Yaara Cohen 👩🏽‍💻

Curiosity killed the cat
Builder, Developer, Explorer & more
Integrator: (Psyche, Emotions & intuition) x (Logic, SW & AI).
Ex-Wix, @CseHuji

春 (ヤラ)

[Alfie Carter @AlfieJCarter]

R.I.P. rebuilding your GTM stack from scratch every session.

A complete Claude Skill Library can replace a $15,000/month agency retainer.

It is not as easy as hiring someone else to do it.

But if you start today, you can have 130+ skills loaded into Claude Code covering every GTM job by end of this week.

I usually charge $299 for access to this library but today, it's free.

Like this post + comment 'Agents' and I'll DM you the entire skill library for free.

(Must be following, or I can't message.)

Taking this down in 48 hours.

[ClaudeDevs @ClaudeDevs]

We’ve added a CLI for Claude Platform to make every API endpoint runnable from your terminal.

Call the Messages API, stand up Claude Managed Agents, pipe results straight into your shell.

The ant CLI is well understood by coding agents (Claude Code) using the claude-api skill.

[AI_Explorer @ai_explorer25]

Best accounts to follow from each frontier lab to stay constantly up to date

Anthropic
@karpathy
- must-follow account for AI; recently joined Anthropic

@bcherny
- Claude Code creator, always shares great tips

@trq212
- also a Claude Code developer; writes amazing articles on CC

OpenAI
@polynoamial
- works on reasoning research, shares a lot of technical details

@gabriel1
- Sora developer, great career path

@jxnlco
- works on dev experience, shares a lot about Codex

Google AI
@OfficialLoganK
- all the major Google Gemini and AI Studio updates

@ammaar
- product and design; shares great things about vibe-coding in Google AI Studio

@fofrAI
- cool use cases for generative models

Cursor
@leerob
- the loudest voice behind Cursor updates

@ericzakariasson
- shares great insights on using Cursor

@mntruell
- Cursor’s CEO; major releases and usage updates

xAI
@milichab
- recently joined xAI, shares updates on Grok

@skcd42
- also covers major Grok releases

[cat @_catwu]

Excited to share our most powerful new Claude Code feature: dynamic workflows!

Mention "workflow" in a prompt and Claude will dynamically create an orchestration plan that it strictly follows, allowing you to confidently trust that every stage happens in the right order even across 100s of agents.

[darkzodchi @zodchiii]

Anthropic engineer showed how one person can run 5 AI agents, that code, test, review, and deploy at the same time.

In 30 minutes they built the whole thing live in one session.

Here's what they cover:

> when to use one agent vs a full team
> how to split work so agents don't step on each other > the exact framework for deciding what each agent handles

that's exactly why, I put together a guide on building agent teams that actually work.

full guide in the article below 👇

[Vivo @vivoplt]

best 15 accounts to follow in AI:

@karpathy = LLMs king
@steipete = built openclaw
@gregisenberg = startup ideas king
@rileybrown = vibecode king
@jackfriks = solo apps king
@levelsio = startups king
@marclou = startups king
@EXM7777 = AI ops + systems king
@eptwts = AI money twitter king
@godofprompt = prompt king
@vasuman = AI agents king
@AmirMushich= AI ads king
@0xROAS = AI UGCs king
@egeberkina = AI images king
@MengTo= AI landing pages king

follow them all and learn.

[Yaara Cohen 👩🏽‍💻 @yaara4c]

https://t.co/sbGgLdj8VF

[Yaara Cohen 👩🏽‍💻 @yaara4c]

#techHeal #techGym @metiv #מוזיאוןתא
פרויקט מרגש שמתחיל היום בשיתוף פעולה עם מטיב ומוזיאון תא

[Yaara Cohen 👩🏽‍💻 @yaara4c]

@netadror @HilaShmuel דווקא פגשתי אנשי פרודקט טכניים שונים דברים, ומתכנתים שאוהבים פרודקט או להבין ביזנס בסטארטאפים

[Neta Dror @netadror]

יותר מ150 נרשמו לוובינר החינמי בחמישי הקרוב ב11:00 בבוקר על יצירת סרטונים עם קלוד קוד + remotion!

בתכנית: איך מתקינים, מתכננים ויוצרים כמה סרטוני שיווק (כולל עריכות ודיוקים) הכל בלייב. מעניין איך יצא :)

יעלה פה בלייב וביוטיוב, אבל יכולים להירשם פה בלינק כדי לקבל תזכורת + הקלטות למייל אח״כ אם לא זמינים:
https://t.co/ucr3z4xtqd

[Yaara Cohen 👩🏽‍💻 @yaara4c]

@advahandelman @netadror 👸🏼

[Matt Pocock @mattpocockuk]

My Ralph Wiggum breakdown went viral.

It's a keep-it-simple-stupid approach to AI coding that lets you ship while you sleep.

So here's a full explanation, example code, and demo.

[Meng To @MengTo]

I recorded a 43-min video on how to turn a DESIGN.md into landing pages, mobile screens and motion design

[Yaara Cohen 👩🏽‍💻 @yaara4c]

Interesting :)

[MiroFishAI @mirofish_ai]

What if predicting the future was actually… fun?

We built the world's first simple & universal open-source Swarm Intelligence Engine.

Rigorous when you need it. Playful when you want it.

Github: https://t.co/9MhcLAIs7N

[Liad Yosef @liadyosef]

The agentic web needs agent-ready tools.
We spent a decade optimizing websites for humans.

Lighthouse was built by humans to measure human experience.
Ora is powered *by real world agents* that rank the real agentic experience.

Instead of guessing: https://t.co/Ji5voLsiuZ

[Rahul @sairahul1]

Karpathy didn't make a course.

He made THE course.

3 hours. Free.

Tokenization. Attention. Hallucinations. Tool use. RLHF. DeepSeek. AlphaGo.

Every behavior you've ever wondered about in an LLM - where it comes from, why it exists, how it was engineered.

The gap between engineers who understand this and engineers who don't isn't technical depth.

It's the ability to conceive of entirely different things.

[Abraham John 🦄🦓 @Abmankendrick]

Web Designers and Developers, this might be the next-level animation library

ReactBits gives you a growing collection of smooth, modern UI animations you can plug straight into your projects.

Bookmark it for later 💜

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[Yaara Cohen 👩🏽‍💻 @yaara4c]

@liadagmon הי ליעד, שלחתי בקשת חברות, אשמח לשוחח