Urgent.News

What's breaking now, across thousands of outlets.

Tech

From Monk To Startups: One Founder’s Unusual Path To Building Government Software

After living as a monk while managing Graves’ disease, a former Facebook engineer left Big Tech to pursue meaningful work, eventually founding government software startup FSH Technologies which aims to improve processes such as school food services and public records management through the use of artificial intelligence.

From Monk To Startups: One Founder’s Unusual Path To Building Government Software

Lilly Chen, the co-founder and CEO of FSH Technologies, embarked on an unconventional journey from monk to startup founder. At the age of 16, Chen was institutionalized for Graves' disease and her mother suggested they move to China so she could become a monk. After two years of monastic life, she returned to the U.S., homeschooled herself, and enrolled at Colorado College without a high school diploma. Chen graduated in 2019 with a double major in mathematics and economics.

During her college years, Chen briefly played Dota 2 professionally and had an early encounter with AI technology when she played against OpenAI's game-playing bots. After leaving her job at Facebook, she didn't initially have a specific startup idea but was driven by a desire to find work she believed in. This led her to FSH Technologies, a Philadelphia-based company that builds software to address some of the government's least glamorous tasks, such as administering school food services and managing public records.

Chen's motivation to build government software stemmed from her belief that solving everyday problems, even if they didn't generate much excitement, could have a significant impact. She drew inspiration from a monastic experience where she was asked whether she saw himself as the fish or the pond. This question stayed with her and influenced her approach to addressing complex administrative processes within governments.

Through an introduction by Envision, a student-run accelerator, Chen connected with Contrary, a venture firm that supported her startup journey. Contrary invested in FSH in 2021, even before Chen had a fully developed company pitch. The company raised $25 million in total funding, including a $20 million Series A round led by Contrary. Chen's passion for solving meaningful problems and her background as a monk shaped FSH Technologies' mission to tackle the unsexy but essential tasks in the government sector.

Written by urgent.news from Crunchbase News's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

Read the original at news.crunchbase.com →

More in Tech

Stop Pasting Production JWTs into Random Websites: The Security Guide to JSON Web Tokens

How many times this week have you opened a random online "JWT Debugger", pasted an Authorization: Bearer eyJhbGci... token from your production network tab, and hit enter?

  • Developers often paste production JWTs into random debuggers, exposing sensitive user data
  • JWTs are signed, not encrypted; use JWE for confidentiality
  • Three critical vulnerabilities: alg: none attack, asymmetric key confusion, and ignoring expiration

How to integrate a legal secretary with artificial intelligence into WordPress

Conversational artificial intelligence is opening up new possibilities for developing more interactive and useful websites.

  • Digital legal secretary integrates with WordPress for legal inquiries
  • Conversational AI interprets messages, generates responses within limits
  • Privacy compliance with GDPR and Spanish data protection regulations

MCP tool cost: 20 job Actors on Apify, 2.1K to 32.7K tokens each

Here is what an agent pays before it asks a job-search tool anything. I pinned 20 job-search Actors from the Apify Store, one at a time, on the hosted Apify MCP server and read what tools/list sends…

  • Twenty Apify MCP Actors used for job search cost analysis
  • Token counts range from 2,139 to 32,737 per tool definition
  • Author's input schema is 406 tokens, median is 3,829

Which Microcontroller to Choose for Matter and Thread (ESP32-C6, H2, and nRF52840)

Originally published on iot.gripe. Matter is the standard that aims for the same device to work with Apple Home, Google Home, Alexa and Home Assistant.

  • ESP32-C6 is best for Matter due to Wi-Fi and Thread compatibility and Espressif support
  • ESP32-H2 or nRF52840 alternatives for battery-powered devices using only Thread
  • Matter is an application layer standard, not a radio, traveling over Wi-Fi, Thread, and Internet

More from Wednesday 28 October →