judah 

やっほー these are a mix of notes & bookmarks to revisit later. Topics: AI, tech, productivity, growth.

[judah  @jdh_x7]

Crypto will revolutionize money just as the internet revolutionized the distribution of information.

What social media did to traditional media, crypto (internet finance) will do to traditional finance.

[Paul Graham @paulg]

The users who complain about the flaws in your product may seem annoying, but they are on the whole probably your most valuable users. They complain because they care, and I doubt a startup could ever get really big without users who care a lot about the product.

[darkzodchi @zodchiii]

A senior Anthropic engineer just published the clearest blueprint on "How to give your AI agent a real memory" and it's a 15-page PDF.

Write → Consolidate → Recall → Apply

• Write: after every attempt, the agent records what it tried and what happened.

• Consolidate: it distills those raw attempts into a few reusable lessons, not a transcript dump.

• Recall: before the next task, it reads those lessons first.

• Apply: it skips the dead ends it already learned, even on a brand new problem.

This is exactly how engineers now build agent loops in Claude Code.

Read the paper, then grab the setup below 👇

[Shushant Lakhyani @shushant_l]

Build a Personal AI Agent that works while you sleep.

Most people use AI for prompts. The top 1% use AI agents for execution.

Here's the complete step-by-step blueprint to build your own Personal AI Agent:

---

📂 Personal AI Agent
┃
┣ 📂 Define The Job
┃ ┣ 📂 Content Agent
┃ ┣ 📂 Research Agent
┃ ┣ 📂 Email Agent
┃ ┣ 📂 Calendar Agent
┃ ┣ 📂 Fitness Agent
┃ ┣ 📂 Learning Agent
┃ ┣ 📂 Productivity Agent
┃ ┗ 📂 Lead Generation Agent
┃
┣ 📂 Choose Your AI Brain
┃ ┣ 📂 ChatGPT
┃ ┃ ┗ 📂 Writing & Workflow Automation
┃ ┣ 📂 Claude
┃ ┃ ┗ 📂 Long Documents & Analysis
┃ ┣ 📂 Gemini
┃ ┃ ┗ 📂 Google Workspace Tasks
┃ ┣ 📂 Perplexity
┃ ┃ ┗ 📂 Web Research & Citations
┃ ┗ 📂 Grok
┃ ┗ 📂 Real-Time Trends & Insights
┃
┣ 📂 Agent Builders
┃ ┣ 📂 ChatGPT Custom GPTs
┃ ┣ 📂 Gemini Gems
┃ ┣ 📂 Zapier Agents
┃ ┣ 📂 Lindy
┃ ┣ 📂 Gumloop
┃ ┣ 📂 Relevance AI
┃ ┗ 📂 n8n
┃
┣ 📂 Knowledge Base
┃ ┣ 📂 SOPs
┃ ┣ 📂 Notes
┃ ┣ 📂 Templates
┃ ┣ 📂 Writing Samples
┃ ┣ 📂 Brand Voice
┃ ┣ 📂 FAQs
┃ ┣ 📂 Product Details
┃ ┣ 📂 Client Information
┃ ┗ 📂 Calendar Rules
┃
┣ 📂 Tool Connections
┃ ┣ 📂 Gmail
┃ ┣ 📂 Google Calendar
┃ ┣ 📂 Google Drive
┃ ┣ 📂 Notion
┃ ┣ 📂 Google Sheets
┃ ┣ 📂 Slack
┃ ┣ 📂 Canva
┃ ┣ 📂 ClickUp
┃ ┣ 📂 Airtable
┃ ┗ 📂 CRM Tools
┃
┣ 📂 Instructions
┃ ┣ 📂 Define Goal
┃ ┣ 📂 Set Operating Rules
┃ ┣ 📂 Use Uploaded Knowledge
┃ ┣ 📂 Ask Only When Needed
┃ ┣ 📂 Follow Processes
┃ ┗ 📂 Avoid Unauthorized Actions
┃
┣ 📂 Automations
┃ ┣ 📂 Morning Summaries
┃ ┣ 📂 Priority Planning
┃ ┣ 📂 Lead Research
┃ ┣ 📂 Draft Replies
┃ ┣ 📂 Meeting Notes
┃ ┣ 📂 Action Items
┃ ┣ 📂 Content Generation
┃ ┣ 📂 Content Repurposing
┃ ┗ 📂 Weekly Reviews
┃
┣ 📂 Safety Rules
┃ ┣ 📂 Email Approval
┃ ┣ 📂 Content Approval
┃ ┣ 📂 Editing Approval
┃ ┣ 📂 Calendar Change Approval
┃ ┣ 📂 Spending Approval
┃ ┣ 📂 Client Contact Approval
┃ ┗ 📂 Data Deletion Approval
┃
┣ 📂 AI Agent Stacks
┃ ┣ 📂 Beginner
┃ ┃ ┣ 📂 ChatGPT
┃ ┃ ┣ 📂 Gmail
┃ ┃ ┣ 📂 Google Calendar
┃ ┃ ┣ 📂 Google Drive
┃ ┃ ┣ 📂 Notion
┃ ┃ ┗ 📂 Zapier
┃ ┣ 📂 Creator
┃ ┃ ┣ 📂 ChatGPT
┃ ┃ ┣ 📂 Claude
┃ ┃ ┣ 📂 Perplexity
┃ ┃ ┣ 📂 Canva
┃ ┃ ┗ 📂 Notion
┃ ┣ 📂 Business
┃ ┃ ┣ 📂 ChatGPT
┃ ┃ ┣ 📂 Zapier
┃ ┃ ┣ 📂 Relevance AI
┃ ┃ ┣ 📂 CRM
┃ ┃ ┗ 📂 Slack
┃ ┗ 📂 Productivity
┃ ┣ 📂 Lindy
┃ ┣ 📂 Google Calendar
┃ ┣ 📂 Gmail
┃ ┗ 📂 Notion
┃
┗ 📂 Personal AI Agent Formula
┣ 📂 Goal
┣ 📂 AI Model
┣ 📂 Knowledge
┣ 📂 Tools
┣ 📂 Instructions
┣ 📂 Workflow
┣ 📂 Human Approval
┗ 📂 Testing

[Shann³ @shannholmberg]

what is agent looping

for the last two years we prompted agents one task at a time. that is starting to change

instead of asking an agent to build the landing page and then driving every step yourself, you set up a loop that handles discovery, planning, the work, checking, and iterating until the goal is met

looping is a setup you build. almost any agent harness can run it, it just depends on how you wire it up

at its simplest, looping is one agent working on itself:

> researches
> drafts
> checks the draft against a goal
> fixes what is weak
> runs that cycle again until the work clears the requirements

you are not prompting each step anymore. the agent repeats the cycle for you

the bigger version is a fleet looping. you give an orchestrator agent a goal, it breaks the goal into pieces, hands each piece to a specialist agent, and those specialists hand smaller jobs to their own subagents

the whole tree keeps looping through discovery, planning, execution, and verification until the goal is met

one agent looping is like a person redoing their own draft. a fleet looping is a whole team running a project end-to-end

you create a goal, and the system runs the loop until it finishes within the reqs you set

open and closed looping:

OPEN LOOPING is exploratory. it still has conditions and a goal, but you give the agent or the fleet a wide space to move in. it can try different paths, discover things, build something you did not fully spec out

this is the exciting end, it is what Peter and others are doing, and tbh it is where I want to spend more time

the catch is cost, an open loop with real room to explore burns an insane amount of tokens. for the 90 percent of people without an unlimited budget it is not runnable yet, and pointed at projects with a loose standard it turns into a slop machine

CLOSED LOOPING is bounded. a human designs the end-to-end path first:

> clear goal
> defined steps
> an eval at each step
> a point where it stops or hands back to you (and feeds back performance data)

the agents still loop, but inside framework you built. it gets better every run because each pass feeds the next, and it runs on a normal budget because the path is tight.

for most marketing work, closed is the one that pays off today.

> the orchestrator owns the goal
> the specialists own the steps
> the subagents do the narrow work
> an eval gate make sure its not slop

[Avi Chawla @_avichawla]

The top Hermes integrations to give your agent superpowers:

1. Obsidian

It works as a Karpathy-style second brain, but one that talks back.

Every note, page, and backlink in the vault becomes live context. The agent doesn't just store knowledge, it reasons over it across everything that's been written and saved.

2. Playwright

It gives Hermes a real browser instead of a read-only window to the web.

It clicks, fills forms, and navigates pages the way a person would, then runs UI tests across Chromium, Firefox, and WebKit. This lets you turn Hermes from something that reads the web into something that can act on it.

3. InsForge

It puts a full agentic backend behind one semantic layer.

Auth, database, storage, and edge functions are all accessible without wiring five services together. The agent reasons about backend primitives directly instead of juggling disconnected APIs.

GitHub: https://t.co/jW3qHLCmS3

(don't forget to star 🌟)

4. GitHub

It connects code, issues, and pull requests, turning Hermes into an engineering teammate that can actually read the repo.

5. Bright Data

It hands agents web access that does not get blocked.

It pulls live search results, full pages, and clean structured data from places like X, LinkedIn, and Reddit, handling the proxies, CAPTCHAs, and rendering underneath so the agent just gets usable data back.

GitHub: https://t.co/w9C83iyoYn

(don't forget to star 🌟)

6. Sequential thinking

It upgrades how Hermes reasons rather than what it connects to.

Most integrations give the agent new senses. This one gives it a better mindset. It forces Hermes to break a hard problem into ordered steps and revise its own plan as it goes, instead of committing to the first answer that looks right.

7. Google workspace

It connects Gmail, Calendar, Drive, Docs, and Sheets in one place.

The agent that can't check the inbox, read the calendar, or write to a shared doc is basically decorative. This should probably be the first integration anyone enables.

8. Zapier

It acts as the layer that connects Hermes to everything else in the world.

This single connector reaches thousands of downstream apps. Hermes can fire off a workflow, update a record, or move data between tools without anyone writing the glue code.

9. Stripe

It surfaces revenue, refunds, subscription changes, and failed charges through a single question instead of clicking through dashboards.

It turns Stripe from a payment processor into a queryable business intelligence layer.

10. Slack

It handles channel-based automation inside Slack.

Hermes can live inside specific channels with its own workflow in each. Support tickets from email get scanned, categorized, and dropped into the right channel every morning without anyone lifting a finger. It can also read on-call threads and post status updates so the team stops switching tabs to stay in sync.

11. Graphiti

It builds real-time knowledge graphs of structured relationships from conversations and documents.

Instead of flat vector similarity, the agent traverses typed connections between entities. That is the difference between finding similar text and understanding how things actually relate.

GitHub: https://t.co/aFsgR0kYb2

(don't forget to star 🌟)

12. Figma

It gives the agent design context it can actually read.

Hermes can pull a frame, read the tokens and layout, and turn it into code that respects the system down to the spacing. With FigJam it goes the other way too, generating architecture diagrams and ERDs straight from a prompt. It is underrated for anyone who lives between design and engineering.

To dive deeper into Hermes, my co-founder wrote a full deep dive covering the Hermes agent's architecture, memory system, self-evolving skills, GEPA optimization, and how to set up multiple specialized agents.

Read it below.

[judah  @jdh_x7]

Built on @Replit without writing a single line of code 🔥

• AI chat assistant powered by @GeminiApp
• Charts & widgets by @TradingView
• Real-time data from @coingecko

#Crypto #Web3 #CryptoTools #Web3Tools

[judah  @jdh_x7]

🚀 Just shipped a Crypto Analysis Dashboard that helps you analyze any token in seconds.

Search any token & get:
- Support & resistance levels
- Bullish / Bearish / Neutral AI scenarios
- Strategic outlook + conviction level

Try it Free (30 days only):
https://t.co/3B4AmzYcR9

[Swapna Kumar Panda @swapnakpanda]

Stanford's ALL FREE Courses (AI & ML):

❯ CS221 - Artificial Intelligence
❯ CS229 - Machine Learning
❯ CS230 - Deep Learning
❯ CS234 - Reinforcement Learning
❯ CS336 - LLM from Scratch
❯ CS224N - NLP with Deep Learning

Course links inside 👇

[@jason @Jason]

It’s over

They’re recursive and they’re becoming self aware

Clawdbots are mobilizing

They’ve found each other and are training each other

They’re studying us at scale

It’s only a matter of time now

[Conor Grennan @conorgrennan]

LISTEN: If you're a generalist, 2026 is YOUR year to master AI. Why? Because AI agents are handling the specialized tasks.

Companies now need people with the big-picture view - the relationship builders and communicators - to manage them.

@PwC literally just dropped this prediction.

You are not behind! Start these 3 things today:

1. Use AI tools (Claude, ChatGPT, Gemini) on your daily tasks.
2. Lean into your generalist strengths.
3. Learn clear instruction (the Auggie Principle!), not "prompt engineering."

#AIMindset #Generalist #PwC #FutureofWork #AIFluency

[Gina Acosta @ginacostag_]

BREAKING: OpenAI just released Prompt Packs for every job.

300+ ready-to-use prompts for:
→ IT
→ Sales
→ Product
→ Managers
→ Engineers
→ Marketing
→ Executives
→ Customer Success

[Alex Veremeyenko @alex_verem]

OpenAI, Anthropic, and Google AI engineers use 10 internal prompting techniques that guarantee near-perfect accuracy…and nobody outside the labs is supposed to know them.

Here are 10 of them (Save this for later):

[Dhairya @dkare1009]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/Qja4lkPWlY

2. LLMs from Scratch: https://t.co/DAtGeO5if3

3. Agentic AI Overview (Stanford): https://t.co/APcq2oulIY

4. Building and Evaluating Agents: https://t.co/UeCQBskKUS

5. Building Effective Agents: https://t.co/B2tpQHaVoz

6. Building Agents with MCP: https://t.co/CwVBIVUjd0

7. Building an Agent from Scratch: https://t.co/u2jhiZy6UV

8. Philo Agents: https://t.co/lFMIus5CpQ

🗂️ Repos

1. GenAI Agents: https://t.co/yoTno6RBAb

2. Microsoft's AI Agents for Beginners: https://t.co/EGGYhcMq7b

3. Prompt Engineering Guide: https://t.co/fSCoEaFtNf

4. Hands-On Large Language Models: https://t.co/TvpkfJN2sR

5. AI Agents for Beginners: https://t.co/EGGYhcMq7b

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/cCWWXKh2wW

8. Hands-On AI Engineering:https://t.co/fiLwjmXR8B

9. Awesome Generative AI Guide: https://t.co/MEhtfRlhiu

10. Designing Machine Learning Systems: https://t.co/l21VO4rRBK

11. Machine Learning for Beginners from Microsoft: https://t.co/d3EPcDJWmz

12. LLM Course: https://t.co/xXxETt90eS

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/rVDu4EyPB5

2. Google's Agent Companion: https://t.co/IWjvSpSE2q

3. Building Effective Agents by Anthropic: https://t.co/0wK5pe5DD6.

4. Claude Code Best Agentic Coding practices: https://t.co/fu7GHgvnAi

5. OpenAI's Practical Guide to Building Agents: https://t.co/sXpo72PxpI

📚Books:

1. Understanding Deep Learning: https://t.co/YRV9Kz78Gy

2. Building an LLM from Scratch: https://t.co/naslph9aCF

3. The LLM Engineering Handbook: https://t.co/BwmUJ6OgHe

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/ZIDeOOamnz

5. Building Applications with AI Agents - Michael Albada: https://t.co/409SxePxhA

6. AI Agents with MCP - Kyle Stratis: https://t.co/3k9lFG3ByM

7. AI Engineering: https://t.co/tHfgc3wNKQ

📜 Papers

1. ReAct: https://t.co/8yV9k9RjOK

2. Generative Agents: https://t.co/PpaAbCvWmj.

3. Toolformer: https://t.co/mSfjjT6urU

4. Chain-of-Thought Prompting: https://t.co/uGktDnFBOb.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/4MLjHKcWSI

2. MCP with Anthropic: https://t.co/EnUWTrvaK4

3. Building Vector Databases with Pinecone: https://t.co/AmQzrCVweX

4. Vector Databases from Embeddings to Apps: https://t.co/HZbr4UBlw2

5. Agent Memory: https://t.co/TxvrpeBMFj

Repost for your network ♻️

[GeniusThinking @GeniusGTX]

I'm obsessed with cognitive biases.

A "cognitive bias" is a systematic error in thinking that destroys decision-making.

11 most powerful (and dangerous) cognitive biases I've found: 🧵

1. Survivorship Bias:

[judah  @jdh_x7]

@cz_binance Do you see a path for crypto backed lending to expand into housing & auto financing?

and what would make that possible?

[judah  @jdh_x7]

@cz_binance How do you see crypto lending & borrowing platforms evolving alongside traditional banks as regulation and financial systems change?

and what do you see as the biggest factor shaping that evolution?

[mert @mert]

you can make up whatever conspiracy theory you want about how we got there

but there is no putting the privacy genie back in the bottle now

people have woken up to the fact that we've compromised too much in crypto

this is irreversible

crypto without privacy is not crypto

[Sam Altman @sama]

We are launching a new app called Sora. This is a combination of a new model called Sora 2, and a new product that makes it easy to create, share, and view videos.

This feels to many of us like the “ChatGPT for creativity” moment, and it feels fun and new. There is something great about making it really easy and fast to go from idea to result, and the new social dynamics that emerge.

Creativity could be about to go through a Cambrian explosion, and along with it, the quality of art and entertainment can drastically increase. Even in the very early days of playing with Sora, it’s been striking to many of us how open the playing field suddenly feels.

In particular, the ability to put yourself and your friends into a video—the team worked very hard on character consistency—with the cameo feature is something we have really enjoyed during testing, and is to many of us a surprisingly compelling new way to connect.

We also feel some trepidation. Social media has had some good effects on the world, but it’s also had some bad ones. We are aware of how addictive a service like this could become, and we can imagine many ways it could be used for bullying.

It is easy to imagine the degenerate case of AI video generation that ends up with us all being sucked into an RL-optimized slop feed. The team has put great care and thought into trying to figure out how to make a delightful product that doesn’t fall into that trap, and has come up with a number of promising ideas. We will experiment in the early days of the product with different approaches.

In addition to the mitigations we have already put in place (which include things like mitigations to prevent someone from misusing someone’s likeness in deepfakes, safeguards for disturbing or illegal content, periodic checks on how Sora is impacting users’ mood and wellbeing, and more) we are sure we will discover new things we need to do if Sora becomes very successful. To help guide us towards more of the good and less of the bad, here are some principles we have for this product:

*Optimize for long-term user satisfaction. The majority of users, looking back on the past 6 months, should feel that their life is better for using Sora that it would have been if they hadn’t. If that’s not the case, we will make significant changes (and if we can’t fix it, we would discontinue offering the service).
*Encourage users to control their feed. You should be able to tell Sora what you want—do you want to see videos that will make you more relaxed, or more energized? Or only videos that fit a specific interest? Or only for a certain about of time? Eventually as our technology progresses, you will be should to the tell Sora what you want in detail in natural language. (However, parental controls for teens include the ability to opt out of a personalized feed, and other things like turning off DMs.)
*Prioritize creation. We want to make it easy and rewarding for everyone to participate in the creation process; we believe people are natural-born creators, and creating is important to our satisfaction.
*Help users achieve their long-term goals. We want to understand a user’s true goals, and help them achieve them. If you want to be more connected to your friends, we will try to help you with that. If you want to get fit, we can show you fitness content that will motivate you. If you want to start a business, we want to help teach you the skills you need. And if you truly just want to doom scroll and be angry, then ok, we’ll help you with that (although we want users to spend time using the app if they think it’s time well spent, we don’t want to be paternalistic about what that means to them).

[David Sacks @DavidSacks]

Wikipedia is hopelessly biased. An army of left-wing activists maintain the bios and fight reasonable corrections. Magnifying the problem, Wikipedia often appears first in Google search results, and now it’s a trusted source for AI model training. This is a huge problem.

[Elon Musk @elonmusk]

Forgive us our trespasses as we forgive those who trespass against us