MurphyA/hacker-news huggingface dataset:
by hinkley | 2026-03-04 20:23:14+00:00
Someone gave me that idea about eight years ago and I spent the next several trying to look for a nail for that hammer.

I eventually expanded the one I wrote to include URLs to the right places in Bamboo to do things like disable triggers or start manual deployments. By the time I finished that we were doing 10x as many canary deployments as we had been before, and we’re retiring tech debt way faster because of it. 10/10 would do again.

npm publish will open a web browser for you for passcode entry, and I think I’ll do that next time instead of using cut and paste.
Show HN: FadNote – Zero-knowledge secret sharing for your CLI and AI workflows
by easyFloyd | 2026-03-04 20:23:25+00:00
Hi HN,

I built FadNote because I realized that while services like Privnote or Yopass exist, they often feel like an "interruption" to a developer's flow. You have to leave your terminal, open a browser, paste, and copy again.

FadNote is designed for integration, not just web-visiting.
The goal is to provide a zero-knowledge "utility layer" that lives where you already work:

OpenClaw Skill: For AI-powered workflow automation.

CLI First: A zero-dependency Node.js script to pipe secrets directly from your terminal (echo "secret" | fadnote).

Obsidian Plugin (In Dev): To share snippets directly from your knowledge base.

The Security Model (Trust-less):

Local Encryption: Secrets are encrypted via AES-256-GCM with 600,000 PBKDF2 iterations before they leave your machine.

The URL Fragment: The decryption key is stored in the URL fragment (#key). Since browsers never send fragments to the server, I literally cannot read your data even if I wanted to.

One-Time & Faded: The encrypted blob is deleted from the server immediately after the first read or after the TTL expires.

Why I’m posting here:
I’ve integrated this into OpenClaw and am finishing the Obsidian plugin. However, I’m looking for feedback on where this "fading note" utility belongs next.

The real question for you:
Aside from IDEs (VS Code) or CI/CD pipelines, where else should I integrate this to make sharing temporary secrets frictionless in a professional workflow?

GitHub: https://github.com/easyFloyd/fadnote

Live Instance: https://fadnote.com

I’m eager to hear your thoughts on both the crypto implementation and the next integration targets!
by shadowgovt | 2026-03-04 20:24:07+00:00
My understanding of LLMs with attention heads is that they function as a bit of a mirror. The context will shift from the initial conditions to the topic of conversation, and the topic is fed by the human in the loop.

So someone who likes to talk about themselves will get a conversation all about them. Someone talking about an ex is gonna get a whole pile of discussion about their ex.

... and someone depressed or suicidal, who keeps telling the system their own self-opinion, is going to end up with a conversation that reflects that self-opinion back on them as if it's coming from another mind in a conversation. Which is the opposite of what you want to provide for therapy for those conditions.
by jgraettinger1 | 2026-03-04 20:24:39+00:00
Maintaining a high-quality requirements / specification document for large features prior to implementation, and then referencing it in "plan mode" prompts, feels like consensus best practice at this stage.

However a thing I'm finding quite valuable in my own workflows, but haven't seen much discussion of, is spending meaningful time with AI doing meta-planning of that document. For example, I'll spend many sessions partnered with AI just iterating on the draft document, asking it to think through details, play contrarian, surface alternatives, poke holes, identify points of confusion, etc. It's been so helpful for rapidly exploring a design space, and I frequently find it makes suggestions that are genuinely surprising or change my perspective about what we should build.

I feel like I know we're "done" when I thoroughly understand it, a fresh AI instance seems to really understand it (as evaluated by interrogating it), and neither of us can find anything meaningful to improve. At that point we move to implementation, and the actual code writing falls out pretty seamlessly. Plus, there's a high quality requirements document as a long-lived artifact.

Obviously this is a heavyweight process, but is suited for my domain and work.

ETA one additional practice: if the agent gets confused during implementation or otherwise, I find it's almost always due to a latent confusion about the requirements. Ask the agent why it did a thing, figure out how to clarify in the requirements, and try again from the top rather than putting effort into steering the current session.
by asdff | 2026-03-04 20:25:04+00:00
People still hold the ipad at the exact same height against their lap. You mean to tell me they hold it like arm outstretched in front of them? So easy to position my laptop. Edge of couch with screen tilted up, done. On lap of course, done. On table, done. Resting on my back, with the laptop on my chest, done. These things weigh only like 2lbs now. It isn't like 2010 when the ipad came out and the macbook was a good 5-6 awkward lbs and smoking hot.
by tptacek | 2026-03-04 20:25:36+00:00
Wait: "write tests first" isn't simple and it's controversial. The benefits of TDD in pure-human development are debatable (I'd argue, in many cases, even dubious). But the equation changes with LLMs, because the cost of generating tests (and of keeping them up to date) plummets, and test cases are some of the easiest code to generate and reason about.

It's not as simple an observation as you're making it out to be.
by intenex | 2026-03-04 20:25:38+00:00
When you open up Activity Monitor, to the immediate left of the "Memory Used" and "Cached Files" that you see, you'll see the Memory Pressure graph that the guy above is talking about.

On my 64 GB M1 Macbook Pro right now, I have 53.41 GB of Memory Used and 10.72 GB of Cached Files and 6.08 GB of swap, but Memory Pressure is green and extremely low. On my 8 GB M1 Macbook Air I just bought for OpenClaw, I'm at 6.94 GB Memory Used and 1.01 GB of Cached Files with 2.05 GB of Swap Used, and Memory Pressure is medium high at yellow, probably somewhere around 60-70%.

You can open up the Terminal and run the command memory_pressure to get much more detailed data on what goes into calculating memory pressure - more than just the amount of swap used, it tracks swap I/O and a bunch of page and compressor data to get a more holistic sense of what's going on and how memory starved you're going to feel in practice.

In any case - I've been absolutely mindblown at how fast my 3 8GB M1 Macbook Airs I just bought for ~$350 brand new have been - even with tons of Chrome tabs open, multiple terminal windows open, running OpenClaw and Claude Code and VS Code and doing a ton of development and testing, never once have they ever felt slow. Oftentimes they actually feel faster than my 64 GB M1 Macbook Pro, which kind of blows my mind and makes me wonder wtf is going on on my monster machine. Moreover, my M1 Macbook Pro drains battery like crazy and uses a ton of charge, whereas the Macbook Airs stay constantly below 10 watts essentially always and even with Amphetamine keeping them on 24/7, with the display off and being fully on, they'll drop to a single watt of power draw. Truly insane stuff. I've lost all my concern about RAM, to be honest (which is shocking coming from someone who bought a top of the line maxed out RAM primary machine in 2021 specifically because I felt like RAM was so important)
by alnwlsn | 2026-03-04 20:25:41+00:00
At least you are supposed to be allowed to transmit on AM (and FM) bands at very low power, at least under FCC rules. That's how they can sell those adapters for car radios. Some other ones you definitely are not supposed to use as far as I know, like the old analog TV signals - I've seen a couple of those sorts of projects recently. On the other hand, you can go and buy an illegal TV transmitter on Amazon right now.

Also a reasonably even bet that you already own a low-quality wall power supply that will produce more interference than anything you're going to be doing with a Pico and a 4" jumper wire (I've found a couple of offending devices in my house), but I'm certainly in no position to tell you if you should or shouldn't do something.
by cosm00 | 2026-03-04 20:25:49+00:00
Good idea — thanks!

Right now qlog is a Python CLI, so the cleanest “npm” story is probably a small wrapper package that installs qlog (pipx/uv/pip) and shells out to it, so Node projects can do `npx qlog ...` / `import { search } from 'qlog'` without reimplementing the indexer.

A native JS/TS port is possible, but I wanted to keep v0.x focused on correctness + format parsing + index compatibility first.

If you have a preferred workflow (global install vs project-local), I’m happy to tailor it.
by Alive-in-2025 | 2026-03-04 20:25:51+00:00
Yes, I'd like to hear more of their background, because they seem very naive about writing software, adding to it, testing it, etc.

You can't just whip up a replacement for salesforce using claude code. Who's going to fix the bugs, who is going to have tests, manage performance testing? People will still pay for software that is tested and performant. I could get a replacement for an online spreadsheet, google docs like thing. Suppose you tell it to copy the google docs or whatever programming language. You won't know if it's buggy because you won't haev the same test coverage. You'll never know about bugs that took a long time to reveal in some combination of features.

You can create a new system with a few features together to do something. Again, not tested, not perf tested, isn't away of a compiler bug you had to work around.

But lots of simple things can be claude coded and replaced. Say something that took a photo of a person, centered it, then say put some kind of log on the pic. Something you paid $5 a month to do.
by burningChrome | 2026-03-04 20:25:56+00:00
>> This is a quite scary map.

It can be. FLOCK data was used to put Bryan Kohberger at the scene along with other people's security camera's. Cops regularly use FLOCK camera's to get hits for criminals that have warrants for violent crime.

I can see why people are ok with them when they're used to get criminals off the streets. However, I've seen multiple times where cops initiate a felony stop (where people are pulled out at gunpoint and detained) against a car they got a hit on - only to find out the person they really wanted wasn't driving or even in the car at all.

What's interesting is businesses and houses have so many cameras nowadays that the first thing cops do when they get to the scene of a violent crime is canvas the area for camera's. So yeah, you can avoid FLOCK, but there are most likely hundreds of other camera's that will capture you driving through any given area.
by tugaypala | 2026-03-04 20:26:00+00:00
Thank you, this is exactly the kind of feedback I needed to hear.

You're right — I'm asking for commitment before I've earned it. The blurred images and early premium mentions are creating friction before you've even had a chance to see whether the platform is worth your time.

I'm going to open up visuals for guest users on featured chains and push the monetization messaging much further back in the flow. The value should speak for itself first.

Appreciate you taking the time to articulate this so clearly.
by cosm00 | 2026-03-04 20:26:26+00:00
Totally fair pushback.

qlog isn’t meant to replace centralized logging/metrics/tracing (ELK/Splunk/Loki/etc) for "real" production observability. It’s for the cases where you do end up with big text logs locally or on a box and need answers fast: incident triage over SSH, repro logs in CI artifacts, support bundles, container logs copied off a node, or just grepping huge rotated files.

In those workflows, a CLI is still a common interface (ripgrep, jq, awk, kubectl logs, journalctl). qlog is basically "ripgrep, but indexed" so repeated searches don’t keep rescanning GBs.

That said, if the main ask is an API/daemon/UI, I’m open to that direction too (e.g. emit JSON for piping, or a small HTTP wrapper around the index/search). Curious what tooling you do reach for in your day-to-day?
by 01HNNWZ0MV43FF | 2026-03-04 20:26:30+00:00
> Rather than take its standard 30 percent cut of in-app purchases through the Play Store, Google is lowering its cut to 20 percent

> Third-party app stores will be able to apply to the company's new "Registered App Stores" program to see if they meet "certain quality and safety benchmarks."

> users will still be able to sideload alternative app stores that aren't part of the program

I'll wait to hear how the F-Droid team responds
by markhahn | 2026-03-04 20:26:30+00:00
people say this a lot, but with little technical justification.

gpus have had cache for a long time. cpus have had simd for a long time.

it's not even true that the cpu memory interface is somehow optimized for latency - it's got bursts, for instance, a large non-sequential and out-of-page latency, and has gotten wider over time.

mostly people are just comparing the wrong things. if you want to compare a mid-hi discrete gpu with a cpu, you can't use a desktop cpu. instead use a ~100-core server chip that also has 12x64b memory interface. similar chip area, power dissipation, cost.

not the same, of course, but recognizably similar.

none of the fundamental techniques or architecture differ. just that cpus normally try to optimize for legacy code, but gpus have never done much ISA-level back-compatibility.
by fc417fc802 | 2026-03-04 20:26:55+00:00
Can they actually prevent it though? In typical cases there would be IP licenses involved. But in this case it's a valuation based (AFAICT) on a team of people plus their infra. What happens if they all just happened to get hired by "AnthropicEU GmbH" a new entity which has been gifted hundreds of millions in computing resources?
Show HN: Composable middleware for LLM inference Optimization Passes
by human_hack3r | 2026-03-04 20:27:06+00:00
Hey HN,

I've been building AutoAgents, an AI agent framework in Rust. Today I'm sharing a feature I haven't seen done well elsewhere: composable middleware layers for LLM inference pipelines.

The problem Every agent framework lets you swap LLM providers. Almost none of them give you a structured way to enforce safety, caching, or data sanitization in the inference path itself. You end up with guardrails as application-level if-statements, caching bolted on as a separate service, and PII handling as a "we'll add it later" TODO that never ships.

This gets worse with local models. Cloud APIs have provider-side moderation. When you run Qwen or Llama locally, you get raw inference with zero safety net. If that model has tool access or touches a database, that's a real liability.
The solution

A Tower-style middleware stack for LLM inference. You wrap any provider with composable layers:

```rust
let llm = PipelineBuilder::new(llama_cpp_provider)
.add_layer(CacheLayer::new(CacheConfig {
chat_key_mode: ChatCacheKeyMode::UserPromptOnly,
ttl: Some(Duration::from_secs(900)),
max_size: Some(512),
..Default::default()
}))
.add_layer(
Guardrails::builder()
.input_guard(RegexPiiRedactionGuard::default())
.input_guard(PromptInjectionGuard::default())
.enforcement_policy(EnforcementPolicy::Block)
.build()
.layer(),
)
.build();
```

That's it. The llm variable implements LLMProvider — you pass it to any agent and the layers are structurally enforced. Can't bypass them, can't forget them.

The broader framework:
AutoAgents is a full agent framework — memory, tool use, multi-agent orchestration, the works. The pipeline feature works with any provider: llama.cpp (local), Ollama, OpenAI, Anthropic, etc. Same .add_layer() API regardless of backend.
Written in Rust. No GC pauses. Memory-safe. The framework has ~400 stars and is being used in production for edge AI deployments.
A note on maturity: The guardrails and pipeline layers are still early — the guard implementations are basic, observability isn't there yet, and we're iterating on the API surface. But the underlying architecture is solid and stable. The middleware pattern, the trait-based guard system, and the provider-agnostic pipeline contract aren't going to change. We're building on a foundation we're confident in, and shipping the layers incrementally. Early feedback shapes what gets built next.

I'd genuinely like feedback on:

The layer ordering question — should we enforce a recommended order or keep it flexible?
What guardrail implementations would you actually use in production?
Is the Tower-middleware mental model the right framing, or is there a better analogy?

Full example with local Qwen3-VL-8B: https://github.com/liquidos-ai/AutoAgents/tree/main/examples...

Thanks
by unethical_ban | 2026-03-04 20:27:38+00:00
I have never considered metadata a part of the term E2EE. It has always been about the message contents.

I understand that metadata is valuable information for spies/governments and that encrypting or hiding it is valuable for privacy. But if you use that definition, there are almost no E2EE protocols on the planet in use.

First and foremost, any protocol that uses Apple or Google push notifications is giving metadata to those organizations. Even Whatsapp, iMessage, Signal, Telegram private messages, all of that leaks metadata but the contents of messages are hidden from the provider.
by einr | 2026-03-04 20:28:11+00:00
PS. Wonder why they didn't use A19 in this? Imagine they thought "yeah, that A18 will do for an entry-level laptop", but the entry-level iPhone 17e with A19 needed more kick? What for, our social media apps and mobile websites? This is soooo absurd!

You have to be able to get enough of the things made, too. A19 and A18 Pro are made on different TSMC processes, and most likely it's easier to get production capacity on the older N3E process.
by Larrikin | 2026-03-04 20:28:30+00:00
Have you experienced racism? In Japan atleast, it was evenly applied. That company won't rent to foreigners but this one will. That company won't hire foreigners but this one will. Police will bother you if you ride a bike, but they will be polite while they waste 10 minutes of your time asking for your gaijin card for biking while foreign.

In the US people try to hide it and are far more sinister about it, since there are a lot of laws against obvious racism. The cops are also happy in the US to just kill you.

The racism in the US comes out of hate where as what I experienced abroad was more, we don't think you'll fit in and follow the rules and you have to constantly prove that you can.

I didn't spend too much time in China so maybe it is a racist hell hole.

But my experience in Japan was that white immigrants were way more inclined to make a huge deal about the lighter racism they experienced because they had never been somewhere where their skin color was a disadvantage.
by hrmtst93837 | 2026-03-04 20:29:22+00:00
I've found simplicity rarely earns promotions because it's invisible on a P&L and executives respond to hard numbers. In one role I converted a refactor into a business case with a 12-month cost model, instrumented KPIs in Prometheus and Grafana, and ran a canary that cut MTTR by 60% and reduced on-call pages by two-thirds. Companies reward new features over quiet reliability, so slow feature velocity for a quarter while you amortize the simplification. If you want the promotion, make a one-page spreadsheet tying the change to SLO improvements, on-call hours saved, and dollar savings, then own the instrumentation so the numbers are undeniable.
by tonymet | 2026-03-04 20:29:23+00:00
I volunteer for my city & county , and I'm a privacy advocate, so I have an ambivalent opinion on Flock cameras. Given the completely untenable demands on law enforcement, and extreme driver recklessness , the only practical way to enforce law and order with drivers is some sort of automated surveillance.

Since covid, driver recklessness has been out of control. Running reds, extreme speed, escaping police are all common. Pedestrian and cyclist injuries and deaths remain extreme. At the same time, the public demands more oversight and constraints on police , which reduces their ability to enforce the law.

Imagine you are a policy maker, with worse driver behavior, and police force that are less able to enforce the law. What tools would you use to maintain law and order?

If you don't want surveillance, you will have to make some other tradeoffs to allow human beings to better monitor the public and enforce the law. They are not omnipotent and omniscient creatures.
by chimeracoder | 2026-03-04 20:29:48+00:00
> As for non LLM legal resources, they are just fine if you have all day to read them and all of another day to draft the required filings, but most people have jobs.

You misunderstand. If you are facing tenant harassment in New York City, there are other avenues for you to resolve it that don't involve engaging a lawyer at all.

> True, but it is only an incredibly narrow subset of legal cases where contingency based lawyers exist.

Not really? If anything, there's a pretty narrow subset of cases where it's not possible to get someone on contingency but it is possible to use an LLM to meaningfully push your case forward without one.
Show HN: Runlocal – Open-source localhost tunnel, no signup, no tracking
by gautema | 2026-03-04 20:30:46+00:00
I wanted to do something fun in Elixir and ended up building a localhost tunnel. I've used ngrok for showing my local dev to others and testing webhooks, but figured I'd see what I could make myself.

npx runlocal 3000 - Gives you a public HTTPS URL forwarding to localhost. No account, no config.

It's a single Phoenix app using WebSocket to relay HTTP in real time. No database, no Redis. Fully open source, EU owned and hosted, MIT licensed. Please give feedback

Code is hosted at github: https://github.com/runlater-eu/runlocal-server
by dijit | 2026-03-04 20:31:18+00:00
> I actually used a netbook when I was in school, it wasn't all that bad.

The reputation problem was kind of baked in. Vista launched the same year netbooks did, and even though Vista was a disaster, "runs the latest Windows" is the smell test normal people use for whether something is a real computer.

Netbooks didn't pass.

The storage situation made Windows users miserable anyway. The SSD models had 4-8GiB of flash, and XP alone ate well over half before you'd done anything. So people bought the HDD variant instead, more space, sure, but spinning at 4,200rpm, which wasn't even the slow-but-acceptable 5,400 of a normal laptop drive. Then pile the standard bloatware on top of that.

Bear in mind, people chose the HDD version because it ran Vista: the thing that made it a "real" computer. The SSD variant, the one that actually worked, got ignored for exactly that reason.

Run Linux on the SSD variants though, and the thing was actually great.
by QBitFlow | 2026-03-04 20:31:31+00:00
Fair point — and honestly, paywatcher.dev looks clean for that use case. If all you need is "did the money arrive?" then $0.05 flat is hard to argue with.

You're right that it's apples and oranges. The way I see it: paywatcher solves verification, QBitFlow solves the billing lifecycle (authorization, recurring charges, cancellation, merchant dashboard). Different layers of the same stack.

Which actually makes me wonder — have you thought about integrations? A developer could use paywatcher for one-time payment confirmation and QBitFlow for subscriptions, depending on the use case. Or we could even use paywatcher as a verification layer under the hood for chains we don't natively support yet.

To your question: I think the answer is "both, depending on the builder." Someone spinning up a weekend project with a tip jar wants a primitive. Someone running a SaaS with MRR tracking and automated renewals wants the full stack. The market is big enough for both approaches.

Either way — cool to see someone else building in this space. Would be happy to chat offline if you ever want to explore how the tools could complement each other.