@prashantmitali
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bending spoons has been in the news a bunch so i was looking through their portfolio. it feels like a graveyard of software that was cool once and almost nobody would choose today.
is the model basically: buy once-great products with captive users, cut everything to the bone, then keep turning the monetization dial on people too locked-in to leave?
speaking with an incumbent whose product leads the market because it sucks the least.
their VPs are quizzing me with this condescending undertone that we’re overclaiming and don’t know what we’re talking about. meanwhile, i’ve used their product and all i can think is how out of touch they are.
so bullish on startups and openai. incumbents need zero ego and a willingness to start from the bottom.
why do so many retailers have better pricing on amazon than on their own sites?
surely they’re netting less per order after amzn’s cut, yet they discount more aggressively/frequently there. aren’t they just teaching the customer where to shop next time and throwing away margin?
prashant retweeted
Today we're launching the Agents API, a brand new way to build Agents in the cloud, backed by the Codex harness. Bring along all your favorite tools and connectors, connect it to any sandbox, and let Astra cook.
Can't wait to see what you whip up 👨🍳
openai.com/index/introducing…
every bending spoons acquisition is another group of former startup employees discovering their zirp-era options were, in fact, commemorative pdfs
got to work with wonderful customers like @TheCiridae and @getsafetykit on some iterations of this product. check out what they said in our blog post -- openai.com/index/introducing…
agents api is available now in public beta! please try it out and give us feedback so we can make it even better 😊
this model is priced at just 5c/min. the value is unreal.
native support for delegation effectively removes the intelligence ceiling. oh, and you can delegate to our models or bring your own.
game changing.
prashant retweeted
*ring ring*
Today, we’re releasing a preview version of Tomo Voice. Tomosapiens can talk with their Tomo by calling the phone number they already text with, powered by OpenAI Live.
In rare cases, your Tomo may also decide to call you first.
Calling is especially joyful knowing that Tomo can take real-world actions and update your garden of apps with a realtime verifiable trail of texts and images.
Voice is another step towards making accomplishing more in life even more delightful for everyday people.
It’s been a ton of fun to try. I hope you pick up!
we are only at the beginning of the takeoff 🛫
The first-ever purchase made on Tomo came from a user in Hawaii who started using it because she'd always wanted to learn Japanese. Tomo spent $81 on a Japanese textbook for her.
Next came $55 whey protein from a user working with Tomo to get in shape, then a $109 sunrise alarm clock from a user working with Tomo to sleep better.
Consumers let their personal agent make purchases when they trust that it has their best interests in mind.
Pushing people towards their personal ambitions is a great way to start building that trust among everyday people: purchasing items as aspirational as a textbook earns the right to purchase items as mundane as toilet paper.
We’re partnering with Stripe so that tomosapiens can approve purchases directly through Link, further closing the gap between aspiration and action for real people.
ChatGPT Images 2.5—faster, sharper, smarter, with better tools for creating whatever you can dream of.
- Faster image generation to keep your ideas flowing
- Improved fidelity for more natural, recognizable images
- Consistent details across multiple edits
- Comment-based edits to change only what you want
i suspect both the “this is AGI” and the “i don’t get this model at all, everyone is shilling” camps are describing real experiences with the model. it’s made me wonder if frontier models need onboarding flows?
my logic: the more differentiated your work, the more likely you have specific preferences, exacting judgment, and strong opinions about what good looks like. those things might not live in the model’s defaults.
so when working with a new model, it’d be cool to systematically discover that delta:
work together → find where its defaults diverge from my preferences → distill the important bits into AGENTS.md / skills → get cooking.
very pleased to see us share this view on our internal model usage.
it seems more and more likely that the most effective companies in the near future will be “RSI-pilled” — groups of individuals who build an improvement loop around the core value drivers in their business.
increasingly the jtbd is to tend to the loop: make outcomes measurable, expose the right systems and context to agents, build the infrastructure for safe experimentation, and continuously expand the surface area over which models can improve the system themselves.
the foundation model does more and more of the actual work inside the loop — writing code, running experiments, analyzing failures, proposing changes, testing them, and eventually improving the machinery that enables the next iteration.
this makes tokens less like a software cost and more like leverage on your most valuable resource: people. foundation labs already operate this way; increasingly i expect the best companies will too.
Today we're releasing data on models accelerating research at OpenAI.
Recursive self-improvement could be the most important contributor to AI capabilities over the next few years, but by default it will only be seen inside a few frontier AI labs. Being transparent is more urgent than ever, so we can inform the public discussion on whether and how to pace model development. I ask other AI companies to do the same.
openai.com/index/research-ac…
all else equal, if you’re young and ambitious, i’d bias heavily toward places where tokens are effectively unlimited and experimentation is encouraged. avoid companies practicing financial engineering on token spend while treating inference as a cost center to be squeezed.
the compounding difference between spending your formative years somewhere that asks “how do we use 10x more intelligence?” vs “how do we spend 20% less on intelligence?” is going to be enormous.