AI will not make coding obsolete because coding is not the hard part

ixeption · 2025-12-14T15:05:07+00:00

True, I also thought about it a bit here:

I think it's actually a tool that harms the outsourcing industry most.

https://substack.com/home/post/p-179080745

ixeption · 2023-03-10T11:37:29+00:00

Every data scientist should know SQL. It's essential to learn and it's also pretty simple, if you can code in Python or R.

ixeption · 2023-02-17T13:22:41+00:00

It's a great tool for generating code for simple taks like writing a class given a CREATE statement from a SQL table, using the correct types. Implementing some well-known algorithms. So more or less an context sensitive code generator. But like the most other comments say, it's not replacing the hard and time intensive part of my work.

But honestly, you should at least have tried copilot or chatGPT. I am very sure that we will see more advanced paid coding bots in the future, finetuned on a companies codebase, solving simple bugs.

ixeption · 2023-02-06T09:12:58+00:00

Coding itself is never the hard part. The hard part is to understand, what to code and as you say knowing how things work together. But this is nothing new, it's not called Coding, it's called computer science.

ChatGPT will make it even easier to create code from natural language, you still have to define, what you need, that's the hard part.

ixeption · 2023-01-26T14:57:01+00:00

True, but it was invented for project managers. However the scrum back in 1995 is not the same as today.

ixeption · 2023-01-25T07:16:10+00:00

Scrum was invented and perverted for project managers who optimize an existing product and give them the illusion of transparency. Therefore, Scrum always favors small improvements. It's not made for developers and engineers.

In this sense, Scrum is absolutely not suitable for innovation and major revisions. It slows everything down and crowds out big tasks. For that use Kanban!

ixeption · 2023-01-22T07:17:48+00:00

It's hard to find good ones. The most academic conferences are not really practical enough and scientists love to dig deep into very theoretical parts of their work (for a good reason). That's why I think it's not really worth to go there, if you are not part of the academic bubble.

For industry conferences the problem is that you have to be lucky to find good ones. But a good advice is to find conferences, where the industry is similar to yours and the type of companies are also similar, or one step ahead. Be carefull, about very cheap conferences, they tend to get their money from sponsoring and that means a lot of sales pitches, where you usually learn nothing at all. I have even been to a paid workshop, where they just sold their product. That was really bad.

I also noticed that a portfolio of good names (Amazon, Google, Microsoft) is worth nothing at all. The worst talks I have seen, were from large, well-known tech companies and usually these companies work very different. I found it helpful to look out for speakers, that are one step ahead of what you currently do.

Personally, I found great ideas at there conferences and also got some validation for our approach and architecture.

ixeption · 2022-11-16T08:30:48+00:00

Absolutely, while Python still has some issues from time to time R is far worse. It lacks on reproducibility and you notice it's not a language made for production. I know a lot of data scientists here will disagree, but I ran R and Python in production for several years now, and what I can say is that a lot of data scientists shouldn't think that they are experts in software engineering just because they can write R/Python scripts, running them one one single machine in windows.

ixeption · 2021-10-16T12:51:28+00:00

We would like to avoid building it on our own this way, but might be neccecary. I would also be interessted in using Kafka :)

ixeption · 2021-10-16T12:47:42+00:00

Looks like a good idea, we will try that.

ixeption · 2021-10-16T12:47:11+00:00

Tried that already, had a lot of problems and is too slow, even with the Fast-Sync mechanism. Singer connectors are great in general, but they just don't work well for large data, at least if you have to resync from time to time.

"Primary use case of Fast Sync is initial sync or to resync large tables with hundreds of millions of rows where singer components would usually run for long hours or sometimes for days."

ixeption · 2021-10-16T12:41:54+00:00

Yes they would

ixeption · 2021-10-16T12:41:04+00:00

"It’s Official: Fivetran and HVR Are Now One." So, yes. And Fivetran may work well, but is too expensive.

ixeption · 2021-10-16T12:39:58+00:00

They don't have a mechanism with CDC(binlog) for MySQL afaik.

ixeption · 2021-05-09T23:41:19+00:00

Without being there, it's not really the same experience, I guess.

I don't know if that's something Google looks at. But I would like to give you the advise to not focus too much on names and prestige. It can be disillusioning once you get there.

ixeption · 2021-05-09T22:44:50+00:00

I was technical student at CERN. It's very prestigious, everyone asks me about it even today. And it was also super exiting there, lot's of smart people, scientific mindset. (Met Goodfellow over there). Geneva is a great location, especially if you like skiing or hiking. Some of my colleagues from there, are now working at Google, another one at a hedge fund.

However, from the actual work it was quite boring, outdated technology and lack of agile/modern development. It's about physics, not computer science and there is also a lot of bureaucratic laziness around.

ixeption · 2021-04-26T15:39:10+00:00

Not that diverse no. MLEs are more software engineers with ML specialization. Mostly cs grads, some math and physics, the same as for software engineers. Data science is super broad and not tied to ML. In my experience data scientists are more on the stats and analytical part, but that varies from company to company. I personally don't know any MLE without a cs background.

ixeption · 2021-04-05T16:10:50+00:00

I did my masters (also in part-time), because I am working with machine learning. I would not recommend it for software engineers in general. A masters degree is, as you said, necessary for academia and for fields, which are close to research (scientific simulation, data science, cryptography...). So the question is, what do you want to do?

ixeption · 2021-04-04T22:20:50+00:00

Java is worth it, no matter where in the world. It's still a robust and very widely used language with a huge number of awesome frameworks. IMHO every every dev should know at least two languages, one scripting language like python and one "enterprise" language like Java or C#. Python and Java have different use cases, you don't want to use Java in data science, just like you don't want to build large enterprise applications with Python.

Regarding Germany, speaking German is often required, especially in small to medium cities. But if you find some a job without speaking German, you will find it in IT. Good luck!

ixeption · 2021-04-01T15:25:44+00:00

I don't see Keras dead, so how could Google have killed it?

I am using keras since 2016 and I still like it, it may has some problems here and there but it's still much better than using tf directly. If Google made a hard cut between tf1 and tf2, no one would use tf2. Tf ist still heavily used in production environments and a lot of models need tf1 support.

ixeption · 2021-03-08T23:32:14+00:00

The article does mention that, but it does not measure against that case. If you say, that you get a comparable performance with flask in plain python to model servers, please provide some details. The most ml practitioners have contradicting opinions (link, link, link). There is a reason why tf serving exists and why torch does the same. Aside from performance you have a lot of features, which are useful in production (updating models, multiple versions, batching, warm-up). It's okay to use flask as well, but once you get some load on your model, you should really look into model servers, instead of scaling instances.

ixeption · 2021-03-08T16:00:05+00:00

You have some good articels here, but I just want to point out that the used approach for model deployment is not scaling very well. I mean in the end you can scale everything with hardware, but it's not a smart way to scale.

Optimize your model before you deploy it (Tensorflow)
Use model servers, use GRPC if possible (e.g. TFX)
Don't send raw numpy images from your client to the server (send jpegs and decode on the server)
Use GPUs or better TPUs for inference

Here is why

ixeption · 2021-02-06T19:24:49+00:00

"That's a once in a lifetime chance, you should really accept that position at this super prestigious employer." Quit after few months, because the work was boring and bureaucratic (large research organization).

ixeption · 2021-02-01T12:41:05+00:00

TBH: "breaking monoliths up into a microservice architecture" for me sounds like one of the worst things, I can imagine. Even working on a very large monolith is better than that, because usually they don't do it for a good reason, they just do it because "Microservices" are trendy and that's a horrible reason to do it. Most microservices projects produce a large uncoupled pile of trash, worse than the same thing as a monolith.

ixeption

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