IA News & Research

I've been thinking about this lately. The Qwen team has released several new models recently, but they appear to be holding back the 122B, 35B, 27B, and 9B versions for now.

One possible reason is that these larger models performed so strongly that the team chose not to release them immediately as open weights. If that's the case, they will likely wait until they have even more capable models before making them available.

Recent analyses suggest open-source models are currently lagging 2–4 months behind state-of-the-art systems. With Qwen now adding further 1–2 month delays (or longer) before releasing open weights, I'm concerned the gap could continue to widen. Could this eventually lead to another significant shift in the open-source landscape, similar to what happened with Meta-Llama models?

To clarify my focus: I'm particularly interested in Qwen models because they currently offer the best performance among models that can realistically run on consumer-grade hardware.

While I understand some community members maintain more substantial local setups capable of running 500B or bigger models, my question is aimed at those of us working with standard consumer GPUs.

New 18 dimensional math that will allow us to time travel

I feel like I can sum up this entire ad campaign and concept as “so the person is like, and they have a thing and then they put it and then they like look and stuff real close tho”

I feel like I could, not using chat gpt, come up with 100 fake scenarios better than this without breaking a sweat.

I've been writing a no-compile no-dependencies node.js based AI Harness for llama.cpp as a learning exercise and can really use some help. I'm basing my code off https://github.com/av/mi and https://pi.dev/ with really basic agentic loops. It basically loop until there are no more tool calls being made then returns the control to the user prompt.

My biggest problems are

1. often times the LLM will ignore the tool call and the results and call the same tools again.
2. or worse, sometimes it'll drift it's attention to answer a previously answered question and tries to work from there instead of the latest tool call or continue its plan.

I'm using a q4 quant of qwen3.6 27b. I don't experience this problem when I run the same model under pi. I've looked at pi's agentic loop implementation and there doesn't seem to be any special sauce.

I added reminder messages after tool calls to remind it to review them before moving on and it helps a bit, but I would like to know if anyone has experienced the same problem in their own AI harness development and how do you address it?

So far the reminder messages I've implemented kinna work, but it feels like band-aids than real cures.

Edit: add bare minimal source code.

coffee.mjs

tools/bash.mjs

if you have node.js installed 'node coffee.mjs' will run it. no dependencies. just make sure llama-server is running. all config information are stored as variables at the top of coffee.mjs. Very basic stuff, but should be very human readable code.

I have more tools and skills implemented, but this is the bare minimum that forms a basic AI coding agent/harness. Like I said, it's a learning project, not competing for anything. I've been using it as daily driver tho.

Oh, and if you have free AI resource, feel free to have it scan the code to see if it can help answer the question. thank you!

Anthropic has accused Alibaba of creating tens of thousands of fake Claude accounts to scrape Claude of its intellectual property via distillation attacks.

Alibaba retaliates by telling their official (not contracted) employees to stop using Claude Code.

I'm noticing from Reddit posts and comments that Claude has gotten much more wary of what it determines as strange prompting requests?

There is an article indicating that Fable 5 has been "hardened" against distillation attacks, but it's locking out some legitimate users and refusing on innocuous requests.

Seems like a lot of users are caught in the middle?

I’ve been working on Murmur, a local text-to-speech app for Apple Silicon Macs.

The new feature I’m building is called Projects / Story Studio, and it solves a problem I kept running into:

TTS tools are fine for one-off clips, but messy for actual audio projects.

If you’re making a podcast segment, audiobook chapter, course lesson, ad, or game dialogue, you usually need multiple speakers, multiple takes, pauses, reactions, music, edits, exports, and a way to come back to the project later.

So I built a project-based workflow:

Write a script → assign voices → generate dialogue → edit clips on a timeline → add music/SFX → export final audio.

It supports things like:

• multiple scripts inside one project
• Host / Guest / Narrator / Character speakers
• inline tags like [pause], [laugh], [chuckle]
• per-block regeneration
• timeline editing with waveforms
• media lane for music and SFX
• ripple editing and gap tools
• WAV/M4A export
• transcript and stem export

Everything runs locally on Mac, so long scripts and voice samples do not need to be uploaded to a cloud service.

I’m still polishing the workflow and would love feedback from Mac users, especially people who make podcasts, audiobooks, courses, YouTube narration, or game dialogue.