MurphyA/hacker-news huggingface dataset:
by YaraDori | 2026-02-16 03:55:30+00:00
This is a great approach — giving agents their own compute environment is a huge unlock.

One challenge I keep running into on the other side of this: even with a full VM and browser available, the bottleneck is often teaching the agent what to do. Writing detailed step-by-step instructions for each workflow is tedious and error-prone.

I've been working on a complementary tool (SkillForge — https://skillforge.expert) that takes a different angle: you record your screen doing the task once, and AI extracts every click/keystroke/navigation into a structured skill file the agent can replay.

The combo of something like CloudRouter (agent gets its own machine) + structured skill extraction (agent knows exactly what to do) feels like it could make agent automation much more practical for non-trivial workflows.
by dimitri-vs | 2026-02-16 03:56:05+00:00
API Opus 4.6 will tell you it's still 2025, admit it's wrong then revert back to being convinced it's 2025 as it nears it's context limit.

I'll go so far as to say LLM agents are AGI-lite but saying we "just need the orchestration layer" is like saying ok we have a couple neurons, now we just need the rest of the human.
by YaraDori | 2026-02-16 03:56:46+00:00
Really interesting approach to the coordination problem. File locking + knowledge flywheel is a clean pattern.

I've been thinking about the adjacent problem: even once you solve coordination, each agent still needs to know HOW to do the workflow. Right now that means someone writes detailed instructions or the agent fumbles through with generic prompts.

We've been experimenting with screen recording as a way to capture workflows (https://skillforge.expert) — you record yourself doing the task, and AI extracts every click/keystroke into a structured skill file. The agent replays it autonomously.

Curious: with Forge's knowledge flywheel, have you thought about storing reusable skill definitions that agents could query before attempting a task? Feels like the "capture once, replay many" pattern could stack well with multi-agent orchestration.
by CuriouslyC | 2026-02-16 03:58:02+00:00
Human in the loop isn't the only defense, you can't achieve complete injection coverage, but you can have an agent convert untrusted input into a response schema with a canary field, then fail any agent outputs that don't conform to the schema or don't have the correct canary value. This works because prompt injection scrambles instruction following, so the odds that the injection works, the isolated agent re-injects into the output, and the model also conforms to the original instructions regarding schema and canary is extremely low. As long as the agent parsing untrusted content doesn't have any shell or other exfiltration tools, this works well.
by skeptic_ai | 2026-02-16 03:58:02+00:00
They won’t “sell”. Imagine LV selling originals in Africa , Africa would immediately resell them in Europe and us and Asia for much higher price and dilute the brand. It will be officially sold to a reseller, not officially they will pay a special African company to destroy it.

So same shit as before. Slightly more expensive. No big brand would ever sell their originals that didn’t sell cheap.
by ggm | 2026-02-16 03:58:03+00:00
Porque no los dos?

Keep on doing IPR focussed design.

Grow deeper roots into a foundry, work on integration of the tech into FPGA and big chip/platter stuff for AI.

If AI tanks, the work will find a value point. We all want more memory and more execution cycles per clock tick be they on one ALU or many.

Lots of work to come in optical related areas. ARM has green fields to dig, beyond the instruction set.
by drecked | 2026-02-16 03:58:24+00:00
Hey, when American workers decided to spend the 3 years or so they had leverage over their bosses after the pandemic to insist that, well, actually, there is no advantage in working together and in person, and that there is no advantage to having an American college degree, is it really surprising that their bosses then decided, well, if that’s the case, why are we hiring people at 5x the cost we could get someone in a different country?
by kjellsbells | 2026-02-16 03:58:24+00:00
I guess HN leans strongly towards the pro-anonymity viewpoint, and sure, the "think of the children!" claim has worn very thin.

But: what methods could that reduce the harm that anonymous internet discourse so often produces? People send death threats, threats of sexual assault, harassment of all kinds, unsolicited pics of their genitals, swat attacks, just absolute nonsense all day every day, hiding behind the veil of anonymity and the asymmetry between the cost of sending such trash and the cost to track it down and do something about it.

There is, quite unusually in politics, a bit of a bipartisan consensus that this is a real, real problem, and that steps like this, or repealing Section 230, would help. Would it, or would it not, and if not, what alternatives are there?
by scalefirst | 2026-02-16 03:59:11+00:00
I built this because I kept running into the same problem at work: an AI agent (Claude Code, Cursor, etc.) would make changes to our codebase, and a week later nobody could explain why it chose to delete a file instead of editing it, or why the same prompt produced different results on Tuesday vs Thursday.
The debugging loop was brutal. No execution trace. No way to compare two runs. No way to prove that an artifact was produced by a specific agent invocation. We were flying blind.
ContextSubstrate is a CLI tool that treats agent runs the way git treats code changes — as immutable, content-addressed snapshots you can inspect later.
Three core operations:
ctx capture — Records an agent run into a Context Pack: the prompt, tool calls, model params, input files, timestamps, environment metadata. Everything gets hashed. You get back ctx://sha256:abc123 — share that hash, and anyone can reproduce the run.
ctx diff <hash-a> <hash-b> — Compares two runs and shows where decisions diverged. Did the model pick a different tool? Did a parameter drift? Did the prompt change? It outputs a structured diff of decision points, not just text diffs.
ctx verify <artifact> — Traces any file back to the Context Pack that produced it. "Who wrote this code, when, and with what instructions?"
What it's not:

Not an agent framework (use whatever agent you want)
Not an observability dashboard (no cloud, no SaaS)
Not a prompt IDE

It's infrastructure. Local-first, Git-compatible layout, JSON + content-addressed blobs. Written in Go.
The closest analogy is: git gives you blame, log, and diff for human-written code. ContextSubstrate gives you the same primitives for agent-written code.
Early stage — the CLI works, Context Packs are solid, and diff/verify are functional. Looking for feedback from anyone who's dealt with "my agent did something last week and I can't figure out what" problems.
GitHub: https://github.com/scalefirstai/ContextSubstrate
by crazygringo | 2026-02-16 04:00:08+00:00
Oh, I didn't learn it from LLM's, believe me.

That style of writing has been around forever. LLM's learned it from us. I'd basically call it "American sales pitch". It's a little bit product landing page, a little bit political opinion column, a little bit self-help book or motivational blogger.

It's always been a style of writing that tries to maximize engagement. The issue is that now we see it creeping into areas that never used to use it. It's not how developers tend to write. But now developers toss their original draft into an LLM asking it to "punch it up for engagement" -- or the LLM has just been trained to assume that's what someone wants by default -- and so now it stands out like a sore thumb.
by 12345hn6789 | 2026-02-16 04:00:29+00:00
https://imgur.com/a/qYmznHa

I just asked gemini 3 5 times: `what temperature I should take a waterfowl out of the oven`

and received generic advice every single time it gave nearly identical charts. 165F was in every response. LLMs are unpredictable yes. But I am more skeptical it would give incorrect answers (raw goose) rather than your mother preparing the fowl wrong.
by bombcar | 2026-02-16 04:00:43+00:00
The group of people who read the instruction manual for video games, and then the hint guide, and then played a bit - these people love new board games.

And you can sometimes get a group of them and try a new game each time!

But the average person just wants to play the same game each week for fun - we used to do things like this with cards (poker, bridge, cribbage).
by ggm | 2026-02-16 04:00:48+00:00
One of those counterfactual moments. Like many other 5:2 practicing people I did it, and I moved on, but the weightloss was real to me, and I have managed to keep below my peak.

Other forms of dieting did not "stick" and so if its no better, I kept at it longer and I got to the place I needed to be.

There's "better" and there's "practicable and achievable" and I have to say, it was the latter.

(peak weight 98kg. current weight 5+ years in 84-87 with slow upward drift)
by thisislife2 | 2026-02-16 04:01:17+00:00
I have faced this some time too - I think if you post some "controversial" comment (judged by many quick upvotes and downvotes) it triggers a "cooling down" period before you can post a reply to your immediate child comments in the thread. This ensures you don't dominate the thread, and allows a conversation with other participants to develop. (Based on how others react to the comments, I assume it also gives the mods a better picture if they need to intervene). I found it a minor annoyance at first, but have learnt to appreciate it - thoughtful comments (with careful moderation) from a diverse group of people is what makes a community like this valuable.
by conartist6 | 2026-02-16 04:01:52+00:00
You could also think of it this way: if I abuse git's protocols to do my thing, I pretty much give up my chance to become a standard in the future. If people are going to adopt a new standard they want it to be clean: you don't want to be trying to sell a greenfield technology that's loaded up with legacy tech debt. Making a data format that renders broken in every existing tool that touches it would be showing a lot of bad faith up front.
by anonymid | 2026-02-16 04:02:14+00:00
Thanks for reading, and I really appreciate your comments!

> who feed their produced tokens back as inputs, and whose tuning effectively rewards it for doing this skillfully

Ah, this is a great point, and not something that I considered. I agree that the token feedback does change the complexity, and it seems that there's even a paper by the same authors about this very thing! https://arxiv.org/abs/2310.07923

I'll have to think on how that changes things. I think it does take the wind out of the architecture argument as it's currently stated, or at least makes it a lot more challenging. I'll consider myself a victim of media hype on this, as I was pretty sold on this line of argument after reading this article https://www.wired.com/story/ai-agents-math-doesnt-add-up/ and the paper https://arxiv.org/pdf/2507.07505 ... who brush this off with:

>Can the additional think tokens provide the necessary complexity to correctly
solve a problem of higher complexity? We don't believe so, for two fundamental reasons: one that
the base operation in these reasoning LLMs still carries the complexity discussed above, and the
computation needed to correctly carry out that very step can be one of a higher complexity (ref our
examples above), and secondly, the token budget for reasoning steps is far smaller than what
would be necessary to carry out many complex tasks.

In hindsight, this doesn't really address the challenge.

My immediate next thought is - even if the solution space can be represented within the model, do we actually feel like CoT is moving us towards generalized solutions, or that the solution space is navigable through reinforcement learning? I'm genuinely not sure if I feel one way or another.

> I don't have an opinion on this, but I'd like to hear more about this take.

I'll think about it and write some more on this.
by dangus | 2026-02-16 04:02:21+00:00
But Google sells the productivity apps and also does the exact same things OpenAI does.

If their work on Gemini is this leading world-class stuff, why aren’t Google’s software products not suddenly becoming better?

Was the most recent release of Android demonstrative of a significant uptick in product iteration? Shouldn’t we suddenly be seeing Android pulling far ahead of iOS in an unusually rapid fashion because Apple doesn’t have access to the same quality of shovels?

What about Microsoft Windows 11? Isn’t Microsoft a major OpenAI investor with full access to their latest and greatest?

Why aren’t we seeing release schedules accelerate or feature lists growing at a faster rate?

Supposedly we are selling a lot of shovels here but I don’t see a lot of holes being dug.
by 4b11b4 | 2026-02-16 04:02:32+00:00
Put it in plan mode.

Then, repeatedly ask Claude to criticize the plan and use the "AskUserQuestion" tool to ask for your input.

Keep criticizing and updating the plan until your gut says Claude is just trying to come up with things that aren't actually issues anymore.

Then unleash it (allow edits) and see where you get. From there you may ask for one off small edits. Or go back into plan mode again
by 12345hn6789 | 2026-02-16 04:02:38+00:00
https://imgur.com/a/qYmznHa

I just asked gemini 3 5 times: `what temperature I should take a waterfowl out of the oven`

and received generic advice every single time it gave nearly identical charts. 165F was in every response. LLMs are unpredictable yes. But I am more skeptical it would give incorrect answers (raw goose) rather than your mother preparing the fowl wrong.

Cooking correctly is a skill, just as prompting is. Ask 10 people how to cook fowl and their answers will mimic the LLM.
by noelsusman | 2026-02-16 04:03:19+00:00
It likely won't matter much in the end, but I do think this could be a significant mistake for OpenAI.

OpenAI has two real competitors: Anthropic in the enterprise space and Google in the consumer space. Google fell far behind early on and ceded a lot of important market share to ChatGPT. They're catching up, but the runaway success of ChatGPT provides OpenAI with a huge runway among consumers.

In the enterprise space, OpenAI's partnership with Microsoft has been a gold mine. Every company on the planet has a deep relationship with Microsoft, so being able to say "hey just add this to your Microsoft plan" has been huge for OpenAI.

The thing about enterprise is the stakes are high. Every time OpenAI signals that they're not taking AI safety seriously, Anthropic pops another bottle of champagne. This is one of those moments.

Again, I doubt it matters much either way, but if OpenAI does end up blowing up, decisions like this will be in the large pile of reasons why.
by jader201 | 2026-02-16 04:04:27+00:00
As someone that hosts and co-hosts several game nights (once or twice a week, most weeks), the key is to try to feel out the group, and if necessary/helpful/desired, send out "how to play" videos in advance (if they prefer learning that way vs. live).

But yeah, see if the group wants to learn a new one vs. playing one they already know.

When I set up games for my coworker group, I always send out new options along with a number of games we've already played, and let them pick. (More times than not, they pick a new game.)

When I host games with my family, I always bring a bunch of games, both new and already played, and let them pick (these are usually much lighter [easier] games). I'll sometimes suggest a new game if I think they'll like it, and they usually do. The key is to get to know what the group typically enjoys.
by ggm | 2026-02-16 04:05:18+00:00
We're beyond peak oil. Not peak from lack of available reserves, peak because we need and consume less.

OPEC and other market/supply manipulation, plus collusive behaviour explains pricing more than availability.

Why would anyone exploit thick, heavy, sulphur rich expensive-to-extract oil over light sweet crude? Oh right: Tar sands. Like, this hasn't been a thing for ages.

TL;DR It's politics, its bullshit. Oil is a great feedstock for drugs, fertiliser, plastics and explosives. We don't depend on it for fuel as much as we used to and we will depend on it for fuel much much less in the future.

Venezuela's problems are going to take more than US market manipulation to fix.

(we do obviously both depend on, and need continuance of access to oil, and oil prices affect inflation and industrial production in all kinds of ways. "less" is not none)
by srcreigh | 2026-02-16 04:05:32+00:00
It's just stored as a [256]u8 in the struct.

// sqlite.zig
pub const ErrorPayload = struct {
message: [256]u8,

pub fn init(db: *c.sqlite3) @This() {
var self = std.mem.zeroes(@This());
var fw = std.Io.Writer.fixed(self.message[0..]);
_ = fw.writeAll(std.mem.span(c.sqlite3_errmsg(db))) catch |err| switch (err) {
error.WriteFailed => return self, // full
};
return self;
}
};
by adgjlsfhk1 | 2026-02-16 04:05:37+00:00
Engines have a significantly bigger edge in frc. Humans generally know enough about openings to minimize mistakes for the first 10 or so moves (such that computers playing humans generally are trained to make a couple highly dubious moves to get far away from theory in the opening). engines on the other hand don't go based on theory and are just as capable in 960 as they are in regular chess.
by gip | 2026-02-16 04:05:59+00:00
I think that’s fair.. building a competing product would likely be relatively easy and inexpensive. But that’s true for most software now: it’s becoming easier to build, and the barriers to entry are lower.

I love Anthropic and OpenAI equally but some people have a problem with OpenAI. I think they want to reposition themselves as a company that actively supports the community, open source, and earns developers’ goodwill. I attended a meeting recently, and there was a lot of genuine excitement from developers. Haven't seen that in a long time.
by somenameforme | 2026-02-16 04:06:53+00:00
Chess ability seems distributed in a power law, rather than any sort of a normal distribution. There are repeatedly, throughout history, players that are just much better than everybody else, including the 2nd best player in the world. Lasker, for instance, was world champion for 26 consecutive years while also regularly dominating tournaments during that period as well. Kasparov was #1 for 21 years, and so on.

I'd go further to say I think this is true in many things. For instance if you're into wrestling, you know the name of Alexander Karelin [1] who ended his career with a record of 887 wins and 2 losses (both losses by a single point and both highly controversial). He was winning olympic gold, repeatedly, not only without a single defeat but without his opponents even scoring a single point against him. His ears tell the story - 889 world class matches, and he doesn't even have cauliflower ear.

[1] - https://en.wikipedia.org/wiki/Aleksandr_Karelin
by yoyohello13 | 2026-02-16 04:07:33+00:00
In this case I think it is largely jealousy, it's just a guy getting a new job at the end of the day.

But come on, negativity around a rugpull is jealousy? Are you so jaded you can't imagine people objecting to the total lack of morality required to do a crypto rugpull? I personally get annoyed about something like Trump Coin because seeing people rewarded for being dirt bags offends my sense of justice. If you need a more pragmatic reason, rewarding dirtbaggery leads to a less safe society.
by justinzhou01 | 2026-02-16 04:08:14+00:00
I built this to practice PTE Core Read Aloud more efficiently. It has 47 prompts, in-browser recording, and AI scoring for content/
pronunciation/fluency on the native 0-90 PTE scale, with word/syllable/phoneme-level feedback and cloud-synced progress. I’d love feedback on
whether the scoring report is actually actionable for learners, and what would make this more useful for daily speaking practice.
by braiamp | 2026-02-16 04:08:28+00:00
I would recommend reading the paper rather than the article, or at least the abstract:

> Compared to a noise-free control night, EN reduced N3 deep sleep (p < .0001) while PN reduced REM sleep (p < .001). Adding PN to EN worsened sleep structure, despite minor dose-dependent improvements of EN-induced sleep fragmentation and N3 sleep increases. Earplugs mitigated nearly all EN effects on sleep but started failing at the highest EN level (65 dBA). Morning cognition, cardiovascular measures, and hearing were not affected by nighttime noise, but subjective assessments of sleep, alertness and mood were significantly worse after EN and PN exposure.
by bitwize | 2026-02-16 04:09:46+00:00
It became painful for me to move my left arm for months after I got my Shingrix. It still hurts a little, especially in the morning upon awakening. This isn't a common side effect but I did find a case study of a woman who had worse pain for longer (about a year). Still would get the vax though. My mother-in-law actually had shingles. I don't want it.
by samrus | 2026-02-16 04:09:53+00:00
> but modern computers are insanely fast, and browsers, insanely optimized

I think these facts have been used as excuses to shit up the app layer with slow mal-optimized js code.

An example of a recent high performance app is figma, which blows normal js only apps out of the water. And it does so by using c++ wasm/webGPU for its more domanding parts, which is most of it

I think we have to let go of the "just get more ram" approach and start optimizing webapp code like figma does
by crazygringo | 2026-02-16 04:09:55+00:00
He almost certainly doesn't "lisp" his S's if you heard him in real life, but says them perfectly normally.

What you're hearing is the way microphones deal with the hissing of an "s", same as they struggle with plosives like "p", from the whoosh of air. It's an artifact of microphones close to the mouth, so it makes sense that Google replicates it.

You can use physical pop filters or digital audio filters to reduce the effect, but podcasters don't usually use the physical ones, and the level of audio processing podcasters do really depends on their level of expertise and how much they even care.
by hunterpayne | 2026-02-16 04:10:10+00:00
Continuous batching is a nonsense term. Systems that behave this way are usually called streaming (as opposed to batch).

Batch is started and stopped and produces one result (or set of results). Computation happens when the job is started until the job completes.

Streaming is on continuously and computation happens when new data arrives. It produces a stream of results.

This is a streaming system. And most of the analysis doesn't really understand how high throughput (as in >16Mb/s/core) are architected. It doesn't even use the correct language for what is being done to the data (relational algebra) so the proper optimization techniques can't be applied.

It is however a very nice explanation of the preprocessing used in LLM systems.
by lucaspiller | 2026-02-16 04:10:40+00:00
I'm in the early phases of working on a game that explores that.

The backstory is that in the late 2050s when AI has its hands in everything, humans loose trust of it. There are a few high profile incidents - based on AI decisions -, which cause public opinion to change, and an initiative is brought in to ensure important systems run hardware and software that can be trusted and human reviewed.

A 16bit CPU architecture - with no pipelining, speculative execution etc is chosen, as it's powerful enough to run such systems, but also simple enough that a human can fully understand the hardware and software.

The goal is to make a near-future space exploration MMO. My Macbook Pro can simulate 3000 CPU cores simultaneously, and I have a lot of fun ideas for it. The irony is that I'm using LLMs to build it :D