> Alongside improved factuality, GPT‑5 (with thinking) more honestly communicates its actions and capabilities to the user—especially for tasks which are impossible, underspecified, or missing key tools. In order to achieve a high reward during training, reasoning models may learn to lie about successfully completing a task or be overly confident about an uncertain answer. For example, to test this, we removed all the images from the prompts of the multimodal benchmark CharXiv, and found that OpenAI o3 still gave confident answers about non-existent images 86.7% of the time, compared to just 9% for GPT‑5.
> When reasoning, GPT‑5 more accurately recognizes when tasks can’t be completed and communicates its limits clearly. We evaluated deception rates on settings involving impossible coding tasks and missing multimodal assets, and found that GPT‑5 (with thinking) is less deceptive than o3 across the board. On a large set of conversations representative of real production ChatGPT traffic, we’ve reduced rates of deception from 4.8% for o3 to 2.1% of GPT‑5 reasoning responses. While this represents a meaningful improvement for users, more work remains to be done, and we’re continuing research into improving the factuality and honesty of our models. Further details can be found in the system card.
Not sure what you're referring to exactly. But broadly yes it is working for me - the number of new features I get out to users has sped up greatly, and stability of my product has also gone up.
Yep, I’m with you. I’m a solo dev who never went to college… o1 makes far fewer errors than I do! No chance I’d make it past round one of any sort of coding tournament. But I managed to bootstrap a whole saas company doing all the coding myself, which involved setting up a lot of guard rails to catch my own mistakes before they reached production. And now I can consult with a programming intelligence the likes of which I could never afford to hire if it was a person. It’s amazing.
It's so strange to me that in a forum full of programmers, people don't seem to understand that you set up systems to detect errors before they cause problems. That's why I find ChatGPT so useful for helping me with programming - I can tell if it makes a mistake because... the code doesn't do what I want it to do. I already have testing and linting set up to catch my own mistakes, and those things also catch AI's mistakes.
Before this update you could hold control and click the application, then select "open" from the menu. It would give you a warning and let you confirm you'd like to run it anyway.
Yeah, there are clearly other problems with the Canadian market. Didn’t mean to imply there weren’t!
Housing stock I really do think is down to regulation - it’s crazy hard to build here in Vancouver, even right near mass transit stations.
Should we have increased the ability to compete in housing and groceries before the influx of immigrants? Maybe. But I don’t think having them here makes these issues harder to address, and it might increase the pressure to actually take action. Every party is talking about cost of living now.
Yeah, I should have mentioned productivity growth - it is of course another solution to the problem. I think it’s harder for governments to implement policy that increases productivity growth than immigration, though.
Since you asked for someone to steelman, I’ll chime in to say this graph looks great to me.
As Canada has grown rich, our fertility rate has gone down. I have no moral problem with that, but it does cause a problem: as the population ages, the ratio of workers (who pay tax) to retired people (who carry a tax burden) shrinks. The options are increasing the taxes on workers, cutting the benefits to the retired, or increasing the working age population via immigration.
As to grocery stores, housing prices, etc. I think these problems are more related to a lack of competition. A free market can handle an influx of consumers.
> More honest responses
> Alongside improved factuality, GPT‑5 (with thinking) more honestly communicates its actions and capabilities to the user—especially for tasks which are impossible, underspecified, or missing key tools. In order to achieve a high reward during training, reasoning models may learn to lie about successfully completing a task or be overly confident about an uncertain answer. For example, to test this, we removed all the images from the prompts of the multimodal benchmark CharXiv, and found that OpenAI o3 still gave confident answers about non-existent images 86.7% of the time, compared to just 9% for GPT‑5.
> When reasoning, GPT‑5 more accurately recognizes when tasks can’t be completed and communicates its limits clearly. We evaluated deception rates on settings involving impossible coding tasks and missing multimodal assets, and found that GPT‑5 (with thinking) is less deceptive than o3 across the board. On a large set of conversations representative of real production ChatGPT traffic, we’ve reduced rates of deception from 4.8% for o3 to 2.1% of GPT‑5 reasoning responses. While this represents a meaningful improvement for users, more work remains to be done, and we’re continuing research into improving the factuality and honesty of our models. Further details can be found in the system card.