I agree with this statement. And because it churns less tokens, it's just generally faster too - noticeably, throughout the day, across a range of tasks I get more shit done with Codex.
It's not better at reasoning on complex coding tasks, Claude Opus is still ahead there, but not by a lot.
A bit slower? I think for most of my tasks, Claude takes easily 2x longer for almost everything, even things like just analyzing code. It churns tons of tokens for quite simple things.
IMO that's exactly why it's a bit better at actual problem solving.
You absolutely do not "always have to correct" Codex. I'm not sure what you're doing, but I'd say 80-90% of its edits on my side it doesn't need any revisions.
I do almost all my regular coding tasks with Codex 5.5 on medium. Sometimes for niche edge cases, or when I run out of tokens on my Codex sub, I'll switch to Claude. Some recent examples where Claude was able to solve things Codex couldn't:
- 3D gamedev layout: I asked Codex to render a solar system in a certain camera positioning, saying it needed to fit the planets of the system to the viewport. Codex just couldn't do it, even on high reasoning: Claude Opus did it first attempt.
- Tricky Tiptap image drag-n-drop layout implementation: Codex failed this after numerous iterations. Claude Opus also struggled mightily to get it to work, but I think around 3 attempts it nailed it. Both of them ended up grepping the Tiptap code from node_modules - that's the kind of task it was.
But these are really isolated examples. Across all my projects (I have many; mostly TypeScript, but also things like C#), Codex "Just Works" (tm), with minimal prompting effort from me.
It's a bit more nuanced than that. Earlier models definitely benefited a lot more from prompt engineering. I remember this distinctly from building data pipelines to do things like extract data from PDFs over the last year or two - there are numerous "tricks" like negative prompting, including the right number of examples, massaging the mock data in the JSON examples so it wasn't "too realistic", and so on. I saw how this impacted recall by running evals, so it wasn't pseudoscience.
But what has happened is the models have gotten better - which OpenAI is making explicit for some cases in this release. You need that stuff less and less as they become more human and better at inferring what's required implicitly.
You still do need to be explicit, and you probably always will, but you don't need as much "engineering" of the way you're asking for things with more recent models.
In December 2015, CEO Zuckerberg listed as one of Meta’s goals for 2016: “Time spent [on
the Platorms] increase by 12%” over the following three years. And as of November 2016,
Meta’s “overall goal remain[ed] total teen time spent … with some specific efforts (Instagram)
taking on tighter focused goals like U.S. teen total time spent.”
Between October 2022 and April 2023, Meta’s own internal metrics show that an average of
208,000 Mississippi young adults used Instagram daily and 345,000 used it monthly. In fact,
Meta monitored key metrics for Mississippi, including:
• Ratio of teen daily active users to monthly active users: 0.72
• Increase in monthly active users over a two-month period: 7,894
• By 2020, Meta estimated 100% of MS teens were monthly active users of Instagram
A 97-page internal presentation, “Teen Fundamentals,” in May 2020, described its goal as to “look … to biological factors that are relatively consistent across adolescent development and gain valuable unchanging insights to inform product strategy….”
That presentation conceded, “due to the immature brain they have a much harder time stopping even
though they want to – our own product foundation research has shown teens are unhappy with the
amount of time they spend on our app.”
One internal communication noted that Meta could “[l]everage teens’ higher tolerance for notifications to push retention and engagement,” while another noted that some users are “overloaded because they are inherently more susceptible to notification dependency.”
As it noted in its 2019 internal presentation, “Teen Mental Health Deep Dive,” “Young people are acutely aware that Instagram can be bad for their mental health, yet are compelled to spend time on the app for fear of missing out on cultural and social trends.”
In another internal presentation, Meta employees express concerns about “content on IG triggering
negative emotions among tweens and impacting their mental well-being (and) our ranking algorithms
taking into negative spirals & feedback loops that are hard to exit from.”
> China’s Ministry of Commerce has led meetings over the past month with major AI companies, including Alibaba, ByteDance, and http://z.ai/, to discuss measures that would restrict overseas access to cutting-edge AI models, including models that have not yet been released.
> The discussions reportedly include not only closed-source models but also open-weight models.
> Future regulations could take the form of a tiered framework based on technological capability. Basic open-source AI models may be managed through a filing system, high-performance models may be subject to security reviews, and the most sensitive frontier models may be banned from public release or restricted to use within China
I'd love to read more about how to use this workflow. What kind of top level instructions does this actually work with? Is there an article out there with some concrete examples of how to do this effectively?
An expressive and well designed language (elixir) is objectively better than a less well designed language like python. Python probably needs more LoC than elixir for the same task. Python is also untyped by default.
I don't really see the moat for frontier AI labs being "more efficient models" although that could help their margins - I think moats will be built by expanding the horizontal and vertical market expansion - like Anthropic is doing the most at the moment
Wan Streamer is a native-streaming, end-to-end interactive foundation model, designed from the ground up for real-time, low-latency, full-duplex audio-visual interaction. It models language, audio, and video as both input and output within a single Transformer: the sequence is an interleaving of visual, audio, and text input tokens with visual, audio, and text output tokens, coordinated by block-causal attention for incremental streaming.
> The standard way to reduce liability risk is to get liability insurance
Exactly! That's what I do in the Netherlands. It's also common to cover this contractually too - you can negotiate where liability falls for many cases.
Getting a limited liability company for a one-person operation is just overkill.
Working on https://octoloops.com - Get your vibe-coded app its first users and grow it.