afiodorov

  Location: Las Palmas, Spain
  Remote: Yes
  Willing to relocate: Yes (can work in EU & UK)
  Technologies: Go, Python, Kubernetes, AWS, Apache Spark, Airflow, LangGraph, RAG, gRPC, PostgreSQL, Docker
  Résumé/CV: https://cv.fiodorov.es/
  Email: hn@fiodorov.es

A RAG search accross all HackerNews comments is back online with a new design: https://hn.fiodorov.es/

The running costs are very low. Since posting it today we burned 30 cents in DeepSeek inference. Postgres instance though costs me $40 a month on Railway; mostly due to RAM usage during to HNSW incremental update.

Daily updates I do on my m4 mac air: takes about 5 minutes to process roughly 10k fresh comments. Historic backfill was done on an Nvidia GPU rented on vast.ai for a few dollars. If I recall correctly took about an hour or so. It’s mentioned in the README.md on GitHub.

I've been embedding all HN comments since 2023 from BigQuery and hosting at https://hn.fiodorov.es

Source is at https://github.com/afiodorov/hn-search

Apparently Persian and Russian are close. Which is surprising to say the least. I know people keep getting confused about how Portuguese from Portugal and Russian sound close yet the Persian is new to me.

I've found that building my side projects to be "scalable" is a practical side effect of choosing the most cost-effective hosting.

When a project has little to no traffic, the on-demand pricing of serverless is unbeatable. A static site on S3 or a backend on Lambda with DynamoDB will cost nothing under the AWS free tier. A dedicated server, even a cheap one, is an immediate and fixed $8-10/month liability.

The cost to run a monolith on a VPS only becomes competitive once you have enough users to burn through the very generous free tiers, which for many side projects is a long way off. The primary driver here is minimizing cost and operational overhead from day one.

Data all-rounder with 10 years building everything from low-latency Go microservices to training ML models to large-scale AWS data pipelines. Looking for a senior, autonomous role at a small company/startup.

  Location: Las Palmas, Spain
  Remote: Yes
  Willing to relocate: No
  Technologies: Go, Python, SQL, Kubernetes, Docker, AWS (S3, EMR, RDS, Aurora, Athena), Apache Spark, Apache Airflow, TypeScript, React, gRPC, REST APIs, PostgreSQL, Google BigQuery, LangChain, LangGraph, RAG, faster-whisper
  Résumé/CV: https://cv.fiodorov.es
  Email: hn@fiodorov.es

RAG search that contains all HN comments since 2023

https://hn.fiodorov.es

I treat it more like a homework exercise for a Coursera course but I like the result.

It was uncomfortable at first. I had to learn to let go of reading every line of PR code. I still read the tests pretty carefully, but the specs became our source of truth for what was being built and why.

This is exactly right. Our role is shifting from writing implementation details to defining and verifying behavior.

I recently needed to add recursive uploads to a complex S3-to-SFTP Python operator that had a dozen path manipulation flags. My process was:

* Extract the existing behavior into a clear spec (i.e., get the unit tests passing).

* Expand that spec to cover the new recursive functionality.

* Hand the problem and the tests to a coding agent.

I quickly realized I didn't need to understand the old code at all. My entire focus was on whether the new code was faithful to the spec. This is the future: our value will be in demonstrating correctness through verification, while the code itself becomes an implementation detail handled by an agent.

  sign of serious organizational disfunction.

The reality is, most engineers spend far less than half their time writing new code. This is where the 80/20 principle comes into play. It's common for 80% of a company's revenue to come from 20% of its features. That core, revenue-generating code is often mature and requires more maintenance than new code. Its stability allows the company to afford what you call "dysfunction": having a large portion of engineers work on speculative features and "big bets" that might never see the light of day.

So, while it looks like a bug from a pure "coding hours" perspective, for many businesses, it's a strategic feature!

I live next to an abandoned building from the Spanish property boom. It's now occupied illegally. Hype's over yet the consequence is staring at me every day. I am sure it'll eventually be knocked down or repurposed yet it'd be better had the misallocation never happened.

The crux of the article is asking whether such a large investment is justified; downplaying the article saying it's only X% of the GDP compared to Y doesn't address the issue.

Nice clip yet it does not make it clear why 9% is the good value of GDP. Why not 7%?

My rule of the thumb is that Dataclasses are for compile-time type-checking, pydantic classes are for run time type checking.

You're right, they can try to manipulate you on a thousand tiny things. My counter-argument is that at a certain point, it's not worth the mental energy to fight over what amounts to pennies on the dollar.

Anecdotally, when I bought my car recently, they forgot to even offer me the extended warranty they'd planned to push. I find it funny to think it was so minor, even they forgot to care.

Let's be clear: it's entirely possible to leave the "herd". People can and do go completely off-grid and thus disengage from capitalism. The crucial point is that the vast majority of us choose not to. That choice is what makes your "slowest antelope" analogy so much more complex.

An antelope's greatest desire is to be in the herd, because while it may contain a lion, the world outside contains a thousand wolves.

We've built a herd—society—that is incredibly effective at holding those wolves at bay: famine, plague, and chaos. We willingly participate because it provides "shields" our ancestors could only dream of. The problem isn't the herd itself; it's the lion that we allow to stalk within it.

What I am suggesting isn't to abandon this safety and comfort brought by modern capitalism. It's to improve the herd—to enjoy its protections while finding ways to tame, cage, or evade the lion of exploitation. What we're discussing here aren't futile attempts to escape, but vital tactics for building a better, safer herd for everyone.

There's nothing an algorithm can do against disciplined, intentional engagement.

If you know which car you want to buy it doesn't matter what the salesman has to say.

We should not underestimate the timeless human response to being manipulated: disengagement.

This isn't theoretical, it's happening right now. The boom in digital detoxes, the dumbphone revival among young people, the shift from public feeds to private DMs, and the "Do Not Disturb" generation are all symptoms of the same thing. People are feeling the manipulation and are choosing to opt out, one notification at a time.

We've been visited by alien intelligence that is simultaneously fascinating and underwhelming.

The real issue isn't the technology itself, but our complete inability to predict its competence. Our intuition for what should be hard or easy simply shatters. It can display superhuman breadth of knowledge, yet fail with a confident absurdity that, in a person, we'd label as malicious or delusional.

The discourse is stuck because we're trying to map a familiar psychology onto a system that has none. We haven't just built a new tool; we've built a new kind of intellectual blindness for ourselves.

If you're hiring 16 year olds just because of their ability to write code sounds like you're bottlenecked by writing code. Your comment doesn't clarify why you disagree.

I think the "more capital than ideas" problem is highly contextual and largely a Silicon Valley-centric view.

There is immense, unmet demand for good software in developing countries—for example, robust applications that work well on underpowered phones and low-bandwidth networks across Africa or Southeast Asia. These are real problems waiting for well-executed ideas.

The issue isn't a lack of good ideas, but a VC ecosystem that throws capital at ideas of dubious utility for saturated markets, while overlooking tangible, global needs because they don't fit a specific hyper-growth model.

Even without LLMs, we were approaching a point of saturation where software development was bottlenecked by market demand and funding, not by a shortage of code. Our tooling has become so powerful that the pure act of programming is secondary.

It's a world away from when the industry began. There's a great story from Bill Gates about a time when his ability to simply write code was an incredibly scarce resource. A company was so desperate for programmers that they hired him and Paul Allen as teenagers:

  "So, they were paying penalties... they said, 'We don’t care [that they are kids].' You know, so I go down there. You know, I’m like 16, but I look about 13. They hire us. They pay us. It’s a really amazing project... they got a kick out of how quickly I could write code."

Source: https://youtu.be/H1PgccykclM?si=YuIFsUcWc6sHRkAg

I heard that actors & singers don't necessarily manage to fix the accent in the natural speech so they can only recite extracts perfectly well.

European Portuguese sounds very Slavic; I'm sure Russians have a blast with it. English is a phonetically isolated language, largely due to the Great Vowel Shift. Unlike English, most languages have a closer linguistic relative. This makes English challenging for most people to learn, and it also makes it difficult for native English speakers to learn a foreign language without a heavy accent.

When you spend some time transcribing live, impromptu speech, you'll notice that it often doesn't follow the rules of written grammar; speakers frequently abandon sentences midway through.

For example, in the linked clip[^1], the speaker says:

  "uh the European Union uh that's not a US creation that's a you guys creation so don't ex..[abandoned word] the strength of the west [abandoned sentence] and the west is a really I don't know what"

[^1]: https://youtu.be/_hBd8w-Hlm4?si=7-kvpUoeYo5ODPiI&t=787

The phonetic similarity between Russian and Spanish is a huge relief. As a Russian speaker, pronouncing English has always felt like a workout for my mouth; the sounds are completely alien. Spanish, on the other hand, is effortless. It just flows, since I'm using the same phonetic toolkit I grew up with.

There's another level after fluency (C1), which is near-native fluency (C2). At the level of such mastery you don't feel the need to simplify just to be understood, your utterances now define the language itself as you've achieved the level of the crowd whom the language belongs to in the first place.

P.S. I've typed this out in English after having achieved such unlock.

I use my third language, Spanish, every day, and my second, English, for work. On top of that, my partner is a native Portuguese speaker, so I'm passively soaking up a fourth. (I usually reply to her in Spanish, but we watch everything in Portuguese—though this month it's been all Italian, just for fun).

To this day, I still find Spanish a bit more challenging than my native language or even English. I think it's because even though I moved to Spain over seven years ago, I never fully immersed myself in the culture. I'm pretty sure I haven't read a single book in Spanish.

I still do that classic thing non-fluent speakers do: I'll get halfway through a sentence, realize I don't know a specific word, and have to rephrase my thought more simply. To be clear, I'm far from a beginner, just not yet fluent.

Anyway, I can attest that grappling with a language you haven't quite mastered is a daily mini-puzzle that definitely keeps the brain working a bit harder than it otherwise would.

On a side note, I love that LLMs can handle so many languages now. After 17 years of living abroad, I still feel most at ease speaking my native language, Russian, even though my vocabulary is a bit lacking these days for more complex topics. It makes me completely understand why people prefer to receive medical care in their native tongue.

I am not saying rewrites are always warranted, but I think LLMs change the cost-benefit balance considerably.