jampa

When COVID hit, I knew a lot of engineers who decided to move to rural areas / small farms because they could leverage Starlink to work remotely.

Last year, when I asked whether they still liked Starlink, all of them said it is amazing, but they had gotten fiber coverage in their area from a local provider, so they don't use it anymore, or just use it as a backup.

I think Starlink was a huge demand signal that there were people willing to pay a premium for faster-than-radio internet. So, unless they manage to be cheaper and faster than fiber, I don't think there is much of an endgame there.

But there are a few places that will need Starlink, like planes, cruise ships, and islands. I'm just not sure if that will justify that $1T valuation.

I feel the same way about consumer AI tools now. Gemini and ChatGPT have been abysmal lately. They can no longer be relied on to do multi-turn searching and thinking.

Before, they could stay in thinking mode for more than 7 minutes. For example, "find a source for this claim" would search, analyze, and self-adjust the query. Nowadays, even if I push for it, I cannot make these tools work for more than 30 seconds before they give generic answers, even in "Pro" mode.

From what you said: Not looking at code is bad, not because Claude can slip a few bugs (it can), but because LLMs tend to default to writing more code and features than needed, which isn't a good thing. I see a lot of people making 10+ PRs per day, but most of them are just going back to fix earlier PRs.

Claude always likes to "go big," for example, by choosing tools that can support millions of concurrent users or by adding unnecessary layers of abstraction that create more maintenance pain. I guess that's good for LLM companies, since more tokens are spent fixing the mess it caused.

Every time I enter plan mode for a huge feature, I end up cutting about 30-60% of the task scope before the LLM can actually start the work. I review the final code, and I still find things to cut. As said before "The best code is no code, or code you don’t have to maintain" [0]

0: https://www.simplethread.com/20-things-ive-learned-in-my-20-...

This post needs some examples, because I have never had an interaction with Claude that made me think this way.

LLMs generally have a way to "play a role" (most earlier prompt guides ask you to start with "You are a <role> expert in a <domain>"). So maybe if you interact with it by asking questions, it might assume that it knows more than the operator and adopt that attitude?

I tested it to fix React Native bugs in a project, comparing it with Opus. It fared better on harder bugs, taking less time to find the root cause, but after implementing a fix, it spent a lot of time and effort on validation. This was mostly unnecessary, since most of the bugs were in the JS code, so for most things, hot reloading is enough for E2E validation and to run just the right tests. No need to run a full build and test suite (which takes 10+ minutes); the CI can do this.

I switched back to Opus because of this validation quirk. Overall, Fable spent 20% of the time on coding and 80% on validation.

I think using Fable for planning and Opus for execution could be a "best of both worlds" approach (I need to test this more), but for most cases, it's not necessary, and Opus is enough.

Fable feels like a version of Opus running on a harness that won't let it halt until it's sure the issue is fixed, which makes sense if what you want is a model that's better at benchmarks.

It's a very good model, but it comes at a huge premium: not only do the tokens cost more, but the model itself really wants to spend them all. For example, working with React Native, Fable never just says "okay, I did the thing, that's it." It tries to rebuild the entire app from scratch, run the whole test suite, and watch every log and warning.

This is the first time with LLMs I've felt that upgrading to a model isn't worth it, even if my company lets me use it, because all the building / testing was just destroying my machine and its battery, which keeps me from working on other things.

For now, it feels like Opus with ultracode is a better choice (less pollution of the main context, more parallelism in investigations).

It is hallucinating many flights in my region, some that never existed (so it is not an outdated data problem).

I also see some logic flaws. It overlooks the option of going to a major hub to access faster aircraft, rather than hopping on local hubs.

Also, immigration and customs are cleared at the first airport you arrive at in the country, not at the last one.

In some countries, you need to clear immigration even while going to a third country, so 1 hour is not enough to do it.

there's not enough money to be made via speculation

I mean, there is money to be made. CATL stock (the major producer of EV batteries with 50% market share, with billions of contracts for stationary batteries) rose 48.81% over the last 6 months, for example.

But I agree that news about renewables goes unnoticed. I only see news about renewables because I actively seek out channels and websites that cover it. I wonder if it is because most companies in the industry are Chinese and don't focus on PR in the West as AI companies do.

The biggest competitor to Starlink is, ironically, traditional fiber.

When COVID hit, I knew a lot of engineers who decided to move to rural areas / small farms, because they could leverage Starlink to work remotely.

Last year, when I asked whether they still liked Starlink, all of them said it was amazing, but they had gotten fiber coverage in their area from a local provider, so they don't use it anymore, or just use it as a backup.

I think Starlink was a huge demand signal that there were people willing to pay a premium for faster-than-radio internet. So, unless they manage to be cheaper and faster than fiber, I don't think there is much of an endgame there.

The last three times I filed detailed bug reports as a client, all I got back were AI replies asking the same questions I’d already answered in the original report and suggesting alternatives I’d explicitly said I’d already tried. No wonder people don’t write bug reports anymore.

This oil crisis was a huge boon for EVs. In Brazil, despite the "hate" most people have against EVs, BYD went from breaking into the top 10 in March to taking the #1 spot in consumer sales for the first time ever.

Mythos release feels like Silicon Valley "don't take revenue" advice:

https://www.youtube.com/watch?v=BzAdXyPYKQo

""If you show the model, people will ask 'HOW BETTER?' and it will never be enough. The model that was the AGI is suddenly the +5% bench dog. But if you have NO model, you can say you're worried about safety! You're a potential pure play... It's not about how much you research, it's about how much you're WORTH. And who is worth the most? Companies that don't release their models!"

I think the main point is that "reinventing the wheel" has become cheap, not software design itself.

For example, when a designer sends me the SVG icons he created, I no longer need to push back against just using a library. Instead, I can just give these icons to Claude Code and ask it to "Make like react-icons," and an hour later, my issue is solved with minimal input from me. The LLM can use all available data, since the problem is not new.

But many software problems challenge LLMs, especially with features lacking public training data, and creating solutions for these issues is certainly not cheap.

Not sure if WhatsApp paid off, though. There are reports of up to $1 billion in annual revenue with the Business API, so this is far less than what they paid. I think Meta's strategy was to create a Western version of WeChat, which has a very high ARPU, but for some reason, they never invested in it properly... They added Stories, a "Venmo" feature, and then gave up.

Thanks for the write-up! Yes, this clearly shows it is malware. In VirusTotal, it also indicates in "Behavior" that it targets apps like "Mail". They put a lot of effort into obfuscating the binary as well.

I believe what you wrote here has ten times more impact in convincing people. I would consider adding it to the blog as well (with obfuscated URLs so Google doesn't hurt the SEO).

Thanks for providing context!

This article is so frustrating to read: not only is it entirely AI-generated, but it also has no details: "I'm not linking", "I'm not pasting".

And I don't doubt there is malware in Clawhub, but the 8/64 in VirusTotal hardly proves that. "The verdict was not ambiguous. It's malware." I had scripts I wrote flagged more than that!

I know 1Password is a "famous" company, but this article alone isn't trustworthy at all.

I am using API mode, and it's clear that there are times when the Claude model just gives up. And it is very noticeable because the model just does the most dumb things possible.

"You have a bug in line 23." "Oh yes, this solution is bugged, let me delete the whole feature." That one-line fix I could make even with ChatGPT 3.5 can't just happen. Workflows that I use and are very reproducible start to flake and then fail.

After a certain number of tokens per day, it becomes unusable. I like Claude, but I don't understand why they would do this.

There are already some great responses, but I want to add that one effective way to coach senior employees is to give them responsibilities one level above their current role and then provide feedback.

For engineers aiming to move into management or staff engineering, you can assign them a project at the level they aspire to reach and give feedback once they complete it. For example, for an engineer aiming to be an EM, I expect them to lead not only meetings but also all communications related to this project, while I act as their director. Afterwards, I provide feedback.

It doesn't have to be that extensive right away. You can start small, like asking them to lead a roadmap meeting, and then increase responsibilities as they improve. Essentially, create a safe environment for them to grow.

I am glad that I don't need to use Windows anymore. When I did, the LTSC version (the one made for ATM and Kiosks) was the only one that was productivity-friendly.

Microsoft doesn't want to accept that no one cares about Windows, and the OS is the thing that gets you to the thing you want to do.

I saw 2 instances of people getting "updating windows" in their personal laptops when they tried to present something and lost everyone's time. I imagine this happens a lot of times every day. And now they are just breaking everyone's system by forcing updates as well.

I usually agree with this. But I am using the same workflows and skills that were a breeze for Claude, but are causing it to run in cycles and require intervention.

This is not the same thing as a "omg vibes are off", it's reproducible, I am using the same prompts and files, and getting way worse results than any other model.

Slightly off topic, but does anyone feel that they nerfed Claude Opus?

It's screwing up even in very simple rebases. I got a bug where a value wasn't being retrieved correctly, and Claude's solution was to create an endpoint and use an HTTP GET from within the same back-end! Now it feels worse than Sonnet.

All the engineers I asked today have said the same thing. Something is not right.

I imagine they're building this system with the goal of extracting user demographics (age, sex, income) from chat conversations to improve advertising monetization.

This seems to be a side project of their goal and a good way to calibrate the future ad system predictions.

After years of lurking, I made some posts this year here. Here is what my Google Analytics and Substack traffic from HN shows:

51% US

5% United Kingdom

4% Germany

3% Australia, Canada, India

The rest are primarily European countries, with Sweden, Denmark, France, and Spain leading the way.

* Most people here use ad blockers. I imagine this data is incomplete and would reflect the mobile portion of the users. I don't log IP addresses.

I've used it in some repos. It doesn't catch all code review issues, especially around product requirements and logic simplification, and occasionally produces irrelevant comments (suggesting a temporary model downgrade).

But it's well worth it. It has saved me some considerable time. I let it run first, even before my own final self-review (so if others do the same, the article's data might be biased). It's particularly good at identifying dead code and logical issues. If you tune it with its own custom rules (like Claude.md), you can also cut a lot of noise.

I wrote about this recently. You need to prompt better if you don't want AI to flatten your original tone into corporate speak:

https://jampauchoa.substack.com/p/writing-with-ai-without-th...

TL;DR: Ask for a line edit, "Line edit this Slack message / HN comment." It goes beyond fixing grammar (because it improves flow) without killing your meaning or adding AI-isms.

I recently got a Samsung device for testing, and the experience was terrible. It took three hours to get the device into a usable state.

First, it essentially forces you to create both a Samsung account and a Google account, with numerous shady prompts for "improving services" and "allowing targeted ads."

Then it required nine system updates (apparently, it can only update incrementally), and worst of all, after a while, it automatically started downloading bloatware like "Kawai" and other questionable apps, and you cannot cancel the downloads.

I wonder how much Samsung gets paid to preinstall all that crap. The phone wasn't cheap, either. The company seems penny wise and pound foolish.

While I think there's significant AI "offloading" in writing, the article's methodology relies on "AI-detectors," which reads like PR for Pangram. I don't need to explain why AI detectors are mostly bullshit and harmful for people who have never used LLMs. [1]

1: https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...

Me neither, but I remember that when searching for hotels and Airbnbs, I only filter for hotels that are 8+/10 domestically and 9+/10 internationally, which filters out many of the hotels that have those kinds of issues (and score doesn't affect budget much).

In the beginning of Android / iOS, just installing an app and registering was enough for the company to get your device's MAC address and thus your indoor location with accurate precision.

They could access your Wi-Fi network's BSSID (whose location is often public due to wardriving databases), and in public places, they had partner companies (malls, airports, etc.) whose routers would triangulate your position based on Wi-Fi signal strength and share information like "John is in the food court near McDonald's."

All of this happened without you even needing to connect to their Wi-Fi, because your phone used to broadcast its MAC address if the Wi-Fi was simply on. But now your MAC is now randomized, but it took a lot of time for Google / Apple to this.

I've worked as an EM at four different companies, from large enterprises to small startups, and I think "the role of engineering manager" is a myth. Your role varies wildly from one company to another. In every company I've worked at, my job has never been the same:

In the end, engineering management basically requires you to counter-balance whichever of the four pillars your team needs most: Product, Process, People, and Programming.

- Too few people? You'll work on scope to make the deliverables meet reality. Since there's not much communication overhead, you'll be able to program.

- No PM? You now own the product pillar entirely. This takes a lot of your time: You'll need to validate features, prioritize the roadmap, and even talk directly with clients. None of the rest matters if your team is shipping features with no user value.

- Too many people in the team/company? Say goodbye to programming. You'll be responsible for careers, making everyone work cohesively, and navigating the org to get the right resources and support for your team.

- Reporting close to the CEO? You'll handle the bridge between sales, operations, client communications, and other functions.

The common thread is that your focus constantly shifts based on where your team's bottlenecks are. The key is identifying which pillar needs attention and adapting accordingly.