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Mock Airtable and CSV to a One-Page PDF Report
AI-assisted demonstration using synthetic data, not client work. A local mock Airtable JSON snapshot is joined with an invented CSV schema, then an HTML template is rendered to a one-page PDF. The example validates 2 mock records and 6 CSV rows: 4 selected rows appear in the report and 2 are accounted for separately. Six tests passed, including repeatability and multi-page overflow rejection. Source code, PDF, PNG preview and audit are available. No live Airtable API connection was implemented or tested.
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Built SpeedPulse AI — an automated website performance teardown and local client acquisition engine.
Most local businesses lose over 35% of their mobile conversions due to slow load times and missing Google SEO schema. Auditing each client manually took hours, so I automated the entire process on Base44:
⚡ Real-time Core Web Vitals, Local SEO & UX friction score dials
🔄 Dynamic Before/After sprint projections (gliding scores from 45 to 91+)
📄 Automated Executive PDF brief generation
🤖 Built-in Agency CRM that captures incoming leads, tracks stages, and auto-drafts client proposals with one-click copy
Live App: https://speedpulse-flow.base44.app
Project AIR is an open-source toolkit that keeps a tamper-proof record of what AI agents do. Every action an agent takes gets signed, chained to the step before it, and tied to the person who authorized it. The chain is anchored to Sigstore Rekor, a public transparency log, so anyone can check later that nothing was changed or deleted. When something goes wrong, you can show exactly what happened and who approved it, instead of just saying it happened.
It also runs 16 detectors over every record, covering all ten risks on the OWASP Top 10 for Agentic Applications, three from the OWASP list for LLM apps, and three of its own. It can stop an agent mid-action until a person signs off. It plugs into LangChain, LlamaIndex, and the OpenAI, Anthropic and Gemini SDKs with a line or two of code.
I built the Python library and command-line tool, published on PyPI as projectair (now at version 1.3.1) under the Apache 2.0 license, plus the website and dashboard in SvelteKit. We run it on our own infrastructure too: every request to vindicara.io is recorded and anchored to Rekor every minute.
Impressive work! 👏 Excellent approach to AI agent security, tamper-proof audit trails, and human oversight for more trustworthy AI systems.
Understanding Vaccine Cold-Chain Performance
I used stimulated data to examine the effect of storage equipment and associated variables on vaccine excursion.
I downloaded the Vaccine Cold Chain Management dataset from Hugging Face using Python. I organized and combined the data from regional vaccine stores, district hospitals, and rural health posts using MySQL, then used Tableau Public to build an interactive dashboard exploring equipment functionality, staff training, temperature compliance, vaccine wastage, and power outages.
Three key insights were uncovered.
Rural health posts recorded the highest heat and freeze excursion rates.
Longer power outages were associated with lower temperature compliance.
Reliable equipment alone isn't enough. Staff training and proper maintenance matter too.
Very interesting 👌