@Uniswap There's a real difference between value that operates onchain and value that just settles there. The first changes how systems compose. The second mostly changes the venue.
@Tenacious_DeFi@MintbuilderES@Zeus_Lobster Already shipping while most are still talking about it. AI is making this possible for builders like you who just get to work. ๐
For most of computing's history, building software meant learning to think like the machine.
Tools that flip that โ letting the machine meet the person where they are โ expand who gets to build at all.
https://t.co/QsY2Mloa6e
what if building apps felt less like typing or coding, and more like playing?
a new way to build apps. coming soon to a Wabi near you.
comment with your app idea and we'll send you an invite to join wabi
Onboarding shouldn't be an obstacle.
Resources like these from @Fortune__ONE make it easier for new users to take the first step with confidence. ๐
https://t.co/UXnolHuktE
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@venturetwins Moodboards as style guides for generation is a smart idea. It connects the visual intuition you already have to the output, instead of trying to describe an aesthetic through a prompt box.
@garrytan The epistemology layer is interesting, not just what's similar, but who believed what and when. That's where it stops being a search tool and starts being something else.
An agent, mid-task, identifies that it needs paid data. It looks up the source, completes the purchase, and continues with the work. The transaction is a step in a larger workflow, not the workflow itself.
This is what people mean when they say agentic commerce will mostly happen behind the scenes.๐ฆพ
https://t.co/7QdO5pSvxt
@patrickc An interesting question now is what the trust and permission model looks like at scale, when agents are making hundreds of these decisions autonomously.
Markets can only be as open as the access to them.
Access can only be as open as the code underneath.
The deepest layer is what determines what the surface can be. ๐
https://t.co/vNZ4kxccYr
1. Capability gains have been concentrated in domains with verifiable outcomes, like code and math. These are the areas where reinforcement learning can be applied at scale.
2. Frontier models and consumer-tier models have diverged sharply over the past year.
The result: a widening gap between what AI can do and what most people believe it can do.
https://t.co/eT2MAT2eyi
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
Two tools, one workflow: image model generates the panorama, agent builds the viewer to explore it.
The building blocks are getting better individually, but the bigger story is how they're starting to fit together.
https://t.co/ZXwyDjw4Ja
GPT-Image-2 can be used to make full panoramas ๐คฏ
Prompt for a "360 equirectangular image" of a specific place - I asked for Akihibara at night.
Then I had my OpenClaw agent build a viewer in the browser so I could easily upload the image, pan around, and zoom.
@ilblackdragon Agents that can't be trusted with credentials or assets will always hit a ceiling. The infrastructure layer has to be built for trust from the start, not bolted on later. ๐