Technology EntrepreneurshipTechnology (ENT600) : Secret Lab.CO / Faridatul Wahida Saini
Bibliographic record
Abstract
Secretlab is gaming chair manufacturing company that was founded in 2014. It operates in the United States, Canada, United Kingdom, Europe, Australia, and South-East Asia, with its headquarters in Singapore. This company can be classified as the well-known company because it’s popular in many countries. This Company was founded in 2014 by two former competitive gamers Ian Alexander Ang and Alaric Choo, who set out to create and market computer chairs targeted at computer gamers, having grown disgruntled at the lack of affordable and quality options in the gaming chair market. They got the idea to start the company after they could not find a chair that can be used for long hours and fit for both gaming and office setups. After the launch of their first chair in 2015, Ang, who as CEO oversees engineering, marketing and product strategy, and Choo, who is technical and partnerships director, have expanded their product line with three models: Throne, Omega and Titan. Each chair delivers comfort with a level of firmness for good posture. Secretlab has sold over 500,000 chairs to customers in more than 60 countries. In this case study, there are five problems that have been discussed. The designs are simple and minimalism makes the customers not attract with it. Design is important because as a consumer the first thing they will attract is the design of the product itself. Next, chairs which are too large and not suitable for the users that have narrow space. Furthermore, having the lumbar support pillow is nice but as it does not attach to the chair, it can easily slide down when adjusting in the seat. Lastly, the seat height of the chair cannot be adjustable according to the position of the table. It will be a problem as the customers cannot seat with comfortable when doing their works. To summarize, we need to produced more advanced gaming chairs that may well resolve and satisfy the customers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.465 | 0.246 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".