MurphyA/hacker-news huggingface dataset:
by alexey-pelykh | 2026-03-11 13:17:05+00:00
The bottleneck isn't agent capability. It's captured institutional knowledge.

I structure system prompts (CLAUDE.md) with verification gates: pre-task checkpoints, approach selection, post-completion rescans. When an agent writes an ADR during a refactor, future agents reference it before touching the same code. The context compounds across sessions.

Commit messages capture what. ADRs capture why. Skill files capture how the team works. That last layer is what most setups miss. The gap isn't level 6 vs 8, it's whether architectural reasoning is machine-readable or trapped in someone's head.
by cubefox | 2026-03-11 13:17:38+00:00
Yeah, "1.58 bit" is 1 trit with three states, since log2(3)≈1.58.

So it's not a inference framework for 1-bit models (two states per parameter) but for 1.58 bit models (three states per parameter). Annoying that they try to mix up the two.
by ErroneousBosh | 2026-03-11 13:17:46+00:00
That doesn't solve the problem I have, because I already have a 1/4" ratchet and I don't have a 1/4" T50 bit.

Furthermore, a T50 bit with 1/4" drive would just snap instantly. If the bit didn't break, you'd twist the end off the extension bar.

I have a specific problem, which I already know how to solve, which has a specific solution, for which I need a specific component.
by LuxBennu | 2026-03-11 13:18:00+00:00
Agreed. The real value proposition of Apple Silicon for local inference is running models that won't fit on consumer GPUs. I run Qwen 70B 4-bit on an M2 Max 96GB through llama.cpp and it's usable — not fast, but the unified memory means it actually loads. Would be interested to see MetalRT benchmarks at that scale, since the architectural advantages (fused kernels, reduced dispatch overhead) should matter more as models get memory-bandwidth-bound.
by snek_case | 2026-03-11 13:18:17+00:00
First, believe it or not, 3 years is not that long. It's also not a given that LeCun was given the resources he needed to work on this tech at Meta. Zuck wanted another llama.

Second, AMI Labs just secured a billion in funding, and while that's a lot of money, it's literally just a fraction of the yearly salary they are paying to Wang. Big tech companies are literally throwing tens of billions to keep doing the same thing, just on a bigger scale. Why not try something else once in a while?
by piva00 | 2026-03-11 13:18:18+00:00
It's incredibly naïve to think regime change supported by the people is actually the objective. It's a good thought but which is absolutely out of control of the military actions by Israel or the USA.

The main objective is to neuter the Iranian regime to diminish how threatening it could be to Israel, behead the government, destroy military targets, destroy its lifeline from the oil industry. If regime change happens because conditions worsen it's a good bonus but without forcefully removing the regime with boots on the ground it's just wishful thinking that it's the main objective.

Iraq was also under a brutal dictatorship with Saddam, it took more than a decade of ground operations to actually change it. Iran is more populous, has a much more loyal regime security force, is more ideologically driven, and has a much worse geography for any ground invasion.

When the bombing stops there will be so much destruction that the regime can point towards the USA and Israel that it will keep having loyalists behind to defend them, the IRGC will absorb the more loyal ones and grow to keep stamping out revolutionaries.
by Nevermark | 2026-03-11 13:18:22+00:00
The fact that humans can learn to do X, sometimes well, often badly, and while many don’t, strongly supports the conjecture that X is not how they naturally do things.

I can perform symbolic calculations too. But most people have limited versions of this skill, and many people who don’t learn to think symbolically have full lives.

I think it is fair to say humans don’t naturally think in formal or symbolic reasoning terms.

People pattern match,

Another clue is humans have to practice things, become familiar with them to reason even somewhat reliable about them. Even if they already learned some formal reasoning.

—-

Higher level reasoning is always implemented as specific forms of lower order reasoning.

There is confusion about substrate processing vs. what higher order processes can be created with that substrate.

We can “just” be doing pattern matching from an implementation view, and yet go far “beyond” pattern matching with specific compositions of pattern matching, from a capability view.

How else could neurons think? We are “only” neurons. Yet we far surpass the kinds of capabilities neurons have.
by Kave0ne | 2026-03-11 13:18:27+00:00
The self-modification-without-restart point is underrated. The current model where you have to restart an agent to change its behavior creates an implicit coupling between the agent lifecycle and its configuration. When the session restarts, you lose working context. Mog sidesteps this cleanly with dlopen-style dynamic loading. The capability propagation design also addresses something I have not seen well-solved elsewhere: agent-written code that inherits the agent session permissions rather than getting a fresh capability set. That is the right primitive for safe self-extension.
by alexey-pelykh | 2026-03-11 13:18:45+00:00
The distinction that matters is whether the contributor can defend their work in review, not what tool produced it.

I maintain a 300-commit fork built with heavy AI assistance. The AI writes a lot of the code. I review every line and can explain every choice. The test: can they respond to feedback, explain why they chose this approach over the simpler one, iterate on edge cases? That works regardless of how the code was produced.

Debian's problem isn't AI. It's distinguishing "used a tool well" from "dumped output." Code review already does this. Tighter process for new contributors (smaller patches, demonstrated understanding through review conversation) filters on engagement quality, not tool choice.
by jqpabc123 | 2026-03-11 13:18:56+00:00
Amazon had outages caused by humans in the past.

Everyone has had and will most likely continue to have outages. The question is --- what is the trend? Are they increasing or decreasing with increased use of AI at Amazon?

Amazon has allegedly discussed internally the “trend of incidents" related to “Gen-AI assisted changes".

“Folks, as you likely know, the availability of the site and related infrastructure has not been good recently,” Dave Treadwell, a senior vice-president at the group, told employees in an email, also seen by the FT.

https://www.ft.com/content/7cab4ec7-4712-4137-b602-119a44f77...
by alexey-pelykh | 2026-03-11 13:19:13+00:00
Most agent orchestration projects skip session persistence across restarts. That's the feature that actually matters for human-in-the-loop workflows, because rebuilding context kills the iteration speed. The ask/delegate/autonomous mode spectrum maps well to how trust builds with these systems.

Genuine question: in delegate mode, when the agent encounters something that needs escalation, does it block until you approve or can it context-switch to other work while waiting? The blocking vs. async decision shapes the whole UX of human-in-the-loop systems.
by pmf_hunter | 2026-03-11 13:19:13+00:00
Been running 6 AI agents for my solo operation for a few months now. One does market research, another writes content, third handles video scripts. Not coding agents - business operations agents.

The overnight thing is real but overhyped. What actually works is giving agents very narrow tasks with clear success criteria. "Research top 10 Reddit threads about X and summarize pain points" works great. "Build me a feature" overnight is a coin flip.

Biggest lesson: the bottleneck moved from execution to context management. Getting agents to remember what matters and forget what doesn't is harder than the actual task delegation.
by LuxBennu | 2026-03-11 13:19:15+00:00
This is the right call. llama.cpp has dozens of hand-tuned CUDA kernels across Q4_K_M, Q5_K_S, Q8_0 and other quant formats, each targeting different hardware profiles. An autoresearch approach that could optimize these per-GPU would be huge — right now performance varies wildly between, say, an RTX 3090 and a 5070 Ti on the same quant format because the kernels are tuned for specific architectures. The hardware diversity in the llama.cpp user base is exactly where automated kernel search has the most to gain.
by giancarlostoro | 2026-03-11 13:19:18+00:00
One of the things I often wonder is "what will be the minimally viable LLM" that can work from just enough information that if it googles the rest it can provide reasonable answers? I'm surprised something like Encyclopedia Britanica hasn't yet (afaik) tried to capitalize on AI by selling their data to LLMs and validating outputs for LLM companies, it would make a night and day difference in some areas I would think. Wikipedia is nice, but there's so much room for human error and bias there.
by etienne_l | 2026-03-11 13:19:32+00:00
I spent the last few weeks building an abstraction layer for n8n to fix a specific problem: LLM hallucinations in AI IDEs.

When you ask Cursor or Windsurf to build an n8n workflow, it often breaks the JSON schema. By bringing the n8n ontology into TypeScript with decorators, we provide the AI with the strict context it needs to be 10x more reliable.

It's a pure GitOps approach to automation. Curious to see if others here are moving away from 'No-Code' UI towards 'As-Code' for better AI reliability.
by wongarsu | 2026-03-11 13:19:55+00:00
I've also always though that it's an interesting opportunity for custom hardware. Two bit addition is incredibly cheap in hardware, especially compared to anything involving floating point. You could make huge vector instructions on the cheap, then connect it to the fastest memory you can buy, and you have a capable inference chip.

You'd still need full GPUs for training, but for inference the hardware would be orders of magnitude simpler than what Nvidia is making
by benlatger | 2026-03-11 13:20:00+00:00
Really helpful feedback, thanks a lot. i have to admit i mostly agree with you. i started from the assumption that today Getweeks is a one-off, lifetime purchase. i didn’t build it to make millions — that wouldn’t be realistic anyway. it’s more part of a broader flow of building small apps until i land on an idea that solves a truly painful problem, and by then my execution will be really dialed in
by suckathought | 2026-03-11 13:20:22+00:00
I wrote a tutorial that builds an AI agent from first principles.

The core minimal model:

Agent = Triggers → Loop(Thinking + Tools + Memory) inside a Container

The tutorial walks through:

1. channels (triggers + tools)
2. adding an LLM for reasoning
3. building the decision loop
4. adding memory
5. running the agent inside a container

The goal is to show the minimal architecture behind most agent frameworks.

Would love feedback.
by LuxBennu | 2026-03-11 13:20:53+00:00
rfw300 nails it. In production LLM serving, correctness at the tail matters more than median throughput. MMLU and Hellaswag validate general capability, but they don't catch subtle issues like KV cache corruption under high concurrency or numerical drift accumulating over long generations. The single-model specialization approach is smart in theory — the question is whether the generated stack handles all the edge cases that vLLM has fixed over thousands of issues and PRs. 15% throughput gain means nothing if you get one garbled response per thousand requests.
by naravara | 2026-03-11 13:21:01+00:00
For now the code assistance mostly works within your existing IDE and dev tools. So as long as there is rough parity across the different model vendors I think it should actually be pretty straightforward to swap between them. In fact the few enterprise tools I’ve seen shipping now have a drop-down to pick which model you want to use per instance. You can pick Gemini to do one thing and Claude to do something else and Llama for a third thing if you want. They’re all outputting into conventional programming languages so they can all read and proof each others’ work.
by SilverBirch | 2026-03-11 13:21:21+00:00
What you are talking about isn't inference cost. Yes, fundamentally what matters is all the work that goes into the models, including R&D, training, and inference.

But we talk about inference separately for a reason: largely inference cost is the scaling cost. Once you have a model the margin on your inference is how you get to profitability, as long as your margin is positive you can make the entire enterprise profitable by just selling more tokens. This is the same fundamental business that chip fabs work on. Yes it costs them a lot to get to the next node, but what's important is the margin they can get on the wafers they sell, because they sell tonnes of wafers.

It's pretty core to the concept of SAAS businesses that yes, you do consider all costs. But you want to focus on the margin of the bit that scales. This is why WeWork exploded, the thing they were scaling only scaled up at negative margin.

The point is that if their inference margin is positive, they can "just" scale up and become profitable. If their inference margin is negative, then scaling up the business actually causes problems.
by sarchertech | 2026-03-11 13:21:22+00:00
The skilled trades are better than unskilled work. Better than retail or food service, but they certainly aren’t a replacement for white collar jobs.

The median salaries for skilled trades aren’t great. You can make good money if you are willing to work a ton of overtime, or if you can manage to get one of the very limited union spots in the right city. Or if you become a business owner (and accept the corresponding risk) and mostly manage other skilled employees.

It’s also not a viable solution for more than a small percent of the population. Let’s say AI comes along and forces 25% of the white collar workers out of a job, there is only enough room in the skilled trades to handle a tiny fraction of those displaced workers.

That’s ignoring what massive unemployment does to salaries in the trades. And the fact that to make decent money in the trades you need years of working for peanuts first. And if you think age discrimination is a problem in tech, try breaking into the trades as a gray beard. The entry level jobs are built on the assumption that you are 20 years old and can do 12 hours of hard physical labor without needing a week off to recover.

Again it’s not impossible, it’s just not a solution at any kind of scale.
by embedding-shape | 2026-03-11 13:22:14+00:00
> Framework is ready. Now we need someone to actually train the model.

If Microslop aren't gonna train the model themselves to prove their own thesis, why would others? They've had 2 years (I think?) to prove BitNet in at least some way, are you really saying they haven't tried so far?

Personally that makes it slightly worrisome to just take what they say at face value, why wouldn't they train and publish a model themselves if this actually led to worthwhile results?
Do monitoring tools still miss early signals before incidents?
by gabdiax | 2026-03-11 13:22:30+00:00
I'm curious how teams detect early signals of infrastructure problems before they turn into incidents.

In some environments I've seen cases where monitoring alerts arrive only after the system is already degrading.

Examples:
- disk usage spikes faster than expected
- network latency gradually increases
- services degrade slowly before failing

Tools like Datadog, Zabbix, Prometheus etc. are great for alerts, but they still feel mostly reactive.

How do you deal with this in your infrastructure?

Do you rely more on:
- anomaly detection
- predictive monitoring
- custom scripts
- or just good incident response?

I'm trying to understand what actually works in real-world environments.
by Arch485 | 2026-03-11 13:23:05+00:00
It's not so much a workaround as it is an elegance in design. In Zig, when you @import a file, that file is converted to a struct with comptime fields for all of the public members. Similarly, a "namespace" in Zig is just a nested struct with more fields. Usually it's just another import of something else.

N.B.: Coffee hasn't reached my bloodstream yet; accuracy not guaranteed.
by safety1st | 2026-03-11 13:23:13+00:00
I mean it's a title. Titles are under no obligation to condense the entire content of the article into one sentence. People who want to comment on the article should read it first, and then write in good faith.

The problem lies in the HN comments which have taken that title and interpreted it through the lens of unrelated political arguments: class warfare, anti-offshoring, etc. etc. I don't think any title would be immune from these people. They're just angry because the Internet has its hooks in their brain, and they're going to post about it.

His points are good and people would be wise to read the article and take them to heart. His key points are:

1) If you're a rent seeker, current trends will probably see you lose out to a bigger and more powerful rent seeker. He's probably right about that.

2) Creating more value than you consume is a great form of self-preservation, when you do this no one wants to get rid of you.

None of it's political. It's just good advice for life. I hereby forbid the masses from responding to these points with political rage bait.

HN has better moderation than a lot of places but from my vantage point the entire Internet is sinking into this garbage - we're more aware of the problem these days, at least, but everything and everywhere is more consumed by political hot takes than ever before.

If there was tech that forced commenters to read the article before they could comment on it - now THAT would be a valuable innovation!
by simullab | 2026-03-11 13:23:16+00:00
o I spent the last few months building an AI prediction analyzer (because apparently I hate free time).

I fed it a ton of data and asked it the most pressing question of our generation: "Will Jesus Christ return before GTA 6 drops?"

The answer... was unsettlingly confident.

I showed it to my friends and now they're divided into three camps:

"This is blasphemous, delete it."

"This is the funniest thing I've ever seen."

"So you're saying there's a chance for both?"

Should I keep optimizing this thing, or just delete it, or leave it as it is now and go touch grass?
by ChrisSD | 2026-03-11 13:23:34+00:00
> Many projects including Chromium, boringssl, Firefox, and Rust call SystemFunction036 from advapi32.dll because it worked on versions older than Windows 8.

That's not true. They use ProcessPrng since versions earlier than 10 are no longer supported (well, rust also has a windows 7 target but that couldn't use ProcessPrng anyway since it wasn't available). The issue they linked is from a decade ago.

> If [ProcessPrng] fails it returns NO_MEMORY in a BOOL (documented behavior is to never fail, and always return TRUE).

From Windows 10 onward ProcessPrng will never fail. There's a whitepaper that gives the justification for this (https://aka.ms/win10rng):

> We also have the property that a request for random bytes never fails. In the past our
RNG functions could return an error code. We have observed that there are many callers
that never check for the error code, even if they are generating cryptographic key
material. This can lead to serious security vulnerabilities if an attacker manages to create
a situation in which the RNG infrastructure returns an error. For that reason, the Win10
RNG infrastructure will never return an error code and always produce high-quality
random bytes for every request...

> For each user-mode process, we have a (buffered) base PRNG maintained by
BCryptPrimitives.dll. When this DLL loads it requests a random seed from kernel mode
(where it is produced by the per-CPU states) and seeds the process base PRNG. If this
were to fail, BCryptPrimitive.dll fails to load, which in most cases causes the process to
terminate. This behavior ensures that we never have to return an error code from the
RNG system.
by bstsb | 2026-03-11 13:23:35+00:00
your own source quotes as following:

> To use Communication features like Experience chat, Voice chat, and Party chat, all users must successfully complete an age check.

your words were "They can just disable chat for non age verified accounts". which they have. all "non age verified accounts" cannot use chat.

i have never attempted to assert that Roblox is a completely safe platform, or indeed that their safety team is as effective as it could be - however, your flippant statements were provably untrue with any research.

as for Schlep - while i applaud his stated aims, he was effectively "baiting" predators, offering to move conversations off-Roblox through platforms like Discord to bypass the moderation enforced by Roblox. in general, i fundamentally hate the idea of predator hunters; it seems so strange to monetize justice in this way, especially when it seems they care more about the content than the outcome of their "cases"
by utopiah | 2026-03-11 13:23:43+00:00
> validating outputs for LLM companies

How? They can validate thousands if not millions of queries but nothing prevent the millions-th-and-one from being a hallucination. People who would then pay extra for a "Encyclopedia Britanica validated LLM" would then, rightfully so IMHO, complain that "it" suggested them to cook with a dangerous mushroom.
by NexusCore | 2026-03-11 13:23:51+00:00
I've spent 25 years in systems architecture, and I wanted to prove that .NET 10 can compete with C++ in hard real-time scenarios. By enforcing a strict NANO-standard (Native-AOT, No-Allocation, Open-Contracts), I've managed to bypass the managed heap and achieve sub-microsecond determinism.
The core uses a memory-mapped binary search that finishes in 6-7 memory hops. I've also deployed a Telemetry Portal (linked on the site) for real-time verification of these metrics. Happy to answer any technical questions about the memory-mapped state or the AOT-isolation!
by ilaksh | 2026-03-11 13:23:58+00:00
okay so they say text continuation only without fine tuning. I assume that means that we can't use it as a replacement for TTS in an AI agent chat? Because it will not work without enough context?

Could you maybe trick it into thinking it was continuing a sample for an assistant use case if the sample was generic enough?

I appreciate them being honest about it though because otherwise I might spend two days trying to make it work.
by pydry | 2026-03-11 13:25:13+00:00
It's often way easier to capture value than to get compensated for creating it.

It's definitely indicative of an unhealthy organization or society when this happens but generally I've still found this to be the norm.

Indeed, maybe one of the reasons why free market capitalism functions is because it has a built in check (bankruptcy) against this natural human organizational tendency.

I think a large part of why software devs were so well compensated in the last decade was because we were helping build the systems which made the capture of value more efficient (e.g. from taxi drivers, smbs, property rentals), not because we were facilitating its creation.
by glimshe | 2026-03-11 13:25:14+00:00
I was about to say the same thing. How can people compare Apple to a NIAKUN throwaway laptop? I'm no Mac fanboy - I use Windows, Linux and Mac at home. I find MacOS somewhat annoying, but as a Internet browsing laptop, I'd much rather pay for the Mac Neo than "NIAKUN".

PS: I wrote this on my Macbook Air.