Bibliographic record
Abstract
In today's digital world, advertisements have a big impact in promoting small business, products or organizations. However most of them struggles to create best and quality advertisements because it can be challenging to design and requires creativity and skills. Also the softwares or tools available in market are costly and difficult to understand. To solve amd overcome this problem, PromoBot was developed as an AI based platform that helps users to easily generate advertisements for their business without requiring any design experience or knowledge of software or tools.PromoBot only requires a simple text input from the user and after submitting, it automatically creates complete and professional advertisements in the form of short videos or images. PromoBot used Natural Language Processing (NLP) for generating ad scripts, Text-to-Speech (TTS) for voiceovers, and AI avatars to make the advertise more human-like. The web application is built using React.js, Next.js, Tailwind CSS. To manage user data, authentication and media storage, Convex Database, Clerk API and ImageKit.io was used.During testing, PromoBot actually reduced the time and effort needed to design an advertisement. It was truly beginner friendly. The generated ads maintained professional quality and were visually appealing. PromoBot let user to customise ad and ready for sharing it on social media. In conclusion, PromoBot shows how AI can speed up and simplify advertisement creation, making it more accessible and efficient for beginners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".