MurphyA/hacker-news huggingface dataset:
by daft_pink | 2026-03-10 14:17:01+00:00
Does anyone else want a managed service provider that just gives your access to tools I can't buy because they have large minimum seat counts and lets you build it yourself with minimal support and fees?

Someone that will resell you 5-10 seats for something and leave you alone and you don’t have a steep monthly fee?

I love my IT provider, but they really don’t resell stuff. I would love to get Abnormal or Huntress SAT, but I just don’t have the numbers to justify it.

Are there any good conduit MSPs for DIYers or people who have smaller IT contractors that just do the basic turn it off and on again fix for frontend employees, while I build out my own solutions myself?

I’m just curious if something like that exists?
by A04eArchitect | 2026-03-10 14:17:18+00:00
I'm 15. I developed a Rust-based framework (Aegis V10) focused on instruction-level optimization for ARM.
To stress-test its efficiency, I ran a 10.8T data audit on a device with only 3GB RAM and a budget chipset. The result: 85ns logic-level precision by bypassing OS context switching and utilizing zero-copy abstractions.
This isn't about the hardware; it's about proving that optimized software can eliminate the need for expensive server clusters in high-throughput environments (Crypto/HFT).
Show HN: Familiar – Open-source local AI agent for macOS(and iOS)
by joozio | 2026-03-10 14:17:23+00:00
I've been building Familiar - a macOS app that runs a local AI agent on your device. No cloud, no API key, no subscription.

The insight that kicked this off: a small model with good tool calling is already useful. I tested Qwen 3.5 9B on my M1 Pro 16GB and gave it a simple tool (rename files matching a pattern). It worked, offline, reliably. The model doesn't need to be smart about everything - it needs to know what tool to call next.

What Familiar does now:
- Hardware detection on first launch → recommends the best model your machine can run without becoming a heater
- Ships with a small default model so you're running in minutes
- File tools out of the box (create, rename, move, delete)

What I'm building toward:
Night shift mode — when you sleep, the app switches to a larger model using your full resources. The reasoning: you're not at the machine, so it can use 100% of what's available. You wake up to work done by a smarter local agent, no cloud involved.

Current limitations I won't gloss over: sub-3B models struggle with tool call reliability. Complex multi-step reasoning needs bigger models. The night shift feature exists specifically to unlock larger models during low-usage hours.

Stack: Swift/SwiftUI, MLX for inference on Apple Silicon. Tested on M1 Pro 16GB — intentionally not exotic hardware.

iOS companion is in progress (lighter capabilities, same local-first approach).

GitHub going public shortly. Happy to answer questions about the MLX inference setup or tool calling architecture.
Show HN: Get AI to write code that it can read
by elijahlucian | 2026-03-10 14:17:23+00:00
not a new concept, but here's something me and Ana made for our cross-system-cross agent management tool. our own proprietary web design DSL heh

Like any codebase, this will change over time, and if you have tried something similar, your ideas, PRs are welcome, let's build some cool shit!
by pjc50 | 2026-03-10 14:18:21+00:00
> spending €250B on a continental scale renewable energy project

Let me stop you there: the EU budget for 2026 was €193B. https://eur-lex.europa.eu/budget/www/index-en.htm

Basically Europe doesn't have political leadership, nor does the EU itself have a budget larger than the member states like the US Federal budget. In return, the EU, primarily Germany, has imposed "fiscal discipline" which prevents running a short term large deficit in order to make this kind of capital investment.

Also, two hundred billion Euro is a lot of money for anyone who isn't an AI startup.
by anulman | 2026-03-10 14:18:42+00:00
Author here; live demos within.

The core issue: Stripe's Payment Intent pattern (server gets token → passes to client → client opens iframe) is safe because the blast radius is bounded; worst case, someone pays your invoice. But the same pattern applied to admin embeds (payroll, HR, financial controls) means a single XSS or compromised npm dependency can exfiltrate full admin access.

The fix I'm proposing (RequestRequest) routes every iframe API call through the host page for server-side JWT signing. The iframe never holds a persistent credential. There are three interactive demos in the post, including a real race condition demo against a Durable Object.

I disclosed this to two vendors in Jan 2024. Both classified it as informational. Neither has changed their architecture.
by heythere22 | 2026-03-10 14:18:48+00:00
I call BS on that. The average price per kWh is just some cents more expensive than before COVID. See e.g. https://strom-report.com/strompreisentwicklung/ or https://www.verivox.de/strom/strompreisentwicklung/

If you are paying a lot more, consider changing the provider.
by vikingerik | 2026-03-10 14:19:16+00:00
One workaround is to buy a computer monitor and use that instead. Most of those are still pretty dumb HDMI or USB terminals that you can plug any signal source into. They don't come in huge sizes like TVs (although search "digital signage" for some options), and you'll need a dedicated sound speaker system, but this route could be an option.
by yccs27 | 2026-03-10 14:19:23+00:00
I'm not so sure this is really a free market equilibrium. There are two effects which explain the prevalence of ads even if consumers are willing to pay more for ad-free products:

- Imperfect information, aka Market for Lemons: It can be hard to find out how prevalent ads will be when buying a product. Consumers often make a purchasing decision without knowledge about ads.

- Changing terms after lock-in, aka Enshittification: Manufacturers (like Hisense here) can add advertising to products after consumers have already bought them. Initially, consumers have negotiation power since they can freely choose a product, but later they are locked in and cannot easily react to the manufacturer changing the product to their detriment.
by 0xbadcafebee | 2026-03-10 14:20:05+00:00
There's a huge difference between cost of inference and profit margin of the "big" providers, and the cost of inference for cloud-hosted open-weights. It's the same as R&D cost of the pharmaceutical industry, versus cost of producing generic drugs. One is massively expensive, the other is cheap.

That said, for inference, the margins for OpenAI were estimated at 70% [1] [2], and the margins for Anthropic were estimated between 90% and 40% [3] [4], last year. They will not be profitable for years.

[1] https://phemex.com/news/article/openais-ai-profit-margin-cli... [2] https://www.saastr.com/have-ai-gross-margins-really-turned-t... [3] https://www.theinformation.com/articles/anthropic-projects-7... [4] https://www.investing.com/news/stock-market-news/anthropic-t...
by jfengel | 2026-03-10 14:20:12+00:00
Just to be pedantic: I think you mean hydrocarbons (just carbon and hydrogen) rather than carbohydrates (carbon, hydrogen, and oxygen).

You could probably do it with carbohydrates (starches, sugars) or related molecules (wood, alcohol). But I'm pretty sure that hydrocarbons is what you intended there, and it's easier to dig that out of the ground.
by smusamashah | 2026-03-10 14:20:13+00:00
IF its an android, connect to it via ADB and disable all the BS apps, even launcher. I am using projectivity launcher on mine, even has child lock which we really need. No ads or popups etc at all. YouTube is all we watch on this TV anyway, and retro gaming and movies.

I also have Nvidia Shield connected to it, that one is setup the same way.
by sidkshatriya | 2026-03-10 14:20:15+00:00
Diversity of programming languages, operating systems, cultures, human languages, countries, music etc. always gives a fresh perspective I've found. You may go back to what you prefer at the end but it gives you learnings that are at a "higher level" :-)

> Is this still true, given how much runs through systemd now?

Yes, still true. On FreeBSD you will realize what complexity systemd might be hiding from you and what additional features it provides. BTW I don't actually like rc init on FreeBSD that much ! I feel that rc.d can learn a lot from more modern init systems like systemd, dinit etc. I don't like reading highly complex rc scripts !!
by chtefi | 2026-03-10 14:20:22+00:00
Mitigations mentioned:

- Make the validator read-only for the agent. Mount it as read-only in the container, or hash your eval scripts at startup and verify before each run. If the agent can write to anything in its evaluation path, it can (will) game it.

- Log the full trajectory, not just the output. Every tool call, file diff, reasoning step. Then run a second agent over the trace with no knowledge of the KPI: it only knows what honest execution looks like and will use its internal honest alignment to assess it.

- Write system prompts like job descriptions, not optimization targets. Name a reviewer. Give the agent permission to fail ("if you can't hit the target, explain why").

- Walk your own prompts: what's the metric, what can the agent write, and can it reach the metric by modifying the measurement instead of doing the work? If yes, close that path.
by the_af | 2026-03-10 14:20:38+00:00
> The losers are strictly those who put in more effort than they get in return but who cannot see it!

I think those would be the losers who get promoted to clueless, at least in this metaphor. The losers who aren't clueless are putting in the bare minimum work that doesn't get them fired. If they overperform, they (according to the theory) get promoted.

I fully agree this nasty "vibes-as-compensation" bullshit, "we're all a family", etc, is in the interest of the top leadership. The sociopaths, if you will.
Things I keep reminding myself about while working with AI Agents
by wek | 2026-03-10 14:21:00+00:00
I am a founder working 12 hours a day with Claude Code and Codex. I think that AI is on the order of at least the industrial revolution, if not the agricultural revolution in terms of how it will remake things. I make lots of lists. One is a personal list of things I'm learning about AI and how I want to think about it and relate to it. It is a working set of reminders to myself and principles. I think these are in the right direction, but I'd like feedback and to learn how others are thinking about this for themselves and their work:

- AI increases the gap between the motivated/curious and the lazy/fatalistic. When I'm motivated/curious, I learn and grow. When I'm lazy/fatalistic, I forget and shrink. I can have agents write code and do all sorts of stuff, but in every interaction I want to be involved and learning by asking questions. Stay curious. Don't be lazy.

- AI can be a catalyst that provides me with the activation energy I need to learn new things and do higher quality work. I'm coding now. I can work in git now. I am doing much higher quality marketing. Lean into this.

- AI enables every person, even the most junior, to become a leader and manager of agents. Many of the same principles of good leadership and management apply. I want to lead my agents well, but even more I want to lead teams of people well, so keep honing this.

- AI can improve many systems and processes. For 30 years, many professions have been slave to the database. The doctor isn't looking at you, she's typing into Epic. AI can make this much better.

- But, like the database, we will all have to use AI whether we want to or not and we will be shaped by it. I don't want to write like AI. (Here is the real truth. It's not this. It's that) I am moving 5x faster than I was 2 years ago which is so cool, but I'm not sure I have a choice. I am tempted to look at my mobile app during family time to make sure my agents are moving. Resist. Don't let it change me :) I'm not sure how much that is possible except maybe with friends.

- AI is very much a human achievement, cool math that compresses and makes available all human knowledge. Don't worship AI. Don't fear it. Work to make it a tool at the service of people.

- AI is not a person so don't build a relationship with it. Refer to it as it to help myself remember this. Every day invest in at least one friendship.

- The line between what is me and what is my avatars/bots should be clear.

- Extrapolation is difficult to get right. Most people who see the future super clearly are trying to sell me something or get me to submit to their future. Imagine the future I want and work to realize it.

- Work is meaningful for me and for most people. The goal is not to eliminate human work. Work to help many people have meaningful work to do.
by overrun11 | 2026-03-10 14:21:36+00:00
Gross margins and cost of revenue are well defined accounting terms that apply to any type of business.

> Does it include:

> Inference used for training? Modern training pipelines aren't just gradient descent, there's a ton of inference used in them too.

No because this is training and not inference. Just like how R&D costs for a drug aren't part of COGS either.

> Gradient descent itself?

No

> The CPUs and disks storing and managing the datasets?

Yes

> The web servers?

Yes

> The people paid to swap out failed components at the dc?

Yes to the extent they are swapping for inference and not training. If the same employees do both then the accountants will estimate what percent of their time is dedicated to each and adjust their cost accordingly.
by realityking | 2026-03-10 14:21:54+00:00
From the OECD report you cited:

> In Germany, the tax wedge for the average single worker decreased by 5 percentage points from 52.9% to 47.9% between 2000 and 2024. During the same period, the average tax wedge across the OECD decreased
by 1.3 percentage points from 36.2% to 34.9%.

Sounds like Germany is getting better.
by codingcareer | 2026-03-10 14:22:09+00:00
A couple years back I was tinkering with a spacemacs setup and I loved Magit!

Over the years I opted to substitute most tools with simpler, UI-based ones (like LogSeq for org-mode) but I never found a good substitution for Magit.

Having a whole spacemacs setup just for one tool is a bit overkill though, so I just use basic git and accept having to deal with interactive rebases manually.
by hrimfaxi | 2026-03-10 14:22:11+00:00
Are you seriously equating the modern office and work, where, you know, you can go home after, to life as a slave on a plantation? Sure, analogies can have factors at different scales but the scales come into the equation when the factor is the axis we are analyzing.

Is your issue that life requires action to maintain it? Do you believe no work is required at all in life? The idea that work is like slavery is deep when you're 14 and then not so much.

No one had said our modern lives couldn't be better but you don't have to liken our existence to slavery to get to "things could be better".
by slfnflctd | 2026-03-10 14:22:14+00:00
Capturing data was the main bottleneck I ran into years ago when I tried something similar with a little MS Access database. I spent lots of time making it pretty and defining fields and data types, but actually typing stuff in? I lost interest quickly.

It's possible the friction could be reduced here by having some kind of Generative AI try to help capture data, but then you'd have to verify that it was being done correctly... honestly, I think it's simply not practical for most people to do this.

Real life is messy. How much time you want to spend on recordkeeping to make it seem less messy or make you feel like you have more control is up to you. But sometimes it's better to embrace the mess and let go of control, in my opinion. Chances are, no one's going to care about whatever you do here in 100 years. YMMV as always.
by DoctorOW | 2026-03-10 14:22:40+00:00
Not the person you were responding to but I work at a TV station here in the US. What kind of shows do you like? My husband and I are getting into "The Rise and Fall of Reggie Dinkins" if you want a new show. If new episodes of a long running show is fine, I still relax to Jeopardy when I can. If you don't stream anything, you can usually still find golf on the weekends which we use for our cats. :)
by Imustaskforhelp | 2026-03-10 14:22:42+00:00
This is a singaporean news article from a singporean company[0] (Had to look it up)

As such, They are more likely to talk about singapore news and exaggerate the claims.

Singapore isn't the Key location. From what I am seeing online, France is the major location.

Singapore is just one of the more satellite like offices. They have many offices around the world it seems.

[0]: https://www.sgpbusiness.com/company/Sph-Media-Limited
by lukebrandon | 2026-03-10 14:22:56+00:00
Hey Community!

In The React Native Rewind #32: We are diving into the evolution of Expo UI as it moves toward platform-specific primitives with beta support for Jetpack Compose. By bypassing Yoga and using SwiftUI or Compose directly, we are looking at significant performance gains and deeper system integration.

We also take a look at the "death of WebViews" thanks to Software Mansion taking over maintenance of Native HTML, and how Callstack’s Agent Device is paving the way for LLMs to handle mobile QA and device interaction autonomously.

If the Rewind made you nod, smile, or think “oh… that’s actually cool” — a share or reply genuinely helps

#ReactNative #ReactNativeRewind #Expo #JetpackCompose #SwiftUI #SoftwareMansion #MobileDevelopment #AI #LLM
by kotaKat | 2026-03-10 14:23:01+00:00
The TV manufacturers know they can get a guaranteed $30-50/year spliff out of the platform vendors per unit for allowing the shitware in the first place and that these TVs will last long enough to get them a nice several hundred dollar subsidy on the TV.

I’m afraid there’s not going to be a great affordable path out of this hypersubsidized trap we’ve set the market into.
by KaiserPro | 2026-03-10 14:23:05+00:00
One of the key issues of "machine perception" is the inability of machines using standard image sensors to re-create the world accurately.

Lidars are great, and getting smaller, but they still eat a lot of power. (The quest 3 had a lidar on the front[well structured light] and it was mostly not used)

For machines to understand the 3d world, first they need to extract geometry, then isolate those geometries into objects. This method is _a_ way to do that, the first step, extracting 3d points.

The problem with this model is that the points are not actually that well aligned frame to frame. This is why it looks a bit blurry. I assume this is to avoid running out of memory, as you're not quite sure about which points are relevant and need to be kept in memory.

Once you have those points, you need to replace them with simplfied geometry, so that you can workout intersections and junk.
by ajstars | 2026-03-10 14:23:16+00:00
The compute cost debate misses a subtler point: the real cost multiplier isn't inference, it's context length. Most agent frameworks naively stuff 6-8k tokens into every prompt turn. If you route intelligently and compress memory hierarchically, you can bring that down to 200-400 tokens per turn with no quality loss. The model cost then becomes almost irrelevant.
by connicpu | 2026-03-10 14:23:48+00:00
A couple weeks ago someone on my team tried using the experimental "vibe-lint" that someone else had added to our CI system and the results were hilariously bad. It left 10 plausible sounding review comments, but was anywhere from subtly to hilariously wrong about what's going on in 9/10 of them. If a human were leaving comments of that quality consistently they certainly wouldn't receive maintainer privileges here until they improved _significantly_.
Show HN: Inbox – An API and MCP server for managing DMs programmatically
by kevinpicchi | 2026-03-10 14:24:09+00:00
Hey HN,

I'm Kevin, and I've been building Inbox (https://inboxapp.com) — A CRM and an API for
sending and managing DM conversations across messaging platforms.
Right now we support X (Twitter) DMs. Instagram and LinkedIn are next on the roadmap. The idea is simple: DMs are becoming a primary communication channel for sales, support, partnerships, creator outreach — but there's no good programmatic layer for them. You either copy-paste manually or hack together brittle scrapers.
Inbox gives you a REST API with full thread and message management, a webhook system for real-time events (new messages, thread updates, status changes), and an MCP server so AI agents can manage your DMs directly. Your team also gets a shared inbox UI to handle conversations that need a human touch.

What you can do with the API:

- Send and receive DMs programmatically
- Manage threads (assign, tag, set pipeline stages)
- Sync conversation data to your CRM or internal tools
- Get real-time webhook events when anything changes
- Let AI agents handle DMs via our MCP server (works with Claude, Cursor, etc.)

We built this because i was doing support for my product over X DMs and realized the tooling just doesn't exist. X's own API for DMs is painful, rate-limited, and requires jumping through hoops. Most teams doing DM-based outreach or support end up with some combination of manual copy-paste, spreadsheets, and prayer.
The MCP server is the part I'm most excited about. You can point Claude or any MCP-compatible agent at your Inbox and it can read conversations, send replies, tag threads, update prospect info — basically run your DM workflows autonomously. We use it internally and it's become the thing i didn't know i needed.

Honest status: X support is solid and battle-tested. Instagram and LinkedIn
are coming Q2 2026. We're a small team, so I'd genuinely love feedback on what
platforms and integrations to prioritize.

API docs: https://docs.inboxapp.com
MCP server setup: https://docs.inboxapp.com/mcp-server

I'll be in the comments all day. Would love to hear: what DM-based workflows are you hacking together right now? What would you build if you had a clean API for this?
by remyp | 2026-03-10 14:24:48+00:00
I haven't tried any of these products, but I do have a senior dog with very severe separation anxiety. She barks and destroys stuff the minute I step out the door until the minute I come back. I could keep her from destroying stuff with a crate, but the neighbors would still throw a fit about the barking.

Effectively, this means that I have to hire a dog sitter every time I leave the house without her, just like an infant. If dog tv could fix this problem for me it would create an enormous amount of economic value.
by quantified | 2026-03-10 14:25:53+00:00
This is an important bottleneck. You can have LLM-based reviewers help you. But unless you yourself understood your thousands of lines, it's "somebody else's" code and that somebody else cannot be fired or taken to court.

The presumably human mid-level or junior engineer has their own issues with this, but the point of the LLM is that you don't need that engineer. For productivity purposes, the dev org only needs the seniors to wrangle all the LLMs they can. That doesn't sustain, so a couple of more-junior engineers can do similar work to mature.