:: Father of 4 &&&& 👉🏻 AI
Оn-chain / Based 👉🏻
Joined October 2008
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Vlad retweeted
🇺🇸 Microsoft just showed 60,000 employees the exit door... and asked them nicely to walk through it.
The company is offering voluntary buyouts to workers in the U.S., Canada, India and the UK, all the way up to vice president level.
That's about 27% of its entire workforce of 223,000.
Meanwhile, its spending on AI data centers is expected to top $50 BILLION in a single quarter, up from $34.9 billion a year ago.
The servers are getting the raise, the humans are getting the buyout.
Source: CNBC / Writer: Julie
Vlad retweeted
This is f*cking insane. SpaceXAI senior engineer (ex-Cursor):
"99% of people open one GrokBot, ask it a question and close the tab
I run 50+ GrokBot agents like a company. A Chief of Staff runs my day, 3 PMs hand out the work, 40+ workers ship it overnight, and I just check the results over coffee. That team does the job of 20 engineers"
In this 1-hour workshop, SpaceXAI builds that GrokBot team from scratch, step by step
GrokBot → Chief of Staff → PMs → 40+ Workers → Autonomous Company
At SpaceXAI, 97% of engineers already run a team like this, and the ones who do are getting $750K+ offers. The rest are about to compete with them
Worth more than any $5,000 agentic engineering course
Bookmark it and watch today
Then read how to build your own team of GrokBot agents in the harness engineering guide below ↓
Vlad retweeted
10 GitHub repositories to learn the Agentic AI concepts you need before 2027 👇
1- Harness Engineering
github.com/walkinglabs/aweso…
2- Loop Engineering
github.com/cobusgreyling/loo…
3- Context Engineering
github.com/yzfly/awesome-con…
4- Tool Design & MCP
github.com/modelcontextproto…
5- Agent Memory
github.com/mnemoverse/awesom…
6- Agent Orchestration
github.com/vivy-yi/awesome-a…
7- AI Guardrails
github.com/enguard-ai/awesom…
8- Agent Evals
github.com/benchflow-ai/awes…
9- Human-in-the-Loop
github.com/cloudflare/agents
10- Agent Observability
github.com/anhermon/awesome-…
These cover the production side of agentic AI:
→ Harness
→ Loops
→ Context
→ Tools
→ Memory
→ Orchestration
→ Guardrails
→ Evals
→ Human oversight
→ Observability
✅ Save this for your Agentic AI learning journey.
Join the tech community for more resources:
whatsapp.com/channel/0029Va9…
Follow for more developer resources, AI tools, interview prep, jobs & opportunities.
Vlad retweeted
One of my friend makes $1.2 million a year as an Anthropic engineer.
I asked him how he learned prompting so well. He sent me a video that was never supposed to get out. Their core team's prompting playbook.
You won’t find anything better about prompting than this 30 minutes video.
I watched it last night. Halfway through, I realized I've been using Claude completely wrong for two years.
Watch it, then read the article below.
Vlad retweeted
🇮🇶🇸🇾 Iraq's workaround for the Strait of Hormuz is to full-send tanker trucks packed with oil over the border into Syria towards Mediterranean ports
And it looks fking insane
Writer: Ian
Readers added context they thought people might want to know
This appears to be an AI generated video, with an implausibly sideways car at the end and more trucks than are likely available for this route.
The roads look somewhat different:
bloomberg.com/news/articles/…
Vlad retweeted
🇷🇺 He shoved the store robot, then the robot came for him.
It wasn't a cute glitch or a stumble, the staff LITERALLY had to grab it and drag it down while it kept moving.
This kind of clip used to be funny... now its not.
Writer: Val
Vlad retweeted
Andrew Ng just released a 2-hour course on full Graph Engineering.
How to go from one prompt to 100 agents that loop, improve themselves, and run without you:
0% → 09:14 - build your first AI agent
25% → 33:11 - run agents with loop engineering
50% → 1:02:46 - turn agent loops into graphs
75% → 1:30:15 - build agents that rewrite themselves
100% → 1:49:05 - run the entire graph system without you
Most people are still building one agent and calling it done.
Andrew Ng is already teaching what comes next:
Prompt → Agents → Loops → Graphs → Self-Improving Systems
Single agents are the old workflow.
Systems that improve and run without you are the next one.
This 2-hour course is worth more than most $500 agent engineering courses.
Bookmark it and watch before everyone starts catching up.
Then read how to run 1,000 agents from one prompt below ↓
Vlad retweeted
a community member built a fully private research portal, and the engine inside it is a headless Hermes Agent (we love it!)
type a query into a local web UI, and behind it a Hermes run does the whole research pass:
searching, fetching and crawling through a MCP server , then writing you a dated, styled HTML report. everything stays yours.
this is my favorite thing about Hermes Agent: it runs headless inside other software just as happily as it chats with you.
github.com/rpmalouin/AI-Rese…
Vlad retweeted
🚨 JD VANCE DECLARES THEY’RE ENDING THE DOLLAR AS RESERVE CURRENCY
JD Vance just confirmed it: the dollar’s global status is over because it lets Americans “consume too cheaply.”
This is not a casual statement to be made.
Every crisis was financially engineered to force the new system, just like 1971 when they killed gold convertibility. Even the COVID money printing came when the U.S. Repo market was about to collapse in 2019.
JP Morgan CEO warned that U.S. dollar will no longer be a reserve currency within the next 25 years.
China’s been on a massive gold-buying spree. Every central bank is doing the same. South Korea just started buying gold after 13 years.
Japan is the biggest threat.
BoJ’s Yuto apologized for the measures being prepared. The BoJ and finance minister threatened of bold actions to save the yen.
Then Boom, the US Treasury intervened and effectively took control of BoJ operations to stop Japan dumping its $1.4T in Treasuries, which would collapse the entire US debt market and trigger a global liquidity crisis.
Scott Bessent even warned the Fed: expand the FIMA facility for Japan… or watch the U.S. Treasuries implode.
Central banks, IMF, BIS and the biggest financial institutions engineer the financial crisis to get out of the mess they made.
Fiat Money is next.
Vlad retweeted
Anthropic just released a 4-hour course to getting a $500k AI engineering job:
00:15 - The right way to prompt Claude
33:21 - What makes Claude act dumber on your code
01:33:39 - How Anthropic use Claude every day
02:50:56 - The fix that makes Claude way smarter This
4-hour Anthropic free course replaces about 10 paid engineering courses.
Watch it today, then read the step-by-step guide on building loops below.
Vlad retweeted
Andrej Karpathy spent 8 years working at OpenAI and Tesla
Last week, he condensed everything he knows into one free 2-hour lecture
People spend $15K on bootcamps that teach less than half of this
You probably don't have two hours to watch it right now
Just don't let it disappear in your feed
Save it, watch it later, then read the guide below and build your first loop
Vlad retweeted
Ilya Sutskever:
“My document gently flowed to where AI is today - and agents are the future”
He wrote the paper that made LLMs like Claude and ChatGPT possible - and explains it from scratch in 20 minutes
Never touched AI or use it every day - you need to watch this course
Watch - bookmark, then put the entire lecture into practice with the article below ↓
Vlad retweeted
A Chinese developer just explained the shift from Loop Engineering to Graph Engineering better than anyone.
most people are still building agents the way that's about to be obsolete.
> why single-agent loops break and go "goal blind"
> the 4 parts of a graph: nodes, edges, state, policy
> 3 topologies that run everything: diamond, supervisor, pipeline
> Anthropic's 5 official workflow patterns
the punchline: it's not how many agents you run. it's the determinism you build with verifiers, code fallbacks, and reality anchors.
I broke the same architecture down with Kimi K3. Full A-Z guide below.
Vlad retweeted
This is the best 2 hours on graph engineering ever recorded, Andrew Ng breaking down how to build agentic knowledge graphs from scratch:
9:14 - your first working agent
33:11 - how loops actually work
1:02:46 - turning loops into working graphs
1:30:15 - agents that improve their own code
1:49:05 - a system that runs the whole thing for you
I've seen $500 courses that teach less than this video alone.
Watch it, then take it further with my step-by-step guide on graph engineering below.
Vlad retweeted
this is f**king dangerous
A guy built a system of 7 Claude agents on his MacBook.
No assistant. No sales team. No office.
Every day it scans Google Maps across 3 cities, finds small businesses with no website or one from 2014, builds a landing page mockup, renders a 10-second video of it, and sends a personalized cold message — before he wakes up.
47 clients a month. $400 each.
$18,800/month. $480 in API costs.
Traditional web agencies run 8-person teams for the same order flow.
He runs it alone from a MacBook and an iPhone.
When a positive reply comes in while he's in a taxi, his Mobile agent books the Zoom call. He taps "approve" and joins 10 minutes later.
The only time the system wakes him is when a deal breaks $3,000 or the reply rate drops below 12%.
Everything else runs without him.
Here's the complete playbook for building such $10K/month passive income machine with AI ↓
Vlad retweeted
Andrej Karpathy’s 1-hour Stanford lecture on AI engineering is one of the best explanations I’ve seen of how AI systems actually work.
The progression is simple:
10% → LLM
30% → Prompt
50% → Agent
70% → Loop
100% → Graph
The key takeaway:
AI engineering isn’t just about writing better prompts.
It’s about building systems around models — giving them context, memory, tools, feedback loops, and data flows.
“Delete everything, keep Graph.”
Definitely worth watching if you’re building with AI agents.
Watch → Bookmark →
Vlad retweeted
Anthropic’s unreleased Model 2 is only 22.2% points below the CoBench v2 score it says a system capable of fully substituting for its research staff should achieve.
Model 2 scored 62.8%. Anthropic estimates that a truly substitutive system would score at least 85% and these results were already a month old when the report was published.
RSI is nigh.
Anthropics new Model 2 doesn’t just slightly outperform Mythos 5 as they stated in the report. It scores 62.8% on Anthropic’s internal CoBench v2 AI R&D benchmark, vs 50.3% for Mythos 5 and 54.8% for Mythos Preview.
Also keep in mind the reports date is July 15, so these results are already a month old snapshot of Anthropic’s internal capabilities. Their current internal frontier is most likely further ahead.
Vlad retweeted
Demis Hassabis:
"These days, one person who truly knows AI can outperform an entire startup team."
I've watched hundreds of AI talks, this 60-minute Cambridge lecture is the one I wish I had seen a year ago.
A Nobel Prize winner and the CEO of Google DeepMind just told you where this goes.
The person who outperforms a whole team isn't smarter, they just know their tools deeper.
Watch it, then read the full breakdown of the Claude features 99% never find below.
Vlad retweeted
Don't waste 2 years figuring out how AI agents actually work.
ex. Anthropic & Google engineers at Stanford just dropped a 1-hour lecture on self-improving AI agents:
• 00:00 - Introduction to self-learning AI systems
• 31:22 - inside a LLM: self-correction, backtracking
• 42:24 - goal → loops → graphs→ feedback: the Agent loop
• 45:47 - 6 agent orchestration patterns
• 50:37 - the generator-verifier gap - why self-improvement stalls
Planning, multi-step reasoning, self-correction. That's the entire agent roadmap for 2026.
Bookmark this & give 70 minutes today. Then read the article below.
