Right, the point of this from YC's perspective is to identify and pre-qualify the minority of enterprise AI buyers that can move quickly. Even those buyers aren't going to research and inbound to early-stage YC startups very often. The startups aren't intentionally staying quiet, but often they haven't nailed product-market fit and/or messaging yet.
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Waymo also lets you pick, but only from a predetermined list of pickup/dropoff locations. Sometimes there's not a spot on the side of the street you'd want, other times it will default to a spot on the other side of the street but you can override.
Your market may vary, but I can assure you that in Phoenix, you can drop the pin practically anywhere that's safe and legal.
The app has changed the method a few times. It used to show green or blue segments for valid spots, but today as it is, I just pick up the pin, drop it, hope for the best. It also demonstrates which way the car will be facing.
Dropping a precise pin on the map is no guarantee that it will actually stop right there, depending on the situation on the ground when it arrives. But, you can still adjust the pin while the car waits, or you can hit the "Pull Forward" button to try and improve it.
The app has also been updated to disallow adjustment of multiple dropoffs beyond the initial stop. So if you want a precise pin-drop in a multi-stop trip, you need to intervene along the way, instead of during your initial setup.
There was a recent case reported in the news, where people were trying to Waymo to the San Bruno airport, but their pin got dropped off in a residential neighborhood. This was apparently because Waymo doesn't directly serve the airport's lanes yet, so the pin dropped as close as possible, but the residents of that street were nonplussed about the results. So were the airline passengers who were lugging luggage!
I'm also at a startup. My workflow is similar but I have Fable 5 xhigh drive the whole thing: it gets Codex CLI installed in its environment with an API key, and it's instructed to delegate ~everything to Codex and review its work, especially for code quality/conciseness. Fable delegates to Sol or Luna (fast mode) xhigh/max depending on the task - I think Luna xhigh on fast mode is basically a Pareto improvement over Sol medium.
Most evidence indicates that OPT graduates create more jobs for US-born workers through business formation than they "consume," so this change would get us further from full employment for US-born workers. See e.g. [0] [1]
I'm not sure I fully trust this data anymore. If all of the major US companies have a CEO that is Indian, and these companies still employ a lot of US people, but that number is changing fast - is it really just a study that's trying to convince you not to believe your lying eyes
what are your eyes telling you? whatever they are telling you, you would feel the pain if you actually ran a business and were restricted in hiring to make "US 100%" this is why companies spend tens/hundreds of millions lobbying politicans and why we have a charade of OPT / H1B by the current administration while no one is talking about much bigger issue which is outsourcing. In a company I contract for, 47% of all employees (out of 4k) are outsourced. but you won't hear a peep about that cause you just go to mar-a-large with a large white envelope and problem solved while the "masses" are eating up this "OPT" and "H1B" charade. Too funny...
not much reason to think this won't happen except unconfirmed gossip, but I fully expect the next one to not be released. actually I won't be surprised if even this release was withheld and the announcement withdrawn.
Thinky's main commercial product AFAIK is Tinker [0] - companies pay them to host their fine-tuning workloads and then the resulting fine-tuned models. I don't know if this is a good business plan, but I'm sure at least one person there has read Joel on Software [1].
I don’t know if it’s a great business model but it makes perfect sense to me. Open models when fine tuned are capable at better than frontier performance at a fraction of the price for many (probably most) domain specific tasks. If companies help make that easy to implement, there is value to capture. But I kind of like Unsloths model here which is to be really good at just layer, and not bothering with building their own models.
I don't get this - I can do LORA on my mac... ok I can't do LORA on a 1tn param model, but if I was in the tn parameter model game I would get some kit that I could use to do that...
Like, buy and set up the physical hardware? I cba with that. Plus the hardware you want for LoRA (the type but especially the quantity) is different than what you want for inference, so either you'd under-spec it and wait forever for fine-tuning runs, or over-spec it and have low utilization most of the time. And even then who knows if it would be good enough to LoRA next year's best open source model. AWS gets great margins for renting out commodity hardware as a service because it built the right abstractions and can serve them efficiently at scale, I think the arguments here are basically the same.
I assume this is ~equivalent to ultracode in Claude Code, which can deploy a tree of hundreds of nested subagents and was just released experimentally 5 weeks ago IIRC.
It's still just a bad answer across the board. Having opinions and being able to articulate and defend them clearly is itself an extremely important hiring signal regardless of a company's stance on generative AI. An AI-forward company will be looking for an answer like "I haven't written code manually since 2025, I use ..., I stay on top of new tools without drowning in hype by ..." If that's not your answer, you probably aren't a good fit for those companies, but companies that would be a fit will still want a similar level of decisiveness. Much better to give an honest answer that will sound good to the right people than a wishy-washy answer that will sound bad to everyone.
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