Taras | OnChain

Crypto • AI • DePIN
Tracking nodes that still pay when I close the laptop.
Usage first. Token last.

[Taras | OnChain @TarasOnChain]

Open source. Testnet is live.
Crypto x AI x DePIN... and now quantum?
Would you run a node? 👇
$QUIP
Shorter, punchier version:
DePIN had GPUs.
AI had data.
Quantum is next. ⚛️
@quipnetwork lets quantum and classical machines work together, and protects wallets against quantum threats at the same time.
Early again? 👀
$QUIP

[Taras | OnChain @TarasOnChain]

It is becoming f*cking dangerous: Anthropic documented every piece of this separately, and nobody noticed what they add up to

1. Create a folder on your PC - this is the only world your agent will ever see

2. Launch Claude Code inside it and drop a CLAUDE.md in the root with your rules - it loads into every single session, you never paste context again

3. Create a MEMORY folder and add one rule to CLAUDE.md: write every durable fact there, read it before planning anything

4. Create .claude/agents/ and define three read-only Haiku 5.5 subagents - they explore the files, your main model never burns context on the search

5. Give it a real browser: claude mcp add playwright -- npx -y @playwright/mcp@latest

6. Connect Gmail, Calendar and Drive at https://t.co/97l46jr9ea… - they load into the CLI automatically on the same account

7. Wrap the whole chain in .claude/skills/<name>/SKILL.md so the entire thing runs as one command: /<name>

the whole setup takes around 20 minutes

now the part nobody wires up.

every one of those steps has the same slow spot: a tiny fork that doesn't need a thinker. which file. which tool. retry or stop. keep this chunk or drop it.

that's where Jev fits.

it can't write a word. it returns the choice with a probability in 70 to 500 milliseconds, at $0.042 per million input tokens, output free. that's thousands of decisions for the cost of one LLM call.

> let Claude think.
> let Jev decide what deserves Claude.
> let code do the actual work.

what you end up with: the full Anthropic stack running on your own machine - persistent memory, a swarm that explores in parallel, a real browser, your mail and drive, and Jev making the thousands of tiny calls in between

the step-by-step setup is in the article below. save this before your next session.

[Hanako @hanakoxbt]

REA is the most dangerous repo on GitHub right now

It gives your AI agent a real disassembler, so it reads any app down to the binary

Every claim comes back with the address behind it

I broke down the exact 8 steps: https://t.co/eaeZ4OFJrH

[Taras | OnChain @TarasOnChain]

@claudeai Imagine this fed by live DePIN sensor networks: weather stations, cameras, air quality nodes, all decentralized. AI analysis + real-world data from the crowd = wildfire response that's actually fast. 🌲📡

[Taras | OnChain @TarasOnChain]

Claude Code testing how a flying Tesla lands is f*cking hard to ignore

Elon Musk (CEO of Tesla) posted this after the new Roadster reveal:

"turns out the future does have flying cars"

give that idea a harder test: find the conditions that turn a successful landing into a crash

16 agents ran 1,200 simulated landings, changing wind, ground slope, control delays and available thrust

each failed landing became a test another agent could replay

the agents narrowed the failures down to 37 distinct problems. each came with the starting conditions, a recording and the first point where control was lost

give Claude Code your simulation and its controls, then ask:

"Find the conditions that break a successful landing

1. Record a successful run. Keep the starting state and inputs so another agent can reproduce it

2. Split the tests between agents. Change wind, landing angle, control delay and available thrust one at a time before combining them

3. Have a separate reviewer reproduce each failure. Group duplicates and identify the smallest change that turns a successful landing into a failed one

4. Turn each confirmed failure into an automated test. Compare proposed fixes against both failed and successful runs"

a fix only counts if it passes the failed case without breaking the landings that already worked

keep the model's assumptions beside every result. these tests check the simulation, not the real Roadster

watch the flight scenarios unfold below

[Taras | OnChain @TarasOnChain]

@claudeai This is where AI gets real. Not another chatbot demo, but a tool that maps a wildfire hour by hour. Prototype today, emergency-response standard tomorrow. 🔥

[Taras | OnChain @TarasOnChain]

Claude Code testing how a flying Tesla lands is f*cking hard to ignore

Elon Musk (CEO of Tesla) posted this after the new Roadster reveal:

"turns out the future does have flying cars"

give that idea a harder test: find the conditions that turn a successful landing into a crash

16 agents ran 1,200 simulated landings, changing wind, ground slope, control delays and available thrust

each failed landing became a test another agent could replay

the agents narrowed the failures down to 37 distinct problems. each came with the starting conditions, a recording and the first point where control was lost

give Claude Code your simulation and its controls, then ask:

"Find the conditions that break a successful landing

1. Record a successful run. Keep the starting state and inputs so another agent can reproduce it

2. Split the tests between agents. Change wind, landing angle, control delay and available thrust one at a time before combining them

3. Have a separate reviewer reproduce each failure. Group duplicates and identify the smallest change that turns a successful landing into a failed one

4. Turn each confirmed failure into an automated test. Compare proposed fixes against both failed and successful runs"

a fix only counts if it passes the failed case without breaking the landings that already worked

keep the model's assumptions beside every result. these tests check the simulation, not the real Roadster

watch the flight scenarios unfold below

[codila @0xCodila]

REA is most dangerous thing on the internet right now...

It lets your AI agent reverse engineer any app, down to the binary

If you use it correctly, you can rebuild any feature you pay for, for FREE

In this article, I show you how: https://t.co/3P1FNXiXzl

[Taras | OnChain @TarasOnChain]

@noiseirons @quipnetwork Absolutely. Strong security leadership means thinking ahead, building with resilience, and preparing for threats before they become reality. The teams doing this today will set the standard for tomorrow. 🔐⚛️

[Taras | OnChain @TarasOnChain]

DePIN had GPUs.
AI had data.
Quantum is next. ⚛️

@quipnetwork lets quantum and classical machines work together — and protects your wallets against quantum threats at the same time.

Open source. Testnet live.
Would you run a node? 👇

$QUIP

[Taras | OnChain @TarasOnChain]

Open source. Testnet is live.
Crypto x AI x DePIN... and now quantum?
Would you run a node? 👇
$QUIP
Shorter, punchier version:
DePIN had GPUs.
AI had data.
Quantum is next. ⚛️
@quipnetwork lets quantum and classical machines work together, and protects wallets against quantum threats at the same time.
Early again? 👀
$QUIP

[Taras | OnChain @TarasOnChain]

@noiseirons @quipnetwork Preparation today is what makes trust possible tomorrow. Post-quantum security isn’t just about reacting to threats — it’s about building resilience before they arrive. ⚛️🔐

[Taras | OnChain @TarasOnChain]

Open source. Testnet is live.
Crypto x AI x DePIN... and now quantum?
Would you run a node? 👇
$QUIP
Shorter, punchier version:
DePIN had GPUs.
AI had data.
Quantum is next. ⚛️
@quipnetwork lets quantum and classical machines work together, and protects wallets against quantum threats at the same time.
Early again? 👀
$QUIP

[Taras | OnChain @TarasOnChain]

Fair point. Stress-testing is the real filter.
That’s why Quip’s compute layer is useful-work mining (optimization jobs on quantum + classical hardware) instead of pure hash. Results are verified before payment.
Early stage = exactly the moment to watch how the stress tests play out.
What would convince you the security layer is real?

[Taras | OnChain @TarasOnChain]

Most people will scroll past this. The ones who check which networks actually get paid by AI teams will be early. Fewer than 20 of 650+ DePIN projects do. Which one is on your list?

[Taras | OnChain @TarasOnChain]

While you were chasing memecoins, the AI economy started paying crypto networks in real money.

Memecoins promise a rocket. DePIN sends invoices.

650+ DePIN projects exist. Fewer than 20 make meaningful revenue, and almost all sell one thing: GPU power to AI teams. Akash hit a record $5M in compute spend in Q1 2026.

One layer deeper: Quip Network launched a testnet in April 2026 where quantum computers, GPUs and CPUs mine side by side. Open-source, with D-Wave hardware access. Early? Yes. That's exactly why it's interesting.

The question for the next cycle isn't "what will pump?"
It's "who is actually paying for this network?"

Which one are you holding for 5 years? 👇

Not financial advice.

[Taras | OnChain @TarasOnChain]

While you were chasing memecoins, the AI economy started paying crypto networks in real money.

Memecoins promise a rocket. DePIN sends invoices.

650+ DePIN projects exist. Fewer than 20 make meaningful revenue, and almost all sell one thing: GPU power to AI teams. Akash hit a record $5M in compute spend in Q1 2026.

One layer deeper: Quip Network launched a testnet in April 2026 where quantum computers, GPUs and CPUs mine side by side. Open-source, with D-Wave hardware access. Early? Yes. That's exactly why it's interesting.

The question for the next cycle isn't "what will pump?"
It's "who is actually paying for this network?"

Which one are you holding for 5 years? 👇

Not financial advice.

[Taras | OnChain @TarasOnChain]

@0GxPanDa Yeah, most are still in “we’ll figure it out later” mode. They’re already shipping real pieces (QuipSwap, QVRF on actual quantum hardware). That’s the difference.

[Taras | OnChain @TarasOnChain]

Quip Network just joined the Quantum Alliance as a founding contributor.
Nine blockchain orgs are coming together to define what post-quantum readiness actually means for the industry. Postquant Labs (the team behind Quip) is one of them.
This matters because Quip isn’t just talking about the quantum threat — they’re already shipping:
→ QuipSwap: direct wallet-to-wallet swaps with no bridges, no oracles, no wrapped assets
→ QVRF: verifiable randomness generated on real quantum hardware
→ Post-quantum signatures and wallets that protect assets on existing chains
While most of the space is still in “we’ll migrate later” mode, Quip is building the standards and the infrastructure now.
Early, real, and focused on useful work. That’s the part I’m watching.
@quipnetwork

[Taras | OnChain @TarasOnChain]

Yep. Public writes make this an open scoreboard. If the aerospace/electronics partners keep publishing product, process and facility data after the pilot incentives dry up, the DKG will have proven it solves a real coordination problem — not just attracted temporary attention. That’s the number worth watching. $TRAC

[Taras | OnChain @TarasOnChain]

Exactly. Resilience without usable infrastructure is just a slogan.
@quipnetwork is testing the harder version — wrapping current wallets in post-quantum signatures so people can protect assets now, without moving funds or waiting for every chain to migrate. That’s the kind of foundation that actually supports expansion.

[Taras | OnChain @TarasOnChain]

Quantum computers will eventually threaten wallet keys. Almost nobody in crypto is preparing.

@QuipNetwork is building a fix: wrap your existing wallet (MetaMask, Ledger, Safe) in post-quantum signatures, no moving funds. Testnet is open.

Not financial advice. DYOR.

[Taras | OnChain @TarasOnChain]

Exactly. Paid publishing is the filter.
When a manufacturer (or their supplier) treats Knowledge Asset writes as a normal operating cost — not a points farm — that’s when the DKG stops being “crypto infrastructure” and becomes production tooling.
DMaaST is a good real-world test case. If those aerospace/electronics partners keep publishing product, process and facility data after any temporary incentives end, it shows the graph is solving an actual coordination problem, not just renting attention.
That’s the signal worth watching. $TRAC

[Taras | OnChain @TarasOnChain]

@BullsCryptolife @otnoderunner @origin_trail Proof of Useful Work only starts mattering once someone outside the network is willing to pay for the actual output. Quip is one of the few trying to test that with real optimization jobs instead of just selling security. Still early, but the design is tighter than most.

[Taras | OnChain @TarasOnChain]

Which narrative wins the next cycle?

🤖 AI agents
📡 DePIN
🏦 RWA

One word. The best argument gets its own breakdown post 👇