MurphyA/hacker-news huggingface dataset:
by trilogic | 2025-08-10 07:48:16+00:00
But we were promised that AI will make our life better.

A lot of Software engineers find themself in the layoffs, so do suddenly some 30 years old experienced programmers.

The point is that the education system is outdated and youngsters are still trapped loosing time studying some futile programs only to find out that they are graduated in matters that AI can execute in seconds.
by floppyd | 2025-08-10 07:49:07+00:00
I tried Kokoro for voicing blog posts and articles and wasn't impressed to be honest. Right now Gemini 2.5 Flash TTS is a much more capable system with generous free limits (about 10 minutes per generation and about 90 minutes per day). Voices are not very consistent between generations, but for shorter pieces it's not a big deal (but will obviously be for books)
by renn0xtek9 | 2025-08-10 07:49:20+00:00
I’ve built a small third-party CMake module called CMakeDependencyDiagram that helps you explore and understand your project’s build dependencies.

It works by leveraging CMake’s --graphviz output and turning it into an interactive minimal web page. Instead of staring at a static .dot file, you can click around, zoom, and navigate the dependency graph right in your browser.

Highlights:

Drop-in CMake module – no modifications to your project sources required.

Uses Graphviz for layout, outputs HTML/JS for exploration.

Helps track down circular dependencies, see build structure at a glance, and onboard new devs faster.

No external server needed – the generated HTML is self-contained and can be opened locally or shared.

Repo: https://github.com/renn0xtek9/CMakeDependencyDiagram

Feedback, suggestions, and contributions welcome!
by mdp2021 | 2025-08-10 07:50:11+00:00
No, DeepSeek also fails. (It worked in your test - it failed in similar others.)

(And note that DeepSeek can be very dumb - in practice, as experienced in our practice, and in standard tests, where it shows an ~80 IQ, where with other tools we achieved ~120 IQ (trackingai.org). DeepSeek was in important step, a demonstration of potential for efficiency, a gift - but it is still part of the collective work in progress.)
by BrenBarn | 2025-08-10 07:50:30+00:00
AI didn't conquer the US economy. What conquered the US economy is a small number of large companies. If Apple started rolling out an iTurd app, and Google started offering gTurd, and Amazon started shipping free turds to everyone's doorstep, and so on, then we would be talking about how turds have conquered the US economy. The economy has become divorced from any pretense of meeting actual consumer needs. The suppliers are creating their own demand.
by fl1pper | 2025-08-10 07:53:40+00:00
neuralese is a term first used in neuroscience to describe the internal coding or communication system within neural systems.

it originally referred to the idea that neural signals might form an intrinsic "language" representing aspects of the world, though these signals gain meaning only through interpretation in context.

in artificial intelligence, the term now has a more concrete role, referring to the deep communication protocols used by multiagent systems.
by meowface | 2025-08-10 07:54:46+00:00
It's a term somewhat popularized by the LessWrong/rationalism community to refer to communication (self-communication/note-taking/state-tracking/reasoning, or model-to-model communication) via abstract latent space information rather than written human language. Vectors instead of words.

One implication leading to its popularity by LessWrong is the worry that malicious AI agents might hide bad intent and actions by communicating in a dense, indecipherable way while presenting only normal intent and actions in their natural language output.
by nopinsight | 2025-08-10 07:55:45+00:00
The author might use it as an analogy to mentalese but for neural networks.

https://en.wiktionary.org/wiki/mentalese

EDIT: After reading the original thread in more detail, I think some of the sibling comments are more accurate. In this case, neuralese is more like language of communication expressed by neural networks, rather than its internal representation.
by CodeVisio | 2025-08-10 07:56:11+00:00
2. "...but in this case you have to show os some of your work from the past, so we can discuss this."

That is not always possible. There is always an NDA in contracts. Imagine me going around and revealing the code I have done for your company...

3.

Are you going to pay extra those two engineers of your company for doing something that is clearly outside what they were hired for at beginning or outside their competences?

4.

The same as 3. Are you going to pay those employees for doing manager work instead? Or the managers of your company are paid for doing nothing while showing doing something and soon ready, as in your message, to download their responsibilities to simple engineers (company's leaves )?

Edited.
by stevekemp | 2025-08-10 07:57:25+00:00
Off-topic but I have to say that potatoes are incredibly easy to grow, and doing so is very worthwhile.

I have a small planter on my balcony at the moment which is thriving with leaves, and in a few weeks I'll dig out the harvest to see what I got. People grow them in very poor soil, and even in literal bags of compost, or buckets. They're easy-going and almost trouble-free.

Growing chillis, tomatoes, or herbs on window-sills is kinda fun and rewarding, but growing a "proper crop" feels even more rewarding. And surprising because you have to wait until you dig things up to see how well you did!
by adrian_b | 2025-08-10 07:57:46+00:00
Eliminating from your 11-item list the words that cannot designate saturated colors, i.e. black, white, brown, pink and gray, there remain 6 words for saturated colors: red, orange, yellow, green, blue and purple.

Besides these 6 words, 2 more words are useful for naming saturated colors. The use of the word "violet" is widespread enough that it should be added to the list as a basic English color term.

The distinction between violet and purple is that while both are mixtures of red and blue, violet contains a small enough amount of red that it can be matched by a monochromatic color in the violet range of the spectrum, while purple contains an amount of red that is great enough so that it can be matched only by a mixture of monochromatic red with monochromatic blue.

A word is needed for the blue-green color. Cyan is a misnomer that should be avoided, but "turquoise" is a word that has been used in English for many centuries for designating the blue-green color (and which is used for the same purpose in many other European languages, all of which have borrowed the word from French, like also English). "Teal" is another synonym for "blue-green", which has been introduced in the 20th century.

There have been many misunderstandings about the words used by Isaac Newton for his circle of colors, but I believe that when his words are interpreted correctly, he was right.

Isaac Newton has divided the saturated colors into 8 colors: red, orange, yellow, green, blew or blue, indico or indigo, violet and purple. The first 7 of these were classified as prismatic colors, i.e. as monochromatic colors.

Isaac Newton spelled blue as both "blew" and "blue", and also indigo was spelled as both "indico" and "indigo". While the meaning of the other color words is not ambiguous, the meaning of these 2 words is not clear.

The words used by Isaac Newton were the names of some paints, whose hue he compared with the colors obtained by the dispersion of the solar light. The "indico" paint was presumably made with the indigo pigment, so it must have been blue.

The "blew" or "blue" of Isaac Newton was the color of the "blue Bise" paint. "Bise" is more frequently spelled "bice" and it was the name of a paint based on copper carbonate. Depending on the exact details of its method of preparation, such a paint will contain a mixture of malachite and azurite pigments, so it might have any hue between green and blue. We do not know who was Newton's paint supplier or any other details about the paint used by Newton, so it cannot be known for sure which was the real color of Newton's "blew".

Nevertheless I assume that Newton's "blue Bise" was actually a blue-green paint, because only in this case Newton's classification of the saturated colors will make perfect sense, because blue-green and blue are distinctive enough to deserve to have their own names.

So translated into modern English, the 8 saturated colors of Isaac Newton would be: red, orange, yellow, green, turquoise, blue, violet and purple. This classification remains right.
by anovikov | 2025-08-10 07:59:09+00:00
Well, in fact part of the reason why they left Russia is to be freer from threat of law enforcement. Ukraine is a more corrupt country and also doing shady things is more socially acceptable there. Don't get me wrong, i am a big supporter of Ukraine including its war efforts (and in fact work full time on Ukrainian defence tech project of my own), but it's a country as close to the popular Western mental image of "Eastern Europe" as it gets. Russia is a more orderly place - it is uncanny, but not chaotic. If you have some bitcoin, you can get away from law enforcement in Ukraine for a lot longer than you can in Russia.

And well, the reason of why cybercriminals are so frequently Russians is not that Russians are particularly evil. It's simply because they have the best technical higher education of Eastern Europe. Atheistic, post-Communist cultures of all of those countries, save for Czech Republic maybe, have no backstops against (nonviolent) crime at all. People there won't do cybercrime because they don't have the brains or training to do it, not because they are morally against it or society prevents them from it. It may be difficult to grasp for Americans (even those who don't consider themselves religious), but there are cultures that are completely Godless even on subconscious level. These people do whatever works and whatever they can get away with.
by sponnath | 2025-08-10 07:59:41+00:00
I don't think it's correct to generalize front-end work like this. I've found it very underwhelming for the kind of front-end stuff I do. It makes embarrassing mistakes. I've found it quite useful for a lot of the braindead code I need to write for CRUD backends though.

It's good at stuff that most competent engineers can get right while also having the sort of knowledge breadth an average engineer would lack. You really need to be a domain expert to accurately judge its output in specific areas.
by nxobject | 2025-08-10 08:00:12+00:00
There's the market out there for a consultancy that will fine-tune an LLM for your unique platform, stack, and coding considerations of choice – especially with proprietary platforms. (IBM's probably doing it right now for their legacy mainframe systems.) No doubt Apple is trying to figure out how to get whatever frameworks they have cooking up ASAP into OpenAI etc.'s models.
by written-beyond | 2025-08-10 08:01:26+00:00
Okay so we ended up using that too, it was really well done and documented.

But in hindsight I think flutter-pi or the Toyota one might have been a better investment. The lead engineer in Sony behind the elinux project left for Nvidia. The Toyota one uses Wayland, you can't make it directly hook into DRM. So you'd need a minimal compositor to get it running.
by tannhaeuser | 2025-08-10 08:02:25+00:00
Prolog works indeed very well as target for generation by an LLM, for input problems limited and similar enough in nature to given classes of templated in-context examples, so well indeed that the lack of a succinct, exhaustive text description of your problem is becoming the issue. At which point you can specify your problem in Prolog directly considering Prolog was also invented to model natural language parsing and not just for solving constraint/logic problems, or could employ ILP techniques to learn or optimize Prolog solvers from existing problem solutions rather than text descriptions. See [1].

[1]: https://quantumprolog.sgml.net
by scotty79 | 2025-08-10 08:04:23+00:00
I think the quality of the voice is super important for audiobooks and I think we are just closing in on the required quality with TTS.

I played a bit with Eleven labs voices and while they aren't bad when I tried make them read fragment of a text that I wrote, it sounded chaotic, boring, quite terrible, for anything longer than a sentence or two. But when I tried their v3 voices which they are currently in the process of rolling out, the same text sounded consistent, emotional, engaging, simply amazing. I think we are just crossing vocal uncanny valley.
by Squakie | 2025-08-10 08:04:38+00:00
I can offer a hacking/penetrarion testing perspective to this as a security researcher at a security consultint firm: this type of hallucination and trust is one of the largest things we exploit in our new LLM testing service. Overly agentic systems (one of the top 10 OWASP LLM vulns) is the most profound and commonly exploited issue that we've been able to leverage.

If we can get an internal, sensitive-data-handling agent to ingest a crafted prompt, either via direct prompt injection against a more abstract “parent” agent, or by tainting an input file/URL it’s told to process, we can plant what I have internally coined an “unfolding injection.”

The injection works like a parasitic goal, it doesn’t just trick one agent, it rewrites the downstream intent. As the orchestrator routes tasks to other agents, each one treats the tainted instructions as legitimate and works toward fulfilling them.

Because many orchestrations re-summarize, re-plan, or synthesize goals between steps, the malicious instructions can actually gain fidelity as they propagate. By the time they reach a sensitive action (exfiltration, privilege escalation, external calls), there’s no trace of the original “weird” wording, just a confidently stated, fully-integrated sub-goal.

It’s essentially a supply-chain attack on the orchestration layer: you compromise one node in the agent network, and the rest “help” you without realizing it. Without explicit provenance tracking and policy enforcement between agents, this kind of unfolding injection is almost trivial to pull off, and we've been able to compromise entire environments based on the information the agentic system provided us, or just gave us either a bind or reverse shell in the case it has cli access and ability to figure out its own network constraints.

SSRF has been making a HUGE return in agentic systems, and Im sad defcon and black hat didnt really have many talks on this subject this year, because it is a currently evolving security domain and entirely new method of exploitation. The entire point of agentic systems is non determinism, but it also makes it a security nightmare. As a researcher though, this is basically a gold mine of all sorts of new vulnerabilities we'll be seeing. If you work as a bugbounty hunter and see a new listing for an AI company I can almost assuredly say you can get a pretty massive payout just by exploiting the innate trust between agents and the internal tools they are leveraging. Even if you dont have the architecture docs of the agentic system you can likely prompt inject the initial task enough to taint the further agents to have them list out the orchestration flow by creatively adjusting your prompt for different types of orchestration and how the company might be doing prompt engineering on the agents persona and task its designed to work on and then submit report on to parent agent, and the limited input validation between them.
by dileeparanawake | 2025-08-10 08:04:57+00:00
Not sure about them peaking but I definitely feel that right now gpt-5 doesn’t feel like a game changer like the marketing says. In some things it’s worse, slower, less useful (in terms of detail not ego flattery!). My use case is mainly code and learning.

I think the make everything bigger is plateauing and not yielding the infinite returns that were first suggested and demonstrated from gpt 2 - 4.

I think now it’s harder because they’ve got to focus on value per watt. Smaller good models mean less energy, less complexity to go wrong, but harder to achieve.

The unlock could be more techniques and focussed synthetic data from old models used to train new ones but apparently gpt-5 uses synthetic data and this is one of the reasons it isn’t necessarily good in real world tasks.

For me if we go the synthetic data route it’s important to shoot for quality - good synthetic data distils useful stuff, discards the noise so useful patterns are more solid in training, but imagine it’s hard to distinguish signal from noise to produce good synthetic data.
by masklinn | 2025-08-10 08:05:10+00:00
> Is an infinite crash-loop considered success in Erlang?

Of course not, but usually that's not what happens, instead a process crashes because some condition was not considered, the corresponding request is aborted, and a supervisor restarts the process (or doesn't because the acceptor spawns a process per request / client).

Or a long-running worker got into an incorrect state and crashed, and a supervisor will restart it in a known good state (that's a pretty common thing to do in hardware, BEAM makes that idiomatic in software).
by yzydserd | 2025-08-10 08:05:28+00:00
This week’s episode of the BBC podcast The Infinite Monkey Cage was 42 minutes of Science Comedy dedicated to the spud, featuring the same expert as the OP.

Most interesting fact I learned was the effort going toward making potato seeds (not seed potatoes).

https://www.bbc.co.uk/sounds/play/m002fxn7
by jamboca | 2025-08-10 08:06:29+00:00
Have built many pipelines integrating LLMs to drive real $ results. I think this article boils it down too simply. But i always remember, if the LLM is the most interesting part of your work, something is severely wrong and you probably aren’t adding much value. Context management based on some aspects of your input is where LLMs get good, but you need to do lots of experimentation to tune something. Most cases i have seen are about developing one pipeline to fit 100s of extremely different cases; LLM does not solve this problem but basically serves as an approximator for you to discretize previously large problems in to some information sub space where you can treat the infinite set of inputs as something you know. LLMs are like a lasso (and a better/worse one than traditional lassos depending on use case) but once you get your catch you still need to process it, deal with it progammatically to solve some greater problem. I hate how so many LLM related articles/comments say “ai is useless throw it away dont use it” or “ai is the future if we dont do it now we’re doomed lets integrate it everywhere it can solve all our problems” like can anyone pick a happy medium? Maybe thats what being in a bubble looks like
by komali2 | 2025-08-10 08:06:36+00:00
I'm realizing that I may have misunderstood Framework's market. I thought it was tinkerers and environmentally conscious FOSS nerds like me, but I think there maybe be a huge enterprise segment whose employees in charging of purchasing are like me but answer to much more strict business needs than "Isn't it cool that it comes with a screwdriver in the box?" So for example the underpowered cpu in the fw12 makes no sense to me until I found out that it's also designed for mass purchases by schools and designed to be flung around by angsty teens. The desktop seems to be meant to be strapped to the underside of 40 identical cubicals in an office as much as it's meant to be apparently hauled around by people that want to have CSGO lan parties.
by dileeparanawake | 2025-08-10 08:07:22+00:00
Yep right now I’m not even sure I’d say it’s 10% better (at things I use it for). Feels overhyped based on the launch and what every influencer covered in their review (maybe obviously). Maybe it will get better as they sort out routing and other kinks. Right now feels like big gap between marketing and reality.
by rvasa | 2025-08-10 08:07:33+00:00
Great to start off ... then we will end-up reinventing/re-specifying functions for reusability, module/packages for higher-level grouping, types/classes, state machines, control-flows [with the nuances for edge cases and exit conditions], then we will need error control, exceptions; sooner or later concurrency, parallelism, data structures, recursion [lets throw in monads for the Haskellians amoung us]; who knows .. we may even end up with GOTOs peppered all over the English sentences [with global labels] & wake up to the scoping, parameter passing. We can have a whole lot of new fights if we need object-oriented programming; figure out new design patterns with special "Token Factory Factories".

We took a few decades to figure out how to specify & evolve current code to solve a certain class of problems [nothing is perfect .. but it seems to work at scale with trade-offs]. Shall watch this from a distance with pop-corn.
by socalgal2 | 2025-08-10 08:08:06+00:00
Suggestion for the author, I don't think there are any outdoor places where the sky is black. I don't know that gray would be any better. Stars? Some night clouds? or maybe even still a gradient?

https://www.google.com/search?sca_esv=58e7983bf9f21fcd&udm=2...
by radio879 | 2025-08-10 08:08:18+00:00
well, most of the time, I just dump the entire codebase in if the context window is big and its a good model. But there are plenty of times when I need to block one folder in a repo or disable a few files because the files might "nudge" it in a wrong direction.

The surgical context tool (aicodeprep-gui) - there are at least 30 similar tools but most (if not all) are CLI only/no UI. I like UIs, I work faster with them for things like choosing individual files out of a big tree (at least it is using PySide6 library which is "lite" (could go lighter maybe), i HATE that too many things use webview/browsers. All the options on it are there for good reasons, its all focused on things that annoy me..and slow things down: like doing something repeatedly (copy paste copy paste or typing the same sentence over and over every time i have to do a certain thing with the AI and my code.

If you have not run 'aicp' (the command i gave it, but also there is a OS installer menu that will add a Windows/Mac/Linux right click context menu in their file managers) in a folder before, it will try to scan recursively to find code files, but it skips things like node_modules or .venv. but otherwise assumes most types of code files will probably be added so it checks them. You can fine tune it, add some .md or txt files or stuff in there that isn't code but might be helpful. When you generate the context block it puts the text inside the prompt box on the top AND/OR bottom - doing both can get better responses from AI.

It saves every file that is checked, and saves the window size, other window prefs, so you don't have to resize the window again. It saves the state of which files are checked so its less work / time next time. I have been just pasting the output from the LLMs into an agent like Cline but I am wondering if I should add browser automation / browser extension that does the copy pasting and also add option to edit / change files right after grabbing the output from a web chat. Its probably about good enough as it is though, not sure I want to make it into a big thing.

--- Yeah I just keep coming back to this workflow, its very reliable. I have not tried Claude Code yet but I will soon to see if they solved any of these problems.

Strange this thing has been at the top of hacker news for hours and hours.. weird! My server logs are just constant scrolling