SKU Science
SKU Science delivers a fast and intuitive solution for sales forecasting and performance tracking. Implement your demand planning process in as little as two days! Created by seasoned experts, it’s specifically designed for operations managers, S&OP managers, supply chain professionals, and demand planners. With 644 statistical combinations, the platform generates highly accurate and tailored sales forecasts at any level. For even greater precision, AI models can be trained on your unique dataset. Automatically calculated KPIs highlight the most critical items, helping you focus on what matters most for your supply chain and business success. The platform’s operational dashboards refresh every cycle, ensuring efficient activity monitoring and data-driven decision-making. Combining advanced capabilities with ease of use, SKU Science is trusted by clients across manufacturing, food and beverage, healthcare, retail, and e-commerce sectors.
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Checksum.ai
Engineering teams shipping with AI have a new bottleneck: validation. Code output has accelerated. Quality hasn't. Checksum closes the gap.
Checksum is a continuous quality platform with a suite of AI agents that handle testing end-to-end, at every stage of the development lifecycle. Where most tools wait for a human to trigger them, Checksum runs autonomously in the background, generating tests, executing them, and repairing failures without manual intervention. Seventy percent of test failures are resolved automatically through real-time auto-recovery.
The platform covers every layer: end-to-end UI flows via Playwright, API endpoint chains, and targeted CI tests scoped to exactly what changed in a PR. All tests land as real code in your repository and are delivered as standard Playwright, owned by your team.
Checksum is fine-tuned on 1.5+ million test runs and integrates natively with Cursor, Claude Code, and 100+ AI coding agents. Type /checksum and your coding agent's output gets tested before it ever reaches review. Generation and healing happen on Checksum's cloud infrastructure which means no LLM tokens consumed, no local resources required.
The result: test suites that stay green as the product evolves, fewer regressions reaching production, and release confidence that scales alongside AI output.
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Ango Hub
Ango Hub is an all-in-one, quality-oriented data annotation platform that AI teams can use. Ango Hub is available on-premise and in the cloud. It allows AI teams and their data annotation workforces to quickly and efficiently annotate their data without compromising quality.
Ango Hub is the only data annotation platform that focuses on quality. It features features that enhance the quality of your annotations. These include a centralized labeling system, a real time issue system, review workflows and sample label libraries. There is also consensus up to 30 on the same asset.
Ango Hub is versatile as well. It supports all data types that your team might require, including image, audio, text and native PDF. There are nearly twenty different labeling tools that you can use to annotate data. Some of these tools are unique to Ango hub, such as rotated bounding box, unlimited conditional questions, label relations and table-based labels for more complicated labeling tasks.
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StableCode
StableCode provides an innovative solution for developers aiming to enhance their productivity through the utilization of three distinct models designed to assist in coding tasks. Initially, the foundational model was developed using a broad range of programming languages sourced from the stack-dataset (v1.2) by BigCode, with subsequent training focused on widely-used languages such as Python, Go, Java, JavaScript, C, Markdown, and C++. In total, our models have been trained on an impressive 560 billion tokens of code using our high-performance computing cluster.
Once the base model was created, an instruction model was meticulously fine-tuned for particular use cases, enabling it to tackle intricate programming challenges effectively. To achieve this refinement, approximately 120,000 pairs of code instructions and responses in Alpaca format were utilized to train the base model.
StableCode serves as a perfect foundation for those eager to deepen their understanding of programming, while the long-context window model provides an exceptional assistant that delivers both single-line and multi-line autocomplete suggestions seamlessly. This advanced model is specifically designed to efficiently manage larger chunks of code simultaneously, enhancing the overall coding experience for developers. By integrating these features, StableCode not only aids in coding but also fosters a deeper learning environment for aspiring programmers.
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