Tasks That Code Interpreters can Automate

Large Language Models (LLMs) possess vast capabilities that extend far beyond conversational AI, and companies are actively exploring their potential. In a recent tech blog, engineers at Faire share how they’re leveraging LLMs to automate key aspects of code reviews, unlocking new ways to enhance developer productivity. At Faire, code reviews are an essential part of the development process. While some aspects require deep project context, many follow standard best practices that do not. These include enforcing clear titles and descriptions, ensuring sufficient test coverage, adhering to style guides, and detecting backward-incompatible changes. LLMs are particularly well-suited for handling these routine review tasks. With access to relevant pull request data—such as metadata, diffs, build logs, and test coverage reports—LLMs can efficiently flag potential issues, suggest improvements, and even automate fixes for simple problems. To facilitate this, the team leveraged an internally developed LLM orchestrator service called Fairey to streamline AI-powered code reviews. Fairey processes chat-based requests by breaking them down into structured steps, such as calling an LLM model, retrieving necessary context, and executing functions. It integrates seamlessly with OpenAI’s Assistants API, allowing engineers to fine-tune assistant behavior and incorporate capabilities like Retrieval-Augmented Generation (RAG). This approach enhances accuracy, ensures context awareness, and makes AI-driven reviews genuinely useful to developers. By applying LLMs in code reviews, Faire demonstrates how AI can enhance developer workflows, boosting efficiency while maintaining high code quality. As companies continue exploring AI applications beyond chat, tools like Fairey provide a glimpse into the future of intelligent software development. #Machinelearning #Artificialintelligence #AI #LLM #codereview #Productivity #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/deaMsxZy 

Field notes: AI agents aim for an objective, create a step-by-step plan to get there, and then work toward it. Amazon Q is packed full of them. Let's dive in. You all know Q as the world's most capable assistant for software development and leveraging your company's data, and you probably know that Q can recommend code, tests, and documentation - with the highest acceptance rate of any system. But... Q has some superpowers hidden behind the backslash. Q includes unique developer agents which can autonomously perform a range of tasks, from implementing features, documenting, and refactoring code, to performing software upgrades. Meet the crew... 💫 Slash transform Just type /transform, and Q's agent for code transformation will appear. To transform your code, Q generates a plan that it uses to upgrade the code language version of your project. As Q makes changes, it re-builds and runs unit tests to fix any encountered errors iteratively, updating deprecated components, libraries, and frameworks as it goes. Afterwards, Q provides a transformation summary and a file diff for you the review changes before accepting them. Q always asks a human to check its work before incorporating the results. 🌟 Slash dev Typing /dev accesses the feature development agent. You can ask Q to implement an new feature (such as asking it to create an “add to favorites” feature in a social sharing app), and /dev will analyze you existing application code and generate a step-by-step implementation plan. You can collaborate with the agent to review and iterate on the plan before it gets implemented, connecting multiple steps together and applying updates across source files, code blocks, and test suites. Voila, a new feature. Carrying out these tasks, Q has achieved the highest scores of any software development assistant available today, scoring 13.4% on the SWE-Bench Leaderboard and 20.5% on the SWE-Bench Leaderboard (Lite), a dataset that benchmarks coding capabilities. Not too shabby, and we're just getting started. We envision dozens of friendly, helpful agents in Q - many of which will operate with minimal guidance, and some of which will interact with each other - to complete increasingly complex tasks on your behalf, automatically. Below, you can see Q tackling a code transformation from Java 8 to Java 17: you can see the agent building, following, and updating the step by step migration plan - checking off the items as it goes. I could actually watch this all day long. Full disclosure: the embedded video below is edited to show the highlights, but I'll post the real-time, unedited video in the comments for transparency. Q is available to everyone, and so it's easy to dive in and experience for yourself (making overpromising and underdelivering a risky strategy - other providers may want to take note). (thank you, Christos Matskas, for the videos!) #genai #agents #aws

Developers today don’t just write code—they manage intelligent coding agents. That’s why I open-sourced SimulateDev, a tool that runs AI-powered coding IDEs like Cursor, Claude Code, and Windsurf to automatically build features, fix bugs, and generate pull requests across any GitHub repo. Imagine a swarm of specialized AI agents collaborating on your codebase—a Planner decomposing complex tasks, a Coder implementing the solutions, and a Tester validating the output. This is already how engineers at top AI labs like Anthropic and OpenAI work, and SimulateDev brings this collaborative approach to everyone by orchestrating multiple agents like a real engineering team. Key capabilities: (1) Multi-agent workflows - coordinate agents with defined roles (Planner, Coder, Tester), each bringing distinct strengths. (2) Universal compatibility - works with Cursor, Windsurf, and Claude Code (with Codex, Devin, and Factory support on the way). (3) Automated PR creation - Clone → Analyze → Implement → Test → Create PR, all automated. I’ve already tested SimulateDev on eight widely used open-source projects, where it automatically opened pull requests, some of which have already been approved and merged. (PR links in the comments) What’s next? Integrating web-based coding agents like Cognition’s Devin and OpenAI’s Codex, along with remote execution support (e.g., @Daytona or @E2B), enabling coding agents to run continuously in the background. Repo (Apache 2.0) https://lnkd.in/exn2evFs

Coding with AI isn't just about speed anymore. It's about strategy. And Claude Code (and OpenAI’s Codex) might be the first agent that actually thinks like a teammate. Not a chatbot that happens to write code. But a programmable co-worker with real autonomy. Here's how the engineers at Anthropic actually use it: They write README-style memory just for Claude → A file called CLAUDE.md sits in your repo and teaches the AI how to work with your stack, your tools, and your team's quirks. They set up slash commands for reusable workflows → Think: /fix-linter-warnings or /triage-open-issues. These are markdown prompt templates you drop into .claude/commands and reuse across sessions. They use Claude like a project lead, not an intern → The best engineers don’t ask Claude to just "write code." They: Ask it to read and understand files Prompt it to "think hard" or "ultrathink" before building Then ask it to write a plan before shipping code They automate onboarding → New hires just start talking to Claude. Instead of asking a team lead, they ask: "How does logging work here?" "Why are we using X over Y on line 134?" "How do I add a new API route?" They run multi-agent workflows → One Claude writes code. Another reviews it. A third patches it. Each runs in a separate terminal or worktree. They even automate Claude itself → Headless mode lets you run Claude programmatically inside CI pipelines, git hooks, or across massive code migrations. — Agentic coding isn’t just about making an AI write functions. It's about making it collaborate across your entire stack. (👉 Credit to Anthropic's engineering blog for this breakdown) — Enjoyed this? 2 quick things: - Follow me for more AI automation insights - Share this a with teammate 

The ChatGPT Code Interpreter is surreal: It creates and executes Python code for whatever task you describe, debugs its own runtime errors, displays charts, does file uploads/downloads, and suggests sensible next steps all along the way. Whether you write code yourself today or not, you can take advantage of GPT-4's stellar natural-language input/output capabilities to interact with the Code Interpreter. The mind-blowing experience is equivalent to having an expert data analyst, data scientist or software developer with you to instantaneously respond to your questions or requests. As an example of these jaw-dropping capabilities (and given the data science-focused theme of my show), I use today's episode demonstrate the ChatGPT Code Interpreter's full automation of data analysis and machine learning. If you watch the episode on YouTube, you can even see the Code Interpreter hands-on in action while I interact with it solely with natural language. Over the course of today's episode/video, the Code Interpreter: 1. Receives a sample data file that I provide it. 2. Uses natural language to describe all of the variables that are in the file. 3. Performs a four-step Exploratory Data Analysis (EDA), including histograms, scatterplots that compare key variables and key summary statistics (all explained in natural language). 4. Preprocesses all of my variables for machine learning. 5. Selects an appropriate baseline ML model, trains it and quantitatively evaluates its performance. 6. Suggests alternative models and approaches (e.g., grid search) to get even better performance and then automatically carries these out. 7. Optionally provides Python code every step of the way and is delighted to answer any questions I have about the code. The whole process is a ton of fun and, again, requires no coding abilities to use (the "Code Interpreter" moniker could be misleadingly intimidating to non-coding folks). Even as an experienced data scientist, however, I would estimate that in many everyday situations use of the Code Interpreter could decrease my development time by a crazy 90% or more. The big caveat with all of this is whether you're comfortable sharing your code with OpenAI. I wouldn't provide proprietary company code to it without clearing it with your firm first and — if you do use proprietary code with it — turn "Chat history & training" off in your ChatGPT Plus settings. To circumnavigate the data-privacy issue entirely, you could alternatively try Meta's newly-released "Code Llama — Instruct 34B" Large Language Model on your own infrastructure. Code Llama won't, however, be as good as the Code Interpreter in many circumstances and will require some technical savvy to get it up and running. The SuperDataScience Podcast is available on all major podcasting platforms and a video version is on YouTube. I've left a comment for quick access to today's episode below ⬇️ #superdatascience #machinelearning #ai #ml #generatieveai #llms #gpt4