nibab
looks great btw. congrats
The Abundance Mirage: why the road to hell is paved in unchallenged assumptions
Discovery vs. Tokenmaxxing
at the same time, context poisoning is a real cognitive problem for humans too and I can't tell you the number of times I've seen irrelevant details become a drag on execution. my fear is that having too much context will only cause bikeshedding and a revisiting of prior decisions.
frankly, our organizational structures were already pretty good at creating mechanisms for eliciting the right implicit context at large scales. it is possible that we're just going to come up with the same mechanisms from first principles...
Show HN: An unstructured data workspace for data transformations with LLM
hi HN!a couple of months ago I had to analyze a few thousand audio recordings to help identify issues with customer support. i was able to get some raw high-level initial results with python scripts invoking LLM APIs, but they were too general and unhelpful. writing basic prompts is easy, but tuning them and making them specific enough to ensure no faint signal is missed is hard. you need to...
Free alternative to Harvey/Legora's tabular document review
Search, not AI, is still king
First to Be Second: Why Enduring AI Companies Will Be Started After This Wave
its interesting to see how "lock-in" is the main pitch here. all things considered, i don't think "lock-in" is relevant at all unless the activity performed with the tool is highly strategic to the company.
you could argue that some orgs may not want openai/anthropic to have their sensitive data leave the parameter, but im also here to tell you that even the most privacy sensitive companies in the world probably resolve this by having a proxy in between the users and the LLM APIs from the labs.
so where does this leave you ? cost savings from OSS? maybe, but its hard to imagine that we are in the phase of the adoption cycle where companies have become as acutely aware of costs as you think they are.
my 2c - focus on the integrations and see which one gets most traction. that will be your value capture mechanism long-term.
Ilya Sustkever's deposition reveals previously unknown details [pdf]
ng3n is more of a grid-like workflow solution on top of documents. it's a user-facing application geared towards non-technical users that have processing needs.
if there are all these new problems that became solvable, what exactly are they?
id be interested in replacing datalab with extend, but im not sure what avenues that opens for ng3n. would be very curious to learn!
ChatGPT is the new browser and memory is the new cookie
Engineers that refuse to acknowledge constraints, whatever the nature of those constraints may be, are not engineers. At best they are ideologues, at worst they are just incompetent. The most pathetic thing you can do is just continuously deny the laws of physics and reality, because it doesn’t suit you at some ideological level.
Truly elegant solutions are those that account for all constraints in the simplest, most concise way. It is those that do more with less.
i've seen this happen with people that have had much much smaller financial success in the industry..or even ones that haven't had any at all. you are either naturally inclined to identify with the culture or you trick yourselves into it so that you may belong.
<insert paragraph about social desire to be connected and how we construct an image of ourselves through others>
the culture of SV today is an amalgamation of Taylorist ideas, Randian objectivism, Utilitarianism etc etc. there is a lot of social capital to be earned by embodying the values of these currents. DOGE is a quintessential representation of this. it is not surprising at all that author had such a visceral reaction to it.
its important to emphasize that there have been very successful companies that have gone against the current (ie Apple), with an emphasis on craftsmanship, obsession with the process, taste-driven vs data-driven decisions and appreciation for things that are outside of profit maximization.
As many have pointed out, with time you notice the principles being used in all sorts of ways against you depending on the context. The management class is conditioned to tell you that "principles are deliberately in contention with one another"...which gives everyone the necessary cover to spin a particular guiding principle in whichever way suits them in that moment. Or you could buy the kool-aid and pretend that whoever architected these principles was so linguistically adept that they truly figured out the perfect way of articulating a set of contentious principles that taken together distill the exact cultural nuance that Amazon is all about. Wittgenstein is rolling in his grave.
In their current state, the leadership principles are simply a way of defining the lingo for work conversations and providing some sort of framework for decision making (emphasis on framework)...which still has a bounding effect on how things are done at Amazon, albeit in a very very limited way. I do think most people would have trouble coming up with their own decision-making framework if they had to, never-mind articulating or communicating it to their peers. However, it would be preposterous to claim that the Amazon principles have any significant cultural value, at least in the narrow definition that most people commonly ascribe to "business culture" (ie language is also culture, but in this context its more about unique behavior specific to a company).
What drives the culture more than anything at Amazon is the insane growth that the company has seen in the past couple of decades. People there have felt it very deeply and have the battle scars to prove it. Everyone that has been at the company long enough will point to the often counter-intuitive things that one should do to succeed at sustaining this type of growth. Bear in mind this is a different type of business than the other high growth behemoths of the industry and Amazon has its own peculiar aspects..
As far as the article goes, I think that the most important aspect that the author gets right is that. the decline is probably due to the influx of people in middle management that don't have any idea about what it really took to build Amazon into what it is today.
It is as close as you can get to India/Chinas growth without leaving Europe. Chisinau is bustling with activity and full of foreigners chasing entrepreneurial opportunities. The leading Eastern European private equity funds are making hundreds of millions of investments in the country and there appears to be a gold rush in all service oriented areas: IT Consulting, Banking, BPO outsourcing etc.
It is lazy, easy and dishonest to draw a portrait of a derelict country. Diving into the essence of what is actually happening behind the scenes takes a lot more effort. The description of a horse carriage in the center of the capital city is outright false. Chisinau is on par with Kiev, Warsaw, Bucharest, Sofia etc, but bustling with opportunity at every corner.
Like many post Soviet countries, Moldova is incredibly corrupt. Fortunately, it now has a former US federal prosecutor leading anti-corruption efforts. The current president and more than half of the current prime ministers cabinet is made up of Ivy League educated technocrats under 40 yrs of age. It is a very exciting place.
U.S. surgeon general declares firearm violence a public health crisis
So, they cant release science that the googles of the world can use to their advantage BUT they kind of have to because that's their whole mission.
The whole thing was sort of dead on arrival and Ilya's email dating to 2016 (!!!!) only amplifies that.
"Not only that, but any algorithmic advances published in a paper somewhere can be almost immediately re-implemented and incorporated. Conversely, algorithmic advances alone are inert without the scale to also make them scary."
This is reinforced by the fact that infrastructure / training details are much more sparse in papers than algorithmic adjustments.
It's all about 1. researchers (capable of navigating the maze and carefully chosing what to adopt from the research space) 2. clean data 3. infrastructure