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mahimairaja

posted an update 1 day ago

Post

3180

Happy New Years 2026!

For next 365 days I will be commit to work on:

- Document AI and OCR Automations
- Voice Agents
- Long Running Tasks - Durable Agents

1 reply

mike-ravkine

posted an update 1 day ago

Post

1367

Happy 2026 everyone!

I've been busy working on some new ranking/position methodologies and excited to start sharing some results.

Plot legends:

- X = truncation rate (low = good)
- ? = confusion rate (low = good)
- blue bars = average completion tokens (low = good)
- black diamonds = CI-banded performance (high = good)
- cluster squares = models inside this group are equivalent

openai/gpt-oss-120b remains the king in all dimensions of interest: truncation rates, completion lengths and performance. If I had but one complaint it's the reason_effort does not seem to actually work - more on this soon.

Second is a 3-way tie in performance between the Qwen3-235B-2507 we all know and love with an unexpected entrant - ByteDance-Seed/Seed-OSS-36B-Instruct

This is a very capable model and it's reasoning effort controls actually works, but you should absolutely not leave it on the default "unlimited" - enable a sensible limit (4k works well for 8k context length).

Third place is another 3-way tie, this one between Seed-OSS-36B (it straddles the CI boundary between 2nd and 3rd place), Qwen/Qwen3-Next-80B-A3B-Instruct (demonstrating that full attention may be overrated after all and gated is the way to go) and the newly released zai-org/GLM-4.7 which offers excellent across the board performance with some of the shortest reasoning traces I've seen so far.

dhruv3006

posted an update about 12 hours ago

Post

1320

gRPC support in Voiden

gRPC has become a popular choice for building high-performance, scalable APIs, especially in microservices and real-time systems. Unlike traditional REST APIs, gRPC uses HTTP/2 and Protocol Buffers to deliver fast, efficient communication with strong typing and contract enforcement.

Many developers in our community asked for it, so we have now added gRPC support in the latest Voiden release.You can now test and document gRPC APIs side-by-side with your REST and WebSocket APIs.

Keep everything in one file-centric, Git-native workflow.

Use reusable blocks and version control for gRPC requests, just like any other API call.

Download the latest Voiden beta here: https://voiden.md/download

omarkamali

posted an update 1 day ago

Post

151

New year, new dataset 🚀

I just released omarkamali/wikipedia-labels, with all the structural labels and namespace from wikipedia in 300+ languages. A gift for the data preprocessors and cleaners among us.

Happy new year 2026 everyone! 🎆

sadpig70

posted an update 1 day ago

Post

158

**HAO (Human AI Orchestra)** is a next-generation collaborative development framework that maximizes synergy between **human intuition** and the **diverse strengths of multiple LLMs**—turning the human from a “coder” into a **conductor**.

At its core is an **11-step workflow** you can run immediately: divergence for wild ideas → convergence into architecture → critique & voting → synthesis → blueprinting (Gantree) → prototyping (PPR) → cross-review → refinement → roadmap → implementation. The philosophy is intentionally **anti-standardization**, treats **conflict as a resource**, and keeps **orchestration** (human-in-control) as the center.

This repo includes the **developer manual** (with concrete prompt templates), plus real artifact histories from two full runs: **Dancing with Noise** and **Dancing with Time**.

**GitHub:** [sadpig70/HAO](https://github.com/sadpig70/HAO)

MikeDoes

posted an update 3 days ago

Post

1891

Anonymizing a prompt is half the battle. Reliably de-anonymizing the response is the other.

To build a truly reliable privacy pipeline, you have to test it. A new Master's thesis does just that, and our data was there for every step.

We're excited to showcase this work on handling confidential data in LLM prompts from Nedim Karavdic at Mälardalen University. To build their PII anonymization pipeline, they first trained a custom NER model. We're proud that the Ai4Privacy pii-masking-200k dataset was used as the foundational training data for this critical first step.

But it didn't stop there. The research also used our dataset to create the parallel data needed to train and test the generative "Seek" models for de-anonymization. It's a win-win when our open-source data not only helps build the proposed "better solution" but also helps prove why it's better by enabling a rigorous, data-driven comparison.

🔗 Check out the full thesis for a great deep-dive into building a practical, end-to-end privacy solution: https://www.diva-portal.org/smash/get/diva2:1980696/FULLTEXT01.pdf

#OpenSource
#DataPrivacy
#LLM
#Anonymization
#AIsecurity
#HuggingFace
#Ai4Privacy
#Worldslargestopensourceprivacymaskingdataset

Reubencf

posted an update 4 days ago

Post

2545

As 2025 is ending i would like to thank everyone for trying out
Reubencf/Nano_Banana_Editor

looking forward to build and release more in the future for the open source community

Kseniase

posted an update 5 days ago

Post

2011

What we learned about memory in 2025: 8 comprehensive resources

If models forget everything, how can they be reliable? AI systems need to remember past interactions, update knowledge, stay consistent over time, and work beyond a single prompt. That's why many start to talk more about memory in AI.
Here’s a useful set of studies and videos on where AI memory stands today:

1. Memory in the Age of AI Agents (2512.13564)
A great survey that organizes agent memory research. It gives concrete taxonomies across memory form, function, and dynamics, summarizes benchmarks, frameworks, and emerging directions for building systematic agent memory systems

2.When Will We Give AI True Memory? A conversation with Edo Liberty, CEO and founder @ Pinecone -> https://youtu.be/ITbwVFZYepc?si=_lAbRHciC740dNz0
Edo Liberty discusses what real memory in LLMs requires beyond RAG - from scalable vector storage to reliable knowledge systems - and why storage, not compute, is becoming the key bottleneck for building dependable AI agents.

3. Why AI Intelligence is Nothing Without Visual Memory | Shawn Shen on the Future of Embodied AI -> https://youtu.be/3ccDi4ZczFg?si=SbJg487kwrkVXgUu
Shawn Shen argues AI needs a separate, hippocampus-like memory to move beyond chatbots, enabling long-term visual memory, object permanence, and on-device intelligence for robots, wearables, and the physical world

4. From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs (2504.15965)
Links human memory types to LLM memory, introduces a taxonomy across object, form, and time, and identifies concrete limitations and future research directions

5. Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions -> https://arxiv.org/abs/2505.00675v2
Proposes a concrete taxonomy, core operations, and research directions to systematically organize and advance agent memory systems.

Read further below ⬇️
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3 replies

sergiopaniego

posted an update about 2 hours ago

Post

The list of hands-on notebooks (some beginner-friendly!) to get started with fine-tuning using TRL keeps growing!!

• SFT
• GRPO
• Tool calling & agents
• RL environments with OpenEnv
• LLMs and VLMs
✨ Many run on FREE Colab, making it super easy to get started fast!

https://github.com/huggingface/trl/tree/main/examples/notebooks

branikita

posted an update about 7 hours ago

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We tested the maximum dynamic payload of the SO-ARM101 with our parallel gripper and a base servo replaced by a Feetech STS3250. The maximum load before failure was 630 g, at which point the Feetech STS3215 in joint 3 failed — its large brass output gear was completely worn down.

The Feetech STS3250 in the base with a metal gear train withstood a significantly higher load.

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