MurphyA/hacker-news huggingface dataset:
by ilaksh | 2026-03-05 11:43:57+00:00
For my framework, since I am using it for outgoing calls, what I am thinking maybe is I will add a tool command call_full_duplex(number, persona_name) that will get personaplex warmed up and connected and then pause the streams, then connect the SIP and attach the IO audio streams to the call and return to the agent. Then send the deepgram and personaplex text in as messages during the conversation and tell it to call a hangup() command when personaplex says goodbye or gets off track, otherwise just wait(). It could also use speak() commands if necessary maybe with a shutup() command first. Need a very fast and smart model for the agent monitoring the call.
by jacquesm | 2026-03-05 11:44:27+00:00
If you don't understand the meaning of what a 'derived work' is then you should probably not be doing this kind of thing without a massive disclaimer and/or having your lawyer doing a review.

There is no such thing as the output of an LLM as a 'new' work for copyright purposes, if it were then it would be copyrightable and it is not. The term of art is 'original work' instead of 'new'.

The bigger issue will be using tools such as these and then humans passing off the results as their own because they believe that their contribution to the process whitewashes the AI contributions to the point that they rise to the status of original works. "The AI only did little bits" is not a very strong defense though.

If you really want to own the work-product simply don't use AI during the creation. You can use it for reviews, but even then you simply do not copy-and-paste from the AI window to the text you are creating (whether code or ordinary prose isn't really a difference).

I've seen a copyright case hinge on 10 lines of unique code that were enough of a fingerprint to clinch the 'derived work' assessment. Prize quote by the defendant: "We stole it, but not from them".

There is a very blurry line somewhere in the contents of any large LLM: would a model be able to spit out the code that it did if it did not have access to similar samples and to what degree does that output rely on one or more key examples without which it would not be able to solve the problem you've tasked it with?

The lower boundary would be the most minimal training set required to do the job, and then to analyze what the key corresponding bits were from the inputs that cause the output to be non-functional if they were dropped from the training set.

The upper boundary would be where completely non-related works and general information rather than other parties copyrighted works would be sufficient to do the creation.

The easiest way to loophole this is to copyright the prompt, not the work product of the AI, after all you should at least be able to write the prompt. Then others can re-create it too, but that's usually not the case with these AI products, they're made to be exact copies of something that already exists and the prompt will usually reflect that.

That's why I'm a big fan of mandatory disclosure of whether or not AI was used in the production of some piece of text, for one it helps to establish whether or not you should trust it, who is responsible for it and whether the person publishing it has the right to claim authorship.

Using AI as a 'copyright laundromat' is not going to end up well.
by alphager | 2026-03-05 11:44:35+00:00
Those usecases are at least 5 years if not 10 years out. They require software support which won't come until a significant part of the pc market has the necessary hardware for it. Until then, paying extra for the hardware is foolish.

This will only come if Windows 12 requires a TPU and most of the old hardware is decommissioned.
by fzeroracer | 2026-03-05 11:44:43+00:00
There are two important failures I see with this logic:

First, I am not arguing for reusability. Reusability is one of the most common mistakes you can make as a software engineer because you are over-generalizing what you need before you need it. Code should be written for your specific use case, and only generalized as problems appear. But if you can recognize that your specific use case fits a known problem, then you can find the best way to solve that problem, faster.

Second, when you're using an LLM to make your code more 'reusable' you are taking full responsibility for everything that LLM vomits out. You're no longer assembling a car from well known parts, taking care to tailor it to your use case as needed. You're now building everything in said car, from the tires to the engine and the rearview mirror.

Coding is a constant balance between understanding what you're solving for and what can solve it. Using LLMs takes the worst of both worlds, by offloading both your understanding of the problem and your understanding of the solution.
by ourmandave | 2026-03-05 11:44:51+00:00
Fear not citizen! Ya boi Elon got you covered with the new Megablock.

Ta da! It's 3 Megapaks wired together with a transformer. Now that's innovation.

See? Without billionaires, who would save us from billionaires?

Tesla’s Megablock: Breaking the Transformer Bottleneck
https://www.rebellionaire.com/post/tesla-megablock-transform...
by edelans | 2026-03-05 11:46:20+00:00
very nice! I'll give it a shot! I thought about something similar in the past few years. I like the fact that you insist on "no social pressure" and a "gentle" nudge. The goal is not to make the user feel bad :). I also like that you make me import just a small subset of my contacts, to not feel overwhelmed right from the beginning (start small! make it work then make it better).

some quick feedback:
- I feel a more natural path to add an "extra checkin" would be from the "flower" in the garden (home page).
- I would love to be able to add a date in the checkin. Let's say I know the person I just checked in with told me she has an important event coming up (exam, interview, work deadline, baby...) I'd love to be able to add a specific date for a future checking on a specific topic.
by theshrike79 | 2026-03-05 11:46:23+00:00
We're solving this with two different tracks for Vibed stuff and Actual Code.

Vibed stuff can do whatever it wants, with some basic CI checks and Agent instructions

BUT if any of that crosses specific thresholds (writes to dangerous APIs, reads from unvetted sources, is deployed on the public internet), an actual developer MUST review the code - with the associated costs billed to the creator's BU.

Works fine, zero projects have been made public yet, but we have a bunch of Vibed internal tools in use that can't be accessed outside our internal network (or VPN) that are actually helping people do their work more efficiently.
by Smalltalker-80 | 2026-03-05 11:46:29+00:00
Imo, the problem with the 4-pane browsers are:
1 - The left-most 'package' pane is a flat list. And there are 10K + classes in e.g. Pharo. There are 900+ (!) packages in Pharo, in the pane, so its not easy to scroll through. This is solved 'manually' by have a hierarchical naming of packages. I think this pane should just have tree view.
2 - When developing an app, you may be working on classes in multiple packages. You want to have 'project' view with a simple way of switching between them, without having 10+ system browser windows open, like shown in the article. Dolphin Smalltalk has implemented this concept.
Shameless plug: SmallJS (https://small-js.org) has these too..
by jacquesm | 2026-03-05 11:46:34+00:00
No, that depends on whether or not the AI work product rests on key contributions to its training set without which it would not be able to the the work, see other comment. In that case it looks like 'a new work doing the same' but it still a derived work.

Ted Nelson was years ahead of the future where we really needed his Xanadu to keep track of fractional copyright. Likely if we had such a mechanism, and AI authors respected it then we would be able to say that your work is derived from 3000 other original works and that you added 6 lines of new code.
by Sharlin | 2026-03-05 11:47:07+00:00
I was talking about libraries, higher-level units of reuse than individual functions. And your "syntactic" vs "semantic" reuse makes zero sense. Functions are literally written and invoked for their semantics – what they make happen. "Syntactic reuse" would be macros if anything, and indeed macros are very good at reducing boilerplate.

You might have a more compelling argument if instead of syntax and semantics you contrasted semantics and pragmatics.
by dathinab | 2026-03-05 11:47:43+00:00
> "We still have no legal conclusion on whether AI model generated code, that is trained on all publicly available source (irrespective of type of license), is legal or not."

it depends on the country you are in

but overall in the US judges have mostly consistently ruled it as legal

and this is extremely unlikely to change/be effectively interpreted different

but where things are more complex is:

- model containing training data (instead of generic abstractions based on it), determined by weather or not it can be convinced to produce close to verbatim output of the training data the discussion is about

- model producing close to verbatim training data

the later seems to be mostly? always? be seen as copyright violation, with the issue that the person who does the violation (i.e. uses the produced output) might not known

the former could mean that not just the output but the model itself can count as a form of database containing copyright violating content. In which case they model provider has to remove it, which is technically impossible(1)... The pain point with that approach is that it will likely kill public models, while privately kept models will for every case put in a filter and _claim_ to have removed it and likely will get away with it. So while IMHO it should be a violation conceptually, it probably is better if it isn't.

But also the case the original article refers to is more about models interacting/using with code base then them being trained on.

(1): For LLMs, it is very much removable for knowledge based used by LLMs.
by andy_ppp | 2026-03-05 11:48:40+00:00
How much better is an LLM at mass surveillance? Obviously RAG with everyone’s details in it is useful but it’s also likely prone to hallucinations. I’m not sure LLMs are the right AI for even finding patterns in such data. As for letting LLMs autonomously kill people they clearly won’t be ready for that any time soon.

Does the administration really believe these AIs are like digital humans?
Show HN: CodeDrift – static analysis for AI-generated code
by hamzzaamalik | 2026-03-05 11:49:55+00:00
Hi HN,

I built *CodeDrift*, a CLI tool that detects bugs commonly introduced by AI coding assistants like Copilot, Cursor and ChatGPT.

Over the last year I noticed that AI tools often generate code that compiles correctly, passes linting and looks reasonable in code review but still contains subtle issues.

Some common examples I kept seeing:

* async `forEach` loops that never await promises
* missing authorization checks (IDOR)
* hallucinated dependencies that don’t exist
* stack traces leaking sensitive information
* request data used without validation

These bugs often slip past ESLint, TypeScript and even human reviewers because the code looks correct.

CodeDrift parses the code using the TypeScript compiler API and runs a set of detectors looking for these patterns.

Example:

```
async function syncProducts(items) {
items.forEach(async (item) => {
await updateStock(item.id);
});
}
```

CodeDrift output:

```
CRITICAL: async forEach does not await promises
Fix: use Promise.all or a for...of loop
```

Another example it detects:

```
Database query using user-supplied ID without authorization check
→ potential IDOR vulnerability
```

The goal isn’t to replace tools like ESLint or TypeScript, or security scanners like Snyk.
It’s meant to act as a safety layer for code generated with AI assistants.

The tool runs locally, requires no cloud access, and can be tried with:

```
npx codedrift
```

I’d love feedback from developers who are using AI coding tools in production.
Show HN: Scrollie – turn iPhone recordings into long screenshots
by foamzou | 2026-03-05 11:50:28+00:00
Built Scrollie, an iOS app for long screenshot workflows.

Main use case: convert recordings (or screenshot sequences) into one long image with less manual work.

Current focus:
- on-device stitching
- export editing (blur/mosaic, text, shapes, watermark)
- draft auto-save + resume

App Store:
https://apps.apple.com/app/scrollie/id6757146579

Launch promo is live now (~48h): Lifetime unlock is free.
Ends: Mar 7, 2026 08:01 UTC
Claim path: Settings -> Upgrade to Pro -> Lifetime (Free / $0)

If claimed at $0 now, Lifetime stays unlocked permanently with no auto-charge later. Early-bird pricing applies only to users who miss this free window.
by janwilmake | 2026-03-05 11:51:13+00:00
Hi, i just open sourced my alternative at https://github.com/janwilmake/forgithub.badge, which is backed by uithub.com. The advantage is that you don't need a github action to calculate and it still auto-updates daily; also, if you click, you go to uithub to further trim the context window which can be useful if the whole codebase doesn't fit or if you want higher quality outputs.
Tell HN: We modeled the cost of boilerplate (it's ~80% of the budget)
by thesssaism | 2026-03-05 11:51:33+00:00
We spent the last month modeling software budgets to figure out why velocity often feels so low even with senior teams. The short answer seems to be structural: about 80% of engineering time goes to non-differentiating infrastructure (auth, pipelines, CRUD) rather than unique business logic.

We call it the "Infrastructure Tax." We analyzed an anonymized $2.4M engineering spend, and honestly, the breakdown was depressing. Only about 20% of that budget went to features that actually differentiated the product. The rest? Auth flows, database provisioning, CI/CD pipelines, and writing the same user schema for the 50th time. Even with modern frameworks, it feels like we're constantly paying a toll just to get to the starting line.

To quantify this, we built a theoretical model (yeah, I know, models aren't reality, but bear with me). We took a standard startup scenario:

- 5 developers
- $500k/year combined cost (conservative inputs)
- Standard SaaS requirements

We mapped hours to "Setup/Boilerplate" vs. "Core Business Logic." Then we modeled an "AI-native" approach where the AI handles ~90% of that initial scaffolding—not just writing functions, but wiring the stack together.

The math suggests a structural inversion. In the traditional model, you're looking at roughly an 80/20 split (Maintenance/Infra vs. Features). With heavy AI automation handling the boilerplate, that shifts toward 30/70.

For a 5-person team, the model implies saving roughly 900+ hours a year. But the real metric isn't hours saved; it's opportunity cost. If you ship 20 features instead of 12, the value difference is non-linear. It's the difference between finding product-market fit and running out of runway.

Obviously, this is where the skepticism kicks in (and rightfully so). There are massive caveats here:

- *Maintenance Burden:* Generative code is easy to create but can be a nightmare to debug. If the AI spits out spaghetti code, you haven't saved time; you've just deferred the pain to the maintenance phase.
- *Architectural Lock-in:* Automated setup usually means rigid opinions. If your app doesn't fit the "standard SaaS" mold, fighting the generated abstraction might cost more than writing it from scratch.
- *The "Black Box" Risk:* There's a real danger of junior devs generating infrastructure they don't fundamentally understand, leading to security holes that senior devs have to patch later.

We're using these models to think about internal resource allocation, but I'm curious if this matches your reality.

- Does the 80/20 split hold true for your stack, or have modern tools (Vercel/Supabase/etc.) already solved this for you?
- Is "Opportunity Cost" a metric you actually track in engineering, or just something CFOs talk about?
- For those using AI for scaffolding: are you paying for it later in debugging costs?

I suspect the "Infrastructure Tax" is the main reason software feels expensive, but maybe I'm underestimating the value of bespoke plumbing.
by jmyeet | 2026-03-05 11:52:12+00:00
This is a bad argument.

Think of a rewrite (by a human or an LLM) as a translation. If you wrote a book in English and somebody translated it into Spanish, it'd still be a copyright issue. Same thing with translations.

That's very different to taking the idea of a body of work. So you can't copyright the idea of a pirate taking a princess hostage and a hero rescuing her. That's too generic. But even here there are limits. There have been lawsuits over artistic works being too similar.

Back to software, you can't copyright the idea of photo-editing software but you can copyright the source code that produces that software. If you can somehow prompt an LLM to produce photo editing software or if a person writes it themselves then you have what's generally referred to as a "cleanroom" implmentation and that's copyright-free (although you may have patent issues, which is a whole separate issue).

But even if you prompted an LLM that way, how did the LLM learn what it needed? Was the source code of another project an input in its training? This is a legal grey area, currently. But I suspect it's going to be a problem.
by dathinab | 2026-03-05 11:52:33+00:00
iff it went through the full clean room rewrite just using AI then no, it's de-facto public domain (but also it probably didn't do so)

iff it is a complete new implementation with completely different internal then it could also still be no LGPL even if produced by a person with
in depth knowledge. Copyright only cares if you "copied" something not if you had "knowledge" or if it "behaves the same". So as long as it's distinct enough it can still be legally fine. The "full clean room" requirement is about "what is guaranteed to hold up in front of a court" not "what might pass as non-derivative but with legal risk".
by jacquesm | 2026-03-05 11:52:34+00:00
Humans can't claim ownership, but they are still liable for the product of their bot. That's why MS was so quick to indemnify their users, they know full well that it is going to be super hard to prove that there is a key link to some original work.

The main analogy is this one: you take a massive pile of copyrighted works, cut them up into small sections and toss the whole thing in a centrifuge, then, when prompted to produce a work you use a statistical method to pull pieces of those copyrighted works out of the centrifuge. Sometimes you may find that you are pulling pieces out of the laundromat in the order in which they went in, which after a certain number of tokens becomes a copyright violation.

This suggests there are some obvious ways in which AI companies can protect themselves from claims of infringement but as far as I'm aware not a single one has protections in place to ensure that they do not materially reproduce any fraction of the input texts other than that they recognize prompts asking it to do so.

So it won't produce the lyrics of 'Let it be'. But they'll be happy to write you mountains of prose that strongly resembles some of the inputs.

The fact that they are not doing that tells you all you really need to know: they know that everything that their bots spit out is technically derived from copyrighted works. They also have armies of lawyers and technical arguments to claim the opposite.
by Joel_Mckay | 2026-03-05 11:54:11+00:00
Much like the price of RAM, SSD, and GPU. The ballooning data-center energy consumption costs have already broken the middle class economic-loop Westinghouse electric drove in the 1950s. Some are seeing their utility bills double.

People are not voluntarily going to build things that make less profit.

It is a suckers bet assuming the unscrupulous will grow a conscience. =3
by tayyab1122 | 2026-03-05 11:54:38+00:00
Really nice work. The async forEach bug alone has burned so many teams and it's the kind of thing that looks completely fine in code review. The fact that it runs locally with no telemetry is a big deal for teams that can't pipe source code to external services.

The hallucinated dependency detector is underrated — AI tools confidently import packages that don't exist and it's embarrassing how easily that slips through. Good to have something that catches it automatically.

Adding this to our dev toolchain.
by theshrike79 | 2026-03-05 11:54:47+00:00
I'm kinda leaning towards the analogy that LLMs are to programming as textile machines were to the loom.

People still pay for hand-knit fabrics (there's one place in Italy that makes silk by hand and it costs 5 figures per foot), but the vast majority is machine made.

Same thing will happen to code, unless the bubble bursts really badly. Most bulk API Glue CRUD stuff and basic web UI work will be mostly automated and churned off automated agentic production lines.

But there will still be a market for that special human touch in code, most likely when you need safety/security or efficiency/speed.
by youchen_ | 2026-03-05 11:55:24+00:00
Hi HN,

I’m Yassine. I built Feedvote.

If your team lives in Linear, sharing a public roadmap usually means manual updates or using a separate feedback tool that drifts out of sync.

Feedvote is a public roadmap + feedback board with real-time 2-way sync with Linear:

Import issues/projects and publish them as a public roadmap

Customer votes and feedback flow back into Linear

Map internal statuses to public states so you can keep your internal workflow intact

Control what is public vs private

It’s early, but working with a few paying teams. I’m switching to subscription now (Pro is $49/mo).

I’d love feedback on positioning and what would make this a must-have for Linear teams.
by hypeatei | 2026-03-05 11:57:24+00:00
Steve Witkoff is a real estate developer who, for some reason, got put into a special envoy for foreign affairs. The fact that you take his word (or really anyone in the executive) at face value says a lot. Many lawmakers were briefed recently and still have no clear motive for this war.

Rep. Warren Davidson (R-Ohio) on Monday said the Trump administration has not effectively explained the U.S. military objective in Iran, accusing the White House of giving “multiple definitions.”[0]

0: https://thehill.com/homenews/house/5765223-warren-davidson-t...
by ilaksh | 2026-03-05 11:57:49+00:00
Does anyone have working code for fine-tuning PersonaPlex for outgoing calls? I have tried to take the fine tuning LoRA stuff from Kyutai/moshi-finetune and apply it to the personaplex code. Or more accurately,various LLMs have worked on that.

I have something that seems to work in a rough way but only if I turn the lora scaling factor up to 5 and that generally screws it up in other ways.

And then of course when GPT-5.3 Codex looked at it, it said that speaker A and speaker B were switched in the LoRA code. So that is now completely changed and I am going to do another dataset generation and training run.

If anyone is curious it's a bit of a mess but it's on my GitHub under runvnc moshi-finetune and personaplex. It even has a gradio app to generate data and train. But so far no usable results.
by dathinab | 2026-03-05 11:58:02+00:00
that is a very good formulation of what I have been trying to say

but also probably not fully right

as far as I understand they avoid the decision of weather an AI can produce creative work by saying that the neither the AI nor it's owner/operator can claim ownership of copyright (which makes it de-facto public domain)

this wouldn't change anything wrt. derived work still having the original authors copyright

but it could change things wrt. parts in the derived work which by themself are not derived