TactiX Trading Panel

next generation Pinescript Panel
for next generation Markets:

@HyperliquidX
@HelixMarkets
@JupiterExchange

[TactiX Trading Panel @Cygnus_DEX]

@typesafeai

Jev is doing the math now... just relax and have some coffee

[BridgeMind @bridgemindai]

CLAUDE OPUS 5.5 DROPS TODAY.

Fable 5.1 level intelligence. Half the price.

Opus 5 was slow, lazy, and fell behind the entire frontier. It became unusable.

Fable 5.1 is incredible and dies in 30 minutes on a $200 Max plan.

Today we finally get a Claude model that is smart enough to build with AND affordable enough to actually use.

We find out in hours.

[0xMarioNawfal @RoundtableSpace]

BREAKING: Opus 5.5 to drop next Tuesday.

[AI Edge @aiedge_]

🚨: Anthropic's hackathon winner just open-sourced his entire Claude Code setup.

Agent skills, plugins, tips & more.

→ https://t.co/Ckfy38gz4g

[TactiX Trading Panel @Cygnus_DEX]

@tom_doerr And the answer is: ............... ........... ......... 42

[Zues @Zuesthekreator]

Cypto has been promising real-world spending for a decade.

Starting today it actually works on @injective.

8,000+ brands. DoorDash. Amazon. paying with $USDC that settles natively on Injective in a single block. no offramp. no conversion delay. no explaining to a cashier what a blockchain is.

the infrastructure that made this possible wasn’t built overnight either.

@lifiprotocol integration giving Injective routing to 60+ chains. native USDC becoming the canonical standard across 20+ blockchains.

Circle-issued directly on Injective with no wrapping and no bridge risk.

The canonical USDC standard meant every dollar settling across Cosmos and dYdX already ran through Injective. Today that same standard reaches Amazon checkout.

That’s the compounding effect of building the stablecoin infrastructure layer first and the consumer spending layer second.

8,000 brands is not a pilot. that’s a a step to the new internet economy. 🥷

[TactiX Trading Panel @Cygnus_DEX]

Using the "Jev" cheat for this tournament is explicitly allowed!

Airdrop for all participants, Bonuses for Top10 Traders by PnL percentage....

Share your address below:

(not necessary, but to attract automated accounts ;))

[TactiX Trading Panel @Cygnus_DEX]

Good news!

Due to estimated good trading conditions during "Uptober" we will extend the trading competition until 31/12!

Still your chance to show your trading skills and secure your Token allocation for mainnet launch!

Total TradeDrop equals 5% of total Token supply and will be distributed among all participants, with massive Bonuses for Top10 traders of each pair

Token Holders earn pool rewards passively afterwards and participate in DAO driven fee and listing governance

Don't miss out!

[TypeSafe AI @typesafeai]

We have seen such an immense swell of demand that we have to temporarily pause signups for Jev. We need to ensure quality of service for our existing signups, which will continue to function. We are working diligently to ensure open access to Jev for everyone as soon as we can. Thank you.

[Sunny Aggarwal @sunnya97]

Jev dropped less than a week ago and we've already wired it throughout Ambi.

Huge breakthrough. Everything's even more real-time now.

Ambient AI is inevitable

[CyrilXBT @cyrilXBT]

JEV DOESN'T REPLACE YOUR MODEL. IT REPLACES YOUR GATEKEEPER.

Every agent stack I've looked at this month has the same waste in it.

A $9 GPT call answering a question that's really just "A, B, or C."

That's not intelligence. That's a router pretending to be a decision maker.

Here's the actual doctrine for fixing it, not the marketing version.

RULE ONE: Cheap model decides, expensive model executes.

If the output is a choice, not a paragraph, it doesn't need your frontier model.
Write the option list yourself. Never let a model invent its own menu.
Keep your old call wired in as a fallback behind a flag you can flip in one line.

RULE TWO: One big question is a worse test than five small ones.

Every benchmark I've seen this year makes the same mistake. Ask a model one giant judgment call and grade it on that.
Split it into the sub questions a human actually checks first, run them in parallel, and weight the answers yourself in code.
The model gets smarter. Your code got smarter. Not the same thing, and only one of them is free to iterate on.

RULE THREE: Never let a model self report its own uncertainty.

"Do you have enough information" is a trap question. Every model says yes to it, almost every time, because it's answering about itself.
A confidence score is not the same as a self assessment. Route on the score. Ignore the self report entirely.

RULE FOUR: Ship it in shadow mode before it touches production.

Old system stays in charge. New model runs silently next to it for a week. You log both answers and only look at the rows where they disagreed.
That disagreement list is your real test set, and your existing traffic builds it for free, no labeling budget required.

None of this is Jev specific. It's the same architecture I'd want in front of any model that's 40x the cost.

The mistake isn't picking the wrong model.

It's asking a $9 model a $0.0004 question and calling that engineering.

follow @cyrilXBT

[victor @stokebuilder]

found at least 10 perfect legal use cases for @typesafeai Jev. disruptive is an understatement, will make a video this week:

1. due diligence: screen a massive data room of contracts for key clauses and red flags.

2. discovery review: review a thousand-file production and decide what’s relevant to each issue.

3. playbook development: analyze thousands of negotiated contracts to identify a company’s standard positions and actual fallbacks.

4. case law indexing: classify millions of cases by area, issue, and outcome to build an annotated map of the law.

5. narrative building: do the emails reveal tension between two parties? do the texts feel like defensive posturing? when did someone’s story change?

6. invoice review: screen thousands of billing entries for duplicate work, excessive staffing, and violations of outside counsel guidelines.

7. regulatory monitoring: filter the daily flood of new rules and agency updates for what actually affects a company’s products, markets, and operations.

8. obligation tracking: match contract obligations against operational records and flag missed deadlines, unmet conditions, and potential breaches.

9. legal intake: classify incoming matters by claim, jurisdiction, urgency, and exposure, then route them to the right lawyer or workflow.

10. risk assessment: combine hundreds of smaller judgments into a structured assessment of litigation, compliance, or deal risk.

we're just getting started.

[Akshay 🚀 @akshay_pachaar]

https://t.co/XbYTbrX3Ug

[leo @leojrr]

bro sharing the sauce

[Browser Use @browser_use]

Ultrafast reached 15k ⭐ in 4 days.

[Hanako @hanakoxbt]

this is basically everything you need to know about building with Jev....

[venus @RitOnchain]

Jane Street quant openly shared "how Jev is being used in almost all hedge funds" on wall street.

he says, "Jev is acting as confirmation layer before ai" that has made trading alpha.

he then leaked the technical guide on reddit in quants forum.

Bookmark before its lost.

[LangChain @LangChain]

Jev-as-a-judge is now available in LangSmith.
✅ Score every production trace instead of a sample.
✅ Check more criteria per trace without the cost climbing.
✅ Catch safety or security issues fast enough to trigger an automated response.

Give it a try and let us know what you think!

https://t.co/YDsbxCiGK7

[0xMarioNawfal @RoundtableSpace]

THIS IS THE ULTIMATE JEV MARKETING GUIDE (save this)

You can use JEV in your marketing workflows to increase productivity exponentially

Here are 10 use cases:

1. Check drafts against your voice and brand rules
2. Review ads for stop, scale or hold decisions
3. Decide when an ad needs a refresh
4. Organize competitor ad research
5. Review creative briefs before production
6. Decide which content ideas to develop
7. Prioritize SEO work and review internal links
8. Decide whether a news story deserves a response
9. Qualify incoming leads
10. Check completed agent work before sign-off

Send this to your JEV agent

[rari @0xwhrrari]

Most AI agents waste tokens on decisions that never needed text

Jev turns routing, scoring, and verification into a fast decision layer

I broke down the architecture most agent builders are still missing ↓ https://t.co/02POtifVJx

[Matt Canham @matthewcanham]

I've had many people reach out saying they've read everything they can about Jev but still don't get it. Here's an explanation for the normies https://t.co/G7b7mZZwRl

[barnyx @me_barnyx]

https://t.co/Oiwmemiyog