MurphyA/hacker-news huggingface dataset:
by afrazullal | 2026-01-07 14:47:49+00:00
I’m building Vect AI, an experiment in turning marketing execution into software instead of managing it through fragmented tools.

The idea behind Vect AI is a marketing operating system where campaign planning, content creation, SEO workflows, and distribution live in one coherent system. Rather than relying on one-off prompts or disconnected SaaS products, the system is designed around stateful workflows that retain context across tasks and campaigns.

Some principles that guided the build:

Replace multi-tool marketing stacks with a single execution layer

Focus on deterministic workflows instead of dashboards and reports

Treat SEO and content as compounding infrastructure, not just traffic sources

Use AI for planning and execution, not just copy generation

Vect AI started as a personal project to solve my own marketing workflow problems, but it’s turned into a broader exploration of how AI-driven systems can reduce operational overhead in SaaS growth and distribution.

Sharing this here to compare notes with others working on AI tools, marketing automation, or developer-first growth systems, and to discuss what works (and fails) when marketing is treated as software rather than process.
Ask HN: Is CS the exact opposite of hobby programming (in terms of motivation)?
by amichail | 2026-01-07 14:48:09+00:00
There is a recurring pattern where people who love building games or apps as a hobby end up frustrated or disillusioned in computer science programs. The issue is often framed as difficulty or lack of preparation, but the deeper problem is a mismatch in motivation.

Hobby programming, especially game and app development, is driven by construction. The enjoyment comes from making something exist, seeing it run, experimenting, and iterating quickly. The feedback loop is immediate and visual. Creativity, clever hacks, and shipping something that works are rewarded.

Academic computer science removes most of those incentives.

Instead of building, the focus is on reduction and abstraction. Problems are formalized, implementations are stripped away, and reasoning happens independently of any concrete program. Progress is measured through proofs, asymptotic bounds, classifications, and impossibility results. Feedback is slow and symbolic. Success means correctness and generality, not expressiveness or playfulness.

From a motivational standpoint, this is not merely different from hobby programming. It is the opposite. Many of the things that make building games or apps fun are irrelevant or actively discouraged in computer science courses.

This helps explain why:

* People who struggle in CS can become excellent software engineers.

* People who enjoy theory often dislike real-world programming.

* Hobby programmers feel misled when entering a CS degree.

The core issue is expectations. Computer science is frequently marketed using apps, games, and “learning to code,” even though the discipline is much closer to applied mathematics and logic than to building software products.

Computer science is not bad or useless. It is a deep and valuable field. But for people motivated by making things, iterating quickly, and creating interactive experiences, it is often a poor motivational fit.

What do you think of this view? Is computer science the exact opposite of hobby programming?
by 2OEH8eoCRo0 | 2026-01-07 14:48:47+00:00
https://lite.cnn.com/2026/01/07/politics/oil-tanker-us-seizu...

> Originally called the Bella 1, the tanker was sanctioned by the US in 2024 for operating within a “shadow fleet” of tankers transporting illicit oil.

> The US Coast Guard had tried to seize the tanker last month when it was near Venezuela, but US forces were unable to board it after the ship turned around and fled.

> At some point while being pursued, the crew of the tanker painted a Russian flag on its hull, claiming it was sailing under Russian protection.
by kurtis_reed | 2026-01-07 14:49:06+00:00
Suppose there are 1000 events and 500 will have outcome A and 500 will have outcome B. If you predict a 50% chance of A for every event you'll be perfectly calibrated. On the other hand, if you predict a 90% chance of a certain outcome and you're right for 800 events, you're not perfectly calibrated but you have a lower Brier score (lower is better).
by sjw987 | 2026-01-07 14:49:18+00:00
Garmin are pretty decent. I have an Enduro 2 which lasts a fortnight on a charge (currently 37% with 6 days of charge left).

It's waterproof. Unfortunately no wireless charging (proprietary cable) but it charges 2 weeks worth in about 2 hours.

It doesn't automatically track exercise, but it does collect a lot more (and higher quality) data than Withings for activity. Automatic sleep. The app has the trends and such, and there's no subscription (they recently added some AI stuff you can pay for but which is optional).
by RockstarSprain | 2026-01-07 14:49:47+00:00
Was just looking for a more general extension like this, thanks!

Just a small nitpick / feature request:
I try not to install extensions that require "Read and change all your data on all websites" permission, so may I ask you to change it to something less general, such as requiring access to specific websites once the corresponding new URL is added to a white list?

I saw some Chrome extensions doing this as of late.
by tonyarkles | 2026-01-07 14:49:49+00:00
This works fantastic for building furniture as well, where the absolute dimension doesn’t matter as much as all of the pieces having matching dimensions. A cabinet with drawers, for example. The story stick captures the spacing between the drawers, the width of the drawer, the slightly smaller height of the drawer face, etc.

It feels really imprecise the first time you set the fence on a table saw based on a marking on a stick instead of a precise specific value but the results are hard to argue with.
by qcnguy | 2026-01-07 14:49:53+00:00
Nobody says math is a conspiracy, except maybe the far left educators who think math is some kind of racist white supremacy. That's a ridiculous straw man.

https://www.hoover.org/research/seattle-schools-propose-teac...

> China is a conservative country but they believe in education

China is a communist country that banned private tutoring because some children were getting ahead, creating inequality (the "double reduction" policy).
by jfengel | 2026-01-07 14:50:13+00:00
I'm envisioning an editorial written in early 1914 saying "Hey, I really hope nothing happens to the Austrian heir in Sarajevo, because everybody is tied into that and it would really blow up into a Europe-wide crisis."

I want to pair it with a scholarly article written in 2125 asking, "Why the heck did they throw away the entire world order because of a frozen-over, barely-inhabited island?"
by yurishimo | 2026-01-07 14:50:25+00:00
It's also important to consider the developers risk tolerance as well. It's all fine and dandy that the project manager is okay with the risk but what if none of the developers are? Or one senior dev is okay with it but the 3 who actually work the on-call queue are not?

I don't get paid extra for after hours incidents (usually we just trade time), so it's well within my purview on when to take on extra risk. Obviously, this is not ideal, but I don't make the on-call rules and my ability to change them is not a factor.
by dsign | 2026-01-07 14:50:32+00:00
I've been working with paper sizes a lot for the last year, and I've rarely thought about the square root of two ratio and when I've, it has been just to amuse myself. However, knowing that to get an A5 piece of paper I just need to cut/fold in half an A4, and that I can get to A3 and A2 by adding A4s, has been really useful. If I were in USA, didn't have that, and instead would have to install yet another new size system in my head[^1], I would despair.

[^1]: This is fun! https://papersizes.io/us/
by krisoft | 2026-01-07 14:50:37+00:00
> Decisions will not be made based on emotions from a demo at CES.

Sure. It is not a mistake with grave consequences. Something can be a mistake and not matter much in the long run. Like the CEO could have went on stage wearing mismatched shoes, or wearing a red clown nose. It wouldn't ruin everything. Wouldn't bankrupt them. If the robots are good they will be still sold. But it would just undermine the message a little bit. For no good reason whatsoever.

The fundamental questions will be: Do the robots work? Are they cheaper than the equivalent labour from humans? (including all costs on both sides of the comparison.) Nothing else matters in the long run. They could have just never went to CES and it would be all the same.

> Im sorry, but this is just too much.

ok :) if you say so. But then tell me. What did the stage presence of the second robot add to the show?
by bflesch | 2026-01-07 14:50:58+00:00
I understand that one cannot expect a reddit hobbyist community to compensate for a medical system that has already "failed" the patients. But if you feel your disease is progressing despite the medications the doctors give you, you will desperately try every single thing that people post on social media.

The cycle of "long covid > mold > lyme > candida > parasites" can only be broken by clever people building better, cheaper analytical methods for detection of these diseases.

Doctors really should show some humility and remember that 200 years ago it was an innovation for them to wash and disinfect their hands, and the guy who told them, Ignaz Semmelweis, was "red herring" chased into psychiatric asylum.
by rancar2 | 2026-01-07 14:51:09+00:00
For a bit more context, most email infrastructures I’ve worked with are for transactional and marketing DTC and B2B companies. I would read my response in this context.

Re one-time setups and one-time changes: I think this will answer both questions and the implied PMF question as well. For internal FTE staff, this will be handle as a one off exception consistently (it’s really no one’s full-time job or responsibility). You may wish to speak with teams that offer professional services / SaaS including self-hosted where this infrastructure would be helpful. Their jobs are made easier with additional predicable / dependable infrastructure software (ie chat with (a) Twilio’s messaging team which remains the SendGrid acquisition, (b) related Red Hat / IBM) vs more work for an individual who is just doing this one-off. You may wish to consider a revenue share and/or white-labeling as they co-install the infrastructure for your business.
by techblueberry | 2026-01-07 14:51:29+00:00
Ok, so here’s a question. A lot of this to me seems to assume that like stuff works the way it’s supposed to. And half of my job (SRE, not SWE) is figuring out why it’s not working the way it’s supposed to, which often involves diving into the code, because if it worked the way it was supposed to then I could just read the docs.

Is the assumption here that code goes away because LLM’s write perfect code?

What do you do if two people write competing specs? Which spec takes precedence? What is the hierarchy of a bunch of spec.md files? And if you can write all that in not code, How do you predict how the compiler will interpret it?

I could maybe see a higher level language come out of this, but software engineering often isn’t the art of writing code, but figuring out why the code that’s there isn’t doing what it’s supposed to.

The agile manifest has this principle:

Working software over comprehensive documentation

This basically flips that in its head. Does this like work? Documentation is immensely tricky to get right. How do you maintain “code” when there’s no shared objective understanding of what symbols mean.

If I read python code, what it does is objective and predictable. If I read documentation, I have to trust the words you use make sense to me? I often read code instead of documentation, because only the code can tell you what it _really_ does.

Maybe “writing essentially all the code” is like the 80/20 rule or 99/1 rule. Engineers only write 1% of code, but that code represents a wide swath of time bug troubleshooting, distributed systems management, optimization, etc.
by joshuaisaact | 2026-01-07 14:51:49+00:00
I notice one of the things you don't really talk about in the blog post (or if you did, I missed it) is unnecessary tests, which is one of the key problems LLMs have with test writing.

In my experience, if you just ask an LLM to write tests, it'll write you a ton of boilerplate happy path tests that aren't wrong, per se, they're just pointless (one fun one in react is 'the component renders').

How do you plan to handle this?
by MajidAliSyncOps | 2026-01-07 14:51:56+00:00
I’ve had a similar experience. HTMX really shifts the complexity back to the server in a way that feels more honest and easier to reason about.

For many apps, especially CRUD-style or internal tools, it removes a lot of accidental complexity without giving up interactivity. The trade-off seems worth it unless you truly need a highly stateful client.
by vladsh | 2026-01-07 14:52:11+00:00
It’s a bit strange how anecdotes have become acceptable fuel for 1000 comment technical debates.

I’ve always liked the quote that sufficiently advanced tech looks like magic, but its mistake to assume that things that look like magic also share other properties of magic. They don’t.

Software engineering spans over several distinct skills: forming logical plans, encoding them in machine executable form(coding), making them readable and expandable by other humans(to scale engineering), and constantly navigating tradeoffs like performance, maintainability and org constraints as requirements evolve.

LLMs are very good at some of these, especially instruction following within well known methodologies. That’s real progress, and it will be productized sooner than later, having concrete usecases, ROI and clearly defined end user.

Yet, I’d love to see less discussion driven by anecdotes and more discussion about productizing these tools, where they work, usage methodologies, missing tooling, KPIs for specific usecases. And don’t get me started on current evaluation frameworks, they become increasingly irrelevant once models are good enough at instruction following.
by sanderjd | 2026-01-07 14:52:21+00:00
Yeah we're not too far from agreement here.

Something I think though (which, again, I could very well be wrong about; uncertainty is the only certainly right now) is that "so the person after me would have a good clean idea of what is going on" is also going to continue mattering even when that "person" is often an AI. It might be different, clarity might mean something totally different for AIs than for humans, but right now I think a good expectation is that clarity to humans is also useful to AIs. So at the moment I still spend time coaxing the AI to write things clearly.

That could turn out to be wasted time, but who knows. I also think if it as a hedge against the risk that we hit some point where the AIs turn out to be bad at maintaining their own crap, at which point it would be good for me to be able to understand and work with what has been written!
by MMAFRAZ | 2026-01-07 14:52:25+00:00
I built a system I call a Resonance Engine to answer a problem I kept running into: content performance is usually judged after publishing, when it’s already too late to change direction.

The Resonance Engine is designed to simulate how content might land with different audience contexts before it goes live. Instead of guessing tone, clarity, or intent alignment, the system evaluates messaging against multiple audience lenses and scores it on factors like clarity, relevance, emotional alignment, and friction.

This isn’t traditional A/B testing or analytics. There’s no traffic involved. The goal is pre-publication feedback that helps decide what to publish, not just how to optimize after the fact.

Some ideas behind the system:

Treat audience reaction as a system that can be modeled, not intuition

Evaluate resonance before distribution, not after engagement drops

Focus on messaging clarity and intent alignment, not vanity metrics

Use simulation to reduce wasted content cycles

The Resonance Engine is part of a broader marketing OS I’m building called Vect AI, but this component started as a standalone experiment to reduce guesswork in content and campaign creation.

Sharing this here to get feedback from others working on content systems, decision-support tools, or alternative approaches to testing messaging before it reaches real users.
by ViccAlexander | 2026-01-07 14:52:32+00:00
I open sourced a small CLI tool I’ve been using personally for about a year.

Smart Commit is an AI-assisted `git commit` workflow that stays entirely in the terminal. It stages changes, looks at what files changed, drafts a clean commit message, and shows it to you before committing. You can approve, edit, or cancel.

Yes, I know Cursor, VS Code, Copilot, etc. can generate commit messages. I use those too. This exists because I personally prefer doing git-related work manually in the CLI, and I wanted something that fit cleanly into that flow instead of pulling me into an editor UI.

There’s no magic, no git hooks, no lock-in. It’s just a thin wrapper around the commit process that tries to make histories more readable and consistent. I’ve been using it daily and finally decided to open source it in case it’s useful to anyone else.
by DonHopkins | 2026-01-07 14:53:03+00:00
@dev_l1x_be: The answer isn't a new typed scripting language. It's recognizing what the interpreter already is.

LLMs are eval(). Skills are programs. YAML is the motherboard.

@unkulunkulu nails it -- "library as the final language", languages all the way down. Exactly. Skills ARE languages. They teach the interpreter what to understand. When the interpreter understands intent, the distinction dissolves.

@conartist6: "DSL is fuzzy... languages and libraries don't have to be opposing" -- yes. Traditional DSL: parse -> AST -> evaluate. LLM "DSL": read intent -> understand -> act. All one step. You can code-switch mid-sentence and it doesn't care.

The problem with opinionated frameworks like ROR and their BDFLs like DHH is that one opinion is the WRONG number!

The key insight nobody's mentioned: SPEED OF LIGHT vs CARRIER PIGEON.

Carrier pigeon: call LLM, get response, parse it, call LLM again, repeat. Slow. Noisy. Every round-trip destroys precision through tokenization.

Speed of light: ONE call. I ran 33 turns of Stoner Fluxx -- 10 characters, many opinions, game state, hands, rules, dialogue, jokes -- in a single LLM invocation. The LLM simulates internally at the speed of thought. No serialization overhead. No context-destroying round trips.

@jakkos, @PaulHoule: nushell and Python are fine. But you're still writing syntax for a parser. What if you wrote intent for an understander?

Bash is a tragedy -- quoting footguns, jq gymnastics, write-only syntax. Our pattern: write intent in YAML, let the LLM "uplift" to clean Python when you need real code.

Postel's Law as type system: liberal in what you accept. Semantic understanding catches nonsense because it knows what you MEANT, not just what you TYPED.

Proof and philosophy: https://github.com/SimHacker/moollm/blob/main/designs/stanza...
Show HN: YoloForge – Create object detection datasets using Gemini 3 Pro
by Olibier | 2026-01-07 14:53:17+00:00
Hi HN, I’m the creator of YoloForge. I built this because I hit a wall with a hobby computer vision project: I needed a custom dataset, and zero-shot tools like Grounding DINO just weren't accurate enough for my specific classes. I decided I’d rather write code for a couple of weeks than draw another box by hand.

I previously experimented with Grounding DINO and SAM3. While they are amazing for generic objects, I found they struggle with specific semantic requests (e.g. specific manufacturing parts, game characters or distinguishing "a worker" from "a worker without a helmet").

I discovered that Gemini 3 Pro is surprisingly underrated for bounding box tasks if you prompt it with detailed visual descriptions. It handles semantic understanding significantly better than standard zero-shot detectors.

url: yoloforge.com

The Workflow:

Upload a zip of raw images (stored in Cloudflare R2).
Describe class/classes in plain English.
The system generates a .jsonl batch file and sends it to the Gemini Batch API. This allows us to process thousands of images in parallel at 50% of the standard cost.
You review/correct boxes in the UI and export the YOLO train/val/test dataset.

Technical Challenges:

One hard part was getting valid JSON out of the LLM consistently. I ended up writing a robust parser that uses regex fallback strategies to literally "salvage" valid bounding boxes from malformed responses.

The Stack:

- Frontend: Next.js
- Backend: FastAPI, Celery (for async zip processing and polling the batch API), Redis.
- Storage: Supabase (Auth/DB), Cloudflare R2 (Image Storage).
- Model: Google Gemini 3 Pro via Batch API.

There is a live demo on the landing page (no sign-up required) where you can upload a single image to test the detection logic. But of course the tool really shines with datasets that have thousands of images with multiple classes.

If you have any technical questions please ask!
by scottLobster | 2026-01-07 14:53:26+00:00
Show me the incentive, I'll show you the outcome. More than once I've had my hand slapped professionally for taking ownership of something my immediate superiors wanted to micromanage. Fine, here I was trying to take something off their plate that was in my wheelhouse, but if that's where they want to draw the line I guess I'll just give less of a shit.

If you actively deny your employees ownership, then the relationship becomes purely transactional.

It's also possible OP is just a bad employee, but I've met far more demoralized good employees than malicious bad ones over the course of my career.
by bluGill | 2026-01-07 14:53:31+00:00
I don't know, that is why I'm asking.

My general thought as someone who doesn't know how rail maintenance is done is that rails should not crack in normal operation. If there is a crack that implies either the rail is end of life anyway and you replace it, or there is likely a manufacturing problem and you want to replace all the rail from that branch. Either way rail is manufactures in long sections (20 meters is my guess, but that is slightly educated guess that I won't stand by), so you would only need within a few meters to find the track section in question.

However I don't know how track maintenance is done. It is entirely possible that they grind/cut out the crack and then fill in (either cast in place, fill with weld, or just replace a a few cm) and in that case you would need to know within a few mm (though maybe inspection could find it if you are within a few cm).

Again, I am not a rail expert here. This is a place where I want to know and thus would like an expert to say. (though likely no experts are reading this...)