The AI Daily Brief (Formerly The AI Breakdown): Artificial Intelligence News and Analysis
What AI Coding Agents Can Do Right Now
Thu, 20 Feb 2025
AI coding tools are advancing rapidly, but how effective are they for freelance jobs? OpenAI's new SWE Lancer benchmark evaluated top AI models on 1,400 software engineering tasks from Upwork. The outcome? Claude 3.5 Sonnet surpassed OpenAI’s models, completing more tasks and earning the highest simulated payout. Additionally, "vibe coding" is transforming software development into a more interactive, less technical process. Brought to you by:KPMG – Go to www.kpmg.us/ai to learn more about how KPMG can help you drive value with our AI solutions.Vanta - Simplify compliance - https://vanta.com/nlwThe Agent Readiness Audit from Superintelligent - Go to https://besuper.ai/ to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Subscribe to the newsletter: https://aidailybrief.beehiiv.com/Join our Discord: https://bit.ly/aibreakdown
Chapter 1: What is the AI Daily Brief and today's main topic?
Today on the AI Daily Brief, OpenAI released a paper effectively seeking to test how competent their leading models are in real-world coding applications. Before that in the headlines, former OpenAI CTO Meera Muradi has officially announced her new company Thinking Machines. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
To join the conversation, follow the Discord link in our show notes. Welcome back to the AI Daily Brief Headlines Edition, all the daily AI news you need in around five minutes. OpenAI has had a lot of talent departures over the last year and a half or so. In some cases, it's felt like a protest on how the direction of the company was going and indeed has explicitly been shared as such.
In others, it's about people making a boatload of money and just wanting to do something different for a while. And then still in others, it's about building something new outside of the constraints of that company. And among that set, one of the most closely watched people has been former CTO Mira Marotti.
For months now, there have been rumors around what she's building, mostly fueled by departures and recruitment from OpenAI and Anthropic to join Marotti on some as yet unrevealed company. Now, however, that company has been officially announced. Yesterday, Mira tweeted, I started Thinking Machines Labs alongside a remarkable team of scientists, engineers, and builders.
We're building three things, helping people adapt AI systems to work for their specific needs, developing strong foundations to build more capable AI systems, fostering a culture of open science that helps the whole field understand and improve these systems.
Our goal is simple, advance AI by making it broadly useful and understandable through solid foundations, open science, and practical applications. Alongside it, they published a website, thinkingmachines.ai. They write, Knowledge of how these systems are trained is concentrated within the top research labs, limiting both the public discourse on AI and people's abilities to use AI effectively.
And despite their potential, these systems remain difficult for people to customize to their specific needs and values. To bridge the gaps for building thinking machine labs to make AI systems more widely understood, customizable, and generally capable. Now, if you're sitting there thinking, boy, I have absolutely no idea what these folks are actually building. You, my friend, are not alone.
Cosmic Chaos writes good luck, but I'm still not sure what exactly you are building. Is it one product that does all three or separately? Is it a service or a product? And what's your roadmap? William Wolfe writes, I'm rooting for Thinking Machines, but I wish projects like this had product to both engineering and design in their founding philosophies.
Otherwise, it kind of just feels like yet another group of world-class researchers vaguely gesticulating at the future. Where is the vision? Swicks pointed out what he called two notable omissions from the Thinking Machines manifesto. The website does not use the word reasoning or agent at all. So what are these folks building? I have absolutely no idea.
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Chapter 2: What is Thinking Machines and who is behind it?
Agents pinpoint the source of an issue remarkably quickly, using keyword searches across the whole repository to quickly locate the relevant file and functions, often far faster than a human would.
However, they often exhibit a limited understanding of how the issue spans multiple components or files and fail to address the root cause, leading to solutions that are incorrect or insufficiently comprehensive. We rarely find cases where the agent aims to reproduce the issue or fails due to not finding the right file or location to edit.
For the managerial tasks, each model displayed better performance. Quad 3.5 Sonnet was again the best-performing model, earning $314,000 of a possible $585,000, completing 54% of tasks. O1 was hot on its heels, correctly completing 52% of tasks for a total of $302,000. And even GPT-4O, bringing up the rear, still managed 47% of tasks to earn $275,000.
This showed that the models were all decent at choosing the right solution when presented with several options, but still have a long way to go until they can fully replace a technical lead. Overall, Claude 3.5 Sonnet won the day, earning $403,000 overall with a 40% completion rate.
O1 earned $380,000 while completing 38% of the full set of tasks, and GPT-40 finished 30% of tasks, earning $304,000. Now, to be clear, no money was actually earned. These tasks were all simulated, but that's how much they would have earned had the AI actually been in charge of that job from Upwork or Expensify.
Part of what's so interesting about this, and we'll get to this in a moment in the commentary, is that this absolutely reflects the broad consensus that people have had for some time, which is that Cloud 3.5 Sonnet is just by far and away the best coding model.
We've even talked about how its ubiquity as a coding model created some challenges for Anthropic's economic report, given what a high percentage of Claude's use comes from those coding use cases. Now, in terms of commentary and the response to this so far, a lot of it is focusing on exactly this weird contrast that we've identified. Mihir Patel writes, As always, evals remain hard and messy.
And still, somehow, Sonnet is the best code model. Benjamin DeKraker, who was previously on the team at XAI but fired for saying that Grok 3 wasn't the second coming, noted that it was bold of OpenAI to show that Claude 3.5 Sonnet outperformed O1 on their own benchmark. Synthetica Lab responded, I'm not benchmarking, but in a real project that I'm working on in C++. O1 was basically unusable.
They then went to share their experience with O1, Claude 3.5, and Grok 3, again pointing out that these benchmarks are really not necessarily useful for understanding how things are going to work in the real world. Another interesting comment came from Henry Shi, the founder of Super.com.
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