Disclaimer

1. General Information & Accuracy

The information, technical tutorials, blog articles, architectural blueprints, and code examples published on altercoding.vercel.app are provided for general educational, illustrative, and informational purposes only.

While we strive to ensure that all information on this website is accurate, complete, and up-to-date at the time of publication, technology stacks and software ecosystems (such as Python packages, JavaScript libraries, and cloud platforms) change rapidly. Alter Coding makes no representations or warranties of any kind, express or implied, regarding the completeness, accuracy, reliability, or suitability of the information contained on this website.

2. Educational Projects & Academic Integrity

Our mission is to help students, researchers, and learners understand software architecture, neural network fundamentals, database schemas, and modern engineering principles.

• Student Ownership & Compliance: The student or client bears sole and ultimate responsibility for ensuring that any materials, source code, research prototypes, or documentation templates provided by Alter Coding are utilized in strict compliance with their educational institution's academic honor codes, ethics regulations, and plagiarism policies.
• No Submission Representation: Alter Coding does not encourage, endorse, or promote academic dishonesty or direct uncredited submission of code where prohibited by university rules. Our deliverables are intended as educational references, prototypes, and study guides to facilitate independent understanding.
• No Grade Guarantees: While we guarantee that all delivered software code compiles and runs according to agreed specifications, we do not and cannot guarantee academic grading outcomes, internal scores, university approvals, or examination panel results.

3. Artificial Intelligence & Machine Learning Limitations

Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, and Large Language Models (LLMs) operate fundamentally upon probabilistic, statistical pattern recognition rather than deterministic certainty.

When commissioning or deploying AI and ML models with Alter Coding, clients must acknowledge the following inherent realities:

• Accuracy & Generalization Variance: Model accuracy, precision, and recall metrics obtained during laboratory training or on test datasets do not guarantee identical performance in real-world production environments. Models can experience performance degradation ("model drift") due to distribution shifts in real-world data.
• Hallucinations & Inaccuracies in Generative AI: LLMs and generative pipelines (including RAG architectures and conversational chatbots) may occasionally generate incorrect, plausible-sounding falsehoods ("hallucinations"), biased answers, or unexpected outputs. Output should never be trusted unconditionally without automated guardrails and human verification.
• Dataset Quality & Bias: Machine learning algorithms reflect the quality, noise, and latent biases of the datasets on which they are trained. Alter Coding cannot be held responsible for biased or skewed outputs resulting from client-supplied or publicly scraped benchmark datasets.
• Not Autonomous Decision-Makers: AI models should be implemented as decision-support systems rather than fully autonomous agents in critical workflows.

4. No Professional Advice (Medical, Legal, Financial)

Software prototypes, predictive algorithms, or machine learning models developed by Alter Coding that touch upon healthcare diagnostics, legal document processing, stock forecasting, or financial trading are exploratory technology demonstrations and educational prototypes only.

They do not constitute professional medical, legal, financial, accounting, or investment advice. Always consult licensed medical doctors, attorneys, or certified financial advisors for domain-specific professional judgment.

5. Software Reliability & Third-Party Dependencies

Modern software solutions depend upon third-party platforms, operating systems, cloud providers (e.g., AWS, Vercel, Supabase), open-source libraries (e.g., PyTorch, Hugging Face, Node packages), and external APIs (e.g., OpenAI, Google Cloud).

Alter Coding is not responsible for software downtime, API deprecations, sudden price changes, or outages caused by upstream third-party service providers.

6. Third-Party Links & Advertising Disclosures

Our website may contain hyperlinks to external websites and resources that are not provided or maintained by Alter Coding. We do not endorse, guarantee, or assume responsibility for the accuracy, relevance, or completeness of any external information or privacy practices on third-party sites.

Advertising Disclosures: This website may display advertisements served by Google AdSense and third-party advertising networks. The appearance of advertisements does not constitute an endorsement, recommendation, or testimonial by Alter Coding of the advertised products, services, or companies.

7. Limitation of Liability

In no event shall Alter Coding, its founder, contributors, mentors, or affiliates be liable for any direct, indirect, incidental, consequential, special, or exemplary damages—including but not limited to loss of data, loss of academic credit, business interruption, or financial loss—arising out of or in connection with your use of this website, our technical resources, or software deliverables.

8. Contact Information

If you have any questions, concerns, or requests regarding this Disclaimer, please reach out to us: