Noureddine Haouari

Researcher in CS 💻• Interested in: PKM🗃️ | AI 🧠 | NLG ✍️

[Noureddine Haouari @haouarin]

🚀 Want to develop your skills in computer science? https://t.co/NeUvxue4XJ is a great resource, but here's how to take it to the next level using AI.
Thread🧵
#computerscience #learning #development #ChatGPT #obsidian

[Z.ai @Zai_org]

Intelligence should be open, accessible, and ready to build with, empowering every developer, everywhere.

GLM-5.2 is now available to all GLM Coding Plan users, including Lite, Pro, Max, and Team plans.
https://t.co/AedZACyzej

As our new flagship model, GLM-5.2 delivers powerful coding capabilities, usable 1M-context support, and continued strengths in long-horizon tasks.

API and Chatbot services will launch next week. The model will also be officially open-sourced next week under the MIT License.

The future of AI is open, and it belongs to the people.

[Noureddine Haouari @haouarin]

@dreamina_ai wow

[Noureddine Haouari @haouarin]

#secondmind

[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.

[Nandkishor @devops_nk]

Honestly, this is the most accurate diagram I've seen.

Waterfall: You plan for 18 months and deliver exactly what nobody needs anymore.

Agile: You deliver something usable at every step, but the CEO keeps asking, "Where's the car?"

AI: You get the car on day one. It has six wheels, the doors are on backwards, and it has a rocket launcher. You spend more time making it yours than actually "building"; it's shaping. owning. verifying. That's what the best AI developers do now. They don't build. They shape and own.

[Anthropic @AnthropicAI]

New Anthropic research: Emotion concepts and their function in a large language model.

All LLMs sometimes act like they have emotions. But why? We found internal representations of emotion concepts that can drive Claude’s behavior, sometimes in surprising ways.

[Logan Kilpatrick @OfficialLoganK]

Introducing Gemma 4, our series of open weight (Apache 2.0 licensed) models, which are byte for byte the most capable open models in the world!

Gemma 4 is build to run on your hardware: phones, laptops, and desktops.

Frontier intelligence with a 26B MOE and a 31B Dense model!

[Noureddine Haouari @haouarin]

@burkov it is well explained in this article https://t.co/Q76eSIg2jW

[Thariq @trq212]

https://t.co/taVRqwm4dG

[Noureddine Haouari @haouarin]

@Fried_rice

[Noureddine Haouari @haouarin]

@theo

[Noureddine Haouari @haouarin]

@Fried_rice

[Noureddine Haouari @haouarin]

@chrysb @openclaw check @Cloudflare way of solving this issue https://t.co/yO7fHVTK5C

[Noureddine Haouari @haouarin]

@ArbieuL Hype !

[Noureddine Haouari @haouarin]

@stevekrouse Google file search

https://t.co/gDZsJrIuCh

[Noureddine Haouari @haouarin]

@sama The power of openai models with speed of @cerebras

[Noureddine Haouari @haouarin]

@gdb explaining to the board why they just signed a $10B deal with Cerebras:
“...a model like Codex. Which sometimes is too slow. For sure. And needs to be reformed. For sure. But which is predictable. Loyal.”

[Noureddine Haouari @haouarin]

Used Gemini 3 Nano Banan Pro to sketch-note the latest @dwarkesh_sp podcast with @ilyasut, mapping the shift from the “Age of Scaling” to the “Age of Research”.

[Noureddine Haouari @haouarin]

#opus 4.5

[Noureddine Haouari @haouarin]

@ericzakariasson @cursor_ai Make Cursor extensible with Vibe Coding.

[Noureddine Haouari @haouarin]

@OfficialLoganK Cool!
Does the PDF parsing support scanned documents?
Is it multimodal for example, can it handle images, figures and diagrams within PDFs?