Tagged: ai
288 posts
PayPal autonomous SRE
How Agents Run in Production
The industry does not have a shared vocabulary for agent execution. Six execution modes and four scale archetypes give you a framework for deciding what you actually need to schedule, isolate, and sandbox these workloads.
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acquired Manus for roughly $2B
Anthropic announced MCP
Berkeley AI Research group published a piece on compound AI systems
built roughly one million lines of production software
Claude 3.5 Sonnet
Google introduced the Agent-to-Agent (A2A) protocol
Linux Foundation AI
McKinsey's 2024 "State of AI" report
moved their coding agent from outside the top 30 to the top 5
named it in February 2026
offered a useful analogy
official MCP server registry
Open Responses spec
OpenAI's Agents SDK
Salesforce acquired it
writing on Martin Fowler's site
Beyond LLMs: Anatomy of Agentic Systems
From compound AI systems to the agentic loop, MCP, APIs, frameworks, and the rise of the Claws — a technical map of what it takes to build with AI agents in 2026.
Beyond LLMs: Moats, Distribution, and the Value Lifecycle
What makes one AI product defensible against another? Data, specialization, compound moats, and how the whole AI product stack maps to Moore's technology lifecycle.
Beyond LLMs: The Whole Product Framework for AI
What Maslow's hierarchy of needs, Levitt's whole product model, and Geoffrey Moore's crossing the chasm can teach us about building AI products that actually ship.
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a PR against Liquid
AI Exposure of the US Job Market — Karpathy
autoimprove on GitHub
autoimprove presentation
Canaries in the Coal Mine — Stanford/MIT
Karpathy's autoresearch
Labor Market Impacts of AI — Anthropic Research
Measurability-biased technical change thread
nailed the framing
put it well
Shopify Liquid PR #2056
treemap of AI exposure across 342 US occupations
AI Exposure — Theory vs Reality
Cross-referencing Karpathy's AI exposure treemap with Anthropic's observed coverage data, Stanford's entry-level impact research, and the measurability thesis. Four interactive views of the gap between what AI could disrupt and what it actually is.
From Autoresearch to Autoimprove: Generalizing the Agentic Experiment Loop
Two unrelated projects discovered the same pattern: point an AI agent at code, give it a score to chase, and let it run experiments until morning. I generalized it into a tool that works for any domain.
The Measurability Trap
Karpathy mapped which jobs AI could disrupt. Anthropic measured which ones it actually is. The gap tells us everything about where we are, and the fault line isn't skill or education. It's whether your output can be measured.
17% lower on comprehension tests
Anthropic published a study
highly-upvoted post on r/ClaudeAI
large-scale study with 234 students
Observing Without Doing: Pseudo-Apprenticeship Patterns in Student LLM Use
one post with 2,700+ upvotes
put it well on X
senior developer on r/learnprogramming
University of Saarland study on knowledge transfer in AI pair programming
Shadowing AI: What I Learned by Watching Agents Code
I've been learning new programming languages by watching AI coding agents work — like shadowing a colleague. The research says this should make me worse. Here's why I think it's more complicated than that.
1.7x more issues
Agentic Engineering Patterns
AI Agents and Code Review
AI Doesn't Reduce Work - It Intensifies It
As one practitioner put it
between 0.7% and 94%
CIO.com described it well
Faros AI study
HBR study from UC Berkeley
Making Coding Agents Reliable
METR randomized controlled trial
METR Study: AI Tools and Developer Speed
new paper from MIT and Washington University
Some Simple Economics of AGI (arXiv)
Some Simple Economics of AGI
Stack Overflow 2025 Developer Survey - AI Section
Stack Overflow 2025 survey
State of AI vs Human Code Generation Report
The 80% Problem in Agentic Coding
The AI Coding Trust Gap
The AI Productivity Paradox Research Report
The New Asymmetry: When Generation Outpaces Verification
Transitioning to the Verification Economy
Using Linters to Direct Agents
Why Your AI Coding Assistant Keeps Doing It Wrong
The Verification Bottleneck: Why AI's Real Cost Is Human Attention
AI scales execution to near-zero cost. But verifying that output stays biologically bounded. The bottleneck was never intelligence. It's human verification bandwidth.
Shrinking the Verification Gap: Practical Patterns for AI-Assisted Development
If AI scales execution and verification is the bottleneck, the winning move is to make verification cheaper. Here are the patterns that actually work.
Agent Skills (SKILL.md)
Galileo's research on multi-agent coordination
Jimmy Song wrote
OpenAI's September 2025 research
Tetrate wrote about securing the MCP supply chain
what CoSAI has been calling for
I Vibe-coded an open registry for AI agents
Container registries store bytes. They don't know what's inside. The Agent Registry is a governance layer for AI agents, skills, and MCP servers, built on A2A AgentCard, MCP server.json, and Agent Skills as native identity formats.
Coleman et al., 2022
— Coleman et al., 2022
10 Things Developers Want from Agentic IDEs
Agent Access Control on GitHub
Agent Access Control Presentation
Agent Access Control
Claude Code is the Inflection Point
Claude Code vs Cursor
Coleman et al. (2022)
CyberArk wrote about this
DevEx: What Actually Drives Productivity
Do Cloud Developers Prefer CLIs or Web Consoles?
GitHub Octoverse 2025
Gravitee surveyed 900+ practitioners
JetBrains Developer Ecosystem 2025
MCP Gateway on GitHub
Measuring AI Impact on Developer Productivity
most agents hold 10x more privileges than required
OWASP released their Agentic AI Top 10
OWASP Top 10 for Agentic Applications 2026
Securing Agentic Applications Guide 1.0
Stack Overflow Developer Survey 2025
State of AI Agent Security 2026
The Rise of AI Agents and the Reinvention of Kubernetes
Warp State of the CLI 2023
What's Shaping the AI Agent Security Market in 2026
Why CLIs Are Better for AI Coding Agents Than IDEs
Don't Teach Your Agents Karate
Every agent team rebuilds the same auth, rate limiting, and credential management. I built an agent gateway for Kubernetes that generates it all from two CRDs so the people building agents never touch security and the people running the platform never touch agent code.
Your terminal is an AI runtime now
AI coding agents pulled developers back to the terminal. But the data says the real split isn't CLI versus GUI. It's about what you're doing.
1,000+ servers in the community registry
10-15% of IT spending in 2026
Agent Registry in Copilot
Agent Registry on GitHub
AI 2026: Infra Agentic Runtime
AI Supply Chain Security Imperative
an MCP catalog
announced an enterprise MCP registry
Cloud API Registry integration
Deploying Agentic AI with Safety and Security
Gartner also predicts
Gartner analyst noted
MIT researcher posted in r/artificial
Multi-Agent Coordination Failure Mitigation
official MCP Registry
one r/kubernetes commenter put it
OpenAI Research on Training Procedures
realized LLMs are terrible at infra
Research measuring hallucination rates
Securing the MCP Supply Chain
Tray.ai coined the term "Shadow MCP"
METR Study
— METR Study
Naval Ravikant
— Naval Ravikant
Naval Ravikant
Bryce York
— Bryce York
ACM published a piece
AI Is Outpacing Your Workforce Strategy
Atlassian wrote about
BCG workforce report
How AI Turns Product Managers Back Into Builders
killed their APM program
Llama Stack documentation
made this point well
n8n Environment Variables
n8n-openshift Kustomize repo
Red Hat OpenShift AI documentation
Rise of AI Coding Developers
study by METR
The Era of the Builder Product Manager
The Vibe Coding Imperative for Product Managers
Vibe Coding is the New Product Management - Naval Ravikant
Vibe Coding Prototypes
vLLM documentation
Why LinkedIn is Replacing PMs
Will AI Replace Product Managers?
If AI Can Code, Who Makes the Call?
The builder model is appealing and mostly wrong. Faster building means more need for someone deciding what to build, not less.
n8n on OpenShift: Workflow Automation That Talks to Your Models
n8n is good at workflow automation. But when your workflows touch internal models and sensitive data, you need it on the same cluster. Here's how to deploy n8n on OpenShift and wire it into vLLM and OpenShift AI.
Agent Registry
A vendor-neutral registry for AI agents, skills, and MCP servers. Covers the problem of agent discovery, the registry architecture, and how agentctl works.
Standardizing AI Value? The Tech and Economics Behind Anthropic's MCP
Decoding MCP's Technology, Market Impact, and Potential as a Foundational AI Standard.
Building Agents with Model Context Protocol - Full Workshop with Mahesh Murag of Anthropic
Zapier MCP actions
Generative AI's Growing Tech Debt: Managing the Ripple Effect
Prepare for the Next Wave of (Generative) AI
technical debt of traditional AI,
The Transient Nature of Prompt Engineering: A Call for More Robust Language Models
Moving Beyond the Hack Towards Robust and User-Friendly Language Models
The AI Cybersecurity Market: Navigating Opportunities and Risks
Understanding the key trends and opportunities.
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AI | 2024 Stack Overflow Developer Survey
AI In Cybersecurity Market Size, Share | CAGR of 22.3%
Finding The Sweet Spot For India’s Sovereign AI
Forecast Analysis: AI Software Market by Vertical Industry, 2023-2027
How to Craft a Proactive Generative AI Strategy To Manage Cybersecurity Risks
NIST AIRC - Playbook
Sovereign AI: What it is, and 6 ways states are building it | World Economic Forum
Study: 51 percent of Indonesian financial institutions are focusing on deploying GenAI for daily tasks - TNGlobal
The state of AI in early 2024: Gen AI adoption spikes and starts to generate value
Why The UK Should Have Its Own Exascale AI/HPC Machine, And How
World Economic Forum's Global Cybersecurity Outlook 2023
Balancing the Yin/Yang of AI Emergence
With Great Power Comes Great Responsibility
IBM’s AI pillars
Customer-Obsession, Powered by the Internet & AI as Enablers: The Bazos Narrative
Invent on the customer's behalf
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2010 Letter to Shareholders
2016 Letter to Shareholders
Jeff Bezos' 2016 Letter to Amazon Shareholders
Beyond LLMs: Compounds Systems, Agents, and Whole AI Products
A Framework for Building Great AI Products
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Compound AI Systems
Federal AI use-case inventory
perceives its environment
Whole Product Model
Jacks of All Trades, Masters of One, and the Model Production Frontier!
Do the right thing!
Production possibilities frontier (PPF)
AI Market Dynamics: Open Vs. Closed, Direct Vs. Indirect
Understanding the AI Market Dynamics, to be or not to be!
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32 AI startup acquisitions
38% of Microsoft's total revenue
$4 billion in investments
$4 billion investment in Anthropic
Amazon Flywheel: The Secret to Success for Earth's Most Customer-Centric Company
Apple is also exploring partnerships with AI companies like OpenAI and Google to integrate their large language models into future products and service
around product design and vertical integration
Azure as of recently
Control Alt delete: Exploring OpenAI's corporate structure, after Sam Altman’s shock dismissal
How Apple Makes Money
investing in generative AI and plans to "break new ground"
q4 earnings report
rank higher in the foundational model transparency index
see the quadrant
which gets rotated/killed often
History, AI, and Non-Consumption: Part I, Winter is Coming
From Ancient Dreams to Modern Realities
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A proposal for the Dartmouth Summer Research Project on Artificial Intelligence
accurately predicting the structure of proteins, DNA, RNA, ligands and more, and how they interact. We hope it will transform our understanding of the biological world and drug discovery
Artificial Intelligence (AI) Coined at Dartmouth
attention with transformers
COMPUTING MACHINERY AND INTELLIGENCE
created a programmable music automaton
Ed Feigenbaum's Search for A.I.
Eliza, Computer Therapist
even in biology
Generative Adversarial Networks
Handbook of Artificial Intelligence
IBM introduced Watson, which defeated human champion Jeopardy
introduced the self-driving car project
it still beat
knowledge-generating machine in "Gulliver's Travels
Left: Marvin Minsky, Claude Shannon, Ray Solomonoff and other scientists at the Dartmouth Summer Research Project on Artificial Intelligence (Photo
LISP programming language
machine consciousness in "Darwin Among the Machines"
“made out of about 400 vacuum tubes and a couple of hundred relays and a bicycle chain”
mageNet large scale visual recognition challenge
MYCIN (the medical expert)
Nathaniel Rochester III (1919-2001) - Dharmendra S. Modha
Newell, Simon & Shaw Developed the First Artificial Intelligence Program
(PDF) The Fifth Generation
presented by Yan Shi to King Mu
Principia Mathematica by Whitehead and Russell
published a book about the applications of expert systems
published a report
The mother of all demos'' by Douglas
world champion of Go at the time
The History of AI: An Interactive Timeline
An interactive timeline of artificial intelligence milestones, from foundational research and philosophy to creative works and physical devices, overlaid with AI popularity trends.
PESTELing NVIDIA's Market CAP
Analyzing the Impact of External factors on NVIDIA's Market Cap
higher sales of gaming GPUs and professional GPUs
impacting their overall sales and profitability.
sanctions and export controls
Pre-training and Fine-tuning Mindmap!
A detailed mindmap explaining pretraining and finetuning
Pre-training, Fine-tuning, and Kungfu!
From Novice to Apprentice
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economies of scale
Fine-Tuning LLMs: LoRA or Full-Parameter? An in-depth Analysis with Llama 2
Full Parameter Fine Tuning (FPFT)
How Gas Fees Work on the Ethereum Blockchain
Low Rank Adaptation (LoRA)
Parameter Efficient Fine Tuning
Self-Supervised Learning (SSL)
Platforms, Products, APIs, and Indian Food
Navigating the Nuances of Platforms + Strategies for Success
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