19,383 karma · joined October 14, 2012
Terry Tao talks about this challenge in some of his blog posts and interviews, it's pretty interesting.
So this idea that doing math is some kind of fully deterministic & mechanistic process is a bit misleading.
However, when we look at the claim with a more critical eye, it becomes obvious that this view is myopic. In fact, current progress is in fact indebted to the invention various tools, which has led us to grand theories and hypotheses: things the Ancient Greeks could only dream about.
Is coding about writing assembly? Is it about writing boilerplate? About writing endless if-then statements? I would argue that it is not: it's about coming up with ideas, building products, finding customers, iterating, producing code that's easy to read, easy to understand, easy to extend.
Coding (at least the kind LLMs do) was never really the hard part, and I'd like to know specific examples of savvyness the author is losing. They've made me more critical of code and able to create better patterns, simply because I can iterate so quickly.
What are you talking about? I'm running 1.4 canary (the Rust rewrite) right now.
λ bun --version
1.4.0
Apart from pro-AI or anti-AI posturing how have the last few months not looked good for Bun, exactly? I use it daily and I've seen basically zero regressions. I get it, you don't like AI or you like Zig over Rust, or whatever. I just haven't seen any serious argument that Bun has somehow become worse software.
> The project has over 5k open pull requests, which is the largest number of pull requests I’ve seen.
Terrible argument, and not really an argument at all.
> The biggest worry is, of course, the code itself.
I agree, so look at the code and point out what's wrong with it.
Insofar as Andrew Kelley is concerned, it's obvious he has an axe to grind and is salty about Bun embarassing Zig (which he freely admits). Not sure why you'd invoke an unreliable narrator as some sort of final nail in the coffin.
But this is precisely the context of the original webpage: someone writing code at your company and your company not having copyright of that code. Almost everyone that works for any tech company signs an NDA, and code in private repos is just that: private. So even if said intellectual property (AI-written code) is not copyrightable, it's still a trade secret.
This is doubly stupid because I've worked at plenty of companies where we would routinely generate code (using macros or transpilers, or what-have-you), and that code is also not technically copyrightable.
Always has been. My prediction is that both OpenAI and Claude will go bust unless they deliver a killer product. And unlike scrappy startups, they have a pretty serious deadline because creditors will come a-knockin'.
There's little to no functional difference between Kimi, Qwen, Sol, Opus, etc. All flagship models are within like 1-5% of each other and the real moat will be what's always been the hard part: making a good product.
This is true, but I was mostly referring to framework boilerplate (Spring, React, etc.) or plumbing boilerplate. If you've ever written code professionally, you know that most code that you write is just making your thing fit with someone else's thing.
If your consumer or your provider made bad engineering decisions, you have to absorb them. If your company mandates the use of a framework, you have to absorb that, and so on. So I think it's great that AI can write all the dumb shit I didn't come up with anyway.
Frankly, this barely scratches the surface of the hubris of engineers. I personally think AI has been one of the best things to happen to software engineering. Writing boilerplate or my millionth auth implementation was never why I fell in love with the craft in the first place.
Remote: Yes
Relocation: Case-by-case
I'm an engineer and data professional interested in team-building, consulting and architecting data pipelines. At Edmunds.com, I worked on a fairly successful ad-tech product and my team bootstrapped a data pipeline using Spark, Databricks, and microservices built with Java, Python, and Scala.
At ATTN:, I re-built an ETL Kubernetes stack, including data loaders and extractors that handle >10,000 API payload extractions daily. I created SOPs for managing data interoperability with Facebook Marketing, Facebook Graph, Instagram Graph, Google DFP, Salesforce, etc.
More recently, I was the CTO and co-founder of a crypto gaming startup. We raised over $6M and I was in charge of building out a team of over a dozen remote engineers and designers, with a breadth of experience ranging from Citibank, to Goldman Sachs, to Microsoft. I moved on, but retain significant equity and a board seat.
I am also a minority owner of a coffee shop in northern Spain. That I'm a top-tier developer goes without saying. I'm interested in flexing my consulting muscle and can help with best practices, architecture, and hiring.
Would love to connect even if it's just for networking!
Blog: https://ai.dvt.name/whos-david/ (under construction)
GitHub: https://github.com/dvx
Email: [d]@[dvt].[name]
I constantly have to tell agents to just "look it up online" and "don't hallucinate your own components" because people have already done this a million times. Ironically, being more lazy could make these models more useful.
It's trivial to see how many people think Pangram is absolute trash[1] (because it is).
> You appear to be misinformed about how Pangram specifically works, it is not based on pattern detection of that sort. I recommend reading their whitepaper, it's a pretty understandable explanation of exactly how they trained their classifier.
I did read their paper (which is, by the way, very scant on details), and they trained their classifier in the laziest way possible: here's a chunk of "human-written" text and here's a chunk of "AI-written" text, put them in the right bucket, and do this a zillion times. Literally zero sophistication. Also: what do you think "pattern recognition" is, if not a "classifier"?
[1] https://www.reddit.com/r/academia/comments/1rm11rs/pangram_c...
Pangram tries to look for common patterns (rule of three, em dashes, etc.) but these are heuristic methods and not to be taken as gospel. There is no provable method to make a distinction between AI and human-generated other than the fact that AI-generated text tends to reek of pseudo-intellectual undergrad with a thesaurus.
Functions extremely well and the result is a very clear (and consitent) human-readable "output layer." Cool idea, fun to see people converging on similar concepts in the space.
And I explained why this is not feasible given modern network topology. Players move around maps and peek around corners in between these "tick updates" the client gets from the server; therefore, we have to use clever methods of interpolation and prediction as well as server consensus to ensure that gameplay is smooth (e.g. models can't just pop in out of nowhere, movement feels good) while also being as fair as possible (e.g. we don't favor one player's view over another's).
So a server, of course, does send updates (player position, etc.) every "tick," but that doesn't matter. Even assuming zero dropped packets (suppose we're playing over TCP), it would feel like shit (stuttering, rubber-banding, pop-in, jittery physics, etc.) to play a game over a ~60ms latency that updates ~60 times a second vs other people that also are ~60ms from the server, so game engines do a lot of interpolation and servers are in charge of concensus. Hence, it's a bit of a misdirection to say: "can't you just send everyone the right player data every server update?" because servers obviously already do that (and that's not really the hard part, anyway).
Local interpolation and remote concensus is the hard part, and games handle this differently. In CS, for example, two players cannot kill each other (with guns) simultaneously. Valve's engine requires that someone always wins a gunfight (which sometimes can feel random). However, I would argue that feels way better than, e.g. in Halo, where you can headshot each other (and both players die), which feels dumb and frustrating.
So when building these servers, there are lot of tradeoffs to consider (a lot of which might change the feel of the game).
[1] https://en.wikipedia.org/wiki/MDY_Industries,_LLC_v._Blizzar....
Always confuses me why people speak so authoritatively on topics they aren't versed in. PVS culling is not even remotely comparable to occlusion culling, mainly because wallhacks are not relevant accross the map; in fact they are only useful when opponents are always well into your PVS range.
FYI: there are also some clever ways to get around PVS culling (mostly by inferring opponent position based on other indicators, like gunfire).
So it's sort of a "relativistic" temporal system, not a linear "oh now you're at t=1, now you're at t=2" kind of timeline. And there's all kinds of complicated ways you create concensus between multiple clients, between server and clients, etc. (A lot of this remains an active research area.)
Cheating has always been a problem in FPSs, and it likely won't go away. That's why premier competitions have always been on LAN.
[1] https://www.pcgamer.com/introducing-gameref-the-anti-cheat-h...
[2] Hard to fully obfuscate audio sources, hard to obfuscate hitboxes since you still need them for collision checking (e.g. if a grenade bounces off an enemy player behind a wall—the server does not do all physics for all clients), and this is on top of the engine itself sometimes requiring actual entities, so you're stuck with these dummy entities in memory, and so on.
Remote: Yes
Relocation: Case-by-case
I'm an engineer and data professional interested in team-building, consulting and architecting data pipelines. At Edmunds.com, I worked on a fairly successful ad-tech product and my team bootstrapped a data pipeline using Spark, Databricks, and microservices built with Java, Python, and Scala.
At ATTN:, I re-built an ETL Kubernetes stack, including data loaders and extractors that handle >10,000 API payload extractions daily. I created SOPs for managing data interoperability with Facebook Marketing, Facebook Graph, Instagram Graph, Google DFP, Salesforce, etc.
More recently, I was the CTO and co-founder of a crypto gaming startup. We raised over $6M and I was in charge of building out a team of over a dozen remote engineers and designers, with a breadth of experience ranging from Citibank, to Goldman Sachs, to Microsoft. I moved on, but retain significant equity and a board seat.
I am also a minority owner of a coffee shop in northern Spain. That I'm a top-tier developer goes without saying. I'm interested in flexing my consulting muscle and can help with best practices, architecture, and hiring.
Would love to connect even if it's just for networking!
Blog: https://ai.dvt.name/whos-david/ (under construction)
GitHub: https://github.com/dvx
Email: [d]@[dvt].[name]