MurphyA/hacker-news huggingface dataset:
by DeusData | 2026-03-03 16:08:36+00:00
I built this because AI coding assistants (Claude Code, Cursor, Codex) explore codebases by grepping through files one at a time. Five structural questions about a codebase consumed ~412,000 tokens via file-by-file search.

The same five questions via a knowledge graph query: ~3,400 tokens. That's a 120x reduction — and it's not about fitting in the context window. It's about cost ($3-15/M tokens adds up), latency (graph queries return in <1ms vs seconds of file reading), and accuracy (LLMs lose track of relevant details in large contexts — the "lost in the middle" problem).

It's a single Go binary: tree-sitter parses your code into a SQLite-backed knowledge graph. Functions, call chains, routes, cross-service HTTP links — all queryable via Cypher-like syntax or structured search. You say "Index this project" and then ask things like "what calls ProcessOrder?" or "find dead code."

No Docker, no external databases, no API keys. 35 languages. Auto-syncs when you edit files. Benchmarked against real repos (78 to 49K nodes) including the Linux kernel (20K nodes, 67K edges, zero timeouts).

There are other code graph MCP servers — GitNexus being the biggest (7K+ stars, great visual UI). Key differences: we support 35 languages vs 8-11, ship as a single Go binary (no Node.js/npm), have no embedded LLM (your MCP client IS the intelligence layer — no extra API keys or per-query cost), auto-sync on file changes, and are the only one with published benchmarks across real repos.

GitNexus is great for visual exploration. We're building production tooling.

Happy to discuss architecture, benchmarks, or trade-offs.
by mg | 2026-03-03 16:08:50+00:00
What basic office tasks are that?

The last time I was excited about the performance of local computers was in the 90s I think.

Modern laptops are so insanely fast. Not sure if they are 2x, 10x or 100x faster than I need them to be. But I never hear fans. I never have to wait for the machine these days.
by Latty | 2026-03-03 16:09:26+00:00
It's crazy to me that you'd trust the output of an LLM for that. It's something where if you do it wrong it could cause major damage, and LLMs are literally famous for creating plausbile-sounding but wrong output.

If you wanted to use an LLM to identify it, sure, you can validate that, and then find the manufacturer instructions and use those. Just following what it says about the cables without any validation it's correct is just wild to me. These are products with instruction manuals made for them specifically designed for this.
by thirdtruck | 2026-03-03 16:09:29+00:00
Location: New York City, New York, USA

Remote: Yes (preferred)

Willing to relocate: No, but willing to travel

Technologies:

Languages: Rust, JavaScript, TypeScript, Ruby, Python, C#, Java, .NET, Perl, SQL
Frameworks: React, Ruby on Rails, Angular, Svelte
Services and Platforms: Kubernetes, Docker, Amazon Web Services (AWS), Heroku, GitHub and GitHub Actions, Atlassian and JIRA
Methodologies: Agile, Scrum, Test-Driven Development (TDD), Behavior-Driven Development (BDD),
Databases: MySQL/MariaDB, Postgres, SQL Server
Operating Systems: Linux/Unix, Windows, Mac

Résumé/CV: Available upon request

Email: hire.hn@thirdtruck.org
by kianN | 2026-03-03 16:09:32+00:00
Roughtime is a really cool protocol we came across when we were hardening a license server. It provides a distributed mechanism for cryptographically verifiable time via chained requests. It’s not as precise as NTP (hence rough) but in practice it’s more than precise enough. It also has some nice additional properties: for example, NTP servers are often used as DDOS amplifiers, whereas roughtime servers return a smaller payload than the request.

The ecosystem is currently very young. Each additional deployment meaningfully strengthens the ecosystem (ours is only the fifth server) and each additional implementation
helps harden the spec (which is soon approaching 1.0).

We wrote a bit more about it in a separate article: https://blog.sturdystatistics.com/posts/roughtime/

Official protocol document: https://datatracker.ietf.org/doc/html/draft-ietf-ntp-roughti...
by wtallis | 2026-03-03 16:09:46+00:00
It was also really disappointing to see 24" 4k displays disappear from the market instead of becoming the new standard resolution for that size. A few years ago, there were several options including a cheap LG that was usually around $300 or less. Those all seem to be gone, likely for good, even though there are still plenty of 24" displays with 1080p and even a fair number with 1440p.
by bubblewand | 2026-03-03 16:10:15+00:00
Add to it that all our retirement accounts are invested in these companies, and it kinda looks indistinguishable from a really roundabout way to have a very-regressive redistributive retirement scheme that also has crazy-high fees (whatever part of the overpayment to healthcare companies that doesn't make it to shareholders is basically part of the account management fees)

Yes, I'm suggesting that like 10% of our nominal GDP is actually a deeply fucked up regressive wealth redistribution scheme that doesn't buy tangible productivity, but is essentially a tax-like drag on the economy, but way less efficient than most government-run redistribution schemes. Because it is.
by Aurornis | 2026-03-03 16:10:19+00:00
> That's like a software company letting junior engineers release directly to production

This person wasn’t a junior.

Editorial processes don’t actually check every single line of everything that is written. Journalists are trusted to report accurately. This person demonstrated they could not be trusted.

> Why wasn't there any fact checking?

Why do programmers ever let any bugs get to production if they have code review? Journalistic outlets do not fact check literally every line that is ever written before it goes to publication.
by sowbug | 2026-03-03 16:10:58+00:00
That would effectively rely on the doctrine of trade secret rather than copyright. A major difference is that accidental or malicious disclosure of a trade secret usually ends the trade secret status, forever. In an alternate universe where computer source code had never been copyrightable, famous leaks (Microsoft Windows, 2004; id Quake, 1997) would have effectively open-sourced those codebases, and other companies could have openly and legally used them.

As source code becomes more of a generated artifact of software development the way object code is an artifact of compilation, we might be moving toward a world where secrecy, constant forward motion, and moats become even more of an asset (vs plain IP protection).
by gpm | 2026-03-03 16:11:06+00:00
What are you doing in your loop?

As typically deployed [1] LLMs are not turing complete. They're closer to linear bounded automaton, but because transformers have a strict maximum input size they're actually a subset of the weaker class of deterministic finite automaton. These aren't like python programs or something that can work on as much memory as you supply them, their architecture works on a fixed maximum amount of memory.

I'm not particularly convinced turing complete is the relevant property though. I'm rather convinced that I'm not turing complete either... my head is only so big after all.

[1] i.e. in a loop that appends output tokens to the input and has some form of sliding context window (perhaps with some inserted instructions to "compact" and then sliding the context window right to after those instructions once the LLM emits some special "done compacting" tokens).

[2] Common sampling procedures make them mildly non-deterministic, but I don't believe they do so in a way that changes the theoretical class of these machines from DFAs.
by temp8830 | 2026-03-03 16:11:13+00:00
This article periodically surfaces in some shape or form. There's this idea that there's a "dual ladder", and the IC ladder offers just as much respect and compensation as the management one. This is a lie, and the sooner we stop telling it to the young generation - the better.

Human societies have always rewarded and valued those who built hierarchies more than those who built things. If you focus on building a thing - you will forever be a cog in someone's big project. There's a reason that management ladder is more competitive.
by mgfist | 2026-03-03 16:11:29+00:00
> But here's what I keep wondering: does expanding the scope of the possible eventually erode the deep understanding that makes the expansion valuable in the first place? Like, if you never have to debug a memory leak because the agent handles it, do you lose the intuition that would let you architect systems that don't leak in the first place?

Maybe, but it feels very hard to predict. Neither I nor most engineers I know ~truly~ understands how a computer works at the deepest lowest level. And for those who do, they probably don't understand the deepest lowest levels of chips, and for those who understand that, they probably don't truly understand how those chips are made, and so and so on. Modern life is built on abstractions upon abstractions, and no one can understand it all from the ground up.

My question is whether AI will give us another abstraction on top of what we have, or if it'll just get so smart that it'll do everything, leaving us with no way to contribute (and most likely becoming extinct).
by subscribed | 2026-03-03 16:11:33+00:00
Very different risks.

Physical CC can be used by anyone holding it or actually just standing close.

Contactless payment with phone only after the phone is unlocked().

Physical card contactless payments have a limit (don't remember it) after which you have to use it with pin. At least all mine. Boring. Payments with apple/hoogle/Garmin have higher transaction limit.

I could probably go on longer but these are for me.

() I know it can be done without, but it also can be done with.
Show HN: Herniated disc made me build a back-safe kettlebell app
by blacktarmac | 2026-03-03 16:11:35+00:00
I herniated a disc in 2023 and spent months in physio. Once cleared to train, standard workouts kept tweaking my back, especially when fatigue hit and my form broke down.

I love EMOMs because they make time fly and push my limits without overthinking. Built this generator to combine that structure with exercise selection that won't wreck my back. It excludes american swings, russian twists, and movements that combine loaded spinal flexion with rotation. The algorithm prioritizes anterior-loaded movements (goblet squats, front rack work) based on McGill's spinal loading research.

React 19 + Tailwind + Capacitor for iOS. Lifetime unlock is the main option because nobody needs another Netflix subscription. There's also a low-cost monthly if you want to try premium features without committing.

Not medical advice. This is what worked for my transition from rehab back to lifting. Curious to hear from others: what was the hardest part of getting back to training after disc issues?
Show HN: Demucs music stem separator rewritten in Rust – runs in the browser
by nikhilunni | 2026-03-03 16:11:42+00:00
Hi HN! I reimplemented HTDemucs v4 (Meta's music source separation model) in Rust, using Burn. It splits any song into individual stems — drums, bass, vocals, guitar, piano — with no Python runtime or server involved.

Try it now: https://nikhilunni.github.io/demucs-rs/ (needs a WebGPU-capable browser — Chrome/Edge work best)

GitHub: https://github.com/nikhilunni/demucs-rs

It runs three ways:

- In the browser — the full ML inference pipeline compiles to WASM and runs on your GPU via WebGPU. No uploads, nothing leaves your machine.

- Native CLI — Metal on macOS, Vulkan on Linux/Windows. Faster than the browser path.

- DAW plugin — VST3/CLAP plugin for macOS with a native SwiftUI UI. Load a track, separate it, drag stems directly into your DAW timeline, or play as a MIDI instrument with solo / faders.

The core inference library is built on Burn (https://burn.dev), a Rust deep learning framework. The same `demucs-core` crate compiles to both native and `wasm32-unknown-unknown` — the only thing that changes is the GPU backend.

Model weights are F16 safetensors hosted on Hugging Face and downloaded / cached automatically on first use on all platforms. Three variants: standard 4-stem (84 MB), 6-stem with guitar/piano (84 MB), and a fine-tuned bag-of-4-models for best quality (333 MB).

The existing implementations I found online were mostly wrappers around the original Python implementation, and not very portable -- the model works remarkably well and I wanted to be able to quickly create samples / remixes without leaving the DAW or my browser. Right now the implementation is pretty MacOS heavy, as that's what I'm testing with, but all of the building blocks for other platforms are ready to build on. I want this to grow to be a general utility for music producers, not just "works on my machine."

It was a fun first foray into DSP and the state of the art of ML over WASM, with lots of help from Claude!
by alephnerd | 2026-03-03 16:12:32+00:00
I think you underestimate the job mobility that is lost when you transition from being an individual contributor into someone on the management track.

The reality is, there are very few EM and above jobs, and job security is tough - if I have to choose between firing an EM or a SWE, I'd fire the EM first because I can always find another replacement or split their responsibilities across multiple individual contributors and the PM.

If an EM is laid off or fired, it's extremely difficult to find another role, and it truly is a terminal position. Why would I hire a laid off or fired EM or Director when I can promote internally or hire someone from within my network?

Additionally, back when I was an SE, if we had a deal go bad in order to protect our ass we'd blame the EM so that we can have a head on the platter to hand our CRO, unlike a seasoned SWE who can push back and argue PM requirements were unclear and PM can argue that sales+product was aligned.
by aeghysels | 2026-03-03 16:12:34+00:00
Foundation models unlocked large-scale code generation.
DMX is built for the next step: turning AI reasoning into deterministic, compliance-ready systems.
DMX (Deterministic Matrix Compiler) is a cognitive compiler. It treats AI as a front-end for specification understanding — and compilation as the enterprise trust boundary.
The model reasons once.
From that point forward, everything is deterministic.
Recent production-grade builds compiled from a single natural language specification include:
– 12 interconnected microservices
– 600+ REST endpoints
– 29 containerized services
– Full infrastructure emission (API gateway, routing, monitoring, metrics)
– 0 stubs or placeholders
– 0 LLM calls during deterministic compilation
– Cryptographically sealed build fingerprints (Merkle-based)
– Byte-identical replay when spec + learning state are unchanged
Beyond generation, DMX emits verification and compliance artifacts aligned with enterprise frameworks:
Security Hardening (runtime validated):
– OWASP Top 10 coverage (Injection, XSS, CSRF, SSRF, IDOR, authentication hardening)
Enterprise Control Alignment:
– SOC 2 Type II (access control, audit trails, encryption, monitoring)
– HIPAA (PHI protection, tenant isolation, access logging)
– PCI DSS v4.0 (cardholder data protection, input validation, auth hardening)
– GDPR (PII masking, data protection, auditability)
– ISO 27001:2022 control mapping
Not as documentation added after the fact.
As compiler-enforced verification gates and generated evidence artifacts.
The diagram below illustrates what I call the "Compiler Wall" — the boundary where probabilistic AI reasoning must become deterministic infrastructure. Every AI-generated system eventually hits this wall. DMX was built to break through it.
LLVM decoupled language front-ends from hardware back-ends through a universal intermediate representation. DMX applies the same architectural pattern to AI: decoupling specification understanding from deterministic system compilation. The industry needed LLVM to make compiled languages portable and trustworthy. It needs the same layer now to make AI-generated systems deployable and auditable.
This is not a replacement for foundation models.
It is the infrastructure layer that allows them to operate safely inside regulated and enterprise environments.
Foundation models scale intelligence.
A cognitive compiler scales trust.
Full technical breakdown — IR architecture, determinism guarantees, compilation performance, security validation pipeline, and self-improving learning convergence — linked in the first comment.

Author: Ariel E. Ghysels
Linkedin: https://www.linkedin.com/in/ariel-ghysels-52469198/
Show HN: Free SEO checker for structured data, meta tags and Core Web Vitals
by imai_director | 2026-03-03 16:12:46+00:00
I built CodeQuest SEO Checker because I couldn't find a tool that clearly diagnoses structured data (JSON-LD/Schema.org) alongside basic SEO.

It checks 45+ items across 4 categories:
- Structured Data (JSON-LD/Schema.org)
- Basic SEO (title, meta, headings)
- Content quality
- Technical SEO (HTTPS, mobile, speed)

Free: 10 checks/month, no sign-up needed.
Would love your feedback!
by aseipp | 2026-03-03 16:12:52+00:00
When you do a cache lookup, there is a "tag" which you use as an index during lookup. But once you do the lookup, you may need to walk a few entries in the corresponding "bucket" (identified by that tag) to find the matching cache line. The number of entries you walk is the associativity of the cache e.g. 8-way or 12-way associativity means there are 8 or 12 entries in that bucket. The larger the associativity, the larger the cache, but also it worsens latency, as you have to walk through the bucket. These are the two points you can trade off: do you want more total buckets, or do you want each bucket to have more entries?

To do this lookup in the first place, you pull a number of bits from the virtual/physical address you're looking up, which tells you what bucket to start at. The minimum page size determines how many bits you can use from these addresses to refer to unique buckets. If you don't have a lot of bits, then you can't count very high (6 bits = 2^6 = 64 buckets) -- so to increase the size of the cache, you need to instead increase the associativity, which makes latency worse. For L1 cache, you basically never want to make latency worse, so you are practically capped here.

Platforms like Apple Silicon instead set the minimum page size to 16k, so you get more bits to count buckets (8 bits = 256 buckets). Thus you can increase the size of the cache while keeping associativity low; L1 cache on Apple Silicon is something crazy like 192kb, and L2 (for the same reasons) is +16MB. x86 machines and software, for legacy reasons, are very much tied to 4k page size, which puts something of a practical limit on the size of their downstream caches.

Look up "Virtually Indexed, Physically Tagged" (VIPT) caches for more info if you want it.
by NoLinkToMe | 2026-03-03 16:12:53+00:00
Fair point, I think I'll eat my words then.

The entry-level option however does constitute a forced bump in minimum-spend on the part of the customer of $100, even if in a spec-vs-spec comparison there is no bump. And you can argue this isn't great for an entry-level apple laptop mostly for casual users that don't need or are willing to pay for 512gb.

But the cheaper macbook is set to be announced tomorrow, probably filling some of the gap the M4->M5 left behind. So I think that probably neatly resolves it. Looks like it all makes sense.
by lnenad | 2026-03-03 16:13:04+00:00
Yeah of course you can choose the level of evaluating how things are similar. Yes they both breathe. Yes they have DNA. Both are objectively true.

Also, you keep saying value comparison like it's something I used against OP. I never mentioned anything about dog <=> child, nor did OP. I just meant that the core decision of having either is different, so it's not comparable even though you could boil it down to "you care for both".
by jacksongwalters | 2026-03-03 16:13:23+00:00
I built a little encrypted messaging webapp that uses two types of post-quantum encryption, `ring-lwe` and `module-lwe`, written by myself and another collaborator in Rust.

Please check the docs at for the API.

We are authorized to ship post-quantum crypto to France.

docs: https://docs.open-encrypt.com
pqc: https://github.com/lattice-based-labs
web: https://github.com/walters-labs/open-encrypt
iOS: https://github.com/walters-labs/open-encrypt-ios
by flurdy | 2026-03-03 16:13:31+00:00
Only when chatting in a large channel at work, did I realise nearly 1/3 of the people there also set theirs as 1/1/1970. Which I presume is the first date that phisers will try to enter to reset people's accounts.

I am fully aware that my standard fake birthday is now used by me in some many places, that I have started to have a fake fake birhday. I should really just randomise and store it in my password manager.

But obviously the context of this OP story ruins all that.
by SiempreViernes | 2026-03-03 16:13:32+00:00
Honestly I think these age verification laws are blunt instruments responding to the decade of avoided moderation the big platforms have managed to pull off.

I've run ad blockers for years now, but I'm still trying to forget those disgusting zit popping pictures that trended in ads for a while. Or those incredibly stupid life hack shorts, like the one where someone tied a cord around a mug and the hack to get it loose was smashing the cup... that crap made me despair for humanity as much as the Gaza genocide.

But google and facebook convinced the legislators that it would be impossible to keep that chum away from kids on their platform, so the legislators are going with the next option: banning the kids from the platforms.
by delecti | 2026-03-03 16:13:33+00:00
Work where "crap" is an acceptable level of quality is work that probably doesn't need to be done.

So I think it's more likely that LLMs unravels the "bullshit jobs" entirely, rather than replacing them with crap. Once people realize it didn't matter if the output sucked, they'll realize the output wasn't needed in the first place.
by AnthonyMouse | 2026-03-03 16:13:45+00:00
> In my own country, digital ID adoption was a political hot topic for a long time after the Orthodox Church realized that the new chips contain 12-digit long IDs that might contain the sequence 666.

Indeed, look at those ignorant Bible thumpers objecting to the spreading installation of barcode scanners for their barcode tattoos. Surely the number is not an allegory for the general principal that we should strongly resist attempts to track human beings like a herd of cattle.

> but somehow the mere possibility of this happening in the digital IDs sparked a huge outrage and made politicians avoid the topic for quite a while.

Maybe because the objection was never really about a specific sequence of digits and was more about the expansion of human tracking.
by guerython | 2026-03-03 16:13:48+00:00
Nice to see Strix hitting GPT-5.3 to finish those HTB machines. On our side we run Strix with --output commands.json and pipe that command list into a tiny replay harness before we accept a vuln. The harness replays each recorded HTTP request / shell command inside a sandbox, compares exit codes, and only the steps that reproduce the same signal survive the report. That keeps stray hallucinated CVEs out of the compliance narrative while letting the agent still explore freely. Have you tried re-running the recorded steps for the hits you liked best?
by inanutshellus | 2026-03-03 16:14:08+00:00
Ha! ^_^

That text is literally the only thing hardcoded. It's inside a PNG, sourced in.

I get it though. Reproducing that cutesy "hand drawn" text would be a pain in the arse if you didn't just have the font.[1]

[1] https://github.com/ipython/xkcd-font