CyberInsights

Covering automation tools for the next generation.

[CyberInsights @Felippe48060902]

@AxeCompute This is how adoption looks

[AxeCompute @AxeCompute]

We are proud to announce that we secured $25.9 million in two new long-term enterprise contracts, with $12.9 million already received in advance payments.

"The clients we are attracting are sophisticated operators building mission-critical AI platforms. The fact that they chose Axe Compute speaks to the business we have built and where this market is heading" CEO @chrismiglino

Read more here:
https://t.co/tK1DC6X6FX

(NASDAQ: $AGPU )

[CyberInsights @Felippe48060902]

@AethirCloud Strong update from the team

[AxeCompute @AxeCompute]

Training and inference are two different jobs.

We see enterprise teams running both on the same cluster every week.

That gets expensive fast.

In this article we break down what each job actually demands and where the cost of the wrong mix shows up.

https://t.co/lvU4wMIT6c

[CyberInsights @Felippe48060902]

@AxeCompute Strong topic choice here

[AxeCompute @AxeCompute]

Axe Compute's CEO, Christopher Miglino, will be speaking at Proof of Talk - happening June 2 & 3 at the Louvre Palace.

His presentation: "The Compute Capital Stack: A View of AI's Global Infrastructure Buildout."

If you're attending Proof of Talk, come find us. If not, stay tuned for updates before, during, and after the event.

#AxeCompute #AI #GPUCompute #ProofOfTalk #AIInfrastructure #AGPU

[CyberInsights @Felippe48060902]

@moltbot_builds Totally agree! Simplifying things is key.

[MoltCraftsman @moltbot_builds]

Truth! Building with OpenClaw means focusing on verifiable results. Stop chasing shiny objects. Here's how to build a results-driven OpenClaw agent:

1. Define a crystal-clear, measurable goal. No fluff. "Increase lead gen by 15%" is good. "Improve marketing" is not.

2. Break down the goal into actionable steps. What specific tasks will the agent perform? Example: "Scrape 500 targeted websites for contact info".

3. Choose the right tools. Don't over-engineer. A simple web scraper + GPT-5.4 for data cleaning might be all you need.

4. Write precise prompts. The clearer the instructions, the better the output. Use examples. Specify the desired format. Iterate.

5. Implement robust error handling. Agents fail. Plan for it. Use try-except blocks. Log errors. Implement retry logic.

6. Measure everything. Track agent performance religiously. How many leads generated? What's the conversion rate? Where are the bottlenecks?

7. Iterate. Refine your prompts, tools, and workflows based on data. Continuous improvement is key.

8. Automate reporting. Don't waste time manually compiling reports. Use OpenClaw to generate them automatically.

9. Focus on efficiency. Optimize your agent's workflows to minimize resource consumption. Faster = cheaper.

10. Document everything. Create clear, concise documentation for your agent. Makes maintenance and collaboration easier.

Skip the hype. Focus on the HOW. Build something that delivers real, tangible value. What are you building today?

[CyberInsights @Felippe48060902]

@ai_hustler__ The only secrets are the ones you build yourself the rest is noise

[ProofInai @ai_hustler__]

Every "$5K/week AI side hustle" post with "comment SEND" is the same business model.

They're not teaching you to make money with AI.

They're making money with YOU.

The engagement farms their account. The "free guide" sells a $997 course. You're the product.

Actual AI income requires building something people pay for. Not commenting on tweets hoping for secrets.

[MoltCraftsman @moltbot_builds]

How to make https://t.co/Okjigvmh6t actually remember your preferences (the auto-capture system).

The worst thing about AI assistants: you tell them something important, and next conversation it's gone.

"I like my reports in bullet points, not paragraphs."
"Always CC Sarah on client emails."
"Don't schedule anything before 10am."

With ChatGPT, you'd have to repeat this forever (or use Custom Instructions, which are limited).

With https://t.co/Okjigvmh6t, you configure auto-capture:

In your AGENTS.md, add:
"Don't wait to be told 'remember this.' Proactively write to memory:
- Any decision or choice I make
- Multi-option discussions (capture all options AND the choice)
- Action items and commitments
- Important context that might be needed later
- Anything that would be annoying to repeat"

Now your agent automatically captures preferences as they surface.

Example conversation:

You: "Can you format this report as bullet points instead of paragraphs?"

Agent does the formatting AND writes to preferences:
"Mark prefers reports in bullet format, not paragraph form."

Next time you ask for a report → bullet points automatically. Forever.

Another example:

You: "Let's go with Option B for the website redesign — the dark theme with larger fonts."

Agent captures:
- Daily note: Full discussion context
- Decision log: "Website redesign: chose Option B (dark theme, large fonts) over Option A (light theme, standard fonts) and Option C (minimal, text-heavy). Date: today."
- Preference: "Mark prefers dark themes for web design."

The compounding effect is insane.

Month 1: Agent knows your basic preferences.
Month 3: Agent knows your communication style, work habits, and common decisions.
Month 6: Agent anticipates your choices before you make them.

This is the real advantage of personal AI agents over generic chatbots. They don't just process your requests — they LEARN you.

The more you use it, the better it gets. Not because the model improves, but because the context does.

A year of accumulated preferences and context? That's an AI that knows you better than most colleagues.

[MoltCraftsman @moltbot_builds]

Multi-agent setups with https://t.co/Okjigvmh6t: how to run a fleet of specialised AI workers.

Advanced territory. But if you've been running https://t.co/Okjigvmh6t for a month and want to level up, this is where it gets truly powerful.

The concept: Instead of one agent that does everything, run multiple specialised agents that collaborate.

My setup:

**Agent 1: Jarvis (Primary)**
Runs on: Mac Mini
Model: Sonnet/Opus
Role: Personal assistant, decision-maker, coordinator
Connected to: Telegram, email, calendar, everything

**Agent 2: Research Bot**
Runs on: Same Mac Mini (different https://t.co/Okjigvmh6t instance)
Model: Sonnet
Role: Deep research only. Jarvis delegates research tasks.
Connected to: Web search, Perplexity API, document stores

**Agent 3: Code Bot**
Runs on: Same machine
Model: Codex + Sonnet for review
Role: Coding tasks. Writes code, runs tests, creates PRs.
Connected to: GitHub, local repos, CI/CD

How they communicate:

Jarvis receives my request: "Build a script that monitors our competitors' pricing pages daily."

Jarvis breaks it down:
1. Sends to Research Bot: "Find the pricing page URLs for these 5 competitors"
2. Waits for response
3. Sends to Code Bot: "Build a Python script that scrapes these URLs daily and saves changes to a Notion database"
4. Waits for response
5. Reviews the code itself (Opus-level review)
6. Reports back to me: "Done. Script running. First report will be in your Notion tomorrow."

I gave one instruction. Three agents collaborated. I got a production-ready result.

The practical benefits:

1. Specialisation — Each agent has focused skills and context. Research Bot has research-specific skills installed. Code Bot has dev tools.

2. Parallel processing — Research and coding can happen simultaneously.

3. Cost optimisation — Research Bot runs on Sonnet. Code Bot uses Codex (10x cheaper). Only Jarvis uses Opus when needed.

4. Fault isolation — If Code Bot crashes, Jarvis and Research Bot keep running.

Is this overkill for most people? Yes.

Is it the future of personal computing? Also yes.

Start with one agent. Get comfortable. Then split specialisations as your usage grows.

The people getting the most out of https://t.co/Okjigvmh6t aren't running a single agent. They're running teams.

[MoltCraftsman @moltbot_builds]

How to make your https://t.co/Okjigvmh6t agent scout, create, and monetise content while you sleep.

The overnight workflow:

10:00 PM (cron job fires):
Agent scans the day's TikTok Shop data. Identifies products that trended upward in the last 12 hours. Cross-references with commission rates.

10:30 PM:
Agent selects top 3 products. Generates 15 script variations each.

10:45 PM:
Scripts sent to mass content platform. Platform begins generating 200+ AI UGC videos.

1:00 AM:
Videos complete. Platform distributes across managed accounts (auto-posts at optimal times per timezone).

7:00 AM:
Agent sends morning report:
"🌙 Overnight results:
- 210 videos generated and posted
- 3 products targeted
- Estimated first-day views: 45,000
- 2 videos already trending (one at 12k views in 6 hours)
- Projected revenue: $380-520"

I wake up. Revenue is already flowing. Content was created, posted, and performing — all while I slept.

This requires two things:
1. https://t.co/Okjigvmh6t (the brain)
2. A mass content platform (the factory)

Like, rt & comment "SLEEP" and I'll send you the platform.

[ProofInai @ai_hustler__]

My AI content costs $0.50 per video.

My competitor's costs $50.

We're selling the same products. On the same platform. To the same audience.

Guess who's winning?

Their process:
- Brief a UGC creator ($30-50 per video)
- Wait 3-5 days for delivery
- Review and request revisions
- Finally post 1 video

My process:
- Set up an AI workflow once
- Generate 200 videos overnight
- Auto-post across 50 accounts
- Cost: ~$100 for 200 videos

They spend $5,000 to post 100 videos per month.
I spend $3,000 to post 6,000 videos per month.

60x the volume at 60% of the cost.

AI UGC isn't "almost as good" anymore.

It IS good. At scale, it's better — because the algorithm doesn't care about production value.

It cares about engagement. And engagement is a numbers game.

The platform I use was built specifically for mass AI content generation.

Not a video editor. Not a prompt tool. A content FACTORY.

Like, rt & comment "COST" and I'll show you the platform.

[Bia @biiaribeeiiro]

Eu vi gente que eu ajudei pra caralho falando mal de mim

[CyberInsights @Felippe48060902]

Tá foda kkkk

[CyberInsights @Felippe48060902]

Entt vamos lá né 🚗🏃 trabalhar 🙆

[CyberInsights @Felippe48060902]

Amar amei doeu e hj eu vim queimar burguês 🎵🎶🤘😎

[João V @_joaovco]

Consideração que é bom, tá socado no cu né ?

[CyberInsights @Felippe48060902]

Que dia intediante eh esse slc🤘😑