MurphyA/hacker-news huggingface dataset:
by tptacek | 2025-06-03 01:44:33+00:00
Yes. It's this "next token" stuff that is a total tell we're not all having the same conversation, because what serious LLM-driven developers are doing differently today than they were a year ago has not much at all to do with the evolution of the SOTA models themselves. If you get what's going on, the "next token" thing has nothing at all to do with this. It's not about the model, it's about the agent.
by efitz | 2025-06-03 01:44:35+00:00
Exactly.

What I’ve been saying to my friends for the last couple of months has been, that we’re not going to see coding jobs go away, but we’re going to run into a situation where it’s harder to grow junior engineers into senior engineers because the LLMs will be doing all the work of figuring out why it isn’t working.

This will IMO lead to a “COBOL problem” where there are a shortage of people with truly deep understanding of how it all fits together and who can figure out the line of code to tweak to fix that ops problem that’s causing your production outage.

I’m not arguing for or against LLMs, just trying to look down the road to consequences. Agentic coding is going to become a daily part of every developer’s workflow; by next year it will be table stakes - as the article said, if you’re not already doing it, you’re standing still: if you’re a 10x developer now, you’ll be a 0.8x developer next year, and if you’re a 1x developer now, without agentic coding you’ll be a 0.1x developer.

It’s not hype; it’s just recognition of the dramatic increase in productivity that is happening right now.
by polishdude20 | 2025-06-03 01:44:52+00:00
Maybe if you see this web of relationships connected to you and your job is to please everyone, how about zooming in and picturing the web that is inside of you?

Have you heard of parts therapy? It operates on the idea that we "contain multitudes" that all are trying to do their best for us. If you learn to include parts of yourself in this web of relationships, where different parts of you are distinct "people" that need pleasing, you may start "pleasing" yourself more often?

Like, I'm imagining zoomed out, there's nuancebydefault4's circle in the middle and everybody in your life is also a circle. You're connected by lines in this web. But zoom in and you can see that inside your circle, is a web of relationships of different parts of you. The part that needs love, the part that needs intellectual stimulation, the part that needs rest etc.

Anyways just a post run thought Im having while the endorphins are kicking in...
by DavisKolakowski | 2025-06-03 01:45:25+00:00
Location: Williamsport, PA

Remote: Yes

Willing to relocate: No, unless for the right opportunity

Technologies: JavaScript, TypeScript, C#, TSQL, HTML, CSS, React, ASP.NET Core, .NET 8, Vue.js, Blazor, SQL Server, PostgreSQL, Azure, Azure DevOps, RESTful APIs, Microservices, ETL Pipelines

Résumé/CV: https://daviskolakowski.com

Email: contact@daviskolakowski.com
by Animats | 2025-06-03 01:45:32+00:00
I tried Github Copilot, free mode, on #1. The Python code outsourced all the hard work to numpy and pymeshlab, which is fine. Copilot wrote code to generate triangles. It's a reasonable job. Copilot's knowledge of what to call is better than mine. I have to try it now, but it did OK.

On #2, it set up the problem, but bailed on the gap filling part with

# (This is a complex step; see note at bottom.)

That's not surprising, since I didn't tell it how to solve the problem. Can any of the premium systems do #2?
by mediaman | 2025-06-03 01:45:52+00:00
Just like some people who wrote long sentences into Google in 2000 and complained it was a fad.

Meanwhile the rest of the world learned how to use it.

We have a choice. Ignore the tool or learn to use it.

(There was lots of dumb hype then, too; the sort of hype that skeptics latched on to to carry the burden of their argument that the whole thing was a fad.)
by StackOptimist | 2025-06-03 01:46:03+00:00
Location: India Remote: Yes Willing to relocate: Yes Technologies: Go, Kubernetes, Terraform, AWS, GCP, TypeScript, React, Kafka, PostgreSQL, Temporal Resume: https://linkedin.com/in/prasanthmj/ Email: prasanthmj[at]gmail.com Portfolio: https://prasanthmj.github.io/

10+ years of product development experience, primarily focused on backend engineering and platform development.

Core expertise in: - Building and scaling microservices architectures using Go - Kubernetes (CKA and CKAD certified) and cloud infrastructure - System design and architecture - DevOps and CI/CD pipeline development

I'm particularly interested in backend roles where I can leverage my experience in Go, Kubernetes, and distributed systems. While I'm comfortable with DevOps, I prefer focusing on core product development. I bring strong experience in both startup and enterprise environments, with a focus on delivering quality, maintainable solutions.

Looking for full-time/part-time remote positions with teams that value technical excellence and sustainable development practices.
by dataguy_ | 2025-06-03 01:46:48+00:00
Location: South America

Remote: Yes

Willing to relocate: Yes

Technologies: PostgreSQL, GCP, BigQuery, Looker Studio, Python, FastAPI, ElasticSearch, Neo4j

Résumé/CV: Please email me

Email: nothingcomeseasy@protonmail.com

3+ years as a Data Analyst and Generalist with strong experience in analytics, pipelines, and product-oriented data work.

Past year working with semantic search, document parsing, entity extraction, audio transcription, and classification workflows using LLMs.

Highlights:

Semantic search and re-ranking (Neo4j + Elasticsearch + embeddings)

LLM + OCR pipelines for extracting structured data from PDFs/images

Whisper-based audio transcription with post-processing and hallucination detection

Ran model experiments and published internal docs + dashboards (Looker Studio, BigQuery)

Always happy to collaborate on practical, data-driven problems. Feel free to reach out!
by BigCatStuff | 2025-06-03 01:46:53+00:00
I believe Youtube videos are a great resource for learning this! I picked up electronics and computer/gpu repair as a hobby over the pandemic lockdowns.

A cheap multimeter and a cheap soldering iron/hot air station combo will get you very far in the hobby. I enjoy these channels:

MyMateVince (a guy who fixes many useful household items): https://www.youtube.com/user/mymatevince

StezStixFix: https://www.youtube.com/@StezStixFix

Electronics Repair School (more advanced, he fixes laptops, tvs, and sometimes gpus): https://www.youtube.com/@electronicsrepairschool
by mdnahas | 2025-06-03 01:47:04+00:00
I wonder if that will make the great generation of human coders. Some of our best writers were the generation that spanned between oral education and mass production of books. Late generations read and wrote, rather than memorized and spoke. I think that was Shakespeare’s genius. Maybe our best coders will be supercharged with AI, and subsequent ones enfeabled by it.

Shakespeare was also popular because he was published as books became popular. Others copied him.
by lixiasky | 2025-06-03 01:47:18+00:00
Great question! I chose Go mainly because it's simple, efficient, and widely used — and honestly, it's the language I'm most comfortable with right now.

I'm still a student, and I don’t have super big ambitions yet — I just wanted to build something I could actually finish and understand

Rust is amazing, but I haven’t started learning it seriously yet. It feels a bit overwhelming at this stage. Maybe one day, when I'm ready to dive deeper!

Good luck with your Burp project too — I’d love to see it if you share it someday!
by userbinator | 2025-06-03 01:47:23+00:00
I use "dynamic finger allocation", close to the standard fingering but not exactly. It's hard to describe as anything other than "closest finger that isn't currently in use". My burst speed is over 200 (peaked roughly 240) but I usually stay around 160-170 in 1-minute tests.

Try typing words like "number", "bus", "zany", "excretion", etc. and you'll soon learn why sticking with the "proper" fingering is absolutely idiotic if you want speed and comfort. You can waste time and effort moving the "right" finger all the way from the bottom to the top of the keyboard, or just hit the key with the perfectly usable one sitting there idle.

To elaborate, when typing "number" I use the index and middle finger to chord "nu", then "mb" is the next chord with the left index and right middle, which naturally pulls down my left hand enough that "er" is easily done as a final chord with the left ring and middle fingers.
by skissane | 2025-06-03 01:48:13+00:00
Right, but the “finite setting” is the real world. Yes, if we talk about C++ as a mathematical abstraction, maybe a C++ program containing a googolplex tokens could be in a theoretical sense syntactically and semantically valid. But such a program can’t exist in the real world

Let’s say a compiler has a (very plausible) implementation restriction that the source code of a program can be no longer than 2^32 bytes. Then, even if the abstract grammar is (e.g.) an unbounded context-free language, the actually accepted language has a finite upper bound on length-and it is well-known that applying a length constraint turns a context-free language into a regular language. This doesn’t require us to “to suitably alter most of these concepts to make sense in the strictly finite setting”, because formal language theory in an infinite setting permits constraining languages to be finite, and we have a very good understanding of what that the results of that are - this doesn’t require any new or different math, just applying the same math.

Now, it is true that, we will say that certain algorithms only work for regular languages, not context-free - but once we impose a finite bound on length, those algorithms do actually work in principle. In practice, of course, they are likely impractically slow - but the formalism we are talking about (the Chomsky hierarchy) is based on computability (can it answer the question in a finite but unbounded amount of time), not asymptotic computational complexity (nor real-world performance, which isn’t the same thing, as e.g. galactic algorithms demonstrate)
by Retr0id | 2025-06-03 01:48:20+00:00
YouTube. Pick any piece of consumer electronics and you'll find videos of people diagnosing and repairing it, at both hobbyist and pro-repair-shop level.

The Nintendo Switch Lite is a fun piece of hardware because they're cheap to buy used/broken and there are many opportunities for component-level repair.

Some great written info (specific to the switch) here:

https://www.retrosix.wiki/first-stage-boot-short-checks

https://repair.wiki/w/Nintendo_Switch
by mikegreenberg | 2025-06-03 01:48:25+00:00
FIRST and most of its teams are very open to collaborating and supporting rookies new to the org. I recommend reaching out to as many nearby teams as possible and see what support they can provide. Any serious teams are motivated to help as this directly contributes to their team's success during the competitive season. Competitions require a teams to have a pro-social aspect to their operations. Helping other teams is a strong signal in this area.

Additionally, find a nearby FRC competition and volunteer for at least one event (do more, if you can). Wander the robot pit and interact with the teams. There will be a lot of good intel for you there just wandering around and asking questions.

Source: Am volunteer judge for FRC.
by tptacek | 2025-06-03 01:48:38+00:00
This is cope. I know my own experience, and I know the backgrounds and problem domains of the friends I'm talking to that do this stuff better than I do. The productivity gains are real. They're also intuitive: if you can't look at a work week and spot huge fractions of work that you're doing that isn't fundamentally discerning or creative, but rather muscle-memory rote repetition of best practices you've honed over your career, you're not trying (or you haven't built that muscle memory yet). What's happening is skeptics can't believe that an LLM plus a couple hundred lines of Python agent code can capture and replicate most of the rote work, freeing all that time back up.

Another thing I think people are missing is that serious LLM-using coders aren't expecting 100% success on prompts, or anything close to it. One of the skills you (rapidly) develop is the intuition for when to stop a runaway agent.

If an intern spun off hopelessly on a task, it'd be somewhat problematic, because there are finite intern hours and they're expensive. But failed agent prompts are nickel-denominated.

We had a post on the front page last week about someone doing vulnerability research with an LLM. They isolated some target code and wrote a prompt. Then they ran it one hundred times (preemptively!) and sifted the output. That approach finds new kernel vulnerabilities!

Ordinary developers won't do anything like that, but they will get used to the idea of only 2/3 of prompts ending up with something they merge.

Another problem I think a lot of skeptics are running into: stop sitting there staring at the chain of thought logs.
by bccdee | 2025-06-03 01:49:01+00:00
To quote an excellent article from last week:

> The AI has suggested a solution, but the added code is arguably useless or wrong. There is a huge decision space to consider, but the AI tool has picked one set of decisions, without any rationale for this decision.

> [...]

> Programming is about lots of decisions, large and small. Architecture decisions. Data validation decisions. Button color decisions.

> Some decisions are inconsequential and can be safely outsourced. There is indeed a ton of boilerplate involved in software development, and writing boilerplate-heavy code involves near zero decisions.

> But other decisions do matter.

(from https://lukasatkinson.de/2025/net-negative-cursor/)

Proponents of AI coding often talk about boilerplate as if that's what we spend most of our time on, but boilerplate is a cinch. You copy/paste, change a few fields, and maybe run a macro on it. Or you abstract it away entirely. As for the "agent" thing, typing git fetch, git commit, git rebase takes up even less of my time than boilerplate.

Most of what we write is not highly creative, but it is load-bearing, and it's full of choices. Most of our time is spent making those choices, not typing out the words. The problem isn't hallucination, it's the plain bad code that I'm going to have to rewrite. Why not just write it right myself the first time? People say "it's like a junior developer," but do they have any idea how much time I've spent trying to coax junior developers into doing things the right way rather than just doing them myself? I don't want to waste time mentoring my tools.
by lixiasky | 2025-06-03 01:49:07+00:00
Thanks a lot for sharing this — it's super helpful!

Yeah, I’m currently using gopacket mainly to get something working fast, but I’ve been thinking about writing my own parsers from scratch to understand the protocols better.

Your Hdr example is really clean — definitely saving this as reference! I love how direct and readable it is.

I’ll definitely try going lower level when I revisit the packet layer logic. Thanks again for the nudge
by tguvot | 2025-06-03 01:49:17+00:00
i tried to use chatgpt month ago to find systemic fungicides for treating specific problems with trees. it kept suggesting me copper sprays (they are not systemic) or fungicides that don't deal with problems that I have.

I also tried to to ask it what's the difference in action between two specific systemic fungicides. it generated some irrelevant nonsense.
by steveklabnik | 2025-06-03 01:49:23+00:00
People make productivity arguments for using various languages all the time. Let's use an example near and dear to my heart: "Rust is not as productive as X, therefore, you should use X unless you must use Rust." If using LLMs makes Rust more productive than X, that changes this equation.

Feel free to substitute Y instead of Rust if you want, just I know that many people argue Rust is hard to use, so I feel the concreteness is a good place to start.
by prmph | 2025-06-03 01:49:32+00:00
Not really a counter-argument.

The abstraction over assembly language is solid; compilers very rarely (if at all) fail to translate high level code into the correct assembly code.

LLMs are nowhere near the level where you can have almost 100% assurance that they do what you want and expect, even with a lot of hand-holding. They are not even a leaky abstraction; they are an "abstraction" with gaping holes.
by sirendi | 2025-06-03 01:49:38+00:00
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by MobiusHorizons | 2025-06-03 01:49:49+00:00
I suppose you are talking about UB? I don't think that is anything like Halucination. It's just tradeoffs being made (speed vs specified instructions) with more ambiguity (UB) than one might want. fast math is basically the same idea. You should probably never turn on fast math unless you are willing to trade speed for accuracy and accept a bunch of new UB that your libraries may never have been designed for. It's not like the compiler is making up new instructions that the hardware doesn't support or claiming the behavior of an instruction is different than documented. If it ever did anything like that, it would be a bug, and would be fixed.
by TheAlchemist | 2025-06-03 01:50:14+00:00
His arguments are mostly right I would say. I tend to use a bit of LLMs for coding - just Copilot though - and I do like how it quite often saves me time with autocomplete spanning entire lines.

ChatGPT is also pretty usefull - especially for naming things.

However, on a global scale, I would compare the shift towards 'vibe coding' and massive LLMs use to the one we got when people doing the coding don't even know what Assembly even is. Current state of software is that it's incredibly bloated and slow (and I do love python). Doesn't matter that much since we've got massive improvements in hardware, but still. In 5 years, instead of not really realizing that we're writing slow and bloated code, we will not really realize we're writing incorrect code.

I don't know anything about web developement, but in the environements I've seen during my career - where most code is quite critical and a stupid mistake, one that an LLM could easily hallucinate, can cost a lot - I don't see it replacing humans anytime soon. It's a pretty good and usefull tool though.
by vunderba | 2025-06-03 01:50:20+00:00
The idea of using "smaller LLMs" to control the agency of RPG characters has been a pretty common one ever since AI Dungeon back in 2019. The hardest aspect of it would be locking the AI down to a well-defined character dossier so that it is hard to jailbreak them, and also to limit knowledge leakage of things they don't know, etc.

It would also lend itself very well towards interactive fiction, point-and-click adventure games, etc.
by hatthew | 2025-06-03 01:50:42+00:00
I think the main difference between shortcuts like "compilers" and shortcuts like "LLMs" is determinism. I don't need to know assembly because I use a compiler that is very well specified, often mathematically proven to introduce no errors, and errs on the side of caution unless specifically told otherwise.

On the other hand, LLMs are highly nondeterministic. They often produce correct output for simple things, but that's because those things are simple enough that we trust the probability of it being incorrect is implausibly low. But there's no guarantee that they won't get them wrong. For more complicated things, LLMs are terrible and need very well specified guardrails. They will bounce around inside those guardrails until they make something correct, but that's more of a happy accident than a mathematical guarantee.

LLMs aren't a level of abstraction, they are an independent entity. They're the equivalent of a junior coder who has no long term memory and thus needs to write everything down and you just have to hope that they don't forget to write something down and hope that some deterministic automated test will catch them if they do forget.

If you could hire an unpaid intern with long term memory loss, would you?
by thomassmith65 | 2025-06-03 01:51:08+00:00
All these chatbots already have public APIs which tens of thousands of random developers are using for various tasks.

While it's fun to debate airy fairy philosophical topics, there is a practical issue that needs to be dealt with: people are using chatbots to do things, and these chatbots are unpredictable in sophisticated, occasionally dangerous ways.

It doesn't take much imagination. One difference between a ten year old Garmin GPS device and ChatGPT is that there's a 0% chance the former will regurgitate behavior from Demon Seed and purposefully direct me to drive off a cliff.
by smcin | 2025-06-03 01:51:11+00:00
I started using DL in 2019 and it was then still very much a respectable language-learning app for semi-casual users up to CEFR A1/A2/B1/B2 standard, with good user-contributed community forums (they killed those 3/2022, so they could milk them with AI to resell as an add-on - for me that act was the jump-the-shark telltale). Don't know about the goal of translating documents but that's something they'd want to pivot away from to get beyond a tiny niche audience. Some gamification and user evangeliation is good and necessary (users should be able to turn it off). If nothing else, DL's antipattern whenever they eventually crash will show limits on how far it can be pushed in the name of $$$ growth hacking. Meanwhile, bona-fide langage learners are jumping ship off DL. (Analogously, what is the point when Zynga/FB games Candy Crush/FarmVille/MafiaWars stopped being cool? or Pokemon Go? Ingress?) How much of the stickiness is from the thing being inherently hooked, vs the social competition agaonst your friends?