Ed Vayn

#Technology, science, self-development, organizational methods, #SM, #SMM.
Following #business trends and developments.
Top 1% Kred.

[Ed Vayn @evgone]

New album from Wolves of Agra - Installment One (EP, 2022 Release) https://t.co/K63Hom5Exe via @YouTube #heavymetal #music #wolvesofagra

[Connor Bates @ConnorJBates_]

$SOXL Pretty wild.

From the April 2026 Follow Through Day to its highs, $SOXL rallied nearly 350%.

And now it has completely retraced that entire move, sitting 77% below its highs.

Massive round trip and a reminder of just how quickly gains can evaporate in leveraged ETFs.

[Novavax @Novavax]

#NEWS: Novavax continues to deliver through our partnerships with @sanofi and Takeda, with Nuvaxovid™ approvals in the U.S., European Union and Japan for the 2026-2027 vaccination season.

Read more: https://t.co/Ps609LHIAG

[Ryan Carter @Grow_withAI]

Don't spend 2 years trying to become an AI agentic engineer in 2026.

Andrew Ng, one of AI's pioneers, just shared a complete roadmap to go from beginner to building real AI agents.

1 hour. Completely free:

• 00:00 - AI agent fundamentals
• 12:12 - Agentic workflows & design patterns
• 53:27 - Practical strategies for building AI agents
• 1:20:30 - Self-improving agent loops
• 1:30:19 - Multi-agent AI systems

I watched it last night.

Halfway through, I realized how much faster you can learn this stuff when you follow the right roadmap.

Bookmark this. Watch it. Then build your first AI agent.

[Jimmy @jimmy_aibrief]

50 legendary websites that feel like the internet’s hidden toolbox 🧰

1. https://t.co/dTadi8WXEa — Free research papers
2. https://t.co/2dPp4541SX — Borrow books online
3. https://t.co/n6kOg9UQ2P — Free academic journals
4. https://t.co/YSKnnRxEzF — 70K+ free books
5. https://t.co/y4sLiWWaSP — Free textbooks
6. https://t.co/pYcx5MeuZf — Free online courses
7. https://t.co/GgZZmG4COX — Solve complex problems
8. https://t.co/vh9pIYZxnZ — AI research assistant
9. https://t.co/9JSenzybYX — Research-backed AI answers
10. https://t.co/7WUaC0laQ8 — Find research connections
11. https://t.co/6vLPOnNx1X — Academic search engine
12. https://t.co/XYi1MDFRmA — Understand research papers easily
13. https://t.co/9JMaUsDkRv — Free Photoshop alternative
14. https://t.co/aAhwJwxd8q — Compress & optimize images
15. https://t.co/D933WAHILq — Remove image backgrounds
16. https://t.co/tS1cRDBtl3 — Remove objects from photos
17. https://t.co/gFVKB5DHYj — Remove video backgrounds
18. https://t.co/zfeFtKIK4w — Beautiful code images
19. https://t.co/GYbeCSiGlN — Share elegant code screenshots
20. https://t.co/l43hJrGTdx — AI search engine for developers
21. https://t.co/HC8TaRskDO — Test and debug regex
22. https://t.co/sqqu2lIQ9B — Format & clean code
23. https://t.co/a3Pvg79qb8 — Format & validate JSON
24. https://t.co/xTZBd6T003 — Understand terminal commands
25. https://t.co/Nh0w742UKx — Stunning product mockups
26. https://t.co/FxWXbMGnxK — Powerful online mockup generator
27. https://t.co/TxIoy6nvCE — Find alternatives to any app
28. https://t.co/4kVNihC8Lw — Check data breaches
29. https://t.co/jtftC2cchU — Scan files & URLs for malware
30. https://t.co/QXYA6K88qC — Self-destructing text notes
31. https://t.co/y90Yfi11MN — Disposable temporary email
32. https://t.co/LhWr8ZrACS — 10-minute temporary email
33. https://t.co/PATmtbTaN6 — Temporary & secure file sharing
34. https://t.co/CGvxb6SB2e — Explore global live radio
35. https://t.co/h0uKV2sWdl — Discover similar music & artists
36. https://t.co/PNW23LP7E7 — Find songs from movies & shows
37. https://t.co/Vb9IcObRE9 — Focus music for coding
38. https://t.co/BciaHkPIUk — Custom background sounds
39. https://t.co/gB4WUHw8JQ — Ambient café sounds for productivity
40. https://t.co/scTXewpkDg — Find where to stream movies
41. https://t.co/z5BafNPha8 — The internet’s digital library
42. https://t.co/dVq5zdfhrL — Create a personal copy of any webpage
43. https://t.co/9JohLiVbME — Find related websites
44. https://t.co/XPDv47r62u — AI YouTube video summaries
45. https://t.co/Kl7Z2y9fJs — Ultimate smart bookmark manager
46. https://t.co/jcrYgh4FR8 — Check real-time service outages
47. https://t.co/jOohjiJlmT — Smart reverse image search
48. https://t.co/2pYszKdruY — Quick internet speed test
49. https://t.co/PzFts8BTbY — All-in-one PDF tools
50. https://t.co/YLOJ7jazKC — Merge, split & convert PDFs

Save this. You’ll definitely need some of these later! 🔖

Follow @jimmy_aibrief for more useful websites, AI tools & tech resources.

[David J Phillips @davj]

Top 5 angel investors in SF, NYC, LA, Boston, and Austin by # of deals:

San Francisco Bay Area
@naval — 272 deals
@eladgil — 217 deals
@gokulr — 203 deals
@Benioff — 186 deals
@paultoo — 171 deals

New York City
@fabricegrinda — 258 deals
@scottbelsky — 217 deals
@edyson — 123 deals
@wayne — 92 deals
@nbanta — 91 deals

Los Angeles
Daniel Curran — 185 deals
@aplusk — 71 deals
@spencerrascoff — 68 deals
@BrendanFWallace — 67 deals
@BaronDavis — 54 deals

Boston
@dharmesh — 76 deals
Paul Forster — 61 deals
@willherman — 57 deals
Jonathan Milner — 53 deals
Walter Winshall — 48 deals

Austin
Vishal Rao — 65 deals
@bradharrison1 — 50 deals
@merket — 30 deals
@brettahurt — 28 deals
David Bonderman — 20 deals
@maxaltman — 20 deals (tied for fifth)

[Dipanshu Kushwaha @Dipanshu_AI]

Don't waste 2 years learning to become an AI agentic engineer in 2026.

Andrew Ng, the godfather of AI, gave the complete playbook to become one from scratch.

1 hour course. Free:

• 00:00 - AI agent basics
• 12:12 - AI Agentic workflows & design patterns
• 53:27 - Practical tips for building AI agents
• 1:20:30 - self-improving AI agent loops
• 1:30:19 - multi-agent AI systems

I watched it last night.

Halfway through, I realized I could get into Anthropic in weeks, not years.

Bookmark now. Watch it. Then build your own AI agent

[Frank Curzio @FrankCurzio]

$NVDA's CEO is telling you to buy energy stocks.

"The largest infrastructure buildout in human history"

Data centers will go from 6% of US electricity to 20% by 2035.

Here are 6 stocks positioned to benefit: 🧵

[Joseph Tsar @joseph_tsar_]

Full Course on How To Communicate Brilliantly (by Joseph Tsar)

[The SI Maximalist @thesimaximalist]

tom lee looking at the 8000 target like it’s a rental listing in a safe neighborhood
https://t.co/QldWZcCLK3

[Arti Shah @TechByArti]

IF YOU DIED TOMORROW, YOUR FAMILY WOULDN'T BE ABLE TO ACCESS A SINGLE THING YOU OWN DIGITALLY.

BANK ACCOUNTS. PASSWORDS. CLOUD STORAGE. ALL OF IT PERMANENTLY LOCKED AWAY.

HERE'S HOW TO FIX IT IN 30 MINUTES:

[Lunar @LunarResearcher]

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering.

How to go from one LLM to coordinating 100 AI agents:

01:45 - Build an LLM from scratch
17:22 - Learn how to actually use AI models
30:03 - Master prompt engineering
52:35 - Coordinate 100 agents as one human

Most people are still learning how to prompt one model.

Jeff Dean is showing the full stack:

LLMs → Prompts → Agents → Agent Teams → Graphs

Prompting is the old workflow.

Coordinating agent systems is the new one.

This is 27 years of Google AI compressed into one hour.

Bookmark and watch it today

Then read the full Graph Engineering guide below ↓

[Yarchi @undefinedKi]

Everyone tells you to build a second brain. It is pointless until you understand how one actually works

The whole thing turns on one idea. Your documents are source code. The wiki is the compiled binary. You compile once and run the binary forever.

That is why the raw folder is never edited, why one paper comes out as a dozen short linked pages, and why every question after that reads the pages instead of the papers. Same reason nobody recompiles a program each time they open it.

Once you see it that way the rest follows on its own. The cleanup pass, the linking, the answers getting filed back in, none of it is a trick. It is what compilation always did.

Karpathy's version, drawn exactly as he described it, on one page above.

Bookmark this

[Mark Hyman, M.D. @drmarkhyman]

Do not eat these. Discard immediately if you recognize any of these in yours or a loved one’s kitchen, it’s the easiest act of love you can do for them today.

A French study in PLOS Medicine examined the impact of emulsifiers (food additives used to improve texture and extend shelf life) on cancer risk.

Researchers analyzed data from 92,000 adults over nearly 7 years and found that certain emulsifiers significantly increased cancer risk:

Mono & diglycerides of fatty acids (E471)—found in processed baked goods, margarine, and ice cream—were linked to:
15% higher risk of overall cancer
24% higher risk of breast cancer
46% higher risk of prostate cancer

Carrageenan (E407 & E407a)—a thickening agent in dairy alternatives, deli meats, and plant-based products—was linked to:
32% higher risk of breast cancer.

Cancer is complex and multifactorial, but one thing is certain: the food we eat plays a massive role in shaping our long-term health. Cutting out ultra-processed foods and focusing on whole, nutrient-dense foods is one of the most powerful steps you can take to reduce disease risk.

Thank you to my friend Vani Hari for this image + bringing awareness to this critical issue.

[Max Mynter @MaxMynter]

You asked, I deliver (2 weeks late).

A blogpost about what I did over the last 18 months to sign as a research engineer at a foundation model lab.

Link and summary below

1/n

[Noah Frydberg | Tiktok Shop For Brands @maverickecom]

BREAKING: Anthropic released Skills for Claude.

Most ecom brands will use them like fancy prompts.

I built them into an operating system that helps run my ai affiliate campaigns 👇

Here’s the difference:

A prompt is something you retype every week. A skill is a module Claude already knows. You call it by name, point it at a brand, and it runs.

Built on Opus 4.8, so it handles the longer, multi-step work a real content engine actually needs.

Here's what's in the system right now:

AI UGC modules:
→ brand-rulebook, authority-figure, hook-library
→ script-writer, persona-roster, account-warmer
→ caption-seo, comment-to-dm, compliance-check
→ campaign-tracker, winner-finder

And here's what makes it a system, not a folder of prompts:

We call one module, point it at a brand, and it runs. Reads the data, does the job, returns the fix.

winner-finder → last 7 days → scale the hooks that hit 10K views. One call. Done.

No retyping the prompt. No re-explaining the brand. No copy paste from twenty docs. The same locked persona, the same compliance lines, the same hook library, every single time.

The shift nobody has priced in yet:

A prompt works once. A skill works every time, the same way, forever.

That is the line between using Claude and building with it.

Knowing how to use Claude like a C-Suite employee is enabling small brands to scale like the big boys.

Want the full AI UGC operating system, every module plus how to wire it? It's free:

1️⃣ Comment "METHOD" below 💬
2️⃣ Add me
3️⃣ Like this post 👍

Then it will auto send. (Make sure you are following)

[Aakash Gupta @aakashgupta]

Half of what a PM does today is already an agent's job. Most just haven't run the audit yet.

Take any PM workflow and break it into atomic steps. Sort each one into two buckets. Human judgment: taste, strategy, relationships, the calls only a person can own. Information processing: following a pattern, moving data, applying a rule, producing a deterministic output. The processing bucket is always bigger than people guess, and it's exactly what an agent runs with almost no friction.

So the role splits. The judgment half stays human and gets more valuable. The processing half becomes something you can hand off this week for the price of a few coffees: user feedback synthesis, competitor monitoring, weekly metrics rollups, PRD first drafts, release notes, meeting prep, interview synthesis. None of it needs a sprint. It needs a trigger, the right tool tier, and one human review step before anything reaches a customer.

That split is the death certificate for the old operating model. Judgment concentrated into fewer, more valuable hands. The processing moved to agents. The PM still doing the processing half by hand is now competing with the PM who automated it and spent the recovered hours on strategy and go-to-market.

The on-ramp is almost insultingly cheap. Free to start, about $30 a month to run something real. The only barrier left is noticing the job already moved.

Full teardown of how the PM, engineer, and designer roles get rebuilt: https://t.co/IJA1QkT99m

[Chris Parjaszewski @cparjaszewski]

Lesson 1: Speak the customer's language, not your product's.

"You knock, the door opens — people want to talk. Your problem won't be opening the door. It will be: can you speak the language?"

If you didn't come from the industry, take the time to learn it.

[Alex Lieberman @businessbarista]

I built a content machine.

It turned me into a one-person media company, has driven tens of millions in pipeline for @tenex_labs, and is allergic to AI-slop.

It has also turned all of my employees into content creators.

I may opensource the whole thing, but for now, I'm going to share how I built it & how it works.

Feel free to copy & paste the steps to Claude/Codex if you want to build your own content machine.

Step 1: Map out the process

In order to make any of your work AI-native, you need to understand the way in which it's been done historically. This is why business context & domain expertise REALLY matters, even in a post-AI world.

Content has been my bread & butter for the last decade, so I started by pulling out an 8.5x11 sheet of printer paper and drawing the traditional process.

1) Look for inspiration
2) Pick a 10x content idea
3) Research the idea
4) Brain dump all of my thoughts about the idea
5) Decide the post format I want to create
6) Create a draft of the post
7) Edit the post
8) Create derivative versions of the post
9) Go live
10) Track performance

Step 2: Where am I needed vs. not needed?

I am needed for the first & final mile:

First mile: picking the idea/direction & providing all of the necessary context
Final mile: going through the final draft with a fine tooth comb & giving final sign-off.

AI can handle the rest:

Looking for inspiration, researching the idea, pulling my thoughts out, writing the post, doing a first edit, creating derivative content, and tracking performance.

Step 3: Build the Content Machine

The machine is one pipeline, run end-to-end or step-by-step. It is a directory of skills that mimic the steps in the content process that I've delegated.

1) The Oracle [AI]
Mines my Slack, Notion, call transcripts and Gmail for spikes, moments I naturally said something worth expanding, while the Internet Reader curates an external feed of X accounts & websites I've selected.

Qualifying ideas (≥6/10) are written to The Vault (a notion database of content ideas).

2) Select the idea from The Vault [Human]

3) The Researcher [AI]
Before any interview, build a sourced research-report.md: TL;DR, key facts with links, current developments, what's already been said, contrarian angles, and open questions for the interview. Claims are adversarially checked; fact is separated from opinion.

4) Interview Panel [AI + Human]
Six world-class interviewers (Joe Rogan, Howard Stern, Michael Barbaro, etc) ask 12–15 questions, one at a time, each pushing a different dimension...and never satisfied with vague answers. Won't advance without 2–3 specific stories, real numbers, and emotional specificity.

5) Production [AI]
The interview becomes a raw .md file: transcript, key stories, core insights, quotable moments, emotional anchor, surprising reveals, and the "so what." This raw file is sacred: my exact words, never paraphrased away.

6) Refinement [AI + Human]
I tell the machine what content type I want to create. It reads my custom style guide + past feedback lessons + content-type spec, then drafts in my voice...pulling real stories and quotes from the raw file. The #1 rule: write like you're texting a friend. Supports long posts, LinkedIn, X threads, and more.

7) Writer's Council [AI]
Six expert reviewers (Shaan Puri, Morgan Housel, David Perell, etc) score the draft through their own lens, splitting fixes into editorial (the machine can rewrite) and information gaps (only the creator can answer...these route back to the interview panel).

8) Revision Loop [AI]
Iterate until council scores 9/10.

9) Repurposing Engine [AI]
One anchor → 10+ natively-formatted derivatives, each re-hooked for its platform and each held to the same full Council → revision bar of 9/10. This is how two people produce like a hundred.

10) Final revision [Human]

11) Learning Loop [AI]
After approval, the machine compares first draft vs. final, extracts confirmed lessons, and saves them to that creator's content-lessons.md. Every future first draft starts smarter. Lessons override the style guide when they conflict.

Feel free to steal the machine & ask me any questions about how it works!

[Dan Holbrook @DanHolbrook]

@PeterDiamandis Most people quit before the proof becomes visible. They mistake delay for failure because they cannot see what their discipline is already producing. The future usually arrives after the weak have already talked themselves out of reaching it.