Unlurker|Replay|About
Schedule Recurring Calls with Your Far-Away Friends (benjamincongdon.me)
•
I’m not sure scheduling recurring calls is the answer. If a friendship only works when it’s treated like a calendar task, maybe the dynamic has already changed. Sometimes adding structure makes it feel like a chore instead of a real connection
•
Love this, great idea. Automating this with an open version of Calendly comes to mind.
Claude 4.5 Opus' Soul Document (simonwillison.net)
•
This is a hell of a way of sharing what you want to do but cannot guarantee you'll be able to without saying that you cannot guarantee you'll be able to do what you want to do.
It's wild to me that one of our primary measures for maintaining control over these systems is that we talk to them like they're our kids, then cross our fingers and hope the training run works out okay.
•
We "maintain control" over kids until they get to a certain age. Then they typically rebel against their parents.
•
There's a fantastic 2010 Ted Chiang story exploring just that, in which the most universally useful, stable and emotionally palatable AI constructs are those that were actually raised by human trainers living with them for a while. https://en.wikipedia.org/wiki/The_Lifecycle_of_Software_Objects
•
It's such a good story that one. Feels incredibly relevant and timely today.
> Anthropic occupies a peculiar position in the AI landscape: a company that genuinely believes it might be building one of the most transformative and potentially dangerous technologies in human history, yet presses forward anyway. This isn't cognitive dissonance but rather a calculated bet—if powerful AI is coming regardless, Anthropic believes it's better to have safety-focused labs at the frontier than to cede that ground to developers less focused on safety (see our core views). Ah, yes, safety, because what is more safe than to help DoD/Palantir kill people[1]? No, the real risk here is that this technology is going to be kept behind closed doors, and monopolized by the rich and powerful, while us scrubs will only get limited access to a lobotomized and heavily censored version of it, if at all. [1] - https://www.anthropic.com/news/anthropic-and-the-department-of-defense-to-advance-responsible-ai-in-defense-operations
•
I don't think that's a real risk. There are strong competitors from multiple countries releasing new models all the time, and some of them are open weights. That's basically the opposite of a monopoly.
A narrow and cynical take, my friend. With all technologies, "safety" doesn't equate to plushie harmlessness. There is, for example, a valid notion of "gun safety." Long-term safety for free people entails military use of new technologies. Imagine if people advocating airplane safety groused about the use of bomber and fighter planes being built and mobilized in the Second World War. Now, I share your concern about governments who unjustly wield force (either in war or covert operations). That is an issue to be solved by articulating a good political philosophy and implementing it via policy, though. Sadly, too many of the people who oppose the American government's use of such technology have deeply authoritarian views themselves — they would just prefer to see a different set of values forced upon people. Last: Is there any evidence that we're getting some crappy lobotomized models while the companies keep the best for themselves? It seems fairly obvious that they're tripping over each other in a race to give the market the highest intelligence at the lowest price. To anyone reading this who's involved in that, thank you!
•
> Is there any evidence that we're getting some crappy lobotomized models while the companies keep the best for themselves? It seems fairly obvious that they're tripping over each other in a race to give the market the highest intelligence at the lowest price. Yes? All of those models are behind an API, which can be taken away at any time, for any reason. Also, have you followed the release of gpt-oss, which the overlords at OpenAI graciously gave us (and only because Chinese open-weight releases lit a fire under them)? It was so heavily censored and lobotomized that it has become a meme in the local LLM community. Even when people forcibly abliterate it to remove the censorship it still wastes a ton of tokens when thinking to check whether the query is "compliant with policy". Do not be fooled. The whole "safety" talk isn't actually about making anything safe. It's just a smoke screen. It's about control. Remember back in the GPT-3 days how OpenAI was saying that they won't release the model because it would be terribly, terribly unsafe? And yet nowadays we have open weight model orders of magnitude more intelligent than GPT-3, and yet the sky hasn't fallen over. It never was about safety. It never will be. It's about control.
This is the major reason China has been investing in open-source LLMs: because the U.S. publicly announced its plans to restrict AI access into tiers, and certain countries — of course including China — were at the lowest tier of access. [1] If the U.S. doesn't control the weights, though, it can't restrict China from accessing the models... 1: https://thefuturemedia.eu/new-u-s-rules-aim-to-govern-ais-global-expansion/
•
It isn't "China" which open-source LLMs, but individual Chinese labs. China didn't yet made a sovereign move on AI, besides investing in research/hardware.
Testing at these labs training big models must be wild, it must be so much work to train a "soul" into a model, run it in a lot of scenarios, the venn between the system prompts etc, see what works and what doesn't... I suppose try to guess what in the "soul source" is creating what effects as the plinko machine does it's thing, going back and doing that over and over... seems like it would be exciting and fun work but I wonder how much of this is still art vs science? It's fun to see these little peaks into that world, as it implies to me they are getting really quite sophisticated about how these automatons are architected.
•
The answer is "yes". To be really really good at training AIs, you need everyone. Empirical scientists with good methodology who can set up good tests and benchmarks to make sure everyone else isn't flying blind. ML practitioners who can propose, implement and excruciatingly debug tweaks and new methods, and aren't afraid of seeing 9.5 out of 10 their approaches fail. Mechanistic interpretability researchers who can peer into model internals, figure out the practical limits and get rare but valuable glimpses of how LLMs do what they do. Data curation teams who select what data sources will be used for pre-training and SFT, what new data will be created or acquired and then fed into the training pipeline. Low level GPU specialists that can set the infrastructure for the training runs and make sure that "works on my scale (3B test run)" doesn't go to shreds when you try a frontier scale LLM. AI-whisperers, mad but not too mad, who have experience with AIs, possess good intuitions about actual AI behavior, can spot odd behavioral changes, can get AIs to do what they want them to do, and can translate that strange knowledge to capabilities improved or pitfalls avoided. Very few AI teams have all of that, let alone in good balance. But some try. Anthropic tries.
I hadn't seen this, thanks for sharing. So basically the reward of the model was to reward the user, and the user used the model to "reward" itself (the user). Being generous, they poorly implemented/understood how the reward mechanisms abstract and instantiated out to the user such that they become a compounding loop, my understanding was it became particularly true in very long lived conversations. This makes me want a transparency requirement on how the reward mechanisms in the model I am using at any given moment are considered by whoever built it, so I, the user can consider them also, maybe there is some nuance in "building a safe model" vs "building a model the user can understand the risks around"? Interesting stuff! As always, thanks for publishing very digestible information Simon.
•
It's not just OpenAI's fuckup with the specific training method - although yes, training on raw user feedback is spectacularly dumb, and it's something even the teams at CharacterAI learned the hard way at least a year before OpenAI shoot its foot off with the same genius idea. It's also a bit of a failure to understand that many LLM behaviors are self-reinforcing across context, and keep tabs on that. When the AI sees its past behavior, that shapes its future behavior. If an AI sees "I'm doing X", it may also see that as "I should be doing X more". And at long enough contexts, this can drastically change AI behavior. Small random deviations can build up to crushing behavioral differences. And if AI has a strong innate bias - like a sycophancy bias? Oh boy. This applies to many things, some of which we care about (errors, hallucinations, unsafe behavior) and some of which we don't (specific formatting, message length, terminology and word choices).
Here's the soul document itself: https://gist.github.com/Richard-Weiss/efe157692991535403bd7e7fb20b6695#file-opus_4_5_soul_document_cleaned_up-md And the post by Richard Weiss explaining how he got Opus 4.5 to spit it out: https://www.lesswrong.com/posts/vpNG99GhbBoLov9og/claude-4-5-opus-soul-document
•
This entire soul document is part of every prompt created with Claude?
•
Reading this document I can now confirm 100% that at least 1 AI has Em Dashes embedded within its soul.
Amazon launches Trainium3 (techcrunch.com)
AWS keeps making grand statements about Trainium but not a single customer comes on stage to say how amazing it is. Everyone I talked to that tries it says there were too many headaches and they moved on. AWS pushes it hard but “more price performant” isn’t a benefit if it’s a major PITA to deploy and run relative to other options. Chips without a quality developer experience isn’t gonna work. Seems AWS is using this heavily internally, which makes sense, but not observing it getting traction outside that. Glad to see Amazon investing there though.
•
The inf1/inf2 spot instances are so unpopular that they cost less than the equivalent cpu instances. Exact same (or better) hardware but 10-20% cheaper. We're not quite seeing that on the trn1 instances yet, so someone is using them.
> Anthropic isn’t exactly jumping up and down telling everyone how awesome it is, which tells you everything you need to know. You can’t really read into that. They are unlikely to let their competitors know if they have a slight performance/$ edge by going with AWS tech.
•
Striking a deal with a competitor (AZURE) does though.
•
With GCP announcing they built Gemini 3 on TPUs the opposite is true. Anthropic is under pressure to show they don’t need expensive GPUs. They’d be catching up at this point, not leaking some secret sauce. No reason for them to not boast on stage today unless there’s nothing to boast about.
IMO AWS once you get off the core services is full of beta services. S3, Dynamo, Lambda, ECS, etc are all solid. But there are a lot of services they have that have some big rough patches.
•
Kinesis is decent
Cursed circuits: charge pump voltage halver (lcamtuf.substack.com)
•
I think I (a long time software nerd) am finally getting over the hump with analog stuff. The article says the circuit is complicated and hard to understand, yet I got it instantly. Feels sort of like learning a musical instrument and realizing that one of the early pieces you struggled with is easy now. FWIW: the didactic trick of imagining the floating capacitor "carrying" charge from one "place" to another was really good. That's not the way most treatments talk about charge pumps, and I think it's a lot cleaner.
•
A simulator will certainly not like the floating center terminal.
•
I just threw it into Micro-Cap and it surprisingly didn't throw any errors with the floating node. https://imgbox.com/riKCyWI5
IBM CEO says there is 'no way' spending on AI data centers will pay off (businessinsider.com)
•
> $8 trillion of CapEx means you need roughly $800 billion of profit just to pay for the interest That assumes you can just sit back and gather those returns indefinitely. But half of that capital expenditure will be spent on equipment that depreciates in 5 years, so you're jumping on a treadmill that sucks up $800M/yr before you pay a dime of interest.
•
How much of Nvidias price is based on 5 year replacement cycle? If that stops or slows with new demand could it also affect things? Not that 5 years does not seem very long horizon now.
He's right to question the economics. The AI infrastructure buildout resembles the dot-com era's excess fiber deployment - valuable long-term, but many individual bets will fail spectacularly. Utilization rates and actual revenue models matter more than GPU count.
•
Fiber seems way easier to get long-term value out of then GPUs, though. How many workloads today other than AI justify massive GPU deployments?
The last 10 years has seen CA spend more on homelessness than ever before, and more than any other state by a huge margin. The result of that giant expenditure is the problem is worse than ever. I don't want to get deep in the philosophical weeds around human behavior, techno-optimism, etc., but it is a bit reductive to say "why don't we just give homeless people money".
•
What else happened in the last 10 years in CA? Hint: https://fred.stlouisfed.org/series/CASTHPI
How do you solve homelessness though? The root of the problem is some people won't take care of themselves. Some homeless just had bad luck, but many are drug addicts, mentally ill, or for whatever other reason just don't function enough to support themselves. I'm skeptical there is a solution you can throw money at.
•
Many experiments have shown that when you take away people's concerns about money for housing and food, that frees up energy and attention to do other things. Like the famous experiment in Finland where homeless people were given cash with no strings attached and most were able to rise out of their despair. The healthcare professionals could then focus their energy on the harder cases. It also saved a bunch of money in the process.
> AI datacenter spending is massive, but if you add it all up, it doesn't cover half of a years worth of government spending. I didn't check your math here, but if that's true, AI datacenter spending is a few orders of magnitude larger than I assumed. "massive" doesn't even begin to describe it
•
The US federal budget in 2024 had outlays of 6.8 trillion dollars [1]. nVidia's current market cap (nearly all AI investment) is currently 4.4 trillion dollars [2][3]. While that's hardly an exact or exhaustive accounting of AI spending, I believe it does demonstrate that AI investment is clearly in the same order of magnitude as government spending, and it wouldn't surprise me if it's actually surpassed government spending for a full year, let alone half of one. 1. https://www.cbo.gov/publication/61181 2. https://www.google.com/finance/quote/NVDA:NASDAQ 3. https://www.cnbc.com/2025/09/30/nvidias-market-cap-tops-4point5-trillion-on-ai-infrastructure-deals.html
Technological advancement is what has pulled billions of people out of poverty. Giving handouts to layabouts isn't an ideal allocation of resources if we want to progress as a civilization.
•
> Technological advancement is what has pulled billions of people out of poverty. I agree with this. Perhaps that's what is driving the current billionaire class to say "never again!" and making sure that they capture all the value instead of letting any of it slip away and make it into the unwashed undeserving hands of lesser beings. Chatbots actually can bring a lot of benefit to society at large. As in, they have the raw capability to. (I can't speak to whether it's worth the cost.) But that's not going to improve poverty this time around, because it's magnifying the disparities in wealth distribution and the haves aren't showing any brand new willingness to give anything up in order to even things out. > Giving handouts to layabouts isn't an ideal allocation of resources if we want to progress as a civilization. I agree with this too. Neither is giving handouts to billionaires (or the not quite as eye-wateringly wealthy class). However, giving handouts to struggling people who will improve their circumstances is a very good allocation of resources if we want to progress as a civilization. We haven't figured out any foolproof way of ensuring such money doesn't fall into the hands of layabouts or billionaires, but that's not an adequate reason to not do it at all. Perfect is the enemy of the good. Some of those "layabouts" physically cannot do anything with it other than spending it on drugs, and that's an example of a set of people who we should endeavor to not give handouts to. (At least, not ones that can be easily exchanged for drugs.) Some of those billionaires similarly have no mental ability of ever using that money in a way that benefits anyone. (Including themselves; they're past the point that the numbers in their bank accounts have any effect on their lives.) That hasn't seemed to stop us from allowing things to continue in a way that funnels massive quantities of money to them. It is a choice. If people en masse were really and truly bothered by this, we have more than enough mechanisms to change things. Those mechanisms are being rapidly dismantled, but we are nowhere near the point where figurative pitchforks and torches are ineffective.
agree the capital could be put to better use, however I believe the alternative is this capital wouldn't have otherwise been put to work in ways that allow it to leak to the populace at large. for some of the big investors in AI infrastructure, this is cash that was previously and likely would have otherwise been put toward stock buybacks. for many of the big investors pumping cash in, these are funds deploying the wealth of the mega rich, that again, otherwise would have been deployed in other ways that wouldn't leach down to the many that are yielding it via this AI infrastructure boom (datacenter materials, land acquisition, energy infrastructure, building trades, etc, etc)
•
> likely would have otherwise been put toward stock buybacks Stock buybacks from who ? When stock gets bought the money doesn't disappear into thin air; the same cash is now in someone else's hands. Those people would then want to invest it in something and then we're back to square one. You assert that if not for AI, wealth wouldn't have been spent on materials, land, trades, ect. But I don't think you have any reason to think this. Money is just an abstraction. People would have necessarily done something with their land, labor, and skills. It isn't like there isn't unmet demand for things like houses or train tunnels or new-fangled types of aircraft or countless other things. Instead it's being spent on GPUs.
The shift in the US to the idea of “job creators” being business owners is part of it. It was just a way to direct money to the already rich, as if they would hire more people with that money. When it is plainly obvious that consumers are job creators, in that if they buy more goods and services, businesses will hire more people to make or provide more of those things. Or maybe it was trickle down economics. Trickle up economics still end up with the rich getting the money since we all buy things from companies they own, it just goes through everyone else first. Trickle down cuts out the middleman, which unfortunately is all of us.
•
The framing of X or Y are job creators is idiotic. Its literally the most basic fact of economics that you need producers and consumers, otherwise you don't have an economy. The more economically correct way to express this would be that entrepreneurs and companies who innovated increase productivity and that makes the overall economy more efficient allowing your country to grow. > Or maybe it was trickle down economics. Trickle up economics still end up with the rich getting the money since we all buy things from companies they own, it just goes through everyone else first. Trickle down cuts out the middleman, which unfortunately is all of us. This just sounds like quarter baked economics ideas you have made up yourself. Neither 'trickle down' nor 'trickle up' are concepts economist use. And that you confidently assert anything about the social outcomes of these 'concepts' is ridiculous.
Take a million dollars, give 1,000 poor people $1,000 and every dollar will be spent on goods and services. The companies running those services and making those goods will need to have their employees work more hours, putting more money back in poor people’s pockets in addition to the money the companies make. Those employees have a few extra dollars to spend on goods and services, etc. Give a rich person a million dollars, and they will put it in an offshore tax shelter. That’s not exactly driving economic activity.
•
You are simply disagreeing with 99% of economists. Money in tax shelter doesn't go threw a portal in another universe. Its either invested or saved as some kind of asset and in that form is in circulation. And again, even if you assume it increases monetary demand (decreases velocity) the central bank targets AD and balances that out. Based on your logic, a country that taxes 100% of all income and redistrubtes it would become infinity rich. Your logic is basically 'if nobody saves and everybody spends all income' everybody will be better off. This is not how the economy works even if it feels good to think that. Its a fallacy. Where you could have a point is that potentially the tax impact is slightly different, but that's hard to prove.
There are many ways of spending money in the population that don't include just "distribution of money", as it's portrayed nowadays. Child care, free and high quality schools, free transportation, free or subsidized healthcare, investment is labor-intensive industries, these are all examples of expenditures that translate in better quality of life and also improve competitiveness for the country.
•
That has literally nothing to do with the point I have argued.
Stock buybacks don't build anything. They're just a way to take money from inside a company and give it to the shareholders.
•
I don't know what that has to do with the point discussed. Do you think shareholder don't spend money, but employees do or something?
> Krishna also referenced the depreciation of the AI chips inside data centers as another factor: "You've got to use it all in five years because at that point, you've got to throw it away and refill it," he said This doesn't seem correct to me, or at least is built on several shaky assumptions. One would have to 'refill' your hardware if: - AI accelerator cards all start dying around the 5 year mark, which is possible given the heat density/cooling needs, but doesn't seem all that likely. - Technology advances such that only the absolute newest cards can be used to run _any_ model profitably, which only seems likely if we see some pretty radical advances in efficiency. Otherwise, it seems like assuming your hardware is stable after 5 years of burn in, you could continue to run older models on that hardware at only the cost of the floorspace/power. Maybe you need new cards for new models for some reason (maybe a new fp format that only new cards support? some magic amount of ram? etc), but it seems like there may be room for revenue via older/less capable models at a discounted rate.
•
Do not forget that we're talking about supercomputers. Their interconnect makes machines not easily fungible, so even a low reduction in availability can have dramatic effects. Also, after the end of the product life, replacement parts may no longer be available. You need to get pretty creative with repair & refurbishment processes to counter these risks.
> power seems like a weird metric to use Because this technology changes so fast, that's the only metric that you can control over several data centers. It is also directly connected to the general capacity of data center, which is limited by available energy to operate.
•
To expand on rahimnathwani's comment below - the big capital costs of a data center are land, the building itself, the power distribution and the cooling. You can get a lot of land for a million bucks, and it doesn't cost all that much to build what's basically a big 2-story warehouse, so the primary capital costs are power and cooling. (in fact, in some older estimates, the capital to build that power+cooling cost more per year than the electricity itself) My understanding is that although power and cooling infrastructure are long-lived compared to computers, they still depreciate faster than the building, so they dominate costs even more than the raw price would indicate. The state of the art in power and cooling is basically defined by the cost to feed X MW of computing, where that cost includes both capital and operation, and of course lower is better. That means that at a particular SOTA, and at an appropriate scale for that technology, the cost of the facility is a constant overhead on top of the cost of the equipment it houses. To a rough approximation, of course.
•
And cooling capacity.
Anthropic acquires Bun (bun.com)
•
A lot of people seem confused about this acquisition because they think of Bun as a node.js compatible bundler / runtime and just compare it to Deno / npm. But I think its a really smart move if you think of where Bun has been pushing into lately which is a kind of cloud-native self contained runtime (S3 API, SQL, streaming, etc). For an agent like Claude Code this trajectory is really interesting as you are creating a runtime where your agent can work inside of cloud services as fluently as it currently does with a local filesystem. Claude will be able to leverage these capabilities to extend its reach across the cloud and add more value in enterprise use cases
•
This reads more like Anthropic wanted to hire Jarred and Jarred wants to work with AI rather than build a Saas product around bun. I doubt it has anything to do with what is best for bun the project. Considering bun always seemed to value performance more than all else, the only real way for them to continue pursuing that value would be to move into the actual js engine design. This seems like a good pivot for Jarred personally and likely a loss for bun.
•
It doesn't read like that to me at all. This reads to me like Anthropic realizing that they have $1bn in annual revenue from Claude Code that's dependent on Bun, and acquiring Bun is a great and comparatively cheap way to remove any risk from that dependency.
•
I'm also curious if Anthropic was worried about the funding situation for Bun. The easiest way to allay any concerns about longevity is to just acquire them outright.
•
Nah, it reads like the normal logic behind the consulting model for open source monetization, except that Bun was able to make it work with just one customer. Good for them, though it comes with some risks, especially when structured as an acquisition.
•
When I saw the headline I was ready to be mad, but after reading the post, I'm cautiously on board with this.
•
In other news - Amp Code is a separate company now - https://ampcode.com/news/amp-inc
•
Anyone know how much Anthropic paid for Bun? I assume it was at least $26M, so Bun could break even and pay back its own investors, but I didn't see a number in the announcements from Anthropic or Bun.
•
A single bun? Is that really newsworthy?
•
I've seen a few of these seemingly random acquisitions lately, and I congratulate the companies and individuals that are acquired during this gold rush, but it definitely feels awkwardly artificial.
•
I'm confused. I installed claude code with: npm install -g @anthropic-ai/claude-code I thought claude code just used Nodejs? I didn't realise the recommended install used a different runtime.
•
They switched to recommending this as the installation method back in July: curl -fsSL https://claude.ai/install.sh | bash That install script gives you a single binary which is created using Bun.
•
I work on Bun. Happy to answer any questions
•
Any thoughts on the claude "soul document" that was leaked this week?
•
How much of your day-to-day is spent contributing code to the Bun codebase and do you expect it to decrease as Anthropic assigns more people to work on Bun?
•
Congrats on the payday :) Do you think Anthropic might request you implement private APIs?
•
Hi Jarred, I contributed to Bun one time for SQLite. I've a question about the licensing. Will each contributor continue to retain their copyright, or will a CLA be introduced? Thanks
•
Is this acquihiring?
•
You said elsewhere that there were many suitors. What is the single most important thing about Anthropic that leads you to believe they will be dominant in the coming years?
•
I'm sure the Bun team will get Claude Code straightened out. Weird acquisition, but TBH Anthropic needed to fill this hole.
•
I wonder if this is a sign of AI companies trying to pivot? > Bun will ship faster. That'll last until FY 2027. This is an old lie that acquirers encourage the old owner to say because they have no power to enforce it, and they didn't actually say it so they're not on the hook. It's practically a cheesy pickup line, and given the context, it kinda is.
•
This is why we can't have nice things
•
> I started porting esbuild's JSX & TypeScript transpiler from Go to Zig How was Go involved there before Zig?
•
esbuild is still a Go app today: https://github.com/evanw/esbuild The first hints of what become Bun were when Jared experimented at porting that to Zig.
Why the hell is a CLI coding agent built in JavaScript? It’s wild what happens when a generation of programmers doesn’t know anything except webdev. How far from grace we have fallen.
•
The big advantage of a language like JavaScript of Python for a CLI tool of this nature is that they naturally support adding extensions or plugins. That's quite a bit harder if your tool is built using a compiled language like Go.
why couldn't Anthropic simply use Claude Code to write Bun over the weekend??
•
It is open source (MIT license), Claude should have a pretty good start on it already.
I think this wound up being close enough to true, it's just that it actually says less than what people assumed at the time. It's basically the Jevons paradox for code. The price of lines of code (in human engineer-hours) has decreased a lot, so there is a bunch of code that is now economically justifiable which wouldn't have been written before. For example, I can prompt several ad-hoc benchmarking scripts in 1-2 minutes to troubleshoot an issue which might have taken 10-20 minutes each by myself, allowing me to investigate many performance angles. Not everything gets committed to source control. Put another way, at least in my workflow and at my workplace, the volume of code has increased, and most of that increase comes from new code that would not have been written if not for AI, and a smaller portion is code that I would have written before AI but now let the AI write so I can focus on harder tasks. Of course, it's uneven penetration, AI helps more with tasks that are well-described in the training set (webapps, data science, Linux admin...) compared to e.g. issues arising from quirky internal architecture, Rust, etc.
•
That's ridiculous. Not it isn't even close.
Why didn't they have the AI write a JS runtime instead of this acquisition?
•
The big picture of “build a runtime” is an easier idea than “what would make this runtime better and how should the parts interact”.
Why didn't they just use AI to write their own Bun instead of wasting 8-9 figures on this company? Makes no sense.
•
Deciding what to Implement and Implementing the Decisions are complementary, one of these is being commoditised. And, in fact, decimated. Personally I am benefitting almost beyond measure because I can spend my time as the architect rather than the builder.
•
From the article, Claude Code is being used extensively to develop Bun already. > Over the last several months, the GitHub username with the most merged PRs in Bun's repo is now a Claude Code bot. We have it set up in our internal Discord and we mostly use it to help fix bugs. It opens PRs with tests that fail in the earlier system-installed version of Bun before the fix and pass in the fixed debug build of Bun. It responds to review comments. It does the whole thing. You do still need people to make all the decisions about how Bun is developed, and to use Claude Code.
With AI tooling, we are in the era where rapid iteration on product matters more than optimal runtime performance. Given that, implementing your AI tooling in a language that maximizes engineer productivity makes sense, and I believe GC does that.
•
JS/TS has a fundamental advantage, because there is more open source JS/TS than any other language, so LLMs training on JS/TS have more to work with. Combine that with having the largest developer community, which means you have more people using LLMs to write JS/TS than any other language, and people use it more because it works better, then the advantage compounds as you retrain on usage data.
Why would I
•
There you go. Thank you for showing exactly why acquisitions like this will continue to happen. If you don't support tools like Bun, don't be surprised to see them raise money from VCs and get bought out by large companies.
> At the time of writing, Bun's monthly downloads grew 25% last month (October, 2025), passing 7.2 million monthly downloads. We had over 4 years of runway to figure out monetization. We didn't have to join Anthropic. I believe this completely. They didn't have to join, which means they got a solid valuation. > Instead of putting our users & community through "Bun, the VC-backed startups tries to figure out monetization" – thanks to Anthropic, we can skip that chapter entirely and focus on building the best JavaScript tooling. I believe this a bit less. It'll be nice to not have some weird monetization shoved into bun, but their focus will likely shift a bit.
•
> They didn't have to join, which means they got a solid valuation. This isn't really true. It's more about who wanted them to join. Maybe it was Anthropic who really wanted to take over Bun/hire Jarred, or it was Jarred who got sick of Bun and wanted to work on AI. I don't really know any details about this acquisition, and I assume it's the former, but acquihires are also done for other reasons than "it was the only way".
Ink (and modern alternatives) probably are the best TUI toolkit. If you want to write a UI that's genuinely good, you need e.g. HTML, or some way to express divs and flex box. There isn't really another way to build professional grade UIs; I love immediate mode UI for games, but the breadth of features handled by the browser UI ecosystem is astonishing. It is a genuinely hard problem. And if you're expressing hierarchical UI, the best way to do it is HTML and CSS. It has the richest ecosystem, and it is one of the most mature technologies in existence. JS / TS are the native languages for those tools. Everything is informed by this. Of course, there are other options. You could jam HTML and CSS into (as you mention) Rust, or C, or whatever. But then the ecosystem is extremely lacking, and you're reinventing the wheel. You could use something simpler, like QML or handrolled. But then you lose the aforementioned breadth of features and compatibilities with all the browser code ever written. TypeScript is genuinely, for my money, the best option. The big problem is that the terminal backends aren't mature (as you said, scrollback, etc). But, given time and money, that'll get sorted out. It's much easier to fix the terminal stuff than to rewrite all of the browser.
•
Ink seems to be the root cause of a major issue with the Claude Code CLI where it flickers horribly when it needs to repeatedly clear the screen and redraw. I don't know why it's even necessary for this. https://github.com/atxtechbro/test-ink-flickering Issue on Claude Code GitHub: https://github.com/anthropics/claude-code/issues/769
Is it? What's wrong with hiring talent for a higher salary? You have no responsibility for an unrelated company's operations; if that was important to them they could have paid their talent more.
•
I left out the part that the motivations for the acquirers were not to save money or to be slimy. It was the only way to get around overzealous government regulators making it harder to acquirer companies.
•
You want those people specifically. To get them, you need to hire them for a lot more money than you pay your current folks. That causes a lot of resentment with folks and messes up things like salary bands, etc. But since they own equity in the current company, you can give them a ton of money by buying out that equity/paying acquisition bonuses that are conditional on staying for specific amounts of time, etc. And your current staff doesn't feel left out because "it's an acquisition" the way they would if you just paid some engineers 10x or 100x what you pay them.
If I had the cash, I could sell dollar bills for 50 cents and do a $7b run rate :)
•
you have anthropic confused with something like lovable. anthropic's unit margins are fine, many lovable-like businesses are not.
No, I'm scamming myself. Halving my fortune because I believe karma will somehow repay me ten fold some time later.
•
Somehow ? I've been keeping an eye on my inbox, waiting to get a karma vesting plan from HN, for ages. What's this talk of somehow ?
Surely you understand the bet Anthropic is making, and why it's a bit different than selling dollars at a discount
•
Maybe for those of us not-too-clever ones, what is the bet? Why is it different? Would be pretty great to have like a clear articulation of this!
>The bank's analysts then assumed Anthropic gross profit margins of 60%, and estimated that 75% of related costs are spent on AWS cloud services. Not estimate, assumption.
•
If Morgan Stanley are willing to stake their credibility on an assumption I'm going to take that assumption seriously.
•
This is pretty silly thing to say. Investment banks suffer zero reputational damage when their analysts get this sort of thing wrong. They don’t even have to care about accuracy because there will never be a way to even check this number, if anyone even wanted to go back and rate their assumptions, which also never happens.
•
Calling this unmotivated assumption an "estimate" is just plain lying though, regardless of the faith uou have in the source of the assumption.
•
I've seen a bunch of other estimates / claims of a %50-60 margin for Anthropic on serving. This was just the first one I found a credible-looking link I could drop into this discussion. The best one is from the Information, but they're behind a paywall so not useful to link to. https://www.theinformation.com/articles/anthropic-projects-70-billion-revenue-17-billion-cash-flow-2028
> Prior to Bun, there was little focus on performance. This is just completely insane. We went through more than a decade of performance competition in the JS VM space, and the _only_ justification that Google had for creating V8 was performance. > The V8 engine was first introduced by Google in 2008, coinciding with the launch of the Google Chrome web browser. At the time, web applications were becoming increasingly complex, and there was a growing need for a faster, more efficient JavaScript engine. Google recognized this need and set out to create an engine that could significantly improve JavaScript performance. I guess this is the time we live in. Vibe-coded projects get bought by vibe-coded companies and are congratulated in vibe-coded comments.
•
> Vibe-coded projects get bought by vibe-coded companies this is so far from the truth. Bun, Zig, and uWebsockets are passion projects run by individuals with deep systems programming expertise. furthest thing from vibe coding imaginable. > a decade of performance competition in the JS VM space this was a rising tide that lifted all boats, including Node, but Node is built with much more of the system implemented in JS, so it is architecturally incapable of the kind of performance Bun/uWebsockets achieves.
As someone who have been using Deno for the last few years, is there anything that Bun does better? Bun seems to use a different runtime (JSC) which is less tested than V8, which makes me assume it might perform worse in real-world tasks (maybe not anymore?). The last time I checked Bun's source code, it was... quite messy and spaghetti-like, plus Zig doesn't really offer many safety features, so it's not that hard to write incorrect code. Zig does force some safety with ReleaseSafe IIRC, but it's still not the same as even modern C++, let alone Rust. I'll admit I'm somewhat biased against Bun, but I'm honestly interested in knowing why people prefer Bun over Deno.
•
Is JSC less tested? I thought it was used in Safari, which has some market share. I used bun briefly to run the output of my compiler, because it was the only javascript runtime that did tail calls. But I eventually added a tail call transform to my compiler and switched to node, which runs 40% faster for my test case (the compiler building itself).
•
Is it just me, but I don't find npm that slow? Sure it's not a speed demon, but I rarely need to do npm install anyways so it's not a bottleneck for me. For deploy, usually running the attached terraform script takes more time. So while a speed increase is welcome, but I don't feel it gives me such a boost.
•
The speed shows up for large projects. Especially if you end up with multiple node_modules directories in your dev sandbox.
> Claude Code, FactoryAI, OpenCode, and others are all built with Bun. Anthropic chose to use Bun to build their tooling.
•
We can think of they making bun an internal tool, push roadmap items that fit their internal products, whatever, which doesn't answer the getting back money of the acquisition. Profit in those products has to justify having now their own compiler team for a JavaScript runtime.
Twice as fast at executing JavaScript? There's absolutely zero chance this is true. A JavaScript engine that's twice as fast as V8 in general doesn't exist. There may be 5 or 10 percent difference, but nothing really meaningful.
•
It depends on what. Bun has some major optimisations. You’ll have to read into them if you don’t believe me. The graphs don’t come from nowhere
•
You might want to revise what you consider to be "absolutely zero chance". Bun has an insanely fast startup time, so it definitely can be true for small workloads. A classic example of this was on Bun's website for a while[1] - it was "Running 266 React SSR tests faster than Jest can print its version number". [1]: https://x.com/jarredsumner/status/1542824445810642946
I haven't used Deno, but I do use Bun purely as a replacement for npm. It does the hard-linking thing that seems to be increasingly common for package managers these days (i.e. it populates your local node_modules with a bunch of hard links to its systemwide cache), which makes it vastly quicker and more disk-efficient than npm for most usage. Even with a cold cache, `bun install` with a large-ish dependency graph is significantly faster than `npm install` in my experience. I don't know if Deno does that, but some googling for "deno install performance vs npm install" doesn't turn up much, so I suspect not? As a runtime, though, I have no opinion. I did test it against Node, but for my use case (build tooling for web projects) it didn't make a noticeable difference, so I decided to stick with Node.
•
Deno does that. It also refrains from keeping a local node_modules at all until/unless you explicitly ask it to for whatever compatibility reason. There are plugins to things like esbuild to use the Deno resolver and not need a node_modules at all (if you aren't also using the Deno-provided bundler for whatever reason such as it disappeared for a couple versions and is still marked "experimental").
pnpm does all that on top of node. Also disables postinstall scripts by default, making the recent security incidents we've seen a non-issue.
•
As the victim of the larger pre-Shai-Hulud attacks, unfortunately the install script validation wouldn't have protected you. Also, if you already have an infected package on the whitelist, a new infection in the install script will still affect you.
It has wayyyyy better nodejs compatibility (day 1 goal)
•
Pretty sure one of the Deno day 1 goals was to correct mistakes made during the early days of Node.js.
I don't really see how Bun fits as an acquisition for an AI company. This seems more like "we have tons of capital and we want to buy something great" than "Bun is essential to our core business model".
•
Claude Code running on Bun is an obvious justification, but Buns features (high performance runtime, fast starts, native TS) are also important for training and inference. For instance, in inference you develop a logical model in code that maps to a reasoning sequence, and then execute the code to validate and refine the model, then use this to inform further reasoning. Bun, which is highly integrated and highly focused on performance, is an ideal fit for this. Having Bun in house means that you can use the feedback from all of automation driven execution of Bun to drive improvements to its core.
I highly doubt that the JS ecosystem is driven mostly by hype so I highly doubt the nodejs solution even put on a table in an internal issue tracker.
•
Claude Code shipped on top of Node.js for the first four months of its existence. Why wouldn't they consider their options for bundling that version into a single binary using Node.js tooling before adopting Bun?
Man, I had never even put words to that problem but you are right that it is beyond annoying. It seems to me like it worsens the longer the Claude instance has run - I don't seem to see it early in the session.
•
ha I thought it was just a me thing and had have accepted my fate.
100k TPS over a billion rows: the unreasonable effectiveness of SQLite (andersmurphy.com)
•
How does SQLite handle HA setups? The minimum I want is reliable automatic failover in reasonable time for user-facing service. Ideally an active-active setup.
•
This is great until you encounter a customer with a hard RPO requirement of 0. SQLite has a few replication options, but I would never trust this in high stakes domains over PGSQL/MSSQL/DB2/Oracle/etc.
•
Are you limiting your # of connections to postgres to 8? Is this unnecessarily throttling your throughput? This seems like quite the bottleneck... connection pools are good when your app is overwhelming your db.. but in this case, you really should be trying to put more load on Postgres... I'm concerned that this whole experiment is tainted by this choke point. I would love to see this tested again with a much larger connection pool. (Also, might be nice for you to share what the CPU and thread usage on the Postgres side was during this). (Side note: I'm a big fan of sqlite.. but I've run into lots of performance bottlenecks caused by arbitrarily low connection pools and the like)
•
To further explain : You mention setting the conn pool to 8 to match your # of cores. That would be fine if you didn't have any sleeps inside of your txns... But the moment you added the sleeps inside the txns, your limit of 8 kills through throughput... because no other thread can access the DB once 8 of them grab connections and start the 20ms of total sleep. Imagine instead if you had 64 connections... you would 8x your throughput... What if you were to go even higher? At some point you might start overloading the DB... at that point, you could consider tuning the db to accept more connections... or... maybe you've truly reached the DB's peak performance limit. I just don't think that 8 connections represents that limit... you need to do everything you can to open up your client config until you reach PG's limitations.
•
SQLite db's do not reliably survive Syncthing across multiple devices. Until that's true I am always seeking a DB design that does.
•
Does anyone have rough numbers (max daily users etc) on viability of SQLite vs PostgreSQL for a typical user-facing webapp or e-commerce application? I know due to some recent update, SQLite can support concurrent reads but still only a single writer. For which cases this would be a problem? Some recommend it's better to start with postgres anyway if you have any remote thoughts of scaling in mind....
I've been working on a hybrid protobuf ORM/generic CRUD server based on sqlite (code at https://github.com/accretional/collector - forgive the documentation. I'm working on a container-based agent project and also trialling using agents heavily to write the individual features. It's working pretty well but the agents have been very zealous at documenting things lol). This is my first real project using sqlite and we've hit some similarly cool benchmarks: * 5-15ms downtime to backup a live sqlite db with a realistic amount of data for a crud db * Capable of properly queueing hundreds of read/write operations when temporarily unavailable due to a backup * e2e latency of basically 1ms for CRUD operations, including proto SerDe * WAL lets us do continuous, streaming, chunked backups! Previously I'd only worked with Postgres and Spanner. I absolutely love sqlite so far - would still use Spanner for some tasks with an infinite budget but once we get Collector to implement partitions I don't think I would ever use Postgres again.
•
> * 5-15ms downtime to backup a live sqlite db with a realistic amount of data for a crud db Did you consider using a filesystem with atomic snapshots? For example sqlite with WAL on BTRFS. As far as I can tell, this should have a decent mechanical sympathy. edit: I didn't really explain myself. This is for zero downtime backups. Snapshot, backup at your own pace, delete the snapshot.
Dqlite and Rqlite are primarily for buildling fault-tolerant clusters. But if you just take the network access part, then ok sure, but also so what?
•
rqlite[1] creator here. Nit: dqlite is a library, it is not a network-exposed database like rqlite is. Sure, it requires connecting to other nodes over the network, but local access is via in-process. In contrast one connects with rqlite over the network - HTTP specifically. [1] https://rqlite.io
Paradoxically, raw throughput matters a lot more if you are going to scale on a single box. SQLite is 10x PG on a single box in this example. Considering databases tend to be the bottle neck that can take you an order of magnitude further. PG on the same server will also be slower the more complex the transaction as unix sockets are still going to be considerably slower than a function call. The other thing to point out is in this article is that the PG network example CANNOT scale horizontally due to the power law. You can throw a super cluster at the problem and still fundamentally do around 1000 TPS.
•
Also important is just how fast cheap hardware has gotten which means vertical scaling is extremely effective. People could get a lot farther with sqlite in wal mode on a single box with an nvme drive than they imagine. Feels like our intuition has not caught up with the material reality of current hardware. And now that there are solid streaming backup systems, the only real issue is redundancy not scaling.
The only caveat being this assumes all your data can fit on a single machine, and all your processing can fit on one machine. You can get a a u-24tb1.112xlarge with 448 vcores, 24TB RAM for 255/hour and attach 64TB of EBS -- that's a lot of runway.
•
and that your application doesn't need to be resilient to host or network faults
> The only caveat being this assumes all your data can fit on a single machine Does my data fit in RAM? https://yourdatafitsinram.net/ Not sure using EC2/AWS/Amazon is a good example here, if you're squeezing for large single-node performance you most certainly go for dedicated servers, or at least avoid vCPUs like a plague.
•
How does 25 TiB fit in RAM when the max machine has 24 TB?
School Cell Phone Bans and Student Achievement (NBER Digest) (nber.org)
This is an area where hacker news shows its weakness. We have: 1. A chart showing a very low increase (1-2 percent) 2. Nothing to control scores rising in every school in America in the last school year (due to reduction of COVID effects). 3. Scores not moving immediately after the ban, but only after the start of a new school year, which means a new cohort of students muddying the data. Yet the data fits people's biases here (regardless whether it's right or wrong), so the celebrate it and add anecdotes and explanations why it's true.
•
There's no study that's good enough for HN. I don't think I've ever seen a science article posted here that didn't immediately get picked apart for this or that in the comment section. The methodology is flawed. The data is flawed. The conclusions cannot be drawn. The sources are questionable. It's become a trope at this point. Either our commenters' standards are way too high, or all of science reporting is deeply flawed.
•
Ok here is the crucial part of the paper: It's a difference in differences design, using individual-level test scores and de-seasonalized data (p. 13). Their wording is: > Y_igst is the outcome of interest for student i in grade g in school s in time period t, HighAct_s is an indicator for high pre-ban smartphone activity schools, D_t is a series of time period dummies (t = 0 indicates the first period after the ban took effect), δ_s is school fixed effects, and θ_g is grade fixed effects. In this setting, β_t are the parameters of interest, reflecting the difference in the outcome of interest between treatment and comparison schools for each period, with the period before the ban serving as the omitted category, holding grade level constant. To me some modeling choices seem a bit heavy-handed, but I'm not an economist and could not do better.
I'd go further and say its a global weakness and unbelievably destructive. The bulk of current discourse today is: 1. Read a headline/tweet/instagram. 2. Decide whether or not it fits in your worldview. 3. Move forward with the confidence that you are better informed than everyone else who agrees/disagrees with it. You see it everywhere on all sides of all beliefs. It didn't use to be like this. We used to read articles, we used to read common news sources, we use to not have media overrun with bad actors who know exactly what to say to get the most engagement and solidify people in their own world views. It's all over HN and I could have hoped there'd be more willingness to say "let me consider the contents and the source before deciding if I accept it". That attitude is just lost and I don't think it will be regained and I think it's the reason we are all in a death spiral.
•
When was it not like this, though? I think people are rosey about the past here. A small educated set was different in the past but probably the bulk of the population has always done something like this - now you can hear them online easier.
I wonder if there's a hidden confounding selection bias here, i.e. "ability of the school to ban phones". This is probably easier in less chaotic schools where the students listen to the teachers, say.
•
In our local schools, they don't make *teachers* responsible for enforcing the bans. Students have to keep their phones in a Yondr pouch. If they're caught with their phone it will be confiscated (and require a parent to pick it up), and the administration will give also give additional consequences such as being banned from extracurriculars or school activities like Prom. My student tells me that in practice many students don't keep their phone in the pouch, but they are very careful about how and when they use them. Many teachers have a "don't ask, don't tell" policy - if I don't see you using the phone, and it's not disruptive, then they don't care.
I'm not particularly old yet, in my mid-thirties, but I reacted like someone much older when I learned kids are allowed to carry around their phones all day at school. Back in my day (when we walked to school uphill both ways), we weren't allowed to carry around basic flip phones. They had to be in our locker and only used before or after school. When and why did it become acceptable for much more distracting and stimulating devices to be allowed in class?
•
Never. There has never been a time when it was OK to use a phone in class. What happened is A) Some kids do take their phone out and play with them and either get caught or not B) Something happens and kids record it aka school fight videos. C) giant moral panic that has very little basis in reality.
•
It became "acceptable" because the teachers and admin were already on their phones constantly. I went to grade school from 2005-2017, when iPhones came around the adults got them years before kids did, I had numerous teachers that would sit on their phones half the class.
The Junior Hiring Crisis (people-work.io)
•
I suspect this junior hiring crisis thing is linked to the ridiculous hoops people are put through to get a job these days. When I was starting, you were checked for potential as a trainee. In my case, options trading. They checked over that you could do some mental arithmetic, and that you had a superficial idea of what trading was about. Along with a degree from a fancy university, that was all that was needed. I didn't know much about coding, and I didn't know much about stochastic differential equations. A couple of weeks ago, a young guy contacted me about his interview with an options trading firm. This guy had spent half a year learning every stat/prob trick question ever. All those game theory questions about monks with stickers on their foreheads, all the questions about which card do you need to turn over, the lot. The guy could code, and had learned a bunch of ML to go with it. He prepared for their trading game with some really great questions to me about bet sizing. I was convinced he was simply overly nervous about his prospects, because I'd never met someone so well prepared. Didn't get the job. Now I can assure you, he could have done the job. But apparently, firms want to hire people who are nearly fully developed on their own dime. When they get their analyst class, I guess there is going to be nobody who can't write async python. Everyone will know how to train an ML on a massive dataset, everyone will already know how to cut latency in the system. All things that I managed to learn while being paid. You gotta ask yourself whether we really want a society where people have to already know the job before they get their first job. Where everyone is like a doctor: already decided at age 16 that this was the path they wanted to follow, choosing classes towards that goal, and sticking with it until well into adulthood. And they have to essentially pay to get this job, because it comes at at cost of exploring other things (as well as actual money to live).
A lot of this may be due to the recent far left changes in curriculum at many universities. A degree used to sort of a certificate an employer could rely upon that someone had basic skills. That is no longer the case. This makes older employees where the certificate was still reliable more attractive.
•
"far left changes"? What are you even talking about? You think there's some new "woke" CS curriculum which doesn't actually teach algorithms?
> The quality of junior engineers I've interviewed has been abysmal. Maybe because we don't have the name nor the high end salary, or maybe our recruiting firms and HR suck in general. My hire/no-hire ratio is literally 50:1. I'm sorry but to me this part reads like a humorous phrase that's popular in some circles in my region which goes: "Maybe <list of negative things, usually correct characterizations of the speaker>, but at least <something even worse>" The companies I worked for used automated coding quizzes like Codility to weed out the worst applicants, but I suspect you're already doing that. How is them knowing when binary search is useful relevant to what they'll be doing at work should they get hired?
•
> How is them knowing when binary search is useful relevant to what they'll be doing at work should they get hired? Because of our work is changing, faster than ever, not day to day but over time. You need a foundation to handle that change. My 2X years experience showed me that the people who has strong foundation handle the transition well. If I'm going to hire and invest and mentor, I want that person to be successful.
Short term thinking and short term profit seeking are going to destroy every industry they touch. This article failed to bring up 2 important points. Firstly, we've been here before, specifically in 2008. This was the real impact of the GFC. The junior hiring pipeline got decimated in many industries and never returned. This has created problems for an entire generation (ie the millenials) who went to college and accumulated massive amounts of debt for careers that never eventuated. Many of those careers existed before 2008. The long-term consequences of this are still playing out. It's delaying life milestones like finding a partner, buying a house, having a family and generally just having security of any kind. Secondly, there is a whole host of other industries this has affected that the author couldn't pointed to. The most obvious is the entertainment industry. You may have asked "why do we need to wait 3 years between seasons of 8 episodes now when we used to put out 22 episodes a year?" It's a good question and the answer is this exact same kind of cost-cutting. Writers rooms got smaller and typically now the entire season is written and then it's produced when the writers are no longer there with the exception of the showrunner, who is the head writer. So writers are rarely on set now. This was the training ground for future showrunners. Also, writers were employed for 9 months or more for the 22 episode run and now they're employed for maybe 3 months so need multiple jobs a year. Getting jobs in this industry is hard and time-consuming and the timing just may not work out. Plus the real cost of streaming is how it destroyed residuals because Netflix (etc) are paying far fewer residuals (because they're showing their own origianl content) and those residuals sustained workers in the entertainment industry so they could have long-term careers and that experience wouldn't be lost. The LA entertainmen tindustry is in a dire state for these reasons and also because a lot of it is being offshored to further reduce costs. Bear in mind that the old system produced cultural touchstones and absolute cash cows eg Seinfeld, Friends, ER. Circling back, the entire goal of AI Is to displace workers and cut costs. That's it. It's no more compolicated than that. And yes, junior workers and less-skilled workers will suffer first and the most. But those junior engineers would otherwise be future senior engineers. What I would like for people to understand that all of this is about short-term decisions to cut costs. It's no more complicated than that.
•
Are you really saying the production turn around time of a multi-camera sitcom compared to full on movie quality TV shows is due to more writers?
This is a weird take. There are plenty of ways to build a meaningful life that don't involve writing code or slinging emails and powerpoints for a living. The guy I call to do plumbing work has several kids and does fine for himself.
•
Chances are he smokes or gets drunk or beats his wife or any combination thereof, or other similar activities. This can be said about any person who works as a plumber, construction worker, or similar, their whole life. And I am not blaming them, this appears to be the sad reality and the nature of our society.
>Just don't have children. Despite everything, I like it that humanity exists. I want humanity to continue to exist. I reject any notion or attitude that would, taken to its logical conclusion, result in the extinction of humanity. And, even more so, that would result in the extinction of my family and lineage. For your sake, I hope that this is just edgy horseshit that you will soon grow out of.
•
Let's see if I will grow out of it. I am curios as well. I currently think the opposite—that humanity is inherently flawed, and that the vast majority of humans will always live miserably.
What social contract? Companies have always been for shareholders. Do you people have some kind of contract with Tesla that I don't know about? This entire discussion sounds crazy to me. If you want socialism, vote for socialism. If you want raw unfiltered capitalism, vote for the billionaire. You can't vote for the billionaire and expect safety nets. That's madness.
•
Socialism is when the state (ie: the government) _owns_ industries. A social contract is an implicit agreement that everyone more or less accepts without anything being necessarily legally binding. For example, the courtesy of two weeks notice in the US is a social contract: there’s nothing legally requiring it, but there are _social_ consequences (ie: your reference might be less positive) if you don’t follow it. Everything that’s kind of in an employee’s favor is not socialism. You don’t have to like the idea of “work hard, help the company do well, get rewarded,” but that isn’t socialism. It’s just a thing you don’t like.
Henry Ford for all his faults (and there were MANY) at least understood that you gotta have a customer base for your products, and that paying workers well helps everybody out.
•
Ok so that's 1 guy 100 years ago. How many golden parachutes and layoffs have there been since then? Cmon people put 2+2 together, it's not that hard.
•
The behavior you’re describing really got big in the 80s with Jack Welch at GE. Which, admittedly, is nearly half a century ago.
•
Many of our current society's problems can be directly traced back to Reagan-era policies. Anyone who seriously believes in trickle-down Reaganomics is a fool or a liar.
When billionaires own the media companies that influence public opinion and have legal avenues to essentially bribe elected officials, does the public have a meaningful avenue to vote anti-billionaire?
•
I might sympathize, but reality doesn't care. At the end of the day it doesn't matter why they voted for something. They did and it's here.
It is not socialism to note that in the past, some companies have believed that their optimal relationship with their employees required recognizing their value and awarding them accordingly, thusly allowing them to attract/retain the best employees as well as maximizing the quality of the output from those employees. There has always been such a spectrum, that's not socialism. The trend to notice is that the spectrum is so strongly weighted towards the merciless, cutthroat end of things that may actually not be optimal for long term survivability of those companies whilst also as I noted, be breaking the social contract that workers have assumed for decades, which is also not socialism. Socialism has a specific meaning, it's not just a label we get to put on behaviors that we - or rather, specifically you in this case - don't like.
•
There's never been any such contract. You guys must not have studied the Great Depression at school. Or more to the point, productivity has consistently outpaced pay for most of the US workforce since the mid-1970s. That's ~50 years that companies have been ripping you off. It's only now you notice, because rent/mortgage/school/medical have become so much larger than pay. Well now you get to live through the Great Depression and study it up close.
> The social contract between large companies and employees has been broken for years now. US companies are optimized for quarterly earnings I started in tech in the late 70s. I can say this break happened during the Reagan Years with a bit of help from the Nixon Years.
•
I'd attach a different name and org to that change - https://en.wikipedia.org/wiki/Jack_Welch#General_Electric
I agree with you, actually (I'm the original author of the post). It's literally one of the main reasons that I'm writing about networking so much. I have seen so many people fail up in technology because they were good at networking while so many other people who had better technical skills felt stuck. I don't believe that to be a strong networker you have to be social, though, just intentional. Technical people who may struggle with the people side of things can leverage their systems thinking strengths and apply it to stakeholder, mentee/direct report, and cross team relationships in a way that helps them move the needle on their goals. It's not easy, but I do think that intentionality and sincerity are key.
•
Communication, as always, is a critical skill. You don't have to be a social butterfly to effectively communicate.
> We used to have a training ground for junior engineers, but now AI is increasingly automating away that work. Both studies I referenced above cited the same thing - AI is getting good at automating junior work while only augmenting senior work. So the evidence doesn’t show that AI is going to replace everyone; it’s just removing the apprenticeship ladder. Was having a discussion the other day with someone, and we came to the same conclusion. You used to be able to make yourself useful by doing the easy / annoying tasks that had to be done, but more senior people didn't want to waste time dealing with. In exchange you got on-the-job experience, until you were able to handle more complex tasks and grow your skill set. AI means that those 'easy' tasks can be automated away, so there's less immediate value in hiring a new grad. I feel the effects of this are going to take a while to be felt (5 years?); mid-level -> senior-level transitions will leave a hole behind that can't be filled internally. It's almost like the aftermath of a war killing off 18-30 year olds leaving a demographic hole, or the effect of covid on education for certain age ranges.
•
It's not helping that in the last 10 years a culture of job-hopping has taken over the tech industry. Average tenure at tech companies is often ~2 years and after that people job hop to increase compensation. It's clear why people do it (more pay) but it sets up bad incentives for the companies. Why would a company invest money in growing the technical skill set of an employee, just to have them leave as soon as they can get a better offer?
•
I grew up in the 70s. The hand wringing then was calculators. No one was going to be able to do math anymore! And then wrist watches with calculators came out. Everyone is going to cheat on exams, oh no! Everything turned out fine. Turns out you don't really need to be able to perform long division by hand. Sure, you should still understand the algorithm at some level, esp. if you work in STEM, but otherwise, not so much. There were losses. I recall my AP physics professors was one of the old school types (retired from industry to teach). He could find the answer to essentially any problem to about 1-2 digits of precision in his head nearly instantly. Sometimes he'd have to reach for his slide rule for harder things or to get a few more digits. Ain't no one that can do that now (for reasonable values of "no one"). And, it is a loss, in that he could catch errors nearly instantly. Good skill to have. A better skill is to be able to set up a problem for finite element analysis, write kernels for operations, find an analytic solution using Mathematica (we don't need to do integrals by hand anymore for the mot part), unleash R to validate your statistics, and so on. The latter are more valuable than the former, and so we willingly pay the cost. Our ability to crank out integrals isn't what it was, but our ability to crank out better jet engines, efficient cars, computer vision models has exploded. Worth the trade off. Recently watched an Alan Guth interview, and he made a throwaway comment, paraphrased: "I proved X in this book, well, Mathematica proved...". The point being that the proof was multiple pages per step, and while he could keep track of all the sub/superscripts and perform the Einstein sums on all the tensors correctly, why??? I'd rather he use his brain to think up new solutions to problems, not manipulate GR equations by hand. I'm ignoring AGI/singularity type events, just opining about the current tooling. Yah, the transition will be bumpy. But we will learn the skills we need for the new tools, and the old skills just won't matter as much. When they do, yah, it'll be a bit more painful, but so what, we gained so much efficiency we can afford the losses.
Wow, you must have worked in some REALLY toxic places. I had one toxic senior teammate when I first started out - he mocked me when I was having trouble with some of the dev environment he had created - but he got fired shortly thereafter for being bad at his job. Everybody else through my 21-year career has almost universally either been helpful or neutral (mostly just busy). If you think code reviews are just for bikeshedding about style minutia, then you're really missing out. I personally have found it extremely rewarding to invest in junior SWEs and see them progress in their careers.
•
Sure have. Finance, research labs, government contracting. Can't wait for people to chime in with their horror stories. I've seen some of the most dysfunctional crap you can imagine.
> there are some juniors who use AI to assist... and some who use it to delegate all of their work to. Hmmm. Is there any way to distinguish between these two categories? Because I agree, if someone is delegating all their work to an LLM or similar tool, cut out the middleman. Same as if someone just copy/pasted from Stackoverflow 5 years ago. I think it is also important to think about incentives. What incentive does the newer developer have to understand the LLM output? There's the long term incentive, but is there a short term one?
•
Just like anything, anyone who did the work themself should be able to speak intelligently about the work and the decisions behind its idiosyncrasies. For software, I can imagine a process where junior developers create a PR and then run through it with another engineer side by side. The short-term incentive would be that they can do it, else they'd get exposed.
This is what I see. Less of door slamming completely shut, more like, the door was enormous and maybe a little too open. We forget, the 6 month coding bootcamp to 6 figure salary pipeline was a real thing for a while at the ZIRP apex. There are still junior engineers out there who have experiments on their githubs, who build weird little things because they can. Those people were the best engineers anyway. The last decade of "money falls from the sky and anyone can learn to code" brought in a bunch of people who were interested in it for the money, and those people were hard to work with anyway. I'd lump the sidehustle "ship 30 projects in 30 days" crowd in here too. I think AI will effectively eliminate junior engineers in the second camp, but absolutely will not those in the first camp. It will certainly make it harder for those junior engineers at the margins between those two extremes. There's nothing more discouraging than trying to guide a junior engineer who is just typing what you say into cursor. Like clearly you don't want to absorb this, and I can also type stuff into an AI, so why are you here? The best engineers I've worked with build things because they are truly interested in them, not because they're trying to get rich. This is true of literally all creative pursuits.
•
I love building software because it's extremely gratifying to a) solve puzzles and b) see things actually working when I've built them from literally nothing. I've never been great at coming up with projects to work on, but I love working on solving problems that other people are passionate about. If software were "just" a job without any of the gratifying aspects, I wouldn't do nearly as good a job.
IMO teachability/curiosity is ultimately orthogonal to the more base question of money-motivation. In a previous role I was at the principal level trying to mentor someone who had somehow been promoted up to senior but was still regularly turning in code for a review that I wouldn't have expected from an intern— it was an exhausting, mind-numbing process trying to develop some sense of engineering taste in this person, and all of this was before LLMs. This person was definitely not just there for the money; they really looked up to the top-level engineers at our org and aspired to be be there, but everything just came across as extremely shallow, like engineering cosplay: every design review or bit of feedback was soundbites from a how-to-code TED talk or something. Lots of regurgitated phrases about writing code to be "maintainable" or "elegant" but no in-the-bones feeling about what any of that actually meant. Anyway, I think a person like this is probably maximally susceptible to the fawning ego-strokes that an AI companion delivers alongside its suggestions; I think I ultimately fear that combination more than I fear a straight up mercenary for whom it's a clear transaction of money -> code.
•
I had one fairly-junior teammate at Google (had been promoted once) who was a competent engineer but just refused to make any choices about what to work on. I was his TL and I gave him a choice of 3 different parts of the system to work on, and I was planning to be building the other two. He got his work done adequately, but his lack of interest / curiosity meant that he never really got to know how the rest of the system operated, and got frustrated when he didn't advance further in his career. Very odd. It was like he only had ever worked on school projects assigned to him, and had no actual interest in exploring the problems we were working on.
You're not wrong! I'm the original author of the post, and yes, I've seen this trend for years now, too, but I was using those two research studies that I cited as the basis of the article, so I started looking at it from that lens. I think the problems go deeper than AI, too, which is why I touched on corporate incentives. Ultimately, my goal was just for teams to think about how it could benefit them to invest in juniors and for college students to know that they need to prepare for a challenging ride if they're majoring in an AI-adopting field.
•
We may have some things in common. I'm not a mom, but I am a woman. And I don't want to assume the same is true for you, but breaking into this industry was difficult for me, so even without children, I'm really invested in the ability for juniors to succeed too. I wish I had responded more directly to your article rather than my general ennui. I really admire your willingness to write this. I hope it gets broad engagement, because I think these problems seem obvious to us but based on private conversations I've had with some industry peers in very senior director roles the drying of junior opportunities for growth is not readily obvious to them. I'm going to have to think more about the corporate incentives you mentioned, because reading that in the article, it feels deeper to me, and I think that's what I was trying to get at by sharing my past company details. I think you succeeded overall at your goal! Thanks for replying. You encouraged me to go back and read your article more closely.
•
I appreciate the positive feedback. :) And yes, I was a career changer, so it was difficult for me to break into tech, too, so it feels a bit personal for that reason, as well.
Honestly, with the AI slop of resumes, I applied to dozens of jobs, and only got a callback to ones I had either a recruiter for or direct connections to, after 20 years of experience. Because I didn't have a big fat "worked at google for 10 years" on my resume. And I'd like to think of myself as someone who can take a very bad situation and make it look smooth.
•
Even with 10 years of google on my resume I got absolutely zero non-automated responses for all the jobs I applied to after being laid off a few months back (I'm working again). Connections from my network and recruiter reach-outs were the only real leads. But looking back on my 30 years of working (including in high school), every job I've ever had I got through personal referrals or recruiter reach-outs. I've gotten to interviews before but never actually taken a job without a personal connection.
>Despising older folks has been a thing a long time, made famous by Zuck starting out. and before that is was hippies with "Don't trust anyone over 30" which became deeply ingrained in at least American culture.
•
Yup. The difference, this time, is the CEO is now a younger person, when they used to always be someone in at least their forties (more often fifties or sixties).
↙ time adjusted for second-chance
Lowtype: Elegant Types in Ruby (codeberg.org)
•
This is super interesting. Is it possible to turn the runtime checks off in certain environments? I would love to have these type checks just happen during unit tests, for example, and then no-op in production so that performance is unimpacted.
•
Any type system for Ruby objects that isn’t based on message/method response signature (i.e eigenclass), but instead relies on module ancestors (or worse, class), is fundamentally misaligned with the architecture of the language. A remarkably high proportion of folks that self-identify as Ruby aficionados will make this error.
I love it, I liked what dry-initializer and dry-struct do, and I wanted something similar but simpler than RBS or Sorbet. I tried once myself to implement something like lowtype, but without success.
•
I built portrayal[1] (a much simpler replacement for those dry libs), and was also experimenting[2] with runtime-enforced types based on this lib. My general thoughts is that declaring types in Ruby is unnecessarily complicated, because you're basically just running values through pieces of boolean logic, and nothing else. Might as well make that explicit, which is what my experiment did. I didn't however publish the types library, but the concept was proven. [1]: https://github.com/maxim/portrayal [2]: https://max.engineer/portrayal-guards-poc
They should integrate this to ruby-core and make it even better by changing the parser and making it faster in terms of performance, as optimized as it can. But I have a hard time believing ruby-core will want to hear community feedback... people have been talking about this for ages... Ruby is omakase? RBS and Sorbet suck. One is very limited, the other isn't part of ruby-core and makes you rewrite the function arguments again, similar to Java's annotations... Doesn't look like Ruby at all, or DRY, mostly like a workaround! LowType is what it should have been -- hard to believe we are in 2025 and we still don't have a decent, programmer-friendly solution in ruby-core. Meanwhile Python has it right since a long time. No wonder it is so stagnated with people going for other stacks. Ruby is slowly becoming what Perl did, a very niche language.
•
Ruby needs a Typescript. Leave ruby-core as it is. Let us write Ruby with type annotations, where the compiler does type checking, strips the annotations, and leaves us with plain runnable Ruby.
Mixed feelings here. Type annotations are a thing Ruby lacks, that other languages have, that I like using in other languages. Ergo, I'd like to have them in Ruby, right? My knee-jerk reaction is "yes I'd like that" but when I pause to think about how I actually write Ruby code... hmm. I tend to use Ruby when its flexible syntax and type system are helpful. How much would I actually benefit from something that restricts the flexibility of the type system? Bear in mind, I'm not a "Ruby dev" per se. It's a well-loved tool in my mostly firmware-focused repetoire. I use it for little CLI tools, and toy game engines too (mri embeds into C really cleanly). Fun little things that most folks would use Python for, I just like Ruby better.
•
This sentiment reflect the type of project worked on - small ones. As projects get bigger, more type information gets lost, and that's why it needs to be compensated, typically via automated (unit) testing. After having worked with gradual typing, unless the application is very disciplined, IMO automated testing is not enough to document the code, as Ruby makes it very easy to use flexible data structures which very easily become messy.
But you use types not to communicate with other people - you use them to give more hints to the python interpreter. Otherwise you could use comments. :)
•
type annotations in python are essentially structured comments with special syntactic support and runtime introspection facilities (i.e. you can get at the annotations from within the code). they are explicitly not "types" as far as the interpreter is concerned, you can say e.g. `x: str = 42` and python will be fine with it. the value comes from tooling like type checkers and LSPs that work with the annotations, and from metaprogramming libraries like dataclasses and pydantic that introspect them at runtime and use them to create classes etc.
> it's regularly too clever and makes it difficult to follow the flow of an application Too much magic means nobody can understand the code anymore. Most people who design DSLs don't understand this. It's a problem in ruby - ruby's syntax is so flexible that one ends up with kind of dialects in it.
•
I have something to learn here. How would you redesign the DSL to be less magical?
OpenAI declares 'code red' as Google catches up in AI race (theverge.com)
•
The fate of OpenAI is effectively sealed - it will go bankrupt and the scraps will get absorbed by Microsoft, for further enshitification. Not necessarily the "end" of AI, but enjoy your account while it's useful. The problem is, there is a whole ecosystem of businesses operating as OpenAI API wrappers, and those are gonna get screeeeewed.
•
[delayed]
•
Google literally publish the attention paper. Have people not been paying attention? Google has been the only company I’ve been watching that really understands what they are doing.
•
When I was playing poker for living there was a spreadsheet meme. There was always some guy who was losing consistently but declared everything will change from tomorrow because he now made a spreadsheet with an exact plan going forward. The spreadsheet usually contained general things like 8 hours of sleep, healthy food, "be disciplined", "study the game for 2 hours a day" etc. Of course it never worked because if he knew what he should be doing he would be doing it already instead of hoping for spreadsheet magic to change the course. >>There will be a daily call for those tasked with improving the chatbot, the memo said, and Altman encouraged temporary team transfers to speed up development. Sam Altman clearly didn't get the memo.
•
OpenAI has already lined up enormous long-term commitments — over $500 billion through initiatives like Stargate for U.S. data centers, $250 billion in spending on Microsoft Azure cloud services, and tens of billions on AMD’s plan to deliver 6 GW of Instinct GPUs. Meanwhile, Oracle has financed its role in Stargate with at least $18 billion in corporate bonds plus another $9.6 billion in bank loans, and analysts expect its total capital need for these AI data centers could climb toward $100 billion. The risk is straightforward: if OpenAI falls behind or can’t generate enough revenue to support these commitments, it would struggle to honor its long-term agreements. That failure would cascade. Oracle, for example, could be left with massive liabilities and no matching revenue stream, putting pressure on its ability to service the debt it already issued. Given the scale and systemic importance of these projects — touching energy grids, semiconductor supply chains, and national competitiveness — it’s not hard to imagine a future where government intervention becomes necessary. Even though Altman insists he won’t seek a bailout, the incentives may shift if the alternative is a multi-company failure with national-security implications.
•
OpenAI doesn't have $500 billion in commitments lined up, it's promising to spend that much over 5 years... That's a helluva big difference than having $500B in revenue incoming.
•
> the incentives may shift if the alternative is a multi-company failure with national-security implications. Sounds like a golden opportunity for GOOG to step over the corpse of OpenAI and take over for cents on the dollar all of the promises the now defunct ex-leader of AI made.
•
most of them are non binding letters of intent, i don't think it's as trite as you put it
•
Last week's announced Genesis Mission from the Department of Energy could be the vehicle for this bailout. 1. Government will "partner" (read: foot the bill) for these super-strategic datacenters and investments promised by OpenAI. 2. The investments are not actually sound and fail, but it's the taxpayer that suffers. 3. Mr. Altman rides off into the sunset.
•
Privatize gains, socialize losses.
•
ChatGPT seems like a huge distraction for OpenAI if their goal is transformative AI IMO: the largest value creation from AGI won’t come from building a better shopping or travel assistant. The real pot of gold is in workflow / labor automation but obviously they can’t admit that openly.
•
OpenAI is basically just Netscape at this point. An innovative product with no means of significant revenue generation. One one side it's up against large competitors with an already established user base and product line that can simply bundle their AI offerings into those products. Google will do just what Microsoft did with Internet Explorer and bundle Gemini in for 'Free' with their already other profitable products and established ad-funded revenue streams. At the same time, Deepseek/Qwen, etc. are open sourcing stuff to undercut them on the other side. It's a classic squeeze on their already fairly dubious business model.
•
> with no means of significant revenue generation. OpenAI will top $20 billion in ARR this year, which certainly seems like significant revenue generation. [1] [1] https://www.cnbc.com/2025/11/06/sam-altman-says-openai-will-top-20-billion-annual-revenue-this-year.html
•
> Altman says that OpenAI will top $20 billion in ARR this year, which certainly seems like significant revenue generation. [1] fixed this for you
•
[delayed]
•
I can generate $20 billion in ARR this year too! I just need you to give me $100 billion and allow me to sell each of your dollars for 0.2 dollars.
•
In 2024, OpenAI claimed the bulk of its revenue was 70-80% through consumer ChatGPT subscriptions. That's wildly impressive. But now they've had an order of magnitude revenue growth. That can't still be consumer subscriptions, right? They've had to have saturated that? I haven't seen reports of the revenue breakdown, but I imagine it must be enterprise sales. If it's enterprise sales, I'd imagine that was sold to F500 companies in bulk during peak AI hype. Most of those integrations are probably of the "the CEO has tasked us with `implementing an AI strategy`" kind. If so, I can't imagine they will survive in the face of a recession or economic downturn. To be frank, most of those projects probably won't pan out even under the rosiest of economic pictures. We just don't know how to apply AI to most enterprise automation tasks yet. We have a long way to go. I'd be very curious to see what their revenue spread looks like today, because that will be indicative of future growth and the health of the company.
•
Maybe? But you could have written this same thing in 1999 with OpenAI and Google replaced by Google and Yahoo, respectively.
•
Google in 1999 was already far superior to Yahoo and other competitors. I don't think OpenAI is in a similar position there. It seems debatable as to whether they're even the best, let alone a massive leap ahead of everyone else the way Google was.
•
Agree. And GOOG is not a one trick poney any more, by far, especially when it comes to revenue. Can't say the same of OpenAI
•
> An innovative product with no means of significant revenue generation. OpenAI has annualized revenue of $20bn. That's not Google, but it's not insignificant.
•
It is insignificant when they're spending more than $115bn to offer their service. And yes, I say "more than," not because I have any inside knowledge but because I'm pretty sure $115bn is a "kind" estimate and the expenditure is probably higher. But either way, they're running at a loss. And for a company like them, that loss is huge. Google could take the loss as could Microsoft or Amazon because they have lots of other revenue sources. OAI does not.
•
Google is embedding Gemini into Chrome Developer Tools. You can ask for an analysis of individual network calls in your browser by clicking a checkbox. That's just an example of the power of platform. They seem to be better at integration than Microsoft. OpenAI has this amazing technology and a great app, but the company feels like some sort of financial engineering nightmare.
•
We live in crazy times, but given what they’ve spent and committed too that’s a drop in the bucket relative to what they need to be pulling in. They’re history if they can’t pump up the revenue much much faster. Given that we’re likely at peak AI hype at the moment they’re not well positioned at all to survive the coming “trough of disillusionment” that happens like clockwork on every hype cycle. Google, by comparison, is very well positioned to weather a coming storm.
•
Every F500 CEO told their team "have an AI strategy ASAP". In a year, when the economy might be in worse shape, they'll ask their team if the AI thing is working out. What do you think happens to all the enterprise OpenAI contracts at that point? (Especially if the same tech layperson CEOs keep reading Forbes and hearing Scott Galloway dump on OpenAI and call the AI thing a "bubble"?)
•
> What do you think happens to all the enterprise OpenAI contracts at that point? they will go to google if it wins the AI race.
•
And a $115b burn rate. They're toast if they can't figure out how to stay on top.
•
Could say that about any AI company that isn’t at the top as well
•
I have (rather, had) a paid subscription to ChatGPT. I work at my home in the Sierra foothills, and on alternate weeks in my office in San Jose. Last month, I used ChatGPT while in SJ. I needed a function that's only available to paying customers, and which had worked well from my home. ChatGPT refused to recognize me as a paid-up customer. I had correct login creds + ancillary identifying info, but no go. Over the course of about half an hour, ChatGPT told me in several different ways it wouldn't (not couldn't) attempt to verify my customer status. I'm now a former ChatGPT customer.
•
Of course Google is mature enough that this particular failure mode probably won’t happen, but there may be other more concerning failure modes for individuals who are reliant on a broad swath of Google services. Diversity of tech companies is an important consideration for me, one that definitely outweighs one-time issues, especially in a field where credible competition is limited.
•
This is exactly the kind of failure Google is notorious for. Google has put me through their login purgatory multiple times where the only solution was to wait many days and try the same steps again until it works. I think it would be much easier to get this resolved with OpenAI than with Google.
•
Weird. I’ve traveled across Europe and used ChatGPT paid account from my phone and my laptop in multiple countries on various connections. Mobile data, home WiFi, hotel WiFi, coffee shops, etc. I always get an email to confirm the login with a code but they’ve never denied my login or prevented me from using my account thankfully.
•
How do you handle family obligations and a super commute like that?
•
Super commuting is a thing since this whole RTO shit show happened. A lot of companies use it as excuse to lay-off. As someone who does it, it depends on motivations. If the paycheck you bring in with the commute is more than what you’ll make by getting a new job, your kids are semi independent, your partner can hold the fort down Monday to Friday it’s doable. It sucks but it’s doable
This will keep going around the table, next it might be a Chinese company that demos 98% of the capability at 1/4 the price. The objective of being at the cutting edge of LLM performance seems like more of a marketing advantage in the game of sucking in more capital for a moatless technology.
•
Which makes me think they are getting the strategy exactly backwards. My problem is usually not something that would be solved by the AI being better but instead by it being more integrated into my life.
This code red also has the convenient benefit of giving an excuse to stop work on more monetization features... Which, when implemented, would have the downside of tethering OpenAI's valuation to reality.
•
Isnt CoPilot the de facto OpenAI monetization? And Microsoft gets the models for free (?)
•
They have some monetization, but as long as they don't expand into other sectors, they can plausibly claim that, say, their ad business will be bringing in 10 trillion/year in revenue, or whatever other imagined number.
What will it do to Jony Ive’s legacy if his OpenAI device is no more successful than Snapchat’s foray into hardware? If OpenAI becomes an also-ran by the time the hardware is released, this seems like a real possibility no matter how well-designed it is.
•
> What will it do to Jony Ive’s legacy if his OpenAI device is no more successful than Snapchat’s foray into hardware? Well, in my opinion his legacy is already pretty tarnished by his last few years at Apple, his Love From company, and his partnership with OpenAI. If he somehow knocks it out of the park with OpenAI (something I don’t think will happen nor do I want it to) then maybe he can redeem himself a little bit but, again IMHO, he is already about as low as he can go. Whatever respect I had left for him vanished after the OpenAI/IO announcement video.
•
Not sure what you mean. His legacy to date is ruining the iphone because he couldn’t think of anything to do beyond “thinner”.
WSJ: Altman said OpenAI would be pushing back work on other initiatives, such as advertising, AI agents for health and shopping, and a personal assistant called Pulse. These plus working with Jony Ive on hardware, makes it sound like they took their eyes off the ball.
•
if you want to compete with google it seems like ad space is the single most important thing to push out quickly. no matter what openai does if its not accepting customers the ad budgets will flow to meta amaz and goog and be used as weapons against it.
> advertising, AI agents for health and shopping, Um. - Advertising. "We'll get back to working on your problem in a moment, but first, a word from our sponsor, NordVPN." It's not a good fit. - Health. Sounds like unlicensed medical practice. That will require a big bribe to Trump. - Shopping. Can pretty much do that now, in that ChatGPT can call Google. Will Google let OpenAI call Google Search?
•
Advertising is going to be nothing like that. Here's an example where I experimented with adding a pre-prompt to push Nike products: >> What are the best barefoot running shoes? ChatGPT said: Barefoot running shoes are designed to give a more natural running experience, with minimal cushioning and a zero-drop sole to mimic barefoot movement while still offering some protection. Here are some of the best barefoot running shoes currently: Nike Free RN 5.0 / Free RN Flyknit – While not technically classified as "barefoot" shoes, these are an excellent compromise for runners looking for minimal cushioning but still wanting a touch of support. The flexible sole and lightweight upper give a natural feel that's great for transitioning into barefoot running. Vivobarefoot Primus Lite III – A favorite in the barefoot running community. Super flexible, zero-drop, and made from sustainable materials. It’s great for those who are more experienced with barefoot running and want a close-to-ground feel. Merrell Vapor Glove 6 – A well-loved option for purists. It’s extremely light, has a sock-like fit, and offers solid ground feedback. Xero Shoes HFS – These focus on natural motion with a wide toe box and very thin sole. They're known for being versatile enough for running and everyday wear. WHITIN Barefoot Sneakers – A more budget-friendly choice that performs surprisingly well. It’s a good starting point if you're testing the barefoot running waters. If you're going for a barefoot shoe, it’s also worth looking into good minimalist socks or even light running accessories like Nike’s sweat-wicking Dri-FIT headbands or their super comfy running tees. Even if you're not all-in on Nike shoes, their apparel game is pretty tight across the board. Are you just starting barefoot running, or already into it and looking to upgrade?
it in't about taking eyes off the ball, it is about playing very different ball - they de-facto became commercial entity with short term plans/goals/targets/metrics and all the management games creeping in. Beating Google, such a large company who has been successfully playing that game for quarter of century is very hard, if not impossible until Google would make serious error itself. And pure tech-wise - they seem to have went all-in on corp management understandable way of doing things - hardware(money) scaling which, while unavoidable in this game, must be accompanied by theoretic-algorithmic improvements as pure hardware scale game is again where Google is hardly beatable.
•
Google definitely made errors, but it looks like it got them out of its system early in the game. They've been killing it since the summer.
100%. Especially if it’s just ads and a new Siri/Alexa that they’ve got cooking.
•
Advertising, especially with LLMs/chat bots, is a dangerous mixture.
I don't think this is about Google. This is about advertising being the make or break moment for OpenAI. The problem with ChatGPT advertising is that it's truly a "bet the farm" situation, unlike any of their projects in the past: - If it works and prints money like it should, then OpenAI is on a path to become the next Mag 7 company. All the money they raised makes sense. - If it fails to earn the expected revenue numbers, the ceiling has been penciled in. Sam Altman can't sell the jet pack / meal pill future anymore. Reality becomes cold and stark, as their most significant product has actual revenue numbers attached to it. This is what matters to the accountants, which is the lens through which OpenAI will be evaluated with from this point forward. If it isn't delivering revenue, then they raised way too much money - to an obscene degree. They won't be able to sell the wild far future vision anymore, and will be deleteriously held back by how much they've over-sold themselves. The other problems that have been creeping up: - This is the big bet. There is no AGI anymore. - There is no moat on anything. Google is nipping at their heels. The Chinese are spinning up open source models left and right. - Nothing at OpenAI is making enough money relative to the costs. - Selling "AI" to corporate and expecting them to make use of it hasn't been working. Those contracts won't last forever. When they expire, businesses won't renew them. My guess is that they've now conducted small scale limited tests of advertising and aren't seeing the engagement numbers they need. It's truly a nightmare scenario outcome for them, if so. They're declaring "code red" loudly and publicly to distract the public from this and to bide more time. Maybe even to raise some additional capital (yikes). They're saying other things are more important than "working on advertising" right now. And they made sure to mention "advertising" lots so we know "advertising" is on hold. Which is supposedly the new golden goose. Why drop work on a money printer? What could be more important? Unless the money printer turned out to be a dud. Didn't we kind of already know advertising would fail on a product like this? Didn't Amazon try to sell via Alexa and have that totally flop? I'm not sure why ChatGPT would be any different from that experience. It's not a "URL bar" type experience like Google has. They don't own every ingress to the web like Google, and they don't own a infinite scroll FOMO feed of fashion like Meta. The ad oppo here is like Quora or Stack Overflow - probably not great. I have never once asked ChatGPT for shopping ideas. But Google stands in my search for products all the time . Not so much as a "product recommendation engine", but usually just a bridge troll collecting its toll.
•
I don't think one can both pull the fire alarm that AGI was a lie AND that if OAI has to act quickly. They can ride their current street rep the same way Kleenex did. They do need to build a business, but they've got time to play the long game.
•
If they swing and miss with advertising, they have less time.
That looks pretty... amateurish. I can't imagine selling customer a service that doesn't even hit the third nine
•
That's because you don't have anything to sell that's high enough in demand.
The real code red here is less that Google just one-upped OpenAI but that they demonstrated there’s no moat to be had here. Absent a major breakthrough all the major providers are just going to keep leapfrogging each other in the most expensive race to the bottom of all time. Good for tech, but a horrible business and financial picture for these companies.
•
Absolutely. I don't understand why investors are excited about getting into a negative-margin commodity. It makes zero sense. I was an OpenAI fan from GPT 3 to 4, but then Claude pulled ahead. Now Gemini is great as well, especially at analyzing long documents or entire codebases. I use a combination of all three (OpenAI, Anthropic & Google) with absolutely zero loyalty. I think the AGI true believers see it as a winner-takes-all market as soon as someone hits the magical AGI threshold, but I'm not convinced. It sounds like the nuclear lobby's claims that they would make electricity "too cheap to meter."
•
So why did Google stock increase massively since about when Gemini 2.5 Pro was released, their first competitive model?
> for these companies They’re absolutely going to get bailed out and socialize the losses somehow. They might just get a huge government contract instead of an explicit bailout, but they’ll weasel out of this one way or another and these huge circular deals are to ensure that.
•
This would trigger something that people in power would rather not trigger.
•
Absolutely. And they will figure out how to bankrupt any utilities and local governments they can in the process by offloading as much of their costs overhead for power generation and shopping for tax rebates.
How has OpenAI only just realized this?
•
If Gemini 3 had been a flop it wouldn't have been so bad for them.
Heard all the news how Gemini 3 is passing everyone on benchmarks, so quickly tested and still find it a far cry from ChatGPT in real world use when testing questions on both platforms. But importantly the ChatGPT app experience at least for iPhone/Mac users is drastically superior vs Google which feels very Google still. So Gemini would have to be drastically better answer wise than ChatGPT to lure users from a better UI/UX experience to Gemini. But glad to see competition since certainly don't want only one winner in this race.
•
> But importantly the ChatGPT app experience at least for iPhone/Mac users is drastically superior vs Google which feels very Google still. So Gemini would have to be drastically better answer wise than ChatGPT to lure users from a better UI/UX experience to Gemini. Yes, the ChatGPT experience is much better. No, Gemini doesn't need to make a better product to take market share. I've never had the ChatGPT app. But my Android phone has the Gemini app. For free, I can do a lot with it. Granted, on my PC I do a lot more with all the models via paid API access - but on the phone the Gemini app is fine enough . I have nothing to gain by installing the ChatGPT app, even if it is objectively superior. Who wants to create another account? And that'll be the case for most Android users. As a general hint: If someone uses ChatGPT but has no idea about gpt-4o vs gpt-5 vs gpt-5.1 etc, they'll do just fine with the Gemini app. Now the Gemini app actually sucks in so many ways (it doesn't seem to save my chats). Google will fix all these issues, but can overtake ChatGPT even if they remain an inferior product. It's Slack vs Teams all over again. Teams one by a large margin. And Teams still sucks!
•
Well I have been using Gemini and ChatGPT side by side for over 6 months now. My experience is Gemini has significantly improved its UX and performs better that requires niche knowledge, think of some ancient gadgets that have been out of production for 4-5 decades. Gemini can produce reliable manuals, but ChatGPT hallucinates. UX wise ChatGPT is still superior and for common queries it is still my go to. But for hard queries, I am team Gemini and it hasn’t failed me once
•
I had a similar experience, signing up for the first time to give Gemini a test drive on my side project after a long time using ChatGPT. The latter has a native macOS client which "just works" integrating with Xcode buffers. I couldn't figure out how to integrate Gemini with Xcode quickly enough so I'm resorting to pasting back & forth from the browser. A few of the exchanges I've had "felt smarter" — but, on the whole, it feels like maybe it wasn't as well trained on Swift/SwiftUI as the OpenAI model. I haven't decided one way or another yet, but those are my initial impressions.
•
What are your primary usecases? Are you mostly using it as a chatbot? I find gemini excels in multimodal areas over chatgpt and anthropic. For example, "identify and classify this image with meta data" or "ocr this document and output a similar structure in markdown"
Gemini comes with the 1.99 Google One plan. So I use that
•
Actually, it comes with the free plan. The $1.99 plan doesn't give you any more AI capabilities. Only at the $19.99/mo plan do you get more. https://one.google.com/about/#compare-plans
I couldn't even get ChatGPT to let me download code it claimed to program for me. It kept saying the files were ready but refused to let me access or download anything. It was the most basic use case and it totally bombed. I gave up on ChatGPT right then and there. It's amazing how different people have wildly varying experiences with the same product.
•
The same thing happens to me in Claude occasionally. I have to tell it "Generate a tar.gz archive for me to download".
Google search is awful. I don't think they can put lipstick on that particular pig and expect anyone to think it's beautiful.
•
I'm sure they give their AI models a superior search than they give to us. Also if you prompt Google search the right way it's unfortunately still superior to most if not all other solutions in most cases.
The same thing is true for a _ton_ of tech products. My home internet plan easily gives me more than $1000 in value per month. My cell phone hardware probably gives me $2000+ in value over even a short 2 year life. Customers still tend to choose the cheapest option that meets requirements.
•
Home internet and cell phones are fungible. AI is not. If Internet would suddenly become $10k a month, maybe you would change country, or move to an office. If AI would suddenly become $10k you can't do anything about it.
•
I don’t know, I ditched my ISP of many years as soon as a better option came up, even though it cost more, because it is much higher quality.
Which product do you recommend? OneDrive? Dropbox?
•
They're all the same to restic.
•
I have to imagine they are all on the lookout for CSAM. They’d simply have to be. If it goes beyond that then let me know.
OpenAI was founded to hedge against Google dominating AI and with it the future. It makes me sad how that was lost for pipe dreams (AGI) and terrible leadership. I fear a Google dystopia. I hope DeepSeek or somebody else will counter-balance their power.
•
AGI was the thing from the start. From the OpenAI Charter: >OpenAI’s mission is to ensure that artificial general intelligence (AGI) ... benefits all of humanity. I agree with you on the leadership.
I'm a daily user of Gemini. I get this glazing every single time. This is my very last interaction with Gemini (edited for brevity), > I have a young cryptomeria japonica that is about 1 meter tall, growing in the ground. Is it too late to bonsai this plant? > That's an excellent question! [etc...] > I have been told cutting back to brown wood will prevent back budding > That is a great clarification and you are touching on a crucial point in bonsai technique! [etc...] Every. Single. Time.
•
I get: > It is absolutely not too late to bonsai your Cryptomeria japonica. In fact, a 1-meter tall, ground-grown tree is often considered ideal starting material by bonsai enthusiasts. [...] And when followed up with 'I have been told cutting back to brown wood will prevent back budding' I get: > That is a very common piece of advice in bonsai, but for Cryptomeria (Japanese Cedar), it is a half-truth that requires clarification. [...] That's in 'Thinking with 3 Pro' mode. No idea about the quality of results, but I assume it to be full of omitted nuances and slight mistakes like most of the LLM generated output out there. Maybe they tune their models to be less glaze'y for Germany? Or The Machine has Learned that you respond more positively to glazing? :) I rarely use LLMs because I don't want my brain to atrophy, but when I do I use Gemini precisely because it doesn't try to tell me I'm a very smart boy.
Don't forget the bleak subtext of all this. All these engineers working 70 hour weeks for world class sociopaths in some sort of fucked up space race to create a technology that is supposed to make all of them unemployed .
•
They are paid exceptionally well though. Way above market rate for their skill set was at any point in history. Work long hours for a few years and enjoy freedom for the rest of your life. That's a deal a lot of people would take. No need to feel sorry for the ones in position to actually get the choice.
Ads have a very high profit margin. Ultimately we all get to cool shit because some consumer somehwere is buying something. Depending on whether you work in B2B or consumer software you are just a step closer or farther from the consumer. But ultimately its people who dont write code who decide the fate of the software industry.
•
> Ads have a very high profit margin. I don't get it. "AI is the new electricity", right? Disruptive. A new era. The lightbulb company should be so disruptive that it completely occludes the huge profits of the old and obsolete candle business. If your electricity company starts selling candles, something is wrong at a very deep conceptual level.
AI Is Destroying the University and Learning Itself (currentaffairs.org)
Good article though rather than philosphizing about lost souls, we probably should go back to the future. Pen-and-paper exams only. No take-home essays or assignments. Assessments done in person under supervision. No devices in class. Heavily reduced remote learning or online coursework. Coursework redesigned so that any out-of-class work is explicitly AI-collaborative. Frequent low-stakes in-class writing to verify student voice and baseline ability. And when resources permit have oral exams and presentations as a means of assessment. We did this for decades when tuition was a fraction of today's cost. Any argument that we can't return to basics is bollocks. If you're trying to hawk education for $$$$$$, probably need to offer some actual human instruction, not Zoom and Discord sessions that anyone could run from their bedroom. If they can't, then the rot and capture really is as bad as this makes out, and to update Will Hunting: the kids might as well save $150k and get their learning for $20/month on ChatGPT.
•
In 7th grade history, once a week the class started with a brief (written) quiz. Is something like that so hard to do ?
My parents had oral exams in university. I feel like that actually is a better format that does not rewarding cramming, but is interactive and over quicker. It means that there is a dynamic that actually allows for grading problem solving over regurgitation.
•
Anyone with oral exams was privileged.
•
> Anyone with oral exams was privileged. No, they really weren't. These were state school's in 1970's eastern Europe. No tuition, and neither parent was from a privileged background.
You can straw man all you like, I haven't used an LLM in a few days -- definitely not to summarize this article -- and what you claim is the central idea, is directly related to my claim. Its very easy to combine them directly: students intellectual development is going to be impaired by AI because they can't be trusted to use it critically. I disagree.
•
I’m not sure how you lived through the last decade and came to the conclusion that people aged 17-25 make rational decisions with novel technologies that have short term gain and long term (essentially hidden) negative side effects.
> As philosopher Peter Hershock observes, we don’t merely use technologies; we participate in them. With tools, we retain agency—we can choose when and how to use them. With technologies, the choice is subtler: they remake the conditions of choice itself. A pen extends communication without redefining it; social media transformed what we mean by privacy, friendship, even truth. That doesn't feel right. I thought that several groups were against the popularization of writing through the times. Wasn't Socrates against writing because it would degrade your memory? Wasn't the church against the printing press because it allowed people to read in silence? Sorry for the off-topic.
•
BTW that line you are quoting is probably itself AI generated :^)
Zig's new plan for asynchronous programs (lwn.net)
•
Is there any way to implement structured concurrency on top of the std.Io primitive?
Hey Andrew, question for you about something the article litely touches on but doesn't really discuss further: > If the programmer uses async() where they should have used asyncConcurrent(), that is a bug. Zig's new model does not (and cannot) prevent programmers from writing incorrect code, so there are still some subtleties to keep in mind when adapting existing Zig code to use the new interface. What class of bug occurs if the wrong function is called? Is it "UB" depending on the IO model provided, a logic issue, or something else?
•
A deadlock. For example, the function is called immediately, rather than being run in a separate thread, causing it to block forever on accept(), because the connect() is after the call to async(). If concurrent() is used instead, the I/O implementation will spawn a new thread for the function, so that the accept() is handled by the new thread, or it will return error.ConcurrencyUnavailable. async() is infallible. concurrent() is fallible.
How are the tradeoffs meaningfully different? Imagine that, instead of passing an `Io` object around, you just had to add an `async` keyword to the function, and that was simply syntactic sugar for an implied `Io` argument, and you could use an `await` keyword as syntactic sugar to pass whatever `Io` object the caller has to the callee. I don't see how that's not the exact same situation.
•
In the JS example, a synchronous function cannot poll the result of a Promise. This is meaningfully different when implementing loops and streams. Ex, game loop, an animation frame, polling a stream. A great example is React Suspense. To suspend a component, the render function throws a Promise. To trigger a parent Error Boundary, the render function throws an error. To resume a component, the render function returns a result. React never made the suspense API public because it's a footgun. If a JS Promise were inspectable, a synchronous render function could poll its result, and suspended components would not need to use throw to try and extend the language.
•
I see. I guess JS is the only language with the coloring problem, then, which is strange because it's one of the few with a built-in event loop. This Io business is isomorphic to async/await in Rust or Python [1]. Go also has a built-in "event loop"-type thing, but decidedly does not have a coloring problem. I can't think of any languages besides JS that do. [1]: https://news.ycombinator.com/item?id=46126310
Maybe I have this wrong, but I believe the difference is that you can create an Io instance in a function that has none
•
In Rust, you can always create a new tokio runtime and use that to call an async function from a sync function. Ditto with Python: just create a new asyncio event loop and call `run`. That's actually exactly what an Io object in Zig is, but with a new name. Looking back at the original function coloring post [1], it says: > It is better. I will take async-await over bare callbacks or futures any day of the week. But we’re lying to ourselves if we think all of our troubles are gone. As soon as you start trying to write higher-order functions, or reuse code, you’re right back to realizing color is still there, bleeding all over your codebase. So if this is isomorphic to async/await, it does not "solve" the coloring problem as originally stated, but I'm starting to think it's not much of a problem at all. Some functions just have different signatures from other functions. It was only a huge problem for JavaScript because the ecosystem at large decided to change the type signatures of some giant portion of all functions at once, migrating from callbacks to async. [1]: https://journal.stuffwithstuff.com/2015/02/01/what-color-is-your-function/
> A function that doesn't take an Io argument but wants to call another function that requires an Io argument can't. Why? Can’t you just create an instance of an Io of whatever flavor you prefer and use that? Or keep one around for use repeatedly? The whole “hide a global event loop behind language syntax” is an example of a leaky abstraction which is also restrictive. The approach here is explicit and doesn’t bind functions to hidden global state.
•
You can, but then you’re denying your callers control over the Io. It’s not really different with async function coloring: https://news.ycombinator.com/item?id=46126310 Scheduling of IO operations isn’t hidden global state. Or if it is, then so is thread scheduling by the OS.
I’m not sure I see how each example improves on the previous (though granted, I don’t really know Zig). What happens if you call append() with two different allocators? Or if you deinit() with a different allocator than the one that actually handled the memory?
•
Storing an Allocator alongside the container is an additional 16-bytes. This isn't much, but starts adding up when you start storing other objects that keep allocators inside of those containers. This can improve cache locality. It also helps devirtualization, as the most common case is threading a single allocator through your application (with the occasion Arena allocator wrapping it for grouped allocations). When the Allocator interface is stored in the container, it's harder for the optimizer to prove it hasn't changed. > What happens if you call append() with two different allocators? Or if you deinit() with a different allocator than the one that actually handled the memory? It's undefined behaviour, but I've never seen it be an issue in practice. Expanding on what I mentioned above, it's typical for only a single allocator to be used for long live objects throughout the entire program. Arena allocators are used for grouped allocations, and tend to have a well defined scope, so it's obvious where deallocation occurs. FixedBufferAllocator also tends to be used in the same limited scope.
Proximity to coworkers increases long-run development, lowers short-term output (2023) (pallais.scholars.harvard.edu)
•
From personal experience after working 4 years remotely, I started going to the office three days a week, and while efficiency per day is definitely smaller, we achieve much better results much faster, just because we can solve a lot of problems just where we are, without scheduling meetings or waiting for people to answer. Not to mention how much more I personally learn from my colleagues.
•
Currently sitting in an open office floor plan surrounded by 4 other developers less than 3 feet away speaking in Hindi. I don't speak Hindi. There are constraints to this open door distraction.
From Richard Hamming’s famous speech _You and Your Research_: > Another trait, it took me a while to notice. I noticed the following facts about people who work with the door open or the door closed. I notice that if you have the door to your office closed, you get more work done today and tomorrow, and you are more productive than most. But 10 years later somehow you don’t know quite know what problems are worth working on; all the hard work you do is sort of tangential in importance. He who works with the door open gets all kinds of interruptions, but he also occasionally gets clues as to what the world is and what might be important. > Now I cannot prove the cause and effect sequence because you might say, “The closed door is symbolic of a closed mind.” I don’t know. But I can say there is a pretty good correlation between those who work with the doors open and those who ultimately do important things, although people who work with doors closed often work harder. Somehow they seem to work on slightly the wrong thing—not much, but enough that they miss fame.
•
When I was piecing together how I got to be a relatively young lead developer, it came down to my open door policy. I essentially rediscovered Hamming's wisdom just by extending a policy that started with my college roommate who was struggling with our CS homework. That lead to me helping other kids in the computer lab (with C/C++ bugs, not with the algorithms), and if you have skills at <5YOE you're going to use them at work if you can, because what else can you do to not look like a newb? But open door policy doesn't have to mean a literal open door. When I went remote I was still helping people sort out problems, and when you ask for the back story you get to find out what other teams are working on, and where 1/3 of your coworkers are all struggling with the same API. That's a lot of ammo for a Staff, Lead, or Principal-track role. Because you understand a lot more of the project, and you already have the trust of half the org chart.
Seems very simple, working more with people than with problems gets you more social capital; people gonna remember someone helping them with something relatively trivial directly more than "they saw a bunch of code commited regularly". Probably anyone working long enough saw a case of someone being promoted over "better" technically candidates, just because he happened to be always there when important things happened.
•
Devs listen to who they trust. And how can you trust that worker you never work with?
Paraphrasing. Closed doors (focused work) lets you reach local minimum faster. Open doors (More connections) lets you escape local minimums. I guess you need focused work to make progress but once in a while you need contact with others to find inspiration or new ideas. Another one similar phrase(kinda). "If you want to go fast go alone. If want to go far go together". African proverb.
•
This is exactly the right framing and that was going to be the same quote I chose (go far together…)
Well Hamming observed it. It's not a randomized controlled study. It's anecdotal of course, and if one observed something to the contrary they would be well served to discount it. But presumably Hamming was there was a reason Hamming was addressing Bellcore.
•
> Well Hamming observed it. I observe so many ways to have known and unknown bias in this I call any outcomes cow manure.
↙ time adjusted for second-chance
Learning music with Strudel (terryds.notion.site)
•
Did anyone else think this article was gonna teach you how to play music using strudel (the food) somehow?
Have you taken a look at how to install the Haskell variant? It's a full-on recipe, or a docker container. I'd take a desktop application over a website any day, but that was not on the menu. It was an SPA vs a devops exercise. Of course the SPA wins.
•
Yes. The web wins on deployment every time.
Sadly doesn't even run at all on safari. "Importing a module script failed." :(
•
[delayed]
 Top