Kris

Intelligence is the ability to avoid doing work, yet getting the work done. -Linus Torvalds;
C++/C# Enterprise Developer exploring AI innovation...

[Todd Spence @Todd_Spence]

In 2003, THE SIMPSONS cast appeared on Inside The Actors Studio and answered host James Lipton's questions in the voice of all of the iconic characters 😂
I attempted to edit down the segment but it was all just too good so here it is in its entirety.

[Kris @KrRyMa]

@aeronxbt 720

[Michel Lieben @MichLieben]

Our Head of Growth runs $300K+/mo in paid ads without opening a single ad manager. Three platforms, one terminal, 12 custom Claude Code skills.

200+ hours of work off real client campaigns. Some clearing 4X ROAS on $1M+ spent. 40+ strategy files, 39 Python scripts hitting the platforms directly.

My bet: the agency that codes its ad-ops owns the margin everyone else burns in clicks.

The whole thing is 4 Claude Code skills:

> /linkedin-ads (15 strategy files, 14 scripts): full-funnel framework with audience-sizing rules, 6 campaign structure models, ABM 1:1/1:few/1:many playbook, 35-item audit checklist, creative by awareness stage. Scripts pull demographics across job function, seniority, company size, industry, country.

> /meta-ads (16 strategy files, 12 scripts): the Meta Ads Operating System, Creative Cadence OS, Pixel + CAPI setup, ABO to CBO to Advantage+ progression, creative fatigue detection. Scripts handle full creative-copy extraction across link, video, carousel, dynamic formats.

> /google-ads (9 strategy files, 13 scripts): the intent ladder, account structure split by intent, Phrase/Exact then Broad keyword progression, Performance Max guardrails, search-terms wasted-spend finder.

> /onboarding: 5-minute interactive setup. Walks credentials platform by platform, tests connections.

Underneath all three sits the foundations layer: the 5-Stage Demand Engine (replaces TOFU/MOFU/BOFU), budget allocation by stage and channel, voice-of-customer copy framework, scaling quadrant, optimization signals split between leading and lagging.

On each platform, one job at a time:

> Google: pulls 30-day search terms, queues every query that spent money with zero conversions as a negative once you approve, audits quality scores in the same loop.

> Meta: click-through fatigue watch across every live ad. Audience oversaturation flag.

> LinkedIn: 35-item account audit, then demographics on the top campaign. The job titles and company sizes that convert.

Read-only until the diff matches what Ivan would've done by hand. Then negatives, then fatigue, then bids.

Weekly pass: Ivan runs the zero-conversion sweep on Google, then the fatigue check on Meta, then pacing. Diff against last week's run.

Comment "ads" under the article and we'll send the repo. Must be following.

[Miles Deutscher @milesdeutscher]

The smartest thing you can do with Hermes right now:

Create a self-evolving loop that trains Her,es on your data - so every session is smarter than the last.

Session 1 → learns → logs it → Session 2 is smarter → logs that → repeat

Here's exactly how to set it up:

1. Create a Memory.md file on your desktop

Structure it like this:

## Preferences
## Corrections
## Patterns
## Lessons learned

2. Attach it to Hermes

Paste this prompt once:

"At the start of every session, read Memory.md and apply everything in it.

After every task, do three things:

• Log what worked and why
• Log what failed and why
• Analyse and write rules you'd apply next time

Never duplicate entries. Rewrite existing rules when you learn something better."

3. Once a week, run this prompt:

"Review everything in Memory.md. Identify patterns across all your logged lessons. Distill them into sharper, more general rules. Delete anything that's been superseded. The goal: fewer, better rules every week."

4. Archive before every cleanup

Copy Memory.md into a dated backup before your agent rewrites anything.

(In case of any mistakes, this is how you secure previous data)

That's the self-evolution loop that everyone should be running with Hermes right now.

Super simple, but it makes Hermes 10x more powerful.

Make sure to bookmark this so you actually maximise your agentic workflows.

[Miles Deutscher @milesdeutscher]

This chart should fire you the f*ck up.

We live in a time where literally anyone with a laptop + WiFi can make life-changing money.

30 years ago, this required a full team, deep pockets, the right network, etc, etc.

Don't get me wrong - these things are still incredibly important (especially if you want to build a true empire), but the average person can now make $10-20k/mo completely solo.

What an exciting time to be alive.

The barrier to entry has never been lower, and the ceiling has never been higher.

[Rahul @sairahul1]

Sam Altman:

"We're going to see 10-person billion-dollar companies pretty soon."

"If I were 22 right now, I'd feel like the luckiest kid in history."

Most people will read this, feel inspired for 3 minutes, and go back to what they were doing.

The ones who act will build a one-person company this weekend.

One tool. Claude Cowork. Full operation.

This is the exact playbook ↓

[Grant @iamgrantowen]

I asked @danmartell to walk me through every level of making money with AI.

He gave me the most simple, practical advice I've ever heard on this subject.

Level 1 - Making $0 - $100k
Level 2 - Making $1m - $10m
Level 3 - Building a $10m++ enterprise.

0:00 Only 5% of the World Has Ever Paid for AI
0:46 The Easiest Thing to Sell With AI Right Now
1:56 The Marcus and Sophie Framework
4:24 Theory of Constraints (Right Problem to Solve)
5:33 What Is the Number One Business Constraint
7:13 How to Leave Your Job and Go All In
8:27 Business Is Simple Find a Problem and Solve It
9:08 Stop Getting Ready to Get Ready
9:33 The Sarah Story One Text and $10K
9:53 Pull Up Your Phone and Message Your Contacts
11:05 Dan's Son Gets His First Client at $800/Month
12:41 Best Employee vs. Best Employer
13:59 What Other Services Can You Sell With AI
14:44 Sales Is Not Talking It's Asking
17:01 What to Do When You Hate Your Business
18:40 Pain and Pleasure Are the Only Two Motivators
19:13 They Haven't Made It a Must Yet
20:29 Make It a Must Not a Nice to Have
21:06 The Jen Story and the Gasping Moment
22:17 How to Find Your First 10 to 15 Clients
28:38 The Personal Brand Play
33:06 Vision Is What AI Cannot Do
34:55 Hard for Computers Easy for Humans
36:13 Level 2 Making Your First Million With AI
37:18 The Replacement Ladder Framework
37:39 Admin First Then Delivery Then Marketing
39:09 Why Marketing Is the Biggest AI Category
39:32 Why You Should Keep Sales for Yourself
40:00 Level 5 Leadership and AI Agents
41:41 What a Fully AI Systems Business Looks Like
43:13 The Gym Owner With Three Locations
46:16 Shutting Down the Company for Two Days
46:37 Teaching the Whole Team to Code in Claude
49:28 Wayne the 62 Year Old Who Made $12K a Month
52:38 I Only Share What Actually Works
53:21 Whisper Flow and Talking to Your AI
56:41 Claude Chat Claude Coworker and Claude Code
57:57 The Claude Browser Extension
58:49 Claude Code Is Not Just for Developers
1:00:06 How to Migrate Your AI Memory Across Tools
1:01:08 Level 3 $1M to $10M and the Brand Play
1:02:05 Nobody Buys AI They Buy Trust
1:03:25 Brand Is Association and Association Is Trust
1:05:12 A Million Followers Is $10M in Activated Revenue
1:07:03 How to Keep AI From Becoming Slop
1:07:42 Human in the Loop
1:08:16 The 10 80 10 Rule and Why AI Is Now the 80
1:10:01 The Team FIRED Themselves
1:11:45 Dan's Free AI Curriculum for Your Team

[Codez @0xCodez]

A senior Anthropic engineer just dropped 11-page PDF on "Loop Engineering" for agentic systems.

The shift: you stop prompting the agent. You build the system that prompts it instead.

Schedule → Discover → Build → Verify → Repeat

Every loop runs one turn, five moves:

• Discovery: it finds its own work - failing CI, open issues, recent commits - instead of being handed a list.

• Handoff: each task gets an isolated git worktree so parallel agents don't collide.

• Verification: a second agent, told to assume the code is broken, reviews the first. The "thing that can say no."

• Persistence: results get written to disk, never left in a context window that gets flushed.

• Scheduling: an automation wakes it on a timer. That's what makes it a loop.

The key insight: an agent grading its own work always praises it.

This 11-page PDF changed how I'm building agentic systems today.

Read it now, then explore the article below.

[Yarchi @undefinedKi]

THIS GUY BUILT A REAL JARVIS THAT TURNS HIS IDEAS INTO FINISHED PROJECTS ON ITS OWN, AND HE JUST SHOWED HOW THE WHOLE THING WORKS

The whole system runs on one clean split he calls a blood-brain barrier.

The second brain, his Obsidian vault, is only for things that aren't real yet. Ideas. Each one sits in a project-shaped folder, waiting to be processed and turned into something real.

Nothing executes there. The vault is the place where ideas get shaped, not built.

Once an idea is signed off, it crosses over to the execution pipeline. That lives on his actual machine, or an always-on box, or a Raspberry Pi, depending on what the project needs.

Inside that machine is a fully Claude-native file system. The key piece is one CLAUDE.md file sitting in the root of his documents folder, acting as the map to everything in there.

So he just opens Claude and says "go to my projects folder," and it already knows the whole structure, because that root file explains it.

Two zones, one rule: the brain holds what isn't real, the machine runs what is. That separation is what keeps the ideas from tangling with the work.

Bookmark this

[Roan @RohOnChain]

https://t.co/7pRx7jJ4GC

[Roan @RohOnChain]

USC mathematicians just published the most dangerous quant paper of the year.

THE MATH BEHIND HOW INSIDERS BEAT THE MARKET WITHOUT GETTING CAUGHT.

This paper will teach you to detect smart money moving before announcements. 43 pages of pure game theory. Bookmark now.

[starmex @starmexxx]

AN AWS ENGINEER QUIETLY BUILT A 2 PETABYTE HOME SERVER FOR $9/MONTH THAT KILLS A $3,400/MONTH CLOUD STORAGE BILL

the lenovo thinkstation pgx ships nvidia's gb10 grace blackwell superchip and 128gb of unified memory in a box the size of a mac mini at 1.2kg

it runs an 80b qwen3 coder model at 25 to 40 tokens per second and a 196b step-3.5-flash moe model at 20 tokens per second locally

the gb10 packs 6,144 cuda cores, 192 fifth-generation tensor cores and rates at 1 petaflop of fp4 with sparsity from a single 240 watt usb-c power supply

fine tuning qwen 2.5 7b with lora took 18 minutes and 41gb of unified memory while the gpu pulled 65 watts and peaked at 77 degrees

the box pulls a docker container from nvidia's registry and serves a frontier model on your local network with tool calling and zero data leaving your desk

bookmark this and read the article below

[leopardracer @leopardracer]

https://t.co/lPLABZCDhv

[Gipp 🦅 @gippp69]

https://t.co/5rl5fTLiV0

[Radha Tripathi @Radha_AI]

AWS engineer built a 2 PETABYTE home server.

Cloud bill: $3,400/month.
Home server cost: $9/month.

It runs frontier AI models locally.
No cloud.
No subscriptions.
No data leaves your desk.

This changes everything.

Bookmark this before everyone finds out.