MurphyA/hacker-news huggingface dataset:
Show HN: C++26 Reflection for Python Algo Trading
by tradrich | 2026-02-22 09:55:10+00:00
There’s a constant tension in quant dev between speed-to-market (Python) and speed-to-book (C++). We've had bridges like PyBind11 [1] for years, but the manual boilerplate is usually too high a tax to use them for daily work.

With the C++26 Reflection (P2996) [2], that bridge can now be made invisible. That's the new syntax: ^^T and [: x :] .

I built this repo to show how you can use the Bloomberg/Clang P2996 fork [3] to automatically reflect a C++ quant engine into a Python execution loop. I used Merton Jump Diffusion [4] as the shell - it’s a standard model (with no special alpha) - to show the tech in a real-world context. I initially found out this was working from Callum Piper's ACCU talk [5].

I've included a justfile and Docker Clang build so people can experiment with the new reflection syntax without having to battle through the build tedium.

1. https://github.com/pybind/pybind11
2. https://isocpp.org/files/papers/P2996R13.html
3. https://github.com/bloomberg/clang-p2996
4. Merton, R.C. (1976). Option pricing when underlying stock returns are discontinuous. Journal of Financial Economics, 3(1–2), 125–144.
4.
5. https://youtu.be/SJ0NFLpR9vE
by ChicagoDave | 2026-02-22 09:55:23+00:00
I’ve been a consultant to fortune 100 companies throughout my career and the amount of pain they willingly endure supporting Excel, Access, and .NET/Java applications is astounding. The desire to eliminate these things is high, but there’s no political will over cost and appeasing departmental management.

I think GenAI opens Pandora’s box and all of these decisions change.
by squigz | 2026-02-22 09:55:46+00:00
> Why bother digging up a carbon laden energy source from the depths of a gravity laden planet instead of using solar energy or wind or any other energy source that is less harmful?

Well at least one reason might be that you're currently unable to use those latter forms of energy as well as you can the former.

Anyway, using the way we act as a comparison for how these other civilizations might act doesn't make sense to me - we're nowhere even remotely close to being a threat to other civilizations. By the time a civilization reaches the point where they can travel between stars, I do suspect they'll be using renewables pretty dang heavily
by ricardobayes | 2026-02-22 09:57:01+00:00
I remember at some point which I think was a bug: it started showing a specific type of food, I think some kind of barbeque, prepared in various ways from across the globe. And by "started showing" I mean the feed was pretty much that for an extended period of time. Also at some point a large part of the feed was reposts of random reddit posts in screenshot format.
by pi-rat | 2026-02-22 09:57:06+00:00
The main issue with evaluating content for what it is is how extremely asymmetric that process has become.

Slop looks reasonable on the surface, and requires orders of magnitude more effort to evaluate than to produce. It’s produced once, but the process has to be repeated for every single reader.

Disregarding content that smells like AI becomes an extremely tempting early filtering mechanism to separate signal from noise - the reader’s time is valuable.
by lstodd | 2026-02-22 09:57:45+00:00
LNG aviation does not make any more sense than H2 aviation. Even LPG does not make any sense since you neither can haul 16 bar fuel tanks, nor can you realistically maintain temperature for 1-2atm pressure. And any leak is not 'oh. look, a kerosene stain on tarmac', it's ready-made fuel-air explosion.

On the plus side we would be able to retire airport fire engines because they would never be able to get to a crash before it completely burns out.
by ACCount37 | 2026-02-22 09:57:54+00:00
Agreed on "green steel".

In general, "green hydrogen" makes the most sense if used as a chemical feedstock that replace natural gas in industrial processes - not to replace fossil fuels or be burned for heat.

On paper, hydrogen has good energy density, but taking advantage of that in truth is notoriously hard. And for things that demand energy dense fuels, there are many less finicky alternatives.
by oblio | 2026-02-22 09:58:28+00:00
That's not (or should not be what's happening).

They write a short high level plan (let's say 200 words). The plan asks the agent to write a more detailed implementation plan (written by the LLM, let's say 2000-5000 words).

They read this plan and adjust as needed, even sending it to the agent for re-dos.

Once the implementation plan is done, they ask the agent to write the actual code changes.

Then they review that and ask for fixes, adjustments, etc.

This can be comparable to writing the code yourself but also leaves a detailed trail of what was done and why, which I basically NEVER see in human generated code.

That alone is worth gold, by itself.

And on top of that, if you're using an unknown platform or stack, it's basically a rocket ship. You bootstrap much faster. Of course, stay on top of the architecture, do controlled changes, learn about the platform as you go, etc.
by danielhanchen | 2026-02-22 09:58:44+00:00
Oh it's more time that's the issue - each benchmark takes 1-3 hours ish to run on 8 GPUs, so running on all quants per model release can be quite painful.

Assume AWS spot say $20/hr B200 for 8 GPUs, then $20 ish per quant, so assuming benchmark is on BF16, 8bit, 6, 5, 4, 3, 2 bits then 7 ish tests so $140 per model ish to $420 ish/hr. Time wise 7 hours to 1 day ish.

We could run them after a model release which might work as well.

This is also on 1 benchmark.
by lucketone | 2026-02-22 09:59:19+00:00
We could say that practically all problems would work them self out one way or another.

I heard facebook allows any language as long as you have packaged it neatly with all required build chain. (I.e. Even a choice of language works out)

Some lowest common denominator will be reached. (1 :) )

On the other hand: Are we happy with the lowest or do we want to aim higher?
by alcasa | 2026-02-22 10:00:34+00:00
Frankly the most critical question is if they can really take shortcuts on DV etc, which are the main reasons nobody else tapes out new chips for every model. Note that their current architecture only allows some LORA-Adapter based fine-tuning, even a model with an updated cutoff date would require new masks etc. Which is kind of insane, but props to them if they can make it work.

From some announcements 2 years ago, it seems like they missed their initial schedule by a year, if that's indicative of anything.

For their hardware to make sense a couple of things would need to be true:
1. A model is good enough for a given usecase that there is no need to update/change it for 3-5 years. Note they need to redo their HW-Pipeline if even the weights change.
2. This application is also highly latency-sensitive and benefits from power efficiency.
3. That application is large enough in scale to warrant doing all this instead of running on last-gen hardware.

Maybe some edge-computing and non-civilian use-cases might fit that, but given the lifespan of models, I wonder if most companies wouldn't consider something like this too high-risk.

But maybe some non-text applications, like TTS, audio/video gen, might actually be a good fit.
by brainless | 2026-02-22 10:00:46+00:00
The trick with small models is what you ask them to do. I am working on a data extraction app (from emails and files) that works entirely local. I applied for Taalas API because it would be awesome fit.

dwata: Entirely Local Financial Data Extraction from Emails Using Ministral 3 3B with Ollama: https://youtu.be/LVT-jYlvM18

https://github.com/brainless/dwata
by zelphirkalt | 2026-02-22 10:00:49+00:00
The issue is that for most things we don't want the fuzzy nature of biology in our systems. Yet some people try to shoehorn it into everything. It is OK for chat or natural language things, which are directed at a human, but most other systems we would like to be 100% reliable, and not 99% or failing after a few years, and at the very least we want them to behave predictably, so that we can fix any mistakes we made, when writing that software.
Show HN: CacheGuard-OS version UF-2.5.1 released
by cacheguard | 2026-02-22 10:01:12+00:00
Change logs are available at https://www.cacheguard.net/doc/guide/changelogs.html

Amongst other enhancements, this version supports DynDNS, allowing the VPN server to be configured for authentication via SSL certificates and enabling access even when its public IP address changes dynamically.
by tomhow | 2026-02-22 10:03:31+00:00
The dupe detector works on full URL, not domain. The dupe detector only has effect within 8 hours of the same URL being submitted earlier, unless the earlier submission gets significant discussion then it has effect for 12 months.

Also, we only saw this because someone else emailed us. @replies don't work on HN. Please email us (hn@ycombinator.com) to ensure we see reports like this, thanks!
by prmoustache | 2026-02-22 10:03:35+00:00
> software creators are more likely to make money if they ship something that is 80% perfect now than if they ship something that is 99% perfect 6 months from now.

Except they are shooting themselves in the foot. I reminds me of the goldrush where the shovel and trousers sellers (here the AI companies) would make more money than the miners (developers).

Soon there will be barely any software to build if the general public can just ask an AI to do the things they want. 10 years ago, people would ask a friend that knew about photoshop to help them edit a picture or create something. Nowadays most of them just ask an AI. Same will happen to any kind of productivity or artistic tool. The people alergic to AI slop will just go full luddite and analog and won't use a computer for anything artistry so software creators will lose them alltogether. Home and professionnal software might gradually just disappear and most software creators will have spent thoundands of dollars in tokens with nothing to sell anymore. What might survive might only be the tools that AI rely one, operating systems, database and storage systems, etc.

But boy you will have been super productive, yet totally cancelled by the increase in competition, for the few years it lasted.
by EastLondonCoder | 2026-02-22 10:04:32+00:00
I don’t use plan.md docs either, but I recognise the underlying idea: you need a way to keep agent output constrained by reality.

My workflow is more like scaffold -> thin vertical slices -> machine-checkable semantics -> repeat.

Concrete example: I built and shipped a live ticketing system for my club (Kolibri Tickets). It’s not a toy: real payments (Stripe), email delivery, ticket verification at the door, frontend + backend, migrations, idempotency edges, etc. It’s running and taking money.

The reason this works with AI isn’t that the model “codes fast”. It’s that the workflow moves the bottleneck from “typing” to “verification”, and then engineers the verification loop:

-keep the spine runnable early (end-to-end scaffold)

-add one thin slice at a time (don’t let it touch 15 files speculatively)

-force checkable artifacts (tests/fixtures/types/state-machine semantics where it matters)

-treat refactors as normal, because the harness makes them safe

If you run it open-loop (prompt -> giant diff -> read/debug), you get the “illusion of velocity” people complain about. If you run it closed-loop (scaffold + constraints + verifiers), you can actually ship faster because you’re not paying the integration cost repeatedly.

Plan docs are one way to create shared state and prevent drift. A runnable scaffold + verification harness is another.
by squigz | 2026-02-22 10:05:18+00:00
Like I said, we should probably care more, and generally speaking, we do, over time. I'm not suggesting we're perfect, that we haven't made any mistakes, or that we won't make any more - just that we're slowly learning how to do better.

> Be it waste handling, colonisation, industrial revolution, slavery, oil extraction etc etc.

Interestingly, most of these have seen lots of progress in reducing the harms - if not practically eliminating it altogether, such as with slavery.
by irthomasthomas | 2026-02-22 10:07:02+00:00
In my own tests I have found opus to be very good at writing plans, terrible at executing them. It typically ignores half of the constraints.
https://x.com/xundecidability/status/2019794391338987906?s=2...
https://x.com/xundecidability/status/2024210197959627048?s=2...
by lucketone | 2026-02-22 10:09:29+00:00
1. Nobody sees this as a big issue.

2. As-is situation is good enough.

3. I guarantee that you will have colleagues that will accidentally add empty dirs. (I definitely have seen when they accidentally add built artefacts; dirs would be lesser issue as they would not increase repo size by much, but still)

4. Actually only first two reasons matter, but for nitpickers there would be also a bit dishonest but formally correct pointing to some doc saying that it is file or content tracker (and formally directory is not a content)
by coppsilgold | 2026-02-22 10:10:45+00:00
How feasible would it be to integrate a neural video codec into the SoC/GPU silicon?

There would be model size constraints and what quality they can achieve under those constraints.

Would be interesting if it didn't make sense to develop traditional video codecs anymore.

The current video<->latents networks (part of the generative AI model for video) don't optimize just for compression. And you probably wouldn't want variable size input in an actual video codec anyway.
Show HN: Cuto – Turn raw footage into viral shorts in 30s with an AI prompt
by zy5a59 | 2026-02-22 10:11:12+00:00
I built Cuto because I was tired of spending hours on the repetitive parts of video editing: splitting long footage, syncing audio and captions, filtering out bad takes, and adjusting the pacing. I just wanted to focus on the storytelling.

Cuto is an AI video editing workspace that automates the grunt work. You drag and drop up to 12 video clips, write a quick prompt detailing what you want (e.g., "Find the best 30s product highlight and add a strong CTA"), and the engine does the rest.

Within 30 seconds, it hands back an editable visual timeline with:
- Auto-split shots and filtered out silences/bad takes
- Synchronized captions with rhythm alignment
- Selected highlight effects and custom watermark branding
- Auto-generated social-ready copy for TikTok, Shorts, and Reels

Unlike traditional NLEs (Non-Linear Editors), we wanted an interface where the visual parameters are controlled intuitively by natural language first, giving you a solid 80% draft instantly. From there, you can still tweak the timeline if needed.

It's currently geared towards talking heads, unboxing videos, reviews, and live stream clips.

I'd love for you to try it out. We still have a lot of edge cases to cover regarding complex scene transitions, but I'd really appreciate any feedback on the UX, the rendering speed, or the AI's editing choices.
by palata | 2026-02-22 10:11:52+00:00
I guess my point is that it is a bit of a gradient. You say you want Stock Android to allow you to get root access, others will say that Stock Android should not allow a normie to be tricked into getting root access and shooting themselves in the foot. Truth is, none of those is a "right": there is a product (Android) that tries to do well for the vast majority of its users. It seems totally reasonable to me that Google doesn't want to invest a lot of resources into making an extremely small minority happy. I am pretty sure that the number of people who want root on their smartphone is a rounding error.

Second thing is: if you have root and change something on the system, you break the secure boot. So you fundamentally cannot have full access, can you?

That's why my opinion is that it's not Google's role to make everyone happy. They should just not be allowed to prevent alternatives. So that the rounding error minority can install the system they want and be happy with it.
by thawawaycold | 2026-02-22 10:11:59+00:00
Agree. Also what upsets me the most is that to a great extent, all of these antics he pulls out are largely ignored by its userbase, meaning that most of the people using OpenAI products are either ignoring or ignorant altogether of what he truly thinks of them.
I'm not advocating for stopping GenAI usage because of this, but considering there are several equivalent competitors out there, it'd be mostly warranted to boycott OpenAI for these exact reason.

Otherwise the message that remains in the eye of these ghouls is that no matter how much you treat the world population as annoying cattle, they'll gobble it up in exchange for restaurant suggestions and rageslop
by quietbritishjim | 2026-02-22 10:12:17+00:00
I found the "perp dot product" an interesting one. It's a pity the description is in a massive pdf (though it looks like a great book). The top Google result is the MathWorld page [1] but it's very brief.

Here how that pdf describes it. It first defines the perpendicular operator on a 2D vector x as

x⟂ := (-x_2, x_1)

which is x rotated 90 degrees anticlockwise. Then the perp dot product of two 2D vectors is defined as

x⟂ . y

This has a few interesting properties, most notably that

x⟂ . y = |x| |y| sin θ

For example, the sign of the perp dot product tells you whether you need to rotate clockwise or anticlockwise to get from x to y. If it's zero then they're parallel – could be pointing in same or opposite directions (or over or both are zero).

In this Reddit post [2] about it, again not much is said, but a redditor makes the astute observation:

> The perp dot product is the same as the cross product of vectors in a plane, except that you take the magnitude of the z component and ignore the x/y components (which are 0).

[1] http://mathworld.wolfram.com/PerpDotProduct.html

[2] https://www.reddit.com/r/learnmath/comments/agfm8g/what_is_p...
by Terr_ | 2026-02-22 10:13:01+00:00
P.S.: Further musing: There's a system-design tension between granting access to people that are "perfect" versus "flawed in ways we are aware of and can manage." Where a process ought to land on that spectrum depends on certain assumptions about baseline applicant quality, an estimate of the organization's accuracy at [false/true] [negatives/positives], and the impacts.

If you auto-reject the people who admit to something sub-criminal like cheating on their spouse, that means no applicant will ever admit to it, so you'll end up with more people hid it. In the long run, that means a higher proportion of employees who have something an adversary can use for blackmail, and the blackmail is more-effective because the repercussions are large.
by miggol | 2026-02-22 10:15:24+00:00
Yes, the entire point is that requiring Google's permission to sideload anything is very bad.

The linked post by F-droid additionally points out that even that very bad case is not certain. We shouldn't trust that Google will even allow sideloading at all based on their words on their own blog.

Media has a responsibility to report that there is no evidence that Google will even allow anyone to sideload at this point.
by jruohonen | 2026-02-22 10:15:52+00:00
"The nightmare we should be focusing on is institutional: generative AI breaks long-standing correlations that society uses to infer things like effort, sincerity, authenticity, and credibility."

Spot on, I believe. It might be a good idea also business-wise; solving the authenticity issue alone might open new opportunities now that the "big platform economics" are dying (by hypothesis).
DietPi released a new version v10.1
by StephanStS | 2026-02-22 10:16:27+00:00
DietPi is a lightweight Debian based Linux distribution for SBCs and server systems, with the option to install desktop environments, too. It ships as minimal image but allows to install complete and ready-to-use software stacks with a set of console based shell dialogs and scripts.

The source code is hosted on GitHub: https://github.com/MichaIng/DietPi
The main website can be found at: https://dietpi.com/
Wikipedia: https://de.wikipedia.org/wiki/DietPi

The project released the version DietPi v10.1 on February 21th, 2026.

The highlights of this version are:
NanoPi Zero2: New hardware support
WhoDB: New software package
DietPi-Banner: Added option to output kernel version
Python3: All Python software options make now use of venvs
Navidrome: Support for RISC-V
MinIO: Console client mc added due to removed WebUI admin features
TigerVNC/RealVNC/XRDP: Lighter setups in case of no desktop needed
Fixes for NanoPi R5C, ZeroPi, Pi-hole, BirdNET-Go, Chromium, ADS-B feeder, Gogs, Plex media server, microblog.pub

The full release notes can be found at: https://dietpi.com/docs/releases/v10_1/
Show HN: Create Spot for creative inspiration, portfolios and critiques
by k_shehadeh | 2026-02-22 10:17:55+00:00
Create Spot is a creative platform for sharing your work, exploring what others are making, and growing through thoughtful critiques. Build and organize your portfolio, browse community pieces and museum collections for inspiration, and get featured in curated exhibits. It’s designed to help artists showcase their work, connect with others, and keep creating consistently.
by zozbot234 | 2026-02-22 10:18:00+00:00
It is an AI thing though. AI makes it far easier to create bespoke software targeted at narrow specialized domains, which is the mainstay of modern SaaS. We'll probably see "proper" FLOSS expand into these sectors too, such that the software won't be simply a matter of internal vibecoding by any single business - instead, the maintenance work will be shared.
by thatwasunusual | 2026-02-22 10:18:13+00:00
> Cost wise it does not seem very effective.

Why is this so damn important? Isn't it more important to end up with the best result?

I (in Norway) use a homelab with Ollama to generate a report every morning. It's slow, but it runs between 5-6 am, energy prices are at a low, and it doesn't matter if it takes 5 or 50 minutes.
Show HN: Claude Flubber – A 3D avatar that expresses Claude's emotions
by binora | 2026-02-22 10:18:19+00:00
I built a desktop companion that gives Claude a face. As you chat in Claude Code, Claude calls an express() MCP tool with 6 emotional parameters (valence, arousal, dominance, genuine, asymmetry, intensity). An MCP server broadcasts them over WebSocket to a 3D Flubber avatar that animates in real-time on your desktop. The avatar is a translucent green goo blob built in Three.js — it stretches when excited, puffs up when angry, droops when sad, and squints when skeptical. Compound emotions emerge naturally from the parameter combinations.