Generative AI

Harvey has released Harvey Tenet, its first post-trained model, as a research preview on August 20, 2026. Tenet is built upon the Kimi K3 base model and has undergone post-training using Fireworks through asynchronous reinforcement learning, specifically targeting long-horizon legal work. The comprehensive training corpus integrated synthetic data, publicly accessible legal datasets, and data provided by human legal experts. Harvey has explicitly stated that no customer data was utilized in the training process. When evaluated against the base K3 model, Tenet demonstrated a significant improvement, completing nearly twice as many held-out tasks on Harvey's proprietary Legal Agent Benchmark (LAB). Furthermore, it achieved 20% more completions on the LAB: Contracts sub-benchmark, leading to an increase in the overall pass rate by 9 percentage points and the contracts pass rate by 2 percentage points. Harvey reports that Tenet has achieved state-of-the-art performance on LAB: Contracts and secured second place on the broader LAB. These performance gains have also shown transferability to other agent systems, including Mercor's APEX Agents and Crosby's Redline Bench, even without further training on those platforms. The dual objectives behind the development of Harvey Tenet are to advance frontier legal intelligence using open-weight models and to provide law firms with the capability to develop and own their specialized AI models. However, Harvey Tenet is not yet deployable as a standalone product. As of its announcement on August 20, 2026, it remains a research preview. Harvey has not yet published the model's weights, a detailed model card, or an API endpoint for external access. While the base Kimi K3 model is open-weight, Tenet itself is a proprietary checkpoint developed by Harvey. The company indicates that the advancements made in this research will be integrated into Harvey's existing products over time, transitioning from research to production. The current offering provides the methodology and training approach rather than a ready-to-use artifact. Access to Harvey's platform, which is sold to law firms, mid-sized firms, and in-house legal teams, is required for enterprise-tier engagement. A laboratory equipped with a reinforcement learning stack could potentially replicate the training methodology. The training process involved approximately 150 NVIDIA B300 GPUs utilized over a two-month period. The primary industries targeted for this technology include legal services, corporate in-house legal departments, private equity and investment banking for M&A diligence, and regulated sectors with high contract volumes such as insurance, financial services, healthcare, and energy. Potential applications encompass the generation of M&A due diligence memos from datarooms, contract drafting, review, and redlining, structured data extraction from up to 10,000 documents, and precedent search across a firm's internal knowledge base.

The rapid advancement of open-weight large language models (LLMs) has outpaced the hardware assumptions traditionally associated with their deployment. Models such as Kimi-K3, GLM-5.2, and DeepSeek-V4-Flash are rapidly closing the capability gap with proprietary AI systems. However, merely releasing model parameters does not address the significant challenge of affordability and accessibility for running these powerful tools. Historically, serving LLMs has necessitated extensive datacenter-class GPU clusters, a prohibitive cost for individual developers and smaller teams, especially as agentic workloads increasingly drive inference demand. This landscape is shifting with the introduction of FreeToken, an edge-native Mixture-of-Experts (MoE) serving engine developed by researchers from the University of California, Berkeley, and the University of Texas at Austin. FreeToken redefines the concept of an inference platform by treating a personal machine as a unified, elastic resource. Instead of being limited by a single GPU's capacity, it intelligently and continuously maps computation and model state across all available hardware components, including the GPU, CPU, system memory, and interconnect bandwidth. This dynamic allocation allows for unprecedented performance on consumer-grade hardware. For instance, a 35 billion parameter model can achieve interactive speeds on a laptop with just 8GB of GPU memory, a 284 billion parameter model can run on a standard gaming desktop, and remarkably, the colossal 753 billion parameter GLM-5.2 model can be served on a single workstation GPU. FreeToken is designed for broad deployability and accessibility. It is open-sourced under the permissive Apache-2.0 license on GitHub and is readily available on the Python Package Index (PyPI) as `freetoken v0.1.2`, installable via `pip install "freetoken[accel]"`. Furthermore, a user-friendly one-click desktop application for both Windows and Linux is distributed through flashml.ai. The command-line interface (CLI) is currently optimized for Linux x86_64 systems equipped with NVIDIA GPUs running driver r580 or later (supporting CUDA 13). The `ft serve` command within FreeToken exposes OpenAI- and Anthropic-compatible API endpoints on port 1919, enabling seamless integration with existing workflows. Additionally, the `ft launch claude` command simplifies the process of wiring up powerful models like Claude Code, Codex, OpenCode, or OpenClaw directly to a user's local machine. FreeToken is particularly well-suited for solo developers, startups, and small to medium-sized business (SMB) engineering teams whose escalating token bills for AI agents are beginning to outweigh the cost of owning dedicated GPU hardware. For larger enterprises, FreeToken presents a strategic solution for implementing air-gapped or regulated workloads, offering a secure, on-premise inference path rather than a direct replacement for established datacenter infrastructure. Industries that stand to benefit most include healthcare and legal sectors, where maintaining strict data privacy and ensuring data never leaves the local machine is paramount. Other strong fits include defense, finance, and intellectual property-intensive research and development environments. Typical applications leveraging FreeToken include the development of local coding agents, private code review tools, offline contract analysis, and the generation of synthetic data, all processed locally without any sensitive information being transmitted externally. This innovation directly addresses the growing disparity between the rapid proliferation of highly capable open-weight AI models and the practical, affordable means for their deployment and utilization by a wider range of users and organizations.