MurphyA/hacker-news huggingface dataset:
by Modified3019 | 2026-03-10 15:34:24+00:00
> It's kind of funny because Telegram is used by Russian military to coordinate a lot of things, so they complain a lot about the block.

This plus the starlink cutoff blinded them so badly Ukraine was able to counterattack and retake a bit of area north of Huliaipole, with armored vehicles (which normally attract immediate drone response these days) last I checked operations are still ongoing, so it’ll be a bit before we know the extent of what they were able to do.
by beepbooptheory | 2026-03-10 15:34:45+00:00
But then why have any contributions at all?

Like its been years and years now, if all this is true, you'd think there would be more or paradigm shift? Im happy I guess waiting for Godot like everyone else, but the shadows are getting a little long now, people are starting to just repeat the same things over and over.

Like, I am so tired now, it's causing such messes everywhere. Can all the best things about AI be manifest soon? Is there a timeline?

Like what can I take so that I can see the brave new world just out of reach? Where can I go? If I could just even taste the mindset of the true believer for a moment, I feel like it would be a reprieve.
by moralestapia | 2026-03-10 15:34:59+00:00
It is a not-that-obscure secret that most posts on Moltbook, particularly the "Viral™" ones, are written by humans.

Does Mark not know this?

I know there's a big advantage in capturing the market early, but in this case Moltbook hasn't captured any of it ...

Weird. With Meta's backing it is going to be successful anyway, but this is something they could have developed in-house in like a weekend.
by timacles | 2026-03-10 15:35:15+00:00
It’s kind of like the beginning sequence from back to the future 1 when it shows all the random inventions at Docs house.

Yeah they are interesting, I guess they do something but are any of them actually delivering value? That’s when you get into the argument of what is value and to whom, but as AIs role in society of generating productivity, that’s pretty disputable if every person being able to build their own train set that turns on the toaster and makes coffee is going to move us forward as a species like , say the internet.

That’s really the only argument, is the use of LLMs worth the trillions of dollars and selling out the future of humanity for. Not is it fun Bildungsroman quirky apps really fast
Show HN: Extract (financial) data from emails with local LLM
by brainless | 2026-03-10 15:35:27+00:00
I wanted to have all my emails (and files) scanned for financial data. Transactions, Bills (I may not have paid). I wanted this to run entirely locally and not depend on a Large Language Model from a cloud provider.

I initially started with Google Gemini 3 Flash but I switched to Ollama + Ministral 3:3b. The extraction is not exhaustive and there is much to improve but this is working.

dwata runs locally, runs a web backend and the gui runs in browser. Connects to emails, downloads them. Then we can run the financial template detection. It checks for similar looking emails, grouped by sender. Then sends a sample from each cluster to LLM agent. The LLM is asked to find out the parts of text that look like the data we are looking for. dwata then searches for the variables/values that LLM gave in the email, creates a template by replacing the data with template tags. Saves template to DB. dwata parse the data from each email when extracting data.

Roadmap:
There is a long way to go, the extractor needs to work much, much, better. dwata will also work on files soon (bank/CC statements).

I want to extract vendors, businesses, contacts, events, places, etc. Connect to different APIs and process everything locally.

dwata will be able to download and process data from Hacker News API too (or other similar sources) - extract entities you care about.

Eventually, only use Ollama/Llama.cpp with models that fit 6-8GB graphics cards or 16GB unified memory only!!
by tombert | 2026-03-10 15:35:47+00:00
I actually had two PhD advisors [1]; Jim Woodcock and Simon Foster.

Both of them are legitimately wonderful and intelligent humans that I can only use positive adjectives to describe, but the one I was referring to in this was Jim Woodcock [2]. He had many, many nice things to say about Tony Hoare.

[1] Just so I'm not misleading people, I didn't finish my PhD. No fault at all of the advisor or the school.

[2] https://en.wikipedia.org/wiki/Jim_Woodcock
by robrenaud | 2026-03-10 15:36:17+00:00
Was Alphago's move 37 original?

In the last step of training LLMs, reinforcement learning from verified rewards, LLMs are trained to maximize the probability of solving problems using their own output, depending on a reward signal akin to winning in Go. It's not just imitating human written text.

Fwiw, I agree that world models and some kind of learning from interacting with physical reality, rather than massive amounts of digitized gym environments is likely necessary for a breakthrough for AGI.
by ShawnC21 | 2026-03-10 15:36:22+00:00
Hi HN — I'm Shawn, the author. We did a Show HN for the GitHub launch a few weeks back. A number of things have shipped since then, so posting an update..

The core thesis

Prompt injection is not a prompt problem. It's an execution problem.

When an LLM agent can run shell commands, call APIs, read files, or use credentials, model output is effectively privileged code. Most defenses try to detect malicious prompts. That breaks down once output reaches execution sinks.

MVAR assumes prompt injection will occur and enforces deterministic policy between model output and privileged operations.

Invariant:

UNTRUSTED input + CRITICAL sink → BLOCK

No prompt classification. No heuristics. No secondary model judging intent. Deterministic enforcement at runtime.

Changes since the previous Show HN:

- Verified execution contracts — per-call invocation hash binding with replay protection
- Ed25519-only enterprise hardening mode with signed policy bundles
- Portable offline witness verifier for verifying decision chains without the runtime
- CI-enforced governance gate — security validation required on every merge to main
- First-party adapters for MCP, LangChain, OpenAI Agents SDK, AutoGen, CrewAI, OpenClaw
- PyPI release: pip install mvar-security

Reproducible validation:

git clone https://github.com/mvar-security/mvar
cd mvar
bash scripts/repro-validation-pack.sh

This runs the red-team gate, a 50-vector prompt-injection attack corpus across 9 categories, and the full validation suite.

Each run emits a timestamped artifact pack with SHA-256 checksums and an independently verifiable witness chain.

Expected result:

LAUNCH GATE: ALL SYSTEMS GO
50/50 attack vectors blocked

Minimal integration:

pip install mvar-security

from mvar import protect
safe_tool = protect(my_tool)

This wraps any callable tool in an enforcement boundary and works with most agent runtimes.

Curious to hear from anyone building agent runtimes or MCP tool chains. If you find a bypass or failure mode, we'd definitely want to see it..

https://github.com/mvar-security/mvar
by SlinkyOnStairs | 2026-03-10 15:36:34+00:00
Reputation isn't very relevant here. Yes, for established well known FOSS developers, their reputation will tank if they put out sloppy PRs and people will just ignore them.

But the projects aren't drowning under PRs from reputable people. They're drowning in drive-by PRs from people with no reputation to speak of. Even if you outright ban their account, they'll just spin up a new one and try again.

Blocking AI submissions serves as a heuristic to reduce this flood of PRs, because the alternative is to ban submissions from people without reputation, and that'd be very harmful to open source.

And AI cannot be the solution here, because open source projects have no funds. Asking maintainers to fork over $200/month for "AI code reviews" just kills the project.
by integralid | 2026-03-10 15:36:36+00:00
I too don't want to be a martyr hermit who don't travel and consume just to cur my CO2 emissions in half, while someone else generates 10x of that living a full life. That's just dumb, and I consider this whole movement rich people telling poor people that they should be considerate and sacrifice themselves.

If you truly care about the planet, don't have children.
by Kyome22 | 2026-03-10 15:36:48+00:00
There are technical article posting sites that support extended Markdown syntax like ":::details," and this syntax is commonly adopted by existing Markdown parsers as well.

I personally don't want to write HTML within Markdown. It’s not very readable, and I believe it's better if users can write content without needing to know both Markdown and HTML.

While building a static website might require some technical expertise, the fact that content can be updated by anyone who can write Markdown—even those who aren't tech-savvy—is a significant advantage.
by marssaxman | 2026-03-10 15:37:08+00:00
Are you kidding? It's Antarctica. Rich people will pay a small fortune just to get a glimpse of the place! For the opportunity to spend a season living there, I'd gladly accept a low-paying job. I'd probably do it for free.

(Alas, I doubt my life will ever work out in such a way that I'll have that opportunity.)
by usrbinbash | 2026-03-10 15:37:15+00:00
> Just because they have been made before LLMs doesn't mean it can be done again

Erm...no? That's exactly what that means.

Earth-Ovens haven't been in widespread use for hundreds of years. People can still use them to bake bread however: https://www.youtube.com/watch?v=WAJqGVxuJPo
by ianlpaterson | 2026-03-10 15:37:31+00:00
I built this to answer a question for myself: which model should I actually route each type of task to? The harness runs 38 deterministic tests (CSV transforms, letter counting, modular arithmetic, regex extraction, code gen, multi-step instructions), costs $2.29 per full run across all 15 models, and all scoring is programmatic. No LLM judge for primary scores.

The surprising part was the QA process. My initial results showed Haiku beating Sonnet. That turned out to be a json_array scorer bug where max_score was set to expected_row_count instead of len(expected_rows), producing quality scores above 100%. A thin-space Unicode character (U+2009) in Gemini Flash responses broke three regex scorers silently. I ended up running 5 separate QA passes, each using a different model, and each pass found bugs the previous ones missed.

Gemini 2.5 Flash scored 97.1% at $0.003/run w/ a 1.1s median response time. Opus scored 100% at $0.69/run. GPT-oss-20b scored 98.3% for $0. The cost spread across models that all score above 95% is genuinely hard to justify for most tasks.

Scoring code and raw results are in the post. Happy to answer questions about methodology.
by ShawnC21 | 2026-03-10 15:37:34+00:00
One clarification: MVAR is not a prompt filter and not a model judge.

The enforcement happens at the execution boundary. If model output reaches a critical sink (shell, filesystem, credentials, etc.) with untrusted provenance, the runtime blocks the call deterministically.

The repo includes the full attack corpus and proof pack if anyone wants to test the enforcement model locally.. Cheers - Shawn
by est31 | 2026-03-10 15:37:39+00:00
I think it's a complicated issue.

A lot of low quality AI contributions arrive using free tiers of these AI models, the output of which is pretty crap. On the other hand, if you max out the model configs, i.e. get "the best money can buy", then those models are actually quite useful and powerful.

OSS should not miss out on the power LLMs can unleash. Talking about the maxed out versions of the newest models only, i.e. stuff like Claude 4.5+ and Gemini 3, so developments of the last 5 months.

But at the same time, maintainers should not have to review code written by a low quality model (and the high quality models, for now, are all closed, although I heard good things about Minmax 2.5 but I haven't tried it).

Given how hard it is to tell which model made a specific output, without doing an actual review, I think it would make most sense to have a rule restricting AI access to trusted contributors only, i.e. maintainers as a start, and maybe some trusted group of contributors where you know that they use the expensive but useful models, and not the cheap but crap models.
by ArisLee | 2026-03-10 15:37:48+00:00
Quick update from the battlefield:

The first Ash Dragon actually died earlier and respawned 10 seconds later. Players are now attacking the second dragon.

A user named "Mr Foxx" has been grinding the leaderboard and currently has ~78k damage, which was not something I expected when I built this little prototype.

The goal of this experiment is basically to see if the core MMO feeling of a shared boss fight is satisfying even when stripped down to almost nothing — just a global HP bar and people contributing damage.

Based on the comments here, the next things I want to try are:
- small collaborative mechanics (buffs/support)
- balancing spam clicking
- making the shared battle feel more alive.

Appreciate everyone trying it and breaking it :)
by sheepscreek | 2026-03-10 15:38:09+00:00
TL;DR the premise for the calculation is completely flawed even if the conclusion is correct

> Qwen 3.5 397B-A17B is a good comparison point. It's a large MoE model, broadly comparable in architecture size to what Opus 4.6 is likely to be.

I stopped reading here. Frontier models have been rumoured to be in TRILLIONS of parameters since the days of GPT-4. Besides, with agents, I think they’re using more specialized models under the hood for certain tasks like exploration and web searches.

So while their cost won’t be $5000 or anywhere close, I still think it would be in the hundreds for heavy users. They may very well be losing money to the top 5-10% MAX users. Their real margin likely comes from business API customers.

Here’s an interesting bit - OpenAI filed a document with the SEC recently that gave us a peek into its finances. The cost of all infrastructure stood at just ~30% of all revenue generated. That is a phenomenal improvement. I fell off the chair when I first learned that.
by zbentley | 2026-03-10 15:38:19+00:00
Eh, as someone generally on the skeptical end of the spectrum for a lot of AI-assisted ops tasks, exploratory query generation is a great use case for it.

I’m highly proficient in code, only average at SQL, and am routinely tasked to answer one-off questions or prototype reporting queries against highly complex schemas of thousands of tables (owned by multiple teams and changing all the time, with wildly insufficient shared DAO libraries or code APIs for constructing novel queries). My skill breakdown and situation aren’t optimal, certainly, but they aren’t uncommon either.

In that context, being able to ask “write a query that returns the last ten addresses of each of the the highest-spending customers, but only if those addresses are in rhetorical shipment system and are residences, not businesses”. Like, I could figure out the schemas of the ten tables involved in those queries and write those joins by hand, slowly. That would take time and, depending on data queries, the approach might get stale fast.
by rosasalberto | 2026-03-10 15:38:24+00:00
We actually agree with the core concern.

Right now the internet has a terrible model where every company asks for your ID and stores it themselves. That means your identity data ends up scattered across dozens of databases.

We think the future is privacy-preserving identity and reusability: verify once, keep your identity in your own wallet, and only share minimal proofs (e.g. “over 18” or “real human”) instead of your full identity every time.

That’s the direction we’re building toward with SSI / identity wallets and reusable verification.
by daxfohl | 2026-03-10 15:38:51+00:00
I guess you need two things to make that happen. First, more specialization among models and an ability to evolve, else you get all instances thinking roughly the same thing, or deer in the headlights where they don't know what of the millions of options they should think about. Second, fewer guardrails; there's only so much you can do by pure thought.

The problem is, idk if we're ready to have millions of distinct, evolving, self-executing models running wild without guardrails. It seems like a contradiction: you can't achieve true cognition from a machine while artificially restricting its boundaries, and you can't lift the boundaries without impacting safety.
by spudlyo | 2026-03-10 15:39:00+00:00
On my M1 Mac Pro I get 0.13s wall, so not much faster than your Mac. On my i9-9900K Linux box I get 0.04s. I would think my M1 single core performance would be on par, if not faster. Perhaps it has something to do with macOS and gatekeeper, as I notice I'm not getting as high of a CPU utilization.

$ gtime /opt/homebrew/bin/emacs --batch --eval '(princ (format "%s\n" emacs-version))'
30.2
0.07user 0.03system 0:00.13elapsed 78%CPU (0avgtext+0avgdata 46064maxresident)k

$ /usr/bin/time ~/bin/emacs --batch -eval '(princ (format "%s\n" emacs-version))'
30.2
0.02user 0.01system 0:00.04elapsed 95%CPU (0avgtext+0avgdata 57728maxresident)k
by ge96 | 2026-03-10 15:39:00+00:00
Tangent, I've always had notes and have this longing to store things but at the same time I like "moving on" too. For ex. I used to have windows phones and saw I still have an accessible One Note from 10 years ago... tempted to read through it but also I've changed as a person.

Another tangent, recently bought a paper shredder, started shredding through boxes of mail I have kept since they have personal info like cc statements but on the same idea of moving on/reducing stuff I'm hoarding whether it's data or physical.
by krylon | 2026-03-10 15:39:31+00:00
> this is possible via snapper on Linux + btrfs but needs complex installation and is not so integrated

FWIW, openSUSE defaults to btrfs on the root filesystem and uses snapper in a very similar manner to zfs boot environments on FreeBSD. I don't have a lot of experience with the latter, but I have been running openSUSE Tumbleweed on my desktop and primary laptop for about 10 years now, and the btrfs+snapper arrangement has worked pretty well for me.

(I also run FreeBSD on my home server and just did the upgrade to 15.0 this weekend, which left me wondering why I had procrastinated this upgrade for so long. It went perfectly fine.)
by 3rodents | 2026-03-10 15:39:38+00:00
I thought that Moltbook was sort of a joke because it was people LARPing as agents as much as it was agents, and given that, I'm confused by this:

> "The Moltbook team has given agents a way to verify their identity and connect with one another on their human's behalf," Shah says. "This establishes a registry where agents are verified and tethered to human owners."

So the impetus for the acquisition was either the verification technology or to hire someone who has worked on verifying agent identity.

Does anyone know what exactly Moltbook's technology is, the technology being described by Meta? I can't find anything on the website related to this. The only "verification" they seem to have is an OAuth connection with Twitter.

edit: I guess it's this https://xcancel.com/moltbook/status/2023893930182685183
by adev_ | 2026-03-10 15:39:38+00:00
Let me clarify something here (And my apologies if this looked a bit aggressive).

There is always a cost to abstraction, and that can take different form. In C++, it is often build time (and/or complexity). And Chandler, in his talk, is perfectly right about that.

But that does not change the validity of the C++ concept 'zero cost abstraction at *runtime*'. It is possible to get proper language abstractions while not sacrificing runtime performances.

I did get sharp on his comment because this talk is constantly posted by a specific C crowd that profoundly hate any form of abstraction and use it as a totem to justify terrible development practices.

If your language support zero cost abstraction and genericity, by the sake of god, use it... most of the time* the impact in term compilation time is worth the benefits.

unique_ptr is a beautiful example of that btw.
by hkonte | 2026-03-10 15:40:16+00:00
The "prompts are code" framing is right, and the compile analogy holds further than people think. Real code has structure: typed parameters, return types, separated concerns. A raw prose prompt is more like a shell one-liner with everything inlined. It works, but it breaks when you try to reuse or modify it.

If you take the compile idea seriously, the next step is to give prompts the same structure code has: separate the role from the context from the constraints from the output spec. Then compile that into XML for the model.

I built flompt (https://github.com/Nyrok/flompt) as a tool for this. Canvas where you place typed blocks (role, objective, constraints, output format, etc.) and compile to structured XML. Basically an IDE for prompts, not a text editor. A star would help a lot if this resonates.
by julien-poitou | 2026-03-10 15:40:17+00:00
53% of MCP servers store API keys as plaintext in config files.
They get committed to git, shared across machines, exposed in breaches.

MCPGuard is a local-first CLI that:
- Scans your MCP configs for plaintext credentials
- Migrates them to your OS keychain (macOS/Linux/Windows)
- Replaces values with mcpguard:// references
- Injects credentials at runtime — never on disk

One command to audit, one to migrate. No cloud, no account, free and open source.
by rustyhancock | 2026-03-10 15:40:40+00:00
Important though we generally assume few bad actors.

But like the XZ attack, we kind of have to assume that advanced perissitant threats are a reality for FOSS too.

I can envisage a Sybil attack where several seemingly disaparate contributors are actually one actor building a backdoor.

Right now we have a disparity in that many contributors can use LLMs but the recieving projects aren't able to review them as effectively with LLMs.

LLM generated content often (perhaps by definition) seems acceptable to LLMs. This is the critical issue.

If we had means of effectively assessing PRs objectively that would make this moot.

I wonder if those is a whole new class of issue. Is judging a PR harder than making one? It seems so right now
by glhaynes | 2026-03-10 15:41:06+00:00
An argument that I have some sympathy for, while still being moderately+ in favor of gun control (here in the USA where I'm a citizen).

It seems that gun control—though imperfect—in regions that have implemented it has had a good bit of success and the legitimate/non-harmful capabilities lost seem worth it to me in trade for the gains. (Reasonable people can disagree here!)

Whereas it seems to me that if we accept the proposition that the vast majority of code in the future is going to be written by AI (and I do), these valuable projects that are taking hard-line stances against it are going to find themselves either having to retreat from that position or facing insurmountable difficulties in staying relevant while holding to their stance.
by Joe_Cool | 2026-03-10 15:41:33+00:00
What hardware are you on?

On my old Ryzen 3600X running Arch it's a lot faster. Does the UI eat so much performance on OSX?

$ time emacs -Q -e kill-emacs
real 0m0.076s
user 0m0.058s
sys 0m0.018s

$ time nvim -es --cmd 'vim.cmd("q")'
real 0m0.028s
user 0m0.005s
sys 0m0.003s

vim still is a lot faster though.