Vikam Sathish

Continuous Learner #Agile #Digital #Cloud #Cybersec #Data #ML #ai #Python passionate about #Startup #Entrepreneurship #Disruption #Innovation #Farming #Growth

[Vikam Sathish @VikamSathish]

@Manu_Sisti Claude

[Vikam Sathish @VikamSathish]

@ISACANews I have been noticing this login issue since the beginning of June, #ISACA #SignOnError #Salesforce

[Vikam Sathish @VikamSathish]

Deep respect for Mr. Clean @ptrmadurai who walked away from a high‑profile investment banking career to serve with integrity. Your pause from active politics leaves a void, but I pray you return soon to continue your legacy of service and inspire us again 🙏💙

[Anthropic @AnthropicAI]

Mythos Preview has already found thousands of high-severity vulnerabilities—including some in every major operating system and web browser.

[Andrej Karpathy @karpathy]

@kepano I just tried it this morning on the 245-page Mythos pdf and it failed badly and the outputs were all mangled. Converting pdfs is really hard, I think it has to probably be a Skill not a program, for a SOTA LLM for it to work properly.

[Brij Pandey @LearnWithBrij]

Claude Code ships with 5 architectural layers most engineers never open.
Not features. Not settings. Layers — each solving a distinct problem that LLMs alone can't solve. And four of them have nothing to do with prompting.
Here's the full Agent Development Kit:

Layer 1 — CLAUDE.md → The Memory Layer
Architecture rules, naming conventions, test expectations, repo map. Always loaded. Always active.
Two scopes:
• ~/.claude/CLAUDE.md → global
• .claude/CLAUDE.md → project
This isn't context you paste in before every session. It's context that never needs repeating. The agent's constitution.

Layer 2 — Skills → The Knowledge Layer
Each SKILL.md carries a description. Claude matches it at runtime and forks the skill into an isolated subagent. On-demand, never always-on.
Task-specific knowledge without inflating your main context window. Modular by design.

Layer 3 — Hooks → The Guardrail Layer
PreToolUse → PostToolUse → SessionStart → Stop → SubagentStop

This is the layer most teams skip. And the one they regret skipping first.

Hooks are NOT AI. They're deterministic event-driven shell commands.
• Auto-lint on every Write
• Hard-block on rm -rf
• Slack notification on Stop
Event fires → Matcher checks → Command runs
Quality enforced at the infrastructure level. Not the prompt level.

Layer 4 — Subagents → The Delegation Layer
Each subagent gets its own context window, model, tools, and permissions.
Main agent delegates down. Receives results up. That's it.
No infinite recursion — subagents can't spawn subagents. Main context stays clean. Hard boundaries by design.

Layer 5 — Plugins → The Distribution Layer
Bundle your skills + agents + hooks + commands into a plugin. One install. Whole team inherits the behavior.
Think npm packages — but for what your agent knows how to do.

Wrapping everything:
→ MCP Servers on the left (GitHub, databases, APIs, custom integrations)
→ Agent Teams on the right (parallel execution, message passing, shared permissions)

The 5-layer stack in one line:
CLAUDE.md sets rules → Skills provide expertise → Hooks enforce quality → Subagents delegate work → Plugins distribute to the team
Most production failures in agentic systems trace back to one missing layer.

Which one is the gap in your current setup?

[Rishabh @Rixhabh__]

Anthropic pays engineers $750,000+ a year to understand how LLMs work.

Stanford just put a 2 hour lecture that covers 80% of it for FREE.

Bookmark this. Give it 2 hours today.

[Ritesh Roushan @devXritesh]

System Design Series - Day 11/30

How to Set Up PostgreSQL Replication in Production

This is the exact setup we used to go from 1 database to 4 and handle 10x more traffic.

Real configs.
Real code.
No fluff.

Thread 👇

[Claude Code Changelog @ClaudeCodeLog]

Claude Code 2.1.81 has been released.

27 CLI changes, 2 system prompt changes

Highlights:
• Added --bare flag: skips hooks, LSP, plugin sync and skill scans for faster, deterministic automation
• Assistant honors 'no memory' requests: ignores stored memory and reports none, reducing data exposure
• Read tool now prefers targeted reads of known file sections, yielding smaller, more relevant results faster

Complete details in thread ↓

[Leonard Rodman @RodmanAi]

10 YouTube channels that teach AI better than most CS degrees in 2026:

1. Andrej Karpathy
Deep, intuitive walkthroughs of neural networks and modern LLMs
https://t.co/38utnbOEJD

2. 3Blue1Brown
Visual intuition for math, linear algebra, and neural networks
https://t.co/XNoBMWP5gK

3. StatQuest with Josh Starmer
Clear, friendly explanations of statistics and ML fundamentals
https://t.co/lEvzdthNYj

4. Stanford Online
University-grade ML and AI lecture series (Andrew Ng, CS229, etc.)
https://t.co/pbgvlTKbaC

5. sentdex
Practical machine learning and Python projects
https://t.co/RU8zwTiO1S

6. Yannic Kilcher
Deep dives into ML and AI research papers
https://t.co/RZmBNiHJJu

7. MIT OpenCourseWare
Rigorous academic courses on ML, AI, and applied mathematics
https://t.co/kDRDUmGLsJ

8. Siraj Raval
High-level overviews and motivation around AI concepts
Link: https://t.co/GZp6gOQpgY

9. DeepLearningAI
Structured learning paths for deep learning and generative AI
https://t.co/rXuWkbWxBs

10. Two Minute Papers
Fast, accessible summaries of cutting-edge AI research
https://t.co/zoDBCoirqg

[The Startup Ideas Podcast (SIP) 🧃 @startupideaspod]

1-person companies are coming

One human. A team of digital employees.

Here's the framework to build one:

Step 1: Pick a sub-niche inside a big market

Not "finance."

Think "FIRE for Gen Z."

That's where the venture boys aren't playing.

Step 2: Map the workflow end-to-end

Every single step.

Lead comes in → quote sent → deposit collected.

Call owners.

Use AI.

Do whatever it takes.

Step 3: Highlight where money changes hands

- Negotiating price.
- Collecting deposit.
- Ordering materials.

That's your wedge into the market.

Step 4: Spot the repetitive mechanical steps

- Checking leads across 4 platforms?
- 10 min/day?

If that person earns $400/hr, you just found a $20,000/year problem.

You don't automate everything.

You automate the mechanical.

You keep humans on the judgment.

That's how you build SaaS that prints cash at $100K+/mo without raising a dollar.

[Shann³ @shannholmberg]

Greg Isenberg just dropped a 23-step framework for building a 1-person AI SaaS

the trick is: start as a service business, not a software company

Here is how to use it 🧵

[Anthropic @AnthropicAI]

A statement from Anthropic CEO Dario Amodei: https://t.co/WnSFrwI9nI

[Vikam Sathish @VikamSathish]

It’s a curse to live in war zones—sleepless nights, sirens, bunkers, and cries echoing in the dark.
I pray for peace to prevail, for humanity to unite toward a greater purpose. ✨🕊️

[Aakash Gupta @aakashgupta]

Karpathy is telling you something most product teams haven’t internalized yet.

The new distribution channel for software is agents. Agents don’t browse your marketing site, watch your demo video, or click through your onboarding flow. They call your CLI. They hit your MCP server. They read your docs programmatically. If none of those surface areas exist, your product is invisible to them.

Look at how fast this moved. MCP went from zero to 97 million monthly SDK downloads in twelve months. 10,000+ active servers. OpenAI, Google DeepMind, Microsoft, and Cloudflare all adopted it. By December 2025, Anthropic donated MCP to the Linux Foundation because the standard had already won. Running an MCP server is now compared to running a web server.

That’s the new baseline for product discovery.

85% of enterprises are expected to have AI agents deployed. Those agents need structured, programmatic access to your product. They need CLIs, MCP endpoints, and machine-readable documentation. A beautiful React dashboard is worthless to an agent trying to pull data into a workflow at 3am.

This tells you everything about why Karpathy’s framing of CLIs as “legacy” technology is so precise. Legacy means battle-tested, standardized, universally parseable. stdin/stdout, flags, JSON output. The entire Unix philosophy was accidentally designed for AI agents decades before they existed.

Your competitor ships an MCP server and suddenly every Claude Code user, every Cursor session, every autonomous workflow can discover and use their product. No human ever visits the website. No sales call. No onboarding email. The agent just finds the tool and starts using it.

The companies that win the next 24 months are the ones building agent-accessible surface area right now. The ones that lose are still optimizing their landing page above the fold.

[Vikam Sathish @VikamSathish]

@s_mohinii Send

[Srishti @srishticodes]

Stanford University is giving out millions of dollars to the public.
Yes, millions.
So what’s the catch?

You have to learn AI and actually use it to solve real problems.

This is not random content.
This is Stanford’s actual learning path from foundations to frontier models.

Stanford AI Learning Path
Artificial Intelligence: Principles & Techniques
Search, logic, planning, intelligent agents
https://t.co/jvCUfYJ7mK

Machine Learning (Andrew Ng’s course at Stanford)
The math and intuition behind ML algorithms
https://t.co/XDH8JvTVXD

Deep Learning
Neural networks, backpropagation, practical systems
https://t.co/xIN8ttT5Fy

NLP with Deep Learning
How language models work from vectors to transformers
https://t.co/VwckdWO44u

Language Modeling From Scratch
Build language models step by step
https://t.co/VwckdWO44u

Transformers and Large Language Models
Modern architectures and scaling laws
https://t.co/B2hHMGXiLx

Deep Generative Models
Create text, images, audio systems
https://t.co/OnC82Jx2cA

Why this matters

Most people use AI.
Some build with it.
Very few understand it:

• how models learn
• where they fail
• what scales
• what breaks
That layer of understanding is where real leverage comes from.

[Claude @claudeai]

Introducing Cowork and plugin updates that help enterprises customize Claude for better collaboration with every team.

[Vikam Sathish @VikamSathish]

AI is reshaping software development. Industry trends show it's not a fad. If you're not experimenting with AI coding tools now, you risk falling behind. Your relevance depends on adapting. #AI #Programming #FutureOfWork #Developer #QA #cloud #DevOps

[Vikam Sathish @VikamSathish]

Many get confused, so let's clarify:
Entrepreneur: invents the wheel.
Businessman: improves the axle.
Startup: finding the road.
Successful Biz: on the highway.
One builds the future, the other perfects the present.
They are distinct not same.
#startup #business #entrepreneurship