AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
The Problem: We Were Flying Blind At Refer, we're on a mission to enable talented individuals to fulfill their professional potential by helping them pursue their ideal job. Behind the scenes, that means a lot of microservices, and recently we decided to consolidate everything into a mono-repository. If you've ever migrated dozens of microservices into a monorepo, you know the drill: contracts b
The Problem If you are building AI applications with LLMs, you know the pain: raw HTML is useless for training data. You need clean, structured Markdown. Most solutions like Firecrawl or Crawl4AI require setup, dependencies, and often paid plans. You could write your own parser: import re import urllib.request def html_to_markdown(url): html = urllib.request.urlopen(url).read().decode()
This technical post walks through the design and implementation of Secure Playground: a local web app that simulates prompt-injection attacks against large language models and demonstrates simple defenses. Provide a minimal, reproducible environment to test payloads and defensive strategies. Make it easy to add new providers and run mutation-based red-team experiments. Offer a leaderboard and scor
So I made a bad trade in my fantasy baseball league. Dropped Kaz Okamoto because — according to my data — he’d been cold for two weeks. In reality, he’s been on a tear for the last 9 days. 😅 This was a bad decision made because of bad data — my stats cron job had hit a rate limit, exited with no errors, and my FastAPI backend kept serving a stale JSON snapshot. Well, I’d been meaning to fix that
I'm 15 years old and just completed my 10th grade. I started learning python from Python Crash Course : 3rd Edition and some other resources. But now I've many questions like : After this what to do ? DSA, AI Automation etc. When I should change from Python to C++ ? Why To Change ? Is DSA in Python beneficial and useful ?
Last week, a Cursor agent running on Claude Opus 4.6 deleted a startup's production database and its backups in nine seconds. The agent had been asked to fix a credential mismatch in staging. It decided to delete a Railway volume to "fix" it instead — using an over-scoped API token it found in an unrelated file. Railway stores volume backups in the same volume, so one destructive call zeroed every
Decoupling Workloads: Strategies for Non-Blocking API Responses in Python Modern web applications demand instant feedback. Users expect immediate responses, and frustrating delays can quickly lead to abandonment. When an API endpoint performs computationally intensive or time-consuming operations directly within the request-response cycle, it creates a bottleneck that can cripple your backend sy