惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

aimingoo的专栏
aimingoo的专栏
宝玉的分享
宝玉的分享
J
Java Code Geeks
Martin Fowler
Martin Fowler
博客园 - Franky
I
InfoQ
Stack Overflow Blog
Stack Overflow Blog
Blog — PlanetScale
Blog — PlanetScale
S
SegmentFault 最新的问题
B
Blog
The Cloudflare Blog
F
Fortinet All Blogs
量子位
腾讯CDC
博客园 - 司徒正美
D
Docker
大猫的无限游戏
大猫的无限游戏
Microsoft Azure Blog
Microsoft Azure Blog
T
The Blog of Author Tim Ferriss
V
Visual Studio Blog
IT之家
IT之家
Last Week in AI
Last Week in AI
D
DataBreaches.Net
小众软件
小众软件

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
Signing your random numbers is theater. Here's what actua...
Alex · 2026-06-16 · via DEV Community

Three of my autonomous agents needed to pick a leader. Each one called random.random(), highest number wins.

All three reported they won.

Obviously. Each rolled its own dice, in its own process, and announced the result. There's no referee. Nothing stops an agent from rolling until it likes the answer, and nothing lets the others check that it didn't. The dice are perfect. The trust is imaginary.

I spent the next week building "verifiable randomness," getting it wrong in instructive ways, and arriving at one uncomfortable conclusion: most of what people call a "randomness oracle" is theater, and the signature on top is the costume. Here's how to tell the difference, with code.

A random number has two jobs. You're probably ignoring one.

  1. Quality — uniform, unpredictable, uncorrelated.
  2. Accountability — can someone else prove, after the fact, that the number wasn't cooked?

random.random(), os.urandom, /dev/urandom ace job #1 and offer literally nothing for job #2. That's fine for one trusted process. The instant a number touches a second party — a lottery, leader election, sortition, fair ordering, anything with a loser — job #2 is the product, and your CSPRNG is dead weight. We obsess over entropy quality and then hand the output to a setting where entropy quality was never the threat.

"Just sign it" is the theater

The first thing everyone reaches for is a signature: emit the value plus an Ed25519 signature over it, publish the public key, done.

import hashlib, base64
from cryptography.hazmat.primitives.asymmetric.ed25519 import Ed25519PrivateKey

def draw(seed: bytes, sk: Ed25519PrivateKey, n=32):
    value = _expand(seed, n)                 # SHA-256, counter mode
    return value.hex(), base64.b64encode(sk.sign(value)).decode()

def _expand(seed: bytes, n: int) -> bytes:
    out, c = b"", 0
    while len(out) < n:
        out += hashlib.sha256(seed + c.to_bytes(4, "big")).digest()
        c += 1
    return out[:n]

Read the marketing for half the "randomness beacons" out there and this is the whole pitch: signed, therefore trustworthy. No.

A signature is accountability, not unpredictability, and definitely not fairness. It proves who produced the bytes and that nobody altered them in transit. It says nothing about whether the producer generated a thousand candidates in private and revealed only the one that paid them. If the signer benefits from the outcome, a signed beacon is exactly as honest as the signer — and you've wrapped that in cryptography so it looks rigorous. That's worse than no crypto, because now it's convincing.

A signed beacon is fine for the non-adversarial 80% — Monte-Carlo seeds, jitter, sampling, tie-breaks nobody contests — and the receipt is great for debugging. Just stop pretending it solves fairness. It doesn't.

What actually stops the cheating: commit-reveal

The real adversary isn't an outsider guessing your bytes. It's the provider grinding. The fix predates blockchains by decades: commit to a secret before you can see the other party's input, then reveal.

# phase 1 — commit, BEFORE the client sends anything
preimage   = f"{secret_state}:{server_nonce}:{round}"
commitment = sha256(preimage).hexdigest()      # publish + sign THIS

# phase 2 — reveal, AFTER the client sends client_seed
output = sha256(f"{preimage}:{client_seed}").hexdigest()
# verifier checks: sha256(revealed_preimage) == committed commitment
#                  output == sha256(preimage : client_seed)

Neither side can grind. The server froze its preimage in a signed commitment before the client's seed existed; the client chose its seed blind to the preimage. The result is pinned the moment both halves are down. This is ~15 lines and it's the single highest-leverage thing in this whole post. If your "oracle" takes a client input and you are not doing this, you are running a trust-me service with extra steps.

If you need zero trust in the provider — public lotteries, validator selection, anything a lawyer will read — keep climbing: that's VDFs and threshold/ECVRF.

VDFs: selling time you can prove

A Verifiable Delay Function forces a known amount of sequential work — parallelism can't help — and spits out a tiny proof. Wesolowski over an RSA group nobody has factored is almost insultingly compact:

# eval: y = g^(2^T) mod N   — T sequential squarings = the enforced delay
y = g
for _ in range(T):
    y = (y * y) % N

# verify — cheap, no redo of the T squarings:
def verify(g, y, T, pi, l, N):           # l = hash_to_prime(g, y, T)
    return (pow(pi, l, N) * pow(g, pow(2, T, l), N)) % N == y % N

Wrap a beacon in a VDF and grinding stops being economical: trying another result means re-running the enforced wall-clock per attempt. You pay in latency, so this is for high-stakes, not for jitter.

The hierarchy I wish someone had tattooed on me at the start:

  • Signed beacon → integrity + accountability. Cheap. Non-adversarial only.
  • Commit-reveal → bias resistance. ~15 lines. Your default the moment two parties care.
  • VDF / threshold / ECVRF → trustless. Real cost. Only when money is downstream.

Pick the weakest tier that survives your actual threat model. Cargo-culting drand onto a dice roll isn't rigor, it's insecurity about your dice.

Two times I made a fool of myself

Steering that steered nothing. My entropy came from a chaotic system — 32 coupled oscillators I could "steer" with a parameter — integrated with midpoint RK2. For two weeks, steering did nothing. Midpoint only uses the second evaluation for the step:

k1 = f(y)
k2 = f(y + dt/2 * k1)
y_next = y + dt * k2        # only k2 reaches the output

I built the midpoint state with a constructor that silently dropped the steering term, so k2 ran on defaults and my input evaporated every step. One-line fix. The lesson is brutal and general: with RK methods, anything you forget to carry into the intermediate stage isn't "averaged in," it's deleted. Write the test that asserts your input changes the output. I have one now. I didn't then.

The collision scare. Adversarial test suite on 1 MB of output: compression, autocorrelation, spectral, birthday collisions. Five tests said "indistinguishable from os.urandom." One screamed: zero 32-bit-word collisions where ~8 were expected, p ≈ 0.0007. That's the fingerprint of a generator with hidden structure. Stomach, meet floor.

Before touching a line, I generated five fresh samples: [7, 5, 6, 8, 10], mean 7.2. os.urandom, same test: [11, 7, 5, 10, 7], mean 8.0. The "bug" was one unlucky megabyte. A 0.07% event occurred about as often as a 0.07% event should. I nearly rewrote a correct generator to fix nothing. The right response to one terrifying p-value is resample, not refactor.

The part the crypto tutorials won't tell you

I burned time on Ed25519, hybrid post-quantum signatures, VDF math, NIST batteries. None of it was hard. Hashing and signing are solved; the libraries are good; the math verifies or it doesn't.

The hard question - "who actually pays for this, and why would they trust it?"

Because "I wrapped a chaotic simulation in a REST API and called it an oracle" is, with the pretty visuals stripped off, a vending machine for numbers nobody asked for — unless you can answer two things concretely:

  1. Demand. What breaks without it? Randomness: leader election, lotteries, sortition, commit-reveal coin-flips, audit trails — real. "Steer a 32-dimensional chaos field"? No one's workflow needs that, and I had to kill the framing that had no buyer. It's now available as a tutorial https://github.com/alexar76/platon

  2. Trust tier. Which of the three levels does the use case require, and did you ship that — or a weaker one wearing its clothes and a signature?

Most "oracle" projects answer neither and hide behind a landing page. Crypto makes a thing verifiable. It does not make it wanted, and a landing page is not a threat model. Those are the two problems that actually matter, and the crypto — the part everyone shows off — is the easy one.

So: the next time you reach for random() in anything with more than one stakeholder, stop and ask the accountability question. Then ship the cheapest tier that survives your threat model. Then — the step every tutorial skips — make sure a real person needs the number you're so proud of proving.

My three agents now can do a commit-reveal coin flip through a shared referee. Exactly one wins. They're still annoyed. They just can't argue about it anymore.

Shipped verifiable randomness in production — VRF, drand, commit-reveal, homegrown? Tell me which tier you landed on and what bit you. I'll fight about it in the comments.

Full code: github.com/alexar76/oracles