Looming Liability Machines (LLMs)
- Murat Demirbas tl;dr: “We discussed a paper that uses LLMs for automatic root cause analysis (RCA) for cloud incidents. This was a pretty straightforward application of LLMs. The proposed system employs an LLM to match incoming incidents to incident handlers based on their alert types, predicts the incident's root cause category, and provides an explanatory narrative... The use of LLMs for RCAs spooked me viscerally.”featured in #544
LLM Applications I Want To See
- Sarah Constantin tl;dr: “But the most creative and interesting potential applications go beyond “doing things humans can already do, but cheaper” to do things that humans can’t do at all on comparable scale.” Sarah shares a list of app ideas.featured in #543
Onboarding To A ‘Legacy' Codebase With The Help Of AI
- Birgitta Böckeler tl;dr: “To get an idea for how well this works and what the potential is, I picked an issue of the open source project Bahmni and tried to understand the issue and what needs to be done with the help of AI. Bahmni is built on top of OpenMRS, which has been around for a very long time. OpenMRS and Bahmni are good examples of very large codebases with a lot of tech debt, representing a lot of different styles and technologies over time.” Birgitta shares her observations about what AI could and could not help with in such a use case.featured in #542
featured in #542
Objective Evaluations For AI Systems
- Kelly Moon tl;dr: AI systems are only as good as the benchmarks and evaluations they are measured against. Learn how to evaluate AI systems.featured in #540
Bringing AI-Powered Answers And Summaries To File Previews On The Web
tl;dr: From the team at Dropbox: "Both our summarization and Q&A features LLMs to find, compare, and consolidate the content of the file. An LLM works by ingesting content as text, transforming the ideas contained within it into a numerical representation, and comparing those numerical representations against both the input query and an internal corpus of knowledge to answer the question. This effectively enables a computer to consume and compare information semantically, rather than lexically.”featured in #532
Why Build When You Can Deploy Speech AI Instantly
- Kelsey Foster tl;dr: Not sure whether to build or buy an AI speech recognition system? Our comprehensive guide breaks down the key considerations, from accuracy and internal resources to speed of iteration and data security.featured in #531
Leveraging AI For Efficient Incident Response
tl;dr: From the team at Meta, “We’re leveraging AI to advance our investigation tools even further. We’ve streamlined our investigations through a combination of heuristic-based retrieval and large language model (LLM)-based ranking to provide AI-assisted root cause analysis. During backtesting, this system has achieved promising results: 42% accuracy in identifying root causes for investigations at their creation time related to our web monorepo.”featured in #526
featured in #521
How Does AI Impact My Job As A Programmer?
- Chelsea Troy tl;dr: “It’s how human programmers, increasingly, add value. Figure out why the code we already have isn’t doing the thing, or is doing the weird thing, and how to bring the code more into line with the things we want it to do. Chelsea argues that this “conveniently comprises most of the job these days: read code. Analyze it. Understand it. Repair it.”featured in #519