Ankit K

Supply chains by day. Price action by night. Learning AI in between. All patterns, different screens.

[Ankit K @prudentttrader]

https://t.co/kHXooAN0qT

[Miles Deutscher @milesdeutscher]

I honestly should've been doing this sooner. It's completely changed how I use AI (I'm way more productive now).

GitHub has a TON of valuable AI skills, agents, prompts, etc. Recently I've been launching agents to install valuable workflows for me.

Try this prompt inside your agent:

"Every single week at [insert day/time], I want you to scan GitHub trending. Identify any workflows that might be valuable to me. Filter through and deliver a report of the top 5-10 workflows. Make sure they're secure and safe. I'll manually approve the ones I want you to set up."

From there, you can customise things to your liking. Add more skills, build more agents - whatever.

Such a simple hack, but so good.

[Moritz Kremb @moritzkremb]

Full Jev Tutorial

What it is, how you can build with it and what new applications it can unlock

→ 0:00 Intro
→ 0:34 Jev explained
→ 4:06 API setup
→ 5:59 Demo 1: Voice-controlled browser
→ 11:33 Demo 2: AI memory
→ 17:27 Demo 3: YouTube predictor

[Zac Gawn @ZacGawn]

This is the Jev explainer for business owners from a non-developer.

I sorted 20,000 emails, slacks and transcripts last night into upsells, complaints, missed follow ups and 5 other things.

It took 7 minutes and cost $1.45.

The short version:

Don't think of Jev as another model. Think of it as a tool for your models.

Claude or GPT does the thinking and works out what questions are worth asking.

Jev answers those questions across everything you've got. Fast, and for almost nothing. "Is the person in this email disappointed in our services?"

It can answer a bunch of questions at once, too. ( something that current models can struggle while keeping accuracy.)

"Are they disappointed" + "is there an upsell here" + "did we use the right opening line" + "is it a technical problem". Adding questions doesn't change the price much. Jev charges on input tokens - output is free.

Long Version: So why does that matter for business owners?

Because unlocking the next level of AI in a business isn't about a better model. It's about better data.

The models are good enough now. The constraint is your data. Anything actually useful, spotting a problem before it lands, knowing which customers are about to leave, needs data that's been organised well enough to learn from. Most businesses don't have that.

You've probably heard the terms structured and unstructured. Your emails, calls and messages are unstructured. A pile.

Having the pile is one thing. Turning it into something you can count, trend and act on means putting a tag on every single item.

That's the whole job Jev does, and it's the step most people skip because it used to be expensive.

Two things I'm excited about doing with it.

1. Mass classification, for cheap.

You can now tag every email, call and message you've got against as many questions as you want, and redo it whenever you change your mind.

That's what makes a better second brain possible. Here's how.

Everyone on Twitter is building a second brain. Plug in your emails, calls, docs, Notion, Obsidian, whatever, ask it anything, it knows your whole business.

But it is always reactive. waiting for you to ask a question.

To make it proactive - flagging stuff for you without you asking - something has to keep re-reading your data looking for things you care about, and that gets expensive fast.

The cheap way, (and until now the only way), is to tag everything as it arrives so you can query the tags later.

I tried that. Thought I was smart. You have to sit down and answer: what will I want to know about this data? What should I be tagging for right now? what connections do I have to make.

And you'll get it wrong, because the technology is moving too fast to plan around and we are all building as we go.

So you pick your ten tags, feel good about it. Six months later you launch a new service and want to know which existing customers have been asking for something like it. That tag doesn't exist, and nothing before today has it.

Until now the choice was rerun the whole history through an LLM, which is a proper project with a proper bill, or live without it. Most people live without it.

With Jev, retagging the whole history is a few dollars and a few minutes. So you stop trying to predict. Tag what matters today. When the business changes, retag everything.

2. Re-analysing old data for new patterns.

If you've got a tonne of historical data, you can now structure it fast enough to actually learn from it. So you can use your tags (and retags) for pattern analysis.

Say you've got three years of customer emails. Tag every thread for what the customer wanted, how we handled it, how it ended. Then ask what the ones that ended well have in common. Which way of communicating led to a good result, and which led to a bad one.

I run a call centre, so here's mine.

We do hundreds of thousands of calls a day and we do our best to classify them.

Every call goes through an LLM and we ask it: "did they use the opener", "did they handle the objection", "did they ask for the appointment". Three or four things.

Metrics I pulled out of my ass after a few years on the phones. It's what I think a good call looks like.

Whether it actually is, I've never been able to check properly. At that volume, changing the questions and rerunning everything is a project I'm not equipped to run.

Now I can hand the LLM the transcripts and the outcomes and say: come up with some hypotheses about what actually leads to appointments.

It comes back with twenty. Did the caller use the prospect's name ten times. Did they mention a neighbour. Did they give a reason for calling in the first ten seconds.

I point Jev at the history, reclassify every call against all twenty, line it up with which ones booked, and see if there's a pattern.

Throw out the ones that didn't matter, ask for twenty more, run it again.

None of that is new btw - It was just expensive and complex, and now it isn't.

Both of these are the same thing underneath. You've got a pile of unstructured data, you've got questions about it, and the questions are going to change.

Jev makes asking, and re-asking, cheap enough that you don't have to be right the first time which is great because businesses are more data driven than ever.

[Ian Nuttall @iannuttall]

Unsure how to get started with Jev?

Install the skill:
npx skills add typesafe-ai/skills --skill typesafe-ai

Then prompt in your project:
use /typesafe-ai to see how Jev can be used to replace slow, expensive LLM usage and find possible new features it would enable for users.

[Deedy @deedydas]

Here are the LinkedIns of the 3 Indian guys who hacked OpenAI with Opus 5 in 2 days for <$3000:
Rahul Maini, Mohan Pedhapati and Harsh Jaiswal

No brand name colleges or companies. Just raw curiosity and skill.

You can just do things.

[Robin Ebers @robinebers]

Jev is a much bigger deal than people believe

the average vibe coder has no fucking idea

this is the first time in a very long time that engineers regain a real advantage, because they can see the value of what this model does

it takes more architectural/systems thinking to understand where this could be useful, because it's not just a "drop in model replacement" but a completely different beast

but holy fuck is this thing amazing

huge kudos to @typesafeai for this

[Miles Deutscher @milesdeutscher]

Corey ( @coreyganim) makes $1,000/hour with AI consulting.

I sat down with him to break down his full playbook.

If you actually want to make money with AI, you need to watch this.

A full masterclass on how to get started, land clients, build offers, and so much more free value.

Thanks for coming on @coreyganim - have to do it again soon!

Watch now 👉 https://t.co/nrayf4GcsN

[Miles Deutscher @milesdeutscher]

Jev is THE most important AI model of 2026.

My take is that it has the potential to be as big (if not bigger) than GPT itself.

If you still don't understand Jev, take 5 seconds to read this diagram, and you'll understand why this is a massive deal:

[Ankit K @prudentttrader]

I read a lot. I observe a lot. I can talk about it well.

But when I sit down to write, nothing comes.

At work this never happens. Someone gives me the problem and the deadline. Then I know exactly what to do.

Outside work nobody gives me that. So the observations just pile up and I forget them.

I don't think I lack ideas. I think I lack somewhere to put them at the moment I have them.

Anyone else like this?

[Ankit K @prudentttrader]

Now zoom out.

Higher bond yields can mean:

More expensive business expansion.
Costlier mortgages.
Higher government interest bills.
Less investment.
Slower hiring.

So when someone says:

“Bond yields are rising.”

It may sound like something only traders care about.

But eventually, it can reach something as ordinary as your next job offer.

[Ankit K @prudentttrader]

You’ve cleared the interview. Then the company says:

“Sorry, we’ve put hiring on hold.”

What could that possibly have to do with government bonds?

More than you might think.

Here’s how rising bond yields can travel from financial markets to your next job. 👇

[Ankit K @prudentttrader]

Why are yields under pressure?

One reason is heavy government borrowing. More debt has to find buyers, and investors may demand higher returns.

Another is inflation.

If investors expect prices to rise faster, a fixed payment years from now becomes less valuable in purchasing-power terms.

Higher energy prices can make those inflation fears worse.

[Ankit K @prudentttrader]

This is one way an economic slowdown spreads.

The workers, factories and skills are still there. What’s missing is enough demand to keep them busy.

New spending from households, businesses, governments or overseas buyers can help break the cycle.

More orders → more production → more work → more income → more spending.

Sometimes an economy doesn’t lack the ability to produce.

It lacks enough demand to put that ability to work

https://t.co/1ldB33FIbi

[Ankit K @prudentttrader]

Now those workers and suppliers are earning less.

So they cut their own spending too.

That means other businesses lose customers, and the slowdown spreads.

The cycle begins to feed itself:

Fewer sales → less work → lower incomes → even less spending.

Because in an economy, one person’s spending is another person’s income

[Ankit K @prudentttrader]

Imagine people start eating out less or postpone buying a new car.

Restaurants serve fewer customers, so they order less food.

Car dealers sell fewer cars, so factories reduce production.

Businesses then slow hiring, cut working hours or even jobs.

What began as lower spending has now reached workers and suppliers.

[Ankit K @prudentttrader]

Ever wondered how an economic slowdown begins, and how it eventually reaches your job?

It can start with something very ordinary:

Millions of people deciding,
“Let’s spend a little less.”

That small decision, repeated across an economy, can eventually lead to fewer sales and job cuts.

Here’s how. 🧵👇