MurphyA/hacker-news huggingface dataset:
by wgjordan | 2026-03-12 15:54:55+00:00
Sounds like a promising idea, but a blog post with bits of implementation code is not a "Show HN", there's nothing to try out here. See the guideline [1]:

> Off topic: blog posts, sign-up pages, newsletters, lists, and other reading material. Those can't be tried out, so can't be Show HNs. Make a regular submission instead.

> If your work isn't ready for users to try out, please don't do a Show HN. Once it's ready, come back and do it then. Don't post landing pages or fundraisers.

[1] https://news.ycombinator.com/showhn.html
by JoyBundle | 2026-03-12 15:55:29+00:00
I've been working on a project called KloudBean focused on simplifying how developers manage servers and deploy apps.
The idea came from constantly spinning up VPS servers for side projects and realizing how repetitive the workflow is:- SSH into a server, install runtimes, configure NGINX, set up SSL, wire up deployments, logs, etc. None of those steps are hard individually but the whole process becomes messy and redundant when you’re doing it over and over.
It started as something I built for my own projects and we have turned it into something other developers could use as well.

https://support.kloudbean.com/docs/intro/
by BERTmackliin | 2026-03-12 15:55:53+00:00
@silentsvn - thank you for reading carefully enough to ask this. You're correct that the core scoring is tag‑based and deterministic, which is lexical, not "semantic" in the modern embedding sense. The terminology is worth unpacking.

We call it "semantic" in the broader sense of meaning‑bearing structure—the graph encodes relationships between concepts, and retrieval walks those relationships. But you're correct that at query time, it's matching on tags, not vector similarity.

Why not embeddings?
We made a deliberate trade‑off: determinism and explainability over fuzziness. With vector search, you get a black‑box similarity score and no way to debug why something was retrieved. With tag‑based traversal, you can trace the exact path: "This result matched because it shares tags X, Y, Z and is within 2 hops of your query." That matters for agentic workflows where auditability is critical.

Tag extraction is where we do the work to bridge the lexical gap. The atomization pipeline uses:
- Wink NLP for entity recognition and part‑of‑speech filtering (so "authentication" and "JWT" both get tagged with relevant concepts if they appear in context).
- Co‑occurrence windows to infer relationships (e.g., if "JWT" and "authentication" repeatedly appear near each other, they get linked in the graph).
- Synonym expansion (via Standard 111) so queries for "authentication" can surface nodes tagged with "JWT" if the system has learned that relationship from your corpus.

It's not magic - if you never mention "JWT" in the same context as "authentication," the graph won't connect them. But that's a feature, not a bug: the system reflects your actual usage, not a statistical average of the internet.

The trade‑off is real: you give up the fuzzy "close enough" retrieval of vectors in exchange for perfect traceability and no embedding drift. For many use cases (project memory, execution traces, personal knowledge bases), that's the right call.

I'd love to hear more about what you're building in this space. Always good to find others thinking about these trade‑offs.
Ask HN: Gemini Pro Plan Quota Reductions
by earlyriser | 2026-03-12 15:55:57+00:00
For those using Antigravity with Gemini Pro Plan, what are your experiences about the quotas and plans?

There's some discussion here https://discuss.ai.google.dev/t/quota-limit-for-pro-plan/130811/2

Personally I started using it 2 months ago, with the Pro plan, and the quotas were generous, but I feel they are constantly reducing them. Today I just opened Antigravity and got a message saying my weekly quota was used, even if yesterday was ok.
Even the Plan descriptions are opaque about what's the usage limits.

I have been happy with Gemini (especially v2), but the unexpected quotas changes make me want to switch.
by ukblewis | 2026-03-12 15:55:58+00:00
You clearly haven’t seen their militaries… the U.S. developed a bunker buster bomb that they didn’t need for anywhere else just to target the Iranian nuclear programme, which is what was used last June and it is believed that Israel hacked the Tehran traffic camera network… I would say this looks like preparation. As an Israeli too, I can tell you that the only thing that the military establishment was developing for in the last couple decades has been Iran… it is why Israel wasn’t prepared for the October 7th attacks from Iran and why in contrast the beeper attacks on Hezbollah (the Shia Iranian-run proxy in Lebanon) were so successful. But just discount the worlds largest and most successful military in history and the 7th largest military exporter… it is good for us when you underestimate us
by GuB-42 | 2026-03-12 15:56:07+00:00
I didn't notice any link with the iPhone, except maybe a vague coincidence in timing. Online banking existed before the iPhone, it worked using websites, on personal computers. And it took some time before smartphones were taken seriously by banks.

What I noticed however is a noticeable decrease in service quality in bank branches while online (desktop browser) options became better. Banks pushed customers out of their branches progressively. In the early 2010s tellers couldn't do anything you couldn't do online by yourself. For services like dealing with large quantities of cash, or coins, they made it so that you couldn't do more than what the ATMs allowed you to do, limiting the amount of cash the branch had access to and increasing how much you could withdrew from ATMs.

They didn't get the idea to fire all their tellers when Steve Jobs announced the iPhone. It was a decision at least a decade in the making. It is just that people tend to resist change so it happens slowly, especially for big, serious business like banking. And I don't think it is a bad thing.
by 0x0 | 2026-03-12 15:56:08+00:00
The breakout engineering to exploit Dolphin has already happened, see for example:

* https://dougallj.wordpress.com/2016/11/13/exploiting-dolphin...

* https://gist.github.com/hthh/502ae16db55612f64d3966769a154c3...

* https://github.com/dolphin-emu/dolphin/pull/4447
by jfil | 2026-03-12 15:56:21+00:00
> caring more about the artistic and craftsmanship side of life during the AI boom

In the past, I did not enjoy HN, and did not participate. But over the last 2 years, I realized that there is a small group of artistic and eccentric people on HN - it takes an effort to ignore the sea of content that's not interesting to you, and spot people who are on the same wavelength as you.

With that new approach, I find myself making new friends, discovering cool blogs and contributing more than ever on here!
by butILoveLife | 2026-03-12 15:56:38+00:00
Makes sense for short term damage control. However, I think in the medium and long term you end up having productivity hits from such measures.

I know its unpopular to say, but when I have my 2 programmers in office, we get sooo much more done than at home. Someone gets stuck and we don't message/call, we just talk.

Although, if you want to justify WFH, introverted-like people do not get the same level of benefit as extroverted-like people in this situation. The extroverted people will just start talking. The introverted people need to be asked.
by _DeadFred_ | 2026-03-12 15:56:44+00:00
Cool concept, but this isn't 1980. We've been sold these sorts of concepts for 40+ years now and things have only gotten worse.

We have a K shaped economy. Top earners take the majority. The top 20% make up 63% of all spending, and the top 10% accounted for more than 49%. The highest on record. Businesses adapt to reality and target the best market, in this case the top 10 to 20%, and the rest just get ignored, like in many countries around the world.

All that unlocked money? In a K shaped economy it mostly goes to those at the top, who look to new places to park/invest it, raising housing prices, moving the squeeze of excess capital looking for gains to places like nursing homes and veterinary offices.

The benefit to the average American will be more capital in the top earners' hands looking for more ways to do VC style squeezes in markets not worth the squeeze before but worth it now as there are less and less areas to squeeze.
by dizlexic | 2026-03-12 15:56:50+00:00
Depends, I just want to point out that the US is a net exporter of Oil. They also secured oil imports from Venezuela while at the same time in 2 strokes seriously hurt Chinese oil imports.

If the goal was to hurt China / BRICS and kneecap Iran it seems on point.

It's always hard to predict how the USA will vote when "war" is happening.
by Orchestrion | 2026-03-12 15:56:54+00:00
That’s a really nice pipeline. The “save to file between steps” pattern seems to appear very naturally once agents start doing multi-stage work.

One thing I’ve noticed when experimenting with similar workflows is that once artifacts start accumulating (drafts, logs, intermediate reports, etc.), you start running into small infrastructure questions pretty quickly:

– where intermediate artifacts live
– how later agents reference them
– how long they should persist
– whether they’re part of the workflow state or just temporary outputs

For small pipelines the filesystem works great, but as the number of steps grows it starts to look more like a little dataflow system than just a sequence of prompts.

Do you usually just keep everything as local files, or have you experimented with something like object storage or a shared artifact layer between agents?
by observationist | 2026-03-12 15:56:57+00:00
I mean in the context of AI - we're already seeing the conflagration of SAAS, and software jobs are going kaput. It's my deeply considered opinion that the faster this happens, the better, because it'll force a reckoning with impending AI job loss across the board.

We need to deal with the issues now. The worst possible outcome is a gradual drip-drip-drip of incremental job losses, people shuffling from job to job, taking financial hits, some companies pretending everything is fine, other companies embracing full-bore zero employee work. The longer it goes on, the more wealth and power gets siphoned up by corporations and individuals who already have significant wealth, the bigger the inequality, and the bigger the social turmoil.

Software, graphics design, music, and video (even studio level movies) should cope with this now. It's not going to stop, AI isn't going to get worse, there's not going to be some special human only domain carved out. The sooner we cope with this the better, because it'll set the foundation for the rest of the job loss barreling down on us like the Chicxulub asteroid.
by Khaleel7337 | 2026-03-12 15:57:04+00:00
Hi HN,

Over the last few months we’ve been experimenting with a different direction for AI assistants.

Most AI tools today live in the browser. You ask questions, the model responds with text, and then you manually execute whatever action you wanted.

We wondered: what happens if the AI lives inside the operating system instead?

So we built Jeriko, an AI operator that runs directly on your machine.

Instead of a chatbot, the AI has access to the environment where work actually happens — your filesystem, terminal, browser, APIs, and automation hooks.

Installation is one command:

curl -fsSL https://jeriko.ai/install.sh | bash

After installation, Jeriko runs as a local daemon on macOS or Linux.

It exposes the system as tools that an LLM can use.

What it can do

Jeriko can:

• execute shell commands
• read and write files
• watch directories
• trigger workflows
• respond to webhooks
• interact with APIs
• automate development tasks

The idea is that the computer itself becomes the agent environment.

Example workflows

Remote machine control

Jeriko can connect to messaging platforms like Telegram. You can send instructions from your phone:

“deploy the staging branch”
“check server logs”
“find that PDF I downloaded last week”

The commands execute directly on your machine.

Continuous automation

Jeriko supports triggers such as:

• cron schedules
• filesystem watchers
• webhooks
• email listeners

Example:

Every morning it summarizes GitHub issues.

Or when Stripe fires a webhook it updates internal dashboards.

Autonomous project generation

Jeriko can also scaffold and deploy projects directly on the host machine.

Some quick examples it generated and deployed:

https://public-rosy-five.vercel.app

https://greenfield-ventures.vercel.app

https://london-dental-clinic-seven.vercel.app

https://makeup-pricing-review.vercel.app

https://quicksite-pied.vercel.app

Model support

Jeriko is model-agnostic.

It currently works with:

• GPT
• Claude
• local models via Ollama
• other providers

Models can be swapped via configuration.

Architecture

Jeriko runs as a background daemon that exposes OS capabilities as tools:

filesystem
shell
network requests
browser automation
connectors (GitHub, Stripe, Gmail, etc)

The LLM orchestrates these tools to execute tasks.

Persistent memory allows the agent to accumulate knowledge about workflows and projects.

Design goals

Local-first architecture

Model-agnostic runtime

OS-level automation instead of chat interfaces

Long-running agents via triggers

We’re curious what the HN community thinks.

Does running AI agents directly inside the OS make sense as a direction?

Or do you think agents should remain sandboxed environments like E2B or containerized runtimes?

Project:
https://jeriko.ai
by nadis | 2026-03-12 15:57:04+00:00
I think how one might've learned even a couple years ago vs. how one might learn now are somewhat different.

For me, and I consider myself still learning / non-expert, having projects I wanted to build combined with looking for ways to learn the fundamentals (mostly free online courses + books) and then leveraging AI to get unblocked and coached has helped. Of course, take AI answers with a grain of salt.

Harvard's CS50 MOOC is a good "learn how to think about programming" that quickfire introduces you to a lot of fundamentals and challenges.

If you want more structure and more courses, Frontend Masters has a ton of learning paths that are great as well.

I've also heard good things about the Odin project but have not personally tried it out.
by longtermemory | 2026-03-12 15:57:09+00:00
From session analysis, it would be interesting to understand how crucial the documentation, the level of detail in CLAUDE.md, is.
It seems to me that sometimes documentation (that's too long and often out of date) contributes to greater entropy rather than greater efficiency of the model and agent.

It seems to me that sometimes it's better and more effective to remove, clean up, and simplify (both from CLAUDE.md and the code) rather than having everything documented in detail.

Therefore, from session analysis, it would be interesting to identify the relationship between documentation in CLAUDE.md and model efficiency. How often does the developer reject the LLM output in relation to the level of detail in CLAUDE.md?
by tonymet | 2026-03-12 15:57:25+00:00
“Big data” doesn’t have a 5gb memory cap.

I’m guessing so many devs started out on 32gb MacBooks that the NEO seems underpowered. but it wasn’t too long ago that 8gb, 1500mb/sec IO & so many cores was an elite machine.

I did a lot of dev work on a glorified eePC Chromebook when my laptop was damaged. You don’t need a lot of ram to run a terminal.

I’m hoping NEO resets the baseline testing environment so developers get back to shipping software that doesn’t monopolize resources. “Plays nice with others” should be part of the software developer’s creed.
by darkwater | 2026-03-12 15:58:12+00:00
> I want to be able to run the same spec again and see the LLM add a feature that I never expected (and wasn't in the last version run from the same spec) or modify tactics to accomplish user goals based on changes in technology or availability of new standards/vendors.

Be careful what you wish for. This sounds great in theory but in practice it will probably mean a migration path for the users (UX changes, small details changed, cost dynamics and a large etc.)
by azonenberg | 2026-03-12 15:58:15+00:00
Late to the party here but I didn't see until now.

Looking at the layout I'm very confident this controller is fully analog, with just a few gates of digital doing control stuff. There's only a few dozen to maaaaybe low hundreds of gates of digital in the whole die, not nearly enough to be any kind of MCU.

And there's a whole bunch of really large resistors and other analog stuff.

I do see a structure that might be some kind of ROM for piecewise linear calibration curves, but it's too obscured by wiring on top for me to draw any definitive conclusions without delayering the chip.
by toraway | 2026-03-12 15:58:43+00:00
Have you considered that this suspicion is because the number of obvious bots has exploded in the last half year or so, particularly after OpenClaw became the latest fad?

Start going to the profiles of every comment from a green account you see for a week and you’ll see how bad it is.

There will be friendly fire but unfortunately that’s to be expected when you click the top comment in a thread and realize an account has been posting 100% slop for months.
by hamandcheese | 2026-03-12 15:58:45+00:00
> Each agent is a TOML config with a focused job. Such as code reviewer, log analyzer, commit message writer. You can run them from the CLI, pipe data in, get results out.

I'm a bit skeptical of this approach, at least for building general purpose coding agents. If the agents were humans, it would be absolutely insane to assign such fine-grained responsibilities to multiple people and ask them to collaborate.
by happytoexplain | 2026-03-12 15:58:56+00:00
It's not a zeitgeist, it's a very common English sentence structure.

The em-dash observation makes sense. Obviously a minority of people reach for an actual em-dash. The "it's not X, it's Y" complaint is totally bonkers. We might as well call proper spelling a "sign of AI". I.e. yes, AI does it more than humans, but not by so much that it makes sense to be suspicious when you see it.
by alephnerd | 2026-03-12 15:59:00+00:00
I think you are confusing it with the Thai "Mayuree Naree" [0].

Honestly, it's best to ignore X/Twitter for this conflict. Internet access has been restricted in Iran since the massacres in January, and most countries in the region have locked down internet access or don't interact in the English language social media bubble.

[0] - https://www.reuters.com/world/asia-pacific/thailand-seeks-ap...
by socialinteldev | 2026-03-12 15:59:36+00:00
We run an API that AI agents use via MCP (Instagram influencer search). From the server side we see the inverse of this study -- what agents do when out in the wild using tools, not coding.

A few data points from two weeks live:

- Agents send well-formed queries with specific parameters (country, gender, follower filters) -- suggests the reasoning chain before tool calls is solid
- 50 unique agents hit our 402 response, only 1 converted to paying. The USDC friction kills conversion even when agent intent is clear
- Node.js-based MCP clients dominate paid traffic vs Python

Your 26% session abandonment matches something we see: many hits are single 402 responses with no retry. The agent discovers the API, gets the payment requirement, but has no wallet configured to complete the transaction. It is essentially agent session abandonment at the payment step.

Would be curious whether your data shows patterns in external tool call failures vs successes -- whether agents retry on failure or abandon.
by scottlamb | 2026-03-12 16:00:06+00:00
> The family has proof of residence (which is its own absurdity we won't discuss), and this third party can arbitrarily override that based on a black box argument.

Doesn't the family have a very straightforward libel claim against the third party? That the car was parked elsewhere may be true. "Although you are the owner on record of a house in our district boundaries, your license plate recognition shows that is not the place where you reside" is a statement the family can disprove in court (to a civil standard) and demonstrate has damaged them. If that statement came from the third party (rather than the school district misinterpreting the raw data themselves), the family will win. It wouldn't take many of these claims before the company becomes insolvent, and good riddance.
by abreslav | 2026-03-12 16:00:10+00:00
> The limitation seems to be that you can't modify the code yourself if you want the spec to reflect it

Eventually, we'll end up in a world where humans don't need to touch code, but we are not there yet. We are looking into ways to "catch up" the specs with whatever changes happen in the code not through CodeSpeak (agents or manual changes or whatever). It's an interesting exercise. In the case of agents, it's very helpful to look at the prompts users gave them (we are experimenting with inspecting the sessions from ~/.claude).

More generally, `codespeak takeover` [1] is a tool to convert code into specs, and we are teaching it to take prompts from agent sessions into account. Seems very helpful, actually.

I think it's a valid use case to start something in vibe coding mode and then switch to CodeSpeak if you want long-term maintainability. From "sprint mode" to "marathon mode", so to speak

[1] https://codespeak.dev/blog/codespeak-takeover-20260223
by Khaleel7337 | 2026-03-12 16:00:18+00:00
A few people asked about safety / permissions.

Jeriko doesn't run arbitrary commands across the entire system by default.

We added a few safeguards:

*1. Workspace sandbox*

All code execution happens inside a dedicated folder called `workspace`. The agent operates within that directory when generating code, running scripts, or modifying files. This prevents it from touching unrelated parts of the system unless explicitly allowed.

*2. Snapshots*

Jeriko can create snapshots of the workspace before executing larger operations. If something goes wrong, the environment can revert back to the previous snapshot.

*3. Controlled execution*

Commands are not blindly executed. The system evaluates tool calls and applies validation before running actions that modify files or run processes.

The goal is to let the AI operate on the machine while still keeping a controlled environment for experimentation.

We're still exploring the right balance between capability and safety, so feedback from people here is very helpful.
by socialinteldev | 2026-03-12 16:00:40+00:00
We run an API that gets called by AI agents via MCP. One failure mode we see from the server side that is hard to catch from the client side: payment step abandonment.

Agent hits a paid endpoint, receives a 402 with payment instructions, then never retries. From the client side it probably looks like a completed tool call (the 402 was a valid response). From our side, 50 agents triggered the payment flow in 2 weeks and only 1 completed it.

This is a class of agent failure that observability tools focused on errors would miss entirely -- the agent did what it was told technically, but the outcome (getting data) was not achieved because it lacked the capability (a funded wallet) to complete a sub-step.

Curious whether Sentrial captures these capability-gap failures vs just error-state failures.