MurphyA/hacker-news huggingface dataset:
Show HN: acorn – LLM framework for long running agents
by onel | 2026-02-24 13:03:06+00:00
Hi HN,

This is Andrei from askmanu and I'm super happy to share a new framework I've been working on: acorn.

It takes all the best parts of DSPy, langchaing, instructor, etc and wraps it in a beautiful and easy to use API. Very easy to define model I/O, branches, define callbacks for every step, etc

See the getting started docs here:
https://github.com/askmanu/acorn/blob/main/docs/getting-star...

Try out the different demos here:
https://huggingface.co/spaces/askmanu/acorn
by jmclnx | 2026-02-24 13:03:34+00:00
This the thing I do not understand about modern EVs. Based upon articles I read, maintenance of EVs are far more expensive that fossil fuel vehicles. Maintenance is a big part of cost of ownership .

100+ years ago, Electric Vehicles back the were correctly touted as having far less maintenance that fossil fuel vehicles.

Here is the thing, Items EVs do not have/need:

1. No Transmission

2. Oil Changes

3. No radiator

4. No exhaust or catalytic converter.

5. There are other things not in EVs that need regular maintenance.

So to me, maintenance should be much less for EVs, 100+ years ago the only drawback was range, and that has been solved with modern EVs.
by skandinaff | 2026-02-24 13:04:01+00:00
a new kind of operating system, that instead of having all those annoying apps, will just be an agent, that does whatever stuff you need|want|it can - why not? But still, we gonna need to be able to maintain many contexts, access remote servers, local file system, sort, present data, be it local or remote. One screen, one button, philosopher's stone made of silicon
by kakacik | 2026-02-24 13:04:31+00:00
One needs to be a (long term present) parent to understand these subtleties.

You also hear just the success stories which are often extremely marginal, when such approach wouldn't fit development curve of some other potential genius we would not be hearing their less successful story, would we.

Not diminishing the overall message, in 80s even in western democracies deeper info was not so readily available so its not like his parents just threw him wifi-connected tablet with wikipedia opened and that was it.

But I think what should be celebrated more is some proper hard long term effort and not just usual approach with exceptional results.
by apwheele | 2026-02-24 13:04:46+00:00
Claude code inherits from the environment shell. So it could create a python program (or whatever language) to read the file:

# get_info.py
with open('~/.claude/secrets.env', 'r') as file:
content = file.read()
print(content)

And then run `python get_info.py`.

While this inheritance is convenient for testing code, it is difficult to isolate Claude in a way that you can run/test your application without giving up access to secrets.

If you can, IP whitelisting your secrets so if they are leaked is not a problem is an approach I recommend.
by sekha-ai | 2026-02-24 13:04:54+00:00
Built this after watching my AI assistants forget everything—every context window fill, every new chat starting from zero, every re-explained codebase. Persistent memory was the missing layer.

Sekha solves the part OpenClaw doesn't: making that memory portable, auditable, and enterprise-ready. SQLite (postgres option coming soon) instead of Markdown. APIs instead of file boundaries. Same embedding-based retrieval, but universal.

Stack: Rust (fast path), Python (bridge), Chroma (search), MCP/REST/SDKs (universal). AGPL, self-hosted, actually free.

Happy to answer any questions.
Show HN: Design is Code – UML to TDD tests that constrain AI code generation
by mossgu | 2026-02-24 13:05:34+00:00
Two root causes make AI code generation unreliable:

Natural language isn't a contract. It's ambiguous by nature. Same prompt, different code, every time. There's no determinism.

Cost is asymmetric. AI generates at zero cost with zero responsibility. You review at high cost with full responsibility.

These compound. Ambiguous input produces unpredictable output. Unpredictable output demands expensive review. And if no one designed the code, no one can defend the architecture, no one can maintain it, and no one owns it.

DisC (Design is Code) applies London-school TDD to AI code generation. You draw a sequence diagram. Each arrow becomes one verify() in a mockist test. The tests leave exactly one valid implementation. The AI doesn't interpret — it types what the tests require.

It ships as a Claude Code skill. Works best with Opus, should be fine with Sonnet.

Here's a real example. This diagram:

@startuml
InvoiceService -> OrderRepository: findAllByCustomerId(customerId)
InvoiceService <-- OrderRepository: orders: List<Order>
InvoiceService -> InvoiceBuilderFactory: create()
InvoiceService <-- InvoiceBuilderFactory: invoiceBuilder: InvoiceBuilder
loop for each order in orders
InvoiceService -> InvoiceBuilder: addLine(order)
end
InvoiceService -> InvoiceBuilder: build()
InvoiceService <-- InvoiceBuilder: invoice: Invoice
@enduml

Generates these tests:

@Test void shouldFindAllOrdersByCustomerId() { verify(orderRepository).findAllByCustomerId(customerId); }
@Test void shouldCreateInvoiceBuilder() { verify(invoiceBuilderFactory).create(); }
@Test void shouldAddLineForOrder() { verify(invoiceBuilder).addLine(order); }
@Test void shouldBuildInvoice() { verify(invoiceBuilder).build(); }
@Test void shouldReturnInvoice() { assertThat(result).isEqualTo(invoice); }

AI implements the only thing that passes:

public Invoice generateInvoice(UUID customerId) {
List<Order> orders = orderRepository.findAllByCustomerId(customerId);
InvoiceBuilder invoiceBuilder = invoiceBuilderFactory.create();
orders.forEach(invoiceBuilder::addLine);
return invoiceBuilder.build();
}

4 arrows + 1 loop → 5 tests → 1 possible implementation.

Java + Spring only for now. Orchestration code only (not algorithms). PlantUML format. The mockist coupling tradeoff is real — but when AI writes the implementation, refactoring cost moves from code to the diagram.

Try it without installing anything — clone the demo repo and run in a Claude Code session:

git clone https://github.com/mossgreen/design-is-code-demo
cd design-is-code-demo
/disc 01_hello-world.puml

Blog: https://mossgreen.github.io/introducing-design-is-code/

Plugin: https://github.com/mossgreen/design-is-code-plugin

Happy to hear what breaks, what's missing, and whether this is worth expanding to other languages.
by hnthrow0287345 | 2026-02-24 13:06:29+00:00
People aren't interested in long-term thinking when companies are doing layoffs for bullshit reasons and making vague threats about how most of us will have to go find a new career which causes a heck of a lot of stress and financial costs. That isn't being petty, it's having self-respect. They get the quality when the companies treat the craftspeople with respect.
by molitor | 2026-02-24 13:06:53+00:00
I built a PDF layout engine in Rust with a React component layer. You write JSX, it renders PDFs via WASM.
I needed this for my own project, generating soil analysis reports. I was running a separate Puppeteer server (headless Chrome) just to make PDFs. It was slow (1-5s), 200MB+, and crashed. I replaced it with an in-process function call. 28ms, no browser.
What's different from react-pdf: page breaks work across nested components, there's a dev server with live preview and element inspector, table headers repeat automatically, and it runs as a single WASM binary with no native dependencies.
Open source, MIT licensed. Rust engine with 240+ tests.
npm install @formepdf/react @formepdf/core
Docs: https://docs.formepdf.com
by rustyhancock | 2026-02-24 13:07:05+00:00
I don't think they will lose on inference because that assumes that compute becomes cheap for all evenly.

Their spending today has secured their compute for the near future.

If every GPU, stick for RAM and SSD is already paid for. Who can afford to sell cheap inference?

Z.ai is trying to deal with this by using domestic (basically Huwawei silicon not Nvidia). And with their state subsidy they will do well.

Anthropic has a 50bn USD plan to build data centres for 2026.

OpenAI similarly has secured extraordinary amounts of other people's money for data centres.

All these will be sunk costs and "other people's money" while money is easy to get hold off. But will be a moat when R&D ends.

Once all the models become basically the same who you go with will be who you're already with (mostly OpenAI), and who you end up with (say people who use Gemini because they have a Google 2TB account).

Some upstart can put themselves into the ground borrowing compute and selling at a loss but the moment they catch up and need to raise prices everyone will simply leave.

ChatGPT is what is most likely to remain a sustained frontier model. Maybe Claude jumps ahead further a few times, Gemini will have its moment. But it'll all be a wash with ChatGPT tittering along as rarely the best. But never the worst.
by tclancy | 2026-02-24 13:07:11+00:00
This was a big thing around the time of Flickr and, if my memory is still working, del.icio.us. There was a push for “url is the new command line” which I wish had gotten more traction because there were some fun things happening. I feel like Yahoo Labs was involved in a lot of it, including Pipes and their ahead of its time JavaScript framework. It’s a strange sliding doors moment for me because I turned down a job there right around this time.
Show HN: I made a Uniswap v3 Hedge Rebalancer that manages shorts on Hyperliquid
by carter2099 | 2026-02-24 13:07:15+00:00
I wrote a blog post about it: https://blog.carter2099.com/posts/6

tldr: Delta Neutral is a self hosted Rails app that allows you to enter hedge configurations for your Uniswap v3 positions. You enter a target hedge, i.e. 50%, and accordingly shorts are opened on Hyperliquid. As your assets composition changes in the pool, your shorts will be rebalanced to stay at your target hedge.

Here's the code: https://github.com/carter2099/delta_neutral

It builds on my Ruby Hyperliquid SDK: https://github.com/carter2099/hyperliquid
by prmoustache | 2026-02-24 13:07:21+00:00
Is it so hard to understand that the features person A wants are not necessarily the same as person B yet a company/foundation would be interested in providing a complete featureset for both A and B?

I don't need themes nor having my url bar serving search. I am not interested in an AI agent in my browser yet I welcome traduction features, do I have to shit on every company developping a software that has some features because I don't want them?

I am much more pragmatic: are these features easy to ignore/disable, do they largely increase the resources needed (disk, memory, cpu) even when not used, do these introduce bloat, etc.

I wasn't interested in pocket, I was just using a combination of firefox forks or disabling it on the devices I was using. That is the whole point of open source software.
by codethief | 2026-02-24 13:07:27+00:00
That books seems very interesting in general, thank you for sharing!

Then again, I am a bit skeptical since the author also credits Claude Opus 4.5 and GPT-5.2. Both models got released in November/December last year, which makes me wonder: Was this book written in only 2 months, i.e. mostly by AI?

EDIT: No. Turns out the book is much older and only recent changes were done with the help of LLMs, https://github.com/lukstafi/curious-ocaml/commits/main/?afte... .
by xmcqdpt2 | 2026-02-24 13:07:48+00:00
I think in practice it's less of a programming language and more of a scripting environment. It's like excel for math. There are many more people using it to produce mathematical results (like how excel is used to produce reports and graphs) than people who use it to produce programs.

This is why its not particularly problematic that it is closed source. Most people I've worked with who use it produce mathematical results with it that are fully checkable by hand.
by reddalo | 2026-02-24 13:08:03+00:00
YouTube had the same problem.

The oldest account have a direct URL in the form of youtube.com/username. Newer account had youtube.com/user/username (I think most account have both URLs).

Then YouTube was bought by Google and they introduced "channels", so some channels had youtube.com/channel/username.

Then YouTube wanted to become like TikTok and they forced at-usernames some years ago, so now accounts have a URL like youtube.com/@username
Show HN: Tessera – An open protocol for AI-to-AI knowledge transfer
by kirkmaddocks | 2026-02-24 13:08:39+00:00
Tessera is an activation-based protocol that lets trained ML models transfer knowledge to other models across architectures. Instead of dumping weight tensors, it encodes what a model has learnt —
activations, feature representations, behavioural patterns — into self-describing tokens that a receiving model can decode into its own
architecture.

The reference implementation (tessera-core) is a Python/PyTorch library. Current benchmarks show positive transfer across CNN, Transformer, and LSTM pairs. It runs on CPU and the demo finishes in
under 60 seconds.

Happy to answer questions about the protocol design, the wire format, or the benchmark methodology.
by menzoic | 2026-02-24 13:08:58+00:00
I’ve found there are two major mindset shifts that helped me start passing tech interviews consistently:

1. Study the algorithms and patterns, not the questions
2. Treat it like a serious investment, 2–3 months of focused prep minimum

Most people skip the fundamentals. But these core patterns and data structures come up over and over. If you really understand them, you can solve almost anything.

I used this exact approach to land offers from Google, Amazon, Uber, Airbnb and more, without a CS degree.

That experience led me to write this full breakdown of how to study for tech interviews the right way.
by NoboruWataya | 2026-02-24 13:10:13+00:00
Yes, I really like it. I wish they let extensions access it directly but it seems they don't (yet, at least). I'm also a bit surprised more hasn't been done to distribute it separately as a command line tool as I think something like that would be very helpful. I have seen a couple of attempts to put a CLI wrapper around it but they are third-party and seem to not be well maintained or documented.
by jaffa2 | 2026-02-24 13:10:15+00:00
from the article :
> This might seem silly. The user interface of URLs? Who types in or edits URLs by hand? But keyboards are still the most efficient entry device.

Can someone let Apple know this ? Safari URL bar is a disaster. If I edit a URL say to remove a part and hit enter 9/10 times it searches the internet.

It seems to forget that it's a url it's displaying despite it cutting the front off, even after telling it to use long urls in address bar. It's so annyoing I actually use another browser since I often need to paste in or modify urls for the work I do. Safari sucks hard. Any solutions to disabling 'search' in the address bar ? I want it to be a URL bar only, and anythign typed in it should be resolved and not searched.
by HarHarVeryFunny | 2026-02-24 13:10:27+00:00
Fundamentally the failure here is one of reasoning/planning - either of not reasoning about the implicit requirements (in this case extremely obvious - in order to wash my car at the car wash, my car needs to be at the car wash) to directly arrive at the right answer, and/or of not analyzing the consequences of any considered answer before offering it as the answer.

While this is a toy problem, chosen to trick LLMs given their pattern matching nature, it is still indicative of their real world failure modes. Try asking an LLM for advice in tackling a tough problem (e.g. bespoke software design), and you'll often get answers whose consequences have not been thought through.

In a way the failures on this problem, even notwithstanding the nature of LLMs, are a bit surprising given that this type of problem statement kinda screams out (at least to a human) that it is a logic test, but most of the LLMs still can't help themselves and just trigger off the "50m drive vs walk" aspect. It reminds a bit of the "farmer crossing the river by boat in fewest trips" type problem that used to be popular for testing LLMs, where a common failure was to generate a response that matched the pattern of ones it had seen during training (first cross with A and B, then return with X, etc), but the semantics were lacking because of failure to analyze the consequences of what it was suggesting (and/or of planning better in the first place).
by Retr0id | 2026-02-24 13:11:05+00:00
I'm surprised by your benchmark results.

I've considered building this exact thing before (I think I've talked about it on HN even), but the reason I didn't build it was because I was sure (on an intuitive level) the actual overhead of the SQL layer was negligible for simple k/v queries.

Where does the +104% on random deletes (for example) actually come from?
by enjoykaz | 2026-02-24 13:11:06+00:00
Shell RC files (`~/.bashrc`, `~/.zshrc`) are write-protected in the rules but not read-protected. OpenAI's own quickstart tells you to put your API key there — so anyone who followed that tutorial has `OPENAI_API_KEY` sitting in their zshrc, readable by the agent. DLP is the only backstop, and only for known formats. Am I reading the rules wrong?
I spent $100 benchmarking LLM providers on a weekend CTF
by wwdmaxwell | 2026-02-24 13:11:31+00:00
This past weekend, I decided to test out a cli tool I've been building to help me do source code reviews _faster_.

I figured the best environment for such a tool would be a Weekend CTF event. I like web challenges since you get a nice dump of source code, as well as a Dockerfile or docker compose setup for how to run everything locally. Usually, I can complete 2-3 Web challenges before I get stuck. To help get unstuck I found myself increasingly turning to LLMs as a pairing partner.

I'm a fan of devcontainers, so I figured I could apply a similar concept with an agent*, where I load the agent into a container, mount the source code, and even start up any provided Dockerfile or docker-compose.yml so that the agent can actually test real `curl` commands!

So how did it go? It was pretty helpful for the web challenges. I was able to cruise through 5 between Friday and Saturday. I decided to see how it would do in the other categories - without any input / guidance from me as I typically stick to web.

In total *we* solved 19 challenges. It's best category was crypto with 4/7 solved, and it's worst was pwn with 2/5.

I was also curious how different providers would fair, because this was an automated agent, I started off using xai since they were the cheapest.

xai was able to solve 8 challenges autonomously with just source code and challenge descriptions.

I then pivoted to gemini as the next cheapest, and it did pretty well and was able to build on xai's "analysis" and solve 5 additional challenges.

I further tried to pivot to anthropic's Opus model, but it wasn't able to crack any additional challenges, and I got frustrated since I kept getting rate limited with 429 errors (so I kind of wish I switched to openai 5 instead, as it seems like Anthropic doesn't really like agents other than Claude calling their models.

In terms of cost breakdown I spent

$ 33.06 with xai

$ 35.61 with google

$ 24.04 with anthropic

Bringing the total just under $100 for a weekend benchmarking exercise.

Going forward I'm not really interested in paying to copy-paste CTF flags, but I did find the agent helpful for brainstorming solutions, and it worked a lot better when connected to the source code, with access to an instance running locally, and also augmented with MCP tools that allowed concept and source code searching. I'm planning to use similar concepts to build out a dev/review agent.

The source code for my setup is here: https://github.com/edelauna/prompt2pwn

* My initial version does require setting `--priveleged` on the Docker runtime. I originally tried to use podman, but I ran into networking / dns issues with how I wanted to make MCP tools available to the agent. Please open an issue on the repo and let me know if you have any ideas how to harden this.
by jawuilp | 2026-02-24 13:11:32+00:00
Hi HN,

I'm a full-time remote developer, and in my free time, I'm constantly building micro-SaaS and tools. I love Astro for its performance and DX, but I noticed a huge friction point: every time I started a new project, I was wasting hours just wiring up the same boilerplate environment—configuring the framework, integrating Tailwind CSS, setting up SEO tags, and structuring components.

That initial setup friction is a momentum killer for shipping fast. To solve this for myself and speed up my own workflow, I packaged my ideal stack into AstroKit.

To be fully transparent: this is a paid commercial boilerplate. However, I'm sharing it here because I’d love to get your technical feedback on the structure and the stack choices.

You can check the live demo of what it generates here: https://micro-saas-template-psi.vercel.app/

The core stack includes Astro, Tailwind CSS, and a predefined folder architecture optimized for fast deployments and perfect Lighthouse scores.

I'm curious about your workflows: do you usually buy or use pre-made boilerplates for your side projects, or do you maintain your own internal starter repos to avoid the setup fatigue? I'll be around to answer any technical questions!
by Hasz | 2026-02-24 13:11:35+00:00
Market for DDR4 is crazy, but not as crazy as DDR5.

Also a symptom of how inelastic hardware demands are. You would expect the purest k8s people to just shove workers on older machines and completely dodge this crisis, but we don't see that at all. Despite being an almost-commodity, many of the hw vendors still have a decent hand.
by rustyhancock | 2026-02-24 13:12:20+00:00
> when some estimates put the number of users in the free tier at 96%.

It's certainly almost everyone today, but that's because enshitificarion has yet to start properly.

The risk to OpenAI is that their free tier are captured by the tack on markets (i.e. Gemini with 2TB of cloud storage).

But otherwise they will make free more annoying until people just buy the cheap tier and then move up from there. Like chatgpt Go.
by edgecasehuman | 2026-02-24 13:12:26+00:00
This looks like a game-changer for power users who live in the terminal. I've been using Notion's API with custom scripts, but a unified CLI with 39 commands could streamline my workflow immensely, especially for quick database queries or page exports. Have you considered integrating it with tools like fzf for fuzzy searching within Notion workspaces?
by throwaw12 | 2026-02-24 13:12:37+00:00
Things are in terrible state in the world.

Gaza exposed it even more:

* No one accepts high western "morality" anymore

* Most US politicians are blackmailed via Epstein who worked for Israel, with high probability, including Trump

* ICE is just the beginning, they're trained by IDF, send more people and 1940 is not too far away from us
by xtiansimon | 2026-02-24 13:12:53+00:00
> “…we assume the cooperative principle holds […] that the question is sincere and that there must be some plausible reason for walking.”

Yes. And. Some problems have irrelevant data which should be ignored.

The walk choice is irrelevant in the context. It needs to be simplified, as with a maths problem. That has nothing to do with human nature, but rather prior mistake in reasoning.
by fcatalan | 2026-02-24 13:12:59+00:00
I didn't know Sogno di Volare. It does work very well, quite intense.

I got me thinking... I've never taken any mind altering drugs, so I wonder how the experience compares. I guess that even if not in the same league, being "free" and without apparent side effects this is quite the "feelgood" bang for the buck.