1 26 37

efe

efecelik

AI & ML interests

NLP, LLM

Recent Activity

posted an update about 3 hours ago

My First MCP Server: DataView Browse HuggingFace datasets directly from your AI assistant. -Search & filter datasets -View rows & stats -SQL queries & Parquet export https://huggingface.co/spaces/efecelik/dataview-mcp

updated a Space about 10 hours ago

efecelik/dataview-mcp

published a Space about 10 hours ago

efecelik/dataview-mcp

View all activity

Organizations

posted an update about 3 hours ago

Post

My First MCP Server: DataView
Browse HuggingFace datasets directly from your AI assistant.
-Search & filter datasets
-View rows & stats
-SQL queries & Parquet export
efecelik/dataview-mcp

updated a Space about 10 hours ago

DataView MCP

🔍

published a Space about 10 hours ago

DataView MCP

🔍

posted an update 1 day ago

Post

169

We Built a Music App with ACE-Step – Looking for Feedback

Hey everyone,

We've been building AceSteps – a platform where anyone can create music using the ACE-Step model ( ACE-Step/ACE-Step-v1-3.5B). You can mint your tracks as NFTs, tokenize them into 100,000 fractional shares, and trade them on Uniswap V4. When your song gets popular, token holders earn from ad revenue automatically. It's a Farcaster Mini-App on Base Network.

But we want to make it better, and we'd love your input:

What's the one feature that would make you actually use an AI music tool regularly?
Andd any suggestions on how we can make this model better? Actually sharing here for this question. 🤗

Any feedback, ideas, or critiques are welcome.
🔗 https://docs.acesteps.com/
🔗 https://docs.acesteps.com/pitch-deck.html
🔗 https://farcaster.xyz/?launchFrameUrl=https%3A%2F%2Fwww.acesteps.com%2F
🔗 https://www.acesteps.com

commented a paper 2 days ago

LTX-2: Efficient Joint Audio-Visual Foundation Model

Paper • 2601.03233 • Published 10 days ago • 121 •

upvoted a paper 2 days ago

LTX-2: Efficient Joint Audio-Visual Foundation Model

Paper • 2601.03233 • Published 10 days ago • 121

liked a model 3 days ago

Lightricks/LTX-2

Image-to-Video • Updated 2 days ago • 1.36M • • 1.09k

replied to their post 3 days ago

Thanks for the detailed feedback! You're right that v1 has its quirks and we've experienced the repetition issues too.

Great to hear v1.5 is coming soon. Actually we built a platform called AceSteps using this model(v1). You can create music, mint it as an NFT, tokenize it into tradeable shares, and earn from ad revenue. It's a Farcaster Mini-App on Base Network.

Planning to integrate v1.5 once it drops.

Didn't know they had a Discord server, thanks for the info.

posted an update 4 days ago

Post

2275

why ACE-Step model isn't popular that much? imo it makes really good music.
ACE-Step/ACE-Step-v1-3.5B

2 replies

liked a model 15 days ago

unsloth/Qwen3-VL-32B-Thinking

Image-Text-to-Text • 33B • Updated 15 days ago • 196 • 2

updated a Space 16 days ago

FLUX.2-dev-Turbo Explorer

🎨

6x faster FLUX.2 image generation with 8-step Turbo

published a Space 16 days ago

FLUX.2-dev-Turbo Explorer

🎨

6x faster FLUX.2 image generation with 8-step Turbo

liked a model 17 days ago

fal/FLUX.2-dev-Turbo

Text-to-Image • Updated 18 days ago • 11k • • 288

liked a model 23 days ago

ACE-Step/ACE-Step-v1-3.5B

Text-to-Audio • Updated May 22, 2025 • 672

replied to Kseniase's post 26 days ago

this is a very good study. it reminded me of a time a few years ago when i found things like "few shots" and similar things ridiculous; that was a big mistake.

reacted to Kseniase's post with ❤️ 26 days ago

Post

3700

From Prompt Engineering to Context Engineering: Main Design Patterns

Earlier on, we relied on clever prompt wording, but now structured, complete context matters more than just magic phrasing. The next year is going to be a year of context engineering which expands beyond prompt engineering. The two complement each other: prompt engineering shapes how we ask, while context engineering shapes what the model knows, sees, and can do.

To keep things clear, here are the main techniques and design patterns in both areas, with some useful resources for further exploration:

▪️ 9 Prompt Engineering Techniques (configuring input text)

1. Zero-shot prompting – giving a single instruction without examples. Relies entirely on pretrained knowledge.

2. Few-shot prompting – adding input–output examples to encourage model to show the desired behavior. ⟶ https://arxiv.org/abs/2005.14165

3. Role prompting – assigning a persona or role (e.g. "You are a senior researcher," "Say it as a specialist in healthcare") to shape style and reasoning. ⟶ https://arxiv.org/abs/2403.02756

4. Instruction-based prompting – explicit constraints or guidance, like "think step by step," "use bullet points," "answer in 10 words"

5. Chain-of-Thought (CoT) – encouraging intermediate reasoning traces to improve multi-step reasoning. It can be explicit ("let’s think step by step"), or implicit (demonstrated via examples). ⟶ https://arxiv.org/abs/2201.11903

6. Tree-of-Thought (ToT) – the model explores multiple reasoning paths in parallel, like branches of a tree, instead of following a single chain of thought. ⟶ https://arxiv.org/pdf/2203.11171

7. Reasoning–action prompting (ReAct-style) – prompting the model to interleave reasoning steps with explicit actions and observations. It defines action slots and lets the model generate a sequence of "Thought → Action → Observation" steps. ⟶ https://arxiv.org/abs/2210.03629

Read further ⬇️
Also subscribe to Turing Post: https://www.turingpost.com/subscribe