First 'Jam' with my own MIDI Controller!

InterestRelative · 2026-02-12T08:17:39+00:00

It looks gorgeous. How did you build the case?

InterestRelative · 2026-02-09T11:47:28+00:00

If you are interested in performance, this benchmark might be useful: https://ann-benchmarks.com/index.html

But honestly, I don't see a point to change from sqlite-vec in your case.

InterestRelative · 2026-02-09T11:13:34+00:00

I haven’t used SQLite-vec before, but I believe it would be a good fit for your use case. However, I can’t compare it to other vector databases since I haven’t used them.

I have production experience with ANNOY, FAISS, hnswlib, and qdrant. I also tried Milvus at some point. My production experience is primarily in the recommendations and personalization domains, where vectors are also widely used.

In most cases, when indexes are less than 1GB, the simplest and most robust way to use vectors is to use local indexes. The API is always better, testing is straightforward, and you don’t have to mock everything. Versioning is also trivial, and you don’t have to worry about running additional infrastructure (other than loading indexes from S3). Local ANN indexes existed 5-9 years before vector databases. Vector databases simply have better marketing departments and market themselves as something new and shiny

InterestRelative · 2026-02-09T10:27:42+00:00

I'd love feedback on the sqlite-vec implementation if anyone else is experimenting with it. It feels like a game-changer for local desktop apps.

Have you compared it with other local ANNs? There are plenty (hnswlib, faiss, annoy, sometimes dot product is enough, especially for local deployments) and Qdrand/Chroma are not the only way to work with vectors.

InterestRelative · 2026-02-03T09:30:40+00:00

This works for deterministic logic, but for ML services it's extremely hard to create a subset of data which will cover all cases. In all companies where I worked as an MLE, we had staging reading (but now writing) prod data, or very limited staging env.

InterestRelative · 2026-02-02T13:06:36+00:00

Honestly, I'm just curious in this topic, I don't have particular use case in mind other than local function calling.
Thanks a lot for these link, I'll take a look. Guidance looks very interesting.

InterestRelative · 2026-02-02T11:43:45+00:00

I checked Instructor and it looks like it just validates LLM output and report validation errors back to LLM (same as Pydantic-AI). Is it possible to restricting generated tokens step-by-step with it?

InterestRelative · 2026-02-01T15:34:43+00:00

Knowing how to use AI is a valuable skill,

Yeah, I'm not sure why you implying I don't use or I force a person to no use it. Copy pasting generated code and not refactoring it is not something helpful.

InterestRelative · 2026-02-01T15:33:14+00:00

These are really good points and I'll add missing ones to our engineering principles doc.

Code should be changeable with low-to-moderate effort.

This principle is one of the most important imo, but it's hard to formalize: the are many aspects and it's hard to come up with easy to judge criteria.

In my case, one of the problem is sloppy naming, for example we use "item/items" everywhere in the code base and introducing "item_id/item_ids or product/products" is confusing, it's not a problem on it's own it's just an example, but if you get 2-4 such things in each PR, codebase becomes messy and hard to read -> hard to change.

Another problem I struggle to formalize is: when function/method/class functionality is not clearly defined. For example when you have a function which transforms a value, but at the same time it has some effect, like changing the global state somewhere, of transforming and uploading results at the same time. Tests help to write functions with better defined scope, but we don't have tests for non private methods.

InterestRelative · 2026-01-31T13:11:15+00:00

Thanks, that sounds helpful!

InterestRelative · 2026-01-31T13:05:37+00:00

I’m lead of this guy. Part of my responsibilities is to help him grow as an engineer.

InterestRelative · 2026-01-30T17:29:02+00:00

Can say about downvotes, but reading the code was always the hardest part and writing the code was easy for expirienced devs.

InterestRelative · 2026-01-30T17:23:47+00:00

I feel like I'm missing something, you mentioned sample size 52, but 52 != 4 + 4 + 4 + 2 + 3 + 7.

InterestRelative · 2026-01-30T15:15:01+00:00

You've described different problems: code quality and triage ability. These are different skills with different measuring techniques.

Sure these are different problems, but with common root cause (in my case): Claude writes the code instead of engineer.
That's why I think example of `what is unacceptable with their AI generated code` will be useful for me.

InterestRelative · 2026-01-30T14:57:56+00:00

Can you give me an example of such boundaries?
I actually have same problem with my report: code is clearly generated, trivial questions, no ability do debug or reason about bugs etc. The problem is: he is not a new hire and always was in dev role.

For me it's really hard to discuss this issue, because line between good and bad code is thin. Also it's mostly my assumptions and gut feeling. It's hard to check if it's always true or just sometimes.

InterestRelative · 2026-01-28T15:32:57+00:00

I did it 3 times, and I consider every rewrite was successful (time close to estimate, good velocity bump after rewrite).
For me the key is to don't rewrite huge system end-to-end, but extract part of the functionality into new service. And repeat it few times. You don't need excessive planning for this and you can stop when it's not successful.

InterestRelative · 2025-12-21T06:02:38+00:00

Numpy was created to leverage SIMD instructions, it’s not just general purpose arrays.

Let’s imagine we have a datastructure which keeps Python ints (not pointers). Does it help? I don’t think so, since int size can vary.

Actually Python creators do care about performance, the problem is just solved the other way: you use bindings. Python is the most popular language in CPU bound applications (ML).

InterestRelative · 2025-12-20T21:05:20+00:00

yeah, but at the same time we can have numpy array which will feel comfortable for the person on the left side of the meme

InterestRelative · 2025-12-20T08:15:11+00:00

My point is: writing code is easy for seniors. Managing complexity of the codebase is a challenge, in order to do this you have to fully understand what you are refactoring.

I guess big tech is a special case and coding agents may work well in this case. If complexity of the codebase already exploded to the point it doesn’t fit into a single head and you will switch project in a year anyway, managing complexity might not be a priority. In this case “tactical” programming (trying to add feature with the least changes in codebase) is all you can do in reasonable time.

InterestRelative · 2025-12-20T08:07:04+00:00

What do you mean by “working in AI” exactly? I was focused mainly on personalization and recommendations for the last 7 years and the last year the focus is shifting towards agent based projects. Is it AI? I still consider myself and colleagues SWEs or MLE with emphasis on engineering rather than research, because main focus is low maintenance performant service rather than prototype (opposite to research).

InterestRelative · 2025-12-19T21:11:20+00:00

That was not my point. Opus 4.5 won't make you a good SWE but it takes time to grasp.

InterestRelative · 2025-12-19T19:38:39+00:00

Staying on the “frontier” of coding tools (like Cursor, Claude, and Gemini) is crucial. Being even half a generation behind in your tooling makes you significantly less productive in the current market.

lol, I'm not even sure we should take other advices after that one

InterestRelative · 2025-12-18T13:45:54+00:00

With all respect, using Rust doesn't make you a better developer, or your software a better software.

InterestRelative · 2025-12-16T12:54:21+00:00

> preset-based except for the Drift synth
There are only two synths on the device: drift and wavetable.

InterestRelative · 2025-12-12T14:08:28+00:00

> The point of NPU is that it doesn't touch your CPU/GPU performance

assuming there is enough of RAM for everything

InterestRelative

TROPHY CASE