skysight-inc/HackerNews huggingface dataset:
And a chunk of Mozilla's funding going towards social justice related projects, as much of the organization has been co-opted by social justice types.

As was stated in the article[1], close to half a million dollars was spent on a social-justice related organization, the Mackenzie Mack Group.

“[Mckensie Mack Group] is a change management firm redefining innovation in the white-dominant change management industry.”

In the article it also says " While The Lunduke Journal does not like to delve too deeply into the Political Woods (tm), it should be questioned why so much money — possibly millions of dollars donated by individuals who thought they were supporting a web browser — is being funneled into highly political organizations that seem to have no involvement with the World Wide Web, Web Browsers, or any related standards. "

1. https://lunduke.locals.com/post/4387539/firefox-money-invest... "
> Shouldn't businesses be operating like the latter? Constantly training?

There's a rampant cultural mind-virus that argues that 95%th percentile is somehow tons of work (rather than a lack of unforced mistakes), so everyone just writes it off. It's on full display at this very site. Just look on any post involving software quality, and read a bunch of comments suggesting widespread apathy from engineers.

Obviously every situation is different, but people seem to be pretty okay with relinquishing agency on these things and just going along with whatever local maxima their org operates in. It's not totally their fault, but they're not blameless either.
The weaker model is just a stand-in for human supervisors.

They are experimenting on GPT-2 supervising GPT-4 as an analogue to humans supervising the superhuman AGI.
Sourcegraph Cody – AI assistant that knows your codebase is GA
> Did you notice the quote marks around adequately?

What is the significance of this question?

> I want stuff to go really well

Right. You need to figure out whether you're talking about someone who is acceptable or not. Right now, you're equivocating.
Weird, I have had the opposite experience that most shit slips through the cracks of automated testing & manual testing by an experienced QA is 10x more effective.
It's only the oldest for purposes of simplifying pedagogy in scientific education. In reality the error bars on half a dozen of the "oldest" cave art in the world overlap so much that it's impossible to really conclude which one was older. There are even older works of art but they're more like patterns and abstract scribbles than what we would consider drawings.

I do believe the Indonesian cave painting have the distinction of being the oldest known art with distinct animal figures.
Though there are a couple caveats as to what code is available. Quoting from the github:

> implementation contains an implementation of the evolutionary algorithm, code manipulation routines, and a single-threaded implementation of the FunSearch pipeline. It does not contain language models for generating new programs, the sandbox for executing untrusted code, nor the infrastructure for running FunSearch on our distributed system. This directory is intended to be useful for understanding the details of our method, and for adapting it for use with any available language models, sandboxes, and distributed systems.
It's not visible or concealed damage, it really looks like poor QA.

All cans (and it's always cans, not bottles) are visibly intact. Sometimes the can itself is "squishy" due to lack of gas. Other times they barely foam when poured. It's usually potable but wildly inconsistent in taste.

Most are fine, but it's gotten too expensive to keep overlooking duds.
Like the Carl Hewitt of ML?
What other project files or GitHub code? It’s a new language. And what does “hoover up” mean? Use the context window for language basics? RAG? Retrain the model?

Also the LLM won’t have been trained on the vast corpus of debugging sessions, blog posts, etc of existing languages. I’d expect the quality to be very poor compared to the current top players.

(It’s effectively the same phenomenon as folks sticking to languages with a big hiring pool.)
The same is true for database rankings (db-engines).

If entrants are not artificially inflating "organic" signals via fake content spam (Twitter/X), then the criteria themselves are losing their signal strength (StackOverflow/GitHub).

The diffusion makes it increasingly difficult to understand which channels are important and which correlate to strength in the market.

Unfortunately, these can be more than vanity metrics.

Some VCs or financial markets may use these as methods towards valuation.
New clues emerge toward possible life on Enceladus
I think the thing missing from a lot of these conversations is what problem domain you're working in. The more "technical" your problem domain is the more valuable automated testing will be over manual. For almost anything based on user experience and especially mass-market customer-facing products, human QA is far more necessary.

In either case, the optimal operating model is that QA is embedded in your product team. They participate in writing tickets, in setting test criteria and understanding the value of the work being done. "Finding bugs" is a low value task that anyone can do. Checking for product correctness requires a lot more insight and intuition. Automated test writing can really go either direction, but typically I'd expect engineers to write unit tests and QA to write e2e tests and only as much or as little as it actually saves time and can satisfactorily indicated success or failure of a user journey.
Now just to convince msft of this fact because their shit's been breaking left and right and all over the place.
I think Congress should pass a law that if you send a registered letter to the legal department of these e-commerce companies with your name, address and credit card number with an order to drop the service then they have to process it and cancel your "subscription". Companies would be required to print out the address of their legal department on the ordering page and sign-up page. Right now if you complain to your credit card company they won'thelp you and will refer you to the company which won't help you.
Exploring How AI Will Impact the Economy
Not even LinkedIn is that keen on Microsoft's cloud
Thanks for sharing the story of your developer mom - a very cool mother to have!

COBOL is not a "cool" language, but mainframes have been around long enough to be "retro cool" now, and most run Linux at least as an optional OS under some virtualization (IBM Z).

As a doctoral candidate in the noughties, I purchased a book about FORTRAN due to its "retro-coolness" and read it, and eventually took on a short university gig to earn the money that the book cost back, tutoring architects/engineers in FORTRAN 95 for a bit, which was fun; to date, I could not bring myself to do the same with COBOL, though, or not yet.

Because it is so verbose, if I had to use it I would probably write in another language and transpile to it.
When I wrote "teletype" I wasn't referring to what teletypes had become by the time by the time Unix was developed. Think further back than that. This is why I mentioned 5-bit codes:

https://en.wikipedia.org/wiki/Baudot_code

This coding scheme is so constrained (32 possible 5-bit values) that it uses the codes FIGURES (01000) and LETTERS (10000) to toggle back and forth between two alternate sets of meanings for the remaining 30 possible codes. Still not enough space for lower case or ASCII's plethora of control codes (just NUL and DEL).
I strongly disagree.

I worked at a company with a world-class QA team. They were amazing and I can't say enough nice things about them. They were comprehensive and professional and amazing human beings. They had great attention to detail and they catalogued a huge spreadsheet of manual things to test. Engineers loved working with them.

However -- the end result was that engineers got lazy. They were throwing code over to QA while barely testing code themselves. They were entirely reliant on manual QA, so every release bounced back and forth several times before release. Sometimes, we had feature branches being tested for months, creating HUGE merge conflicts.

Of course, management noticed this was inefficient, so they formed another team dedicated to automated QA. But their coverage was always tiny, and they didn't have resources to cover every release, so everyone wanted to continue using manual QA for CYA purposes.

When I started my own company, I hired some of my old engineering coworkers. I decided to not hire QA at all, which was controversial because we _loved_ our old QA team. However, the end result was that we were much faster.

1. It forced us to invest heavily on automation (parallelizing the bejesus out of everything, so it runs in <15min), making us much faster

2. Engineers had a _lot_ of motivation to test things well themselves because there was no CYA culture. They couldn't throw things over a wall and wash their hands of any accountability.

We also didn't have a lack of end-to-end tests, as the author alludes to. Almost all of our tests were functional / integration tests, that run on top of a docker-compose set up that simulated production pretty well. After all, are unit tests where you mock every data source helpful at all? We invested a lot of time in making realistic fixtures.

Sure, we released some small bugs. But we never had huge, show stopping bugs because engineers acted as owners, carefully testing the worst-case scenarios themselves.

The only downside was that we were slower to catch subtle, not-caught-by-Sentry bugs, so things like UX transition weirdness. But that was mostly okay.

Now, there is still a use case for manual QA -- it's a question of risk tolerance. However, most applications don't fit that category.
That article actually shows that Mozilla spends most on software development.

It does have a lot of administrative overhead that it could do without, instead of laying off engineers, like they did. But the article itself is trash, due to complaining of $892,000 worth of expenses that seem dubious, in an organization that makes 500 million per year, which paints a certain kind of picture that's disingenuous.
It's not his field, what do you expect?
Sensitive power users.

I didn't say it was sh*t. I'm saying it's not newsworthy.

Clap for the devs. And install all the extensions. But we don't need a hundred posts about it. This isn't the big story Firefox marketing might think it is.
52 snippets from 2023 that struck me as interesting.
Or are you just more likely to notice the overlap?
Ask HN: What are some great examples of bi-directional diagram/code projects?
I've always been interested in this space and there seem to be few examples. Specifically, I'm looking for an example that shows how to use something like codemirror and a drag and drop interface. A change in one would be reflected in the other.
To your point, the QA team is the customer's advocate. As you say, they know the product, from the customer's perspective, better than anyone else in the development organization.

Where I've seen QA teams most effective is providing more function than "just" QA. I've seen them used for 2nd tier support. I've seen them used to support sales engineers. I've also seen QA teams that take their manual test plans and automate their execution (think Selenium or UiPath) and have seen those automations included in dev pipelines.

Finally, the QA team are the masters and caretakers of your test environment(s), all the different types of accounts you need for testing, they should have the knowledge of all the different browsers and OSes your customers are using, and so forth.

That's a lot for the dev team to take on.
> and what kind of back-up plans they have for such a scenario.

I mean, notoriously, stop working, for weeks: https://en.wikipedia.org/wiki/TSB_Bank_(United_Kingdom)#Migr...

(I've got to assume that loss of institutional knowledge was a big factor in that fiasco.)
How is React state not a DAG out of the box? What problems does this solve? The pitch appears to be:

> any outside change (user action, api result) unleashes a graph traversal. Our UI components become much simpler, because they just need to dumbly reflect values in the graph.

But that sounds the same as the reasoning behind React's one-way data flow.
The problem with civil cases is that you still need to have the money upfront (and accept that it will be very hard for you to win so you may never get compensated for those expenses) and then also overcome governmental immunity and qualified immunity

> Even as the proliferation of police body cameras and bystander cellphone video has turned a national spotlight on extreme police tactics, qualified immunity, under the careful stewardship of the Supreme Court, is making it easier for officers to kill or injure civilians with impunity. The Supreme Court’s role is evident in how the federal appeals courts, which take their cue from the high court, treat qualified immunity. In an unprecedented analysis of appellate court records, Reuters found that since 2005, the courts have shown an increasing tendency to grant immunity in excessive force cases – rulings that the district courts below them must follow. The trend has accelerated in recent years. (https://www.reuters.com/investigates/special-report/usa-poli...)
> I don't believe LLM's will ever become AGI, partly because I don't believe that training on the outputs of human intelligence (i.e. human-written text) will ever produce something equivalent to human intelligence.

This is irrelevant because OpenAI's definition of AGI [1] doesn't imply similarity or equivalence to humans at all:

>artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work

I.e. the stated goal of this company is to put humans out of work and become the censors and gatekeepers, not to produce something human-like.

>You can't model and predict the stock market just by training on the outputs of stock trading decisions (the high today, the low yesterday). You have to train on the inputs (company fundamentals, earnings, market sentiments in the news, etc.)

Most of your intelligence is not actually yours. It's social in nature, obtained by distillation of generations' worth of experience, simplified and passed to you through the stored knowledge. Which is, coincidentally, what the models are being trained on.

[1] https://openai.com/charter
SoftwareAndEngineering
it's tempting to write a sh(1) based webapp that uses make(1) under the hood to provide similar functionality...
I share the same experience that the QA team writes test plans not and “code level” tests

That said, those test plans are gold. They form the definition of the product’s behavior better than any Google Doc, Integration Test, or rotating PM ever could.
Man my first job was at Nordea 2000-2003, looking back at it now it was pretty dysfunctional. They were implementing a new everything but the kitchen sink framework that would unify all the customer facing e-banking and it was a mess with an external company providing the framework together with internal architects. In the 3 years I was there I think pretty much zero was delivered.

The you had the markets division that would hire in externals to do in parallel dev and then drop the burning corps of the project on internal dev once they moved onto the next shining thing lol.
https://deepmind.google/discover/blog/graphcast-ai-model-for...
Send them a bug report, they'll likely fix it. I'm not joking, they're not using Webfig often, so sometimes they can overlook these kinds of minor issues.
>of an 8x8 pixel matrix.

Some examples of the time (often 7x8) https://www.nesdev.org/wiki/Fonts

Another open source example for 8x8 and 8x16 is GNU Unifont

You can go down to 5x5 for upper case while still being readable
https://www.dafont.com/5x5-pixel.font

Looked at the topic in the context of silk screen printing with normal steel mesh.
I don't see a single comment suggesting that.
It seems this is essentially an evolutionary algorithm with an LLM generating the pool of new variations at each step. Definitely a very cool idea, but hard to evaluate the results without knowing more about that field of mathematics. Obviously the problem they chose fit well into the FunSearch framework, but I'm curious if this is one of the more popular open problems in that space or if they picked something that was more niche?

Namely, what sort of computational resources had been dedicated to the problem before? Because DeepMind suddenly throwing their weight at a problem that was previously the focus of a handful of random math grad students would make it hard to benchmark the ML advance that was made here -- like would it be possible to find a similar solution with a ton of compute and more traditional genetic algorithms?

I wouldn't be surprised if the answer were no. Protein folding competition was a pretty big space in biology before getting absolutely destroyed by Alpha Fold. But I also wouldn't be entirely surprised if the answer were yes. The amount of hype around LLMs right now is crazy, probably half the news I see about them turns out to be very exaggerated upon further evaluation.

ETA: the headline here is definitely exaggerated, because there was also human in the loop to refine what the LLM was generating. At a glance the technical article doesn't benchmark enough against alternatives to the LLM component in their workflow IMO. But it is entirely possible they tackled well established enough open problems, such that prior work already handled those control cases decently.

I'd love to know what someone in this space of mathematics thinks about the paper! Would it have generated much buzz if they got these same results like 3 years ago? Would it be accepted to Nature if they found they could accomplish something similar using their framework even if it was an RNN in the loop?
I use Kotlin now. Java is merely the reason JVM starts with a "J".

Converting 250kLOC was quite doable. Team couldn't be happier.
Which tools actually pull down those packages though? If anything that's a counter point to the Intel CEO: industry is adopting CUDA as the defacto standard.
I've never seen HN rendered incorrectly in any desktop or android FF version, what do you mean?
I highly recommend the tutorials in Mortal Kombat 11 and Mortal Kombat 1 (the time travelling reboot/sequel to 11). They do a really good job of teaching intermediate/advanced mechanics in a way that are pretty straightforward and understandble. I never understood whate "frame traps" or "plus/minus on block" meant until I did the tutorials for MK11 and I've been (casually) playing fighting games since Street Fighter 2.

The World Tour mode in Street Fighter 6 also starts you off by easing you into the mechanics, giving you mini missions where you get to practice specific mechanics "safely". The dynamic controls which massively simplify inputs is also a godsend for people who are intimidated by quarter circle inputs and the like.
PyTorch includes some Vulkan compat already (though mostly tested on Android, not on desktop/server platforms), and they're sort of planning to work on OpenCL 3.0 compat, which would in turn lead to broad-based hardware support via Mesa's RustiCL driver.

(They don't advertise this as "support" because they have higher standards for what that term means. PyTorch includes a zillion different "operators" and some of them might be unimplemented still. Besides performance is still lacking compared to CUDA, Rocm or Metal on leading hardware - so only useful for toy models.)
I was lucky enough to work in a small eng team with 1 full-time dedicated QA person. One of the very few coworkers from my long career that I have really tried hard to poach away from whatever they were doing after our shared workplace went bust.

Yes, part of the job was to write and run manual test suites, and to sign off as the DRI that a certain version had had all the automated and manual tests pass before release.

But their main value was in the completely vague mandate "get in there and try to break it." Having someone who knows the system and can really dig into the weak spots to find problems that devs will try to handwave away ("just one report of the problem? probably just some flaky browser extension") is so valuable.

In my current job, I have tried for 5+ years to get leadership to agree to a FT QA function. No dice. "Developers should test their own code." Yeah and humans should stop polluting the ocean and using fossil fuels, how's that going?
There are more junk foods out there than potato chips.
Ledger Connect-Kit Supply Chain Attack Hits Decentralized Crypto Apps
I had a buddy who liked to do that kind of thing. I think his favorite trick was just to enter nothing in a form and hit enter to see what happens. It's probably his favorite because it deleted the database on some little thing I wrote at one point and we got a good laugh over it and I got a good lesson out of it.
>Calm down, buddy

I'm not agitated.

>Obviously, a prerequisite to becoming more intelligent than a human, is to become equally intelligent as a human. I don't believe LLM's will ever be equal in intelligence to humans, ergo I also don't believe they will become superior in intelligence to human (which is how the linked article defines "AGI").

You have still not answered my question. saying "equivalent to human intelligence" is easy. The real question is equivalent how ? What are you saying it needs to do ? or is this just a vague "i'll know it when i see it" assertion ?

If it's so easy to say "GPT-4 is not agi" then it should be very easy to say what it can't do that disqualifies it

what is it that you're looking for it to do to be called one ?
Wonder where the goalposts will be moved now by the "stochastic parrot" parroters.
User silvester had better rig up a connection pooler next time ;)
I've seen this. I can get quite a bit of decent responses from LLMs about Python. But using it for Elixir produces more shady/dated answers. And Jetpack Compose, it just completely gets weird (and mostly worthless, being either too synthetically approximated or dated).

It really illustrates (to me) that LLMs are "Plausibility Machines". They produce plausible answers. Plausibility is informed by repeated observability over large sets. We asses something as plausible when it matches the majority of our observations. That's how the parlor trick of seeming to "tell the truth" is pulled off. The venn overlap of "plausible" and "true" is pretty high. But when "truth" is in flux, you start to see the feet of the wizard behind the curtain.
The decrease of Github activity might be caused by their 2FA rollout. Everything that does not have a 2FA workflow (like a git mirror bot) got locked out from non-public repositories this summer.
I though the same.
(even though I personally use beancount for plain text accounting)
I don’t know, imagine relying on remote access and one day when you absolutely need to log in and do your thing you suddenly cannot because the breach and they turned it off for everyone.

And to get on site it’s a 8-hour commute, or you have to cross a border or two, or you need a visa to get there. Or anything at all which necessitated remote access in the first place.
I've heard this expression many times before, still doesn't make any sense to me.
I vastly prefer my setup based around FreeBSD I've used for the last 20+ years, than the corporate hell that is Windows.

Not much has changed over the years - I still use rc.conf and pf.conf to configure my system. Things are still where I expect them to be.

XFCE is still the same, thunderbird hasn't changed much. Vim is exactly how it was. And the initial learning curve wasn't even that steep.

Meanwhile, Windows is just getting worse and worse and worse for each year.
So in that case, I am paying both with my time wasted and with money(though someone elses).