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Autonomous coding agent that runs each task in its own cloud sandbox preloaded with your repo — writing features, fixing bugs, running tests, and opening PRs. Reachable from ChatGPT web, a CLI, desktop apps, and IDEs (VS Code, JetBrains, Xcode).
Interleaves chain-of-thought reasoning with tool-using actions in one LLM loop: the model plans, queries a source like Wikipedia, then revises from results. Cuts hallucination versus reasoning-only prompting and beats trained agents on interactive tasks.
Treats the interface between an LM agent and a computer as a design variable. A custom agent-computer interface (ACI) with concise file-edit, repo-navigation, and test commands plus compact feedback reaches 12.5% pass@1 on SWE-bench, 87.7% on HumanEvalFix.
A graph-based RAG framework pairing a knowledge graph with vector retrieval and a dual-level (low/high) query mode. New documents merge into the graph via set operations instead of triggering a rebuild, cutting the cost of keeping the index current.
Headless AI coding agent that runs a local HTTP server (OpenAPI 3.1) any client can drive — TUI, desktop, IDE plugins. Provider-agnostic: bring keys for any LLM, no vendor lock-in. Ships LSP-aware editing, plan/build agents, and shareable session links.
Run and manage open and community LLMs locally via a compact CLI and REST API—supports model import, Docker deployment, and official Python/JS SDKs for local inference, RAG, and dev workflows.
Patches Hugging Face Transformers and TRL with hand-written Triton kernels to fine-tune LLMs on a single consumer GPU up to 30x faster with about 90% less memory. Does LoRA, QLoRA, and full fine-tuning across 500+ models, exporting to GGUF and Safetensors.
Curated collection of AI courses, books, video lectures, papers and engineering guides for developers building generative models and agentic systems. Actively maintained with a weekly, evidence-backed curation workflow and an opinionated quality bar.
The 2017 paper that replaced recurrence with pure self-attention, making sequence models fully parallelizable — and, almost as a side effect, laying the architectural foundation for nearly every large language model that followed, from BERT to GPT.
Scales any Python or ML workload across CPUs and GPUs with a few decorators, instead of rewriting code for Spark or MPI. Bundles libraries for distributed training, hyperparameter tuning, RL, batch inference, and online model serving on one cluster.
Curates step-by-step, hands-on tutorials for reimplementing technologies from scratch—covering everything from OSs and compilers to neural networks, LLMs, and vision systems—so learners learn by rebuilding real systems across languages.