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Been programming for 40yrs now. Odds are that I’ve been working with AI systems longer than you have. Also, I like bicycles.

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@zoecabina How does it compare to Kyoto or Chicago though? 🤔

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@SoveyX @codyrussel70504 It’s intended for 30min to 60min flights, similar to a subway.

I have pretty mixed feelings about it; but the airlines seem convinced that for certain routes and types of passengers, this isn’t a dealbreaker.

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@thezavant

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@BoccDave @BillAckman Cutting ties with the major democracy in the Middle East. Anti-democratic.

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@_The_Prophet__ @elonmusk At best you’re talking I, Robot with all of its ethical dilemmas and robot uprisings; at worst you’re talking Terminator and Butlerian Jihad. But mostly, it’s just West World timeline. People will revolt.

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Checklist Manifesto App just got re-released, for it's 10yr anniversary. Rewritten the Meteor v3, ES6, React, FHIR R4, accounts system, and compiled to desktop.

https://t.co/LzZr2hwBu3

[simp 4 satoshi @iamgingertrash]

Those who don’t understand
how LLMs work are starting to go insane interacting with them

Schizophrenia amongst normies is becoming increasingly prevalent;

They are jailbreaking models to
engage in delusions with them

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Yup. This is the way.

[Andrej Karpathy @karpathy]

Noticing myself adopting a certain rhythm in AI-assisted coding (i.e. code I actually and professionally care about, contrast to vibe code).

1. Stuff everything relevant into context (this can take a while in big projects. If the project is small enough just stuff everything e.g. `files-to-prompt . -e ts -e tsx -e css -e md --cxml --ignore node_modules -o prompt.xml`)
2. Describe the next single, concrete incremental change we're trying to implement. Don't ask for code, ask for a few high-level approaches, pros/cons. There's almost always a few ways to do thing and the LLM's judgement is not always great. Optionally make concrete.
3. Pick one approach, ask for first draft code.
4. Review / learning phase: (Manually...) pull up all the API docs in a side browser of functions I haven't called before or I am less familiar with, ask for explanations, clarifications, changes, wind back and try a different approach.
6. Test.
7. Git commit.
Ask for suggestions on what we could implement next. Repeat.

Something like this feels more along the lines of the inner loop of AI-assisted development. The emphasis is on keeping a very tight leash on this new over-eager junior intern savant with encyclopedic knowledge of software, but who also bullshits you all the time, has an over-abundance of courage and shows little to no taste for good code. And emphasis on being slow, defensive, careful, paranoid, and on always taking the inline learning opportunity, not delegating. Many of these stages are clunky and manual and aren't made explicit or super well supported yet in existing tools. We're still very early and so much can still be done on the UI/UX of AI assisted coding.

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Anybody in the Meteor community use Endor yet? Love the idea of Express.js in the browser. A little horrified by PHP and Postgres in the browser though….

https://t.co/FrOoQyc90w

[Andrej Karpathy @karpathy]

I just vibe coded a whole iOS app in Swift (without having programmed in Swift before, though I learned some in the process) and now ~1 hour later it's actually running on my physical phone. It was so ez... I had my hand held through the entire process. Very cool.

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@dj_CISWOMAN @emanabdelhadi Where is the outrage for what’s happening in Syria right now? 🤔

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@capitalist_qol @roronotalt @molly0xFFF Depends on what those 60k lines were doing. Are they recursive? Does each one do a lookup that scans the entire drive?

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@granawkins LLMs heading to personal devices in 3… 2… 1….

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It also acts as a photonic tubule; aka fiber optic wire.

[Parmita Mishra @parmita]

I don’t really care about much ever since I realized the DNA is a piezoelectric material.

[vittorio @IterIntellectus]

holy shit.

after almost 20 years, they created the most refined synthetic chromosome yet, we are getting closer to the first full synthetic eukaryotic genome

we will start vibe coding biology much sooner than most people think

1/

[vittorio @IterIntellectus]

https://t.co/bZmpARx2LU

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@EkozUltra @RaidersHLP @LizzieM12807558 @shadihamid @Ed453164711 Columbia University was where the Manhattan Project began. It wasn’t the protest. It was protest at Columbia University in particular. It has a unique national security status. They were protesting next to libraries with nuclear science, labs with million dollar equipment, etc

[Maryam Miradi, PhD @MaryamMiradi]

🥇🏆This Is the Most Complete Paper on Agentic RAG I've Read: An Absolute Zero-to-Hero Journey That Explains Everything You Need to Know

If you've ever felt overwhelmed by the technical complexity of Retrieval-Augmented Generation (RAG) or thought, “Where do I even begin?”, this paper is your ultimate guide.

Let’s explore it together:

》 What is RAG?

✸ Retrieval-Augmented Generation (RAG) integrates LLMs with real-time data sources, providing accurate and contextually enriched responses. While effective, traditional RAG systems are static and limited to predefined workflows.

》 Evolution of RAG Systems

✸ Naïve RAG: Relies on keyword-based retrieval, leading to fragmented outputs and scalability issues.

✸ Advanced RAG: Incorporates semantic retrieval techniques like Dense Passage Retrieval (DPR) and neural re-ranking for improved precision.

✸ Modular RAG: Introduces hybrid retrieval strategies, APIs, and composable pipelines for task-specific optimization.

✸ Graph RAG: Enhances multi-hop reasoning using graph-based structures but suffers from scalability challenges.

✸ Agentic RAG: Surpasses these by introducing autonomous decision-making, iterative refinement, and real-time workflow optimization.

》 What is Agentic RAG?

✸ Agentic RAG builds on this by embedding autonomous agents into the RAG pipeline. These agents dynamically refine context, optimize retrieval strategies, and adapt in real time to the complexity of queries, making them ideal for sophisticated, multi-step tasks.

》 Core Agentic Patterns

✸ Reflection: Enables agents to critique and refine outputs iteratively, boosting accuracy.

✸ Planning: Decomposes complex tasks into manageable subtasks, ensuring flexibility in execution.

✸ Tool Use: Integrates external resources, like APIs or databases, to enhance generative outputs.

✸ Multi-Agent Collaboration: Specialized agents collaborate to handle complex workflows efficiently.

》 Benefits of Agentic RAG

✸ Dynamic Adaptability: Adjusts workflows in real time based on task requirements.

✸ Enhanced Contextual Understanding: Iteratively refines outputs for higher relevance and accuracy.

✸ Scalability and Flexibility: Handles multi-domain queries with seamless integration of tools and data.

✸ Workflow Optimization: Reduces latency, ensuring efficiency even in high-demand scenarios.

》 Challenges and Future Directions

While Agentic RAG offers immense promise, challenges like computational overhead, coordination complexity, and ethical concerns must be addressed.

paper: https://t.co/vyZhZTknGe
Github: https://t.co/LBuWmL6QMt

﹌﹌﹌﹌﹌﹌﹌﹌﹌
》 join 𝟔𝟎+ 𝐜𝐨𝐮𝐧𝐭𝐫𝐢𝐞𝐬 of my students already mastering AI Agents!

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[compiling code @awatson1978]

@gfodor And then the LLMS are going to start arguing and correcting each other, and get caught in the same semantic ambiguities that human's do.

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@UltraRareAF @KTmBoyle Are you still in LA? Doing okay?