Dmytro

[Dmytro @Dmytroo_eth]

STOP SCREENSHOTTING KIMI K3 BENCHMARKS. ROUTE REAL WORK TO IT

Moonshot shipped Kimi K3 on July 16 with 2.8 trillion parameters, 1M context, native text/image/video input and $3 per million input tokens

Most people will treat it like launch-week content

Post the benchmark chart, argue in replies, then send zero real tasks through the model

That misses the actual use case

K3 is not trying to be the smartest model in every situation

It is a volume machine

The important part is the 1M context window, flat long-context pricing, and cache hits at $0.30 per million tokens

That changes the economics of heavy work

Repo-wide analysis. Large docs. Research corpora. Agent loops that reread the same context again and again

A normal setup pays full price every time the model sees the same project

A structured setup keeps the stable context identical, puts the new task last, and lets cache pricing cut repeated context by 10x

But the catch matters

Benchmarks are still self-reported. Reasoning effort is max-only at launch. Open weights are promised for July 27, not live yet. And trivial tasks can get expensive because the model overthinks them at full output price

So the move is not “replace your whole stack overnight”

The move is routing

Keep premium models on hard reasoning and sensitive work

Send long-context reading, repo analysis, doc synthesis and repeated agent context to K3

Most people are still asking which model is king

The winners are asking which task each model should own

[darkzodchi @zodchiii]

https://t.co/H025GtmpxG

[Dmytro @Dmytroo_eth]

MARKETING IS A TAX ON BORING PRODUCTS

Most gaming studios dump millions of dollars into PR agencies, cinematic trailers, and paid influencer campaigns, only to launch their game to absolute silence

The creators of Meccha Chameleon spent exactly $0 on marketing

They just designed a core gameplay loop so visual and satisfying that people started recreating it in real life

Look at this video, people are buying plain 3D-printed figures, grabbing markers, painting them by hand, and hiding them around their rooms blending them perfectly into frames, computer desks, and laptop stands

If your product doesn't create a "how is that even possible" moment in 3 seconds of silent video, you are paying a massive tax to buy rent-free attention

save this

[Asteri @Asteri_eth]

https://t.co/XX1kjU5kiG

[Dmytro @Dmytroo_eth]

YOUR VRAM CAPACITY DECIDES WHAT RUNS, BUT YOUR BANDWIDTH DECIDES WHAT WORKS

Most local AI beginners are falling for the “more VRAM is always better” marketing trap

They spend thousands on unified memory mini-PCs or “AI-ready” personal workstations with 96GB of shared memory, only to watch a basic 13B model crawl at a painful pace

Your GPU cores are not the bottleneck. The memory bus is

Every single token your LLM generates requires the GPU to read the entire model weights from VRAM

If your memory bandwidth is slow, your expensive GPU cores sit idle, waiting for data that hasn't arrived yet

A used RTX 3090 ($800-$1,050) with 936 GB/s bandwidth will absolutely demolish an integrated system with 96GB of slow DDR5 memory running at 256 GB/s

It is a 3.7x speed difference on the exact same model size

Stop buying the capacity hype

Start checking the bandwidth math

[beamnxw ./ @beamnxw]

https://t.co/uquDQ53smA

[Dmytro @Dmytroo_eth]

RENTING TRAFFIC FROM SOCIAL MEDIA ALGORITHMS IS AN OPERATIONAL HAZARD

Most builders post random content pieces or push generic landing pages hoping a lucky algorithmic wave will pay their bills

When hundreds of millions of monthly users are already standing on commercial platforms in active planning mode, guessing what to publish is pure economic waste

The play is structuring an automated, multi-tiered content factory

You define the exact target positioning outside the platform, use high-persistence templates to batch out your catalog, and lock the incoming attention into a self-contained capture funnel

You deliver extreme value upfront with a structured lead magnet, segment the users by geographic risk, and leverage automated email sequences to monetize the list again and again

A single audience database that you own outright removes more financial friction than a hundred disconnected side hustles sitting in your Notion workspace

[Insomnia @insomnia_vip]

https://t.co/9XQnLdfsFc

[Dmytro @Dmytroo_eth]

THE SOLO DESIGN STUDIO IS AN ARCHITECTURE PROBLEM NOT A CREATIVE BOTTLENECK

Most builders think they need a giant creative agency roster or weeks of manual asset assembly to ship a premium product ecosystem

The time between a raw concept and a fully realized brand stack has completely collapsed

One person can engineer a complete corporate identity, pitch deck, and mobile prototype in a single weekend

But indecision inside the canvas is the most expensive operational drag you can introduce

Every vague rewrite eats your execution budget and destroys your project consistency

The teams making serious revenue aren't playing with prompts hoping for a lucky generation

They construct a brutal source of truth brief, establish the core variables, and use the vision model strictly as a manufacturing layer

You don't need to be a professional designer to run an industrial production line anymore

You just need to stop playing with the tools and start structuring the blueprint

Tools create options

[Asteri @Asteri_eth]

https://t.co/V3U5aEPJ66

[Dmytro @Dmytroo_eth]

@diamai_ The real fix is architectural hygiene keep the mess out of the model’s context window

[Dmytro @Dmytroo_eth]

THE ONE-PROMPT WHILE-LOOP IS THE ULTIMATE PRODUCTION TRAP

It looks like magic in a 60-second video but collapses the second it hits real production data

That is the Level 1 amateur setup most builders are still running

They dump every tool into a single canvas and wonder why the agent chokes on its own context history

The production stack is an iceberg, and the real leverage happens entirely below the surface

You don't build a smarter chatbot, you build a three-layer factory line

First, you route the task before it touches the model picker

You match the risk to the right token cost. Luna for routine edits, Sol Ultra for architecture

Second, you enforce hard rails. Clear inputs, clear outputs, and absolute write-access isolation

The tool can fail, but it can never corrupt live state

Third, you deploy the orchestrator. The main agent stays clean while delegating the messy execution, tool noise, and retries to isolated subagents

One subagent researches, one tests, one compares. Your core system never sees the mess

That is how an agent runs for weeks without suffocating on its own logs

Prompts create output

Orchestration creates throughput

[Diam @diamai_]

https://t.co/kyJOW5TqHP

[Dmytro @Dmytroo_eth]

@diamai_ Smart article

I’m already reading

[Dmytro @Dmytroo_eth]

@monokern That’s for sure

[Dmytro @Dmytroo_eth]

THE DIFFERENCE BETWEEN A $20 CHATBOT AND A $20K/MONTH AI WORKFLOW IS NOT THE MODEL, IT IS WHETHER THE AGENT CAN KEEP WORKING AFTER THE FIRST ANSWER

The video is not about writing a cleaner prompt

It is about turning AI into a loop: receive the task, pull the context, take action, check the result, write down progress, notify the human, then come back later and repeat

That is the layer most people still skip

They use AI like a smart search box and manually restart the process every time something changes

A real loop does not wait for another perfect prompt. It checks tickets, PRs, Slack, tasks, docs and project state on its own schedule

That is where the economics change

One agent that reviews the same workflow every night can remove more operational drag than 100 saved prompt templates sitting in Notion

The article is pointing at the same shift: the value is moving from “ask better” to “build systems that keep moving”

Prompts create output

Loops create throughput

[Nekt0 @Nekt_0]

https://t.co/cYwsLS0eXm

[Dmytro @Dmytroo_eth]

@monokern So, I can just copy after him?

[Dmytro @Dmytroo_eth]

@Asteri_eth I’m glad to hear it

[Dmytro @Dmytroo_eth]

@fluixoo If you look at it, he always wins

[Dmytro @Dmytroo_eth]

Trader on Polymarket is sitting at $276,633 profit from one BTC pattern

Joined in June 2026 and already has 652 predictions

> $276,633 all-time PnL
> $23.5K biggest single win
> only $0 in open position value
> still trading BTC Up/Down 5-minute windows

His profile: https://t.co/txdFU7xRKG

[Dmytro @Dmytroo_eth]

ONE AI INCOME STREAM IS USUALLY NOT ENOUGH TO BECOME A REAL BUSINESS

Content gets attention

Automation turns that attention into clients

Discipline stops the money from leaking into random experiments

That is the part most people miss when they talk about “making money with AI.”They pick one lane and treat it like the whole game

A content page with no offer becomes reach that slowly dies. An automation service with no audience becomes a quiet business nobody sees.

The better setup is when the pieces feed each other.

You post breakdowns of real automations. People see a narrow problem they already have.

Some of them ask if you can build it. That service creates cash flow. Then discipline decides what gets reinvested, what stays small, and what should be killed before it eats the business

That is how AI income stops being a lucky post or one random client

It becomes a system

Content brings the market in
Automation gives the market something to buy
Discipline keeps the whole thing alive long enough to compound

[koba @kobaHUB]

https://t.co/xgrtrXPWET

[Dmytro @Dmytroo_eth]

@monokern And they can be properly configured to reveal productive knowledge

[Dmytro @Dmytroo_eth]

YOUR SECOND BRAIN IS USELESS IF YOU’RE THE ONLY ONE MAINTAINING IT

The video shows the nice version of Obsidian: clean notes, calm setup, second brain aesthetic

But the article points at the part most people miss

A second brain doesn’t fail because you chose the wrong app. It fails because you are still the one who has to reread everything, connect old ideas, find duplicates, notice patterns and clean up the mess later

That is where Claude changes the whole thing

You don’t just paste notes into a chat once. You build a loop that rereads your vault on a schedule, checks the output, fixes weak notes, and writes the useful parts back into Obsidian

After a few weeks, it can catch things you keep repeating without realizing it

Same idea written three different ways
Old notes that should be connected
Contradictions you forgot about
Patterns you can’t see because they’re inside your own head

That is the real second brain

Not a prettier folder

A system that keeps noticing things after you stop paying attention.

[Gipp 🦅 @gippp69]

https://t.co/3LXibr1c7u

[Dmytro @Dmytroo_eth]

@vanvster he generator-evaluator split is a total game-changer for capping spend

[Dmytro @Dmytroo_eth]

A SOLO FOUNDER CAN NOW BUILD A SMALL OFFICE TEAM INSIDE CLAUDE

Most people still use Claude like a smart chat box.

They paste a task, get one answer, close the tab, then come back tomorrow and explain the same context again.

Anthropic quietly open-sourced a different setup: role-based plugins that turn Claude into separate workers for sales, marketing, finance, legal, data, support and product.

Each role comes with its own skills, commands and tool connections.

That means sales can prep a call, data can write a query, marketing can audit a page, legal can review a contract, and finance can check numbers without you teaching Claude the job from scratch every time.

The model is not the whole edge anymore.

The edge is the system around it.

A chatbot gives you answers. A plugin stack gives you repeatable labor.

[Asteri @Asteri_eth]

https://t.co/Ac6UJCLxGF

[Dmytro @Dmytroo_eth]

@mimix_xyz @X @Tunnl_io good agency