Readit NewsData.olllo — a local-first, privacy-focused data analysis tool designed for large datasets. Explore and visualize your data interactively without coding or uploading sensitive information to the cloud. Works offline, fast, and secure.”
I’m the creator of Data.olllo, a desktop data tool designed as a powerful alternative to Excel, built to handle large and complex datasets without the usual crashes or slowdowns.
With Data.olllo, you can open, clean, and visualize CSV, Excel, or JSON files instantly — all offline, with no cloud uploads and zero coding required. Whether you’re working with hundreds or millions of rows, Data.olllo stays fast and responsive.
Key highlights: • Blazing-fast loading and exploration of large datasets • 100% local processing for total data privacy and security • AI-powered natural language commands to clean, merge, and analyze data without formulas or scripts • Multi-core and GPU acceleration to leverage your hardware fully • Intuitive no-code workspace suitable for data analysts, scientists, and anyone frustrated by Excel’s limits
I built Data.olllo to solve the headaches of Excel crashing on big files and the privacy concerns of cloud-based AI tools. If you want a seamless, private, and powerful data analysis experience, check it out.
Try it here: [https://olllo.top/excel-alternative-for-large-datasets]
Happy to answer questions or hear feedback!
Hi HN,
I want to share a cool feature in Data.olllo, a local, offline, no-code data analysis tool with AI chat capabilities.
Handling time and date data can be a pain—timestamps, strings, extracting year/month, or converting formats usually require coding knowledge. With Data.olllo’s AI chat, you just describe what you want in plain English, and the AI instantly generates the correct pandas code inside a process(dfs) function you can run on your dataset.
For example, you can: - Convert Unix timestamps to readable date-times - Extract parts like year and month from date columns - Parse messy string dates into proper datetime objects - Convert datetime back to numeric timestamps - Format datetime columns into any string style you want
Here’s a sample snippet the AI generates for converting a Unix timestamp column:
def process(dfs): df = dfs["df"] df["timestamp"] = pd.to_datetime(df["timestamp"], unit="s") return df
You don’t need to know pandas or write any code yourself — just type your request, and the AI does the heavy lifting, letting you explore and visualize your data faster.
Data.olllo runs 100% locally, so your data stays private, and it can handle millions of rows quickly.
If you often struggle with time data or want a fast way to analyze large CSVs without coding, give Data.olllo a try:
https://olllo.top/convert-format-datetime-ai-chat
Happy to answer questions or get feedback!
— Denis
The GitHub repo includes README, setup instructions, and links to guides and demos: https://github.com/olllo-top/data.olllo-helper
Perfect for analysts, researchers, or anyone frustrated with Excel crashing or cloud upload delays.