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PDF table to structured data - an original ADG Media demonstration using fictional stationery data. Three items extracted into an editable CSV, with quantities, decimal precision and all columns preserved. Line totals reconcile to 149.00 USD with a 0.00 difference. AI-assisted production; checked against the sample source.
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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 👌
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.
why every #AI engineer needs #Pydantic:
LLMs don’t return data. They return text that looks like data, most of the time. And “most of the time” is how production systems break.
I made a 6-minute video on why Pydantic is the one library every AI engineer should know:
→ One class does three jobs: it tells the model what to produce, validates what comes back, and powers autocomplete
→ Your function signature becomes the tool definition
→ Validation errors become feedback the model can fix itself
→ It’s already in your stack (OpenAI, Anthropic, LangChain, FastAPI…)
Plus a quick look at Pydantic AI, which runs that whole loop for you.
Prompts are suggestions. Schemas are contracts. 🎯