Thomson Reuters launched Thomson, its first proprietary large language model, on August 24, 2026. The company says it built the model in-house from an open-source foundation and spent $40 million on training, talent, and compute—far below the multibillion-dollar investments associated with many frontier-model developers. Thomson Reuters fully owns and controls the model and says it has lower inference costs than comparable frontier models. Thomson was mid-trained and post-trained using proprietary material from Westlaw, Practical Law, Checkpoint, and Reuters, with hundreds of subject-matter experts involved in setting training goals and evaluations. Less than 10% of the company’s content has been used in training so far. Thomson Reuters says early evaluations place the model on par with recent frontier models across a range of tasks, with gains in following complex instructions and reasoning over dense professional content. The model’s first deployment will be in Tabular Analysis within CoCounsel Legal for law firms and corporate legal departments. CoCounsel Legal will continue using multiple models, applying Thomson to tasks where it has an advantage. Thomson Reuters also plans to extend its models across its legal and tax products and add sovereign-AI options. A small open-weight version of Thomson is available on Hugging Face for academic and non-commercial use, while external legal and AI academics evaluate the model.

A forecast predicts that AI systems will perform most software development because their output will be commercially acceptable at far lower cost, even when it falls short of the quality expected from human-led engineering. It expects businesses to accept software with “99.99” quality rather than “99.999” quality when the cost difference is substantial, and predicts that consumer expectations will adjust accordingly. The forecast also predicts that software engineering outside AI development will slow sharply rather than continue producing broadly adopted new technologies. It argues that developers will have little incentive to create UI libraries when state-of-the-art models are strongest in React, or to create programming languages when those models already know Python, Go, JavaScript, and other established languages best. New libraries and languages may become easier to create, but the prediction is that they will struggle to gain adoption. Large corporations may be exceptions because they can train or fine-tune models on internal technologies, although those technologies could still face difficulty building external communities and talent pools when outside developers lack access to the companies’ models or do not want to use them.

HackEurope 2026 experienced significant issues, including an inaccessible user interface, delays, and miscommunications. Key lessons emphasized the importance of front-end development, as there was no requirement to prove project functionality.

Users retain ownership of Outputs generated by Claude from their Inputs. Training or developing AI models using these Outputs is prohibited without written permission from Claude.

Moonshot has secured a computing power agreement with Alibaba to utilize approximately 20,000 Nvidia chips. This partnership highlights China's dependency on Western semiconductors for its AI advancements, specifically for Moonshot's Kimi models.

AI tools can be built internally for operations, support, and finance teams with scoped and audited access behind company sign-in. Admins approve each data connector, ensuring security and allowing teams to create customized tools such as customer lookups by name, email, or order number.

AI enthusiasm often stems from a desire to manifest ideas without delving into intricate details. However, achieving quality and innovation requires a deep understanding and meticulous attention to the complexities involved.

Moonshot AI's Kimi K3 was launched on July 17 but halted new subscriptions due to overwhelming demand. The company plans to release K3's full model weights by July 27, enabling other organizations to host and modify the model.

Alex Hyett and his wife sought to build an app for tracking habits, hobbies, and chores after finding existing options unsatisfactory. Despite the abundance of habit tracker apps available, the development process took one year.

Creating original projects provides significant personal fulfillment, while relying on others for development diminishes that satisfaction. Concerns about the "AI dev schism" include the decline of hands-on coding, low-level problem-solving skills, and enjoyment in the development process.