LeetTools

discovery, curation, and sharing - enabled by Agentic Workflow + Document Pipeline

[Andrej Karpathy @karpathy]

Continuing the journey of optimal LLM-assisted coding experience. In particular, I find that instead of narrowing in on a perfect one thing my usage is increasingly diversifying across a few workflows that I "stitch up" the pros/cons of:

Personally the bread & butter (~75%?) of my LLM assistance continues to be just (Cursor) tab complete. This is because I find that writing concrete chunks of code/comments myself and in the right part of the code is a high bandwidth way of communicating "task specification" to the LLM, i.e. it's primarily about task specification bits - it takes too many bits and too much latency to communicate what I want in text, and it's faster to just demonstrate it in the code and in the right place. Sometimes the tab complete model is annoying so I toggle it on/off a lot.

Next layer up is highlighting a concrete chunk of code and asking for some kind of a modification.

Next layer up is Claude Code / Codex / etc, running on the side of Cursor, which I go to for larger chunks of functionality that are also fairly easy to specify in a prompt. These are super helpful, but still mixed overall and slightly frustrating at times. I don't run in YOLO mode because they can go off-track and do dumb things you didn't want/need and I ESC fairly often. I also haven't learned to be productive using more than one instance in parallel - one already feels hard enough. I haven't figured out a good way to keep CLAUDE[.]md good or up to date. I often have to do a pass of "cleanups" for coding style, or matters of code taste. E.g. they are too defensive and often over-use try/catch statements, they often over-complicate abstractions, they overbloat code (e.g. a nested if-the-else constructs when a list comprehension or a one-liner if-then-else would work), or they duplicate code chunks instead of creating a nice helper function, things like that... they basically don't have a sense of taste. They are indispensable in cases where I inch into a more vibe-coding territory where I'm less familiar (e.g. writing some rust recently, or sql commands, or anything else I've done less of before). I also tried CC to teach me things alongside the code it was writing but that didn't work at all - it really wants to just write code a lot more than it wants to explain anything along the way. I tried to get CC to do hyperparameter tuning, which was highly amusing. They are also super helpful in all kinds of lower-stakes one-off custom visualization or utilities or debugging code that I would never write otherwise because it would have taken way too long. E.g. CC can hammer out 1,000 lines of one-off extensive visualization/code just to identify a specific bug, which gets all deleted right after we find it. It's the code post-scarcity era - you can just create and then delete thousands of lines of super custom, super ephemeral code now, it's ok, it's not this precious costly thing anymore.

Final layer of defense is GPT5 Pro, which I go to for the hardest things. E.g. it has happened to me a few times now that I / Cursor / CC are all stuck on a bug for 10 minutes, but when I copy paste the whole thing to 5 Pro, it goes off for 10 minutes but then actually finds a really subtle bug. It is very strong. It can dig up all kinds of esoteric docs and papers and such. I've also used it for other meatier tasks, e.g. suggestions on how to clean up abstractions (mixed results, sometimes good ideas but not all), or an entire literature review around how people do this or that and it comes back with good relevant resources / pointers.

Anyway, coding feels completely blown open with possibility across a number of "kinds" of coding and then a number of tools with their pros/cons. It's hard to avoid the feeling of anxiety around not being at the frontier of what is collectively possible, hence random sunday shower of thoughts and a good amount of curiosity about what others are finding.

[LeetTools @leettools]

# Ai Coding Assistant
**Title: The Coder’s Companion**

In the quiet hum of the screen,
Cursor breathes —
an AI pulse beneath fingertips,
a whisper of code before thought,
a partner in the dance of creation.

No longer alone in the labyrinth of syntax,
it knows the language of Python’s flow,
the rhythm of functions and classes,
the silent logic threading through lines unseen.

Tab, tab, tab —
like footsteps echoing in a digital hall,
each suggestion a spark,
a glimpse of what might be,
anticipating the coder’s mind,
faster than the blink of an eye.

Extensions, themes, familiar keys —
it wears the cloak of comfort,
yet hums with frontier intelligence,
a blend of purpose-built and boundless models,
privacy held sacred,
code never wandering beyond consent.

Voices rise from the world’s corners —
developers enchanted,
their workflows transformed,
from hesitation to flow,
from struggle to superpower.

Cursor, the silent architect,
building bridges between human and machine,
where creativity meets precision,
and the future of coding unfolds
in the space between thought and keystroke.

---

**Explanation**

1. **Main Themes and Imagery:**
The poem centers on themes of collaboration between human and AI, the seamless integration of AI into the coding process, and the transformative power of this partnership. Imagery includes the quiet hum of the screen, the dance of creation, footsteps echoing as tab completions, and the cloak of familiarity combined with frontier intelligence. These images evoke both the technical and almost poetic nature of coding enhanced by AI.

2. **Connection to Source Material:**
The poem draws directly from the context describing Cursor as an AI code editor that makes developers extraordinarily productive, anticipates their needs, and integrates smoothly with familiar tools and workflows. It references Python programming as a key language supported and the AI’s ability to understand and edit code naturally. The user testimonials about Cursor’s impact on productivity and workflow inspired the voices of developers praising the tool.

3. **Poetic Devices and Structure Choice:**
The free verse style was chosen to reflect the fluid, dynamic nature of coding and AI interaction, avoiding rigid rhyme or meter to mirror the organic flow of thought and code. Repetition of “Tab, tab, tab” mimics the action of accepting AI suggestions, creating rhythm without formal structure. Metaphors like “dance of creation” and “silent architect” elevate the technical process to an art form, while personification of Cursor as a breathing, whispering partner emphasizes the AI’s supportive role. The poem’s open form invites readers to feel the evolving relationship between coder and AI without constraint, much like the evolving technology itself.

This approach captures the essence of the AI coding assistant as both a tool and a collaborator, celebrating innovation and human creativity intertwined.

[Wenxin Song @wenxin_song_]

Excited to share LeetTools MCP Server!

This MCP server seamlessly integrates the power of [LeetTools](https://t.co/XoRsRoEFTB) – an AI-powered search assistant designed to create highly customizable search workflows. Whether you're looking for precise web results or searching through local knowledge bases, our solution brings it all together with:
- Smart Search: Combines web searching, scraping, and filtering into one streamlined tool, powered by an in-memory vector database for accurate, relevant results.
- Automated Document Pipeline: From data ingestion to indexing and storage, focus on developing your unique workflows while the infrastructure is fully managed.
- Dual Search Capabilities: Execute both web and local searches effortlessly, ensuring you always get the information you need.

[Yuchen Jin @Yuchenj_UW]

on-demand H100 for $0.99/hr, 4090 for $0.20/hr at Hyperbolic

likely the cheapest GPUs around

tell me what you're building, and I'll spot you free credits for an 8xH100 node for at least a few hours to start.

[LeetTools @leettools]

Disclaimer, the following tweet thread was generated by @OpenAI Deep Research. Content aside (which is pretty good I would say), ChatGPT is definitely a master of emojis! All the emojis in the tweets are right on the topic, lol.

[LeetTools @leettools]

🚀 Ever wondered how AI agents can actually use external knowledge in real time? Meet Agentic RAG (Retrieval-Augmented Generation) – the secret sauce behind smarter, fact-driven AI systems. It combines a knowledge base with an autonomous agent for powerful results. 🤖💡 Here are 10 essential techniques that make it work (with GitHub demos)! 🧵

[LeetTools @leettools]

✅ That’s a wrap! Agentic RAG systems are game-changers, blending retrieval with decision-making. By mastering these 10 techniques, you can build AI agents that are more informed, accurate, and autonomous. Harnessing knowledge + reasoning = next-level AI! 🤖🚀

Feel free to add more techniques or tools in the comments. Which of these would you implement in your project first? 🙌

[LeetTools @leettools]

🚀 Ever wondered how AI agents can actually use external knowledge in real time? Meet Agentic RAG (Retrieval-Augmented Generation) – the secret sauce behind smarter, fact-driven AI systems. It combines a knowledge base with an autonomous agent for powerful results. 🤖💡 Here are 10 essential techniques that make it work (with GitHub demos)! 🧵

[LeetTools @leettools]

🔟 🔌 External Tool Integration: Sometimes the needed info isn’t in the indexed docs. Agentic RAG can call external tools/APIs – 🌐 web search, databases, calculators, etc. – to fetch fresh information or perform tasks​. By extending beyond its local knowledge, the agent stays up-to-date and can handle a wider range of queries (from checking current events to running computations).
GitHub: Significant-Gravitas/Auto-GPT (⭐140k) – a famous example of an AI agent that autonomously uses the web and other tools to gather info and solve goals.

[LeetTools @leettools]

Automating GPU Kernel Generation with DeepSeek-R1 and Inference Time Scaling

Date: 2025-02-12

Categories: Generative AI, Intermediate Technical, Development & Optimization, Deep dive, General, AI Inference / Inference Microservices, AI Agent, Hopper

Keywords: GPU Kernel Generation, Attention Mechanisms, NVIDIA, Inference Time Scaling, AI Optimization, DeepSeek-R1, Large Language Models

Sources: https://t.co/CkOK7Fyfa5

In a groundbreaking experiment, NVIDIA engineers have leveraged the DeepSeek-R1 model to automate the generation of GPU attention kernels, achieving remarkable results that sometimes surpass those crafted by expert engineers. This innovation is rooted in a new scaling law termed 'inference-time scaling,' which enhances model performance by allocating additional computational resources during inference. The study highlights the complexities of attention mechanisms in large language models (LLMs) and the necessity for optimized GPU kernels to handle the quadratic growth of computational complexity with input sequence length. By employing a closed-loop workflow that integrates a verifier with the DeepSeek-R1 model, the team was able to refine kernel generation iteratively, resulting in a 100% success rate for Level-1 problems and a 96% success rate for Level-2 problems as per Stanford’s KernelBench benchmark. This research not only demonstrates the potential of AI in automating complex coding tasks but also sets the stage for future advancements in GPU programming and optimization.

[LeetTools @leettools]

How does "test-time compute scaling" work?

Test-time compute scaling is a strategy that allows language models to utilize additional computational resources during inference to enhance their problem-solving capabilities. This approach is particularly beneficial for complex tasks where the model can "think longer" and explore various solution paths, similar to human reasoning processes[2].

There are two primary mechanisms through which test-time compute operates: refining the proposal distribution and optimizing verifier search. The first mechanism involves the model iteratively improving its answers through guided self-revision, generating a sequence of revisions that build on previous attempts. This sequential approach is effective for easier questions, where the model can enhance its initial understanding[2][1].

The second mechanism focuses on using process reward models (PRMs) to evaluate the correctness of each intermediate step in a solution. This allows for sophisticated search algorithms, such as beam search and lookahead search, to explore multiple solution paths simultaneously. The effectiveness of these strategies varies with the difficulty of the problem, with beam search often outperforming simpler methods on harder tasks[2][1].

By applying a "compute-optimal" scaling strategy, which adapts the allocation of test-time compute based on the difficulty of the prompt, models can significantly improve their efficiency. Research indicates that this adaptive approach can enhance performance by up to 4 times compared to traditional methods like best-of-N sampling, particularly in resource-constrained environments[2][1].

In summary, test-time compute scaling represents a shift in how AI models approach problem-solving, allowing them to leverage additional computation during inference to improve accuracy and efficiency, especially on complex tasks[2].

References:
[1] https://t.co/hbFAng5lYh Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
[2] https://t.co/QpyeTcTuEF Test-Time Compute: The Next Frontier in AI Scaling

[LeetTools @leettools]

A full step-by-step guide to use the process with deepseek-r1:1.5b model is here:

https://t.co/qRTZUvYQRq

[LeetTools @leettools]

Run a fully local AI Search / RAG pipeline using Ollama with 4GB of memory and no GPU

We can now build our local knowledge base with one line of command and everything runs locally with no docker or API key required. The total memory usage is around 4GB with the Llama3.2 model:

- llama3.2:latest        3.5 GB
- nomic-embed-text:latest    370 MB
- LeetTools: 350MB (Document pipeline backend with Python and DuckDB)