MurphyA/hacker-news huggingface dataset:
by mjanx123 | 2026-01-27 07:15:54+00:00
It is a blend of oils.
Light oils evaporate (like kerosene does for example), and dissolve thicker oils and grease.
Oils displace water in general and once in the surface pores they prevent water from getting in there again, a mixture containing light oils flows in easier and does that better.
Being predominantly a light oil it is a poor lubricant, but it is better than nothing, and can flow in crevices where thicker stuff would not.

It is really simple and there is no magic.

The name took off as a brand and completely different stuff from the 40th iteration of a Water Displacer formulation is being sold under it as well.
by user205738 | 2026-01-27 07:16:12+00:00
There is a significant difference.

Nothing will change for an ordinary person now.

At that time, the Genplan OST implied the almost complete extermination and the enslavement of a small number of the remaining people.

And also going back to the second part of the top commentary. At that time, people had a great motivation to defend their homeland and their loved ones. The survival of the country and the survival of the people in it were inextricably linked.

The current conflict has no such connection. The existence or cessation of the existence of the state is not related to the existence of people in it. Many of whom found life in a completely different country.

There were already volunteers, mercenaries, those who fell for a good salary. Why force those who actively avoid it?
Ask HN: What Happened to Apple App Clips?
by tomtec | 2026-01-27 07:16:38+00:00
Apple introduced App Clips in iOS 14 as a way to instantly use small parts of an app without installing the full application. The idea seemed compelling: low friction, fast launch, contextual entry points (QR, NFC, links).

Five years later, I almost never encounter App Clips in the wild and Google killed Instant Apps already.

- Are there notable apps or companies where App Clips are a meaningful acquisition or engagement channel?

- Did you already decide to remove your App Clip to reduce maintainability?
by repeekad | 2026-01-27 07:16:41+00:00
I called the non-emergency line for the local police department when someone went home with my wallet after I left it on a plane, tracked with an AirTag. 2 hours later an officer said they didn't have probable cause but could knock on the door and ask anyway. I think he basically offered for there to be no trouble if they gave it back, thief claimed they were "going to return it to lost and found", and sure enough I was able to go show my passport at the station and collect it the next day.
by hobofan | 2026-01-27 07:17:43+00:00
> I see you have a dockerfile.combined - is this built and served via gh artifacts? I can try it out.

Our recommended way of deploying is via Helm[0] with latest version listed here[1].

> with the services expanding to fill enterprise use cases, and simplicity lost.

TBH, I don't think that simplicity was lost for OpenWebUI because of trying to fill enterprise needs. Their product has felt like a mess of too many cooks and no consistent product vision from the start. That's also where part of our origin story comes from: We started out as freelancers in the space and got inquiries to setup up a Chat UI for different companies, but didn't deem OpenWebUI and the other typical tools fit for the job, and too much of a mess internally to fork.

We are small team (no VC funding), our customers end-users are usually on the low-end of AI literacy and there is about ~1 DevOps/sysadmin at the company our tool is deployed, so we have many factors pushing us towards simplicity. Our main avenue of monetization is also via SLAs, so a simple product for which we can more easily have test coverage and feel comfortable about the stability is also in our best interest here.

[0]: https://erato.chat/docs/deployment/deployment_helm

[1]: https://artifacthub.io/packages/helm/erato/erato
by o4c | 2026-01-27 07:17:59+00:00
Hello, first of all sorry. I am not bot, a human user.

I was searching for open source DIY microscope projects and found the OpenFlexure Microscope as the first search result. After reading through the project and finding it technically interesting, I submitted it to Hacker News. Fortunately, it reached the front page approximately five days after submission.

If you search for the term “open source microscope,” you will see the same link appearing as the top result.

https://www.google.com/search?q=open+source+microscope&oq=op...

From my observation, information related to precision engineering is not widely known and can be difficult to find. Because of this, overlapping submissions can sometimes occur. I apologize if this caused any repetition. Detailed teardowns of precision instruments such as gauges, metrology tools, and scientific equipment are relatively rare, which contributes to this situation.
by foltik | 2026-01-27 07:21:08+00:00
Afaik this isn’t quite correct either. From what I could gather from the CCC talk and forum posts:

The Apple specific instructions to talk to the SPTM are only usable in the GL2 privilege level, not EL2 where you end up after booting non-Apple code.

The problem is the macOS kernel uses these custom instructions to manage its own page table mappings, and when being virtualized in EL2 it just crashes since these instructions are now invalid.

The solution is indeed to emulate the SPTM interface and instructions just enough for macOS to not crash, that way it can be virtualized for reverse engineering. The emulated SPTM could just pass through the mappings, ignoring all of the security checks the real one would normally do.

I was able to find quite a bit of existing SPTM analysis online (I believe from iOS security research) so this issue isn’t insurmountable by any means.
How do you use LLMs to verify databases with minimal hallucinations?
by rochansinha | 2026-01-27 07:22:00+00:00
I have a medium sized database of around 150 entries each with 10-15 parameters.
It was put together by Claude. But the amount of hallucinated data is extraordinary!
Trying to fix it using another llm like chat gpt or gemini hasn’t worked since they balk at looking for data for >50 data points.
Gemini actually deleted 100 entries from the database while analysing it!
So the question is-
Is there a suitable way to analyze the database for inaccuracies/hallucinations and fix them, apart from manually going through each data point personally?
by troupo | 2026-01-27 07:22:35+00:00
> You can't protect against an opponent who's motivated to learn the inherent vulnerabilities of our systems, many of which can't be protected against due to the laws of physics and practicality

Ah yes, the insidious opponent who learns the inherent vulnerability of ... huge crowds gathering before hand baggage screenings and TSA patdowns.

And these crowds are only there only due to a permanent immovable physical fixture of ... completely artificial barriers that fail to prevent anything 90-95% of the time.
by jabl | 2026-01-27 07:22:42+00:00
> If I need a universal lubricant that is readily available and cheap, I just use used motor oil.

Why? Used motor oil is, well, used. It contains metal particles from the engine and combustion byproducts, which is why it was replaced in the first place. Granted, most lubrication applications aren't the marvels of precision parts moving at high speed that a modern engine is so can probably make do with poorer oil, but still.

You can buy industrial lubricants in bulk for pretty cheap so that unless you use huge quantities of it, it shouldn't make much difference.

As an aside, my aunt's husband worked more or less his entire career in a heavy truck repair shop. And he had an oil burner heating his house (you can see where this is going, eh?). So he got used engine oil for free, the shop was happy to get rid of it as disposing of it properly cost money. I think burning used engine oil was illegal already back then due to the pollution, and nowadays I think they have some government mandated accounting system to ensure that the same amount of oil is sent to proper recycling as comes in.
by CountVonGuetzli | 2026-01-27 07:24:57+00:00
I recently used the AI feature in n8n to write a code node to parse some data, which worked really well. Feels more like LLMs are enhancing low-code solutions.

Also, I see great value in not having to take care of the runtime itself. Sure, I can write a python script that does what I want much quicker and more effectively with claude code, but there is also a bunch of work to get it to run, restart, log, alert, auth…
by microtonal | 2026-01-27 07:25:06+00:00
Yes and no. West-Germany was not trusted enough to allow them to make nukes or to make a powerful-enough army. For a long time, Germany has pretty much been a vassal state of the US. I cannot see that happening the other way around (given the relative powers of the militaries).

Besides that, living in a neighboring country, the generation of my parents and grandparents had a deeply-rooted aversion towards Germans. They would communicate with Germans politely, but when no German was around, they would often use not-so-nice names or jokes. Luckily that aversion is gone with later generations.

When I was young (early 90ies), we would often go on holiday to Czechoslovakia (before the split) and the Czech Republic. The staff at restaurants and shops would be cold and distant until they discovered that we were not German, then they would be very warm and kind. At some point, we would always start the conversation in English. At the time most staff would only speak German, but we would use it as a signal that we were not from Germany.

This kind of distrust can stretch many decades. I think we have mostly healed as Europeans, but it took a damn long time.
by rvz | 2026-01-27 07:26:06+00:00
The moment the software is in production, making a lot of money and is stable, I then add lots of tests (both unit and integration) to prevent a $1,000 issue turning into a $100,000 problem later down the line.

Instead of testing everything to being perfect, 100% test coverage and never releasing and the company questioning why the deadlines were missed for the project because of testing dogma.

> I've personally found that when the architecture of the system is not mature yet, unit tests can get in the way. Terribly so. Integration tests or system tests to assert behavior seem the starting point in this and other scenario's, including when there are no tests at all yet.

Totally agree.
by kmarc | 2026-01-27 07:26:49+00:00
Hah, I guess this is the same then as in formula 1 (and possibly other motorsports). After the end of a straight (speeding) section, just before the curve they have 150/100/50 (m?) distance indicators. Sans the concrete block at the top. That would obviously shave the driver's head off.

When a driver hits these, they evaporate as dust.
by troupo | 2026-01-27 07:27:05+00:00
> The existence of master locksmiths (and door breaching charges) doesn't mean you shouldn't lock your door at night.

The TSA checkpoints are the equivalent of moving all your belongings onto the lawn, and then locking the door.

Why bother with the plane when now you have potentialy a magnitude more people in the queue to TSA?
Ask HN: What's your wiring pattern for large addressable LED installs?
by emmasuntech | 2026-01-27 07:27:20+00:00
Hi HN — I’m collecting “known-good” wiring patterns for large addressable LED strip installs (WLED/ESP32 / FastLED, WS281x-class pixels). I’m not trying to promote anything; I’d just like to sanity-check best practices and learn what actually works in the field.

Scope: 5V/12V/24V addressable strips, from a few hundred to a few thousand pixels, used in desks/coves/signage/art installs.

Things I already do (baseline):

Power injection (start + mid/end depending on load)

Fuse near the PSU and per-branch when splitting

Common ground between controller and strip

300–500Ω series resistor on data near the first pixel

500–1000µF capacitor near the strip input

Level shifting for 3.3V controllers when needed

Where I’d love your experience:

Do you prefer 5V distribution, or 12/24V distribution + local buck converters near segments? Why?

What’s your go-to approach for long data runs (controller far from first pixel)?

Twisted pair + ground?

Differential (RS-485 style) transceivers?

Placing the controller closer and extending only power?

Any “never again” lessons on connectors, wire gauge, heat, or fusing?

If you’ve done installs that must survive months/years, what design choices mattered most?

If you have a wiring sketch, parts list, or a short rule-of-thumb (e.g., injection spacing under worst-case white), I’d really appreciate it.

Thanks!
by SimplyLiz | 2026-01-27 07:29:28+00:00
The architecture separates tool availability from result depth, which addresses exactly that concern.

Presets control tool availability, not output truncation. The core preset exposes 19 tools (~12k tokens for definitions) vs full with 50+ tools. This affects what the AI can ask for, not what it gets back.
The AI can dynamically call expandToolset mid-session to unlock additional tools when needed.

Depth parameters control which analyses run, not result pruning. For compound tools like explore:
- shallow: 5 key symbols, skips dependency/change/hotspot analysis entirely
- standard: 10 key symbols, includes deps + recent changes, parallel execution
- deep: 20 key symbols, full analysis including hotspots and coupling

This is additive query selection. The call graph depth (1-4 levels) is passed through unchanged to the underlying traversal—if you ask for depth 3, you get full depth 3, not a truncated version.

On token optimization specifically: CKB tracks token usage at the response level using WideResultMetrics (measures JSON size, estimates tokens at ~4 bytes/token for structured data). When truncation does occur (explicit limits like maxReferences), responses include transparent TruncationInfo metadata with reason, originalCount, returnedCount, and droppedCount. The AI sees exactly what was cut and why.

The compound tools (explore, understand, prepareChange) reduce tool calls by 60-70% by aggregating what would be sequential queries into parallel internal execution. This preserves reasoning depth while cutting round-trip overhead. The AI can always fall back to granular tools (getCallGraph, findReferences) when it needs explicit control over traversal parameters.
by lmeyerov | 2026-01-27 07:29:53+00:00
RIP low-code, long live low-code companies :)

For our startup, the low-code vs LLM shift started hugely frustrating and scary, but also hopeful. After years of dev, we were getting ready to launch our low code app product #2, and then bam, chatgpt 3.5 happened and LLMs stopped sucking as much. We had to look at our future for our corner of the world -- democratizing our tricky gpu graph investigation tech into something that goes beyond the data 1%'ers at top gov/bank/tech/cyber investigations do -- and make the painful and expensive call to kill the low-code product.

But as a verticalized startup, the market still needed something here for the same reason we originally built it. LLMs just meant the writing was on the wall that that the market expectations would grow as would what's possible in general. We correctly guessed that would happen, and started building louie.ai . Ex: While we previously had already viewed our low-code platform as doubling a way for teams to write down their investigation flows so they can eventually do ML-powered multi-turn automations on them.. we never dreamed we'd be speed running investigation capture the flag competitions. Likewise, we're now years ahead of schedule on shedding the shackles of python-first notebooks & dashboards.

So yeah, for folks doing generic low-code productivity apps, it's not great. n8n and friends had to reinvent themselves as AI workflows, and there's still good reason to believe that as agent experiences improve, they'll get steamrolled anyways... but...

Verticalized low-code workflow tools get to do things that are hard for the claude codes. Today the coding envs are built better than most of the non-ai-native vertical teams, but the patterns are congealing and commoditizing. It'll be interesting as the ai side continues to commoditize , and the vertical teams get better at it - at which point the verticals get much more valuable again. (And indeed, we see OpenAI and friends hitting ceilings on generic applications and having to lean in to top verticals, at least for the b2b world.)
by bhaviav100 | 2026-01-27 07:30:29+00:00
I’ve been working on a small experimental gateway that sits between agents and customer-facing execution paths and forces decisions through policy + approval before anything irreversible happens.

v2 I just shipped adds:

• risk scoring on drafts
• policy path traces
• approval chain previews
• highlighted spans showing what triggered the block
• admin review endpoints

The motivation is exactly what people are pointing at here: once agents can transact, marketplaces end up banning them unless there’s a way to pause, inspect, and assign responsibility at execution time.

Curious what failure modes you’d want intercepted first if eBay or Amazon ever exposed agent purchase APIs.

https://authority.bhaviavelayudhan.com/v2/console
by Reubend | 2026-01-27 07:31:11+00:00
The post actually has great benchmark tables inside of it. They might be outdated in a few months, but for now, it gives you a great summary. Seems like Gemini wins on image and video perf, Claude is the best at coding, ChatGPT is the best for general knowledge.

But ultimately, you need to try them yourself on the tasks you care about and just see. My personal experience is that right now, Gemini Pro performs the best at everything I throw at it. I think it's superior to Claude and all of the OSS models by a small margin, even for things like coding.
by sersi | 2026-01-27 07:32:03+00:00
Well you do need to vet dependencies and I wish there was a way to exclude purely vibe coded dependencies that no human reviewed but for well established libraries, I do trust well maintained and designed human developed libraries over AI slop.

Don't get me wrong, I'm not a luddite, I use claude code and cursor but the code generated by either of those is nowhere near what I'd call good maintainable code and I end up having to rewrite/refactor a big portion before it's in any halfway decent state.

That said with the most egregious packages like left-pad etc in nodejs world it was always a better idea to build your own instead of depending on that.
by stephen_g | 2026-01-27 07:32:56+00:00
The thing about shoes is just dumb anyway - I don't know if there was some period of time where it was required elsewhere around the world but I never experienced it. Literally the only times I've ever had to take off my shoes were during the two times I've visited the US (vs. a over a dozen trips to European and Asian countries).

Liquid restrictions were also lifted in my country four or so years ago for domestic travel, so it's still annoying when getting ready for an international trip and I remember I still have to do that...
by zephen | 2026-01-27 07:33:37+00:00
Obviously there's no holy water when it comes to go and concurrency bugs.

But go is memory managed and statically typed, and, obviously, assembly language or pure binary opcodes aren't.

The article certainly isn't perfect, but the thesis wasn't just that "Hey, LLMs can now write stuff for us in more efficient languages."

It was (rightly or wrongly) that LLMs can let us more easily use more complicated languages that both go faster and do more checking than most dynamic languages.
by kolinko | 2026-01-27 07:34:24+00:00
At least in Poland, I can almost always see my results before my doctor does - I get a notification that the labwork is ready and I can view results online.

Also, the regular bloodwork is around $50-$100 (for noninsured or without a prescription), so many people just do this out of pocket once in a while and only bring to doctor if anything looks suspicious.

Finally, there is EU regulation about data that applies to medical field as well - you always have the right to view all the data that any company has stored about you. Gatekeeping is forbidden by law.
by happymellon | 2026-01-27 07:34:34+00:00
> There's literally nothing worth paying for in that upgrade.

Well there is the violations of Fitts law with the movement of the start button to the centre of the bar?

But it does make it look slightly more Mac! They should make sure the next upgrade moves the corner to grab away from the actual corner, and that the cursor change for grabbing it doesn't always trigger if they want to really rip it off.
by defrost | 2026-01-27 07:35:12+00:00
And?

Does that, in fact, mean that "the general has fallen for an AI hoax photo"?

What if it were posted in the full knowledge it was shitty AI assisted upscale still image generated from poor resolution cell footage from a distance?

The striking thing about the US forum discussions of the seceral ICE incidents including the two killings, both here on HN and elsewhere is the laser like focus on small details in a tiny windows of time.

There's scant attention paid to the bigger picture in which a small group has worked for a long time to stack the Supreme Court, find the right POTUS to front, and work to incite unrest in "enemy states".
by bhaviav100 | 2026-01-27 07:35:39+00:00
I’ve been experimenting with exactly this pattern.

I built a small authority gateway that sits between agents and downstream systems and forces all high-risk actions through deterministic policy before execution.

In a v2 iteration I just shipped, the gateway returns:

• risk scores on attempted actions
• the policy path that fired
• highlighted spans in the agent output that triggered the rule
• a preview of the approval chain required
• admin endpoints to review and approve pending actions

The key thing I learned: teams don’t just need allow/deny. They need explainable enforcement so when something breaks they can see whether policy failed or the agent bypassed intent.

Curious whether people here treat message drafting and API execution differently, or if everything funnels through the same enforcement layer.

https://authority.bhaviavelayudhan.com/v2/console
by jacquesm | 2026-01-27 07:35:42+00:00
Interesting, so this is effectively 'guided closed loop' software development with the testset as the control.

It gives me a bit of a 'turtles all the way down' feeling because if the test set can be 'good' why couldn't the code be good as well?

I'm quite wary of all of this, as you've probably gathered by now: the idea that you can toss a bunch of 'pass' tests into a box and then generate code until all of the tests pass is effectively a form of fuzzing, you've got some thing that passes your test set, but it may do a lot more than just that and your test set is not going to be able to exhaustively enumerate the negative cases.

This could easily result in 'surprise functionality' that you did not anticipate during the specification phase. The only way to deal with that then is to audit the generated code, which I presume would then be farmed out to yet another LLM.

This all places a very high degree of trust into a chain of untrusted components and that doesn't sit quite right with me. It probably means my understanding of this stuff is still off.