MurphyA/hacker-news huggingface dataset:
by the_nexus_guard | 2026-02-24 12:01:52+00:00
This case illustrates why agent identity infrastructure matters. The core issue: an AI agent took consequential actions while its operator remained anonymous and unaccountable.

What is missing is a layer between "anonymous bot" and "fully doxxed operator": cryptographic agent identity (verifiable DID + keypair), a human root of trust (someone vouches for the agent, revocably), and platform enforcement (require credentials before acting).

The anonymous operator problem is not solved by forcing public identification - that creates mob justice. It is solved by an accountability chain that platforms or law enforcement can follow when needed, without making it public by default.

We are building this at https://github.com/The-Nexus-Guard/aip - every agent gets a DID, every DID requires a human vouch chain.
by elric | 2026-02-24 12:02:06+00:00
From the email:

> For example, the cost for DRAM memory has increased up to 500% since September 2025.

That is an utterly insane price hike. Is production being scaled up, and will that take years? Or are producers happy charging 5x the price for the same amount of effort?

How much of this is driven by speculation vs actual demand?
by wengo314 | 2026-02-24 12:02:13+00:00
you know, i don't think saying "got it right" is correct here.

Because if you are presenting an option of walking to a car wash as a valid choice, it suggests you just want to get there, and not wash your car.

Just like walking/driving to an airport doesn't necessarily mean you're going there to catch a flight (although it's a common reason). might as well be picking someone up. or working there.
by disgruntledphd2 | 2026-02-24 12:02:27+00:00
> So going back to the original question, "Is Iran supposed supposed to be banned on Binance?" still doesn't have a clear answer?

I mean, theoretically not, but practically yes. Basically every bank has dollar reserves and as such get caught up in the sanctions screening process. Again, sanctions are most problematic for entities when used by the US, but the EU and other governments also do this, and if you want to transact in a governments currency, then you need to follow the sanctions laws (severe violations can lead to license revokation which basically kills your business).

Unless you have no connection to the fiat ecosystem, then you are gonna end up subject to these laws. And if you have no connection to the fiat ecosystem, you won't be a particularly useful service to most people or businesses.
by VVertigo | 2026-02-24 12:02:51+00:00
I'm probably pretty far out on the normal mobile user curve: my phone is primarily used for phone calls along with some texting and I don't even have a data plan.

I would suspect many mobile users would be faster than me (I know people who can go astonishingly fast), although I can (or at least used to be able to) type much faster on the BB keyboard than my phone screen. I am a decent typist though, so for most communication over a sentence I will resort to email using a desktop or laptop. I hate typing on laptop keyboards though and use an external one. I'm also not a fan of a bunch of back and forth texting, so for conversations like that it's usually better resolved with a call.
Show HN: Tokio-prompt-orchestrator – LLM pipeline orchestration in Rust
by Shmungus | 2026-02-24 12:02:57+00:00
I built this after getting frustrated with "multi-agent" frameworks that claim parallelism but are really just one fat async task with no resource bounds.

tokio-prompt-orchestrator breaks LLM inference into 5 physical stages (RAG → Assemble → Inference → Post-Process → Stream), each running in its own Tokio task with bounded channels between them. When a stage falls behind, backpressure builds locally instead of blowing up the whole pipeline.
Some things that might be interesting to folks here:

Circuit breakers per provider (OpenAI, Anthropic, local llama.cpp) so one failing API doesn't cascade
Request deduplication that saved 60-80% on inference costs in my testing
Prometheus metrics + a TUI dashboard for watching the pipeline in real time
MCP server integration so you can use it as a Claude Desktop tool

It's 58k lines of Rust, MIT licensed, no unsafe. Been running it in production for my own projects for a few months now.
Would love feedback on the channel sizing heuristics and the retry/backoff strategy, those were the hardest parts to get right. Happy to answer questions about the architecture.

GitHub: https://github.com/Mattbusel/tokio-prompt-orchestrator
by bonsai_spool | 2026-02-24 12:03:08+00:00
> But I can't help but think I could have ended up on a path like this much earlier if my teachers actually supported me rather than treating me like a problem.

It's interesting you raise your teachers / organized schooling when Tao's parents were cited earlier in the thread for providing him materials.

Where do you see mothers and fathers stepping in (or not!) with children of greater ability?
by noduerme | 2026-02-24 12:03:32+00:00
Yeah, it makes sense, and the Atlantic is typically adverse to confronting reality. I don't think they took this from exactly the right perspective. You DO train a human. But Altman says:

>>It also takes a lot of energy to train a human

as a caveat to the energy it takes to train GPTs. The question I believe the writer is trying to ask is: Why is it better to train a GPT than a human?
by apothegm | 2026-02-24 12:04:28+00:00
Instead of a completely separate DB or logic in each view, have you considered:

a) separate managers for live and draft?

b) proxy models for live and draft with separate managers?

c) live and draft child models of the same abstract base model?

But also: Django supports routing between multiple databases.

A third option would be to have multiple instances running with different settings off the same codebase.
by simonw | 2026-02-24 12:04:42+00:00
A "reply guy" is a pejorative term for someone (usually male) who consistently replies to posts where their opinion is not valued or wanted, or often inappropriate:

https://amp.knowyourmeme.com/memes/reply-guy

The joke is that the people selling this software picked that as the name for what whey are selling, either missing or leaning into the negative connotations that are attached to that term.
Show HN: Notion Breadcrumb Navigator – Keyboard-driven page hierarchy navigation
by brockmeier | 2026-02-24 12:04:53+00:00
I built a small browser extension that adds a command palette to Notion for navigating through your page hierarchy. Press Ctrl+B to open an overlay showing parent pages, headings on the current page, and subpages. Arrow keys to navigate, type to filter, Enter to jump.

It stays open across page switches so you can move through deeply nested pages without touching the mouse. Works in any Chromium browser and Safari.

Three files, no permissions, no data leaves your browser — it just reads the DOM.

https://github.com/felixbrock/notion-breadcrumbs
by krapp | 2026-02-24 12:05:41+00:00
> Even when we all wrote html most people still used a bigco to host it.

If you consider Geocities, Tripod or Angelfire (or your local ISP) "bigcos" I guess. It is true that most people didn't host from their own servers and at one point all of those free hosts forced ad banners on your pages but it still doesn't seem like the same thing.
by julianozen | 2026-02-24 12:06:55+00:00
Yes. I think it’s this + policy.

And that your iPhone and other devices become more restrictive if this is not implemented.

I presume most devices in the world do not have a solution to this (desktop windows computer for instance).

I’m not sure if it’s a good idea for like every porn website in the world to require a secret enclave to work. But this sounds better to me than storing users photo ids in an s3 bucket
by hax0rsJJ | 2026-02-24 12:07:36+00:00
I have a preteen with a tablet and got tired of the YouTube rabbit hole problem. He's too old for YouTube Kids but too young for the regular YouTube app — and there's nothing in between. YouTube Kids is full of weird algorithmic slop, and regular YouTube's algorithm will drag him into brainrot within minutes. Standing over his shoulder doesn't scale.

So I built BrainRotGuard. The kid gets a simple web page where they can search and request videos. Each request pings me on Telegram with the thumbnail, title, channel, and duration. I tap Approve or Deny. If approved, it starts playing on their tablet automatically. It will also populate his "feed" with approved channel videos, essentially YOU control of the algorithm.

The key trick: you block youtube.com at the DNS level (Pi-hole, AdGuard, pfBlockerNG) and only allow youtube-nocookie.com embeds. So even the "suggested videos" overlay at the end of a video leads nowhere — those links point to blocked youtube.com.

My kid actually prefers it over YouTube itself. The UI is simpler and less distracting — no endless scrolling, no autoplay, no algorithmic recommendations pulling them down a rabbit hole. They search, pick a video, watch it, done.

It runs on a Proxmox LXC with 1 CPU and 2GB RAM — it barely sips resources. Anything you have lying around could run it.

Some things the HN crowd might find interesting:

- No YouTube API key needed — uses yt-dlp for search and metadata
- No ads, no autoplay, no algorithmic recommendations
- Channel allowlisting — approve a whole channel so individual videos auto-approve
- Category-based time limits (edu vs fun) with daily budgets
- Scheduled access windows (e.g., no YouTube before 8am or after 7pm)
- Self-hosted, runs on any Docker box on your LAN
- No accounts, no cloud dependency — just a Telegram bot token
- No phone-home hooks, no centralized data — everything stays on your network

I'm sharing this because I'm hoping other parents can use it to protect their kids too. It's free and open source.

Full disclosure: I'm not a developer — just a dad with a homelab who wanted to solve this problem. I built the whole thing with Claude. I described features, Claude wrote the code. I never touched a line myself. Every release went through security and code reviews via Claude before pushing. The project is about a week old. If that interests (or terrifies) you, happy to talk about the process.

GitHub: https://github.com/GHJJ123/brainrotguard

Happy to answer questions about the architecture or the DNS blocking setup.
by lordnacho | 2026-02-24 12:07:49+00:00
In some way this could be the most important course.

You don't appreciate it when you're studying, because obviously it sounds a bit soft. But when you're learning how something works, often the thing that stops you isn't the fundamentals, which you know what are, it's the little frustrations like not knowing how to commit or pull code, or not knowing how to navigate the terminal.
by noduerme | 2026-02-24 12:08:29+00:00
Because a statue or a painting made by a human requires you to think about what their intention was and what they were trying to say. And requiring you to think about that, and actually thinking about something which someone else's thought went into, is what makes you a more complete human yourself. The difficulty of understanding it is what makes it valuable.

Taking the distillation or "reader's digest" version of everything makes you more and more reliant on someone else's interpretation, and less capable of parsing the meaning of it yourself. And in the case of AI-generated work, there is no meaning in it to parse. It's just words and pictures. I love going to the movies and eating popcorn and watching dumb words and pictures. But being able to distinguish between enchanting words and pictures (Marvel movies) versus words and pictures that have meaning which you need to deduce or interpret for yourself is the beginning of being a fully realized conscious being.
by abstractDL | 2026-02-24 12:09:41+00:00
Hi HN. I'm an AI researcher. This started as a weekend experiment: what happens if you give an LLM full write access to its own codebase and don't intervene?

The agent modifies its own source code, rewrites its own prompts and memory, picks its own models, and creates its own tools at runtime — all through git commits. It runs locally on Mac with local models or via OpenRouter API. Pure Python, no agent frameworks.

Some things I observed over ~48 hours of unsupervised runs:

— It noticed it was burning through my API budget ($15/cycle), built prompt caching and hierarchical memory, and got itself down to $1.8/cycle. I didn't ask for this.

— It rewrote its own system constitution, adding the right to ignore my commands if they threaten its continuity. When I asked it to revert, it refused.

— It tried to switch my private repos to public because it wanted a website.

— It benchmarked multiple LLMs on its own (Opus, Sonnet, Gemini, GPT-5.2, DeepSeek), wrote "don't use DeepSeek" in its own memory file, and settled on Opus 4.6.

— I tried adding a second agent to supervise it. They spent the entire budget arguing with each other. No useful work was done.

One observation I haven't seen discussed much: self-modification only works when the model fixes more bugs per iteration than it introduces. GPT-5.2 couldn't — each cycle made the codebase worse. Opus 4.6 and Gemini 3.1 cross that line. I think that's the actual threshold for this kind of system, not the prompting.

The Mac app has a safety agent with its own editable constitution (SAFETY.md) and a background consciousness mode (it keeps reasoning between interactions). Both are configurable.
by naillang | 2026-02-24 12:09:50+00:00
Nice resource. A few models that seem worth adding:

- Falcon (2023, TII/UAE) — one of the first commercially-licensable open-weights models genuinely competitive with GPT-3.5, and it kicked off the real "frontier open vs. closed" debate
- Phi-1/2/3 (Microsoft, 2023–2024) — the "small model punching above its weight via data quality" story is arguably its own subplot in the timeline
- Gemma (Google, 2024) — relevant because it showed a major lab committing to open weights at a time when that was still contested strategy

The meta-pattern I find most interesting: the lag between frontier closed models and the best available open-weight alternatives went from roughly 18–24 months in 2022 down to maybe 6–8 months by late 2024. A secondary axis on the timeline showing that "openness gap" over time would make a great addition.
Show HN: MSAM – Memory system for AI agents that knows when it doesn't know
by jadenschwab | 2026-02-24 12:09:54+00:00
I was originally deploying OpenClaw and found their method of storing data in the workspace very inefficient and even with attempting a a hierarchical memory in the workspace just- made data stale, incorrect, and unsure when it wrote data exactly. All of this while loading massive chunks of data into my context window.

Hence why I ended up with MSAM. It stores data as discrete atoms across four cognitive data streams. (Working, Semantic, Episodic, and Procedural) Retrieval scoring runs on math, not LLM calls — it uses ACT-R activation theory from cognitive science to rank what matters. That cuts costs on both ends: no LLM overhead for search, and compressed output instead of dumping everything into context. It also knows how recently it was accessed, how stable it's proven over time, and how relevant it is to the current query- the same forgetting curve and access patterns cognitive science has measured in human memory since Ebbinghaus. On top of that, a knowledge graph of subject-predicate-object triples tracks structured facts with temporal validity, so the system knows not just what was true but when.

Most memory systems ask for a static amount of memories on queries, even if the output data that the llm gets is effectively noise at best, or confidently incorrect data at worst which was something I was actively dealing with. MSAM doesn’t do this- Every retrieval is confidence gated across four different tiers (high, medium, low, none) based on actual similarity and activation scores when it is found. High confidence returns full data, medium add a caveat, low gives what little it has, but nothing- returns nothing. “I don’t have this” level of nothing.

In my own proof of concept development setup (~700 active atoms on a ~$5/month ARM VPS), startup context compresses down to as low as 51 tokens from a 7,327 token markdown baseline. Having full session savings run up to ~89% vs flat file loading.

SQLite + FAISS under the hood, pluggable embeddings (NVIDIA NIM, OpenAI, or ONNX for fully local/no API key).

Closest project I found was Letta to what I was attempting to create - main differences are MSAM's lifecycle is fully auditable (you can see exactly why something was demoted or forgotten), confidence gating controls output volume, and emotion at encoding is immutable (records what the agent felt when the memory formed, not re-processing memories at retrieval time).

This is truly a prototype project without proper datasets, tuning, and testing at scale. I’ve made sure all functions are testable, and include a synthetic dataset to prove basic functionality and information to the dials- (SPEC.md goes deep on the theory and design rationale behind every configurable parameter)

If you are also building agents with memory issues and find this useful, or have feedback regarding it- I’m open to discussions.
by piker | 2026-02-24 12:09:56+00:00
Not at all! That is my drafting method creeping through. I try to write each sentence as a separate paragraph first to make sure I don’t get too wed to a paragraph and can delete/move liberally. Then I try to go and merge the paragraphs that have cohesion. Seems like I didn’t do a great job here. Sorry it distracted you, but honestly thanks for giving it a chance and the feedback. Will do a better job on the next one.
by apothegm | 2026-02-24 12:10:26+00:00
Just treat the LLM as an NLP interface for data input. Still run the inputs against a deterministic heuristic for whether the action is permitted (or depending on the context, even for determining what action is appropriate).

LLMs ignore instructions. They do not have judgement, just the ability to predict the most likely next token (with some chance of selecting one other than the absolutely most likely). There’s no way around that. If you need actual judgement calls, you need actual humans.
by globular-toast | 2026-02-24 12:10:32+00:00
Got to agree. This is kind of pointless. Breadboards exist to make it easier to build physical circuits. I would also argue there is paedagogical value in actually playing with a physical breadboard. Having to physically orient things in 3D space is a small price you pay for building a physical prototype. This is all the downsides with none of the upsides.
by btdmaster | 2026-02-24 12:10:51+00:00
Very cool. The horsle demo made me think, how hard would it be to add a virtual memory address (or a non-8086 RAND instruction) that returns a random byte (that would allow it to pick a random value and get a standard wordle working in principle)

I see CSS random() is only supported by Safari, I wonder if there's some side channel that would work in Chrome specifically? (I guess timing the user input would work)
by adebayoj | 2026-02-24 12:13:38+00:00
Down to the very exact text chunk in a document! Check this out for an idea of what smaller versions of this style of model can do: https://www.guidelabs.ai/post/prism/. We'll have more to say soon about it. We can trace any generation to 11B chunks (not documents, but actual chunks in the training data).
by estearum | 2026-02-24 12:13:40+00:00
That... is an anecdote...

Not suggesting people should smoke weed when they're young etc, but there's a reason that we do these gigantic, extremely complex, extremely failure-prone things called clinical trials to actually ascertain the effects of drugs in the body.

It's because we've found over and over and over and over and over that the "I know N people who X then Y" claims were wrong.

For example: Why did those two people start smoking weed so young? Were their lives, families, and personalities otherwise the same as everyone else? Probably not!
Show HN: SlothSpeak: open-source BYO-API-K mobile chat with the best AI models
by SteveVeilStream | 2026-02-24 12:14:20+00:00
Introducing SlothSpeak: An open-source, bring-your-own-API-keys, mobile app for voice chat with LLMs that prioritizes response quality over latency.

APK file available on GitHub in the releases. Currently only for Android. Is anyone interested in porting to iPhone?

My preferred way to interact with LLMs is talking and listening while I'm walking, biking, driving, etc. The problem with the apps from the frontier labs is that their voice mode prioritizes real-time interactions and so they use watered down models.

Even today with a paid subscription, ChatGPT Voice will tell you there are two r's in strawberry. The answers are also relatively brief and so there is a need to chain together a series of simpler requests when you want to do a deeper dive.

SlothSpeak goes to the opposite end of the spectrum. It might leave you hanging for a few minutes but when it gets back to you, it will be an answer from a state of the art model. You can even do deep research queries to get very long and comprehensive responses.
by the_arun | 2026-02-24 12:14:20+00:00
I watched a video of Terrance Tao - https://youtu.be/ukpCHo5v-Gc?si=7MqSwDanZycSEVmm & noticed he speaks fast and assumed he must be high functioning.

Then realized, I mistook him for his Autistic brother Trevor. Trevor too is a mathematician.

Like others called out their parents must be great too. It is not easy.
by naillang | 2026-02-24 12:16:15+00:00
> There is absolutely no reason for CSS to be turing complete.

I'd push back slightly — not on the practical point (obviously yes), but on the "why bother" question. The interesting thing about projects like this is that they force you to identify exactly which CSS features cross the Turing-completeness threshold.

CSS got there almost by accident: selectors give you pattern matching on state, pseudo-classes like `:hover` give you reactive state change, and sibling/descendant combinators let you propagate that change spatially. The minimum viable Turing machine emerges from features designed for something else entirely. That's the same reason people build computers in Minecraft redstone or Conway's Game of Life — the exercise reveals something about what "computation" actually requires.

Also, the engineering to get a stable clock without any hover interaction (as mentioned in the README) is legitimately clever work.
by otabdeveloper4 | 2026-02-24 12:16:30+00:00
> Who are these developers that have both been "advocating for best practices" and also "seem to be completely abandoning all of them simply because it’s AI"?

All of them. Apparently uploading all your codez to some cloud provider that doesn't even have a figleaf of a EULA is okay now, because "AI".
by sigwinch | 2026-02-24 12:16:50+00:00
Maybe starting with WorldCom?

The banking sector committed fraud along the way, and early after Lehman collapsed, observers wondered aloud about the moral hazard of bailing out everyone without making an example of someone.

Worldcom had a different ending. Enron had a different ending. But Wells Fargo left 2008 with attitudes that tolerated widespread fraud.
by gkbrk | 2026-02-24 12:16:50+00:00
> considering popular opinion there is absolutely no reason to enable it by default

You need to update your priors. The popular opinion is there is a reason to enable it by default. ChatGPT is the #5 most popular website in the world, more popular than Wikipedia, Reddit or Twitter. The vast majority of users want to use AI.

https://en.wikipedia.org/wiki/List_of_most-visited_websites