seattleeng

So they’ll just die when Google has a seamless AI-first mobile experience where consumers just ask their Pixel to do X and it happens. Disruption comes for everyone, hardware isnt a moat

These numbers are certainly wrong lol

It’s more like conditioning the posterior of a response on “Ok, so…” lets the model enter a better latent space for answering logically vs just spitting out a random token.

I think you're almost right

I suspect its more a structural issue

e.g. because of Big VC (a16z, other firms with dozens or hundreds of investing staff), VCs don't stick around firms long enough for real returns (cash distributed) to matter. If you're at a place and your stuff is marked up 10x after 4 years, you just hop to become a GP and try to ride the next markup wave. Even if these people exit VC after 10 years that is still 10 years of deals that all flopped.

This might not even be the individual VC's fault -- there may just be too many venture dollars chasing too few power law returns, so you get a surge of startups circa 2019-2022 that all disappear

literally how openai attracted talent with deepmind as the boogeyman. its a playbook that works

As is usually the case, check the data! A lot of the dataset used has fairly morbid scenarios, so the model is working as expected. All the data was synthetically created with GPT4

Yes, the climate boogeyman shows up as a common anti-tech trope since most people don’t look into the breakdown of emissions. Basically everything in direct consumer control pales in comparison to transit (car/truck/boat/plane).

Speed and scale has a good breakdown: https://speedandscale.com/tracker/

Plus most new AI datacenters are trying to go pure nuclear wherever possible

Im reviewing linear algebra now and would also love to know that book!

I actually think there are a few, only some have achieved critical mass with smaller subnetworks. BlueSky and Threads both seem to have attracted non-tech networks although Threads downranked political content, so BlueSky seems like the natural next mass appeal product.

Others:

Mastodon: many tech subnetworks (security, data)

Nostr and Warpcast: Crypto

This is exactly how Ive seen things occur. It makes a lot of sense, given some tools are great new additions (coding especially), but others fall flat (IMO, search)

You can learn things top-down or bottoms-up. I can read & understand most reverse engineering posts like this because I have a strong "bottoms-up" foundation with an EE degree and worked with microcontrollers. But when I read posts by hobbyist mechanical engineers about some 3D printed piston that uses ball bearings I have to approach it in a top-down "recreate what they did and go deep any time I'm lost" manner.

I think you’re exactly right, it’s a show for insiders to know what these 4 people think so the next time they directly or indirectly encounter them they are known quantities. Its masked as news & informative media but its principally brand marketing for these 4 people and their funds & companies.

Cut 80% of jobs and 80% of revenue

That is not what shareholders want, hence why no one is copying it

preserving the vector art after transforming is really cool, anyone know the relevant papers? or was this original research done by Adobe?

Why is working locally important?

A new programming model for distributed computing is desperately needed. Something between a full operational system a la Temporal, but without the extreme operational overhead + a sane cooperative runtime like Golang.

I think we're probably too early to build this today. Ray is used at my current job for scaling subroutines in our distributed job system. It's the closest I've seen.

A credit based model with minimum add amounts like that does sound like a great alternative if people prefer usage based pricing. From what Ive seen usage pricing actually drives a lot of anxiety even if the end cost is lower

I get your point but its $3/mo or $18/yr. If a diagram isnt worth 1.80 to you then you probably wouldnt have bought it even with pay as you go

Its cool but this feels really late. I just use Ideogram now. The social features are nice

Was gonna say, the email reads similarly to this famous thread about Java (with the old CTO of MS!): https://www.techemails.com/p/bill-gates-im-literally-losing-...

Really makes you think about the structure of mega corps and how powerful the “defender’s advantage” is. These giants knowingly sleep on disruption and wait to time their entry, and are generally rewarded. I dont know if its good or bad, it probably depends, but I think the capitalism game devs need some balance tweaks.

Seems like "open source" as a marketing tactic (or perhaps strategy, if they do continue to release open models) has peaked. I'm not really complaining, we get a lot of stuff for free as engineers (especially software), but it does seem different for a company to release an open model without any future commitments (e.g. Google) vs making open weights your raison d'etre and then pivoting quite quickly. The first feels transactional but honest, and the other a bit too... machiavellian?

I do think it's too soon to pass judgement; this could just be a normal "freemium" strategy from days old, where you just pay up if you like the smaller/cheaper/free versions of their models.

the majority if the employees at openai joined after chatGPT launched, so it's not like there's some sense of nostalgia or forlorn distress for what they built slowly changing. The stock comp (sorry, "PPUs"... which are a phantom stock plan lol) is also quite high (check levels.fyi) and would have been high 7 to low 8 figures for engineers if secondary/tender offers were made.

I agree it's not that deep - they wanted to join a hypergrowth startup, build cool stuff, and get paid. If someone is rocking the boat, you throw them off the boat. No mission alignment needed! :)

I think there’s an element here thats missing: companies can fund small tiger teams (a la skunkworks) if theres executive buy-in to solve specific targeted problems. A small elite unit of engineers with executive support and external partners can certainly make a difference, without reworking the entire company. I think geohotz has the expectation that AMD would try such a thing, as Ive seen and heard of it many times in industry (software or hardware).

Since you're familiar with this, what was wrong with how Farcaster approached name registration? Signing up or rotating your key is (relatively) cheap on Ethereum mainnet, and client apps could front the cost of signing users up. The user registry doesn't need to be maintained by a closed group in the long run (could start out that way, with Bluesky maintaining the contracts but eventually removing upgradeability when out of beta).

I used to joke at work that the fastest, simplest way to get promoted to a staff engineering position was to finish a 1 yr refactor.

Getting existing codebases to do new things is hard, and most of enterprise software engineering is basically "rework this API (in the general sense, not web-API sense) for a new product use case".

This tool seems to borrow the philosophy of Go's keeping the language simple, with the tradeoff of a more complex (but fairly well supported) ecosystem to account for missing language features.

IME, Golang (some would say paradoxically due to a lack of generics) is one of the best languages to refactor because of:

- Forced error handling semantics so that unhappy paths are easily enumerated

- Tooling like `rdep` to quickly grok the impact of a package refactor

- String templates/codegen being a first-class "blessed" part of the ecosystem (i.e. you can find tooling by the Go team as a reference when writing your own)

Some would balk at things like codegen being how Go handles this, but for the iteration loops at most companies at scale (where a few tools to do codegen are written & maintained by one team and consumed by others) it works well. For paradigms where you want everyone to contribute to the tooling, it works slightly less well since the barrier of understanding the ecosystem is greater than just understanding the language. Nevertheless, it seems to be the right tradeoff for most enterprise use cases.

It's cool that governments are taking action now, but this is a chicken vs egg scenario. If services like Yodlee didn't exist to prove that there was significant demand for programmatic access to consumer financial data by building businesses around it, would governments care? And, how many more years will it take before it gets there? With Plaid/Yodlee/Whatever you can build a Fintech app _today_ in the US that supports thousands of banks. If you're an entrepreneur that's a game changer.

OpenTracing doesn’t actually specify when a span is sampled. IIRC Lightstep sends all spans to a service that makes sampling decisions based on moving latency percentiles, while Jaeger works as you described, sampling in the client. Its implementation specific.

what is `fd` in this context?

I agree with what you've written, and the phrasing I used was unclear. I was mainly making a note about joining a mid to late stage pre-IPO startup (series B or later) vs joining a large, low growth public company. You can't easily invest in most pre-IPO companies today (though with secondary markets growing in popularity this might change in 5 years). In the case of choosing between two large public companies, sure, take the cash & liquidate your positions as soon as you vest so you can stick them in SPY or vanguard.

I agree that there are tiers of companies when it comes to compensation, and this site tends to skew towards recording datas for higher tiers (but many of the companies this site provides levels for do NOT pay as much as FAANG - as more salary data is added this will become clearer). And this can certainly be frustrating/depressing when comparing with individual compensation. It should be noted that the pay differential between top paying companies and those below comes from two factors:

- Geography. US based companies in the Bay Area will almost always pay more than companies anywhere else in the world. Many companies have different compensation bands for different regions of the world, even within the US.

- Equity. The base salary for an entry level developer position at a top company will pay somewhere in the range of 110-130k. I've seen many entry level dev jobs at startups in the bay area paying in the 80-110k range (I can't speak to hard data that supports this though, because open salary information is hard to come across!). So, the salary differential when comparing upper/lower bands between top companies and median companies exists but isn't outlandish (between 20-40% more). The difference is, entry level devs at Google & FB will also get a 50k/yr equity grant. Based on my experiences, this is an order of magnitude more than the median company (where lottery tickets or 1-5k/yr grants are common).

So my personal advice for optimizing compensation would be:

1) Move to the US (Bay Area/NYC/Seattle) or work for a US (Bay Area/NYC/Seattle) company remotely or at a satellite office in another city. Obviously, everyone has personal restrictions so this may not possible.

2) Work for a company who you believe will have equity growth. There is a wide spectrum here between 5 person startup lottery tickets and established behemoth that have 0.5% YoY growth stocks. A good recent example of this is Square, which gave out equity grants that were something like 50% lower in cash value than the equivalent role's offer from a FAANG company (this is based off of personal anecdata). However, Square's stock exploded over the past year and that equity today outcompetes many of the equivalent FAANG-level offers. Of course, the opposite could have also been the case -- I've heard stories of underwater options being granted pre-IPO by Square. Sure, a few years later they're worth a lot, but at the time, employees weren't happy.

The world is wider than FAANG and tiny startups, and each company has its own set of hiring criteria (e.g. both Twitter and Square started as Rails shops so your Ruby experience would be more valuable to them than Google or FB). You can't predict the market, but if the choice is between 20 year old Company A that gives you 3k/yr in equity or a recently IPO'd Company B that gives you 1k/yr in equity, I would on average take the gamble with Company B (in practice taking into account team strength, product vision/market fit, & company direction after interviewing).