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
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
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
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!
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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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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?