The ATLAS TUI live, 10× sped up, running the V3 pipeline on a file creation.
Adaptive Test-time Learning and Autonomous Specialization
ATLAS is a local coding agent for open models. It runs on your own hardware and works inside real repositories: reading files, editing code, running commands, and checking the result in an isolated sandbox.
Loading a model locally is only half the problem. Getting a compact open model to stay on task across a real code change is harder. ATLAS puts an agent loop around the model that can plan, generate alternatives, enforce tool calls, run tests, and repair failures. Simple edits take the short path; harder tasks get more compute and verification.
atlas init selects a compatible registry model for the machine, or you can bring another GGUF and build the matching Lens and ASA artifacts locally. Model identity and context sizing are runtime configuration, not baked-in family assumptions. There is no hosted API or per-token bill, and your source code and prompts stay on the machine running ATLAS.
The published 74.6% LiveCodeBench result belongs to the frozen 14B reference build. Formal results for the current registry models are still in progress.
- 2026-06-17 - V3.1.2 "Maia" released - broader hardware reach (ROCm / Metal / Vulkan), bring-your-own-model Lens + ASA training, in-the-loop lens retraining from your own workloads, and an agent-reliability pass
- 2026-05-12 - V3.1.0 "Maia" released - native Bubbletea TUI, one-command bootstrap, streaming Lens + ASA activation steering, AST-aware surgical edits
- 2026-04-13 - "How to Run an AI Coding Assistant on a $500 GPU and Beat Claude Sonnet" - devtrends.ru
- 2026-04-05 - V3.0.1 released - interactive CLI, Docker Compose deployment, 95.8% reliability
- 2026-04-03 - "$500 GPU Beats Claude: Local AI Revolution for Web Devs" - ownet.it
- 2026-03-29 - "A $500 GPU Just Outscored Claude Sonnet on Coding Benchmarks" - Aivy
- 2026-03-28 - "Why a $500 GPU Can Beat Claude Sonnet on Coding Benchmarks" - Data Science Collective
- 2026-03-27 - "ATLAS: A $500 GPU Outperforms Claude Sonnet" - Clauday
- 2026-03-27 - "ATLAS – lokal AI-koding på 5000kr GPU slår Claude på benchmark" - jansverre.net (Norwegian)
- 2026-03-26 - "Local LLM Coding: $500 GPU Beats Claude: Not the Story" - Sarah Fraser, novaknown.com
- 2026-03-26 - "ATLAS: How a $500 GPU Achieves 74.6% LiveCodeBench Performance Through Intelligent Infrastructure" - TechPlanet
- 2026-03-26 - Hacker News front page - 489 points, 285 comments
- 2026-03-05 - V3.0 released - 74.6% LiveCodeBench pass@1-v(k=3) on frozen Qwen3-14B
- 2026-02-18 - V2.0 released - benchmark infrastructure, HumanEval/MBPP/LiveCodeBench/GPQA/SciCode evaluation suite
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atlas-tui - native Bubbletea terminal UI; the canonical chat client (PC-062). Type
atlasin any project directory to launch it.- Live pipeline view - watch V3 stages stream in a side pane
- Slash commands -
/add,/diff,/commit,/runfor local file context and shell-out - Input modes - chat,
!bash, and/slashwith a hint dropdown
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atlas-proxy - Go agent loop that orchestrates the system.
- Tool-call routing - classifies file operations by complexity tier
- Grammar enforcement - GBNF schemas keep JSON output valid
- BiasBusters - four composed mitigations (descriptions, grammar bans, system notes, ASA steering) that push the model toward
ast_editfor structural code edits - Safety limits - turn caps, token budgets, timeouts
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V3 Pipeline - multi-phase code generation; turns a single prompt into a verified candidate.
- PlanSearch - constraint-driven structured planning
- DivSampling - diverse candidates across temperature and strategy
- Budget Forcing - per-phase thinking-token allocation
- PR-CoT Repair - self-generated test cases for iterative fixes
- Refinement Loops - sandbox verify and correct, then repeat
- Derivation Chains - multi-step reasoning for harder problems
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Geometric Lens - energy-based scoring over the model's own embeddings, no external oracle. (What is a "Geometric Lens"?)
- C(x) Cost Field - model-hidden-dim→512→128→1 MLP that scores candidate quality
- G(x) Quality Prediction - XGBoost ensemble used for selection
- RAG / PageIndex V2 - AST-aware code retrieval and project indexing
- Confidence Router - Thompson Sampling routes compute to the candidates that need it
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Sandbox - isolated execution for build verification.
- Multi-language execution: Python, Rust, Go, C, Shell, others
- Compilation and linting before scoring
- Runs both generated and existing test suites
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llama-server - local LLM inference on one consumer GPU.
- GPU-accelerated quantized inference (Q6_K / Q4_K_M) — NVIDIA CUDA, AMD ROCm, Apple Metal (macOS hybrid), and Vulkan; Intel SYCL on the roadmap
- Grammar-constrained decoding at the token level
- Self-embeddings, so the lens doesn't need a second model
Full documentation (setup, architecture, configuration, troubleshooting, benchmark reports, and the research behind each component) lives in the docs/ directory.
One-shot install:
curl -fsSL https://raw.githubusercontent.com/itigges22/ATLAS/main/scripts/atlas-bootstrap.sh | bashThe script detects your distro (Ubuntu, Debian, RHEL, Fedora, Rocky, Alma) and your GPU vendor (NVIDIA → nvidia-container-toolkit; AMD → ROCm device passthrough), installs the appropriate runtime, downloads the model weights, builds the ASA steering vector, and starts the stack. Expect 10-30 minutes; the model download is the bottleneck.
Then in any project directory, run atlas.
Requirements
| GPU | 16 GB+ VRAM. NVIDIA (CUDA), AMD (ROCm), or Apple Silicon (Metal, macOS hybrid); Vulkan covers most other GPUs. See SETUP.md § Supported GPUs. To size a specific model to your card, see What fits on my GPU?. |
| Runtime | Docker (NVIDIA: + nvidia-container-toolkit; AMD: standalone Docker is enough) or Podman |
| Python | 3.9+ |
| Disk | ~20 GB CUDA / ~22 GB ROCm (model weights + container images) |
Apple Silicon runs natively through the macOS hybrid Metal path (native llama-server + Docker for the rest — see SETUP_MACOS.md); Intel Arc (SYCL) is on the roadmap. For the manual install path (Docker Compose, bare-metal, K3s) and the full set of bootstrap flags, see SETUP.md.
- Linux Docker stack, plus a native macOS path. NVIDIA, AMD ROCm, and Vulkan Docker paths ship today; Apple Silicon runs via the native macOS hybrid Metal path (#32). Intel Arc / SYCL is on the roadmap.
- Current registry models are not formally benchmarked yet. The canonical 74.6% LiveCodeBench score is from the frozen 14B reference build. New model-specific numbers are tracked in #28. The reference methodology and ablations live in
docs/reports/V3_ABLATION_STUDY.md; raw traces are on HuggingFace. - Complex feature additions can be inconsistent. Compact models sometimes spend agent turns exploring an unfamiliar codebase before writing code. Reliability has improved through the V3.1.2 agent-reliability pass; fresh model-specific numbers are tracked in #28.
- Grammar-constrained decoding is slow. Around 51 tok/s on llama-server.
V3.1.2 "Maia" - Current release. Broader hardware reach, bring-your-own-model training, and an agent-reliability pass on top of the V3.1.0 base (TUI, one-command install, streaming Lens + ASA).
- Hardware reach: AMD ROCm via llama.cpp incl. RDNA4 / RX 9070 (gfx1200/gfx1201) (#26); Apple Silicon native macOS hybrid Metal path (#32, see SETUP_MACOS.md); Vulkan universal fallback covering AMD / Intel / Snapdragon / Apple-via-MoltenVK / CPU (#114).
- Bring-your-own-model: local Lens training pipeline (
atlas lens build/retrain, #100) and ASA per-model calibration parity (atlas asa check/build/publish, #113) — train Lens + ASA artifacts for additional GGUFs, with per-model operating thresholds that ship with the lens. - In-the-loop lens training: rate passes in the TUI (
/good·/bad·/review·/deny) → collected, weighted samples →atlas lens retrainon your own workloads. - Agent reliability: tool-result visibility fix, read-dedup, traceback → directed-edit,
move_file, pip-install / case-mismatch steers, sandbox shell policy + host-sized cgroup limits. - Structural call-graph reasoning (#39 / #125, thanks @yogthos); ARCHITECTURE.md translated to zh-CN / ja / ko (#25).
V3.2 - Next milestone: deeper code reasoning and planning.
- Architecture-first planning phase — RPG-style plan-then-fill: plan at module scope, then implement at function scope (#120, PR #124).
- Structural code reasoning (tail) — solver-backed reachability + syntax-agnostic wavelet decomposition for multi-resolution "which files matter" retrieval (#39).
- Reasoning with sampling — efficiency and quality gains (#9).
- Deferred infra: automated HuggingFace submission pipeline (#102); ROCm on K3s / Kubernetes; formal registry-model benchmarks — LiveCodeBench, GPQA Diamond, SciCode (#28).
Backlog / help wanted
- Hardware: ARM64 multi-arch builds (#115), multi-GPU for larger models (#34), Intel oneAPI / SYCL (#27).
- Tooling: VS Code / JetBrains extension (#35).
- Sandbox languages: Java / Kotlin (#29), Ruby / PHP (#30).
- Architecture: model-agnostic platform (#66), SQLite over Redis (#57).
ATLAS is built by a single college student in his free time on a single consumer GPU. If the project has been useful to you and you want to help keep it sustainable, please consider sponsoring on GitHub.
Sponsorship directly funds:
- Compute & hardware — more GPUs for faster benchmark iteration, access to architectures the maintainer can't afford (AMD ROCm, higher VRAM cards, cloud rentals for larger-model experiments).
- Contributor bounties — meaningful compensation for external contributors who put real time into substantive PRs, so ATLAS can grow faster than a single-person pace allows.
- Research — continued academic engagement around the architecture, from future workshop and conference submissions to paper writing and collaborations that validate and extend the approach.
- Community — continued support for the community and platforms ATLAS runs on, including documentation, user-facing channels, and educational content that help ATLAS reach more developers and better serve the ones already using it.
Every sponsor is credited in the release notes of the version they helped fund.
We're building ATLAS in the open and we're actively looking for contributors and core maintainers. Whether you're fixing a bug, adding accelerator support, or rethinking a whole subsystem - there's a place for you here. If you believe open models deserve better infrastructure, come build with us.
Found a bug or hit a wall? Open an issue - you don't need to submit a fix. Bug reports and feedback help just as much as code.
See CONTRIBUTING.md for guidelines.
Licensed under the GNU Affero General Public License v3.0 (AGPL-3.0).