gigalord

SMS scams are out of control — here’s a dead-simple way to spot them.

Most of us get at least one sketchy text per week—many designed to look exactly like messages from banks, delivery services, or the government. AI-powered scams are getting harder to detect, and a single wrong click can lead to stolen credentials or drained bank accounts.

We built TextGuard to make scam detection effortless. Just screenshot any suspicious text, send it to 1-866-990-6283, and we’ll tell you if it’s a scam and how it works.

Want a browser-based experience? Try our web app: https://text-guard.com

Would love your feedback—what’s the most convincing scam text you’ve seen lately?

Checking whether or not a particular IP is on any open source blacklists is pretty annoying. Most IP scoring services place paywalls in the way.

So I built a simple tool to make this easier - no paywalls, just put in an IP and see the score.

Only supports IPv4 for now.

This is basically just a wrapper for IPSum (https://github.com/stamparm/ipsum)

The difference between a seemingly well-scoped project landing on time or an order of magnitude over budget can depend on whether engineering leaders with the right business and technical context have taken the time to deeply inspect critical plans, decisions, and designs.

> Unleashing the untamed brilliance of generative large language models (LLMs) like ChatGPT can be both exhilarating and challenging. These wild geniuses demonstrate exceptional problem-solving abilities, but their genius comes at a price.

What do you think? In a year will all of these strategies still be relevant or will we converge on a few consistent patterns?

Whenever I see buzz around “ML for Computer Systems” my first question is always “how debuggable is this?”

A major benefit of the sophisticated heuristics and algorithms that power modern systems is the legion of experts who deeply understand how they work. When we see poor performance, a bit of digging and asking around will usually unearth some principled reasoning about how we can modify our programs to work well with the existing heuristics (think branch prediction).

Unless we are careful, we lose this with an ML-based approach

I learned to code on the side from Project Euler 9 years ago. It changed my life.

This resonates with me. I think that a product ML team can only be successful in the long run if its members can pivot between modeling work and engineering work as the business needs change. There is an entire class of communication and prioritization problems that evaporate when the same people who build the models are pushing them to production.