Interestingly, stagnation started around 2014 (in the number of questions asked no longer rising,) and a visible decline started in 2020 [1]: two years before ChatGPT launched!
It’s an interesting question if the decline would have happened regardless of LLMs, just slower?
> The unhelpful feedback was a consistent push to dumb down the book (which I don't think is particularly complex but I do like to leave things for the reader to try) to appease a broader audience and to mellow out my personal voice
Interestingly, this was my exact experience when working with a publisher (Manning, in my case), and it was the main reason I decided to part ways when writing my book (The Software Engineer’s Guidebook). While I did appreciate publisher’s desire to please a broader crowd by pushing a style they thought would broaden the appeal: but doing so makes technical books less attractive, in my view. And even less motivation to write!
In my case, self publishing worked out well enough with ~40,000 copies sold in two years [1], proving the publisher’s feedback wrong, and that you don’t need to dumb down technical books, like this specific publisher would have preferred to do so.
Even if it wouldn’t have worked out: what’s the point writing a book where there’s little of the author (you!) left in it. Congrats to OP for deciding to stick to your gut and write the book you want to write!
You might be amused to hear that the only exception for Section 174 is software developers working at oil and gas companies!
From the legislation:
“ Section 174(c)(2) provides that the required § 174 method does not apply to
any expenditure paid or incurred for the purpose of ascertaining the existence, location,
extent, or quality of any deposit of ore or other mineral (including oil and gas).”
Is there an explanation how software developers creating software for oil and gas companies are different than for any other industry?
Or can we assume that the oil and gas industry managed to (yet again!) have its lobbyists where it mattered?
Either Klarna is really good at pulling strings to get media coverage, or mainstream media does not fact checking themselves. About a year ago, the company was everywhere in the media when its CEO announced that it created an AI bot that is doing the equivalent of 700 fulltime customer service folks.
I did what seemingly no other publication reporting on it did: signed up for Klarna, bought one item and used this bot.
I was... not impressed?
Klarna's "AI bot" felt like the "L1 support flow" that every other company already has in-place: without AI! Think like when you have a problem with your UberEats order and 80% of cases are resolved without a human interaction (e.g. when an item is missing for your item.)
I walked through the bot's capabilities [1] and my conclusion was that pretty much every other company did this before (automating the obvious support cases.) The real question should have been: why did Klarna not do it before? And when it did, why did it build a wonky AI bot, instead of more intuitive workflows than other companies did?
My sense is that Klarna really wants to be seen as an "AI-first tech company" when it goes public, and not a "buy now pay later loan company" because AI companies have higher valuations even with the same revenue. But at its core, Klarna is a finance or ecommerce-related company: an not much to do with AI (even if it uses AI tools to make its business more efficient - regardless of whether it could use non-AI tools to get the same thing done)
If you think the issue of devs using fake identities is a problem limited to the Fortune 500: I talked with a 6-person startup who very nearly hired a person who could have been from anywhere else than they claimed, including North Korea. All they know is the candidate used an AI filter to make them look like a Polish person [1] - and this startup recorded when they caught this faker.
This is a full-remote startup and they have now added a mandatory in-person interview to their recruitment loop.
Amusingly, in their case, using local job boards did not help: they got candidates pretending to from Poland or Serbia, yet not speaking the language.
A little sad to see how each episode like this casts more doubt and uncertainty into full-remote interviewing.
The most bizarre OpsGenie story was how in 2022, this tool was down for 2 weeks for hundreds of unlucky companies that were Atlassian customers. This was at a time when JIRA had an outage impacting a small percentage of their customer base - but still in the hundreds of organizations (with around tens of thousands of users.)
While most companies can operate for some time without JIRA: losing your paging service means you're flying in the dark. And yet, Atlassian did not prioritize restoring OpsGenie.
I covered the details at the time [1]. To this date, this incident is a real head-scratcher and makes me wonder if Atlassian has internalized how much more critical an incident alerting software is, compared to a ticketing software (JIRA) or wiki (Confluent).
Author of the article - and analyzed the likely impact of Section 174 in detail a year back [1], in Jan 2024, when it became clear that it was not being reversed like most assumed it would be.
I originally didn’t mention this because S174 impacts the US and US-HQ’d companies. In this data other countries like Germany, UK, France all see a similar drop. Also, S174 impact likely really started from early 2024, when companies impacted had to pay high taxes and realized the change is here to stay with no end in sight. Doesn’t explain the drop since 2022.
Updated the article to make this clear though. It was not in the original version - thanks for the note!
As someone who was a paid customer of Quartr: they do not offer the ability to look or search actual filings at the $20/month plan. Full text search starts from $500/month, and is an annual contract (so $6,000/month.)
Pricing for these services is not cheap, given it can be very helpful for professional traders.
(I’m no professional trader, and not even a trader. I just sometimes want to search for interesting things in transcripts, when I research a topic. I would pay for a decent service offering full text search for transcript search, to use it a few times per month (or perhaps even less frequently). Still not found a product that does it at a sensible price point for my use case - likely because my use case is not worth building a business on.)
I’m one of the paying Kagi customer who wants to make Kagi my default iOS search engine, but cannot. It’s maddening that even though I paid for both my iPhone / iPad and for Kagi, Apple for some reason makes it impossible for me to make this choice (that I already made by paying for Kagi).
On Chrome at least this is possible, even if it’s additional steps (I have not used the extension though there.)
For anyone interested in reading full chapters of the book (all are relatively short!), here are three full ones [1], shared with the permission of the author (Kent Beck) and publisher (O’Reilly).
The Netherlands is arguably one that heavily embraces the 4-day workweek. In this country, employees can request a shorter work week (4 days) for proportionality less wages (so eg for 4 days get 80% of wages).
More than 80% of working moms utilise this option and around 10% of dads. [1] Among my friends I have parents where both of them work 4 days, taking the 5th day off on separate days, and their kid goes to daycare 3 days a week.
Note that most government subsidies (for childcare) are set up to encourage working at least 32 hours per day.
I've been using Kagi as my default search engine for about 2 months now. I love it, and feel so far that it's well worth the $108 per year: just by not having to spend mental energy to scroll through the first several sponsored results; and try and decide if a result is paid or not.
I set Kagi to be my default search in my browser (Chrome). For specific searches like stocks, maps or restaurant reviews, I still use the "!g" bang to go to Google.
Never thought I'd pay for search: but very happy with my choice so far. Great to see others agree on this page - as I hope they can maintain this as a viable business, and stick to the principles of users being their customers: and not advertisers.
I was recently in Palo Alto, and bumped into a newly founded startup (I don't remember the name unfortunately) who set themselves the grand the vision of exactly this: winning a gold medal on the international Olympiad using AI. Their plan was to build mostly on LLMs as a start, and iterate as they go. In their barebones office space, they had a poster with a countdown of the number of weeks till the event: it was 36 at the time.
It sounded interesting to wonder how far they could go with this kind of approach. I thought they were aiming for the moon: but also respected the boldness and determination. They had the funding to operate for at least a year, and were very focused to get there.
Seems like this prize will supr hundreds (or thousands) of teams competing in exactly this space. Perhaps it will have a similar effect like the $1M Netflix Prize in 2009 for recommendations algorithms!
If I recall correctly, this was the same frustration that Lobsters[1] was created. In the case of Lobsters, moderation decision have carefully been considered and implemented.
My two cents is that it's when it comes to comments - and moderation - that things get challenging. It's also where both HN and Lobsters managed to find a (hopefully) sustainable model. Good luck with this!
Threads.com is owned by an ex-Facebook employee who launched a Slack alternative in 2017. I would be surprised if they were to give up a strong domain name (and brand that they surely have trademarks for) that easily!
As someone living in the EU who wants to use Threads but cannot - as Meta has blocked it here, while they work out GDPR compliance that has not been solved since launch - its amusing to me to read how Meta is planning to do various growth hacks.
My humble suggestion would be to first, perhaps, roll out to the EU? On one hand: sure, we are “only” talking about 450M potential users. On the other: anyone who has friends in this region or an interest in someone based here: well, that person needs to go to Twitter/Mastodon/BlueSky etc.
I am not saying this definitely explains all growth struggles: but surely doesn’t help true, global adoption?
Surely in a post about Google Cloud Spanner, all examples mentioned use Google Cloud Spanner? It would be moot listing them as examples if they would not: so my assumption is they are all using GCP infra already for Spanner.
I really want to give Google the benefit of the doubt: but it doesn't help that they did not write that eg Gmail is using "Cloud Spanner." They wrote that it uses Spanner.
I tried to find it in this video, but failed. Could you please share a time stamp on where to look?
It’s a pretty big deal if Gmail migrated to GCP-provided Spanner(not to an internal Spanner instance) and sounds like he kind of vote of confidence GCP and Cloud Spanner could benefit from: might I suggest to write about it? It’s easier to digest and harder to miss than an hour-long keynote video with no time stamps.
And so just to confirm: Gmail is on Cloud Spanner for the backend?
“ According to the Amazon Prime Day blog post, DynamoDB processes 126 million queries per second at peak. Spanner on the other hand processes 3 billion queries per second at peak, which is more than 20x higher, and has more than 12 exabytes of data under management.”
This comparison seems to be not exactly fair? Amazon’s 126 million queries per second was purely for Amazon-related services serving Prime Day generating this on DynamoDB, and not all of AWS is my read.
What would have perhaps been a more fair comparison is to share the peak load that Google services running Cloud Spanner, and not the sum of all Spanner services across all of GCP and all of Google (Spanner on non-GCP infra).
I will say that it would show a massive of confidence to say that Photos, Gmail and Ads heavily rely on GCP infra: which would be brand new information for me! It would add to confidence to learn more on how they use it, and if Cloud Spanner is on the critical path for those services.
What is confusing, however, is how in this article "Cloud Spanner" is consistently used... except for when talking about Gmail, Ads and Photos, where it's stated that "Spanner" is used by these products, not "Cloud Spanner!". Like if they were not using the Cloud Spanner infra, but their own. It would help to know what is the case, and what the load of Cloud Spanner is: and not Spanner running on internal Google infra that is not GCP.
At Amazon, practically every service is built on top of AWS - a proper vote of confidence! - and my impression was that GCP had historically been far less utilised by Google for their own services. Even in this post, I'm still confused and unable to tell if those Google products listed use Cloud Spanner or their own infra running Spanner.
It's pretty amusing how a bunch of teams are posting videos on how they built a specific, bespoke hardware/software component or two of MrBeast videos. This one was about the ~100 hardware button sets and LED strips required for the video. And here is another one [1] that is on how another team build 456 "detonator units" for the Squid Game video: also finished in the nick of time.
It seems clever for MrBeast to hire DIY YouTubers to do these bespoke jobs: it's a win-win even after the video, as they teams create their own "how we built this" video. It also gave me an appreciation for both how much work these massive videos are (we only saw one smaller part of the video, a hardware put together) and how chaotic it can all feel!
It’s an interesting question if the decline would have happened regardless of LLMs, just slower?
[1] An annotated visualization of the same data I did: https://blog.pragmaticengineer.com/are-llms-making-stackover...