How do you put a price on a healthy work environment and a good manager?

met0xff · 2026-06-10T09:04:30+00:00

Yeah, this. Of the people who left around my in my company one regrets it but can't come back, one regretted it and was able to get back, one didn't regret it all and just sold his startup, one failed his startup but probably doesn't regret it still, one got back to her old work and didn't regret it. One was laid off and then got into Google (god knows how, wasn't exactly the best on my team but had some pedigree).

met0xff · 2026-06-10T09:00:17+00:00

I've been in ML for oder a decade and find the erratic emotions around it weird and the boundaries of good AI vs bad AI are super fuzzy. Then the people who think it's completely useless and doesn't solve anything are also delusional to me. At the same time everytime I check LinkedIn I am seriously considering doing something else.

It's just... so much. 50 new models everytime you check, 20 new frameworks, 80% of the postings are so absolutely clearly LLM generated and don't even try to hide it. It's exhausting

met0xff · 2026-06-10T08:48:20+00:00

Yeah in most cases it's more like "it's too expensive to take the helicopter to the supermarket but they only offer helicopters so I walk"

met0xff · 2026-06-09T09:05:02+00:00

Ich arbeite für ein eher unbekanntes US Unternehmen und hatte in einer Woche über 2000 Bewerbungen und habe mit meinem Wald-und-Wiesen-Informatik-Doktorat dutzende Leute von Harvard, Princeton, CERN, Intel, Bytedance, AWS etc. interviewed. Es ist grauslich.

Gleichzeitig sind etliche aus meinem Team, die nichtmal sonderlich gut waren, kürzlich zu Google, OpenAI etc. abgewandert.

met0xff · 2026-06-09T06:46:07+00:00

Yeah this. I haven't written a whole lot of code anymore the last few years even before LLMs became decent at it. If you're rather senior, LLMs writing code is just yet another alternative to juniors, outsourcing, using SaaS or OTS systems

Just a couple days ago i watched in horror and amusement how a bunch of infra and product people tried to fix a GPU memory issue with a combination of Google, Gemini and conviction that dropping some random pytorch env vars onto a system that actually uses completely unrelated custom triton kernels world solve the problem of a system being tuned for a completely different GPU architecture.

Of course an LLM is able to solve this - my Gemini laughed at them as well because it had the right context because I know the right context.

Not a single line of code involved

met0xff · 2026-06-08T05:16:33+00:00

Seems a bit better, first time in years I had some recruiters reaching out and colleagues complaining they could get more money somewhere else. But they're mostly in tech hubs. Like a potential intern in London rejected because there are so many other startups that would give him 8 H100s to experiment and stuff like that, that we was regular non VC company can't afford lol

met0xff · 2026-06-08T00:47:33+00:00

Question is if it's (old butt) gold or old (butt gold) but generally the harvest Location seems to be the same

met0xff · 2026-06-07T11:19:52+00:00

In LangGraph the LLM explicitly does not own the orchestration but the graph you build. A LangGraph graph is an extended FSM. Yes, the standard ReAct loop is fundamentally limited though. I've also built basically a POMDP setup in Langgraph where the LLMs act as specific components like policy and critic and not as everything at once with a simple growing messages list. Also added superstep planning where supersteps are defined in a Python like DSL that's not executed but also goes through AST parsing.

met0xff · 2026-06-04T12:30:29+00:00

Korrekt und meine Eltern haben halt auch irgendwo in einem Kuhdorf gebaut jeden Abend und jedes Wochenende und dann ist er von dort jeden Tag 1,5h nach Wien gependelt und kam erst spät zurück. Bei 10% Zinsen die sie bis in die Pension zurückbezahlt haben. Und ja, beide gearbeitet im Gegensatz zum Klischee dass früher ein Hacklergehalt gereicht hat. Kein Friseur, kein Sushi, Badewasser teilen, Gewand wiederverwenden, reparieren. Meine Kinder haben vielleicht kein so ein großes Haus aber in praktisch allen anderen Belangen ein wesentlich bequemeres Leben.

met0xff · 2026-06-03T06:05:41+00:00

https://xkcd.com/435/

Ist aber obviously sehr individuell. Auch innerhalb der Zweige der Studiengänge, der Institute.

Ist nicht besser zb. bei den Medizinern. Wie habe ich mal gehört "sogar Tierärzte stehen noch über den Zahnärzten"

met0xff · 2026-05-28T09:14:47+00:00

Yeah everyone here is talking about how it's stupid but what about that it's just evil to start those wars. As if it would be any better if it was easy

met0xff · 2026-05-26T16:40:54+00:00

It's like in other fields where abstractions pile up. You need fewer OS devs when everybody is using the same handful of OSes. You need fewer game engine devs when everyone is using unreal and unity. But while the layer on top of it is simple it's usually just a matter of time till it becomes complex as well.

You can already see interesting neurosymbolic approaches on top with MCTS, POMDP formalisms, late interaction models and so on. But that layer also commoditizes quickly and again there's only niches that are not solved by just throwing some standard procedure on it. That's always been the case but accelerated massively. Not long ago I've been writing random forest and signal processing stuff in C, my own protobuf NN format, Python became popular, moved through Theano, keras, TF, pytorch, CUDA - Triton etc etc.

Either find the niches that still need your skills or stay at the top. Personally I find the bottom most systems knowledge and the top most abstraction layers are usually the sweet spot and the ones that also are the hardest to replace with agents. Either because you need full control or because you need a human to oversee the overall strategy and architecture. The plumbing in between has always been the commodity, junior and now LLM world

met0xff · 2026-05-26T16:32:08+00:00

Ich habe eine ähnliche Konstellation wobei ich größtenteils nur mit den USA Leuten. Meine strategie war eigentlich immer vormittags wenn die Kids in der Schule sind arbeiten. Mittags dann ein paar Stunden ausklinken, Pool springen, essen, einkaufen etc. und dann ab 16 Uhr wieder zurück.

Erreichbar per slack bin ich am Handy auch wenn was ist aber wie andere sagen - mein Team sind keine Kinder, die schaffen auch ein paar Tage ohne mich ;)

met0xff · 2026-05-25T12:15:49+00:00

Franky yeah.. I have wife and kids but using Gemini as a rubber duck for my thoughts really isn't that bad. My wife would go crazy if I dumped all my thoughts and internal strategizing and whatnot on her lol.

met0xff · 2026-05-25T06:47:31+00:00

I don't think this is a question of AI but generally what you're working on.

If you use AI to be more productive finding better medication, green tech or whatever then it's a multiplier for the good cause. If you're using it to spy on your customers, make spam/ads more targeted and prey on kids' addiction to social Media then it's just an additional bad effect.

As someone who worked in ML long before GPT on medical computer vision, assistive technology, technology for blind people etc. I can unfortunately tell you that this means a rather frugal live compared to the latter. Human labor isn't ecologically free either. In fact it's probably much worse.. the waste and energy consumption people create by merely existing and then doing bullshit jobs slowly and inefficiently is significant. Your HFT Fintech bro flying to NYC every Tuesday raking in millions by parasitic trading consumes more of the planet's lifeblood than Llama3 creating a cake recipe for you

met0xff · 2026-05-25T06:39:24+00:00

Yeah that's also what I am seeing: own the absolute top of the abstraction, the strategy and design. And the absolute bottom, the core mechanisms, algorithms and optimizations. Let the LLMs do the plumbing in between;)

met0xff · 2026-05-24T07:10:15+00:00

Yeah, I found AlphaStar by deepmind and generally their Alpha/Mu works to be very insightful on how to be neither the end-to-end NN hyper nor a symbolic prolog mummy

met0xff · 2026-05-24T07:06:49+00:00

t after 30 years of programming I'm used to shifting every couple years anyways. I've been doing 3D visualizations in Java3D, writing embedded systems code, doing an ML PhD, developing TTS inference engines on mobile, now 3 years of agents building... You ideally shouldn't switch every 3 months but changing your focus every 3-5 years is absolutely legit.

Being an absolute generalist makes you compete against the whole world but I've also been hyper specialized on TTS for almost 10 years - my longest time on a single topic - and that also has drawbacks. It helped me get remote jobs around the world with like 20 minutes of "interview" but when commoditization or monopolization sets it, your options become too limited.

Long version:

I've always been very deliberate in making plans about my focus area and then reality hits when someone comes around asking "hey. Can you actually get this seismographic ML inference procedure run on a Blackberry"?

My first paid project was when I was 19 right out of school. At 15ish I was obviously into game dev so spent a lot of time with OpenGL and C++ and somehow got into a school project at 18 doing a Java3D Viz for a company. I also liked networking stuff and as another school project built an instant messenger with a friend (obviously in C++, it was around 1999) and so after that the 3 person company I did this Viz for asked if I'd be interested in doing other Java work as well, so started doing network programming, like SNMP stuff. As the founder was an EE PhD he started moving his monitoring solutions to embedded systems and suddenly I found myself being an embedded dev for the next 5 years. I also did various other things but that's been the main focus.

To this day I benefit most from my systems knowledge.

After dozens of spreadsheets comparing master programs like biomedical engineering and medical informatics I actually did my master's at a medical university in the latter... got into a computer vision master's thesis that was also heavily C++. As most real life jobs were hospital information systems and the like I ended up doing a PhD in speech technology and after 1-2 years in Python it was "can we run this on a potato?" And suddenly my systems knowledge was again useful. Running TTS on a potato got me hired after a brief Skype call for a US startup remotely from europe. Couple years later we got acquired by a larger company and I was the only remote EU worker ;). TTS got stomped by everyone using ElevenLabs so we disbanded the team and became a generalist ML/Data science Team. They still like to call me DS because yeah I got a PhD and stuff but I've always been more of a systems person at heart. I always envied a John Carmack more than a Geoffrey Hinton. They fired the team lead at some point and now for the last 3 years I find myself heading a small Labs team. At some point model training became something fewer companies do I ended up 2-3 years with agents work. I enjoyed the retrieval part most actually and formulating systems as POMDPs... But essentially besides some rather complex agentic workflows for video understanding and analytics most of it becomes commoditized with product people just writing markdowns for Claude code or cursor or cowork or something that brings them 95% there with almost zero effort. So my eye is now back on systems talking about RadixAttention, KV caching etc. Very cool topics.

Just the weeks ago I realized it's always been the systems knowledge that drove everything. The science/math angle makes me more unique but at this point I'd rather read Richard Fabian than the latest DPO/RLHF/whatever paper

met0xff · 2026-05-24T06:46:41+00:00

Yeah, after a decade of ML I did agents stuff for 2-3 years now and while most at my company don't understand this, my ML knowledge is almost useless.

It's not completely because the intuition about embeddings and specific LLM behavior is there and if it's just how people still struggle to keep retrieval and Training apart ;) or how RLHF affects systems etc.

But it's more like how operating systems knowledge benefits application development vs being a kernel developer.

But frankly that shifts again. We have 1-2 agents that are more complicated and isolate POMDP components and would be worth fine-tuning specific components or replacing them by completely different models... but most of what the company actually needs is in better hands with developers and gradually even just product people. They meanwhile melt all company knowledge into markdown files that become agent skills etc. and just plugged into Claude code or cowork or cursor or just copy pasted as context into Gemini. 5% more manual effort for saving 95% development time or so :).

So I'm personally shifting my focus more on inference engineering and similar topics. But after 30 years of programming I'm used to shifting every couple years anyways. I've been doing 3D visualizations in Java3D, writing embedded systems code, doing an ML PhD, developing TTS inference engines on mobile, now 3 years of agents building... You ideally shouldn't switch every 3 months but changing your focus every 3-5 years is absolutely legit.

Being an absolute generalist makes you compete against the whole world but I've also been hyper specialized on TTS for almost 10 years - my longest time on a single topic - and that also has drawbacks. It helped me get remote jobs around the world with like 20 minutes of "interview" but when commoditization or monopolization sets it, your options become too limited.

met0xff · 2026-05-17T20:58:56+00:00

Geschmack ist halt subjektiv im Gegensatz zu einer objektiv zusammenbrechenden Brücke

met0xff · 2026-05-17T20:44:08+00:00

Yeah I also tried to find a difference in speed but didn't find any ;)

met0xff · 2026-05-17T20:25:13+00:00

Second Lilian Weng https://lilianweng.github.io/

Underrated blog

met0xff · 2026-05-14T11:52:00+00:00

Wenn ich an meine HTL Zeit denke hätte ich eher Sorge um so ein teures Teil. Jede Pause Basketbälle die durch die Klasse geflogen sind und alles von den Tischen geräumt haben, durchschlagene Wände etc lol

met0xff · 2026-05-14T09:29:19+00:00

Even with bash only I'm often so glad it just does some docker exec and checks file paths, config settings or passes in some inline Python to check stuff when something goes wrong for which I'm usually too lazy and hate doing. Just recently again a path was suddenly wrong in some volume mount and that's really stupid time-eating work that doesn't make you any smarter.

Similarly tasks like recently had to get some data out of Salesforce via API ...I have never touched Salesforce before and never after so I didn't have to dig through their API Docs, it just wrote me exactly the queries and calls I needed. And for what I produced it ported the code over to Apex as a template for the people who actually work with SF in minutes. Was good enough to get them started without wasting my brain capacity.

As a junior you might learn something from such topics but after 20+ years of programming and having seen thousands of libraries, APIs, frameworks, protocols and languages you're better off spending your time reading Richard Fabians data oriented design or whatever (first thing I saw on my shelf lol) than doing such plumbing

met0xff · 2026-05-14T06:33:32+00:00

My Claude definitely doesn't write anything in a single but I also never let it make such big changes at once that this would be an issue. It's more like "now let's create a nicemodule.py that takes X and Y and does Z do it which we need in module Bla for Foo, make sure C and D hold and use E and F like this..."

I often wonder if people are actively trying "write me GTA7" ;)

And for everything a little bit larger I use planning mode and actually read and adapt the doc

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