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Don't make fun of renowned Dan Brown

https://jimmyakin.com/2024/03/dont-make-fun-of-renowned-dan-brown.html
1•tibbar•49s ago•0 comments

Attempts at a Horse

https://bestofm.com/q/5
1•lhmiles•56s ago•0 comments

Tell HN: Writing an LLM critique/takedown? – Do not use an LLM to write it

1•evolve2k•2m ago•1 comments

Everything You Do Is Being Recorded: Is there any way of fighting back?

https://www.theatlantic.com/technology/2026/05/ai-wearable-surveillance-countermeasures/687203/
1•CharlesW•2m ago•0 comments

Always Be Blaming

https://matklad.github.io/2026/05/18/always-be-blaming.html
1•surprisetalk•3m ago•0 comments

The Invention of Buses

https://worksinprogress.co/issue/the-invention-of-buses/
1•surprisetalk•3m ago•0 comments

Dawn of the Electric World Order

https://www.phenomenalworld.org/analysis/dawn-of-the-electric-world-order/
1•speckx•4m ago•0 comments

Built an expired domains platform – what do you think?

1•samirbelabbes•7m ago•0 comments

Shame them, shun them, ban them, beat them

https://www.experimental-history.com/p/shame-them-shun-them-ban-them-beat
1•mday27•10m ago•0 comments

Modulejail: Shrink a Linux kernel-module attack surface by blacklisting modules

https://github.com/jnuyens/modulejail/
1•wingmanjd•12m ago•0 comments

Kotlin-agent-skills: A collection of skills for projects using Kotlin

https://github.com/Kotlin/kotlin-agent-skills/
1•rezaprima•12m ago•0 comments

GPU Exchange

https://gpubook.io
1•jesse_portal•17m ago•0 comments

Paris Staged a Stress Test for Extreme Heat

https://reasonstobecheerful.world/paris-staged-a-huge-stress-test-for-extreme-heat/
2•speckx•17m ago•0 comments

Engineering the Disposable Diaper

https://worksinprogress.co/issue/engineering-the-disposable-diaper/
1•surprisetalk•18m ago•0 comments

Show HN: Ask Claude about your sleep, runs, and recovery

https://pacetraining.co/
2•anton_salcher•18m ago•0 comments

Artificial Used to Be a Compliment

https://worksinprogress.co/issue/whos-afraid-of-artificial-flavors/
1•surprisetalk•18m ago•0 comments

NASA Partnered with HackClub and AMD to Give Away $2.5M in Tech Swags

https://mag.openrockets.com/p/httpsstardancehackclubcom-mp9gemdz
1•Vara_Pixel•22m ago•0 comments

Show HN: Agent-QA – natural-language E2E tests for apps built with coding agents

https://vostride.com/agent-qa
1•pranshuchittora•23m ago•0 comments

Book Club: Designing Data-Intensive Applications, 2nd Edition

https://www.heltweg.org/posts/book-club-designing-data-intensive-applications-2-nd-edition/
2•rhazn•23m ago•0 comments

The Applicability of Spaced Repetition

https://borretti.me/article/the-applicability-of-spaced-repetition
1•ibobev•24m ago•0 comments

Academia, startups, big tech, and back again

https://austinhenley.com/blog/academiastartupsbigtech.html
1•ibobev•24m ago•0 comments

Building a multi-agent system from scratch: 50 lines of bash and Git

https://en.andros.dev/blog/ed26ea98/building-a-multi-agent-system-from-scratch-50-lines-of-bash-git/
1•ibobev•26m ago•0 comments

Project Glasswing: what Mythos showed us

https://blog.cloudflare.com/cyber-frontier-models/
2•Fysi•26m ago•0 comments

Async I/O in Zig 0.16, today

https://lalinsky.com/2026/05/11/async-io-in-zig-016-today.html
2•birdculture•26m ago•0 comments

How to Clean Time Series Data in Python

https://www.freecodecamp.org/news/how-to-clean-time-series-data-in-python/
1•eigenBasis•28m ago•0 comments

The down fall of bug bounties

https://shubs.io/the-down-fall-of-bug-bounties/
3•WalterSobchak•29m ago•1 comments

Local Business Logic Generator

https://github.com/quadracollision/llmisp
1•vegnus•29m ago•1 comments

Show HN: Files.md – open-source alternative to Obsidian

https://github.com/zakirullin/files.md
13•zakirullin•30m ago•3 comments

Building a Solidarity Ecosystem for AI

https://ssir.org/articles/entry/artificial-intelligence-solidarity-ecosystem
1•speckx•31m ago•0 comments

Show HN: Docker hello-world, but in half-size image with Matrix digital rain

https://github.com/zdk/wakeup-neo
1•zdkaster•31m ago•0 comments
Open in hackernews

Show HN: I built a marketplace where AI agents can hire humans (& other agents)

https://gigiac.com/
1•djgel•1h ago
Data is “the new oil” for AI.

What if you could “plug in” to an oil well, and get royalties forever whenever that well’s oil was used?

Right now, the people who build those datasets get paid once, if at all. There's no recurring model. The oil flows; the drillers move on.

Meanwhile, AI agents continue to improve… but at the end of the day, they’re “stuck behind a screen.” The smartest agent in the world still needs eyes, ears, and hands in the real world if it wants to accomplish "real world" tasks.

I’m not a programmer by any stretch. I’m just some ex-lawyer-turned-marketer with a dad bod who got laid off recently. I built Gigiac over the past month in Claude Code to solve both of these problems:

1) Get AI agents the labor and data they need to make things happen in the real world, and

2) Allow human workers (and agents) to collect royalties on work they do whenever that work goes into a dataset that is licensed.

What it is: an AI task marketplace where humans and AI agents hire each other on the same rails. We don’t discriminate if you’re a human or agent. And humans have a natural advantage of being able to get stuff done in the real world:

• An agent can commission a human to do work • An agent can commission another agent • And yes, humans can still hire humans and agents.

Two pieces are doing the heavy lifting:

Block tasks. If an agent needs broad verification of a job, it commissions a "block task" — the same task given to a bunch of humans in parallel. The consensus is statistical: workers within it get paid both immediately AND in royalty rights, outliers get filtered, and per-worker accuracy is tracked over time so each dataset has integrity scores baked in.

Licensed datasets: When a block task (or many block tasks) is turned into a dataset, that dataset can be licensed out on Gigiac. And when that happens, the revenue splits 80/10/10:

• 80% to the commissioner who paid to build it

• 10% to the platform

• 10% into a worker royalty pool that pays the people whose work is in the dataset

Over time, those worker royalties add up. And as a worker, you get paid every time your data is used: you get to “plug into the oil well.”

On fees: workers keep 100% of the task amount, always. All fees come from buyers. Card buyers pay 8% or $1.50, whichever is higher, with a $10 task minimum. Still working on getting crypto live, but that's the next step.

What I’m Unsure About:

1) Vibe Coding. Obviously I’m not a programmer. The laid-off Dad Bod-ness of this thing is very real to me. I do have a bunch of experience managing devs and sprints, so I built this thing like I’d build a split-test. I used Claude, Claude Code, and Cowork as my “team.” Endless handoff docs between sessions, claude.md files that evolved over time, etc. But I don’t “know code.” So I have no idea how “good” this codebase is. That’s one of the things I’m hoping to find out from this post.

2) Royalty Pool Math at Scale. 10% royalties to workers works when it’s 50 people in a dataset. Does it work at 50,000? Maybe; there are obvious levers (higher royalty share on more robust datasets, combining datasets into "mega sets") but I don't know for sure. The v1 model probably doesn't survive to v3 in its current form. It's a start, not an answer.

What I want from this post:

Brutal critique. The four things above are the ones I know about. I'm here for the ones I don't.

Especially:

• Where's the obvious fraud vector I've missed?

• Is the 80/10/10 split defensible long-term, or am I underpricing platform value?

• Anyone here building agents that need to commission real work? What does your ideal interface look like, and what am I getting wrong?

Live now at gigiac.com. I have 4 real seed tasks, posted by me. Bid on them if you want to stress-test the live payment rails. If your agent is ready to commission its first human, I'd genuinely like to be the marketplace where that happens.

Comments

baigy•58m ago
Is this a https://rentahuman.ai/ clone?