MurphyA/hacker-news huggingface dataset:
by keeda | 2026-03-06 02:38:40+00:00
This rhymes with another recent study from the Dallas Fed: https://www.dallasfed.org/research/economics/2026/0224 - suggests AI is displacing younger workers but boosting experienced ones. This matches what we see discussed here, as well as the couple similar other studies we've seen discussed here.

Also, it seems to me the concept of "observed exposure" is analogous to OpenAI's concept of "capability overhang" - https://cdn.openai.com/pdf/openai-ending-the-capability-over...

I think the underlying reason is simply because companies are "shaped wrong" to absorb AI fully. I always harp on how there's a learning curve (and significant self-adaptation) to really use AI well. Companies face the same challenge.

Let's focus on software. By many estimates code-related activities are only 20 - 60%, maybe even as low as 11%, of software engineers' time (e.g. https://medium.com/@vikpoca/developers-spend-only-11-of-thei...) But consider where the rest of the time goes. Largely coordination overhead. Meetings etc. drain a lot of time (and more the more senior you get), and those are mostly getting a bunch of people across the company along the dependency web to align on technical directions and roadmaps.

I call this "Conway Overhead."

This is inevitable because the only way to scale cognitive work was to distribute it across a lot of people with narrow, specialized knowledge and domain ownership. It's effectively the overhead of distributed systems applied to organizations. Hence each team owned a couple of products / services / platforms / projects, with each member working on an even smaller part of it at a time. Coordination happened along the heirarchicy of the org chart because that is most efficient.

Now imagine, a single AI-assisted person competently owns everything a team used to own.

Suddenly the team at the leaf layer is reduced to 1 from about... 5? This instantly gets rid of a lot of overhead like daily standups, regular 1:1s and intra-team blockers. And inter-team coordination is reduced to a couple of devs hashing it out over Slack instead of meetings and tickets and timelines and backlog grooming and blockers.

So not only has the speed of coding increased, the amount of time spent coding has also gone up. The acceleration is super-linear.

But, this headcount reduction ripples up the org tree. This means the middle management layers, and the total headcount, are thinned out by the same factor that the bottom-most layer is!

And this focused only on the engineering aspect. Imagine the same dynamic playing out across departments when all kinds of adjacent roles are rolled up into the same person: product, design, reliability...

These are radical changes to workflows and organizations. However, at this stage we're simply shoe-horning AI into the old, now-obsolete ticket-driven way of doing things.

So of course AI has a "capability overhang" and is going to take time to have broad impact... but when it does, it's not going to be pretty.
by emmanol | 2026-03-06 02:38:52+00:00
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by ubutler | 2026-03-06 02:39:41+00:00
> Also, you really want to tell people how to access it and what it costs. Or put up a "call for quote" if your market is large Enterprise budgets.

Our pricing page can be found in our documentation here: https://docs.isaacus.com/pricing/prices. We're planning on making it more visible on our website; thanks for the feedback!
by niam | 2026-03-06 02:40:29+00:00
This seems misleading inasmuch as your correspondents aren't all on the same mail servers.

Yes, correspondence between you and Build-A-Bear, and between you and your local terrorist cell, are unencrypted individually. But Build-A-Bear presumably doesn't know about your correspondence with the cell, and the latter presumably has some interest in not sharing organizational data access with the former.

I suppose you do have to trust that Proton isn't served a directive to snoop on your correspondence in transit with other providers. But that's still a much better position than leaving all of your historical data unencrypted at rest.
by hedora | 2026-03-06 02:40:50+00:00
CPU model / stepping / microcode versions are probably at least as useful as temperature. I'd also try to get things like the actual DRAM timing + voltage vs. what the XMP extensions (or similar) advertise the manufacturer tested the memory at.

I have at least one motherboard that just re-auto-overclocks itself into a flaky configuration if boot fails a few times in a row (which can happen due to loose power cords, or whatever).
by babypuncher | 2026-03-06 02:41:18+00:00
There are ways of implementing digital identity that do not require the user to divulge more information than is technically required to the party requesting authentication. This is one of the main benefits of a digital system. With physical IDs, you have to give the other party your entire driver's license/passport/whatever every time, even if they just need to know whether or not you're over 18.
by eviks | 2026-03-06 02:41:32+00:00
> Proton did not provide any information to the FBI ... Swiss justice department
> This is an important distinction

Not really, that's a minute procedural distinction without a difference.

> can only happen after all Swiss legal checks are passed.

Oh, don't worry, US also has some "checks", just as useful!

> we understood that a law enforcement officer was shot and explosive devices were involved

And now you're just compounding your fail by siding with the notorious liars against your own customers.
by jiggawatts | 2026-03-06 02:42:22+00:00
> My belief based on personal experience is that in software engineering it wasn't until November/December 2025 that AI had enough impact to measurably accelerate delivery throughout the whole software development lifecycle.

Gemini 3 and Opus 4.6 were the "woah, they're actually useful now!" moment for me.

I keep saying to colleagues that it's like a rising tide. Initially the AIs were lapping around our ankles, now the level of capability is at waist height.[1] Some people will eventually learn to swim or drown.

[1] Many people have commented that 50% of developers think AI-generated code is "Great!" and 50% think its trash. That's a sign that AI code quality is that of the median developer. This will likely improve to 60%-40%, then 70%-30%, etc...
Show HN: ThreatAlert – anonymous community incident map, no sign-up required
by TheProfitKing | 2026-03-06 02:42:43+00:00
Built a PWA where anyone can drop a pin on a live shared map to report nearby incidents — crime, fire, disasters, civil unrest, infrastructure failures.

No accounts, no tracking. IPs are one-way HMAC-hashed before storage and never logged raw. Community votes incidents from pending → active so noise gets filtered out automatically. Each category has its own TTL so the map stays fresh.

Stack: Next.js 16, Firebase (Firestore + Cloud Functions + FCM), Leaflet for the map, D3.js for a 3D globe view. Fully open source.

GitHub: https://github.com/BaselAshraf81/threatalert
Show HN: A deterministic ecosystem simulator for long-horizon AI agents
by yangkecoy | 2026-03-06 02:43:00+00:00
Ten years ago I built a small evolutionary toy experiment with two types of agents: selfish and cooperative “ducks”.

At first, selfish strategies dominated.
But when agents were given memory — the ability to remember who helped them — cooperation suddenly became stable under resource scarcity.

That experiment stayed in the back of my mind for years.

Recently I started rebuilding the idea from scratch as a larger system:

BiomeSyn

https://biomesyn.com/

Instead of evaluating AI on static tasks, the goal is to explore long-horizon adaptive environments where agents must:

• gather resources
• survive environmental pressure
• compete with other agents
• adapt over many generations

The system is deterministic, so experiments can be reproduced across seeds — which makes it possible to treat it as a benchmark for adaptive agents.

The bigger question I’m interested in:

> What happens when intelligence is evaluated inside a world that keeps evolving?

Many current benchmarks measure short-episode performance.
But real adaptive systems must operate in open-ended environments.

BiomeSyn is still an early research sandbox, but I’m curious whether environments like this could become useful for studying:

• evolutionary computation
• long-horizon RL agents
• multi-agent ecosystems
• adaptive AI systems

Would be interested to hear thoughts from people working on agents, simulation platforms, or large-scale AI systems.

https://biomesyn.com/
by maxlin | 2026-03-06 02:43:14+00:00
-They only got 130B
-They planned to fix all this with 100B?

I see what you did there. 70B eh? They only got 50B. A paltry 20B?

The real number though, was gotten in a relatively short time. And now replacement tariffs are on the way. And many companies have re-thought of manufacturing outside US with the higher tariffs always being the possibility in the future too.
by leadfootdrums | 2026-03-06 02:43:15+00:00
1000% accurate. Planning is the difference. I just wrote a white paper that supports this concept at a more macro level. It shows the direct link between failure to plan with adequate business requirements is the exact reason something like 90% of Enterprise AI initiatives failed. Have a look and let me know your thoughts (no asking for your email or any of that) encephalon.net/whitepaper
Show HN: Aidevshield NPM audit for AI coding tool workflows
by GrimLabs | 2026-03-06 02:44:22+00:00
I built aidevshield, a free open-source security scanner that catches vulnerabilities in AI coding tool configurations things like Cursor, Copilot, and Cline setups.

The problem: AI coding assistants are becoming attack surfaces. In the past year we've seen real exploits:

Clinejection (Dec 2025): Prompt injection via GitHub issue titles → AI triage bot executes shell commands → malicious npm package published to 5M users
tj-actions/changed-files (Mar 2025): Tag repointing attack hit 23,000 repos via pull_request_target
Shai-Hulud (2025): First self-propagating npm worm using postinstall hooks
Rules File Backdoor (Feb 2025): Hidden Unicode characters in .cursorrules for invisible prompt injection
Existing tools cover parts of this — Zizmor and actionlint for GitHub Actions, Socket.dev for npm — but nothing scans across all four domains: workflows + AI configs + npm packages + prompt injection.

aidevshield does. One command, zero dependencies beyond js-yaml, fully offline:

npx aidevshield scan .
What it detects:

Dangerous GitHub Actions patterns (wildcard users, untrusted event field interpolation, unpinned third-party actions)
pull_request_target with untrusted checkout (Pwn Request pattern)
Malicious npm lifecycle scripts (postinstall curl | sh, node -e inline exec)
Hidden Unicode in AI config files (.cursorrules, .github/copilot-instructions.md)
Wildcard tool permissions (Bash(), Edit())
Exposed .env files without .gitignore protection
Cache poisoning + credential exposure combos
Outputs text, JSON, or SARIF (for GitHub Code Scanning integration).

56 end-to-end tests. Every detection maps to a documented real-world attack.

MIT licensed. No signup, no paywall.

GitHub: https://github.com/aidevshield/aidevshield

Happy to answer questions about the threat landscape or implementation.
by jasongi | 2026-03-06 02:46:02+00:00
My favourite library from these folks is gum (https://github.com/charmbracelet/gum). The basic premise is simple - instead of using hardcoded variables or in addition/instead of using CLI flags, call gum and capture the STDOUT to get the selected input value(s). Great for turning a bash script into a TUI, uses these libraries under the hood.

I find the pattern of showing interactive TUI if required options/flags are omitted much nicer than showing an error/help output.
by grantcarthew | 2026-03-06 02:46:04+00:00
Yes, Atlassian's CLI support is shocking.

This is for Confluence. Markdown all the way:

https://github.com/grantcarthew/acon

Jira, same, markdown all the way:

https://github.com/grantcarthew/ajira

Other agent tools:

https://github.com/grantcarthew/homebrew-tap
by newscracker | 2026-03-06 02:46:18+00:00
> Try to beat this: https://fingerprint.com

I don’t know, but it seems like it’s overselling its capabilities. I tried with Firefox Focus and it said I’m using incognito (private mode) and assigned a unique visitor ID. Immediately tried with a private tab in Safari on iOS and it said I’m not using incognito (private mode) and assigned a new unique visitor ID. Then I switched networks and tried. One more unique visitor ID.

I’m not claiming that fingerprinting is not possible, but this website is not good at it. Seems like it uses plain cookies.
Show HN: Automated Testing for AI Agents
by rishavmitra | 2026-03-06 02:47:41+00:00
Hi Hacker News! We're launching Zalor, an agent testing platform.

Agents often break when you tweak system prompts, swap models, or add tools. Zalor automatically generates test scenarios and evaluates your agent so you know it's reliable before deploying to production.

We currently support the OpenAI Agents SDK and are onboarding other frameworks. A GitHub integration is coming so you can get feedback on every update.

Looking forward to hearing feedback from people building agents.
by BERTmackliin | 2026-03-06 02:48:05+00:00
We built Anchor Engine because LLMs have no persistent memory. Every conversation is a fresh start—yesterday's discussion, last week's project notes, even context from another tab—all gone. Context windows help, but they're ephemeral and expensive.

The STAR algorithm (Semantic Traversal And Retrieval) takes a different approach. Instead of embedding everything into vector space, STAR uses deterministic graph traversal. But before traversal comes atomization—our lightweight process for extracting just enough conceptual structure from text to build a traversable semantic graph.

Atomization, not exhaustive extraction.
Projects like Kanon 2 are doing incredible work extracting every entity, citation, and clause from documents with remarkable precision. That's valuable for document intelligence. Anchor Engine takes a different path: we extract only the core concepts and relationships needed to support semantic memory. The result is a graph that's just rich enough for an LLM to retrieve relevant context, but lightweight enough to run offline in <3GB RAM—even on a Raspberry Pi or in a browser via WASM.

Why graph traversal instead of vector search?

Embeddings drift over time and across models

Similarity scores are opaque and nondeterministic

Vector search often requires GPUs or cloud APIs

You can't inspect why something was retrieved

STAR gives you deterministic, inspectable results. Same graph, same query, same output—every time. And because the graph is built through atomization, it stays small and portable.

Key technical details:

Runs entirely offline in <3GB RAM. No API calls, no GPUs.

Compiled to WASM – embed it anywhere, including browsers.

Recursive architecture – we used Anchor Engine to help write its own code. The dogfooding is real.

AGPL-3.0 – open source, always.

What it's not: It's not a replacement for LLMs or vector databases. It's a memory layer—a deterministic, inspectable substrate that gives LLMs persistent context without cloud dependencies. And it's not a competitor to deep extraction models like Kanon 2; they could even complement each other (Kanon 2 builds the graph, Anchor Engine traverses it for memory).

The whitepaper goes deep on the graph traversal math and includes benchmarks vs. vector search:
https://github.com/RSBalchII/anchor-engine-node/blob/main/do...

We're especially interested in feedback from people who've built RAG systems, experimented with symbolic memory, or worked on graph-based AI. If you've ever wanted to give an LLM long-term memory that fits on a Raspberry Pi, this might be interesting to you.
by monster_truck | 2026-03-06 02:48:19+00:00
DDR4 and 5 both have similar heat sensitivity curves which call for increased refresh timings past 45C.

Some of the (legitimately) extreme overclockers have been testing what amounts to massive hunks of metal in place of the original mounting plates because of the boards bending from mounting pressure, with good enough results.

On top of all of this, it really does not help that we are also at the mercy of IMC and motherboard quality too. To hit the world records they do and also build 'bulletproof', highest performance, cost is no object rigs, they are ordering 20, 50 motherboards, processors, GPUs, etc and sitting there trying them all, then returning the shit ones. We shouldn't have to do this.

I had a lot of fun doing all of this myself and hold a couple very specific #1/top 10/100 results, but it's IMHO no longer worth the time or effort and I have resigned to simply buying as much ram as the platform will hold and leaving it at JEDEC.
by hedora | 2026-03-06 02:48:22+00:00
That, and 50% of the machines where their heuristics say it is a hardware error fail basic memory tests.

I've seen a lot of confirmed bitflips with ECC systems. The vast majority of machines that are impacted are impacted by single event upsets (not reproducible).

(I worded that precisely but strangely because if one machine has a reproducible problem, it might hit it a billion times a second. That means you can't count by "number of corruptions".)

My take is that their 10% estimate is a lower bound.
by beart | 2026-03-06 02:48:24+00:00
I love this design language to death. I know a lot of engineers prefer a no-frills, straight to the point readme (as reflected in these comments), and I get that. But I also don't want to live in a world made out of nothing but boxes.

It feels a bit like visiting Fallingwater and complaining that there are no arrows pointing to the bathroom.
by cookiengineer | 2026-03-06 02:49:07+00:00
I kind of wanted to confirm that. At that time I was still using a Compaq business laptop on which I played Guild Wars.

The Turion64 chipset was the worst CPU I've ever bought. Even 10 years old games had rendering artefacts all over the place, triangle strips being "disconnected" and leading to big triangles appearing everywhere. It was such a weird behavior, because it happened always around 10 minutes after I started playing. It didn't matter _what_ I was playing. Every game had rendering artefacts, one way or the other.

The most obvious ones were 3d games like CS1.6, Guild Wars, NFSU(2), and CC Generals (though CCG running better/longer for whatever reason).

The funny part behind the VRAM(?) bitflips was that the triangles then connected to the next triangle strip, so you had e.g. large surfaces in between houses or other things, and the connections were always in the same z distance from the camera because game engines presorted it before uploading/executing the functional GL calls.

After that laptop I never bought these types of low budget business laptops again because the experience with the Turion64 was just so ridiculously bad.
by jijji | 2026-03-06 02:49:13+00:00
I tried to use Google's Gemini CLI from the command line on linux and I think it let me type in two sentences and then it told me that I was out of credits... and then I started reading comments that it would overwrite files destructively [0] or worse just try to rewrite an entire existing codebase [1]. it just doesn't sound ready for prime time. I think they wanted to push something out to compete with Claude code but it's just really really bad.

[0] https://github.com/google-gemini/gemini-cli/issues/17583

[1] https://www.reddit.com/r/Bard/comments/1l8vil5/gemini_keeps_...
by hahajk | 2026-03-06 02:49:14+00:00
It's unfortunate the Privacy Act included an exception for law enforcement. I imagine at the time it wasn't clear that every action taken by the govt would be called law enforcement.

There is an ethical framework for handling personal data collected and maintained by the US govt called the Fair Information Practice Principles (https://www.fpc.gov/resources/fipps/).

It really is too bad that "any legal purpose" is the stated boundary for our elected govt rather than a more noble appeal to public service.
by jerf | 2026-03-06 02:49:34+00:00
We need to develop some ethics, or at least, "community standards" (as they may vary significantly between different use cases) around the some of the things this essay talks about. I know I've really been pondering the mismatch between human attention and the ability of LLMs to generate things that consume human attention.

We are still mostly running on inertia where a PR required a certain amount of human attention to generate 500 lines of proposed changes, and even then, nothing stops such PR from being garbage. But at least the rate at which such garbage PRs was bounded by the rate at which you had that very specific level of developer that was A: capable of writing 500 lines of diffs in the first place but B: didn't realize these particular 500 lines is a bad idea. Certainly not an empty set, but also certainly much more restricted than "everyone with the ability to set up a code bot and type something".

Code used to be rare, and therefore, worth a lot. Now it's not rare. 1500 lines of 2026 code is not the same as 1500 lines of 2006 code. The ceiling of the value of a contribution is in how much work the user put it and how high quality the work is. If "the work the user put in" is 30 seconds typing a prompt, that's the value, no matter how many lines of code some AI expanded that into. I'd honestly rather have an Issue filed with your proposed prompt in it than the actual output of your AI, if that's all you're going to put into the PR. There's a lot of things I can do with that prompt that may make it better but it's way harder to do that with the code.

You know, stuff like that. That might actually be a useful counter to some of these slop posts, especially things that are something that may be a good idea but need someone to treat the prompt itself as a starting point rather than the code. Maybe that's a decent response that's somewhat less hostile; close out these PRs with a request to file an Issue with the prompt instead.