RakshitAralimatti (Rakshit Aralimatti)

reacted to danielhanchen's post with 🔥 28 days ago

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9248

Gemma 4 12B can now run locally on just 8GB RAM via Dynamic GGUFs.

Google's new model, Gemma 4 12B Unified supports image, audio and 256K context.
You can run and train the model via Unsloth Studio.

GGUF: unsloth/gemma-4-12b-it-GGUF
Guide: https://unsloth.ai/docs/models/gemma-4

5 replies

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reacted to their post with ❤️ 29 days ago

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500

Reading engineering and research blogs from OpenAI, Anthropic, DeepMind, Meta and others has genuinely leveled up my understanding of AI systems and helped me in my day-to-day work. But keeping track of 20+ sites manually is a pain.

So I built AI Blogs Tracker — a Streamlit app that scrapes the actual blog listing pages (not search) of 20+ top AI companies and surfaces titles, dates, and links in one clean feed. Filter by source, by date, star posts to a reading list, or add your own custom sources.

One click. ~30 seconds. Everything in one place.

🔗 GitHub link - https://github.com/rakshit2020/Tech-Blogs-Tracker-of-Top-AI-Companies-Agent

1 reply

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reacted to their post with 🚀 about 1 month ago

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500

Reading engineering and research blogs from OpenAI, Anthropic, DeepMind, Meta and others has genuinely leveled up my understanding of AI systems and helped me in my day-to-day work. But keeping track of 20+ sites manually is a pain.

So I built AI Blogs Tracker — a Streamlit app that scrapes the actual blog listing pages (not search) of 20+ top AI companies and surfaces titles, dates, and links in one clean feed. Filter by source, by date, star posts to a reading list, or add your own custom sources.

One click. ~30 seconds. Everything in one place.

🔗 GitHub link - https://github.com/rakshit2020/Tech-Blogs-Tracker-of-Top-AI-Companies-Agent

1 reply

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replied to their post about 1 month ago

GITHUB LINK - https://github.com/rakshit2020/Tech-Blogs-Tracker-of-Top-AI-Companies-Agent

posted an update about 1 month ago

Post

500

Reading engineering and research blogs from OpenAI, Anthropic, DeepMind, Meta and others has genuinely leveled up my understanding of AI systems and helped me in my day-to-day work. But keeping track of 20+ sites manually is a pain.

So I built AI Blogs Tracker — a Streamlit app that scrapes the actual blog listing pages (not search) of 20+ top AI companies and surfaces titles, dates, and links in one clean feed. Filter by source, by date, star posts to a reading list, or add your own custom sources.

One click. ~30 seconds. Everything in one place.

🔗 GitHub link - https://github.com/rakshit2020/Tech-Blogs-Tracker-of-Top-AI-Companies-Agent

1 reply

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published a Space about 1 month ago

AI Technical Blogs

🏃

A Streamlit app that fetches atest technical blog

reacted to HannesVonEssen's post with 🚀 about 2 months ago

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4919

📣 Add architecture visualization to model card!

🌟 For all creators out there: add a model visualization to your model card to capture your audience's attention!

🖱️ When clicked, it opens an interactive view with multiple levels of granularity!

1️⃣ Paste url at https://hfviewer.com/model-card-embed
2️⃣ Paste generated code in your README.md!
3️⃣ ✨

posted an update 3 months ago

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1572

🔥 GLM-5.1 (zai-org/GLM-5.1) — Quietly One of the Best flagship model for agentic engineering and Coding tasks Right Now

threw some LangGraph agent code at it, a messy RAG pipeline, some async Python stuff and it just handled it. no drama, no hallucinated methods, actually usable output on the first try.

open source closing the gap this fast is genuinely exciting. go check zai-org/GLM-5.1 on HF if you haven't already

Good work @zai-org-3

1 reply

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replied to melvindave's post 3 months ago

Yes, you can run them using llamacpp or ollama

reacted to SeaWolf-AI's post with 🔥 4 months ago

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5099

ALL Bench — Global AI Model Unified Leaderboard

FINAL-Bench/all-bench-leaderboard

If you've ever tried to compare GPT-5.2 and Claude Opus 4.6 side by side, you've probably hit the same wall: the official Hugging Face leaderboard only tracks open-source models, so the most widely used AI systems simply aren't there. ALL Bench fixes that by bringing closed-source models, open-weight models, and — uniquely — all four teams under South Korea's national sovereign AI program into a single leaderboard. Thirty-one frontier models, one consistent scoring scale.
Scoring works differently here too. Most leaderboards skip benchmarks a model hasn't submitted, which lets models game their ranking by withholding results. ALL Bench treats every missing entry as zero and divides by ten, so there's no advantage in hiding your weak spots.
The ten core benchmarks span reasoning (GPQA Diamond, AIME 2025, HLE, ARC-AGI-2), coding (SWE-bench Verified, LiveCodeBench), and instruction-following (IFEval, BFCL). The standout is FINAL Bench — the world's only benchmark measuring whether a model can catch and correct its own mistakes. It reached rank five in global dataset popularity on Hugging Face in February 2026 and has been covered by Seoul Shinmun, Asia Economy, IT Chosun, and Behind.
Nine interactive charts let you explore everything from composite score rankings and a full heatmap to an open-vs-closed scatter plot. Operational metrics like context window, output speed, and pricing are included alongside benchmark scores.
All data is sourced from Artificial Analysis Intelligence Index v4.0, arXiv technical reports, Chatbot Arena ELO ratings, and the Korean Ministry of Science and ICT's official evaluation results. Updates monthly.

reacted to danielhanchen's post with 🚀 4 months ago

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2730

We collabed with HF on showing how you can use HF Jobs and Unsloth! https://huggingface.co/blog/unsloth-jobs

replied to their post 5 months ago

That's True...

reacted to their post with 🚀 5 months ago

Post

3068

Just built my entire AI Engineer portfolio by pasting 2 links (GitHub and LinkedIn) into

moonshotai Kimi 2.5.
That's it. That's the workflow.
Zero coding. Zero iteration. Zero "make the button bigger."
See for yourself: https://rakshit2020.github.io/rakshitaralimatti.github.io/

The model:
✅ Scraped my GitHub repos automatically
✅ Pulled my experience from LinkedIn
✅ Designed an Aurora Glass theme
✅ Mapped every skill to projects
✅ Added animations I'd never code myself

4 replies

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posted an update 5 months ago

Post

3068

Just built my entire AI Engineer portfolio by pasting 2 links (GitHub and LinkedIn) into

moonshotai Kimi 2.5.
That's it. That's the workflow.
Zero coding. Zero iteration. Zero "make the button bigger."
See for yourself: https://rakshit2020.github.io/rakshitaralimatti.github.io/

The model:
✅ Scraped my GitHub repos automatically
✅ Pulled my experience from LinkedIn
✅ Designed an Aurora Glass theme
✅ Mapped every skill to projects
✅ Added animations I'd never code myself

4 replies

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reacted to their post with 🔥 6 months ago

Post

1270

I built a crazy ultra–low latency voice assistant agent using Pipecat, NVIDIA Riva, NVIDIA NIM, and an MCP‑powered tool stack. It can talk in real time, search the web, and manage your project directory files, document your code and docs hands‑free (create, read, summarise, and clean up).

Link - https://github.com/rakshit2020/Voice-Agent-using-Nvidia-Riva-NIM-Pipecat
I put everything into a small demo repo with the full architecture diagram and a short demo video so you can see exactly how it works and adapt it to your own projects.

Check out the GitHub, play with the agent, and let me know if it’s useful or if you want a breakdown of any part of the setup.

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commented on How We Built a Semantic Highlight Model To Save Token Cost for RAG 6 months ago

Congratulations on the release great work!!
I have a doubt and would love some clarification.
Why isn’t top-K reranking sufficient for token cost reduction in production RAG systems, and in which scenarios does semantic highlighting provide the biggest advantage over rerankers?
Additionally, I wanted to ask:
Can the semantic highlight model further break down or split sentences into smaller, more fine-grained relevant spans (instead of selecting full sentences), or is sentence-level pruning the intended granularity?

upvoted an article 6 months ago

Article

How We Built a Semantic Highlight Model To Save Token Cost for RAG

zilliz

•

Jan 15

• 67

posted an update 6 months ago

Post

1270

I built a crazy ultra–low latency voice assistant agent using Pipecat, NVIDIA Riva, NVIDIA NIM, and an MCP‑powered tool stack. It can talk in real time, search the web, and manage your project directory files, document your code and docs hands‑free (create, read, summarise, and clean up).

Link - https://github.com/rakshit2020/Voice-Agent-using-Nvidia-Riva-NIM-Pipecat
I put everything into a small demo repo with the full architecture diagram and a short demo video so you can see exactly how it works and adapt it to your own projects.

Check out the GitHub, play with the agent, and let me know if it’s useful or if you want a breakdown of any part of the setup.

1 reply

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commented on Scaling Real-Time Voice Agents with Cache-Aware Streaming ASR 6 months ago

How does the cache-aware encoder handle cache resets or partial invalidation during real conversational events like interruptions or rapid turn-taking?

reacted to their post with 👍 6 months ago

Post

2001

One of the most practical and genuinely useful use cases of agentic systems is a research assistant.

I built a Deep Research multi-agent system using NVIDIA’s Nemotron-3-Nano-30B-A3B model and CrewAI.
Try it out yourself 👇
🔗 GitHub: https://github.com/rakshit2020/Deep-Research-Agent-using-CrewAI
What truly made this system feel next-level was powering it with NVIDIA Nemotron-3-Nano-30B-A3B, its built for real-world agentic applications.

The agentic system I built:

1. First talks to you and clarifies what you actually want, removing ambiguity
2. Then creates a proper research plan based on that clarity
3. Performs deep research using web search and content extraction tools
4. Finally produces a well-structured research report grounded in sources

Rakshit Aralimatti

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AI Technical Blogs

How We Built a Semantic Highlight Model To Save Token Cost for RAG

Rakshit Aralimatti

AI & ML interests

Recent Activity

Organizations

RakshitAralimatti's activity

AI Technical Blogs

How We Built a Semantic Highlight Model To Save Token Cost for RAG