Filiffa

[J.B. @VibeMarketer_]

update: we are COOKING

[J.B. @VibeMarketer_]

https://t.co/VcmEgmuAGR

[Closer @closer_xyz]

You already have a take on the game.

Closer just gives you somewhere to put it.

[Closer @closer_xyz]

The beauty of a live game is that there's always another moment coming.

Another goal.
Another card.
Another swing in momentum.

Closer turns those moments into predictions.

See it coming. Call it.

[J.B. @VibeMarketer_]

https://t.co/xNVsA7zrSB

[Filiffa @filiffa]

@VibeMarketer_ Absolutely bullish on this harness methodology 🔥

[J.B. @VibeMarketer_]

https://t.co/bNX6yueK7i

[J.B. @VibeMarketer_]

if I could run a commerce agent through just one benchmark before it touches real money, I'd pick CommerceAgentBench.

it already passed 1,000 stars on github and is becoming the default answer when someone asks whether an agent is actually ready for e-commerce work.

the 107 tasks span procurement, product listings, operations, fulfillment, and after-sales. almost all of them force the agent across systems: it reads a messy inbox or a stack of quotes, then has to go finish the job in a browser, a calendar, or a supplier platform.

the tasks come out of real usage. Accio runs an AI agent for 10 million SMEs, and these 107 were cut down from 1.6 million real conversations and 200K execution traces, with Alibaba's 27 years of e-commerce behind them.

the benchmark also assumes humans keep making the decisions. it just tests whether an agent can handle the execution work under them.

pick a task, then hand your agent this template:

"complete [TASK ID] from CommerceAgentBench end to end. work inside the provided environment only. the run counts only if the required state exists afterward: labels applied, drafts saved, events created, bookings confirmed."

every task grades the same way:

> the grader reads what actually changed: gmail labels, saved drafts, submitted listings, shipment IDs
> whatever the agent says it did counts for zero
> checks are deterministic, so you can rerun it and trust the number

the current best result is just 66/107 tasks completed.

whats even more interesting is the same model scores differently depending on the harness running it.

this is much closer to how we should evaluate agents - not by what they say they can do, but by whether the job actually got done.
open-source benchmark:

https://t.co/XKGxEUFxnu

[J.B. @VibeMarketer_]

how do you write articles that go viral on X?

in the last 30 days, my articles have generated over 6.2M impressions

the answer isn’t simply “write better”... at least, not at first

in my experience, banger articles come down to 3 things:

> the banner image

treat this like a YouTube thumbnail... one big visual idea, a clear subject, strong contrast, and enough curiosity to stop the scroll

i make several completely different versions, then preview them in Typefully on mobile to make sure everything is clear...

if the concept isn’t obvious in one second, simplify it

> the headline

the best headlines combine a clear payoff with an unanswered question

be specific about the person, number, result or problem... “how we built X” will always beat “thoughts on X”

write variations around utility, curiosity and contrarian angles, then pick the one that makes the value of clicking obvious

> the opening sentences

the first 2-3 sentences appear inside the feed preview... use them

give away something valuable immediately: your best fact, sharpest observation or most surprising result

then make it clear what the rest of the article will help them understand

once they click, structure the article so it’s effortless to follow:

- state the main argument near the top
- support every major claim with data, screenshots or a real example
- explain why it happened, not just what happened
- keep paragraphs short and make every subheading communicate an actual point
- turn the insight into something the reader can use
- use visualizations and graphs to make key ideas instantly clear

before publishing, ignore the body for a moment

just look at the banner, headline and opening sentences

would you click if the article wasn’t yours?

if the answer isn’t an immediate yes, keep working on that first

[J.B. @VibeMarketer_]

https://t.co/l62nWZhBmO

[Filiffa @filiffa]

@VibeMarketer_ Good 👍👍

[Closer @closer_xyz]

Calling a 50–50 match is easy.

The interesting part is saying:

Team A — 54%
Team B — 46%

You’re still making a call.

You’re just being honest about the uncertainty.

That’s the kind of prediction Closer is built for.

[Closer @closer_xyz]

There are two things that can revive me:

A genuinely good sports match
A prediction I’m irrationally confident about

Closer provides both problems.

[Closer @closer_xyz]

“I think they'll win” is vague.

“I give them a 73% chance” tells us something.

How confident are you?
How often are you right at that confidence level?

That's where sports prediction gets interesting.

Closer.

[Closer @closer_xyz]

Everyone has a prediction.

Very few have a record.

One good call can be luck.

Twenty calls with timestamps?

Now we're talking.

Closer turns sports predictions into something you can actually look back on.

[Closer @closer_xyz]

Sports Twitter has mastered the art of finding old screenshots.

We’re more interested in the call before the chaos.

Make it.
Leave it.
See what happens.

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[Filiffa @filiffa]

@AllWeb3_io Really forward thinking approach to marketing accountability fr

[AllWeb3 @AllWeb3_io]

What would make you trust a campaign report from a project you don't already know?