config") or {}
 
         builder = build_config_builder(recipe)
diff --git a/studio/backend/core/data_recipe/local_callable_validators.py b/studio/backend/core/data_recipe/local_callable_validators.py
index a7e211eca5..5e2749d45d 100644
--- a/studio/backend/core/data_recipe/local_callable_validators.py
+++ b/studio/backend/core/data_recipe/local_callable_validators.py
@@ -5,7 +5,6 @@ from __future__ import annotations
 
 import json
 import logging
-import os
 import subprocess
 from copy import deepcopy
 from dataclasses import dataclass
@@ -13,8 +12,6 @@ from functools import lru_cache
 from pathlib import Path
 from typing import Any
 
-from utils.paths import ensure_dir, oxc_validator_tmp_root
-
 logger = logging.getLogger(__name__)
 
 OXC_VALIDATION_FN_MARKER = "unsloth_oxc_validator"
@@ -238,12 +235,6 @@ def _run_oxc_batch(
         "codes": code_values,
     }
     try:
-        tmp_dir = ensure_dir(oxc_validator_tmp_root())
-        env = dict(os.environ)
-        tmp_dir_str = str(tmp_dir)
-        env["TMPDIR"] = tmp_dir_str
-        env["TMP"] = tmp_dir_str
-        env["TEMP"] = tmp_dir_str
         proc = subprocess.run(
             ["node", str(_OXC_RUNNER_PATH)],
             cwd=str(_OXC_TOOL_DIR),
@@ -251,7 +242,6 @@ def _run_oxc_batch(
             text=True,
             capture_output=True,
             check=False,
-            env=env,
         )
     except (OSError, ValueError) as exc:
         logger.warning("OXC subprocess launch failed: %s", exc)
diff --git a/studio/backend/core/export/export.py b/studio/backend/core/export/export.py
index ef19530a2c..986da0def2 100644
--- a/studio/backend/core/export/export.py
+++ b/studio/backend/core/export/export.py
@@ -21,7 +21,6 @@ from utils.hardware import clear_gpu_cache
 
 from utils.models import is_vision_model, get_base_model_from_lora
 from utils.models.model_config import detect_audio_type
-from utils.paths import ensure_dir, outputs_root, resolve_export_dir, resolve_output_dir
 from core.inference import get_inference_backend
 
 logger = logging.getLogger(__name__)
@@ -129,7 +128,7 @@ class ExportBackend:
             logger.error(f"Error during memory cleanup: {e}")
             return False
 
-    def scan_checkpoints(self, outputs_dir: str = str(outputs_root())) -> List[Tuple[str, List[Tuple[str, str]]]]:
+    def scan_checkpoints(self, outputs_dir: str = "./outputs") -> List[Tuple[str, List[Tuple[str, str]]]]:
         """
         Scan outputs folder for training runs and their checkpoints.
 
@@ -326,9 +325,8 @@ class ExportBackend:
 
             # Save locally if requested
             if save_directory:
-                save_directory = str(resolve_export_dir(save_directory))
                 logger.info(f"Saving merged model locally to: {save_directory}")
-                ensure_dir(Path(save_directory))
+                os.makedirs(save_directory, exist_ok=True)
 
                 self.current_model.save_pretrained_merged(
                     save_directory,
@@ -388,9 +386,8 @@ class ExportBackend:
         try:
             # Save locally if requested
             if save_directory:
-                save_directory = str(resolve_export_dir(save_directory))
                 logger.info(f"Saving base model locally to: {save_directory}")
-                ensure_dir(Path(save_directory))
+                os.makedirs(save_directory, exist_ok=True)
 
                 self.current_model.save_pretrained(save_directory)
                 self.current_tokenizer.save_pretrained(save_directory)
@@ -478,7 +475,6 @@ class ExportBackend:
 
             # Save locally if requested
             if save_directory:
-                save_directory = str(resolve_export_dir(save_directory))
                 # Resolve to absolute path so unsloth's relative-path internals
                 # (check_llama_cpp, use_local_gguf, _download_convert_hf_to_gguf)
                 # all resolve against the repo root cwd, NOT the export directory.
@@ -486,7 +482,7 @@ class ExportBackend:
                 logger.info(f"Saving GGUF model locally to: {abs_save_dir}")
 
                 # Create the directory if it doesn't exist
-                ensure_dir(Path(abs_save_dir))
+                os.makedirs(abs_save_dir, exist_ok=True)
 
                 # On WSL, patch out sudo check before llama.cpp build
                 _apply_wsl_sudo_patch()
@@ -586,9 +582,8 @@ class ExportBackend:
         try:
             # Save locally if requested
             if save_directory:
-                save_directory = str(resolve_export_dir(save_directory))
                 logger.info(f"Saving LoRA adapter locally to: {save_directory}")
-                ensure_dir(Path(save_directory))
+                os.makedirs(save_directory, exist_ok=True)
 
                 self.current_model.save_pretrained(save_directory)
                 self.current_tokenizer.save_pretrained(save_directory)
diff --git a/studio/backend/core/export/orchestrator.py b/studio/backend/core/export/orchestrator.py
index 37604c81df..d6a04a3c1b 100644
--- a/studio/backend/core/export/orchestrator.py
+++ b/studio/backend/core/export/orchestrator.py
@@ -21,7 +21,6 @@ import threading
 import time
 from pathlib import Path
 from typing import Any, List, Optional, Tuple
-from utils.paths import outputs_root
 
 logger = logging.getLogger(__name__)
 
@@ -379,7 +378,7 @@ class ExportOrchestrator:
         return success
 
     def scan_checkpoints(
-        self, outputs_dir: str = str(outputs_root())
+        self, outputs_dir: str = "./outputs"
     ) -> List[Tuple[str, list]]:
         """Scan for checkpoints — no ML imports needed, runs locally."""
         from utils.models.checkpoints import scan_checkpoints
diff --git a/studio/backend/core/training/trainer.py b/studio/backend/core/training/trainer.py
index 5a03cd6653..31e05d0bda 100644
--- a/studio/backend/core/training/trainer.py
+++ b/studio/backend/core/training/trainer.py
@@ -30,21 +30,13 @@ from datasets import Dataset, load_dataset
 from utils.models import is_vision_model, detect_audio_type
 from utils.datasets import format_and_template_dataset
 from utils.datasets import MODEL_TO_TEMPLATE_MAPPER, TEMPLATE_TO_RESPONSES_MAPPER
-from utils.paths import ensure_dir, resolve_dataset_path, resolve_output_dir, resolve_tensorboard_dir
 from trl import SFTTrainer, SFTConfig
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-
-
-def _build_report_targets(training_args) -> list[str] | str:
-    report_to: list[str] = []
-    if training_args.get("enable_wandb", False):
-        report_to.append("wandb")
-    if training_args.get("enable_tensorboard", False):
-        report_to.append("tensorboard")
-    return report_to or "none"
+_BACKEND_ROOT = Path(__file__).resolve().parents[2]
+_ASSETS_DATASETS_ROOT = _BACKEND_ROOT / "assets" / "datasets"
 
 
 @dataclass
@@ -213,14 +205,9 @@ class UnslothTrainer:
             "lr_scheduler_type": lr_scheduler_type,
             "seed": random_seed,
             "output_dir": output_dir,
-            "report_to": _build_report_targets(training_args),
+            "report_to": ["wandb"] if training_args.get('enable_wandb', False) else "none",
         }
 
-        if training_args.get("enable_tensorboard", False):
-            config["logging_dir"] = str(
-                resolve_tensorboard_dir(training_args.get("tensorboard_dir"))
-            )
-
         # max_steps vs epochs
         if max_steps_val and max_steps_val > 0:
             config["max_steps"] = max_steps_val
@@ -1542,7 +1529,6 @@ class UnslothTrainer:
         import numpy as np
         import soundfile as sf
         from datasets import Dataset as HFDataset
-        from utils.paths import ensure_dir, tmp_root
 
         device = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -1639,18 +1625,10 @@ class UnslothTrainer:
                 audio_bytes = buf.getvalue()
 
                 # 1. Get word timings from Whisper
-                with tempfile.NamedTemporaryFile(
-                    suffix=".wav",
-                    delete=False,
-                    dir=str(ensure_dir(tmp_root())),
-                ) as tmp:
+                with tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as tmp:
                     tmp.write(audio_bytes)
                     tmp.flush()
-                    tmp_path = tmp.name
-                try:
-                    whisper_result = whisper_model.transcribe(tmp_path, word_timestamps=True)
-                finally:
-                    Path(tmp_path).unlink(missing_ok=True)
+                    whisper_result = whisper_model.transcribe(tmp.name, word_timestamps=True)
 
                 normalized_transcript = text_normalizations(text)
                 words_with_timings = []
@@ -1838,7 +1816,7 @@ class UnslothTrainer:
                         file_path = dataset_file
                     else:
                         # Fallback: try relative to assets/datasets
-                        file_path = str(resolve_dataset_path(dataset_file))
+                        file_path = str(_ASSETS_DATASETS_ROOT / dataset_file)
 
                     file_path_obj = Path(file_path)
 
@@ -2091,7 +2069,7 @@ class UnslothTrainer:
                        dataset: Dataset,
                        eval_dataset: Dataset = None,
                        eval_steps: float = 0.00,
-                       output_dir: str | None = None,
+                       output_dir: str = "./outputs",
                        num_epochs: int = 3,
                        learning_rate: float = 5e-5,
                        batch_size: int = 2,
@@ -2108,7 +2086,7 @@ class UnslothTrainer:
                        wandb_project: str = "unsloth-training",
                        wandb_token: str = None,
                        enable_tensorboard: bool = False,
-                       tensorboard_dir: str | None = None,
+                       tensorboard_dir: str = "runs",
                        **kwargs) -> bool:
         """Start training in a separate thread"""
 
@@ -2196,8 +2174,8 @@ class UnslothTrainer:
                 wandb.init(project=training_args.get('wandb_project', 'unsloth-training'))
 
             # Create output directory
-            output_dir = str(resolve_output_dir(training_args.get("output_dir")))
-            ensure_dir(Path(output_dir))
+            output_dir = training_args.get('output_dir', './outputs')
+            os.makedirs(output_dir, exist_ok=True)
 
             # ========== AUDIO TRAINER BRANCH ==========
             if self._audio_type == 'csm':
@@ -2407,15 +2385,11 @@ class UnslothTrainer:
                 "weight_decay": training_args.get('weight_decay', 0.01),
                 "seed": training_args.get('random_seed', 3407),
                 "output_dir": output_dir,
-                "report_to": _build_report_targets(training_args),
+                "report_to": ["wandb"] if training_args.get('enable_wandb', False) else "none",
                 "include_num_input_tokens_seen": True,  # Enable token counting
                 "dataset_num_proc": 1 if (self.is_audio or self.is_audio_vlm or self._cuda_audio_used) else safe_num_proc(max(1, os.cpu_count() // 4)),
                 "max_seq_length": training_args.get('max_seq_length', 2048),
             }
-            if training_args.get("enable_tensorboard", False):
-                config_args["logging_dir"] = str(
-                    resolve_tensorboard_dir(training_args.get("tensorboard_dir"))
-                )
             print(f"[DEBUG] dataset_num_proc={config_args['dataset_num_proc']} (is_audio={self.is_audio}, is_audio_vlm={self.is_audio_vlm}, _cuda_audio_used={self._cuda_audio_used})")
 
             # On Windows with transformers 5.x, disable DataLoader multiprocessing
diff --git a/studio/backend/core/training/worker.py b/studio/backend/core/training/worker.py
index 509f99d3ec..7b98865eea 100644
--- a/studio/backend/core/training/worker.py
+++ b/studio/backend/core/training/worker.py
@@ -121,7 +121,6 @@ def run_training_process(
             sys.path.insert(0, backend_path)
 
         from core.training.trainer import UnslothTrainer, TrainingProgress
-        from utils.paths import ensure_dir, resolve_output_dir, resolve_tensorboard_dir
 
         import transformers
         logger.info("Subprocess loaded transformers %s", transformers.__version__)
@@ -306,14 +305,7 @@ def run_training_process(
         # Generate output dir
         output_dir = config.get("output_dir")
         if not output_dir:
-            output_dir = f"{model_name.replace('/', '_')}_{int(time.time())}"
-        output_dir = str(resolve_output_dir(output_dir))
-        ensure_dir(Path(output_dir))
-
-        tensorboard_dir = config.get("tensorboard_dir")
-        if config.get("enable_tensorboard", False):
-            tensorboard_dir = str(resolve_tensorboard_dir(tensorboard_dir))
-            ensure_dir(Path(tensorboard_dir))
+            output_dir = f"./outputs/{model_name.replace('/', '_')}_{int(time.time())}"
 
         # Start training (directly — no inner thread, we ARE the subprocess)
         _send_status(event_queue, "Starting training...")
@@ -339,7 +331,7 @@ def run_training_process(
             wandb_project=config.get("wandb_project", "unsloth-training"),
             wandb_token=config.get("wandb_token"),
             enable_tensorboard=config.get("enable_tensorboard", False),
-            tensorboard_dir=tensorboard_dir,
+            tensorboard_dir=config.get("tensorboard_dir", "runs"),
             eval_dataset=eval_dataset,
             eval_steps=eval_steps,
             max_seq_length=config.get("max_seq_length", 2048),
diff --git a/studio/backend/plugins/data-designer-unstructured-seed/src/data_designer_unstructured_seed/chunking.py b/studio/backend/plugins/data-designer-unstructured-seed/src/data_designer_unstructured_seed/chunking.py
index e226f4f27d..358e7ea701 100644
--- a/studio/backend/plugins/data-designer-unstructured-seed/src/data_designer_unstructured_seed/chunking.py
+++ b/studio/backend/plugins/data-designer-unstructured-seed/src/data_designer_unstructured_seed/chunking.py
@@ -8,13 +8,11 @@ import re
 from pathlib import Path
 from typing import Any
 
-from utils.paths import ensure_dir, unstructured_seed_cache_root
-
 DEFAULT_CHUNK_SIZE = 1200
 DEFAULT_CHUNK_OVERLAP = 200
 MAX_CHUNK_SIZE = 20000
 _MIN_BREAK_RATIO = 0.6
-_CACHE_DIR = unstructured_seed_cache_root()
+_CACHE_DIR = Path.home() / ".cache" / "unsloth" / "data-recipe" / "unstructured-seed-cache"
 
 
 def resolve_chunking(
@@ -87,7 +85,7 @@ def materialize_unstructured_seed_dataset(
         raise ValueError("No text found in unstructured seed source.")
 
     rows = [{"chunk_text": chunk} for chunk in chunks]
-    ensure_dir(_CACHE_DIR)
+    _CACHE_DIR.mkdir(parents=True, exist_ok=True)
     try:
         import pandas as pd
     except ImportError as exc:  # pragma: no cover
diff --git a/studio/backend/routes/data_recipe/seed.py b/studio/backend/routes/data_recipe/seed.py
index f388ce2323..e803c054c2 100644
--- a/studio/backend/routes/data_recipe/seed.py
+++ b/studio/backend/routes/data_recipe/seed.py
@@ -18,7 +18,6 @@ from data_designer_unstructured_seed.chunking import (
     resolve_chunking,
 )
 from core.data_recipe.jsonable import to_preview_jsonable
-from utils.paths import ensure_dir, seed_uploads_root
 
 from models.data_recipe import (
     SeedInspectRequest,
@@ -32,7 +31,7 @@ DATA_EXTS = (".parquet", ".jsonl", ".json", ".csv")
 DEFAULT_SPLIT = "train"
 LOCAL_UPLOAD_EXTS = {".csv", ".json", ".jsonl"}
 UNSTRUCTURED_UPLOAD_EXTS = {".txt", ".md"}
-SEED_UPLOAD_DIR = seed_uploads_root()
+SEED_UPLOAD_DIR = Path.home() / ".cache" / "unsloth" / "data-recipe" / "seed-uploads"
 
 
 def _serialize_preview_value(value: Any) -> Any:
@@ -305,7 +304,7 @@ def inspect_seed_upload(payload: SeedInspectUploadRequest) -> SeedInspectRespons
     if len(file_bytes) > max_size_bytes:
         raise HTTPException(status_code=413, detail="file too large (max 50MB)")
 
-    ensure_dir(SEED_UPLOAD_DIR)
+    SEED_UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
     stored_name = f"{uuid4().hex}_{filename}"
     stored_path = SEED_UPLOAD_DIR / stored_name
     stored_path.write_bytes(file_bytes)
diff --git a/studio/backend/routes/datasets.py b/studio/backend/routes/datasets.py
index b8793fd9d0..25965a847e 100644
--- a/studio/backend/routes/datasets.py
+++ b/studio/backend/routes/datasets.py
@@ -42,12 +42,6 @@ from models.datasets import (
     LocalDatasetsResponse,
     UploadDatasetResponse,
 )
-from utils.paths import (
-    dataset_uploads_root,
-    ensure_dir,
-    recipe_datasets_root,
-    resolve_dataset_path,
-)
 
 
 def _serialize_preview_value(value):
@@ -97,8 +91,9 @@ _ARCHIVE_EXTS = ('.tar', '.tar.gz', '.tgz', '.gz', '.zst', '.zip', '.txt')
 DATA_EXTS = _TABULAR_EXTS + _ARCHIVE_EXTS
 LOCAL_FILE_EXTS = ('.json', '.jsonl', '.csv', '.parquet')
 LOCAL_UPLOAD_EXTS = {".csv", ".json", ".jsonl", ".parquet"}
-LOCAL_DATASETS_ROOT = recipe_datasets_root()
-DATASET_UPLOAD_DIR = dataset_uploads_root()
+BACKEND_ROOT = Path(__file__).resolve().parents[1]
+LOCAL_DATASETS_ROOT = BACKEND_ROOT / "assets" / "datasets"
+DATASET_UPLOAD_DIR = LOCAL_DATASETS_ROOT / "uploads"
 
 
 def _safe_read_metadata(path: Path) -> dict | None:
@@ -282,7 +277,7 @@ async def upload_dataset(
         )
 
     max_size_bytes = 512 * 1024 * 1024
-    ensure_dir(DATASET_UPLOAD_DIR)
+    DATASET_UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
     stem = Path(filename).stem
     stored_name = f"{uuid4().hex}_{stem}{ext}"
     stored_path = DATASET_UPLOAD_DIR / stored_name
@@ -338,7 +333,7 @@ def check_format(
 
         logger.info(f"Checking format for dataset: {request.dataset_name}")
 
-        dataset_path = resolve_dataset_path(request.dataset_name)
+        dataset_path = Path(request.dataset_name)
         total_rows = None
 
         if dataset_path.exists():
diff --git a/studio/backend/routes/inference.py b/studio/backend/routes/inference.py
index 4e7b1e6646..387268edbb 100644
--- a/studio/backend/routes/inference.py
+++ b/studio/backend/routes/inference.py
@@ -576,16 +576,11 @@ def _decode_audio_base64(b64: str) -> np.ndarray:
     import torchaudio
     import tempfile
     import os
-    from utils.paths import ensure_dir, tmp_root
 
     raw = base64.b64decode(b64)
     # torchaudio.load needs a file path or file-like object with format hint
     # Write to a temp file so torchaudio can auto-detect the format
-    with tempfile.NamedTemporaryFile(
-        suffix=".audio",
-        delete=False,
-        dir=str(ensure_dir(tmp_root())),
-    ) as tmp:
+    with tempfile.NamedTemporaryFile(suffix=".audio", delete=False) as tmp:
         tmp.write(raw)
         tmp_path = tmp.name
     try:
diff --git a/studio/backend/routes/models.py b/studio/backend/routes/models.py
index 190924493e..e217ff63d8 100644
--- a/studio/backend/routes/models.py
+++ b/studio/backend/routes/models.py
@@ -31,7 +31,6 @@ try:
     )
     from utils.models.model_config import _pick_best_gguf, _extract_quant_label, is_audio_input_type
     from core.inference import get_inference_backend
-    from utils.paths import outputs_root, exports_root, resolve_output_dir, resolve_export_dir
 except ImportError:
     # Fallback: try to import from parent directory
     parent_backend = backend_path.parent / "backend"
@@ -49,7 +48,6 @@ except ImportError:
     )
     from utils.models.model_config import _pick_best_gguf, _extract_quant_label, is_audio_input_type
     from core.inference import get_inference_backend
-    from utils.paths import outputs_root, exports_root, resolve_output_dir, resolve_export_dir
 
 from models import (
     CheckpointInfo,
@@ -324,8 +322,8 @@ async def get_model_config(
 
 @router.get("/loras")
 async def scan_loras(
-    outputs_dir: str = Query(default=str(outputs_root()), description="Directory to scan for LoRA adapters"),
-    exports_dir: str = Query(default=str(exports_root()), description="Directory to scan for exported models"),
+    outputs_dir: str = Query(default="./outputs", description="Directory to scan for LoRA adapters"),
+    exports_dir: str = Query(default="./exports", description="Directory to scan for exported models"),
     current_subject: str = Depends(get_current_subject),
 ):
     """
@@ -335,12 +333,10 @@ async def scan_loras(
     (from exports_dir) in a single list, distinguished by source field.
     """
     try:
-        resolved_outputs_dir = str(resolve_output_dir(outputs_dir))
-        resolved_exports_dir = str(resolve_export_dir(exports_dir))
         lora_list = []
 
         # Scan training outputs
-        trained_loras = scan_trained_loras(outputs_dir=resolved_outputs_dir)
+        trained_loras = scan_trained_loras(outputs_dir=outputs_dir)
         for display_name, adapter_path in trained_loras:
             base_model = get_base_model_from_lora(adapter_path)
             lora_list.append(LoRAInfo(
@@ -351,7 +347,7 @@ async def scan_loras(
             ))
 
         # Scan exported models (merged, LoRA, base — skips GGUF)
-        exported = scan_exported_models(exports_dir=resolved_exports_dir)
+        exported = scan_exported_models(exports_dir=exports_dir)
         for display_name, model_path, export_type, base_model in exported:
             lora_list.append(LoRAInfo(
                 display_name=display_name,
@@ -363,7 +359,7 @@ async def scan_loras(
 
         return LoRAScanResponse(
             loras=lora_list,
-            outputs_dir=resolved_outputs_dir
+            outputs_dir=outputs_dir
         )
 
     except Exception as e:
@@ -481,7 +477,7 @@ async def get_gguf_variants(
 @router.get("/checkpoints", response_model=CheckpointListResponse)
 async def list_checkpoints(
     outputs_dir: str = Query(
-        default=str(outputs_root()),
+        default="./outputs",
         description="Directory to scan for checkpoints",
     ),
     current_subject: str = Depends(get_current_subject),
@@ -492,8 +488,7 @@ async def list_checkpoints(
     Scans the outputs folder for training runs and their checkpoints.
     """
     try:
-        resolved_outputs_dir = str(resolve_output_dir(outputs_dir))
-        raw_models = scan_checkpoints(outputs_dir=resolved_outputs_dir)
+        raw_models = scan_checkpoints(outputs_dir=outputs_dir)
 
         models = [
             ModelCheckpoints(
@@ -510,7 +505,7 @@ async def list_checkpoints(
         ]
 
         return CheckpointListResponse(
-            outputs_dir=resolved_outputs_dir,
+            outputs_dir=outputs_dir,
             models=models,
         )
     except Exception as e:
diff --git a/studio/backend/routes/training.py b/studio/backend/routes/training.py
index 45fbff0172..3deabb5253 100644
--- a/studio/backend/routes/training.py
+++ b/studio/backend/routes/training.py
@@ -23,7 +23,6 @@ if str(backend_path) not in sys.path:
 try:
     from core.training import get_training_backend
     from utils.models.model_config import load_model_defaults
-    from utils.paths import resolve_dataset_path
 except ImportError:
     # Fallback: try to import from parent directory
     parent_backend = backend_path.parent / "backend"
@@ -31,7 +30,6 @@ except ImportError:
         sys.path.insert(0, str(parent_backend))
     from core.training import get_training_backend
     from utils.models.model_config import load_model_defaults
-    from utils.paths import resolve_dataset_path
 
 # Auth
 from auth.authentication import get_current_subject
@@ -119,8 +117,24 @@ async def start_training(
         if request.local_datasets:
             validated_datasets = []
             missing_datasets = []
+            # Get the backend directory (where this file is located)
+            backend_dir = Path(__file__).parent.parent
+            assets_datasets_dir = backend_dir / "assets" / "datasets"
+
             for dataset_path in request.local_datasets:
-                dataset_file = resolve_dataset_path(dataset_path)
+                dataset_file = Path(dataset_path)
+
+                # If not absolute, try multiple locations
+                if not dataset_file.is_absolute():
+                    # First try: relative to current working directory
+                    candidate = Path.cwd() / dataset_path
+                    if not candidate.exists():
+                        # Second try: relative to assets/datasets folder
+                        candidate = assets_datasets_dir / dataset_path
+                    if not candidate.exists():
+                        # Third try: just the filename in assets/datasets
+                        candidate = assets_datasets_dir / dataset_file.name
+                    dataset_file = candidate
 
                 if not dataset_file.exists():
                     missing_datasets.append(
diff --git a/studio/backend/utils/models/checkpoints.py b/studio/backend/utils/models/checkpoints.py
index c1cc0d0b78..c601cf64db 100644
--- a/studio/backend/utils/models/checkpoints.py
+++ b/studio/backend/utils/models/checkpoints.py
@@ -8,7 +8,6 @@ import json
 import logging
 from pathlib import Path
 from typing import List, Optional, Tuple
-from utils.paths import outputs_root, resolve_output_dir
 
 logger = logging.getLogger(__name__)
 
@@ -34,7 +33,7 @@ def _read_checkpoint_loss(checkpoint_path: Path) -> Optional[float]:
 
 
 def scan_checkpoints(
-    outputs_dir: str = str(outputs_root()),
+    outputs_dir: str = "./outputs",
 ) -> List[Tuple[str, List[Tuple[str, str, Optional[float]]], dict]]:
     """
     Scan outputs folder for training runs and their checkpoints.
@@ -46,7 +45,7 @@ def scan_checkpoints(
         set to the loss of the last (highest-step) intermediate checkpoint.
     """
     models = []
-    outputs_path = resolve_output_dir(outputs_dir)
+    outputs_path = Path(outputs_dir)
 
     if not outputs_path.exists():
         logger.warning(f"Outputs directory not found: {outputs_dir}")
diff --git a/studio/backend/utils/models/model_config.py b/studio/backend/utils/models/model_config.py
index 472f994e2e..8724f4b00b 100644
--- a/studio/backend/utils/models/model_config.py
+++ b/studio/backend/utils/models/model_config.py
@@ -7,15 +7,7 @@ Model and LoRA configuration handling
 from transformers import AutoConfig
 from dataclasses import dataclass
 from typing import Optional, Dict, Any
-from utils.paths import (
-    normalize_path,
-    is_local_path,
-    is_model_cached,
-    outputs_root,
-    exports_root,
-    resolve_output_dir,
-    resolve_export_dir,
-)
+from utils.paths import normalize_path, is_local_path, is_model_cached
 from utils.utils import without_hf_auth
 import logging
 import os
@@ -902,7 +894,7 @@ def download_gguf_file(
     return local_path
 
 
-def scan_trained_loras(outputs_dir: str = str(outputs_root())) -> List[Tuple[str, str]]:
+def scan_trained_loras(outputs_dir: str = "./outputs") -> List[Tuple[str, str]]:
     """
     Scan outputs folder for trained LoRA adapters.
 
@@ -916,7 +908,7 @@ def scan_trained_loras(outputs_dir: str = str(outputs_root())) -> List[Tuple[str
         ]
     """
     trained_loras = []
-    outputs_path = resolve_output_dir(outputs_dir)
+    outputs_path = Path(outputs_dir)
 
     if not outputs_path.exists():
         logger.warning(f"Outputs directory not found: {outputs_dir}")
@@ -945,7 +937,7 @@ def scan_trained_loras(outputs_dir: str = str(outputs_root())) -> List[Tuple[str
         logger.error(f"Error scanning outputs folder: {e}")
         return []
 
-def scan_exported_models(exports_dir: str = str(exports_root())) -> List[Tuple[str, str, str, Optional[str]]]:
+def scan_exported_models(exports_dir: str = "./exports") -> List[Tuple[str, str, str, Optional[str]]]:
     """
     Scan exports folder for exported models (merged, LoRA, GGUF).
 
@@ -958,7 +950,7 @@ def scan_exported_models(exports_dir: str = str(exports_root())) -> List[Tuple[s
         export_type: "lora" | "merged" | "gguf"
     """
     results = []
-    exports_path = resolve_export_dir(exports_dir)
+    exports_path = Path(exports_dir)
 
     if not exports_path.exists():
         return results
@@ -1046,7 +1038,7 @@ def scan_exported_models(exports_dir: str = str(exports_root())) -> List[Tuple[s
                 # Fallback: read base model from the original training run's
                 # adapter_config.json in ./outputs/{run_name}/
                 if not base_model:
-                    outputs_adapter_cfg = resolve_output_dir(run_dir.name) / "adapter_config.json"
+                    outputs_adapter_cfg = Path("./outputs") / run_dir.name / "adapter_config.json"
                     try:
                         if outputs_adapter_cfg.exists():
                             cfg = json.loads(outputs_adapter_cfg.read_text())
diff --git a/studio/backend/utils/paths/__init__.py b/studio/backend/utils/paths/__init__.py
index b13d949924..d196f8ce14 100644
--- a/studio/backend/utils/paths/__init__.py
+++ b/studio/backend/utils/paths/__init__.py
@@ -5,52 +5,10 @@
 Path utilities for model and dataset handling
 """
 from .path_utils import normalize_path, is_local_path, is_model_cached, get_cache_path
-from .storage_roots import (
-    studio_root,
-    assets_root,
-    datasets_root,
-    dataset_uploads_root,
-    recipe_datasets_root,
-    outputs_root,
-    exports_root,
-    auth_root,
-    auth_db_path,
-    tmp_root,
-    seed_uploads_root,
-    unstructured_seed_cache_root,
-    oxc_validator_tmp_root,
-    tensorboard_root,
-    ensure_dir,
-    resolve_under_root,
-    resolve_output_dir,
-    resolve_export_dir,
-    resolve_tensorboard_dir,
-    resolve_dataset_path,
-)
 
 __all__ = [
     'normalize_path',
     'is_local_path',
     'is_model_cached',
     'get_cache_path',
-    'studio_root',
-    'assets_root',
-    'datasets_root',
-    'dataset_uploads_root',
-    'recipe_datasets_root',
-    'outputs_root',
-    'exports_root',
-    'auth_root',
-    'auth_db_path',
-    'tmp_root',
-    'seed_uploads_root',
-    'unstructured_seed_cache_root',
-    'oxc_validator_tmp_root',
-    'tensorboard_root',
-    'ensure_dir',
-    'resolve_under_root',
-    'resolve_output_dir',
-    'resolve_export_dir',
-    'resolve_tensorboard_dir',
-    'resolve_dataset_path',
 ]
diff --git a/studio/backend/utils/paths/storage_roots.py b/studio/backend/utils/paths/storage_roots.py
deleted file mode 100644
index a005d604a8..0000000000
--- a/studio/backend/utils/paths/storage_roots.py
+++ /dev/null
@@ -1,143 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-import tempfile
-
-
-def studio_root() -> Path:
-    return Path.home() / ".unsloth" / "studio"
-
-
-def assets_root() -> Path:
-    return studio_root() / "assets"
-
-
-def datasets_root() -> Path:
-    return assets_root() / "datasets"
-
-
-def dataset_uploads_root() -> Path:
-    return datasets_root() / "uploads"
-
-
-def recipe_datasets_root() -> Path:
-    return datasets_root() / "recipes"
-
-
-def outputs_root() -> Path:
-    return studio_root() / "outputs"
-
-
-def exports_root() -> Path:
-    return studio_root() / "exports"
-
-
-def auth_root() -> Path:
-    return studio_root() / "auth"
-
-
-def auth_db_path() -> Path:
-    return auth_root() / "auth.db"
-
-
-def tmp_root() -> Path:
-    return Path(tempfile.gettempdir()) / "unsloth-studio"
-
-
-def seed_uploads_root() -> Path:
-    return tmp_root() / "seed-uploads"
-
-
-def unstructured_seed_cache_root() -> Path:
-    return tmp_root() / "unstructured-seed-cache"
-
-
-def oxc_validator_tmp_root() -> Path:
-    return tmp_root() / "oxc-validator"
-
-
-def tensorboard_root() -> Path:
-    return studio_root() / "runs"
-
-
-def ensure_dir(path: Path) -> Path:
-    path.mkdir(parents=True, exist_ok=True)
-    return path
-
-
-def _clean_relative_path(path_value: str, *, strip_prefixes: tuple[str, ...] = ()) -> Path:
-    path = Path(path_value).expanduser()
-    parts = [part for part in path.parts if part not in ("", ".")]
-    while parts and parts[0] in strip_prefixes:
-        parts = parts[1:]
-    return Path(*parts) if parts else Path()
-
-
-def resolve_under_root(
-    path_value: str | None,
-    *,
-    root: Path,
-    strip_prefixes: tuple[str, ...] = (),
-) -> Path:
-    if not path_value or not str(path_value).strip():
-        return root
-
-    path = Path(str(path_value).strip()).expanduser()
-    if path.is_absolute():
-        return path
-
-    cleaned = _clean_relative_path(str(path), strip_prefixes=strip_prefixes)
-    return root / cleaned
-
-
-def resolve_output_dir(path_value: str | None = None) -> Path:
-    return resolve_under_root(
-        path_value,
-        root=outputs_root(),
-        strip_prefixes=("outputs",),
-    )
-
-
-def resolve_export_dir(path_value: str | None = None) -> Path:
-    return resolve_under_root(
-        path_value,
-        root=exports_root(),
-        strip_prefixes=("exports",),
-    )
-
-
-def resolve_tensorboard_dir(path_value: str | None = None) -> Path:
-    return resolve_under_root(
-        path_value,
-        root=tensorboard_root(),
-        strip_prefixes=("runs", "tensorboard"),
-    )
-
-
-def resolve_dataset_path(path_value: str) -> Path:
-    path = Path(path_value).expanduser()
-    if path.is_absolute():
-        return path
-
-    parts = [part for part in Path(path_value).parts if part not in ("", ".")]
-    if parts[:2] == ["assets", "datasets"]:
-        parts = parts[2:]
-    if parts and parts[0] == "uploads":
-        cleaned = Path(*parts[1:]) if len(parts) > 1 else Path()
-        return dataset_uploads_root() / cleaned
-    if parts and parts[0] == "recipes":
-        cleaned = Path(*parts[1:]) if len(parts) > 1 else Path()
-        return recipe_datasets_root() / cleaned
-
-    cleaned = Path(*parts) if parts else Path()
-    candidates = [
-        dataset_uploads_root() / cleaned,
-        recipe_datasets_root() / cleaned,
-        datasets_root() / cleaned,
-        dataset_uploads_root() / cleaned.name,
-        recipe_datasets_root() / cleaned.name,
-    ]
-    for candidate in candidates:
-        if candidate.exists():
-            return candidate
-    return candidates[0]
diff --git a/studio/frontend/src/features/chat/api/chat-api.ts b/studio/frontend/src/features/chat/api/chat-api.ts
index 379ee5a8c9..c73b60d440 100644
--- a/studio/frontend/src/features/chat/api/chat-api.ts
+++ b/studio/frontend/src/features/chat/api/chat-api.ts
@@ -49,11 +49,9 @@ export async function listModels(): Promise<ListModelsResponse> {
   return parseJsonOrThrow<ListModelsResponse>(response);
 }
 
-export async function listLoras(outputsDir?: string): Promise<ListLorasResponse> {
-  const query = outputsDir
-    ? `?${new URLSearchParams({ outputs_dir: outputsDir }).toString()}`
-    : "";
-  const response = await authFetch(`/api/models/loras${query}`);
+export async function listLoras(outputsDir = "./outputs"): Promise<ListLorasResponse> {
+  const query = new URLSearchParams({ outputs_dir: outputsDir }).toString();
+  const response = await authFetch(`/api/models/loras?${query}`);
   return parseJsonOrThrow<ListLorasResponse>(response);
 }
 
diff --git a/studio/frontend/src/features/export/export-page.tsx b/studio/frontend/src/features/export/export-page.tsx
index c9bf251520..05d697bd5e 100644
--- a/studio/frontend/src/features/export/export-page.tsx
+++ b/studio/frontend/src/features/export/export-page.tsx
@@ -162,8 +162,8 @@ export function ExportPage() {
     // For other formats, nest under training-run/checkpoint
     const saveDir =
       exportMethod === "gguf"
-        ? `${baseModelName.split("/").pop() ?? selectedModelIdx ?? "model"}-finetune-gguf`
-        : `${selectedModelIdx ?? "model"}/${checkpoint}`;
+        ? `./exports/${(baseModelName.split("/").pop() ?? selectedModelIdx ?? "model")}-finetune-gguf`
+        : `./exports/${selectedModelIdx ?? "model"}/${checkpoint}`;
     const pushToHub = destination === "hub";
     const repoId = pushToHub && hfUsername && modelName
       ? `${hfUsername}/${modelName}`