Seth Burkart

AI enthusiast. Building BetterSEQTA+

[OpenAI @OpenAI]

We’ve designed and built our first AI chip: Jalapeño.

Designed from the ground up by OpenAI and brought to production with @Broadcom, Jalapeño is purpose-built for the LLM workloads powering ChatGPT, Codex, the API, and future agentic products.

Chips are foundational to the AI economy. Building our own expands our full-stack platform from products to models to infrastructure, and will help us scale intelligence, serve more people, and expand access to AI.

[Zyphra @ZyphraAI]

Zyphra is sharing our first work in continual learning where we study: Can LLMs learn forever from new data?

Many see continual learning as a path to AGI through recursive self-improvement (RSI).

The first obstacle is plasticity loss. We derive a scaling law for its onset 🧵

[Exa @ExaAILabs]

Introducing Exa Connect: connecting agents to data beyond the public web.

Available today with ZoomInfo, Crunchbase, Similarweb, and many other leading data providers.

https://t.co/MlEhWoCIiF

[Figma @figma]

FIGMA MOTION IS HERE
FIGMA MOTION IS HERE
FIGMA MOTION IS HERE

Live from Config 2026

[Cua @trycua]

1/ We had early access from @GoogleDeepMind to Gemini 3.5 Flash's native Computer Use. On Cua-Bench it posted the highest mean reward of any frontier model we tested - 0.267, on KiCad tasks no model fully solves. At Flash speed and cost.

[Emile Riberdy @emile_rib22]

Introducing Fractal — the recursive language model CLI agent.

Fractal is a terminal agent that can answer questions, plan tasks, read and write files, run commands, inspect codebases, and work through problems turn by turn.

But at its core, Fractal is not just another CLI harness around an LLM.

It is powered by predict-rlm, our self-harnessed Recursive Language Model runtime. In practice, that means Fractal can recursively reason through context, interact with its own REPL environment, and take action through code as it works.

We built it to make RLMs easy to try and actually useful on real tasks.

If you've been hearing about RLMs but could never come around to using one, this is the best way to do so.

And if you already know what RLMs are capable of, Fractal is the easiest way to use one on your own tasks.

Install it today:
→ curl -LsSf https://t.co/j0Ye0JSpR3 | sh

Drop us a ⭐️:
→ https://t.co/NAbNAseVsT

Or check out the website:
→ https://t.co/w6XzXx8tN3

[Qwen @Alibaba_Qwen]

📣📣 Meet Qwen-AgentWorld — a native language world model that simulates 7 agent environments (MCP, Search, Terminal, SWE, Web, OS, Android) within a single model. Environment modeling is the training objective from day one, not a post-hoc adaptation.

🤔 LLMs are trained to be better agents — better at acting in environments. But nobody has trained them to model the environments themselves.

🗺️ Our roadmap: investigate how language world modeling can push the boundaries of general agent capabilities, along two routes:

1️⃣ Build a foundation model for environment simulation — outperforming Claude Opus 4.8 and GPT-5.4 on AgentWorldBench

2️⃣ Investigate how world modeling enhances agent training:
🔬 Controllable Sim RL (agentic RL with LWM as environments) surpasses training in real environments
🧠 Learning to predict environments (LWM warm-up) makes agents stronger — remarkably, even without any agent-specific training, this predictive knowledge transfers to agentic tasks with zero fine-tuning

📑 Paper: https://t.co/Jx2l5RKq71
📖 Blog: https://t.co/7tVcKyhsx2
💻 GitHub: https://t.co/B5Lvb1UZCn
🤗 HuggingFace: https://t.co/Kw3QBL1TM5
🧩 ModelScope: https://t.co/YBnGYgMWWI

[Tzafon @tzafon_company]

Everyone trains RL by spending compute where the model is uncertain.

That's wrong.

Spend it where it actually teaches the model something. Here's what that looks like

[GDP @bookwormengr]

“I totally see why it is scary to imagine an openly accessible Mythos class model, but if open models get banned now and only closed models get 10 or 100X better in 2 years in the hands of one or two companies, I think we will have bigger problems on our hands.”

Well said @natolambert

Open source is important for a vibrant global economy and startup culture.

[Krea @krea_ai]

today, we release the open weights of Krea 2.

welcome Krea 2 Raw and Krea 2 Turbo, an undistilled model from mid-training meant to be fine-tuned, and a fast distilled version with a wide aesthetic diversity.

read the details below 👇

[Arkil Patel @arkil_patel]

Can we release all the weights of an LLM but still provide differential access to privileged users?

Yes! We introduce: 𝗧𝗶𝗲𝗿𝗲𝗱 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗧𝗟𝗠𝘀). Define access tiers corresponding to different computation graphs over the same set of LLM parameters!

[jun @hyojun_at]

Today, we're launching Aside, the AI browser you’ve waited for.

⋅ Crafted in every detail: vertical tabs and Liquid Glass
⋅ SOTA on agentic browsing benchmarks: outperforms Claude Fable
⋅ Full privacy: Everything runs in local and encrypted
⋅ You can use Claude or ChatGPT subscription

The first AI browser built to do real work for you.

https://t.co/coZkFSGfgc

[Zhuang Liu @liuzhuang1234]

Can a small academic team build a strong text-to-image model using only public datasets?

Introducing i1: a simple, fully open recipe for strong text-to-image models

[Chetaslua @chetaslua]

Seedance 2.5 is mythos moment of Video Models

> more than 30 seconds
> 4k resolution
> prompt adherence is on another level that we never seen before
> our reports was right , this is why follow us and join our server
> July will be do much fun

[🚨 AI News | TestingCatalog @testingcatalog]

BYTEDANCE 🔥: Seedance 2.5 has been officially announced, along with an updated Seedance 2.0.

- Seedance 2.0 now supports 4k output
- Seedance 2.5 will be able to generate 30-second videos in one go
- ByteDance also announced a new AI copyright commercialization platform

This video ad is stunning 👀

[Prime Intellect @PrimeIntellect]

Today we're releasing prime-rl v0.6.0 — enabling RL at trillion-parameter MoE scale on agentic workloads at the highest efficiency.

We've relentlessly optimized our RL infra.

The result: GLM-5 on agentic SWE tasks at 131k context and sub-5-minute step time.

[Sam Altman @sama]

We want to help all companies be secure, working with the USG and the security ecosystem.

*The full version of GPT-5.5-Cyber is here; state of the art performance on CyberGym.

*Patch The Planet and Codex Security will help solve security problems instead of just finding them.

[Sakana AI @SakanaAILabs]

Introducing Sakana Fugu: A full multi-agent orchestration system accessible via a single model API.

Our ‘Fugu Ultra’ model matches the performance of Fable and Mythos, delivering frontier capability without the risk of export controls.

Try it: https://t.co/hhO6qTawgb 🐡

[Alok @analogalok]

gemma-4-12B-agentic-fable5-composer2.5 V2 is out.

the agentic upgrade to the model trained on Fable 5's reasoning. Running it now with TurboQuant llama.cpp on a single RTX 4060( 8 GB VRAM) at 30 tokens/second with full 25000 context and reasoning:

# The benchmarks

v2 is built for coding + agentic work. writing code, running commands, using tools, debugging, multi step technical tasks. The clearest signal is tau2 bench telecom, an agentic tool use benchmark whose diagnose → fix → verify loop mirrors real terminal/debugging work:

tau2 bench telecom numbers:

base Gemma 4 12B: ~15%
this finetune: ~55%. (Self reported)

thats a huge jump

# TheTom/llama-cpp-turboquant flags:

llama-server.exe -m gemma4-v2-Q4_K_M.gguf -ngl 99 -c 25000 --cache-type-k q8_0 --cache-type-v turbo3 --port 8080

Flag breakdown:

-ngl 99 → full GPU offload
-c 25000 → 25K context
--cache-type-k q8_0 --cache-type-v turbo3 → mixed-precision KV cache — K at 8-bit, V at ~3-bit via TurboQuant (Walsh Hadamard rotated polar quant, Google's own KV-compression research).

Not even merged into mainline llama.cpp. running it off a fork.

No API. No cloud. Just llama.cpp. well, a fork of it and any 6gb+ GPU.

If you tried yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF, check this out and share your experience with the models

[clem 🤗 @ClementDelangue]

- 2016-2024: 🇺🇸leads in open-source AI
- 2024-2027: 🇺🇸 leads in general AI & massively benefits

- 2024-2026: 🇨🇳 leads in open-source AI
- 2026-2030: ??

It's not open-source AI leadership OR general AI leadership, it's open-source AI leadership BEFORE general AI leadership!

Open-source AI is the foundation of all AI. It does not only creates more innovation, competition, jobs, and prosperity now, it's also the best (only?) way for a national tech ecosystem to accelerate and ultimately reach the frontier of AI in general.

Because open-source AI reduces siloes, shares learning and innovation, intensify emulation which all lead to an acceleration of the local ecosystem progress that no others can match if they're less open and collaborative.

Same seems to be true for companies btw, OpenAI/Google started with open science and open-source AI which led to their (and Anthropic who spun off from OAI) domination. Meta could have done the same but decided to change course for some reason.