/AI

How I Use LLMs As A Staff Engineer

- Sean Goedecke tl;dr: “Personally, I feel like I get a lot of value from AI. I think many of the people who don’t feel this way are “holding it wrong”: i.e. they’re not using language models in the most helpful ways. In this post, I’m going to list a bunch of ways I regularly use AI in my day-to-day as a staff engineer.”

featured in #589


How Will Your App Survive The AI Bot Wars?

tl;dr: Today’s bots can easily bypass traditional detection — executing JavaScript, storing cookies, rotating IPs, and even solving advanced CAPTCHAs. Their attacks are advanced by the day, fueled by growth in AI agents. So how do you block these bad actors? The answer is WorkOS Radar. A single JS script is all it takes to instantly protect your signup flow. Whether it’s brute force attacks, leaked passwords, or throwaway emails, WorkOS Radar can catch it all, keeping your real users safe from abuse.

featured in #588


[Tutorial Series] Building Interoperable AI Agent Products (RAG & Tool Calling)

tl;dr: Every product & engineering team is being asked to build AI features. But that requires a deep understanding of a few core concepts: Ingesting & index customers' external knowledge, reconciling 3rd-party permissions and ACLs, and automating tasks across your customers' other apps via agent tool calling. This 3+ part video and written series (with repos) walks through how to implement each of these functionalities into your product.

featured in #588


How I Use LLMs As A Staff Engineer

- Sean Goedecke tl;dr: “Personally, I feel like I get a lot of value from AI. I think many of the people who don’t feel this way are “holding it wrong”: i.e. they’re not using language models in the most helpful ways. In this post, I’m going to list a bunch of ways I regularly use AI in my day-to-day as a staff engineer.”

featured in #588


Building Personal Software With Claude

- Nelson Elhage tl;dr: “This experience has shifted a bunch of my thinking about the role of LLMs in software engineering and in my own work. These thoughts are still unfolding, but this piece is an attempt to capture my experience, and to think aloud as I ponder how to update my behaviors and beliefs and expectations.”

featured in #587


AI Coding Agents For Engineering (And Business) Impact

tl;dr: There’s a lot of BS about AI coding agents. Sourcegraph’s AI coding agents actually work. Our code review agent uses specific rules you define, instead of trying to replace humans entirely. They use search + AI to help you define rules precisely and eval against recent PRs.

featured in #586


How Might AI Change Programming?

- Thorsten Ball tl;dr: Thorsten poses questions about future implications of AI: Will this affect programming language adoption? Will code optimization shift to focus on AI readability? Could prompts replace stored code? Will we need new ways to handle AI-generated technical debt? 

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Judging Code

- Thorsten Ball tl;dr: Thorsten creates a simple system where an LLM evaluates website code based on must-have and nice-to-have requirements, scoring it from 0-5. He demonstrates its reliability and consistency, suggesting that LLMs could replace traditional code-based approaches for certain evaluations.

featured in #585


GenAI: Perception vs Reality

tl;dr: In 2024, Jellyfish introduced the Copilot Dashboard to measure the impact of the most widely adopted genAI coding tool. We’ve since gathered data from over 4,200 developers at more than 200 companies, giving us a representative sample of how engineering organizations are using Copilot and what impact it’s having on production.

featured in #579


How To Cut Developer Ramp Time With AI

tl;dr: Developers must ramp up and navigate the new complex codebase of a new customer every quarter. Architects need to do that every month. Learn how they transformed this challenge into a competitive advantage using deep context AI.

featured in #578