Fathercryptos

Multifaceted Entrepreneur, YT Automations| i Build people use for free | Still Building

[Fathercryptos @Praizchris]

Use these 3 AI websites to simplify your work in #2023

1. https://t.co/jE51O2suh3
‍It takes automatic notes in virtual meetings.

2. https://t.co/M9kM8npJv0
Create high-quality content using AI in seconds.

3. https://t.co/4fN7cKIj6T
Krisp’s AI removes background voices, noise

[Scribbler @Defi_Scribbler]

my friend at the gym just showed me how he lands LinkedIn gigs

• he makes 5 figures/month as a SWE
• travels often
• barely posts on social media

I was shocked.

here’s what he does:

go to LinkedIn search and type:

Founder AND “we are hiring”

this shows founders actively hiring.

filter by US, UK, Estonia, Norway, KSA, UAE, Australia.

now you have direct access to decision makers.

instead of sending 100+ connection requests daily

• use Apollo
• install the extension
• find verified work emails

write a simple, direct cold email.

don’t send 10.

send 100.

the real secret is persistence.

he told me he applied for a gig via:

website → got rejected
emailed → got rejected
cold DM’d the founder → got the job ✅

most people stop after few attempt

you shouldn’t

jobs are scarce but non existent

[Praise Akinlami @Praiseakinlami]

Instead of watching another movie, watch this 3hr master class on how to use AI.

[Fathercryptos @Praizchris]

@oviosu @PayPal So Abasi na the connect thanks chief

But you see PayPal they’re still holding a lot of Nigerias hard earned money

[Jesse--The Data Guy @Mbadiwejesse]

If you don’t have an upwork account in 2026, start here:
https://t.co/7GeKBaIcKl

After creating your upwork account, proceed to the following classes

second class 

https://t.co/VN0twI9sl6

third class 
https://t.co/ZzDTKgHlAB

fourth class
https://t.co/uZvy4fkztR

[Fathercryptos @Praizchris]

@gyina_yie @grok which of these offers opportunity to courses related to geosciences or hydro-geology

[Dev Shah @0xDevShah]

Meta acquired @ManusAI. Not a model company, they acquired an environment company, and the distinction is important.

I have a solid argument favoring that intelligence cannot exist in isolation. It cannot be dissociated from the context and environment in which it operationalizes itself. Manus has internalized this completely.

Manus runs on Claude with its custom tools built for orchestration and grounding. Their agentic environment enables the agents to browse, write code, manipulate files, and execute multi-step workflows without human in the loop.

They also beat OpenAI on GAIA. An interesting thing here is that they didn't build a foundation model. They built the most compatible environment for models to reason and act within.

I'm coining a new term here: Situated Agency. Situated Agency is an idea that agentic capabilities are not intrinsic to the model alone, but they emerge from the coupling of a model with tools, memory, and execution environment. Manus is perhaps the first company to productize Situated Agency at scale. And now Meta owns it.

Actually, this changes everything.

Meta spent a lot of time struggling to build SOTA models. Llama 4 was a disappointment. Behemoth was delayed because it couldn't compete with other frontier models. They built the Superintelligence team. Acquired Scale AI. All attempts were made to close the gaps.

And now the execution layer.

Manus has achieved SOTA agentic performance without training a single model. They engineered the environments and let Claude handle the inference-time compute. Meta might be positioning to become an agentic infrastructure company, not a foundation model company.

Meta has -
> Billions of users generating real-world task data and feedback loops daily
> Rayban glasses and Quest headsets as interfaces for agents
> WhatsApp, Messenger, Instagram as mediums for task delegation
> Zuckerberg also mentioned that he is pushing for personal superintelligence on all wearables

None of this requires Meta to have the SOTA model on MMLU. It requires Meta to have the best execution environment for models to act on behalf of users.

The Avocado rumours become interesting here.

Avocado is Meta's tbd closed model, reportedly being developed under @alexandr_wang. If Manus's agentic systems are genuinely model-agnostic, which their architecture suggests, then nothing blocks Meta from swapping Claude for Avocado.

Manus already runs Claude and fine-tuned Qwen interchangeably, routing different subtasks to different models based on capabilities. The architecture abstracts the model layer behind a smartly engineered tool-calling interface.

This gives Meta a production-tested agentic environment with $125M ARR that they can gradually integrate. They inherit the execution layer, the context engineering IP, the sandboxed compute infrastructure, the customer feedback loops, then port it to Avocado when the model is ready.

Things could get hot if Meta fully commits to this thesis.

OpenAI is building vertically. Foundation models, custom chips, agent frameworks, consumer applications. Google is building vertically. TPUs, Gemini, search, workspace integration. Both are betting that owning the foundation model layer is essential to capturing value.

Meta could be betting the opposite. If Situated Agency is correct, then the best strategy would be to build the best orchestration infrastructure. Let others race to improve the SOTA models, and swap in whatever model scores highest on your agent benchmarks at any given moment.

This is how Android beat iOS in market share. Google didn't build the best hardware. They built the best platform layer for hardware makers to build on, then captured the market. Meta making the same bet on agentic AI fits with Zuckerberg's playbook.

Manus may be the first sign that suggests Meta is thinking this way about AI agents.

Congrats to Meta and the complete teams at Manus AI!

[Neha Singhal Trader @nsinghal211]

22 TECHNICAL ANALYSIS TOPICS YOU NEED TO LEARN.📊🎯

Bookmark it.👇🔖

1. Fibonacci Retracements
2.Breakouts
3.Reversal
4.Elliott Wave
5.Fair Value Gap
6.Candlesticks
7.Heikin Ashli
8.Moon Phases
9.Renko
10.Harmonic Patterns
11. Support and resistance
12.Dynamic Support and resistance
13.Trend lines
14.Gann Angles
15.Momentum Indicators
16.Oscillators
17.Divergence
18.Volume
19. Supply &Demand
20. Market Structure
21.BOS
22.CHOCH

[Avinash Singh @AvinashSingh_20]

Remote Startups List You Can't Miss If You're Looking for Remote Opportunities – https://t.co/RpMB8oMqIo

[Omoalhaja @omoalhajaabiola]

Companies hiring remotely NoW

https://t.co/aZ1ykdKT8f

[Equity Edge @EquityEdgeUK]

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[Equity Edge @EquityEdgeUK]

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[Fathercryptos @Praizchris]

Let’s do 50k followers already !!

[Equity Edge @EquityEdgeUK]

As we’re close to 34K followers, we’re giving away 50x 10K Instant Accounts!

How to enter:
1️⃣ Repost
2️⃣ Like
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Good luck 🚀🔥

[Equity Edge @EquityEdgeUK]

As we’re close to 34K followers, we’re giving away 50x 10K Instant Accounts!

How to enter:
1️⃣ Repost
2️⃣ Like
3 of our most recent posts (including this one!)

Winners will be announced 15th December (next Monday)

Good luck 🚀🔥

[Daniel🥷 @Danieltrades]

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48hrs🧭

[King Leo @CoachKingLeon]

Here’s the full document that explains how to legally pay 0% tax in Nigeria using the CAC + Company Income Tax structure.

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https://t.co/RM6gpCxQ1r

Join my WhatsApp channel
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Enjoy!

[Zach Wilson @EcZachly]

AI Engineering has levels to it:

– Level 1: Using AI
Start by mastering the fundamentals:
-- Prompt engineering (zero-shot, few-shot, chain-of-thought)
-- Calling APIs (OpenAI, Anthropic, Cohere, Hugging Face)
-- Understanding tokens, context windows, and parameters (temperature, top-p)

With just these basics, you can already solve real problems.

– Level 2: Integrating AI
Move from using AI to building with it:
-- Retrieval Augmented Generation (RAG) with vector databases (Pinecone, FAISS, Weaviate, Milvus)
-- Embeddings and similarity search (cosine, Euclidean, dot product)
-- Caching and batching for cost and latency improvements
-- Agents and tool use (safe function calling, API orchestration)

This is the foundation of most modern AI products.

– Level 3: Engineering AI Systems
Level up from prototypes to production-ready systems:
-- Fine-tuning vs instruction-tuning vs RLHF (know when each applies)
-- Guardrails for safety and compliance (filters, validators, adversarial testing)
-- Multi-model architectures (LLMs + smaller specialized models)
-- Evaluation frameworks (BLEU, ROUGE, perplexity, win-rates, human evals)

Here’s where you shift from “it works” to “it works reliably.”

– Level 4: Optimizing AI at Scale
Finally, learn how to run AI systems efficiently and responsibly:
-- Distributed inference (vLLM, Ray Serve, Hugging Face TGI)
-- Managing context length and memory (chunking, summarization, attention strategies)
-- Balancing cost vs performance (open-source vs proprietary tradeoffs)
-- Privacy, compliance, and governance (PII redaction, SOC2, HIPAA, GDPR)

At this stage, you’re not just building AI—you’re designing systems that scale in the real world.

What else would you add?

[Fathercryptos @Praizchris]

@BTC88PUNTA_ ,

[Fathercryptos @Praizchris]

@SputnikInt @grok how many united state presidents have come and gone and Putin is still president?