AbbeFaria

There is nothing wrong with using an LLM so long as a human takes ownership for the artefact (be that books, code etc).

I would rather the author automate the mundane and focus on conveying their ideas clearly.

As an aside, is there a Linux version for this ?

I work at MSFT and I feel burnt out too and am in a similar situation where I feel like resigning would be better for my mental health but AI isn’t a big contributing factor. I do have some arguments against speculative uses of AI though.

Experimenting with speculative uses is fine, technological breakthroughs require lot of iterations and some would naturally never make it but with the enormous amounts of capex that companies are investing, these have to impact the top line and eventually the bottom line as well. I just don’t see that happening now, I could be wrong.

1. To me speculative uses of AI like meeting notes summarisers seem to add little value if at all. First off, most meetings are performative work especially at big companies. Add to this, when someone just casually pastes the meeting notes from an AI summary and asks the meeting organiser to “pls check for correctness”, my blood just boils. Are we spending billions of dollars of capex for this ?

2. Every team builds their own “agent” for diagnosing incidents which is announced to huge fanfare but people rarely end up using it irl.

3. Devs and PMs chasing “volume” of work. You prompt GPT for an issue and it is bound to give you pages of text that you can use to show how much of output you can churn. I have seen excessively verbose design docs that only the writer (and prompter) could understand and all this was accepted because “Hey, I used AI for this and it must be good”.

There are legit uses of AI and I do have a 20$ Claude subscription which I like and use but at big companies they are shoving AI into every nook and cranny hoping it shows up in the top line and bottom line and so far it doesn’t add up.

Lot of these uses are driven by fear, by repeated exhortations from upper management about shoving AI into every nook and cranny when they are just as much clueless as us. People’s mortgages, their children’s education and their retirement, in short their whole livelihoods are at stake even more so when companies will happily lay off workers without a second thought. So people have to use AI even when it adds questionable value, if at all.

I am not resistant to change and am not an AI Luddite. I am happy to use AI to become a better developer but most current use cases seem to add questionable value.

At MSFT, Product Managers were Technical Program Managers. Yes, a good PM is a joy to work with.

No, once you spend that much time at a Big co, you start treating it like your own little fiefdom. Your allegiance becomes to how do I increase my own scope and not how do I build great products for customers ? Also “coordination tax” is heavy at big companies, it takes monumental effort to ship even incremental improvements. You have to inject new blood and move fast with the times. Your tenure doesn’t give you a birthright to stay at the company forever. This is not to devalue people who have contributed immensely to MSFT before, there are tenured people who aren’t afraid of change and adapt with the times. But someone close to their retirement might not want to rock the boat too much.

There’s a reason why despite investing so much in building AI tools, MSFT has a bad rep and no one uses their tools. People use CC, Codex, Cursor when MSFT already has supposedly competitive tools.

Note: I work at MSFT.

In the mature service I worked on, adding new code was “templatized”, you had to add feature flags, logs etc which didn’t vary much no matter which feature it was. The business logic was also not that complex, I can see AI tools one-shotting that and it indeed is a productivity boost. You would be surprised to know that most work was exactly this, writing rather mundane code. Majority of the time was spent coordinating with “stakeholders” (actually more like gate keepers) and testing code (our testing infrastructure was laborious). This was at MSFT. There are lot of teams that are innovating at the frontier (mine wasn’t, at-least not technically), I don’t know how AI tools work in those situations.

My experience working at Big tech companies is that people with roles like “agile coaches", "technical project managers", UX testers add questionable value. And the QA is usually outsourced to service companies like MindTree, TCS etc anyway.

Lot of these companies are bloated from having way too many Engineers anyway. Once you have mature software that brings in bagfuls of money, you don’t need that many people to keep the ship steady. I have seen this first hand at MSFT, we started a new team back in 2019 and it probably had ~40 people full time across US and India. By 2024 when I left, we had about 20 people in India who could easily run the service, the US team was dissolved and they moved to other teams in MSFT. The fact was that new features were few and the team was in KTLO mode. I have seen the reverse happen too, the team I was working on was dissolved and we were moved to different teams and everything moved to the US last year, managers were converted to ICs and a few folks were probably fired but it was a ~10 year old service that didn’t need that many people to run, even more so after AI tools became big last year.

I loved High rise and Concrete Island. He was prescient about what modern and current day society looks like.

I will give his other works a read. I tried reading Crash multiple times but it was a bit too gory for me.

I am guessing this is Salesforce and the celebrity is Matthew McConaughey who’s real chummy with Benioff.

Brave of the developer to bring it up. This cult of personality is pervasive throughout the tech industry.

My mistake, yes indeed it’s 6.828. On a side note, for distributed systems 6.824 is on my list after I finish 15-445 from cmu :)

To anyone wanting to learn about OS development, nothing beats MIT 6.824.

I finished the assignments in that course and that covers all the important aspects like processes, context switching, CPU modes, page tables and virtual memory and many other relevant topics like file systems, device drivers etc. And also it’s free.

From the table of contents this course gets too involved in ancillary matters like bootloaders or the Rust language itself whereas the focus of any OS development tutorial should be on core concepts like how processes are implemented, how context switching works, how paging and consequently multi level page tables (actually, in code) work etc.

I work at MSFT. I understand why they migrated to LibreOffice. Outside of work, I use none of MSFT products.

I do have some burning questions though, 1. How are they saving their work to the cloud if they use LibreOffice ? I don’t think it offers the same functionality that M365 suite does. 2. How are they handling IT security? Are they using a different vendor ?

Sad news. HBO has a veritable treasure trove of TV shows like The Sopranos, The Wire, Six Feet Under, Silicon Valley etc. Even their more recent ones like White Lotus, How to With John Wilson are leagues above Netflix. Only HBO can bet on artists’ vision like that.

I cancelled my Netflix subscription 7 years ago, 99% of their content is algorithmic drivel. Mindhunter, Dahmer, House of Cards were something I liked but nothing beyond that. I knew they were trash once I saw the sheer number of spinoffs they have just on Pablo Escobar. They had had one decent run of Narcos but then they just tried to extract every drop of juice out of that one persona. Most of Netflix dramas are just the equivalent of abhorrent and ugly graffiti. Their shows are Exhibit A in what happens if you give into algorithmic drivel and have no human touch to curate them.

HBO has some timeless TV classics that I keep rewatching every year even though I have watched them multiple times. Netflix can’t produce TV dramas like that, ain’t in their blood. Completely different DNAs.

Netflix does deserve all the plaudits wrt to their streaming experience though.

As long as you have the small screwdrivers used to tinker with electronics, you’re good. You can easily open up the laptop.

In my L480, I opened the laptop to change the battery and also install more RAM. Even for a hardware neophyte such as myself , this was straightforward.

Thinkpads are modular, you can easily get the components such as a battery etc. My T14s comes with a 3-year warranty as well.

I just bought a Thinkpad T14s a couple of months ago. It’s lightweight, has great build quality. I installed Ubuntu and it almost ran out of the box but I ended up having to tinker with it to get My Dell docking station and i3 window manager to work. But that is something I was willing to live with. So far, I have had no complaints. If you’re using Linux, the sleep and standby performance aren’t good. But much better than my previous laptop.

Coming to my previous laptop which I still have with me, I bought a Thinkpad L480 in 2018. It was then a dirt cheap version of a Thinkpad. But it did the job with no complaints. I had to replace the battery after 4 years but that wasn’t an issue. It did everything a daily driver is supposed to do, reliable and never threw a fit. I only had to change it as I felt I needed a better screen and performance. The Intel processor was showing its age.

I have only minor complaints running Thinkpad with Ubuntu. But if you start moving away from popular distros, then you have to accept you will occasionally have to tinker to get things work.

The company says no roles are being eliminated as part of these changes.

What in god’s name is the “voluntary exit program” going to do then ? It will obviously eliminate some roles ?

Corporate doublespeak at its finest.

I work at MSFT. Everything the author says is 100% true not only at MSFT but probably at every Mag-7 company.

It’s also the same reason why MSFT doesn’t have a blockbuster AI product.

1. At work or for personal use, I use GPT-5 or Claude Code. I am forced to use Copilot because that has access to internal company data but it’s nowhere close to GPT-5 or Claude.

2. MSFT open sourced VS code but on its own couldn’t engineer products like Cursor or Windsurf. Lets leave aside the economics of these products for now.

The regular down in the trenches engineer like myself is so busy thinking about how I can advance my career by playing political games, currying favour with my manager or manager’s manager that little time gets spent on product building.

Good thing MSFT has all the cash in the world to invest in companies like OAI, GitHub etc because the bureaucracy is stifling at MSFT.

Go do the xv6 labs from the MIT 6.828 course, like yesterday. Leave all textbooks aside, even though there are quite a few good ones, forget all GitHub tutorials that have patchy support, blogs that promise you pie in the sky.

The good folks at MIT were gracious enough to make it available for free, free as in free beer.

I did this course over ~3 months and learnt immeasurably more than reading any blog, tutorials or textbook. There’s broad coverage of topics like virtual memory, trap processing, how device drivers work (high-level) etc that are core to any modern OS.

Most of all, you get feedback about your implementations in the form of tests which can help guide you if you have a working or effective solution.

10/10 highly recommended.

I work at MSFT. There’s top down pressure to use LLMs everywhere. At this point, if you can convince your management about using LLMs anywhere, they would happily head nod and let you go do that. And management themselves are not that technical wrt LLMs, they are being fed the same AI hype slop that we are fed.

Most of these efforts have questionable returns and most projects will usually involve increasing test coverage or categorising customer incidents for better triage, apart from these low hanging fruits not much comes out of it.

People still play the visibility game though. Hey, look at what we did using LLMs. That’s so cool, now where’s my promotion? Business outcomes wise, there’s some low hanging fruits that have been plucked but otherwise it doesn’t live up to the hype.

Personally for me, it is helpful in a few scenarios,

1. Much better search interface than traditional search engines. If I want to ramp up on some new technology or product, it gives me a good broad overview and references to dive deep. No more 10 blue links.

2. Better autocomplete than before but it’s still not as groundbreaking as AI hype hucksters make it out to be

3. If I want to learn some concepts (say how ext4 FS works), it can give a good breakdown of the high level concepts and then I go need to study and come back with more Q’s. This is the only genuine use case that I really like. Where I can iteratively ask Q’s to clarify and cement my understanding of a concept. I have used Claude code and ChatGPT for this and I can barely see any difference between the two.

This is my balanced take.

Well done. I am actually solving the labs rn. I am on mmap, trying to get fork to work :)

I am an employee and stock holder both. Obviously, I would rather have a job than not but at the same time a significant portion of my personal wealth is in MSFT stocks.

I have seen engineering teams at MSFT that provide questionable value to the business so trimming the fat does make sense. Also These multiple rounds of layoffs have made me internalise that we need to be working on something valuable instead of useful.

Can someone discern why these layoffs are being done ? Driven by large shareholders? More runway till we get revenue from GenAI while we keep burning money on GPUs ?

Fun fact: I used to work in this team.

We have come a long way now that we have these advanced classifiers. You would be surprised how low tech the initial product was, by low tech I mean devoid of any ML/AI. We went GA in end of 2019.

Saw a lot of interesting use cases too for e.g Japanese enterprises wanting to detect cases like suicide or intent to suicide, that is why we have multiple types of classifiers.

I worked on the Infra side (not ML). That too was “low-tech” or the more apt term would be “not the latest tech”. Core parts of the app were part of a monolith (think Exchange). Then we were using a really old .NET Framework version for our MVC app. Lot of the storage technologies we used were very MS specific as well. AFAIK, all of this is still valid today.

I am shocked, shocked that Alexa is spying on users and showing them ads.

I’m shocked, shocked that TikTok is invasively gathering user data.

There is a lot of resentment towards Algorithmic challenges here, I guess most of it is from folks with non-CS backgrounds and possibly from folks who are "non-FAANG".

First off, I hate the "FAANG" acronym, its become a status symbol of some sort and is used so frequently in d*ck measuring contests on social media. I am cognizant of the fact that there are SDEs and great SDEs even outside of BigTech.

The reason Algorithmic challenges are used so frequently in FAANG companies is that when done properly they can be standardized pretty well with minimal source of biases. When done properly, you can observe candidates thinking out loud, communicate properly under time pressure which is what is required at most tech companies. Also, LC style problems cannot be memorized, you have to be incredibly lucky to see a problem you solved recently or the company's hiring process is so broken that their questions are already known to public.

Algorithm challenges are basically testing what you learnt in your undergrad CS curriculum, the topics are limited to fairly standard stuff you learn at university, regular DS like Stacks, Queues, LL etc and algorithmic techniques like DFS, BFS, Sorting, DP etc. Once you have done enough of these problems and understand them well enough, the LC interview isn't that tough. This is coming from someone who has been giving at-least 2 interviews a day for the past 2-3 weeks. I won't say my hit rate is 100% but I am fairly confident that given a problem I haven't seen before, I could solve it in an interview setting under time pressure. You face this situation quite frequently at most tech companies as part of your job.

At the same time, I would whole-heartedly agree that 90% of the work at tech companies doesn't involve applying Algorithms in real life. Never have I faced a situation where I have read an algorithm in textbook and applied it at work. Although, having worked at one tech company, I did see quite a few DS/Algorithms being used in real-life in Large scale services. I remember seeing Tries being used to speed up string search in some services in Exchange at MSFT, I did see clever applications of Bloom Filters, if you peek into the source for .NET Dictionary and you see how hashing is implemented in the real world. Say that the Keys you use in your real-world application are for some reason unevenly distributed and you store them in .NET dictionary, your read queries would considerably slow down and that might not be optimal for your customers. I wish that this were a contrived example but it isn't, I have seen many such situations occur at MSFT where Engineers didn't properly think of trade-offs when designing large scale systems. Algorithmic interviews can be a good filter to test these skills.

Also most tech companies don't necessarily want to hire domain experts. That does happen but only rarely and most teams are fairly generic, they aren't working in specific areas like Databases, Networks, OSs etc. I see folks saying that they are really great with specific frameworks but truth be told, Tech companies would rather hire someone who can learn specific frameworks on the job under extreme time constraints. I know that this is the exact opposite of Startups and smaller companies who need "domain experts" to ship stuff sooner. Amazon doesn't require new folks to know Spring MVC, MSFT doesn't require new hires to know .NET MVC etc, they all want developers who can pick up any XYZ skill whenever the job demands it. I know that quite a few of HN users might look down upon that and you have every right to, but that really is the job at BigTech. Most engineers are fungible, even though you cannot like for like replace engineers you lose. But at the scale that they are operating that is the only way.

Algo challenges test for raw intelligence, period. Now, even Algo challenges have varied levels of difficulties. I have heard horror stories of interviewees being asked questions that require particular tricks or complicated DS like Segment Trees etc. Unfortunately that does happen.

Also, most companies have rounds where they do test "domain knowledge", skills like OOD, System Design etc. Although I admit, these two are also beginning to be gamed. There are websites that sell "courses" for these and its getting to a point where interviewees just regurgitate info from these courses even if they haven't used them in real life at work but that is what happens when you deviate to something that is difficult to standardize. Additionally, from the interviewers perspective its difficult to judge what contributions interviewees have made to an organization when they haven't closely worked with them and the work might be in a domain they aren't familiar with, that is unless you have made major contributions to OSS, which can be verified easily.

Finally, I have to admit that in all this frenzy I just seem to have gotten lucky. I finished university four years ago. Algo challenges were wildly popular during my time and I still like solving them, although I am not as prolific as some of the folks I know in Bigtech. I did a lot of it in University and that helped me tremendously in interviewing at Bigtech. I realise that HN folks come from a diverse set of backgrounds. Some of you are much older and have familial commitments which certainly makes it tough to "practice LC" along with your work and spending time with family. In that, LC is really a young man's (or woman's) game and I have to admit that I have been a beneficiary of that. I can only soothe you by saying something cliched, Life isn't always fair.

I don't want to underestimate the skill of being good at LC. In the past couple of months, I have solved about 200+ LC and I have been interviewing vigorously too. Its a tough skill for sure but beyond a certain point, you start seeing patterns you have applied before and once you are in the flow, it becomes slightly easier.

I only wish the real Software Engineering job was as simple as being good at LC, because it clearly isn't.

Skills such as design patterns etc do matter. There are separate rounds for testing just that, think Design rounds (LLD or HLD) but you usually have to pass the Algorithm rounds before to get to that stage.

I recently interviewed with a startup. They had "outsourced" the first round to a 3rd party firm that specializes in taking tech interviews (Mostly Algorithm rounds).

The interviewer posted an LC question and asked me to read it. Since I was already logged into my LC account, he first asked me to show if I had solved it. I said I did. He then posted 9-10 LC questions one by one, all of which I had solved (I was doing LC regularly then). In the end he got tired, and posted a question from another website (Hackerearth) which I hadn't solved. We ended up taking ~5 minutes just going through different LC questions and he was disappointed that I had solved all of them.

I have also faced situations where I have seen an LC question that I solved but couldn't solve it in an interview setting, mostly because of the pressure.

Thanks a lot for the advice. I am only 4 years into my career and my last employer didn't have any such clause. Hence my indecision.

Yes, LC is Leetcode. Its gotten to a point where that is pretty much all you need to get a FAANG job, nothing more complicated than that.

Or perhaps I should try to get away from my web developer job and get into something else?

You already know what to do here.

I just quit my job at MSFT sometime back, worked straight outta college for four years (in the MS-IDC office). I quit to actually pursue Research in "systems". As for you not getting a callback, add the right keywords/buzzwords in your resume and most importantly LC (considering where you are in your career LC is more than enough). Thats all there is to it. You have a Masters degree from US and you seem like a curious person, I have seen people with far less get into a FAANG job (in India at-least).

FAANG job would definitely be a step up in all aspects money, career etc, for a couple of years at-least. But I get a sense maybe you will grow out of it too (just like I did).

Good luck with whatever you do.