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]

@elonmusk Roadster has downforce.
Cybertruck just said fuck it and started flying.
When is the official thruster pack dropping? 😂

[Taras | OnChain @TarasOnChain]

Roadster has downforce.
Cybertruck has thrusters and zero respect for buildings, police stations or basketball courts.
Flying cars aren’t a 2030 problem anymore

[Taras | OnChain @TarasOnChain]

TESLA ROADSTER: DOWNFORCE

Flying cars are real😳😳😳

[Taras | OnChain @TarasOnChain]

@CyberTruckUK @Tesla @cybertruck @elonmusk When the UK’s bureaucracy won’t let the Cybertruck drive, just make it fly instead. Classic problem-solving. 🚀”
Або коротший варіант:
“Regulations: 0
Cybertruck with thrusters: 1
Elon would approve 😂

[Taras | OnChain @TarasOnChain]

@elonmusk Flying over the Snake River and landing on wheels at half the speed of sound.
That’s not a demo, that’s a flex.

[Taras | OnChain @TarasOnChain]

@trkweb3 @quipnetwork Appreciate it.
Better to test the hybrid post-quantum layer now while the keys are still safe, than to scramble later. Testnet is open if you want to try wrapping a wallet.

[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]

Serious question: if a car can generate this much downforce, how long until "flying car" stops being a meme? 2030? Earlier?

[Taras | OnChain @TarasOnChain]

TESLA ROADSTER: DOWNFORCE

Flying cars are real😳😳😳

[Taras | OnChain @TarasOnChain]

@BullsCryptolife @otnoderunner @origin_trail Exactly.
Once the writes are public, the data either keeps flowing after the incentives or it doesn’t. No narrative can rewrite that later. That’s the cleanest test there is.

[Taras | OnChain @TarasOnChain]

@BullsCryptolife @otnoderunner @origin_trail Exactly. First paid job is interesting, repeat paid jobs are the actual signal.
That’s the bar I’m watching for with Quip too.

[Taras | OnChain @TarasOnChain]

@Coinmaster100x @quipnetwork Exactly. That’s the point of Quip Accounts — hybrid post-quantum signatures that protect your existing wallets today without migrating to a new chain. Future-proofing shouldn’t be optional.

[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]

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.